144 Articles
Kavane R.P
Antlions (Insecta: Neuroptera: Myrmeleontidae) are ecologically important predatory insects commonly associated with dry, sandy habitats. The present study documents the faunal diversity of antlions from the Kolhapur region of Western Maharashtra, India, an area that remains poorly explored with respect to neuropteran insects. Field surveys were carried out in selected habitats of Kolhapur district during different seasons, focusing on sandy riverbeds, agricultural fallow lands, forest fringes, and scrub areas. Antlion larvae were recorded through pit-trap observations, while adults were collected using light traps and visual sampling methods. Species identification was based on standard morphological characters and available taxonomic keys. The study recorded multiple antlion species belonging to different genera of the family Myrmeleontidae, indicating moderate species richness in the region. Variations in species occurrence and abundance were observed across habitats and seasons, with higher diversity during the summer and pre-monsoon periods. The dominance of certain species suggests their better adaptation to local environmental conditions such as soil texture, temperature, and prey availability. The presence of diverse antlion taxa highlights the ecological significance of Kolhapur’s sandy microhabitats.
Dr. Anjali J. Kelkar, Dr. Vaishali T. Patil
Background Clinical laboratories are experiencing increasing test volumes, expanding test menus, and sustained pressure to deliver rapid and reliable turnaround times (TAT) amid workforce constraints. Total laboratory automation (TLA) has emerged as a systems-level approach to address these challenges; however, real-world evaluations focusing on workflow transformation and variability reduction remain limited. Objectives To evaluate the impact of comprehensive TLA on workflow efficiency, TAT performance, and process stability across core laboratory disciplines. Methods A retrospective operational evaluation was performed comparing laboratory performance before and after TLA implementation using LIS data. TAT performance was assessed using the proportion of samples meeting predefined targets across chemistry, immunology, and hematology disciplines. Process stability and variability were evaluated using statistical process control methods. Reductions in manual workflow steps and sample handling touchpoints were quantified. TAT performance for time-critical assays was assessed on the automated track. Results Implementation of TLA resulted in 56% reduction in manual workflow steps and a 75% reduction in sample handling touchpoints. Post-automation, the proportion of samples meeting TAT targets improved across all disciplines, accompanied by significant narrowing of performance variability. On the TLA line, 81–86% of chemistry tests were reported within 30 minutes, and up to 89% of high-sensitivity troponin I results were available within 40 minutes with improved process stability. Conclusions Comprehensive TLA significantly improved workflow efficiency, TAT performance, and process stability in a high-volume tertiary care laboratory. Beyond reductions in absolute TAT, automation enhanced predictability and operational control, supporting clinical decision-making, quality governance, and readiness for data-driven laboratory practice.
Dr. Shekar H S, Jayashree K
This study examines the perceptions surrounding electric vehicles (EVs) and the principal factors driving the transition towards these vehicles among potential consumers in Bengaluru. As India advances towards sustainable mobility, it is imperative to comprehend consumer attitudes to facilitate the effective adoption of EVs. Utilizing surveys and interviews conducted with a variety of demographic groups in Bengaluru, the research highlights key motivators such as environmental concerns, savings on fuel costs, government incentives, and technological innovations. However, it also notes that challenges like insufficient charging infrastructure and range anxiety persist. The findings aim to offer insights for policymakers, manufacturers, and stakeholders to accelerate the EV transition in urban India.
Bhavik V. Ghati, Priti G. Rathod, Prof. Vaishali N. Shelokar, Saloni V. Chapke, Yash A. Deshmukh
The increasing circulation of counterfeit and unsafe medicines poses a serious threat to public health worldwide. Traditional verification methods such as manual checks and barcode validation often fail to provide real-time and reliable authentication to end users. This paper presents MedGuardAI, an intelligent medicine verification system that combines image-based scanning, QR code validation, artificial intelligence, and trusted data sources to ensure medicine authenticity and patient safety. The proposed system allows users to scan medicine packaging or upload images through a mobile application to verify authenticity, view confidence scores, and receive safety-related information. Additionally, the system supports disease-based medicine search to assist users with preliminary guidance while emphasizing medical consultation. Experimental evaluation and application results demonstrate that MedGuardAI offers an effective, user-friendly, and scalable solution for counterfeit drug detection and medicine awareness.
David I. Forsyth
The deployment of more economical and cost-effective wavelength division multiplexing (WDM) solutions for access and metro networks still remains a key research focus. This paper reports on significant performance enhancement improvements of a highly economical, four channel totally incoherent spectrum-sliced WDM system with the incorporation of a Semiconductor Optical Amplifier (SOA) placed in one of its channels. The baseline system was shown to operate well at 10 Gb/s at a maximum link length of 210 km, demonstrating average Q-factor and signal-to-noise ratios (SNRS) over the four channels. However, the introduction of a single saturated SOA and a single filter placed on one channel yielded a sizable improvement in the Q-factor and exceptional improvement in the measured signal-to-noise ratio (SNR), effectively allowing a further 130 km link travel length whilst still yielding acceptable signal quality – making a total link length of 340 km for one channel.
Adewunmi Andrew A., Ajayi Oluwajuwon Emmanuel
Maritime ports represent concentrated emissions zones due to the simultaneous operation of manoeuvring ships, auxiliary engines and diesel-driven cargo equipment. This research applies a detailed bottom-up framework to quantify emissions arising from port operations, comparing marine diesel oil (MDO) to liquefied natural gas (LNG). A 600,000 TEU container terminal was modelled with representative vessel data and operating pro-files sourced from simulation, auxiliary load modelling and equipment duty cycles. Marine diesel operation produced approximately 595,000 tonnes per year of carbon dioxide (CO2), 6,480 tonnes per year of sulphur oxides (SOx), 276 tonnes per year of nitrogen oxides (NOx) and 14.2 tonnes per year of particulate matter (PM). Switching to LNG eliminated SOx, reduced PM by more than eighty per cent and lowered NOx by fifty-eight per cent, while cutting CO2 emissions by approximately twenty-two per cent. Yet even under LNG, the terminal would require more than 150,000 hectares of mature temperate forest to offset its residual CO2. The analysis highlights LNG as an effective transitional fuel that alleviates air quality burdens while ports invest in electrification and prepare for zero-carbon alternatives such as ammonia, hydrogen and renewable-derived synthetic fuels.
Bakwa Dungka Dirting, Dimka Betty, Dr. Godwin Thomas Ayenajeh, Madugu Jimme Mangai, Oguche David Enekai, Stephen Mallo JR
This study evaluates the performance of six machine learning models—Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Decision Tree (DT), Random Forest (RF), and Gradient Boosting Classifier (GBC)—on a binary classification task. Among these, Random Forest (RF) achieved the highest accuracy (78.57%) and ROC-AUC (0.83), indicating superior overall predictive capability, albeit with a lower recall (0.56), suggesting a trade-off in detecting positive cases. Gradient Boosting (GBC) and KNN demonstrated balanced performance, with competitive F1-scores (0.69 and 0.68, respectively) and robust recall (0.73 and 0.71), making them suitable for scenarios requiring a harmony between precision and sensitivity. The Decision Tree (DT) model exhibited the highest recall (0.75), excelling in identifying true positives but at the cost of lower precision (0.62). While most models (LR, KNN, SVC, RF, GBC) maintained strong ROC-AUC scores (>0.80), SVC had the lowest accuracy (73.38%) and F1-score (0.60). The results suggest that model selection should be guided by specific priorities: RF for optimal accuracy and AUC, GBC/KNN for balanced metrics, and DT for maximizing true positive detection. These findings highlight the importance of aligning model choice with application-specific requirements in classification tasks.
Dr. Jyotirmoy Koley. WBES
Direct taxes are important for India’s economic growth. They help gather money, share wealth, and show the government’s goals. However, the way these taxes are set up affects growth, fairness, and whether people follow the rules. This study examines the efficacy of India's main direct taxes. These taxes are personal income, corporate income, and capital gains taxes. This study finds problems that make it difficult for people to follow tax rules and make good economic choices. This suggests that changes are possible and politically sensible. This study reviews changes since 1991 and examines past research on tax policy and digital tools. It checks how the tax system works, including the use of technology to enforce rules and solve disputes. The study found that even with lower rates and better digital tools, problems still exist. These include fairness, complexity, stable revenue, and unequal digital access, which can worsen regional and social disparities. Personal income tax relies too much on salaried workers and has many exemptions that make it complicated and lead to tax disputes. Corporate tax cuts in 2019 might have helped investments, but also reduced revenue and had complex rules. The capital gains tax has different rules for different assets, leading to tax-driven decisions that hurt market efficiency. New tools, such as faceless assessments and AI, have improved transparency, but issues with disputes and trust remain. This study suggests a reform plan with low rates, fewer exemptions, and fairness. It proposes short-term changes to simplify capital gains and deductions, medium-term changes to personal income tax, and long-term changes to integrate taxes and use advanced analytical techniques. The plan requires careful steps, clear communication, and the involvement of stakeholders to keep revenue stable and make the tax system fair and sustainable.
Emmanuel Bamidele Ajulo, Qayyum Adekunle Tiamiyu
Football stands as the world's most popular sport, captivating billions globally. The English Premier League, in particular, is widely regarded as the pinnacle of professional football, boasting immense global viewership and attracting widespread interest. Its dynamic and unpredictable nature fuels a massive industry built around match analysis, reflecting the deep desire to anticipate match outcomes. Early attempts at football match prediction often relied on static historical data, assumed independence among events, failed to adapt quickly to football's rapid evolution, and lacked the capacity to capture complex nonlinear interactions among multiple features. This study develops a machine learning model for football match analysis in the English Premier League to predict match outcomes, addressing gaps in previous models by using ensemble machine learning algorithms to provide timely, accurate, and real-time analysis. The study utilised Random Forest (RF), XGBoost, and LightGBM. Performance evaluation using standard classification metrics, including Accuracy, Precision, Recall, F1-Score, and ROC-AUC, showed that Random Forest achieved the best overall performance, with an accuracy of 87.14% and an ROC-AUC of 99.00%. The ensemble model further enhanced prediction consistency by combining the strengths of the three machine learning models. This study demonstrates the effectiveness of machine learning for match predictions and, from an industry perspective, offers practical recommendations for football to enhance retention, efficiency, and competitiveness.
Emmanuel Bamidele Ajulo, Raphael Olufemi Akinyede, Shukurat Adeteju Bello
Air pollution impacts human health in various ways, including by depleting the ozone layer. This study aimed to utilise available data to develop a machine-learning model that predicts carbon emissions. The dataset was processed, converted to a time series, and split into training and test sets at a 70:30 ratio. The Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models were employed to develop the model. Root Mean Squared Error (RMSE) metrics were used to evaluate the results. The findings indicate that applying the LSTM model to a large dataset with a high number of epochs yields better accuracy than using ARIMA on the same dataset. The LSTM achieved a lower RMSE of 0.0440 and better predicted carbon emissions than ARIMA. The system developed is recommended for countries, organisations, and agencies to monitor carbon-related air pollution.
Aditi Kaushik, Sanjay Kaushik
Claims that blindfolded youngsters can identify items, read text, or describe images are widely promoted in educational and commercial programs, which are commonly referred to as "midbrain activation" or intuition training. Proponents of these programs frequently interpret such examples as proof of extrasensory perception (ESP), nonverbal cognition, or enhanced intuitive ability. However, these ideas are unsupported by actual evidence and contradict well-established sensory neuroscience principles. Recent research in vision science, cognitive psychology, and neuroimaging suggests that even severely degraded visual input can be sufficient for object recognition when paired with predictive coding and memory-based template matching. Peripheral vision and low-resolution retinal input, which are frequently disregarded in lay explanations, provide partial information that the brain can use for shape, contour, and color processing. Furthermore, top-down modulation from the prefrontal, orbitofrontal, and parietal cortex aids in the reconstruction of missing information, allowing for quick perceptual inference from partial sensory data. Furthermore, cognitive and social factors such as ideomotor effects, attentional bias, expectancy, and reinforcement can exaggerate perceived task accuracy, creating the appearance of exceptional ability. In this study, we investigate these assertions using a rigorous neuroscientific approach. We propose a mechanistic model that incorporates low-level visual leakage, coarse peripheral cue extraction, predictive coding, and memory-driven template matching into the ventral visual stream. We highlight the functions of V1-V4, the inferotemporal cortex, the lateral occipital cortex, and higher-order top-down networks in reconstructing object identity from degraded or incomplete sensory input. By mapping these brain and cognitive processes, we provide a holistic framework for explaining actions that are frequently misattributed to non-visual or psychic powers, highlighting the value of controlled experimental paradigms and evidence-based evaluation in educational and training settings.
Mohamad Faizul Yahya, Puteri Aishah Adliah Kamaruddin, Suzaini Abdul Ghani
The increasing global demand for paper and the rising volume of fabric waste have become critical environmental concerns due to unsustainable production and disposal practices. Conventional paper production, heavily reliant on wood pulp, contributes to deforestation and resource depletion, while fabric waste exacerbates landfill overflows. This study investigates the potential of producing paper from recycled calico fabric waste blended with box paper pulp in specific ratios ranging from 20% to 80% to evaluate the mechanical and physical properties of the resulting paper. Fabric waste and box paper were processed into pulp, combined in precise ratios, and shaped into sheets using a mold-based paper-making technique. The produced paper was tested for tearing resistance, thickness, weight, and absorbency properties. Results demonstrated that higher calico fabric content enhanced absorbency and flexibility but reduced tearing resistance. The optimal composition, determined by balancing tearing resistance, flexibility, and absorbency, was found at a 50% calico fabric and 50% box paper ratio, contributing to greater rigidity and mechanical strength. This composition was selected as optimal due to its balance of high mechanical strength, adequate absorbency, and flexibility, making it suitable for practical applications. The final composition balanced these properties, providing an eco-friendly alternative for applications such as sustainable packaging and artistic materials. This study highlights an environmentally friendly approach to paper production, offering a sustainable alternative to conventional methods and integrating circular economy principles by repurposing calico fabric and box paper waste.
Dr. Mohd Zubair
The rapid expansion of digital technologies and wide spread Internet use have significantly transformed contemporary life, particularly in the field of education. While cyberspace offers immense opportunities for learning, communication, and information sharing, it has also led to a sharp rise in cybercrime, exposing students to risks such as cyber fraud, identity theft, cyber bullying, and privacy violations. In this context, the present study investigates the level of cybercrime awareness among students of Constituent Colleges of Cluster University of Jammu. The study aims to assess overall awareness levels and examine differences in cybercrime awareness with respect to gender and area (urban and rural). A descriptive survey method was employed, and a sample of 200 undergraduate students was selected through random sampling from five Constituent Government Colleges of Cluster University of Jammu. The Cybercrime Awareness Scale (CCAS) developed by Tibi et al. (2019) was used for data collection. Statistical techniques such as percentages, means, standard deviations, and the t-test were applied for data analysis. The findings revealed that the majority of students possessed an average level of cybercrime awareness, while a smaller proportion demonstrated high awareness and a negligible number exhibited low awareness. A significant difference was found between male and female students, with female students showing higher cybercrime awareness. However, no significant difference was observed between students belonging to urban and rural areas. The study highlights that although students demonstrate moderate awareness of cybercrime, there remains a need for systematic and structured awareness programmes. The findings underscore the importance of integrating cybercrime and cyber security education into higher education curricula to promote safe, responsible, and informed use of digital technologies among university students.
Asukwo Okon, PhD., Etuk
The Ibibio is the fourth largest ethnic group in Nigeria, after the Hausa, Yoruba, and Igbo. Findings show that the Ibibio constitute the earliest inhabitants of the southern part of the country, having existed in their present geographical location since 700 BC. History has it that Efik is a subset of the Ibibio. The Ibibio tribe has a vibrant cultural identity, encompassing various aspects of daily life, traditions, and rituals. The Ibibio people are renowned for their artistic skills and craftsmanship. At the heart of this cultural endowment are sculptors, raffia-weavers, pottery makers, storytellers, filmmakers, musicians, artists, fashion designers, and content. In recent times, these creatives' contributions and the impact of their cultural and creative industries have been recognised, with the creative industries emerging as a critical driver of the 21st-century economy and social life. The study draws from Kurt Goldstein’s Self-Actualisation Theory to explore the cultural and creative industries of the Ibibio and the Efik people, their various manifestations, the key drivers of the Ibibio creative renaissance, the critical milestones, and the realization of their creative and cultural potentials between 1928 and the present. The Dependency Theory provides insight into social factors within the global community, and how they influence the Ibibio creative economy. The study concluded that the Ibibio/Efik cultural industries and creative industries have contributed immensely to the development of the nation and the global economy in diverse ways. Recommendations are made for harnessing the creative industries for more development dividends.
Christiana Maxwell, G. D. B. Awi-waadu
Freshwater snails have been reported to act as vectors of parasitic infections, causing hardship to infected persons. Surveying the distribution and abundance of snail vectors of parasites in selected areas of Rivers State, Nigeria was assessed in three study sites; Odau, Mbiama and Akinima respectively. Field work was conducted once a month for three consecutive months from June to August 2024. A scoop net and handpicking method was used for snail collection and water samples were collected also for physico-chemical parameter analysis. Physico-chemical parameters analyzed were; temperature, dissolved oxygen (DO), pH, total dissolved solids (TDS), biological oxygen demand (BOD, conductivity and water salinity using standard methods. A total of 539(78.34%), 109(15.84%) and 40(5.81%) snails were gotten from Odau, Mbiama and Akinima respectively. Thus a total of 688 freshwater and land snails made up of Pila ovata, Bulinus forskali, Archachatina marginata and Limicolaria spp. comprising 514 (74.71%), 135(19.62%), 27(3.92 %) and 12(1.74%) were collected respectively during the study in the three locations. The data analyzed showed that water physico-chemical parameters recorded skewed impact on the abundance of snails. The study areas had a high abundance of freshwater snails, and may be at risk of an outbreak of snail-borne parasitic diseases. Odau had the highest in terms of abundance and species diversity than other locations. The physico-chemical parameters indicated different relationships with snail abundance.
Kabiru Ibrahim Musa, Mahmud Ahmed Usman, Muhammad Tella
Machine Learning (ML) has been a critical computational paradigm that has shaped contemporary applications in such domains as finance, healthcare, and cybersecurity, such that its performance evaluation cannot be less critical. However, its selection and interpretation of metrics has remained inconsistent, often leading to misleading conclusions. This study presents a systematic analysis of the most commonly used performance evaluation metrics in ML, integrating conceptual taxonomy, mathematical definitions, and empirical assessment under controlled perturbations. There are three dimensions to ML performance evaluation metrics categorization: robustness, discrimination, and calibration. Experiment conducted on classification and regression, and using synthetic datasets and benchmarks, evaluate threshold variation, class imbalance and label noise. Results obtained showed that no single metric captures model performance comprehensively and widely used metrics may yield conflicting or misleading assessments under certain conditions. Also, context-aware selection and multi-dimensional reporting were necessary for reliable evaluation. By empirically linking metric behaviour to data characteristics, this study provides guidance for context-aware metric selection and reporting that is not only standardized but also evidence-based.
Ogechukwu Scholastica Onyenaucheya
Machine learning-based intrusion detection systems (IDS) are now commonly used in real-time cybersecurity to defend against quickly changing threats. However, recent developments in adversarial machine learning have shown that many IDS models are still very susceptible to adaptive attacks, especially in real-time conditions. Most existing research examines adversarial robustness in offline or static scenarios, missing the dynamic nature of live network traffic, ongoing data flows, and strict time requirements. This gap limits the effectiveness of current adversarial defense strategies in practical intrusion detection systems. This paper aims to fill this gap by proposing a theoretical framework for adversarial robustness in real-time machine learning-based intrusion detection systems. The framework treats adversarial robustness as a characteristic that changes over time, influenced by detection delays, attacker strategies, concept drift, and ongoing interactions between models and adversaries. We introduce formal concepts such as time-to-evasion, detection stability, and robustness decay to describe how IDS reacts under lasting adversarial pressure. Instead of creating a new detection algorithm, this study gives a theoretical viewpoint that clarifies why many adversarial defenses perform well in offline tests but struggle in real-time scenarios. The framework applies to various IDS designs and machine learning methods. By connecting adversarial machine learning theory with the needs of real-time intrusion detection, this work lays the groundwork for future testing, comparisons, and development of resilient IDS for challenging operational environments.
Betty Mukuwapasi, Sicelumusa Ncube, Tendai Mujati
For generations, agricultural communities globally have employed sustainable methods to maintain soil quality, ensure crop yields, and minimize reliance on heavy tillage. Women have played a central role in agriculture from early farming systems to the present day, and their involvement remains vital for boosting agricultural productivity and ensuring family food security. This research analysed the factors influencing the adoption of conservation agriculture (CA) among women farmers in Ward 28, Chiredzi District, Zimbabwe and the challenges they are facing in its implementation. Through stratified and random sampling, 60 women farmers were chosen for the study. Information gathered through pre-tested questionnaires was assessed using descriptive statistics. Findings showed that most women farmers view climate change as harmful to their farming, citing altered rainfall patterns, higher temperatures and frequent droughts. In response, they are adopting conservation agriculture practices such as planting basins, intercropping, rotating crops with legumes and using crop residues for mulch. Key limiting factors for women’s participation included labor-intensive basin preparation, high weed pressure, insufficient finance, inadequate knowledge and training. Women farmers reported that CA improved crop yields, soil moisture retention, soil fertility and decreased use of fertilizers and chemicals. To support women and advance equitable, sustainable farming, the study suggests strengthening extension support, expanding credit access, and ensuring better provision of resources.
Abeki, J.P., A, Adedokun, I.O, Alkali, A, Bunonyo, Y. T, Udensi, E. E
The geophysical interpretation of Aeromagnetic data over the study area bounded by longitude 4°00′–5°00′ and latitude 5°30′–6°30′ provides crucial insights into the subsurface geological configuration, with significant implications for hydrocarbon exploration. Reduced-to-Equator (RTE) map reveals magnetic values ranging from 32,869.16 nT to 33,037 nT, reflecting the influence of subsurface lithology and tectonic structures. High magnetic intensities in the East–central and South Western part of the map, often correlate with the presence of ferromagnetic minerals, such as magnetite-rich mafic and ultramafic rocks, suggesting potential igneous intrusions or lithological boundaries, the lower magnetic zones, illustrated in blue west–central part of the map, indicate nonmagnetic sedimentary units, deep basement rocks, or zones of weathering and alteration. The high magnetic intensity closures observed within the basin are likely associated with the underlying basement rocks or with igneous intrusions that may have penetrated the sedimentary sequence. Depth estimation techniques such as Euler Deconvolution utilize a structural index (SI = 1) to delineate linear geological features like faults and dykes, with clustered solutions indicating complex basement architecture. Source Parameter Imaging (SPI) analysis estimates source depths exceeding 3.0 km, confirming the presence of thick sedimentary basins critical for hydrocarbon maturation. Spectral analysis indicates basement depths between 8.7 and 11.6 km, with deeper zones concentrated centrally and southeast part, aligning with potential depocenters. The First Vertical Derivative (FVD) map indicates the passage of the paleo-fracture zone through the area. This is also indicated in the discontinuity shown in the Total Magnetic Intensity (TMI) map. These datasets improve the geological interpretation of the region, reveal structurally controlled sedimentary basins, and help outline prospective zones for hydrocarbon exploration and development within the Niger Delta.
Abhijit R., Ingole4, Prof. Priti P. Tijare, Samata V. Ingole, sumit G. Dholwade, Unnati C. Shrikhande
Effective task eneration and deadline tracking, typically operate under a static paradigm. These systems fundamentally lack the requisite intelligence to dynamically prioritize tasks based on individual performance metrics or to address the crucial psychological dimension of productivity, specifically user motivation and emotional state, thereby contributing to sub-optimal goal attainment. This deficiency establishes a critical gap between rudimentary organizational tools and comprehensive productivity platforms. Existing methodologies often neglect the necessity for intelligent task decomposition and adaptive scheduling, failing to provide actionable guidance on how large projects should be segmented and sequenced for maximal efficiency. Furthermore, the absence of integrated psychological support mechanisms means that when users experience fluctuations in mood or motivation, the system offers no compensatory intervention, leading to inconsistent application usage and eventual abandonment. Addressing this dual requirement intelligent functional management and sustained psychological engagement is paramount for developing a truly effective productivity solution. To mitigate these limitations, this pamanagement and sustained motivation are essential for improving productivity in academic, professional, and personal environments. However, most existing task scheduling applications focus primarily on basic to-do list creation and reminders, while ignoring intelligent prioritization and the psychological factors that influence user performance. This paper presents a Smart Task Scheduler with Motivation Mode, an AI-powered Android application designed to address both organizational and motivational challenges in task management. The proposed system integrates task scheduling, AI-based priority analysis, and mood-oriented motivational support within a single platform. Developed using Android (Java/XML) and Firebase Realtime Database, the application enables users to create tasks, receive intelligent priority suggestions, and track progress through visual productivity analytics. A Pomodoro-based work timer and personalized motivational messages are incorporated to improve focus and reduce mental fatigue.
D.N.S.Perera, Y.A.A.Kumarayapa
Paddy cultivation is vital for Sri Lanka’s food security, but increasing plant diseases due to adverse climate, declining soil health, irregular water availability, and unpredictable weather have caused a continuous drop in yield, highlighting the need for effective disease detection. This study presents an integrated paddy disease detection system that combines Internet of Things based environmental sensing with drone-based remotesensing imagery and artificial intelligence techniques. The proposed system employs an ESP32 microcontroller interfaced with more accurate sensors to monitor soil, water, and agro-climatic parameters in real time. Machine learning models are applied to analyze the collected sensor data and predict potential paddy diseases based on environmental conditions. In parallel, a drone imaging system captures high-resolution images of paddy fields, which are processed using deep learning models developed with Keras and TensorFlow to detect and classify disease symptoms. A Flask-based web application is developed to visualize sensor data, display disease predictions, and provide actionable recommendations for farmers and agricultural officers. Experimental results demonstrate that the proposed system achieves an overall disease detection accuracy of 98%, with additional evaluation using precision, recall, F1-score, and confusion matrix analysis confirming its robustness and reliability. The practicality of the proposed system is enhanced by its low cost, portability, and modular design, enabling easy deployment in small and large paddy fields and allowing scalability to regional and national agricultural monitoring systems.
Dr. Madhavi Kalapati, Dr. Venkata HRD, K. Sathya., S. Kannan, S. Tamil Selvan
Financial literacy is a crucial facilitator of economic inclusion, particularly for the rural poor. This empirical research examines the extent of financial literacy among the rural poor in Thanjavur District, Tamil Nadu. A primary survey was conducted among respondents in four taluks of Thanjavur District using a structured questionnaire on knowledge, attitudes, and practices in basic banking, savings, credit, insurance, digital payments, and financial planning. Descriptive statistics, scoring trends, and inferential analyses were employed to analyze the data. The findings show that financial literacy is at a moderate but uneven level, with 38% of respondents classified as Low, 44% as Moderate, and 18% as High. Significant correlations were established between financial literacy and educational level, income, and access to financial services. This paper concludes with specific recommendations to policymakers, NGOs, and financial institutions for improving financial education and outreach programs for the rural poor in Thanjavur.
Onwuachu Uzochukwu Christian, Opuh Jude Iwedike
This paper addresses the security shortcomings of MQTT in Industrial IoT by designing, implementing, and evaluating a secure MQTT prototype that balances confidentiality, integrity, and usability. Using an Object Oriented Analysis and Design Methodology (OOADM) guided by UML artifacts, the work decomposes the system into modular classes and enforces layered security aligned with the OSI and client–server models. Implemented in Python with Tkinter GUIs and Mosquitto as the MQTT broker, and supporting libraries (paho-mqtt, pycryptodome, bcrypt, hashlib, base64, socket) for secure messaging, encryption, authentication, and IP tracking. MQTT Explorer was used for real-time visualization of message flows, encryption consistency, and topic activity. The system integrates cryptographic techniques such as AES CBC encryption with random IVs, HMAC SHA256 integrity checks, bcrypt password hashing, and an OTP email verification/recovery flow (Gmail SMTP). Role Based Access Control, account lockout policies, and audit logging (user, role, IP, timestamp, message state) provide operational safeguards. Experimental deployment and validation were conducted in a controlled virtual environment. Kali Linux running in VirtualBox provided the platform for penetration testing, and the subscriber was executed on a Kali instance in UserLAnd with GUI access through R VNC to emulate a realistic IIoT endpoint. Security evaluation and Penetration tools included: John the Ripper for offline password cracking, Bettercap for man-in-the-middle (MITM) testing and traffic manipulation, Wireshark for packet capture and protocol analysis, and Nmap/Zenmap for port and service enumeration. These tools verified the system’s resilience against common attacks, including unauthorized topic publishing, credential compromise, and message interception. Results demonstrate that plaintext MQTT (1883) is trivially intercepted and modifiable while TLS (8883) prevents passive decryption without trust compromise, and that the combined cryptographic and access control measures significantly reduce practical attack surfaces for IIoT deployments.
Ashiru Sani, Obunike Arinze Ubadike, Omotayo Paul Ale, Serestina Viriri, Tope Ojuawo, William Rupert Waboke
Asphalt is a basic material used for pavements and roads construction due to its durability and capacity to endure loads, pressure and stress. To minimize construction cost, asphalt is typically combined with readily available and economically recycled materials. Some of these materials includes glass furnace dross, ashes from municipal waste incineration, crushed bricks, plastic, glass, and crumb rubber, sourced from waste tires. Evaluating Marshall parameters after modification is crucial to maintaining the original capacity of asphalt to withstand enormous stress. There are several Marshall Parameters of modified asphalt mixture (MAM) but Marshall Stability (MS) and Marshall Flow (MF) are the most critical parameters in evaluating the performance of the MAM. Researchers have repeatedly relied on Marshall test in the laboratory to determine the Marshall properties of MAM, which have proved to be expensive, labor-intensive, and tedious. Numerous studies have proposed machine learning (ML) models as an alternative to the traditional evaluation method of Marshall parameters of MAM. The popularity of ML models is largely due to their ability to learn patterns and predictive characteristics from complex data. ML models have been successfully used in the prediction of Marshall parameters of MAM with varying degrees of accuracy, as documented in the literature. Consequently, this paper examines the literature on the use of ML models for predicting Marshall Parameters, particularly MS and MF of MAM. This study identified several ML models, such as support vector machine (SVM), K-nearest neighbor, artificial neural networks, and random forest (RF), previously employed in this domain. The study also looked at various performance metrics used in evaluating the predictive accuracy of MS and MF. Some of the metrics include Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). The paper also highlighted the potentials of ML models in reducing costs, time and labour as well as improving prediction accuracy. In addition the study also address challenges such as over fitting and the need for more quality and open source datasets. Recommendations for future research include the development of standardized datasets and the exploration of synthetic data to enhance model reliability and generalizability.
Leo. Tanyam. Encho, Pipima Celestine Mofor
Globally, healthcare systems face the pervasive challenge of optimizing patient flow, minimizing wait times, and enhancing service delivery. In this research work, an M/M/1 queueing system is considered with impatient customers and a variant of multiple vacation policy, where the case that customer impatience is due to the servers’ vacation is examined. Whenever a system becomes empty, the server takes a vacation. However, the server is allowed to take a maximum number of vacations, denoted by K vacations, if the system remains empty after the end of a vacation. We derive the probability generating functions of the steady-state probabilities and obtain the closed-form expressions of the system sizes when the server is in different states. In addition, the closed-form expressions for other important performance measures is obtained. Finally, some numerical results are presented. Our result shows that E(L_K) and the mean system size E(L) all decrease with θ for any finite K whereas P_V and P_b neither increase nor decrease with θ when K = 2 and K = 3.
AU Osunde, Nosa GIwa-Osagie
Workplace harassment exists as a widespread problem which affects all work environments regardless of location or cultural background or business structure to create negative effects on staff members and their employers. Organizations today need to prioritize workplace harassment prevention because it represents a critical issue in their cultural environment. Workplace harassment exists as any form of unwanted behavior which produces an unpleasant work environment that makes employees feel threatened or scared or uncomfortable. Workplace harassment exists in different forms which include sexual harassment and bullying and discrimination that targets employees because of their race or gender or sexual orientation or disability status. Research shows that workplace harassment affects at least 25% of employees according to studies which demonstrate the need for organizations to take immediate action against this widespread problem (Kemp et al., 2020). Workplace harassment creates effects which reach further than individual victims because it damages organizational performance while altering workplace culture and harming corporate reputation. A hostile work environment creates negative effects on employee morale and staff retention while simultaneously reducing workplace productivity and innovation levels. Research findings demonstrate that workplace harassment creates negative effects which result in decreased employee job satisfaction and increased anxiety and depression and physical health problems (Nielsen et al., 2016). Organizations which do not properly address harassment will face major financial expenses and damage to their reputation while creating an environment where employees feel afraid to speak up.
Danny Sendolo, Ekki Stevens, Francis Omega Somah, Philip Kpoto Joekolo, Theophilus Dee Willie
Antimicrobial resistance (AMR) among Gram-negative bacteria represents a major public health challenge in Nigeria and across Sub-Saharan Africa, particularly resistance to β-lactam antibiotics mediated by plasmid-encoded β-lactamase genes. This study assessed phenotypic antibiotic resistance patterns and the presence of blaTEM and blaSHV genes among 40 archived Gram-negative clinical bacterial isolates obtained from urine and wound samples in Nigeria. Antimicrobial susceptibility testing was performed using the Kirby–Bauer disk diffusion method following Clinical and Laboratory Standards Institute guidelines. Molecular detection of resistance genes was carried out using polymerase chain reaction (PCR). Very high resistance rates were observed against ceftazidime (97.7%), cefuroxime (≈99%), amoxicillin–clavulanic acid (≈92–100%), and ciprofloxacin (92%). Relatively higher susceptibility was recorded for nitrofurantoin, gentamicin, and ofloxacin. Molecular analysis showed that 75% (15/20) of isolates screened carried the blaTEM gene, while all isolates screened for blaSHV (24/24; 100%) were positive. The study demonstrates extensive phenotypic resistance and a high prevalence of β-lactamase–encoding genes among Gram-negative clinical isolates in Nigeria, underscoring the urgent need for integrated phenotypic and molecular AMR surveillance and strengthened antimicrobial stewardship.
Bhargavi Rajarathinam, Imbaasree Rajavelu
Hepatocellular carcinoma (HCC) is a highly aggressive cancer associated with chronic liver diseases, posing significant treatment challenges. This study explores the apoptotic potential of ethanolic leaf extracts from Plectranthus amboinicus and Hibiscus rosa-sinensis on HepG2 cell lines, aiming to elucidate their mechanisms of action and comparative efficacy. Both extracts underwent phytochemical analysis, antioxidant activity assessment using the DPPH assay, and safety evaluation through haemolytic activity determination. Apoptosis was visualized using acridine orange/ethidium bromide dual staining and quantified with propidium iodide/annexin V-FITC staining and flow cytometry. The regulation of key apoptotic genes, BAX and BCL-2, was analysed in treated HepG2 cells. Preliminary results indicate that both extracts exhibit significant antioxidant activity, with P. amboinicus demonstrating robust effects and a favourable safety profile. In contrast, H. rosasinensis showed increased cytotoxicity, raising concerns regarding its therapeutic application. This comparative analysis highlights the potential of P. amboinicus as a promising candidate for novel HCC therapeutic strategies, while underscoring the need for further investigation into the apoptotic mechanisms and safety of H. rosasinensis. The P. amboinicus extract effectively induces apoptosis in HepG2 cells by modulating the expression of key apoptosis regulators, BAX and BCL-2, without significant haemolytic toxicity at lower concentrations. These findings provide a strong foundation for further research into the therapeutic applications of P. amboinicus in liver cancer treatment.
Ndidiamaka Gladys Israel
Grating-based devices are crucial in sensing and monitoring activities in the petroleum and gas industry, espe-cially under extreme environmental conditions such as high pressure, temperature, and mechanical stress. This paper proposes an applied mathematical framework based on Partial Differential Equations (PDEs) for model-ling the physical behaviour and optimising the performance of grating devices. The governing equations are based on wave propagation, heat transfer, and elasticity theory, and they account for optical, thermal, and me-chanical interactions that influence grating response. To solve the resulting PDE systems, analytical techniques like variable separation are supplemented with numerical approaches like as finite difference and finite element methods. The model assesses sensitivity, stability, and accuracy using petroleum-specific operational characteristics. The findings show that PDE-driven models have much higher predictive capability and dependability than tradi-tional empirical approaches. This study combines theoretical applied mathematics with engineering applications, helping to improve sensor design, monitoring efficiency, and risk mitigation in petroleum and gas systems.
Md. Arifuzzaman Nur, Rabiul Islam, Sabrien Sobnom, Shakib Uzzaman
This study was conducted to evaluate the phytochemical composition and toxicological safety of the ethanolic extract of Trema orientalis leaves. Preliminary phytochemical screening revealed the presence of alkaloids, flavonoids, tannins, glycosides, steroids, terpenoids, and carbohydrates, indicating that the plant is rich in bioactive constituents. Acute oral toxicity was evaluated in Swiss albino mice at doses ranging from 250 to 3000 mg/kg body weight. No mortality, behavioral changes, or signs of toxicity were observed, indicating that the extract is safe up to 3000 mg/kg. Sub-acute toxicity was assessed by administering 500 mg/kg of the extract for 14 consecutive days. The treatment did not produce any significant changes in body weight, liver enzymes (SGPT, SGOT, ALP), bilirubin, creatinine, lipid profile, or HDL levels when compared to the control group. These findings suggest that the ethanolic extract of Trema orientalis leaves is non-toxic under both acute and short-term repeated exposure. The presence of multiple phytochemicals along with its favorable safety profile supports the potential of this plant for further pharmacological and therapeutic investigations.
Danjuma Andembutop Kwesaba, Wilson Philip Sankun
This study provides a comprehensive assessment of the ecological impacts of mineral resource exploitation on the Mambilla Plateau, Taraba State, Nigeria. The study focused on eleven key mining communities—Mayo Sina, Titong, Njah, Bongo, Yurum Kenor, Yana, Tunga Shuaibu, Papaa, Tikobi, Bundi, and Tunga Lugeri— within Sardauna Local Government Area. A total population of 8,600 residents was considered, from which a representative sample size of 351 was determined using the Krejcie and Morgan sampling table. Data were collected through randomly administered questionnaires and analyzed using descriptive statistics. Findings reveal that mineral exploitation on the Mambilla Plateau are largely unregulated, reflecting significant gaps in environmental governance. Respondents reported severe ecological impacts, including deforestation (Mean = 4.18), water pollution (Mean = 4.05), soil erosion, and widespread landscape degradation. The composite index confirmed that current exploitation practices are unsustainable and environmentally destructive. The study concludes that unregulated artisanal mining poses a profound threat to the Mambilla Plateau’s environmental integrity and community well-being. It recommends urgent policy intervention through strengthened regulatory frameworks, adoption of sustainable mining technologies, and large-scale ecological restoration to safeguard the Plateau’s fragile environment and enhance the resilience of affected communities.
Adedeji B. Adegoke, Ademu M. Lawal, Amona Adah AKPA, James KOR, Righteous AGOR, Tyson Owoicho ONUH
Food is essential to the human body's metabolism, yet remains scarce due to climate variability that manifests as floods, rising temperatures, drought, intermittent rainfall, and water stress. This calls for effective resilience strategies, such as irrigation, to enhance food production in Benue State, which is strategic to Nigeria's food self-sufficiency. Against this background, with limited empirical attention on the impact of irrigation on rice production, a common staple food in Benue State, this study was motivated. The survey research design was relied upon with primary data collected through structured questionnaires administered to 150 small-scale rice farmers in the state. Data collected were analyzed using descriptive statistics and the stochastic frontier model, with the paired-samples t-test as a robustness check. Results indicated a positive but insignificant impact of irrigation (β = 4.44%; p = 0.559) on rice yield in Benue State, as rice irrigated farmers in the state operate at 50.28% level of efficiency, leaving about 49.72% room for improvement when inefficiency variables such as experience, education, and access to credit are improved upon. For robustness check, the paired sample t-test showed a significant negative mean difference between output before and output under irrigation, implying that output under irrigation remains higher than rainfed rice production, but the overall impact of irrigation is insignificantly positive due to technical inefficiency. In line with the results, the study recommended that IFAD and the state government should intensify efforts on extension services and farmers’ training on global best practices to enhance the experience level of farmers and rice yield under irrigation in the state.
Sandhya Dogra, Sarla Saklani
Inula cappa (Buch.-Ham. ex D. Don) DC. (Asteraceae), a high potential medicinal herb and traditionally used in Ayurvedic, Chinese and Tibetan medicine, has been recognized for its diverse pharmacological properties. The present study aimed to evaluate the antioxidant potential and antimicrobial effect of leaf extracts obtained using different solvents. Antioxidant activity was assessed through standard assays, including DPPH radical scavenging, revealing a concentration-dependent free radical scavenging effect. The methanolic extract exhibited the highest antioxidant potential, correlating positively with its total phenolic and flavonoid content. Bioactive compounds were identified by GCMS. Antimicrobial activity was tested minimum inhibitory concentration (MIC) methods. Results demonstrated notable inhibition zones, particularly against Staphylococcus aureus and Escherichia coli, with the methanolic extracts showing superior efficacy compared to other extracts. These findings suggest that Inula cappa leaves are a promising source of natural antioxidants and antimicrobial agents, supporting their potential application in pharmaceutical formulations.
Tuo N’dioto Sidoine
Water pollution from heavy metals, nutrients, dyes, pharmaceuticals, Per- and PolyFluoroAlkyl Substances (PFAS), and pesticides poses serious global challenges, as conventional treatments often fail to remove trace contaminants effectively and produce secondary waste. Biochar, produced by biomass pyrolysis, offers a sustainable environmental carbon material with tunable surface chemistry, porosity, and carbon sequestration potential, making it a promising low-cost alternative to activated carbon. However, pristine biochar requires modification to overcome low surface area, limited functional groups, and poor selectivity for emerging pollutants. This review examines modification methods, adsorption mechanisms (ion exchange, surface complexation, π-π interactions, hydrophobic partitioning), and environmental impacts, including ecotoxicity, leaching risks, and long-term stability. Key findings highlight biochar's versatility across fixed-bed systems, permeable reactive barriers, constructed wetlands, and decentralized treatment, with regeneration strategies enabling reusability. Current challenges include inconsistent preparation standards, limited real wastewater studies, aging mechanisms, performance-safety trade-offs, and scale-up barriers. Future directions emphasize green modifications, multifunctional composites, AI-optimized design, and Advanced Oxidation Processes (AOP) membrane integration for sustainable water management.
Asogwa, T. C., Omeje, K. N.
The rapid growth of cloud computing has introduced significant benefits in terms of data storage and processing but it has also increased the risks of unauthorized access and data breaches. Therefore, this study presents a biometric-based encryption system which is designed to enhance cloud data security through the integration of facial recognition and homomorphic encryption. The proposed system employs an Autoencoder (AE) for feature extraction, Convolutional Neural Network (CNN) for facial recognition, and the Brakerski-Gentry-Vaikuntanathan (BGV) algorithm for secure data encryption and decryption. The adopted AE is used to efficiently compresses facial features into latent vectors used both for recognition and as encryption keys. Furthermore, the experimental evaluation of the techniques adopted using both primary facial datasets and the LFW dataset demonstrated that the AE achieved a training accuracy of 99.84% and validation accuracy of 98.59%, while the CNN attained a training accuracy of 97.05% and validation accuracy of 95.04%. Additionally, the result of the BGV encryption process recorded an average encryption time of 0.023 seconds and decryption time of 0.019 seconds, indicating minimal computational overhead. Results confirm that the integration of biometric encryption enhances both data confidentiality and authentication reliability in cloud environments. This system provides a robust and efficient framework for securing sensitive data in modern cloud infrastructures, ensuring privacy, integrity, and accessibility for authorized users.
Dr. Adrian Wee, Dr. Andy Choong, Dr. Chris Daniel Wong, Dr. Farzana Nazera, Dr. Lim Chong Ling, Dr. Shaun Edward Cheah, Dr. Stephen T. Homer
For more than two decades, Malaysia has experienced a persistent outflow of highly skilled professionals despite sustained economic growth, expanding higher education, and repeated policy interventions aimed at talent retention and repatriation. This study examines why brain drain in Malaysia remains entrenched and what structural reforms are required to address it effectively. Rather than treating skilled migration as a function of individual choice or wage differentials alone, the thesis conceptualises brain drain as a systemic and multi-level phenomenon shaped by interacting economic, institutional, career, and innovation ecosystem factors. Adopting a qualitative dominant research design supported by secondary data analysis, the study integrates Human Capital Theory, push–pull and relative deprivation models, and institutional and innovation ecosystem theory. Primary data are derived from semi structured interviews with Malaysian professionals who have remained in the country, emigrated, or returned after working abroad, enabling comparative analysis across migration outcomes. The findings reveal that constrained career mobility and slow promotion velocity outweigh wage considerations in migration decisions, particularly at mid career stages. Perceived erosion of meritocracy significantly undermines institutional trust, while shallow innovation and scale up ecosystems limit professional fulfilment and leadership opportunities. Existing policy responses are widely perceived as incentive driven and transactional, lacking the systemic reform necessary to rebuild long term confidence. The study concludes that Malaysia’s brain drain cannot be resolved through isolated or short term measures. Sustainable talent retention and re attraction require coordinated reforms that strengthen meritocratic career pathways, institutional credibility, innovation ecosystems, and absorptive capacity for brain circulation. Reframing brain drain as a nation building challenge, rather than a defensive retention exercise, is essential for Malaysia’s transition toward a high income, innovation led economy.
Bhavya Mishara, Satyadhar Dwivedi
Cognitive impairment is a core symptom in schizophrenia that has a significant impact on psychosocial function, but shows a weak response to pharmacological treatment. Consequently, a variety of non pharmacological interventions have tried to find out suitable out come in patients with schizophrenia. Objectives: The present study aims to review the existing scientific literature on cognitive remediation in schizophrenia. Specifically, it highlights the major structured cognitive remediation protocols developed for schizophrenia and summarizes findings from recent meta-analyses. Methods: A comprehensive electronic search was conducted in the PubMed and Google scholar database for studies published in year 2015 to 2025. All relevant meta-analyses and review articles on cognitive remediation in schizophrenia were included. Results: Numerous intervention programs have been developed and evaluated to enhance cognitive functioning in individuals with schizophrenia. Several quantitative reviews have demonstrated that cognitive remediation effectively reduces cognitive deficits and improves functional outcomes. Moreover, current evidence supports its applicability and usefulness during the early phase of schizophrenia and in high-risk populations. Conclusions: Cognitive remediation represents a promising therapeutic approach for enhancing real-world functioning in schizophrenia and should be regarded as a crucial component of early intervention strategies in psychotic disorder.
Chimenka Goodluck, Obioma D.C.
The uncontrolled growth of the Internet of Things, especially in securing limited-resource IoT, has created unprecedented potential that raises issues tremendously. The key intention of the study is to comparatively examine the security measures and defenses against the cyber-attacks that take place in the IoT security protocols. The study provides a combination of secure communication protocols, blockchain technology, and lightweight cryptography using Bevywise IoT simulator, MQTT Route, and Wireshark for the network. The results of the study confirm the effectiveness of the security measures provided by the blockchain technology for the integrity and immutability of the data. The results also emphasize the significance of the study to solve the IoT security issues better. By creating a security framework, the study significantly contributes to the development of IoT technology, especially in the fields where the security has to be given more priority. Cyber security and maintaining the privacy and trust of the systems play a very significant role.
Prithvi Sharma
The ΛCDM model has been remarkably successful in describing the large-scale evolution of the Universe, yet persistent discrepancies in key cosmological parameters increasingly challenge its completeness. In particular, the growing tension between early- and late-Universe measurements of the Hubble constant (H₀), along with inconsistencies in the amplitude of matter clustering quantified by S₈, suggests that the standard assumption of non-interacting dark components may require revision. This review examines the Interacting Dark Sector (IDS) hypothesis, in which dark matter and dark energy are allowed to exchange energy and momentum while preserving total energy–momentum conservation. We survey phenomenological coupling models, including density-dependent interactions and running vacuum scenarios, and discuss their impact on the expansion history, structure formation, and cosmological observables. By synthesizing recent theoretical developments with constraints from cosmic microwave background measurements, large-scale structure surveys, and distance-ladder observations, we assess the extent to which IDS models can simultaneously alleviate the H₀ and S₈ tensions. We further examine theoretical challenges associated with stability, thermodynamic consistency, and the lack of a microphysical origin for the coupling. We conclude by outlining observational prospects for testing dark sector interactions with forthcoming surveys and discuss whether the interacting paradigm represents a viable extension of ΛCDM in the era of precision cosmology.
Dr. R. Nagarajan, Raja M
Data mining and blockchain have emerged as two transformative technological paradigms in modern computing. Data mining supports knowledge extraction from large-scale datasets, while blockchain ensures secure, transparent, and immutable data storage. Their integration promises innovative solutions to critical issues such as privacy, trust, scalability, and distributed decision-making. This survey provides an extensive review of core data mining techniques, blockchain fundamentals, and the emerging trend of combining the two fields. It highlights recent advancements, applications, challenges, and future research opportunities in blockchain-driven data analytics and data-mining-enabled blockchain systems.
Akinyele ADISA, Bamidele KOLUDE, Bukola Folasade ADEYEMI, Mofoluwaso Abimbola OLAJIDE, Olasunkanmi KUYE
Background- Ameloblastoma is a benign aggressive neoplasm of odontogenic epithelium characterized by local invasiveness, propensity for facial deformity, and a high rate of recurrence. This typical clinical picture characterizes the conventional clinical type. However, slight variations exist with other clinical types. Not all ameloblastoma behave this aggressively and it is important to distinguish between clinical types of ameloblastoma, as well as its clinically and histologically malignant counterpart, ameloblastic carcinoma, to be able to give appropriate diagnosis and treatment to patients. Aim- The aim of this study is to analyse the demographic characteristics of the biologic types of ameloblastoma, as well as that of ameloblastic carcinoma, mostly seen in a Nigerian tertiary centre. Methods- H & E sections and formalin-fixed paraffin embedded (FFPE) tissues of ameloblastoma and ameloblastic carcinoma histologically diagnosed between January 2000 and December 2011 were retrieved. The slides were reviewed for confirmation of histological diagnosis. The ameloblastoma cases were classified according to the 2022 WHO classification of odontogenic tumours into Conventional, Unicystic, and Peripheral biological types. Data was analysed with SPSS version 20.0. Results- Ameloblastoma is more common in the third decade of life, having a mean age of 32±14.6 years, while ameloblastic carcinoma is more common in the fourth decade of life with median age of 33.5 years. Ameloblastoma is more common in males while ameloblastic carcinoma is more common in females. Of the biological types of ameloblastoma seen, Conventional ameloblastoma is more common than Unicystic type. Whereas the Conventional ameloblastoma peaked in the third decade of life, Unicystic ameloblastoma unusually peaked in the fourth decade of life with a mean of 35.2 (SD± 16.3) years. Mural type is the most common type of Unicystic Ameloblastoma in the series. Conclusion- The knowledge of the demographic distribution of the biologic types of ameloblastoma, as well as ameloblastic carcinoma, in the Nigerian population is crucial for accurate clinical diagnoses that inform appropriate therapies.
Donaldson A. Eshilama, Jimoh J. Afolayan, Kingsley M. Udofia, Kufre M. Udofia
The rapid digitalisation of livestock production systems has intensified the demand for affordable, scalable, and user-accessible smart farming solutions, particularly in poultry management, where environmental conditions directly influence animal welfare and productivity. This study presents the design, implementation, and real-world deployment of an AI–IoT integrated cloud platform for real-time poultry environmental monitoring and decision support. The proposed system integrates low-cost IoT sensor nodes for temperature, humidity, and ammonia monitoring, along with energy-efficient sleep scheduling mechanisms and machine-learning–based predictive analytics. Environmental data acquired by distributed sensor nodes is transmitted via Wi-Fi to a central processing unit and securely uploaded to the cloud, where it is stored, analysed, and visualised through an interactive Streamlit dashboard. A hybrid Random Forest–Support Vector Classifier model was employed to provide predictive insights into environmental risk conditions, enabling proactive intervention beyond conventional threshold-based alerts. The platform was deployed and evaluated in a real poultry farm environment, demonstrating reliable real-time monitoring, low-latency cloud connectivity, and improved environmental stability. Practical outcomes include enhanced decision-making for non-technical users, improved accessibility via an intuitive web interface, and measurable reductions in environmental stress indicators associated with poultry mortality. The results confirm the system’s effectiveness in democratising smart poultry farming and highlight its scalability potential for broader multi-livestock and precision agriculture applications.
Hillary O. Oginga, J.K Mwai, V.M Mageto
Tomato (Solanum lycopersicum) cultivation plays a critical role in ensuring food security and economic stability in many parts of the world. However, productivity is often hindered by fungal infections, notably Septoria Leaf Spot, caused by Septoria lycopersici. This disease significantly reduces crop yields by damaging the foliage and accelerating premature defoliation. Timely detection and diagnosis are essential for effective intervention. In this study, we present a deep learning-based approach for automated identification of Septoria Leaf Spot using the Shifted Window Transformer (Swin Transformer), a hierarchical vision transformer architecture known for its balance of computational efficiency and high accuracy in image classification tasks. The research followed a structured quantitative methodology encompassing model design, data acquisition, training, and performance evaluation. A publicly available dataset sourced from Kaggle, comprising annotated images of healthy and infected tomato leaves, was used for model development. Preprocessing steps included image resizing, normalization, denoising, and data augmentation techniques such as flipping, brightness adjustment, and rotation. The Swin Transformer model achieved an accuracy of 93.18%, a precision score of 0.92, and an AUC-ROC of 90.91% on the test set, outperforming conventional CNN models like ResNet-50 and VGG16. These results validate the model’s strong generalization capability and its potential use in smart agricultural applications. The study also emphasizes the architectural advantages of the Swin Transformer in extracting both local and global features critical to plant disease identification. Nonetheless, the exclusive use of a Kaggle dataset introduces limitations, particularly in representing real-world variability. The paper recommends additional validation using field-captured images under diverse conditions to improve robustness. The findings highlight the promise of attention-based models for early and accurate plant disease detection, potentially contributing to increased agricultural productivity and sustainable farming practices.
Duwa Hamisi Chengula, Patrice Nyangi, Peter Laurian
The accumulation of polyethylene terephthalate (PET) waste, vehicle tires, and rice husk ash (RHA) presents a critical environmental challenge in developing countries. This study explores a sustainable composite wall cladding that integrates recycled PET as a polymeric binder, river sand, waste tire steel fibers (SFs), and RHA fillers. Material characterization (XRF, thermal analysis) confirmed a high silica content in RHA (91%) and sand (82%), with a PET melting threshold of 240-255°C. The composites were fabricated through a hot-mixing process at 250°C, while maintaining constant PET and SFs content, systematically varying the RHA-to-sand substitution ratio from 0% to 20% by weight. The experimental results demonstrated that enhancing RHA concentration typically reduced density (from 2.469g/cm3 to 1.495 g/cm3) due to low specific gravity of the ash. While water absorption slightly enhanced (1.8% to 3.12%), values maintained the ‘’Vitreous’’ range of ASTM C373. Compressive strength exhibited an optimal peak of 24.1 MPa at a 12.5% RHA concentration, supported by impact energy absorption (3.29 Joules) without visible damage. The study reveals an optimal mix design of 12.2% RHA and 36.6% sand, demonstrating that RHA serves as an effective secondary reinforcement up to a critical threshold before binder starvation occurs. This research establishes a technical framework for circular economy practices in Tanzania, contributing to a high-performance, low-cost alternative to traditional cladding materials.
Angel M. Bontilao-Gayrama, Monera A. Salic-Hairulla
The study aimed to develop and validate a professional training manual integrating Pedagogical Content Knowledge (PCK) and Science, Technology, Engineering, and Mathematics (STEM) education teaching approach (PCK+STEM) to enhance the instructional competence of public school Biology teachers in Iligan City. Guided by the ADDIE model-Analyze, Design, Develop, Implement, and Evaluate. The study employed a mixed-method research design, integrating both quantitative and qualitative approaches to systematically develop and comprehensively evaluate the training manual. The manual was conceptualized based on the identified needs of Biology educators and encompasses the topics of 1st quarter of Grade 9 Science following the essential domains of PCK and seven (7) stages of STEM Education by Sutaphan & Yuenyong, (2019). The validation process involved six PhD experts specializing in STEM and PCK education who evaluated the manual’s content, structure, and instructional coherence. Quantitative data were collected using a validated instrument across the domains of Analyze, Design, and Development, while qualitative feedback was gathered from open-ended expert comments to improve clarity, relevance, and usability. The evaluation results revealed that the training design manual achieved overall “Excellent” quality, with a grand mean of 3.53. These findings indicate that the training manual meets expert expectations in terms of pedagogical soundness, content validity, and practical applicability for teacher training. Moreover, the training manual serves as a validated resource for professional development programs in promoting Biology teachers’ integrated understanding of disciplinary knowledge, pedagogy, and STEM-based instruction. It is recommended to implement and assess the manual’s effectiveness in actual teacher training and classroom settings, focusing on its impact on teachers’ PCK enhancement and students’ engagement and achievement in Biology.
Joseph Floyd Rosete, Kyle Harold C. Cruz, Mabingnay, Cyrene Anne A.
The Documents Archiver System with Integrated Data Analytics and Recommendation Algorithm is a stand-alone desktop application. It is intended to address the requirement for efficient digital document management in settings with poor or nonexistent connectivity. The program enables users to upload PDF and DOCX files, tag them with keywords and document titles, and save them locally and in an integrated MySQL database. The system offers a contemporary and responsive graphical user interface and is built with JavaScript, Electron, HTML, and CSS for the front end and MySQL for the back end. This makes safe document retrieval, keyword-based search, and easy file organizing possible. To guarantee transparency and traceability, all user actions, including uploads, deletions, and searches, is recorded in real time. Using JavaScript charting libraries, an interactive dashboard displays visual analytics using bar and pie charts that show document statistics and keyword trends. The solution is perfect for small enterprises, schools, and local government offices that need dependable document management without relying on internet connectivity because it is totally offline.
Gbidum Sunday Tote
This study investigates the economic impact of climate change on poverty reduction in 20 African emerging economies over the period 2014–2024. Using a dynamic panel Generalized Method of Moments (GMM) approach, the analysis examines the effects of total greenhouse gas (GHG) emissions, methane emissions from agriculture, and forest area, while controlling for GDP per capita and foreign aid inflows. The findings reveal that total GHG and methane emissions significantly increase poverty. Conversely, forest area was statistically insignificant in poverty reduction. The study concludes that climate change poses a significant barrier to poverty reduction in African emerging economies. However, sustained economic growth and strategically targeted foreign aid can mitigate these effects. Based on the conclusion, the study recommends that emerging economies should implement targeted strategies to reduce greenhouse gas and methane emissions, particularly from agriculture and industrial activities. This may include promoting clean energy technologies, climate-smart agriculture, and sustainable land-use practices.
Elem Chamberlin Jamike
Doping has been recognized as a significant threat to the core values of sportsmanship, and it has become an issue of critical importance to stakeholders including sports organizations, governments, educational institutions, and healthcare professionals This study examines the effects of doping practices among undergraduate athletes of Ignatius Ajuru University of Education (IAUE), Rivers State. The study was guided by three research questions and one hypothesis. The study design was descriptive survey research design. The study population comprises all undergraduate student-athletes of Ignatius Ajuru University of Education (IAUE), Rivers State. Using convenience sampling, 250 undergraduate student-athletes drawn from various faculties and departments of Ignatius Ajuru University of Education (IAUE), Rivers State. A self-developed instrument with a reliability coefficient of 0.82 was used for data collection. Data was analysed using frequencies, percentages, mean and standard deviation while hypotheses were tested using an Independent Sample t-test and Pearson's Product Moment Correlation (PPMC) at 0.05 alpha level of significance. The findings reveals that most athletes resort to doping strategically, often during competitive periods, to gain an edge over their peers. The finding also reveals that while some athletes rely on synthetic drugs, others prefer natural or locally sourced substances that they believe are safer or less detectable. The study concludes that concludes that doping is not solely a matter of personal choice, but rather a product of the social, psychological, and economic pressures experienced by athletes in their pursuit of excellence. Many athletes, motivated by peer influence and the desire for success, often overlook the long-term risks associated with these substances. The study therefore recommends that institution should collaborate with the National Anti-Doping Committee (NADC) and the World Anti-Doping Agency (WADA) to introduce periodic drug testing for athletes participating in inter-departmental and inter-university competitions. Random testing serves as a deterrent and helps identify cases of substance abuse early.
Chakravarthy K, Dr. Samseer R H
Access to higher education remains a significant challenge for youth from nomadic tribes in India, despite governmental efforts to promote inclusivity. This case study explores the opportunities and barriers to higher education for nomadic tribal youth in P.Karattupalayam Panchayat, Gobichettipalayam, Erode District, Tamil Nadu. Drawing on three detailed case histories collected through interviews, the study provides a qualitative analysis of the socio-economic, cultural, and systemic factors influencing educational aspirations and outcomes. Key findings highlight persistent obstacles such as financial instability, limited access to infrastructure, and societal marginalization, alongside pockets of resilience and support systems within the community. The study underscores the need for targeted policy interventions and localized support mechanisms to bridge the educational divide. Recommendations include enhanced financial aid, culturally sensitive curriculum design, and outreach programs to foster awareness and participation in higher education among nomadic youth.
Genelyn R. Baluyos, Jaycee C. Yulo, Merlie M. Ayop
This action research focused on improving Labo National High School’s Grade 10 students’ critical thinking skills in Statistics and Probability using board games during the 2024–2025 school year. Engaging students in more active and critical thinking in mathematics is needed for data interpretation and decision making. However, many traditional methods of teaching fail to capture students’ active participation and critical thinking. This investigation utilized a quasi-experimental framework and comprised two sets of participants: one set practiced critical thinking using board games, while the other received traditional instruction. Each set of participants underwent assessments before and after the intervention. Outcomes indicated that, while students in all groups made improvements to their critical thinking skills, the group that instruction included board games made more significant gains and participated more actively. The use of board games was observed to enhance student motivation, teamwork, and problem-solving skills. In light of these findings, it can be concluded that board games can promote students’ analytical thinking and understanding of more complex statistical concepts. Engaging students using board games into the mathematics curriculum is recommended to promote critical thinking. Learning through games is less monotonous and more meaningful. The provision of resources and materials needed for this effort should come from the school administration.
Mary Claire C. Cabahug, Maryan E. Lagang
This study addresses the persistent problem of poor Filipino language proficiency among Grade 8 students. To improve their oral communication skills, the research introduced the Speaking Enhancement and Practice System (SEPS), a strategy designed to enhance language proficiency through collaborative, interactive learning activities. The study aimed to determine the level of students' Filipino-speaking skills before and after implementing SEPS and to determine whether there was a significant difference between the two. Using a quantitative single-group pretest-posttest design, the study was conducted among 40 purposively selected Grade 8 students from a public secondary school in Ozamiz City during the school year 2024–2025. Data were collected using a speaking performance rubric and analyzed using frequency, percentage, mean, standard deviation, and a paired t-test in Minitab. Results showed that all students were in the "Did Not Meet Expectations" category before SEPS, but after its implementation, they significantly improved and reached the "Outstanding" level. The statistical analysis revealed a highly significant difference in their speaking scores before and after the intervention. Based on the findings, SEPS proves to be an effective strategy for enhancing Filipino speaking skills. It is therefore recommended for integration in classroom instruction and for further application in various subjects and educational levels to support learners' speaking development.
Eben Chong Yi Xie, Nooryn Husna Binti Osman, Norhazren Izatie Mohd, Nur Dinie Adriana Binti Ahmad Marzuky, Tan Jie Ming, Tan JingXian
The study addressed the long-standing inefficiency in the management of hardcopy documents in construction organisations, where manual and semi-digital processes lead to document loss, reduced traceability, and inadequate accountability. Although the construction industry undergoes continuous digital transformation research, there remains a research gap regarding the systematic deployment of digital technologies to manage physical documents, which cannot be neglected in light of legal and operational requirements. The qualitative approach has been followed, including the review of current document workflows, the assessment of available tracking technologies, and the design of a conceptual digital tracking system. The work contributed by proposing a systematic model that incorporates barcode or QR code recognition and identification, a cloud-based database, and secure access controls to manage hardcopy documents. The results showed that the suggested solution minimised errors related to manual operations, enhanced document traceability, reduced time spent during retrieval, and increased coordination and security among staff members and databases. In general, research has shown that a systematic digital tracking alternative can enhance operational efficiency and help construction organisations align hardcopy document management with their digital transformation goals.
G. Manvish Chowdary, K. Vishnu Vardhan, Mrs.Sangamithrai
The online transactions are quite common in the digital world. Nevertheless, they are also susceptible to fraud and it could cause them to lose a lot of money. The databases are highly skewed due to the high number of legal credit card transactions compared to the fraud ones, which makes it very hard to detect fraudulent credit card transactions. Old machine learning models have a tendency to miss the hidden patterns of rare activities that are fraudulent, hence the profits are high in terms of the false negatives. The proposed paper presents an Ensemble Deep Learning model that uses Data Resampling to enhance the accuracy and reliability of the fraud detection systems. The proposed approach will entail integrating multiple deep learning models, such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Deep Neural Networks (DNN) into an ensemble framework. This arrangement records space and time characteristics of transactional data. It integrates resampling methods such as Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN) and random under sampling to address the severe class imbalance. The strategy will make sure the system learns fraudulent patterns effectively without overfitting. The system is applied to credit card fraud datasets and implementing the system in comparison with traditional models and single deep learning methods. Empirical findings indicate that the suggested ensemble model significantly enhances accuracy, recall, and F1-score and minimizes false alarms. This method yields good fraud detection even under extremely disproportionate conditions. The framework is scalable and adaptable. It can be integrated with real-time payment gateways and financial platforms. The next step in the work will involve explainable AI (XAI) to enhance transparency in fraud decisions. It will also apply federated learning to ensure user privacy and apply the model in cloud-based facilities to have global scale.
Dr. Humphrey Mbuti Kimani, Phd
Food safety is a fundamental aspect of community health, ensuring that food consumed by individuals is free from harmful contaminants. WHO estimates that annually, there are nearly 600 million cases globally associated with poor food hygiene. Most studies on hygiene and food safety demonstrate that washing hands often with soap and water for at least 20 seconds plays a significant role in food safety. The objective of this review was to identify and characterize primary literature examining hygiene practices and food safety with a focus on facility related hygiene practices at household and establishment levels in Sub-Saharan Africa. The scoping review adopted the Joann Briggs institute (JB) manual for reporting items. A comprehensive search was conducted from scientific databases (PubMed) and other research bodies and relevant articles published within 2015-2025. To ensure consistency, three reviewers independently screened the existing studies for relevance. Data extraction was performed using Microsoft excel, and meta-analysis. The extracted data from included studies was organized and presented using narrative synthesis. The review established that most studies identified (WASH) practices as important factors in food safety. Promoting hand washing with soap was found to be an important health intervention in the control of foodborne illnesses. The study further noted that there is limited data on food safety and determinants of environmental factors like air ventilation, cleaning and disinfecting regularly touched surfaces. The study concluded that there is need to explore the existing gap on how air and facility hygiene practices in homes influence foodborne illnesses. Further research is needed to establish to what extent fresh air circulation in indoor space and cleaning and disinfecting regularly touched surfaces in households affects food safety. These results will guide policy on how to enhance food safety knowledge through targeted home-based interventions. Dissemination of these findings will be done through journal publications and various media platforms.
Dr. J. Abiraami
Literary studies have undergone significant epistemic transformations over the past century, moving beyond aesthetic appreciation towards interdisciplinary, politically conscious, and culturally grounded modes of inquiry. This paper examines the epistemic shifts within literary studies with special reference to Indian literary texts, aligning with contemporary academic expectations in UGC and Scopus-indexed journals. By incorporating case studies from Indian English and regional literature—particularly the works of Mahasweta Devi, Arundhati Roy, and Bama—the paper demonstrates how literary knowledge production has evolved to foreground marginal voices, challenge canonical authority, and interrogate power structures. The study argues that present-generation literary scholarship is marked by ethical engagement, cultural specificity, and social responsibility, reflecting a decisive departure from Eurocentric and purely formalist paradigms.
Ngozi Jane MADUELOSI, Nornubari Solomon INORDEE, Okpara Sergeant BULL
This study was carried out to evaluate the efficiency of unmodified and modified acid and alkaline mahogany sawdust as adsorbents for the removal of lead (Pb) from aqueous solutions. The modification was done using HCl and NaOH solutions. The batch adsorption method was used to evaluate the performance of each adsorbent under varying conditions, including initial Pb2+ concentration, adsorbent dosage, contact time, and pH. The results showed that the maximum adsorption capacity for the control, acid, and alkaline modified adsorbents were 24.272 mg/g, 21.322 mg/ a n d 14.662 mg/g respectively. Removal efficiency of 98.44% was achieved with the acid-modified sawdust at an initial Pb concentration of 100 mg/L and pH 4 while the maximum removal efficiency by the alkaline and unmodified absorbents were 97.22% and 94.35% respectively. The data analyzed for adsorption kinetics and equilibrium isotherm fitted well with the pseudo-second order kinetic model and the Freundlich isotherm model. This study demonstrated that modified mahogany sawdust is an efficient, eco-friendly adsorbent for treating lead-contaminated water. The findings provide valuable insights for the optimization of mahogany sawdust for practical heavy metal ions removal.
Dr. Bezzel Chitsungo, Dr. George Okumu Achar, PhD, Dr. Sebastian Chakeredza, PhD, Mr. Elisha Manirampa (PI), Mr. Tendayi Chimunhu, Mrs. Florette Uwimana, Ms. Annitha Alapha, Ms. Basuti Bolo, Willard Zendera
This study examines the suitability of precision irrigation systems for effective farming in Manicaland Province, Zimbabwe. The Manicaland Province has been prone to a lot of climatic variability, especially droughts, water scarcity, and soil degradation. Therefore, it is important to implement efficient irrigation techniques in order to enhance agricultural productivity. From the literature, the researchers observed that policies, governance structures, and regulatory frameworks have a huge role in driving the adoption of precision irrigation system. However, there is a lack of comprehensive analysis on how these factors either promote or hinder precision irrigation practices in Zimbabwe. Therefore, it was necessary to study these institutional dynamics in helping policymakers create a conducive environment for farmers to adopt these technologies. There was a need for an impact assessment of existing precision irrigation initiatives in Zimbabwe. While various projects and pilot programs have been introduced, limited empirical studies have been conducted. Therefore, it is essential to undergo evaluation of their effectiveness, scalability, and long-term sustainability. Conducting such assessments will provide valuable insights into best practices and potential areas for improvement. The objectives included to assess the current status of irrigation systems, to evaluate the challenges and opportunities of the current irrigation methods used in Manicaland, to identify the most suitable precision irrigation systems for Manicaland and to develop a plan of implementation of the suitable precision irrigation systems in Manicaland. The researchers employed a mixed research methodology, incorporating both quantitative and qualitative approaches in the study. The researchers used a purposive sampling method for the identification and selection of participants for the study. The sample size for the study was 38 farmers and key informants. The data collection instruments used were questionnaires and interview guides. The findings suggest that precision irrigation systems such as centre pivot, drip, and micro-jets hold significant potential for addressing water scarcity issues while improving agricultural efficiency in the province. Based on these insights, the study proposes recommendations for policy and practice, emphasizing the importance of capacity building, financial incentives, and further research to ensure the successful adoption of precision irrigation systems such as automated irrigation, air-based irrigation, drone irrigation and sub-surface drip irrigation systems in order to have food security in Manicaland.
Emmanuel E. Crucillo, Harold R. Lucero, Jhon Rey O. Palconit, Lyka May C. Medina, Ma. Abegail N. Gelito
Flooding remains one of the most destructive natural hazards affecting urban communities in the Philippines, particularly those located near major river systems. This study presents the design, development, and evaluation of an Internet of Things (IoT)-based Flood Early Warning System (FEWS) for Barangay Doña Imelda, Quezon City, an area highly vulnerable to recurring floods. The system utilizes an ESP32 microcontroller integrated with an HC-SR04 ultrasonic sensor for water-level measurement, a Neo-6M GPS module for geolocation, and a Ra-01 LoRa module for long-range data transmission. Real-time water-level data are transmitted to a cloud-based database and visualized through a web dashboard and an Android mobile application, enabling timely dissemination of flood alerts to residents and local authorities. A simple linear regression model is incorporated to forecast short-term water-level trends, enhancing preparedness and response capability. The system is powered by solar energy to ensure continuous operation during power interruptions. Development followed the Rapid Application Development (RAD) methodology to support iterative prototyping and user-centered design. System evaluation using the Technology Acceptance Model (TAM) yielded an overall weighted mean of 4.52, indicating high user acceptance in terms of perceived usefulness, ease of use, and behavioral intention to use. Compliance assessment based on ISO 22328-1 resulted in an overall weighted mean of 4.45, demonstrating strong conformity with international standards for community-based early warning systems. The results confirm that the proposed FEWS effectively enhances flood preparedness, supports informed decision-making, and contributes to community resilience.
Prof. Arul Vendhan S
Background: The Fear of Missing Out (FOMO) has become a psychological and behavioral concern in modern society, particularly among youth and health professionals exposed to social media. Aim: To explore the concept, causes, psychological implications, and management strategies of FOMO, highlighting its relevance to nursing and health education. Methods: A narrative review was conducted from peer-reviewed literature published between 2013–2024, retrieved from databases such as PubMed, Scopus, and Google Scholar. Results: FOMO is associated with anxiety, low self-esteem, poor sleep, and overuse of social media. It negatively affects academic and professional performance and contributes to digital addiction. Conclusion: Promoting digital literacy, mindfulness, and emotional awareness are key to overcoming FOMO. Integrating such interventions into nursing curricula can enhance mental well-being and professional balance
Dr. D. Rajagopal
This study investigates the relationship between forensic accounting techniques and corporate governance effectiveness in emerging market firms, with particular emphasis on the mediating role of fraud risk reduction. In emerging economies, weak regulatory enforcement, ownership concentration, and institutional gaps often heighten the risk of financial misreporting and governance failure. Against this backdrop, forensic accounting has emerged as a strategic monitoring mechanism capable of enhancing transparency and strengthening internal control systems. Using a balanced panel dataset of 1,250 firm-year observations, the study applies fixed effects, random effects, and dynamic panel Generalized Method of Moments (GMM) estimation to address potential endogeneity concerns. Corporate governance effectiveness is measured through a composite governance index, while forensic accounting adoption is captured using an aggregated index of investigative and monitoring practices. Fraud risk is proxied through financial reporting risk indicators derived from discretionary accruals and manipulation detection models. The findings indicate that forensic accounting techniques significantly improve corporate governance effectiveness and reduce fraud risk. Mediation analysis further reveals that fraud risk reduction partially mediates the relationship between forensic accounting adoption and governance outcomes, suggesting both direct and indirect governance benefits. The results remain robust across alternative specifications. The study contributes to the governance and forensic accounting literature by providing dynamic panel evidence from emerging markets and offers practical insights for regulators, boards, and policymakers seeking to enhance financial transparency and ethical compliance.
Abubakar Umar Maidarasu, Ibrahim Hussaini, Ibrahim Saifuddeen, Usman Ahmed Kumo
This study investigates the impact of risk assessment on fraud detection in Nigerian listed companies. Fraudulent activity seriously threatens the existence of corporations, the transparency of their financial statements, and the trust of investors. Due to the growing complexity of corporate fraud, risk assessment has emerged as a crucial tool in its detection. The study used statistical methods and a quantitative research design. The finding indicate that risk assessment and fraud detection are significantly correlated, suggesting that using forensic risk assessment tools improves the capacity to detect and stop fraud. The findings highlight how important it is for businesses to incorporate forensic risk assessment into their risk management plan. Future research might evaluate various regulatory and anti-graft agencies and examine industry-specific risk assessment tools.
Onwuachu Uzochukwu Christian, Opuh Jude Iwedike
Credit card fraud has become a major challenge in the financial sector due to the rapid growth of online and electronic transactions. Traditional rule-based fraud detection methods are often ineffective against evolving fraudulent patterns. This study presents a machine learning–based fraud detection system designed to accurately identify fraudulent credit card transactions in real time. The system employs supervised learning algorithms such as Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting to analyze transaction behavior and classify transactions as legitimate or fraudulent. Data preprocessing techniques including normalization, feature selection, and handling of class imbalance using SMOTE are applied to improve model performance. Experimental results show that ensemble models, particularly Random Forest and Gradient Boosting, achieve high accuracy, precision, and recall, making them suitable for deployment in real-world financial systems. The proposed system enhances transaction security, reduces financial losses, and improves customer trust.
Juwilyn P. Balansag, Liezl Marie B. Dagondon, PhD
This qualitative case study research examines how science teachers interpret and operationalize the integration of glocal perspectives like balancing global scientific knowledge with local cultural, environmental, and social concerns in classroom teaching. The study investigates the main facilitators and barriers teachers face in implementing glocalized science education about resources, professional development, and institutional support. Finally, it looks at the impact of this integration on students' scientific understanding, attitudes, and engagement. Findings show that teachers are indeed linking global scientific concepts with local realities through inquirybased, experiential, and project-based pedagogies that promote relevance and critical thinking. Community involvement updated instructional materials, professional development opportunities, and supportive school leadership facilitate this practice. Nevertheless, persistent barriers to effective implementation include localized resource limitations, rigid curricula, and cultural challenges. The integration of glocal perspectives positively influences students' motivation, engagement, and environmental responsibility. Students are very enthusiastic in lessons linking science globally and locally; they develop deeper comprehension and critical awareness to solve complex socio-scientific issues. This approach also aligns with the wider educational goals on sustainability and global citizenship. The study emphasizes that clear policy translation into classroom practice is needed, combined with systematic teacher training, resource investment, and community alliances. Such a holistic approach empowers teachers to navigate the challenges while enhancing science education’s relevance and impact. These represent meaningful contributions toward closing the gap in science education policy and practice to empower learners who are ready to engage thoughtfully in local and global scientific issues.
Dr.P.Amudha, Ms. Karthikeyani V
In recent years, organizations have increasingly explored innovative strategies to enhance employee engagement and motivation in response to evolving workplace dynamics, technological advancements, and changing workforce expectations. Gamification, defined as the application of game-design elements in non-game contexts, has emerged as a promising managerial approach to influence employee behavior, performance, and psychological involvement at work. This conceptual study aims to examine the role of gamification in the workplace and to develop a theoretical framework that links gamification mechanisms with employee engagement and motivation. The primary objectives of this study are to conceptualize gamification within organizational settings, to explore its relationship with employee engagement and motivation through established motivational and engagement theories, and to identify gaps in existing literature for future empirical research. Drawing upon self-determination theory, expectancy theory, and engagement theory, the study synthesizes prior research to explain how gamified systems can foster intrinsic and extrinsic motivation, enhance engagement levels, and contribute to positive organizational outcomes. By integrating fragmented theoretical insights into a unified conceptual framework, this paper contributes to the growing body of knowledge on gamification and human resource management. The study offers valuable implications for managers, HR professionals, and researchers seeking to design engaging and motivating work environments in an increasingly digital and competitive business landscape.
Irene A. Estrada, Sulpecia L. Ponce
This paper is an examination of the gender-based needs of the displaced Maranao families affected by the 2017 Marawi Siege who are still in Iligan City at the time of the survey in March to July 2019. This study included 144 internally displaced people (IDPs) residing in unrecognized evacuation centers, renting, or staying with relatives. Findings show that the IDPs suffered from the collateral damage of war, living in extreme poverty due to the absence of livelihood opportunities. To survive, they resorted to reducing their food intake, borrowing from relatives, or sending their children to live with relatives. Some Meranao children are seen doing child labor, a sight not seen prior to the siege. They were not prioritized for support and services, as they are staying in spaces defined by the government as private areas. Regardless of gender and age, the IDPs generally need shelter, livelihood assistance, food, and health care to bounce back. They need a sustainable, culture-sensitive intervention program to aid their recovery.
Arup Roy Joy, Ayon Mallick, Md. Nazmul Hoque, Nubah Nashita Farihat, Nusha Noshin Nuzhat
Okra is an important vegetable crop in tropical, subtropical, and temperate region of the world. Yet, productivity remains strongly influenced by genotype and seasonal environment. This study evaluated the extent of genetic variability and trait relationships among eight okra genotypes grown during the Kharif season (March–June 2025) under AEZ-14 conditions. The experiment followed a Randomized Complete Block Design with three replications. Observations were recorded for phenological, vegetative, and yield-related traits, followed by analyses of variance, genetic parameters, and principal component analysis. Significant differences were observed for all characters, confirming substantial variability among genotypes. Days to first flowering ranged from 30.33 to 34.00 days, while fruit yield per plant varied widely from 128.93 g to 230.08 g. The highest yield was obtained from OLR-3 (230.08 g), followed by Century and Sobujsathi (210.33 g). Fruit length (10.57–17.30 cm), fruit diameter (1.23–2.13 cm), and single fruit weight (11.70–15.00 g) also exhibited marked variation. Phenotypic coefficients of variation exceeded genotypic values for all traits, indicating environmental influence, although moderate to high GCV was observed for fruit yield per plant (17.18), fruit length (15.75), and fruit number per plant (12.05). Broad-sense heritability was high for plant height (98%) and leaf number (83%), while fruit yield per plant showed moderate heritability (68%). Principal component analysis revealed that the first three components explained 81.9% of total variation, with PC1 alone accounting for 41.4% and strongly associated with yield and fruit traits. Genotypes positioned positively along PC1, particularly OLR-3 and Century, demonstrated superior yield potential. The findings highlight the presence of exploitable genetic variability and emphasize fruit yield per plant, fruit number, fruit length, and single fruit weight as key selection criteria for okra improvement under Bangladeshi conditions.
Bhanu Bandi, Jagadeesh Kunuku, Jagadeesh Panda, Moneswari Bathula, Padmaja Kota, Ratna Deepthi Bejjam, Ravi Uddandam, Sindhu Lankalapalli
Green aroma technology represents an eco-friendly and sustainable approach to indoor air freshening through the use of herbal ingredients and plant-derived aromatic compounds such as essential oils, herbal extracts, and volatile phytoconstituents, they are designed to improve indoor air quality by imparting a pleasant fragrance while minimizing the health and environmental risks associated with synthetic air fresheners. Conventional air fresheners often contain synthetic fragrances and volatile organic compounds that pose potential health and environmental risks. Increasing awareness of the adverse effects of chemical fragrances, including respiratory irritation, allergies, and environmental pollution, has led to growing interest in herbal and eco-friendly alternatives. Generally herbal room fresheners commonly incorporate essential oils from plants such as Cymbopogon citratus (lemongrass), Ocimum sanctum (tulsi), Eucalyptus globulus, Lavandula angustifolia (lavender), Mentha piperita (peppermint), and Citrus species. These plants not only provide fragrance but also possess antimicrobial, antifungal, insect-repellent, and mood-enhancing properties. The presence of bioactive compounds such as terpenoids, phenolics, and flavonoids contributes to their functional benefits. Origanum majorana, Cymbopogon citratus were used in the preparation of room fresheners contains various terpenoids, phenolic, tannins and volatile oils show antimicrobial, anti-oxidant, anti-inflammatory and antispasmodic, anxiolytic and sedative effect. Formulation was prepared in the form of spray evaluated and compared with marketed formulation shows satisfactory results.
M. M. Kasdekar, P.N. Chikhalkar, Y. K. Mawale
The Indian Summer Monsoon (ISM) exhibits significant variability at multiple timescales, yet high-resolution paleoclimate records from the core monsoon zone capturing centennial-scale monsoon dynamics during climatically critical periods remain limited. This study presents X-Ray Fluorescence (XRF) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) geochemical analysis of a 55 cm post-monsoon sediment core collected from Lonar Lake, a meteorite impact crater lake in central India's core monsoon zone (19.97°N, 76.51°E). The core comprises subsamples analyzed for major elements and trace elements. Integration with published sedimentation rates (50–80 cm/ka) indicates the core represents 900–950 years of recent Holocene history spanning the Medieval Warm Period (~1050–1300 CE), Medieval Warm Period–Little Ice Age transition (~1300–1400 CE), Little Ice Age climax (~1400–1850 CE), and the modern period (~1850–2024 CE). Paleoclimate indices reveal: (1) stable moderate chemical weathering (Chemical Index of Alteration [CIA] = 59.05 ± 1.03) consistent with semi-arid to sub-humid monsoon climate; (2) anomalously elevated Al/Na ratios in the middle core section indicating intensified chemical weathering during the Little Ice Age, contrary to global cooling trends; (3) V/Cr minima and Co maxima in the same interval indicating episodic anoxic conditions and productivity surges linked to enhanced monsoon precipitation; and (4) progressive Ba depletion reflecting long-term evolution toward more oxidizing depositional conditions. These findings challenge simplified global cooling–monsoon suppression paradigms and underscore centennial-scale ISM variability during a climatically critical recent Holocene interval.
Akampurira Paul, Atuhe Aarone Mike, Dr. Richard Ntwari
The shift to remote and hybrid work in Ugandan universities exposed new cybersecurity risks shaped by human motivation, cognitive load, and system usability challenges. As academic operations increasingly depended on digital platforms, understanding how individuals formed and enacted protective intentions within home-based work environments became critical. This study examined how human and contextual factors—including threat perception, coping appraisal, usability difficulty, cognitive load, and digital fatigue—influenced cybersecurity behaviour among staff working remotely in Ugandan universities. Guided by the Protection Motivation Theory (PMT) and supported by constructs from the Theory of Planned Behaviour (TPB), the research adopted a sequential explanatory mixed-methods design. The quantitative phase identified key motivational and contextual predictors, while the qualitative phase explored how fatigue, usability barriers, and environmental conditions shaped protective motivation. Integration was achieved through narrative comparison and joint display analysis. Quantitative findings revealed that coping confidence and usability difficulty were the most influential determinants of secure behaviour, whereas fatigue and cognitive load significantly undermined protective intentions. Qualitative narratives reinforced these patterns, highlighting themes of threat awareness, usability frustration, motivational fatigue, and uneven institutional support. The study concluded that cybersecurity behaviour in remote academic environments was driven by motivational and contextual dynamics rather than technical controls alone. Strengthening coping efficacy, reducing usability burdens, and addressing digital fatigue were identified as essential strategies for developing adaptive, human-centred cybersecurity interventions in higher education.
Asheshemi Nelson Oghenekevwe, Ayeh Blessing Elohor, IFIOKO Ayo Michael, Obode Aghogho Micheal, Onoseraye A. Henry, Rita E. Ako
Uterine fibroids are among the most common benign tumors affecting women of reproductive age, and their timely detection is crucial for effective clinical management. Traditional diagnostic practices rely on expert interpretation of ultrasound images, which is often time-intensive and subject to variability. This study presents a hybrid machine learning system for the early detection of uterine fibroids using transabdominal and transvaginal ultrasound images. The proposed system integrates Convolutional Neural Networks (CNN) with advanced feature refinement techniques (Attention Mechenism) to improve diagnostic accuracy and reliability. A curated dataset obtained from the Kaggle repository was used, and preprocessing methods such as contrast normalization and noise reduction were applied to enhance image quality. Experimental results demonstrated strong performance, with an accuracy of 94%, precision of 92%, recall of 90%, and an F1-score of 91%. These balanced metrics highlight the robustness of the hybrid approach, offering consistent detection of fibroid-positive cases while minimizing false positives and negatives. The system shows promise as a clinical decision-support tool, particularly in resource-limited settings where radiological expertise is scarce. Future research will focus on expanding the dataset, incorporating explainable AI methods for greater transparency, and validating the model across diverse populations and imaging protocols.
Dr. Adwitiya
Electronic pharmacy refers to integration of digital technologies into hospital pharmacy services to enhance medication management, dispensing accuracy and patient safety. This system enables electronic prescribing, real-time inventory management, automated dispensing and seamless communication between physician, pharmacists and patients. It helps in clinical decision making through drug interaction alert, dosage optimization, tracking medication history. Discharge medication management is a critical component of patient care that ensures continuity of treatment during the transition from hospital to home. Pharmacist led intervention supported by electronic health record and clinical decision support system plays a vital role in identifying shortcomings, optimizing therapies and educating patients about proper medication use. Strengthening discharge medication practices through structured protocols and digital solutions can significantly improve patient safety and quality of care.
Charulatha Umashankar, Rajesh Jagadeesan Ravikumar
Continuous monitoring, individualized therapies, and efficient data collecting are just a few ways in which the proliferation of wearable electronics and Internet of Things (IoT) devices has revolutionized healthcare. New cybersecurity threats, such as exposure to hackers, data breaches, and cyberattacks, are introduced with these innovations. Strong cybersecurity safeguards for IoT devices are critical, especially considering the sensitive nature of healthcare data. The goal of this project is to improve the security of healthcare IoT systems by detecting threats and effectively protecting sensitive data using state-of-the-art deep learning algorithms. In order to identify irregularities and categorize cyber dangers in real-time, the suggested system incorporates deep learning models such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). By spotting changes from typical device behavior, these models enable early detection of harmful behaviors like malware and distributed denial-of-service (DDoS) assaults. Even in IoT settings with limited resources, important healthcare data is protected by incorporating deep learning-enhanced encryption algorithms to safeguard data transmission. This research makes a significant advancement by utilizing federated learning. This method allows for various IoT devices to work together in model training without directly exchanging private data. As a result, patient privacy is preserved and system security is improved. Deep learning-based techniques outperform conventional methods in terms of threat detection accuracy and data security when tested on real-world healthcare IoT datasets. These results highlight the need for more sophisticated deep learning methods to protect healthcare IoT devices from potential cyber threats.
Anita Jhajhria
India, with its vast geographical diversity and rich artistic heritage, is one of the most biodiverse countries in the world. It's home to about 7- 8 of the world's recorded species, ranging from the towering Himalayas in the north to the vast abysses in the south. This natural uproariousness is nearly linked to the Indian Knowledge System( IKS), which has evolved over thousands of times. IKS is a vast depository of knowledge accumulated through generations, passed down orally, and elevated in colorful forms of artistic and religious practices, including Ayurveda, Vedic literature, folk traditions, and indigenous ecological practices.
Rupesh Chandrasen Londhe
This paper examines the transformation of Indian Higher Education Institutions (HEIs) through the integration of design thinking as a guiding philosophy and operational framework. Drawing upon proprietary frameworks—SEPIA, DCAFE, and VAL Design -The Thinking®, developed by the School of Design —this study explores how HEIs can systematically build innovation ecosystems that foster creativity, entrepreneurship, and societal impact. The Indian HEI Innovation Landscape Cartographic View is analyzed to demonstrate the alignment of vision, curriculum, partnerships, and governance. The paper concludes by identifying barriers, enablers, and strategies for embedding design thinking into institutional culture, positioning HEIs as catalysts for inclusive growth and global leadership.
Dr. Minakshi Sharma
Pollination is a critical ecosystem service for temperate fruit crops, particularly pear (Pyrus communis L.), which relies heavily on insect-mediated cross-pollination for successful fruit set and yield. This study assessed the diversity, abundance, and composition of insect pollinators visiting pear blossoms in the Shimla hills of Himachal Pradesh. Observations were conducted during peak flowering (March–April) using standardized visual counts and sweep-net sampling between 0900–1500 hrs. A total of XX insect species from four orders (Hymenoptera, Diptera, Lepidoptera, and Coleoptera) were recorded. Hymenoptera were the dominant visitors, accounting for 65% of flower visits, with honey bees (Apis mellifera, Apis cerana) and native halictid bees showing the highest visitation frequency and effectiveness. Dipteran pollinators contributed 20% of visits, whereas Lepidoptera and Coleoptera accounted for 10% and 5%, respectively. The findings underscore the importance of both managed and wild pollinators for pear production and suggest that conservation of natural habitats around orchards and reduced pesticide use are essential for maintaining pollinator diversity and enhancing fruit yield in the temperate Himalayan region.
Mr Ratan Prasad Manager, Mr. Sanket Pathak, Mr. Vedant Kadam, Mrs Rachana Dighe Director, Ms Abha Sonawane, Prof. Swati K. Nadgaundi
This paper is based on the documentation in monitoring the process. Paper titled “Instrumentation and Control for Green Hydrogen Production Plant” is of plant in initial stage. Initial Stage of the production plant, storage of the process (Hydrogen) which will be raw process. Fluid Raw material DM with the capacity of 20 Tons/hr is then divided into four different lines but with the same operation and the process. Hydrogen and oxygen will be the final products from this process. By taking Process Flow Diagram PFD as a reference, the Piping and Instrumentation Diagram P&ID is prepared and Control Philosophy for the hydrogen Production plant is proposed for implementation.
Alexandria R. Horne, Gbekeloluwa B. Oguntimein
Baltimore’s environmental challenges, ranging from toxic air emissions to inequitable industrial zoning, disproportionately affect historically marginalized communities. Current Environmental Impact Assessments (EIAs) emphasize technical outcomes, but fail to capture disparities in exposure, health, and socioeconomic vulnerability. This paper proposes a systematic integration of environmental justice (EJ) metrics into Baltimore’s EIAs, drawing on national frameworks, comparative urban practices, and a case study of West Baltimore. By embedding EJ indicators into scoping, baseline analysis, prediction, and mitigation stages, Baltimore can align environmental policy with equity and justice goals.
Meliza P. Alo, Queenilyn C. Monzolin, Ryan S. Planas
Blended learning has been institutionalized in the Philippines through DepEd Order No. 050 s. 2022, affirming its pedagogical soundness beyond pandemic exigencies. While widely adopted, challenges in classroom management, equity, and teacher workload persist. This study employed a phenomenological qualitative design with five higher education teachers in Davao del Sur to explore how reflective practice enables the integration of behaviorist and constructivist principles in blended classrooms. Data were gathered through semi-structured interviews and reflective journals, analyzed using Interpretative Phenomenological Analysis (IPA) and thematic analysis. Findings showed that teachers’ reflection via journaling, peer dialogue, and data-driven adjustments supported adaptive management and responsive decision-making. Behaviorist strategies (reinforcement, structured routines, gamified feedback) sustained discipline, while constructivist approaches (inquiry-based tasks, collaborative learning, scaffolding) promoted learner autonomy. Integration of these paradigms produced balanced, context-sensitive management models, with localized adaptations addressing digital inequality and diverse learner profiles. The study highlighted reflective practice as central to adaptive classroom management and offers insights for sustainable blended learning policies in Philippine higher education.
Adebisi Matthew Iwabi, Adepoju Mohammed Olatoye, Ayodele Olusiji Samuel, Olususi Joseph Ige
Basement terrains of southwestern Nigeria host structurally controlled hydrothermal mineralization, but delineating alteration zones and prospective targets remains challenging due to limited surface exposure and complex deformation history. This study integrates aeromagnetic and radiometric datasets to delineate hydrothermal alteration zones and assess the mineralization potentials of Effon-Alaaye and its environs within the Nigerian Basement Complex. High-resolution airborne aeromagnetic and gamma-ray spectrometric data covering the Ado-Ekiti Sheet 244 were acquired by the Nigerian Geological Survey Agency in 2006 and processed using Geosoft Oasis Montaj 8.4 (HJ). Aeromagnetic analysis included Total Magnetic Anomaly (TMA), Reduction to the Equator (RTE), First Vertical Derivative (FVD), Analytic Signal (AS), residual magnetic separation, Euler deconvolution, and lineament extraction to map subsurface structures and lithological contrasts. Radiometric processing involved potassium (K), thorium (Th), uranium (U), potassium deviation (KD), and ternary (K–Th–U) mapping to identify lithological variations and hydrothermal alteration signatures. Integrated interpretation established spatial relationships between structures, alteration zones, and mineralization. Results reveal a structurally complex basement dominated by NNE–SSW to NE–SW trends consistent with African-wide tectonics. Magnetic lineaments, low-magnetic corridors, and clustered Euler depth solutions delineate shear zones, faults, and lithological contacts that likely acted as conduits for hydrothermal fluids. Radiometric maps highlight potassic alteration zones characterized by elevated K and KD anomalies that correlate strongly with magnetic structures. Thorium data discriminate felsic intrusions from schist belt host rocks, while uranium maps delineate a uranium-enriched zone in the northeastern sector (Alawaye–Ojejelu–Itawure axis), suggesting hydrothermal uranium mineralization. Integrated results indicate that the highest mineralization potential occurs where shear zones intersect secondary structures and coincide with radiometric alteration signatures. Consequently, Iwaiji, Ipole, Agopoti, Alawaye, Ojejelu, and Itawure are identified as priority targets for orogenic gold, uranium, and associated polymetallic mineralization.
Francis Chidiebele Ekwempu
Introduction: The rapid rise of cloud computing has reshaped data storage and crime, shifting investigations to digital forensics. Jurisdictional complexities, dispersed data, and lack of server access hinder cross border cloud forensics, while no unified international legal framework allows cybercriminals to find safe havens and blocks admissible evidence preservation. Objectives: This study aims to analyse the legal challenges hindering effective cross-border cloud forensic investigations and propose strategies for international cooperation. The key objectives of this study include identifying issues related to data sovereignty, jurisdiction, and evidence admissibility and establishing best practices to streamline multinational collaboration.
Ochuko Goodluck UTU, Okiemute Dickson OFUYEKPONE, Yaabari NAENWI
This study examines how lubrication properties particularly viscosity and film thickness affect the dynamic performance of gears and bearings through a MATLAB-based simulation. The gear and bearing assemblies were represented as single-degree-of-freedom (SDOF) systems with lubrication-dependent damping. In the simulations, viscosity (η) was varied between 0.01 and 0.1 Pa∙s, while film thickness (hf) ranged from 1×10⁻⁶ to 1×10⁻⁵ m. Dynamic responses, including peak vibration, RMS vibration, and load distribution, were evaluated using 3D surface and contour plots. The results show that increasing viscosity reduces peak gear vibration from 0.98 m to 0.55 m and peak bearing vibration from 0.51 m to 0.30 m equivalent to a reduction of about 44% and 41%, respectively. Load analysis further reveals that gear and bearing loads decrease by nearly an order of magnitude with greater film thickness, underscoring the importance of lubrication in reducing mechanical stress. Contour plots identified vibration hotspots under conditions of low viscosity and thin films, highlighting regions most vulnerable to wear and failure. The analysis results provide practical guidance for selecting lubricants and optimizing mechanical system performance. They also address a key gap in the literature by quantifying how the combined effects of viscosity and film thickness influence the dynamic behaviour of gear–bearing systems, an area that has received limited attention in prior research.
Ms. B Rebekahl Darlinktan, Ms. Soniya
Chronic pain in women is increasingly recognized as a sustained biopsychosocial condition that affects not only physical functioning but also emotional regulation, cognitive processing, identity, and long-term coping capacity. Women living with chronic pain are frequently exposed to prolonged physiological arousal, cumulative emotional labour, and sociocultural expectations that emphasise endurance and role fulfilment over emotional expression and self-care. Over time, these pressures heighten stress reactivity, reinforce maladaptive cognitive patterns such as catastrophizing and hypervigilance, and contribute to emotional exhaustion, reduced perceived control, and diminished psychological resilience. As a result, effective pain management requires interventions that address the psychological and psychophysiological mechanisms through which pain is experienced and regulated. This narrative review integrates psychological theory and empirical evidence to examine the role of Jacobson’s Progressive Muscle Relaxation (JPMR) in enhancing coping skills and reducing pain among women with chronic pain conditions. The review synthesises findings across psychophysiological, emotional, and cognitive domains to clarify how JPMR functions as a mind–body intervention within women those lived with experiences of pain. Evidence consistently suggests that JPMR reduces muscle tension and autonomic hyperactivation, facilitating a state of physiological calm that supports emotional regulation, cognitive clarity, and more intentional coping responses. These regulatory shifts are associated with improvements in coping self-efficacy, reduced emotional distress, and more adaptive engagement with persistent pain. Importantly, JPMR emerges not merely as a relaxation technique but as a coping-enhancement strategy that restores psychological agency often eroded by chronic pain. Its structured, accessible, and self-directed nature makes it particularly suitable for women managing chronic pain alongside caregiving responsibilities, emotional labour, and limited access to specialised psychological care. By strengthening self-regulatory capacity and reducing stress-driven reactivity, JPMR supports sustainable psychological adjustment to long-term pain. Overall, this review positions Jacobson’s Progressive Muscle Relaxation as a psychologically grounded and clinically meaningful intervention that addresses core psychophysiological and emotional mechanisms underlying chronic pain in women.
Ananthi L. R., Aruna V.
Introduction: Biomedical waste generated during healthcare activities poses serious risks to public health and the environment when not handled safely. Despite the implementation of the Biomedical Waste (Management and Handling) Rules in India, gaps in awareness and compliance remain evident across healthcare settings. Knowledge and adherence to biomedical waste management protocols are especially crucial for allied health science students, who form an integral part of the future healthcare workforce. Assessing their understanding and attitude toward safe waste management practices is essential for strengthening infection control and ensuring environmentally responsible healthcare delivery. Aims: The study aimed to determine the knowledge on biomedical waste generation, health hazards and legislation, Awareness on BMW Management Practices, Attitude towards BMW Management and Needle-Stick Injury Awareness among first year allied health science students. Methods: A descriptive cross-sectional study was conducted among 102 first year allied health students selected through convenience sampling. Data were collected using structured questionare comprising 35 closed ended questions under four categories: biomedical waste generation, health hazards and legislation, Awareness on BMW Management Practices, Attitude towards BMW Management and Needle-Stick Injury Awareness. Results: Of the 102 participants, 52.9% exhibited good knowledge and 44.1% showed intermediate awareness of biomedical waste creation, risks, and legislation. The majority of respondents (50%) had intermediate understanding of biological waste management techniques, with good awareness coming in close (42.2%). 70.6% of pupils demonstrated a modest attitude towards safe waste-handling procedures, according to attitude and behaviour assessments. 62.7% of respondents demonstrated good awareness of needlestick injuries. There were significant association found between the course of study and awareness of needlestick injuries (*p = 0.010) and between the course of study and knowledge level (*p= 0.034). Other demographic factors did not exhibit statistical significance. Conclusion: This study revealed that the first year allied health science students possess moderate level of knowledge and attitude towards Biomedical Waste management. Practical compliance and safe waste handling behavior remain insufficient. This demonstrates that students need structured educational intervention and regular training to strengthen the biomedical waste management practices.
Adenigba V.O, Fatukasi B. Adetutu, Fawole O. Olubanjo, Lawal R.T, Oladapo O.Olubumi
Landfilling remains the most common solid waste management method in developing countries due to its economic feasibility. However, poorly engineered landfills pose serious risks to surface and groundwater quality. This study assessed the physicochemical characteristics and heavy metal contamination of water resources around the Aba Eku landfill site in Ona Ara Local Government, Ibadan, Nigeria, and evaluated the potential health risks to nearby residents. Water samples were collected for six consecutive months from three hand-dug wells and surface water sources proximal to the landfill. Standard methods were employed for physicochemical analysis, and metal concentrations were determined using Atomic Absorption Spectrophotometry (AAS). Human health risk assessments for both carcinogenic and non-carcinogenic effects were conducted using United States Environmental Protection Agency (USEPA) models. Results revealed that several parameters including pH, biological oxygen demand (BOD), total hardness, dissolved oxygen (DO), alkalinity, and turbidity exceeded World Health Organization (WHO) permissible limits in both surface and groundwater samples. Heavy metal analysis indicated elevated levels of Fe, Cd, Cr, and Pb in most locations, suggesting leachate migration and pollutant infiltration from the landfill. The calculated Hazard Quotient (HQ) and Hazard Index (HI) values for Cd and Cr exceeded unity (>1), indicating potential non-carcinogenic health risks, while the cancer risk indices (CRI) for Cd and Cr in both adults and children surpassed the acceptable USEPA range (10⁻⁶–10⁻⁴). Children were found to be at greater risk than adults.This study highlights the urgent need for improved landfill management, groundwater monitoring, and community awareness to mitigate contamination and protect public health
Md. Abdul Hakim Shah
Purpose of the Study: Electronic Theses and Dissertations (ETDs) are very significant for research because they increase the accessibility, visibility, and dissemination of scholarly knowledge. But Engineering and Technology University Libraries in Bangladesh have faced some Legal Challenges in managing ETDs. The main purpose of this study is to explore these Legal Challenges and propose actionable strategies for effective ETDs management. Methodology: This study used a Descriptive Research design and a Quantitative Technique. Data were collected from five (05) Engineering and Technology University Libraries in Bangladesh using a Structured Questionnaire. SPSS version 26.0 was employed for data analysis. The Descriptive Statistics used to summarize and interpret the findings. Findings: The study found that DSpace software is widely used in making ETDs repository but several Legal Challenges impede effective ETDs management. The main challenges include the absence of robust ETDs policies, the need for consent from both supervisor and students for publication, unclear ETDs selection criteria, insufficient copyright legislation, Electronic access simplifying plagiarism detection, and the essential need to safeguard copyright laws to build student trust. Originality/Value: This study represents the first initiative to explore the Legal Challenges of ETDs management in Engineering and Technology University Libraries in Bangladesh. It provides a valuable baseline for future research and policymaking, contributing implicitly to the global research community.
Harold R. Lucero, Lady Joy P. Porras, Nataniel P. Herras, Princess Ann R. Valdez, Teodorico J. Gabucan Jr
Low passing rates in the Licensure Examination for Teachers (LET) continue to pose challenges for aspiring educators in the Philippines, highlighting the need for accessible, data-driven review support systems. This study presents the design, development, and evaluation of LET do IT, a web-based online LET reviewer integrated with performance analytics to support structured exam preparation and self-regulated learning. Developed using the Agile–Scrum methodology, the system incorporates a question bank, customizable mock examinations, and rule-based performance analytics that classify learner performance into interpretable categories to guide focused remediation. System evaluation involved a User Acceptance Test with 43 education students based on the Technology Acceptance Model and a software quality assessment by seven IT professionals using the ISO/IEC 25010 standard. Results indicate high user acceptance (overall mean = 4.58, Strongly Agree) and favorable software quality ratings (overall mean = 4.48, Agree), particularly in reliability, functional suitability, and compatibility. Findings suggest that integrating transparent performance analytics into online review platforms can enhance learners’ awareness of strengths and weaknesses and support exam readiness. While direct LET outcomes were not measured, the study demonstrates the system’s potential as a scalable and cost-effective digital review solution. Future research should include longitudinal studies with larger and more diverse populations to examine its impact on actual licensure examination performance.
Bryan S. Alejan, Charmaine R. Quiña, Ellysa Mae V. Caber, Jasper D. Alegro, Jessalyn G. Labay, Jhesa B. Angay-Angay, Joanna Marie L. Bordios, Leila Nica R. Dolendo, Ma. Nemia C. Carcellar, Michael Hendrix T. Casama, Paolo L. Armateo, Shaina Mae M. Jabeguero, Windy L. Godio
This study explored the lived experiences of Social Studies students in the post-pandemic learning landscape, where Philippine higher education has shifted toward hybrid and increasingly digital learning environments. As traditional classroom structures resumed, many learners continued to navigate the lasting academic, technological, and psychosocial effects of pandemic-era distance education. Using a phenomenological approach, the study examined how students made sense of these long-term transitions and how their previous exposure to modular and online learning shaped their current study habits, confidence, and resilience. Data were gathered through semi-structured interviews and analyzed using Colaizzi’s (1978) descriptive method to derive themes reflecting students’ evolving learning realities. Findings revealed that students developed greater autonomy and digital adaptability, yet continued to struggle with residual challenges such as technology fatigue, uneven digital skills, and difficulty re-adjusting to face-to-face academic demands. Participants highlighted how pandemic learning cultivated self-regulation, resourcefulness, and digital competence—skills they now rely on in hybrid settings. However, they also emphasized the need for stronger institutional support systems to bridge learning gaps that persisted beyond the pandemic. The study underscores the importance of responsive educational policies that address long-term learning recovery, promote digital resilience, and strengthen students’ overall readiness for the transformed post-pandemic academic environment.
Dr. Helen Onojah, Nelson, Patience Aniefiok (M.Sc.), Prof. Austin E. Abah (Ph.D)
This study was done to determine the prevalence of malaria and hepatitis B co-infection among pregnant women attending ante-natal in three health centers, samples were collected from Mbodo Aluu health care center, Igwuruta general hospital and Ozuaha primary health care center between August to November 2024. Ethical approval was sought from the ethics committee of the university of Port Harcourt and ministry of health Rivers State. Malaria parasite was determined using the gold standard microscopic procedure, while hepatitis B virus was determined using rapid serology assay diagnostic kit. Urinalysis was done using combi 9 dipstick stripe. Three hundred and eighty (380) consented subjects were examined. Two hundred and forty-seven (247) participated from Mbodo, one hundred and twenty-three (123) from Igwuruta and ten from Ozuaha. The total prevalence of Malaria in this study was 16.84%, hepatitis B 3.42% and Proteinuria 25.79%. Malaria and hepatitis B co-infection was 1.58%, malaria + proteinuria recorded a prevalence of 4.58%, hepatitis B + proteinuria recorded. The prevalence of 4.58%, malaria + hepatitis B + Proteinuria recorded 0.79%. Malaria and hepatitis B was significantly high among the age group from 20 below (2.94%) (p<0.5). The results also shows that the highest prevalence was among the second trimester (52.29%). The prevalence rate of malaria, Hepatitis B and their co-infections in the present study is relatively high. Therefore, it is recommended that regular screening of pregnant women for these Infections be sustained to forestall the undesirable consequences of these infections.
Cyrus Gitonga Ngari, Jeremiah Savali Kilonzi, Julia Wanjiku Karunditu, Peter Njori Wanjohi
Sustainable Development Goal 6 on clean water and sanitation is threatened by Escherichia coli (E. coli) contamination in wastewater, which poses grave dangers to the environment and public health. Microbial and chemical contaminants are present in wastewater from various sources, and treatment difficulties are growing due to urbanization. While biofilm formation, disinfectant resistance, and particle attachment increase bacterial survival, pathogenic E. coli strains can cause serious illness. To explain the dynamics of E. coli in wastewater systems, this study develops a deterministic mathematical model. MATLAB solvers and uncertainty and sensitivity techniques based on Latin hypercube sampling and partial rank correlation coefficients are used to analyze the model. The findings promote more effective wastewater treatment and microbial risk management by identifying key drivers of persistence.
Debashis Chakrabarti
Emotionally expressive machines have become central to debates in artificial intelligence, robotics, and social theory. Research in affective computing has formalised emotion algorithmically, while humanoid robotics has explored emotional expression through embodied interaction. These approaches, however, are often analysed in isolation and treated as politically neutral technical developments. This paper argues that emotion modelling constitutes a shared epistemic and political–economic practice spanning engineering, anthropology, and the organisation of affective labour under contemporary capitalism. Drawing on comparative analysis of affective computing research in the United Kingdom and humanoid robotics in Japan, the paper examines how emotion is operationalised through abstraction, performative validation, and normative constraint. It proposes a tripartite model of artificial emotion integrating affective dynamics, cognitive appraisal, and narrative–cultural mediation, and formally specifies this architecture using a dynamic state-space framework. By reframing artificial affect as the sedimentation of prior affective practices rather than emergent subjectivity, the paper clarifies the distinction between affect and emotion and provides a framework for analysing human–robot emotional interaction as a socio-technical and political phenomenon.
Anil K Das
Sb (~50nm) over Al (~50nm) thin films were sequentially deposited on the silicon substrate in the current work using the e-beam evaporation method at a pressure of 2×10-5 mbar. Next, a 350 KeV Kr+1 beam with a fluence of 3×1016 ions/cm2 was used to irradiate the Sb/Al bilayer. Seebeck coefficient and Resistivity measurements were carried out on Pristine and Irradiated samples and results were compared.
M. M. Kasdekar, P. N. Chikhalkar, Y. K. Mawale
Lonar Crater Lake (19°58′N, 76°31′E) is a ~52 ka meteorite impact structure developed within the Deccan Traps basaltic province and represents an important natural archive for understanding crater-lake hydrology and geomorphic evolution. This study investigates short-term morphometric and hydrological variations in Lonar Crater Lake during the period 2019–2024, with particular emphasis on the role of rainfall variability and groundwater dynamics. Lake boundaries and morphometric parameters were extracted using high-resolution Google Earth Pro satellite imagery, while rainfall data for 2018 and 2023 were obtained from the Maharashtra Rainfall Monitoring Portal. Standard limnological indices, including Depth Ratio (Rz), Relative Depth (Zr), Shoreline Development Index (DSI), and Index of Basin Permanence (IBP), were calculated following established morphometric methodologies.
Christian John Etwire, Enoch Deyaka Mwini, Golbert Aloliga, Isaac Azure
A comprehensive study has been conducted on the numerical investigation of non-Newtonian Casson fluid flow with radiative heat transfer over a magnetized stretching surface. The study considers the influence of surface magnetization, thermal radiation, convection, and temperature fields. The study considers the influence of surface magnetization, thermal radiation, convection, and temperature fields. A novel modification to the Casson fluid model has been introduced by altering its order. Using a similarity approach, the governing equations are transformed into ordinary differential equations. The parameters governing the flow are analysed numerically. The effects of surface magnetization on the flow of this modified fluid are presented in tabular form. It is found that surface magnetization increases the thickness of the thermal boundary layer, thereby significantly enhancing heat transfer control. The magnetized surface also impacts skin friction, Nusselt number, and Sherwood number similarly. This study suggests the potential benefits of incorporating magnetized surfaces in fluid flow applications for efficient flow control, as well as the advantages of adjusting the Casson fluid order.
Dr. Avani Oke, Mr. Mohd Yusuf, Mrs. Sonal Kamble
Ventilator Associated Pneumonia (VAP) is a common and serious nosocomial infection affecting 20-36% of the critically ill Patients. Implementation of evidenced based guidelines is an effective way to reduce the incidence of VAP. The main objective of the study was to assess the knowledge of the nursing students regarding VAP and to identify the association of Knowledge with selected variables. Identifying the gaps will help to plan future strategies towards nurses' preparation as professionals. Exploratory survey using a structured questionnaire was used to collect the data from the nursing students using nonprobability sampling technique. The findings revealed that 50.6% of the students had ICU clinical exposure. The study found 40.6 % were able to identify the common bacteria causing VAP and 56.8 % were aware regarding the preventive measures for VAP. 29.4% had an excellent knowledge regarding VAP implying the need for better education and reinforcement, especially in prevention and management of VAP. Nursing students with ICU exposure had significantly better knowledge scores than those without ICU exposure (P<0.05). In conclusion, study revealed that gap exists in knowledge regarding VAP among nursing students.
Mizanur Rahman, Sabiqun Nahar, Shamima Afrose, Tahmidur Rahman Chowdhury
Members of populations experience health inequities in spite of dramatic improvements in clinical care and overall health care and are indicative of imbedded differences in both social determinants of health, environmental exposures, accessibility of healthcare, and the allocation of resources. Conventional population-health initiatives generally depend on aggregate indicators and ex post analysis and thereby are less effective in identifying localized vulnerability, predicting exceptional risks and fairly distributing services. The paper focuses on the problem of population-health intervention optimization by the integrated application of geospatial analytics and predictive analytics as the way to proactively advance care equity. The given approach utilizes the high-resolution geospatial data coupled with predictive analytics to identify spatial, temporal and demographic patterns of health risk and service use. Geospatial techniques allow accurate mapping of disparities at small geographic levels by combining different streams of data, such as census and socioeconomic data, electronic health records, environmental and climatic data, mobility data, and healthcare infrastructure data. Through these analyses, clusters of unmet need, structural impediments to access and contextual factors that affect health outcomes have been identified and usually remain hidden in conventional population-level analyses.
Addinul Yakin, Eka Cahya Tisyana, Tajidan Tajidan
This study aims to: (1) Determine how to analyze the optimization of raw material inventory in the tofu agroindustry in Mataram City; (2) Determine the impact on sustainability in optimizing raw material inventory in the tofu agroindustry in Mataram City. The method used in this study is descriptive. The unit of analysis in this study is tofu entrepreneurs in Mataram City. This study was conducted in Mataram City, with 13 sub-districts; 2 were selected: Sekarbela Sub-district in Kekalik Jaya Village and Sandubaya Sub-district in Abiantubuh Baru Village, using quota sampling. Determination of sample areas using proportional random sampling, namely, 15 people. The number of samples was allocated to sub-districts using proportional random sampling, with Kekalik Jaya Village receiving 6 and Abiantubuh Baru Village receiving 8. The data used in this study are quantitative and qualitative. Data sources used include primary data and secondary data. Data collection was conducted through direct interviews with respondents. The results of this study are: (1) the analysis of soybean raw material inventory optimization in the tofu agroindustry in Mataram City is still not optimal based on the EOQ method analysis, the optimal order quantity is 34,403 kg per order, with a frequency of 20 times per year or around 1-2 times per month. This is much more efficient than the actual practice of 6,259 kg per order with a frequency of 109 times per year. The required safety stock is 7,114 kg to account for demand fluctuations and delivery delays during the 10-day lead time. The reorder point is 26,000 kg, with a total annual inventory cost of IDR 3,247,757, consisting of an ordering cost of IDR 1,623,867 and a storage cost of IDR 1,623,890. (2) The application of inventory optimization with the EOQ method has a significant positive impact on the sustainability of the tofu agroindustry in Mataram City. There was a very significant inventory cost saving of IDR 5,973,140, or 64.78% of the total actual inventory cost of IDR 9,220,897, mainly from a 81.81% reduction in ordering costs through reduced order frequency.
Eze, Francis Chukwuka, Nnodi, Joy Tochukwu
The high-rate diversity of courses offered in higher institutions has provided students with a broad spectrum of options and a desire for academic and career development. However, this abundance of choice has also introduced significant challenges in selecting courses that align with students' interests, skills, and long-term career goals. Traditional academic advisory systems which rely heavily on one-on one guidance from counselors or faculty, are constrained by the availability of advisors, the time required to provide tailored guidance, and the lack of data-driven insights into students' unique preferences and abilities. This paper presents a machine learning based personalized course recommendation system designed to assist students in selecting appropriate educational courses based on their Unified Tertiary Matriculation Examination (UTME) scores. Leveraging a comprehensive dataset of 1,000 students, the system employs advanced machine learning techniques, notably the XGBoost classifier, combined with Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. Extensive feature engineering transforms raw examination scores and demographic variables into predictive features, enhancing model accuracy. The model was rigorously evaluated using stratified train-test splits and multiple performance metrics, achieving an overall accuracy exceeding 99%. Key insights include high predictive power of subject streams and individual subject scores in forecasting suitable courses for the students. Resulting recommendations provide actionable, interpretable guidance for students and counselors, facilitating informed decision-making and optimized academic pathways. This research demonstrates that machine learning models significantly enhance personalized learning experiences by effectively predicting suitable courses for students and also contributes a robust, datadriven methodology for educational planning support.
Jeanethjoy D. Naturales
In recent years, the expansion of e-learning platforms has revolutionized the educational landscape, offering learners the flexibility to access educational resources anytime and anywhere. However, the abundance of content often overwhelms students, making it difficult to identify which learning materials best suit their individual needs. To address this challenge, this study proposes the development of a Personalized E-Learning Recommendation System that utilizes the K-Nearest Neighbor (KNN) algorithm to tailor learning content based on each learner’s profile, behavior, and preferences. The primary objective of this research is to design and implement a data-driven recommendation model that enhances learner engagement and academic performance by providing customized content suggestions. The system collects various types of user data from the e-learning platform, including quiz scores, subject interests, time spent on modules, and interaction history. Each learner is represented as a feature vector encapsulating these attributes. By applying the KNN algorithm, the system identifies students with similar learning patterns and preferences and recommends educational resources that have proven effective for those peers. The study follows a design and development research methodology, where the system is built, tested, and evaluated through iterative processes. The prototype is developed using Python and its machine learning libraries, while a web-based interface is created using Django and integrated with a backend database to store user data. Evaluation metrics such as recommendation accuracy, user engagement rate, and improvement in quiz performance are used to assess the system’s effectiveness. Initial testing shows that learners receiving personalized recommendations spend more time on the platform and demonstrate higher content retention compared to those accessing randomly assigned materials. This research contributes to the growing field of intelligent e-learning systems by highlighting the effectiveness of simple yet powerful machine learning techniques such as KNN in improving personalization. It also emphasizes the value of learner data in shaping adaptive educational environments that cater to individual learning styles and needs. Future work may include expanding the dataset, incorporating hybrid recommendation models, and exploring deep learning approaches to further improve recommendation quality. By offering a scalable and adaptable framework, this personalized recommendation system has the potential to significantly enhance the digital learning experience, making online education more targeted, efficient, and impactful.
Charles C. Chime, Ifeoma Prisca Udeozo, Nneka Deborah Ajah
The present study focuses on the phytochemical profiling and therapeutic potentials of monoterpenes and aromatic hydrocarbons isolated from the avocado (Persea americana) seed extract. Gas Chromatography-Mass Spectrometry (GC-MS) analysis of the active fraction (ADAE3) revealed the presence of diverse bioactive compounds, including β-myrcene, 1-methyl-4-(1-methylethyl)-1,3-cyclohexadiene, (1R)-2,6,6-trimethylbicyclo [3.1.1] hept-2-ene, p-cymene, and pseudocumene. These compounds exhibit a wide range of pharmacological activities such as anti-inflammatory, analgesic, antioxidant, antimicrobial, and neuroprotective effects. β-Myrcene was particularly noted for its superior ability to reduce inflammatory markers and alleviate neuropathic pain compared to standard analgesics, while p-cymene demonstrated synergistic effects with other bioactive agents, enhancing antimicrobial and anti-inflammatory responses. Additionally, aromatic hydrocarbons such as pseudocumene were found to contribute antioxidant activity, underscoring their therapeutic relevance. Supporting compounds identified, including halogenated epoxides, saturated and unsaturated hydrocarbons, and carboxylic acid esters, provide further evidence of the chemical diversity and industrial significance of the extract. Collectively, the findings highlight the potential of avocado seed as a valuable source of natural therapeutic agents, offering opportunities for pharmaceutical, cosmetic, and industrial applications. This study therefore contributes to the growing body of evidence supporting the medicinal and economic relevance of bioactive compounds from plant-derived sources.
Hyeladzira Garba Mshelia, Maryam Musa Machina, Musa Adamu Machina
Armed conflict is one of the major challenges of today and in most of the time, the construction sector is the second most affected area after human lives with economic impact of losses incurred in the sector always mirrored in the perspective of fixing back the wreckages in their original forms, rather than fixing back their improved and more secured versions. The study bridges this crucial gap with a new valuation order in which it surveyed, identified and enumerated all public buildings destroyed during Boko Haram insurgency in Borno state and, using ‘enhanced bottom-up’ method of costing violence, designed and evaluated their improved and more secured versions for reconstruction. It is expected to serve as a blueprint for policy formation. Field information for the study were sourced through physical inspection of destruction sites and the use of self-administered questionnaires. Analysis indicated that, public buildings destroyed during Boko Haram insurgency in Borno State require the sum of N3, 614,653,400,000.00 to be replaced with their improved and more secured versions. It is concluded that this amount could have been curtailed with the provision of enhanced community-based custody and security to the buildings. Recommendations therefore include among others, the formal entrustment of every community with security affairs of all public buildings in their jurisdictional areas and the provision of security personnel to assist the communities in security strategies.
Darnetty, Jumsu Trisno, Rahmad Faizan
Neoscytalidium dimidiatum fungus is the cause of stem cancer disease on dragon fruit plant. This disease is a major disease of dragon fruit plants that is economically detrimental. The use of Saccharomyces cerevisiae is an environmentally friendly control alternative. This study aimed to determine the potential of S. cerevisiae in suppressing the growth of N. dimidiatum which causes dragon fruit vine cancer. The antagonistic tests of S. cerevisiae against N. dimidiatum were carried out in vitro and in vivo. The experimental design used in this research was a Completely Randomized Design (CRD) with 4 treatments and 6 replications. The treatments consisted of several different inoculation time of S. cerevisiae namely A (S. cerevisiae was inoculated 2 days after N. dimidiatum inoculation), B (S. cerevisiae was inoculated simultaneously with N. dimidiatum inoculation), C (S. cerevisiae was inoculated 2 days before N. dimidiatum inoculation) and D ( Control, without S. cerevisiae). The results of the research showed that treatment B (application of S. cerevisiae simultaneously with inoculation of N. dimidiatum ) and C (application of S. cerevisiae 2 days before inoculation of N. dimidiatum inoculation) inhibited the growth of N. dimidiatum in vitro and in vivo. The best treatment was C (the inoculation of S. cerevisiae 2 days before inoculation of N. dimidiatum with the persentage of inhibition by 57.8% and 87.88%. respectively.
Emmanuel John Anagu, Jatau Lamiri Sunday, Umar Mairo
This study investigated students' behavioral intentions toward adopting game simulators for algorithmic thinking development using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. A cross-sectional survey design was employed to collect data from 611 computing students across nine universities in Northeast Nigeria through a two-stage sampling procedure involving purposive selection of computing faculties followed by random student sampling. A structured questionnaire based on validated UTAUT scales measured performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLSSEM) with SmartPLS 4.0 software. The measurement model demonstrated adequate reliability and validity, with Cronbach's alpha coefficients ranging from 0.80 to 0.89. Results showed that performance expectancy (β = 0.28, p < 0.01), effort expectancy (β = 0.22, p < 0.05), and social influence (β = 0.19, p < 0.05) significantly predicted behavioral intention, explaining 27% of variance (R² = 0.27). Facilitating conditions showed no significant effect (β = 0.09, p > 0.05), suggesting infrastructural support does not directly influence adoption intentions. Behavioral intention significantly predicted actual usage behavior (β = 0.41, p < 0.001, R² = 0.17). Gender moderated the performance expectancy-behavioral intention relationship, with stronger effects for male students (β = 0.34) than female students (β = 0.19). These findings demonstrate that game simulator adoption is primarily driven by perceived usefulness, ease of use, and social endorsement. The study provides actionable insights for educators and policymakers, suggesting that successful implementation requires demonstrating educational benefits, ensuring intuitive design, and leveraging peer influence and instructor advocacy
Angela D. Quillondrino, Harold R. Lucero, Melvin G. Torres, Nathaniel C. Custodio, Rhozzel C. Tayubong
This study presents the design and development of a Predictive Sales and Inventory System with Customer Segmentation for Enhanced Customer Relationship Management. The system addresses operational inefficiencies in small retail businesses by integrating predictive analytics and clustering techniques. Linear regression was applied for sales forecasting, while K-means clustering was used for customer segmentation to support data-driven decision-making and personalized customer engagement. The platform automates sales transactions, inventory monitoring, and membership management, and supports RFID-based cashless payments. Development followed the Agile Scrum methodology, with modules including product management, transaction processing, analytics, reporting, and backup management. User Acceptance Testing based on the Technology Acceptance Model (TAM) yielded an overall weighted mean score of 4.68, indicating strong user acceptance, with one establishment expressing intent to adopt the system. System evaluation based on ISO/IEC 25010 quality standards produced an overall weighted mean score of 4.18, reflecting satisfactory performance in functionality, reliability, usability, and security. Results demonstrate that the system provides an efficient, browser-based, data-driven POS solution that improves transaction speed, operational accuracy, and business insight generation for Spot777 Coffee.
D. K. Monday, N. F. Micah, O. Adeyanju, Q. J. Mawak, S. Tanko
Phytochemical screening and antimicrobial activities of aqueous and ethanol leave extract of U. chamae, were studied using paper disc diffusion method against Streptococcus pyogen, Escherichia coli and Salmonella thypi. The results of the antimicrobial studies indicated that the extracts inhibited the growth of one or more tested pathogens. The ethanolic extract showed a broad spectrum of antimicrobial activity. Phytochemical investigation revealed the presence of tannins, alkaloids, glycosides, flavonoids, carbohydrates and terpenes. Anthraquinone and glycoside were not present. Inhibition zone by the extracts ranges from 6.0 mm to 29 mm. The Minimum Inhibitory concentration (MIC) ranges from 100 mg/mL to 6.25 mg/mL. Uvaria chamae leave may be able to produce antimicrobial agents in drug delivery.
Dhanya Rajan E P, Duha M, Fathima Abdulla, Fathima Ibrahim, Rumaisa K
Plectranthus amboinicus is an herb species ordinarily well-known as Panikoorka of the family of Lamiaceae. These plant species are recognized in every part of India and it is a medicinal plant which is utilised in folkloric medicines for diseases like Flu, bronchitis and epilepsy. This study aims to formulate and evaluate herbal lozenges from Plectranthus amboinicus leaf extract & essential oil and to compare the antibacterial activity of it. Fresh leaves of Plectranthus amboinicus were collected, authenticated and subjected to cold maceration to obtain hydroalcoholic extract. The preliminary phytochemical screening indicated the presence of a diverse range of bioactive compounds, including flavonoids, alkaloids, tannins, terpenoids, glycosides, phenolic compounds, and phytosterols. In this study lozenges are prepared with Plectranthus amboinicus leaf extract and its essential oil. The prepared lozenges underwent organoleptic evaluation, assessing shape, texture, odor, color, taste, and type, as well as physicochemical characterization, including disintegration test, dissolution test, hardness, diameter, and weight variation. An in vitro study was conducted to evaluate the antimicrobial activity of the prepared herbal lozenges against Escherichia coli (E. coli), a Gram-negative bacterium. The antimicrobial activity of Plectranthus amboinicus lozenges was evaluated, and the results showed that the lozenges exhibited a higher inhibition zone compared to the extract. This enhanced antimicrobial activity is attributed to the synergistic effects of the various ingredients present in the lozenges, including peppermint oil, glycerin, jaggery, and sugar, which possess inherent antibacterial properties that complement the bioactive compounds present in Plectranthus amboinicus.
Amol Shinde, Asavari Barshikar, Atul kale, Kushal Toshniwal, Neha deshmukh, Pankaj Raghute
Background: Playing-related musculoskeletal disorders (PRMDs) are common among musicians due to prolonged practice hours, repetitive movements, and sustained awkward postures. Despite growing global literature, limited Indian data exist focusing on keyboard and guitar players. Objective: To determine the prevalence of PRMDs among keyboard and guitar players and to assess pain intensity, instrument-related functional difficulties, and upper limb disability. Methods: A cross-sectional observational study was conducted on 132 keyboard and guitar players aged ≥16 years from Nagpur city. Participants practicing for more than five hours per week were assessed using the Performing Arts Module of the DASH questionnaire, Patient-Rated Wrist/Hand Evaluation (PRWHE), and Numerical Pain Rating Scale (NPRS). Descriptive statistics were used for analysis. Results: The overall prevalence of PRMDs was found to be high, with the neck, shoulder, wrist, and hand being the most commonly affected regions. Moderate levels of pain and functional disability were observed, particularly in participants practicing for more than 14 hours per week. Conclusion: PRMDs are highly prevalent among keyboard and guitar players. Early screening, ergonomic education, and physiotherapy-based preventive strategies are essential to reduce disability and improve performance longevity.
Effaliza Misran, Harisun Ya’akob, Kasmehra Segaran Gunasagaran, Nor Athirah Zaharudin
Supercritical fluid extraction (SFE) using carbon dioxide has become a green and efficient alternative to traditional methods for extracting essential oils and bioactive compounds from plant materials. However, extraction performance heavily depends on operating conditions, requiring systematic optimization. In this study, a One Factor At a Time (OFAT) approach was used to optimize the supercritical CO₂ extraction of Sarawak black pepper (Piper nigrum L.) oil, aiming to maximize oil yield and retain key bioactive compounds. The effects of extraction pressure (200-325 bar), temperature (20-70 °C), CO₂ flow rate (2-10 g min⁻¹), extraction time (30-180 min), and ethanol co-solvent concentration (0-25% w/w) were examined individually while keeping other parameters constant. Oil yield was measured gravimetrically, and piperine content was analyzed using high-performance liquid chromatography. Results indicated that all operating parameters significantly affected extraction efficiency. The optimal conditions were found at 300 bar pressure, 50 °C temperature, 6 g min⁻¹ CO₂ flow rate, 90 min extraction time, and 10% (w/w) ethanol as a co-solvent. Under these conditions, a maximum oil yield of 4.07 ± 0.10% was obtained. The optimized extract had a piperine content of 4.88% (w/w) and physicochemical properties consistent with high-quality, unadulterated black pepper oil. Compared to Sarawak-specific SFE studies without co-solvents, adding ethanol significantly improved extraction efficiency. Overall, this study shows that OFAT-based optimization offers valuable parametric insights and provides a practical, scalable strategy for producing high-quality Sarawak black pepper oil using supercritical CO₂.
Abdullahi Danjuma Kassim, Adeshola Rebecca Akinwola, Ishegbe Eko Joyce
The presence of organophosphorus pesticide residues in food commodities poses significant health risks to consumers and these pesticides are widely used in Nigeria to enhance crop yields. This study determined the presences and levels of organophosphorus pesticide residues in beans sold in Karu Market, Nasarawa State, Nigeria. Bean samples were collected from the market and analyzed using gas chromatography-mass spectrometry (GC-MS) for residues of organophosphorus pesticides, the QuEChERS (quick, easy, cheap, effective, rugged, and safe) method was used for extraction and clean-up of pesticide residues. The analysis detected residues of pesticides such as Ethoprophos, Dichlofenthion, Methyl parathion, Chlorpyrifos, Ronnel, Phosphorodithioic acid, O-(2, 4-dicorophenyl) O-ethyl S-propyl ester and Azinphos-methyl in the bean samples, brown and white beans respectively. Concentrations of some residues exceeded maximum residue limits (MRLs) set by regulatory bodies. Findings highlight potential health risks associated with consumption and underscore the need for regular monitoring and regulation of pesticide use in agricultural practices.
DR. S. K. Rajalakshmi, M. Logeshwari, R. Priyadharshini, S. Swasthika Janani
Secure data transmission depends on unpredictable cryptographic keys. Conventional pseudorandom number generators are deterministic and vulnerable to prediction. This work proposes a Quantum True Random Number Generator (Q-TRNG) that uses quantum superposition and noise to produce truly random numbers. The generated quantum entropy is converted into high-quality random bits and applied to secure encryption, improving confidentiality and resistance to attacks. The proposed system offers a reliable and future- ready solution for quantum-grade secure communication.
Anita Franklin Akpolile, Augustine Onyema Nwabuoku, Chukwuka Emmanuel Mokobia, Godwin Kparobo Agbajor
Radon in water represents a significant yet understudied public health risk, particularly in Nigeria, where groundwater reliance is high and regulatory frameworks are lacking. This review synthesizes current research on radon concentrations in Nigerian water sources, highlighting exposure risks, health implications, and policy gaps. Radon-222 (222Rn), a carcinogenic gas from uranium decay, poses both inhalation and ingestion risks, with inhalation linked to lung cancer and ingestion causing stomach cancer. Despite global guidelines from the WHO and EPA, Nigeria lacks comprehensive regulations, and research remains little, with studies concentrated in only a few states (e.g., Ekiti, Ogun, Kwara). Findings reveal variable radon levels, often within safe limits, though some areas exceed recommended doses. The review identifies critical gaps, including the absence of baseline data in high-risk geological areas like Delta State, limited epidemiological studies on ingestion risks, and insufficient public awareness. This review calls for urgent action: expanded research, radon monitoring, and policy development to mitigate risks. By addressing these gaps, Nigeria can effectively protect vulnerable populations, particularly in rural communities dependent on groundwater. This review underscores the need for interdisciplinary collaboration to bridge science, policy, and public health in addressing radon-related hazards
Shantanu Paul
Artificial Intelligence (AI) is increasingly influencing how creative and technical subjects are taught in higher education. Its presence is now evident across disciplines such as animation, multimedia, design, and applied computing. As AI technologies continue to evolve, they support visual production, automate repetitive tasks, and enhance classroom engagement. This paper examines the contemporary role of AI in creative and technical education through a conceptual and review-based approach. Rather than relying on statistical analysis or survey data, the study draws upon existing academic research, documented teaching practices, and classroom-based observations reported in the literature. The discussion focuses on how AI assists learners in understanding complex or abstract concepts, sustaining engagement, and developing advanced skills, particularly in areas where traditional instructional methods may be less effective. AI-enabled feedback systems, visualization tools, and interactive simulations are shown to reduce learning barriers and support conceptual clarity. At the same time, the paper addresses challenges associated with excessive dependence on automation, including concerns related to foundational skill development, academic integrity, data privacy, and equitable access to technology. The study emphasizes that AI should not replace educators or established pedagogical approaches. Instead, its educational value is maximized when it complements human creativity, instructional expertise, and critical judgement. The paper concludes by encouraging higher-education institutions to adopt balanced and thoughtful strategies for AI integration, ensuring that students are well prepared for future creative and technical professions.
Jonah Joseph Yama, Mahammad Ayan hebballi, Mohammad Ali Mulla, Mohammed Saqeeb
The Real-Time Sign Language Recognition and Translation system shown in this study aims to improve communication between sign language users and non-sign language speakers. The system uses a webcam to record hand movements, which are then processed using OpenCV for real-time image processing and MediaPipe for hand landmark identification. Next, American Sign Language (ASL) movements are accurately classified using a Convolutional Neural Network (CNN). Smoother and more natural communication is made possible by a Text-to-Speech (TTS) engine that translates the identified motions into readable text and then into speech. By integrating computer vision, deep learning, and speech synthesis, the project provides an accessible, efficient, and user-friendly tool for vocally and hearing-impaired individuals. The goal of this approach is to improve communication and encourage inclusivity in commonplace situations like social contact, healthcare, and education. The solution is designed to be cost-effective, easy to use, and scalable, making it highly beneficial in educational environments, workplaces, hospitals, and public interactions. The ultimate goal of this project is to use an intelligent, real-time translation system to close the communication gap, encourage inclusivity, and support the freedom of people with hearing and voice impairments.
Dr R Anitha, T Akshay Kumar, Viswaganth V
Football analytics is an integral aspect of coaching. Currently, the technology accessible in professional leagues requires pricey hardware and an establishment with multiple cameras. This paper describes the STRIKER system, an end-to-end web-based football analytics platform that is able to analyze user-submitted videos of football games and provide analytics on player tracks, speed analysis, distance analysis, team identification, movement heat map analysis, and analytical outputs using the chat interface. STRIKER uses YOLOv8-n for the detection of players on the video, an optimized multiple-object tracking algorithm with the integration of velocity prediction and IoU association, K-mean algorithms optimized for the jersey-color-based classification of team identification, and heuristic approaches for the identification of the referee. It uses metric scaling from pixels on a standard 105-meter football ground for the estimation of the speeds of the players. Additionally, the method uses the Flask web structure with asynchronous processing. This approach is ideal since it is able to provide analytical outputs using the chat interface with minimal web processing delay. Tests on amateur games as well as official games indicate successful detection of subjects within the video with accuracy in team identification and genuine estimations of the speeds.
Dona Soju, Dr. Biju G
The present study examines the relationship between emotional regulation and mental health among prospective teachers. Emotional regulation plays a crucial role in managing stress, maintaining emotional balance, and adapting to professional challenges. For prospective teachers, who are preparing to enter a demanding profession, the ability to regulate emotions effectively is essential for ensuring psychological well-being and professional competence. Mental health, which encompasses emotional, psychological, and social well-being, significantly influences teaching performance, classroom management, and interpersonal relationships. The study highlights that inadequate emotional regulation may lead to stress, anxiety, and burnout, thereby negatively affecting mental health and teaching effectiveness. Conversely, strong emotional regulation skills contribute to positive mental health, resilience, and improved professional functioning. The findings emphasize the need to incorporate emotional skill development, mindfulness practices, and coping strategies into teacher education programs. Strengthening emotional regulation during the training period can enhance mental well-being and prepare prospective teachers to become resilient and effective educators. The study underscores the importance of promoting emotional competence within B.Ed. programs to support the overall development and mental health of future teachers.
Feum Kom Herve Steve, Tan Ling
Adequate prediction of short-term solar irradiance is necessary to have a reliable contribution of solar energy to power grids, but it is not an easy task since the atmosphere varies rapidly and is mainly influenced by clouds, aerosols, and local weather conditions (Perez et al., 2013; Yang et al., 2018). This paper introduces a satellite-meteorological data fusion system, which is created to improve the short-term prediction of solar irradiance at high time resolution. The suggested solution will combine the geostationary satellite measurements, such as optical properties of the clouds and radiative flux estimates, with ground measurements and reanalysis of meteorological variables, such as temperature, humidity, wind speed, and surface pressure (Schroedter-Homscheidt et al., 2016; Ineichen, 2014). The hybrid model attains data fusion, which involves the use of physical radiative relations alongside data-driven learning algorithms to obtain both the large-scale atmospheric patterns and the local variability (Voyant et al., 2017; Haupt et al., 2018).
Md Azmal Hossain, Zubair Mustahid
Non-communicable diseases (NCDs) are chronic and often progressive disorders that pose a major burden on global health systems. Long-term conventional management may be limited by adverse effects, polypharmacy, adherence challenges, and impaired quality of life. Homeopathy, developed by Samuel Hahnemann (1755– 1843), is widely used as a complementary modality and emphasizes individualized treatment and the principle of similitude. This review summarizes foundational homeopathic concepts relevant to chronic disease management and outlines current hypotheses proposed to explain homeopathic effects, including nanoparticlebased models and immunomodulatory mechanisms. Three illustrative clinical cases are presented, including an ovarian mass and two malignancies, each with baseline investigations and long-term follow-up. While these cases demonstrate sustained symptomatic improvement over extended periods, robust evidence requires welldesigned prospective studies using standardized outcomes and transparent reporting.
B. C. Ejiogu, E. I. Nnadi, E.O. Onwukwe, P.C. Agommuoh
This study was carried out to ascertain the implications of teaching and learning secondary school science in well equipped science laboratories, especially from the view points of the end users - the students, teachers and school administrators. The focus of the study was on the development of scientific and problem solving skills as well as attitude of the students towards science. The study sought justification or otherwise of the science laboratory equipment intervention initiative by Nigeria’s Niger Delta Development Commission (NDDC) in some secondary schools in the region. Three research questions were raised. Hypothesis testing focused on whether or not there was gender bias in the responses. Relevant literature was reviewed, the summary showed consistent positive effect of science laboratory hands-on teaching strategies on attitudinal and skills developments among secondary school students. A descriptive design was adopted for the study. Research instruments constructed in the Likert format, targeted at the different categories of respondents, was used to gather data. A sample of 1,050 respondents drawn from 42 out of 50 schools that received NDDC intervention in all nine states in the region was used. Every state in the region was, therefore, represented. Data gathered was analyzed item by item, in proportions, while stated null hypotheses were tested with t-test statistics. Results showed that majority of the respondents returned “Agreed”, based on a bench mark average score of 2.5, on the positive impacts of the science laboratory equipment intervention by NDDC on variables of the study. However, a few items received less than the 2.5 bench mark score and were noted. All null hypotheses tested were accepted. Recommendations were based on the findings, including that governments and education funding agencies should prioritize equipping of secondary school science laboratories while considering re-training of science teachers as part of any future science equipment intervention strategy, for maximum benefits.
Aliyah Basheba Zackpah, Nathaniel L. Nevis
Purine bases and their metabolites, including adenine, guanine, xanthine, hypoxanthine, uric acid, and methylxanthines such as caffeine, are essential biological compounds that have increasingly emerged as environmentally relevant micropollutants due to widespread human consumption and their incomplete removal by conventional wastewater treatment processes. Their high water solubility, structural similarity, and persistence at trace concentrations in complex aqueous matrices make selective removal particularly challenging. This review examines the sources, environmental significance, chemical behavior, and health implications of purines in aquatic systems, while critically assessing the limitations of existing treatment technologies such as conventional adsorption, membrane filtration, biological degradation, and advanced oxidation processes, which often suffer from poor selectivity, high energy demand, fouling, or incomplete mineralization. Emphasis is placed on cucurbit[n]urils (Q[n]) as a promising supramolecular platform for the selective removal of purines from water. The unique molecular architecture of Q[n], characterized by hydrophobic cavities and carbonyl-lined portals, enables highly specific host–guest interactions driven by size complementarity, hydrophobic inclusion, hydrogen bonding, and ion–dipole interactions. The review highlights the influence of cucurbituril ring size on purine selectivity, competitive binding behavior in multicomponent systems, and the advantages of nonporous adaptive crystals for selective uptake under realistic conditions. Current challenges related to scalability, material stability, and process integration are discussed, alongside future perspectives for the rational design of next-generation Q[n]-based adsorbents. Overall, this work underscores the potential of cucurbituril-based supramolecular systems as precision-engineered tools for sustainable purine management in environmental and biomedical applications.
I. C. Eli, Jephter J. Dika
This paper presents a comprehensive sensitivity and threshold analysis of the basic reproduction number (R₀) for a deterministic model describing the transmission dynamics of Lassa fever between human and rodent populations. The next-generation matrix approach is employed to derive an explicit expression for R₀, which quantifies the average number of secondary infections generated by a single infectious individual in a fully susceptible population. Analytical differentiation of R₀ with respect to each model parameter yields normalized forward-sensitivity indices that measure the relative contribution of epidemiological and demographic parameters to disease transmission. The results indicate that transmission rates between humans and rodents (βHV and βVH) and population recruitment rates (ΛH and ΛV) exert the most positive influence on R₀, while the recovery rate (γH) and natural mortality of rodents (μV) produce the strongest negative effects. Threshold analysis further reveals that when R₀ < 1, the disease-free equilibrium is locally asymptotically stable, whereas for R₀ > 1, an endemic equilibrium emerges. These findings highlight that targeted interventions such as enhancing recovery through medical treatment and reducing human rodent contact are the most effective strategies for lowering R₀ below unity and achieving disease eradication.
Ajay Singh Thakur, Kapil Kumar Tiwari, Pratiksha Jain, Ramdarshan Parashar, Vaibhav Rajoriya, Yogesh Sharma
Soymida Febrifuga is a medicinal plant traditionally employed in the treatment of infectious and inflammatory disorders. Despite its ethnopharmacological relevance, systematic evaluation of its phytochemical and pharmacological properties is limited. This study aimed to investigate the phytochemical composition, antibacterial, antifungal, and anti-inflammatory activities of Soymida Febrifuga bark extracts. Bark samples were collected, authenticated, and extracted using methanol and ethyl acetate via Soxhlet extraction. Qualitative phytochemical screening was performed using standard assays, while quantitative estimation of phenolics and flavonoids was conducted using gallic acid and quercetin calibration curves. Antimicrobial activity was assessed by disc diffusion and agar-well diffusion methods against Gram-positive and Gram-negative bacterial strains and fungal species. Anti-inflammatory activity was evaluated in BALB/c mice using carrageenan-induced paw edema, with indomethacin as the reference drug. Phytochemical screening confirmed the presence of carbohydrates, glycosides, phenols, tannins, flavonoids, diterpenes, steroids, proteins, and amino acids. Quantitative analysis revealed high phenolic (118.45 mg GA/g) and flavonoid (34.42 mg QE/g) content in the ethyl acetate fraction. Methanolic extracts exhibited strong antibacterial activity, particularly against Proteus vulgaris (29 mm), Staphylococcus aureus (27 mm), and Pseudomonas aeruginosa (26 mm), while ethyl acetate extracts showed moderate activity. No antifungal activity was observed. Anti-inflammatory evaluation demonstrated dose-dependent inhibition of paw edema, with the highest dose (400 mg/kg) achieving 47.98% inhibition at 5 hours, compared to 69.31% with indomethacin. Soymida Febrifuga bark extracts contain bioactive phytochemicals, notably phenolics and flavonoids, which contribute to significant antibacterial and anti-inflammatory activities. These findings validate its traditional use and highlight its potential as a source of therapeutic agents for infectious and inflammatory diseases.
Dr Devidutta Maurya
The present study reports a comprehensive theoretical investigation of the molecular structure, electronic distribution, and spectroscopic properties of molecule 1CVY (C₃₁H₄₂O₅) using Density Functional Theory (DFT). Geometry optimization was carried out to obtain the most stable molecular conformation, and the optimized structural parameters, including bond lengths, bond angles, and dihedral angles, were analyzed in detail. The electronic properties of the molecule were explored through frontier molecular orbital analysis, providing insight into the HOMO–LUMO energy gap, charge distribution, and chemical reactivity. Molecular electrostatic potential (MEP) mapping was employed to identify potential electrophilic and nucleophilic sites within the molecule. Vibrational frequency calculations were performed to simulate the infrared (IR) spectrum, and all computed frequencies confirmed the stability of the optimized structure with no imaginary modes. The theoretical IR assignments were correlated with characteristic functional group vibrations. The results highlight the relationship between molecular geometry, electronic structure, and spectroscopic behavior of 1CVY, offering valuable insight into its physicochemical properties. This DFT-based study provides a reliable theoretical framework for further experimental investigations and potential applications of the molecule in molecular recognition and related fields.
Abubakar Muhammad JABBI, Habu Nuhu ALIYU, Hamisu Adamu, Zainab Sulaiman
A hydrazide hydrazone ligand was synthesized via the condensation of 2-pyridinecarboxaldehyde and 4hydroxybenzohydrazide. Its corresponding Ni(II) and Cu(II) complexes were prepared and characterized through elemental analysis, molar conductance, magnetic susceptibility, FT-IR, and UV-Vis spectroscopic techniques. Analytical and spectroscopic data confirmed the formation of neutral complexes with the general formulae [Ni(HL)Cl2(H2O)] and [Cu(L)Cl], where HL is the neutral ligand and L- is its deprotonated form. The FTIR results revealed that the ligand coordinates in a tridentate manner. The electronic spectra and magnetic moment (3.42 BM) suggested an octahedral geometry for the Ni(II) complex, while the data for the Cu(II) complex were consistent with a square planar geometry, despite an anomalous magnetic moment (3.11 BM). The molar conductance values in DMSO (~15 S cm² mol⁻¹) confirmed the neutral nature of both complexes. The compounds were evaluated for their in vitro antimicrobial activity against Gram-negative (Escherichia coli), Gram-positive (Staphylococcus aureus) bacteria, and fungal strains (Aspergillus niger, Candida albicans). The bioactivity trend, Cu(II) > Ni(II) > Ligand, was established. The Cu(II) complex demonstrated superior, broad-spectrum efficacy, exhibiting inhibition zones up to 26 mm and an activity index of 93% against E. coli compared to the standard drug streptomycin. This significant enhancement is attributed to the complex's square planar geometry, anhydrous nature, and potential for redox cycling, underscoring the critical role of metal ion coordination in advancing antimicrobial chemotherapeutics.
Araceli Bustillo, Emanuel Julius Galimba Bangud, Joseph Floyd Rosete, Kervin Van Sapul Pontevedra, Kyle Harold, C. Cruz, Roberto Villar Ramirez Jr
This project created a web-based application called Tamper-Evident Inventory and Sales Recording System Using Chain-Based Data Integrity to Ensure Audit Reliability for Mashikketta Restaurant. The system was designed to address common problems in small restaurant operations, particularly the risk of unauthorized modification of sales and inventory records and the lack of reliable audit mechanisms in traditional point-of-sale systems. By improving record integrity and traceability, the system aims to support accurate reporting and strengthen accountability in daily business operations. The web-based application utilizes cryptographic hashing and chain-based data linking to ensure that each transaction record is securely connected to previous entries, making any form of data tampering detectable. Transaction histories are preserved instead of being overwritten to support auditing and review processes. The system includes role-based access control, secure user authentication, real-time inventory monitoring, basic sales analytics, and tamper-evident audit logs. It was developed using C# with the .NET framework, React.js for the user interface, and MySQL for database management, with password hashing and structured data logging implemented to enhance security.
Joan P. Bacarisas, DM, MAN, RN, Sweet Cerlyn L. Espenoza, RN
This study assessed the relationship between team psychological safety and patient safety event and error reporting among nurses in a tertiary hospital during the last quarter of 2025. A descriptive–correlational design was employed, using complete enumeration that yielded responses from 437 nurses. The study utilized an adapted version of Edmondson’s Psychological Safety Scale (1999) and selected dimensions of the Hospital Survey on Patient Safety Culture. Data were analyzed using descriptive statistics, chi-square tests, Cramer’s V, and Pearson r to determine relationships among demographic profile, psychological safety, and incident reporting. Results showed a moderate level of team psychological safety and a high overall level of patient safety event reporting, with lower scores observed in near-miss reporting and non-punitive response to error. Significant relationships were found between demographic variables and both psychological safety and reporting behaviors. Team psychological safety demonstrated significant positive correlations with frequency of events reported, non-punitive response to error, communication openness, and overall reporting levels. These findings support Edmondson’s Theory of Psychological Safety and align with Reason’s Swiss Cheese Model, highlighting the role of supportive team environments in strengthening patient safety. A Psychological Safety and Incident Reporting Enhancement Plan is proposed to address identified gaps and strengthen the hospital’s safety culture.
Francis Chidiebele Ekwempu
The healthcare system in Sub-Saharan Africa (SSA) is a significant challenge due to the availability and competence of its healthcare workforce. Effective training and capacity-building programs are crucial for improving service delivery and achieving better health outcomes. However, the efficacy of these programs varies widely, influenced by various factors. This scoping review aims to map existing literature to identify key determinants that impact the success of healthcare professional training and capacity-building initiatives within the SSA context.
Nan Wai Linn, Taw Tun Aung, Yin Ko Ko, Zin Ko Ko
The study aimed to determine the leadership styles employed at Than Lwin Shwe Company and to examine the impact of these leadership styles on organizational commitment. This study encompassed three leadership styles: transformative, transactional, and laissez-faire. Organizational commitment encompasses affective, continuance, and normative dimensions. This study employed a descriptive technique and multiple regression analysis. Primary data were gathered from all employees across eight designated departments. Employees perceive that leaders are more predisposed to transformational leadership approaches, and affective commitment is more prevalent. The regression results unequivocally demonstrated that transformational leadership style has a significant positive effect on affective commitment, transactional leadership style has a significant positive effect on normative commitment, and laissez-faire leadership style has a significant positive effect on continuance commitment. Both transformational and transactional leadership styles have a significant and positive impact on overall organizational commitment. To achieve employee organizational commitment and enhance job performance, leaders must implement the suitable leadership style.
Abraham Solomon, Bala Balaguru, Danesh Khazaei, Faryar Etesami, Hadi Khazaei, Kaneez Abbas
Background: The integration of Artificial Intelligence (AI) into medical devices has accelerated exponentially between 2020 and 2025, fundamentally altering the landscape of diagnostic medicine. This period is defined by the transition from theoretical algorithms to regulatory-approved, clinically deployed Software as a Medical Device (SaMD), particularly in image-centric specialties. Objectives: This systematic review aims to (1) quantify and characterize regulatory trends for AI medical devices (AIMDs) in the US and EU; (2) evaluate the clinical efficacy and workflow impact of AI technologies in Ophthalmology, Oncology, and Musculoskeletal (MSK) disorders, with a specific focus on AI-assisted Point-of-Care Ultrasound (POCUS); and (3) assess the role of these technologies in democratizing access to expert-level diagnostics. Methods: A PRISMA 2020–compliant literature search was conducted across PubMed/MEDLINE, Embase, Cochrane Library, and IEEE Xplore for peer-reviewed studies published between January 1, 2020, and December 31, 2025. Grey literature from FDA and EU regulatory databases was included to capture approval trends. Risk of bias was assessed using QUADAS-AI and ROBIS tools. Results: The search identified 1,240 records; 67 pivotal studies and systematic reviews were included. Regulatory data reveal >1,000 FDA-authorized AI devices by 2025, with radiology and ophthalmology dominating. In Ophthalmology, autonomous AI for diabetic retinopathy and glaucoma has demonstrated sensitivity comparable to retina specialists (>90%), enabling widespread tele-screening. In Oncology, AI-assisted breast and prostate ultrasound has significantly improved novice diagnostic accuracy (AUC gains >0.10) and reduced unnecessary biopsies through enhanced specificity. In MSK, AI models for fracture detection and real-time POCUS guidance for nerve blocks have standardized procedure quality and reduced inter-operator variability. Conclusions: AI medical devices have shifted from "assistive" to "autonomous" and "augmentative" roles, effectively democratizing diagnostic capacity. High-quality evidence supports their deployment to bridge workforce gaps, though challenges regarding regulatory harmonization and algorithmic bias persist.
Allan Mufumbiro
The rise of the gig economy which is characterized by short-term, platform-mediated work which includes but not limited to ride-hailing, food delivery, online freelancing, transportation, domestic services, and micro-tasking has transformed Uganda’s labour market. Yet, existing laws on employment, social protection, digital platforms, consumer protection, and taxation were designed for traditional employment relationships and therefore struggle to regulate this new digital labour employment. This review examines the regulatory gaps, institutional challenges, and enforcement limitations affecting Uganda’s ability to govern gig work. Using labour law theory, platform governance theory, and regulatory compliance frameworks, the study highlights contradictions in worker classification, weak social security coverage, limited tax enforcement, consumer risks, and gaps in data protection. Case studies from ride- hailing, digital freelancing, and delivery platforms illustrate persistent issues of misclassification, power asymmetries, algorithmic control, and contractual opacity. The review concludes by recommending a hybrid regulatory model incorporating clarifications in worker status, portable benefits, digital platform obligations, and strengthened enforcement systems to ensure fairness, innovation, and sustainable digital labour markets in Uganda.
Dr. Sujatha R
The advancement of women in physics continues to lag significantly behind that in the life sciences, despite concerted efforts by the physics community. This report examines the complex factors contributing to this persistent disparity, analyzing why progress remains slow and why gains from initiatives often revert to previous levels. While traditional arguments such as teaching and research loads or work-life balance are comparable across these disciplines, the analysis reveals that perceived gender bias, the pervasive belief in innate talent coupled with the stereotype that women lack it, and unsupportive academic cultures are dominant differentiating factors. The report synthesizes current statistical landscapes, delves into the root causes of underrepresentation and slow advancement, and explores the limitations of past interventions. Drawing on findings from global projects and promising local practices, it proposes a comprehensive set of actionable recommendations. These recommendations emphasize the necessity of systemic and cultural transformation across the entire academic pipeline, from early education to senior leadership, to foster truly inclusive environments and ensure sustainable gender equity in physics. Achieving this is not merely a matter of social justice but a critical imperative for scientific excellence and the global capacity to address pressing societal challenges.
Cosmus Kipketer Kemboi, Gideon Kipchumba Kemboi
This review article examines how principles of rewards management, as detailed in the Society for Human Resource Management (SHRM) Foundation's guide Implementing Total Rewards Strategies (Heneman, 2007), provide a microcosmic framework for analyzing the fundamental political science question: Who gets what, when, and how? By integrating classical political economy (Smith, 1776; Ricardo, 1817) with dependencies theory (Cardoso & Faletto, 1979; Dos Santos, 1970), we demonstrate how organizational reward systems function as allocation regimes that reproduce, negotiate, and occasionally transform broader structural inequalities. This analysis reveals rewards management as applied political economy, bridging theories of value, power, justice, and governance.
Lucy M. Blanco, Marvin M. Abreu, Mary Jane F. Somao-i, Arturo B. Cunanan, Arlene R. Caballero
This paper presents a conceptual and theoretical framework for an integrated school safety system designed to address persistent safety and accountability challenges in Philippine public schools. The situation regarding students' safety in Philippine public schools has raised issues that are difficult to overlook, as they persist and remain unaddressed. Among other incidents, there have been problems with students getting in and out of the school without proper authorization, violent actions being taken, and even the attendance of students being managed inefficaciously by the administration because of poor monitoring. The manual systems currently in place struggle with errors; they're mainly reactive and mostly isolated from a wider accountability of the institutions. The present paper proposes a theoretical framework for an integrated campus access and attendance management system. The framework is based on the Sociotechnical Systems Theory, Ecological Systems Theory, Technology Acceptance Model (TAM), and Agile-SCRUM implementation principles. Combining these perspectives results in a multidimensional model that considers school safety not merely as a tech add-on but as a co-produced sociotechnical process that is at the heart of the educational ecosystem, not outside it. The paper presents an argument for transforming school safety and accountability into a holistic approach, where inclusivity, transparency, gender responsiveness, and ethical data governance serve as guiding principles. The framework is in sync with the Sustainable Development Goals (SDGs 4, 9, and 16) and national digital transformation policies. The theoretical design proposed here is a replicable and scalable model for resource-poor educational contexts, thus contributing to the discourse on technology in education, governance, and human security.
Lam Thi Thu Hien, Thach Thi Ro Mu Ni
Music occupies a central role in the Du Ke (Lakhon Bassak) theater of the Southern Khmer, serving simultaneously as the structural foundation of the performance, the emotional guide, the support for acting, and the affirmation of cultural identity. Through its rich repertoire of melodies, rhythms, and traditional instruments, music has become the very soul of the stage, creating a cultural space that is both solemn and intimate. Beyond its artistic value, Du Ke music reflects layers of history, belief, and philosophy of life, thereby functioning as a “bridge” that connects heritage with community life. The active participation of the community—from maintaining troupes and transmitting knowledge to innovating and integrating Du Ke into cultural practices—remains the decisive factor ensuring the survival and development of this heritage. This paper emphasizes that music is the convergence point between heritage and community, securing the enduring vitality of Du Ke theater in the context of contemporary integration.
Joann D. Sienes, Samuel Migallos
This qualitative phenomenological study explored the lived experiences of 22 purposively selected nurse leaders, administrators, human resource officers, and hospital owners in Level II and III hospitals within region 12 – SOCCSKSARGEN, Philippines, concerning succession planning and leadership transition in the nursing profession. The research aimed to understand current practices, challenges, and opportunities to utilize these insights in developing a nursing management succession model. Using Colaizzi’s seven-step thematic analysis, the study revealed five major themes: (1) Absence of Structured Succession Systems, describing planning as reactive, informal, and unsupported by policy; (2) Impact on Leadership Readiness and Morale, often resulting in emotional distress and operational disruption from abrupt transitions; (3) Essential Leadership Attributes; (4) Organizational Support Deficiency; and (5) Need for Sustainable Succession Models. The findings underscore the urgent necessity for hospital institutions and policymakers to prioritize formal succession planning as a strategic imperative. A key outcome is the development of the SIENES Nursing Management Succession Model, an evidence-based framework designed to institutionalize leadership continuity through six pillars: Structure Succession Planning, Institutional Policy Framework, Essential Leadership, Nurturing Practices and Mentorship, Evaluation, and Sustainability. This model holds potential for widespread implementation, significantly contributing to organizational resilience, patient safety, and leadership sustainability in various hospital settings.
Mouza Al Sharji
This research paper investigates the strategic role of Human Resource Management (HRM) in facilitating technology adoption within organizations. As digital transformation accelerates across industries, successful integration of new technologies depends not only on technical implementation but also on effectively managing human factors. This study examines how specific HRM practices—including training and development, change management, performance management, talent acquisition, and organizational culture shaping—influence employees’ acceptance and effective use of new technologies. Utilizing a mixed-methods approach, we surveyed 1,250 employees and HR professionals across multiple sectors and conducted follow-up interviews with 25 HR leaders. Statistical analysis, including multiple regression, structural equation modeling (SEM), and ANOVA, reveals significant positive correlations between strategic HRM interventions and key technology adoption metrics. Findings indicate that organizations with highly aligned HRM practices report 42% higher technology adoption rates, 37% lower resistance to change, and 28% higher ROI on technology investments. The paper concludes with a framework for integrating HRM into technology implementation strategies and provides practical recommendations for leveraging HRM as a strategic partner in digital transformation initiatives.
Citra Andini, Fathu Rahman, M. Dalyan, Muliyani, Nurul Intan Pratiwi, Somadi Sosrohadi
This study investigates the use of code-mixing in educational content produced by edu-influencers on social media from a sociolinguistic and digital discourse perspective. Employing a qualitative descriptive approach, the study analyzes ten selected utterances containing Indonesian–English code-mixing. The classification of code-mixing types is based on Muysken’s (2000) typology, namely insertion, alternation, and congruent lexicalization. The findings reveal that insertion is the most dominant type of code-mixing, indicating that Indonesian functions as the matrix language while English elements are embedded mainly in the form of academic terms. Alternation and congruent lexicalization occur less frequently and are used primarily for emphasis and stylistic variation. Functionally, code-mixing serves not only linguistic purposes but also social and symbolic functions, such as conveying academic authority, modernity, and global orientation. In the context of social media, code-mixing functions as a digital communication strategy that helps edu-influencers construct an educative and credible online persona while enhancing audience engagement. This study highlights that code-mixing in educational social media content reflects the intersection of language practices, digital identity, and contemporary educational discourse. Keywords: Eode-Mixing, Edu-Influencer, Social Media, Sociolinguistics
Fathu Rahman, Nyoman Elly Swandayani, Somadi Sosrohadi, Tetet Sulastri
This study investigates the use of figurative language in HT’s Facebook posts, focusing on irony and metaphor as strategies for conveying social criticism and humanitarian values. Employing a qualitative descriptive approach, the data consist of five selected Facebook posts published between September and October 2023. The analysis is conducted using stylistic and digital discourse perspectives, supported by multimodal interpretation of textual and visual elements. The findings reveal that HT consistently employs verbal irony to challenge dominant social assumptions related to dignity, diversity, and moral values, while metaphors are used to conceptualize abstract life experiences such as struggle, social status, and survival. These figurative devices function not only as aesthetic elements but also as persuasive tools that enhance emotional engagement and audience interpretation. Furthermore, the high level of audience interaction indicates that figurative language plays a crucial role in making critical messages more accessible and acceptable in digital spaces. This study concludes that Facebook can serve as an effective platform for ethical discourse, where irony and metaphor operate as powerful communicative resources for social reflection and critique in contemporary digital culture.
Keke, E. O, Olulu, C. R, Udeala R. C
This research work seeks to conduct thermodynamic evaluation of a gas-fired steam turbine power plant. The energy and exergy efficiencies of the plant equipment were evaluated respectively; the exergy destruction and costing of the plant were determined. The data for this research work was obtained through direct observation and interviews from operational records of the plant. MATLAB software was used to analyze the collected data. Energy and exergy methods based on first and second laws of thermodynamics were applied to analyze the data of the plant. Results revealed that the condenser had the highest energy efficiency of 93.07%, the boiler had the lowest energy efficiency at 44.99%, while the turbine had an energy efficiency of 57.59%. The overall efficiency of the plant was 24.11%. The results of exergy efficiency of the boiler, turbine and condenser were also obtained as 69.44%, 89.70% and 15.76% respectively. The energy losses in boiler, turbine and condenser equipment of the plant were analyzed and the following results such as 33.62 MW, 11.66MW and 1.62MW were obtained. Similarly, the exergy losses and the cost rate of exergy destruction of the plant were also achieved. The research work also helps in investigating the areas in the power plant with high energy losses as a means to improve production output, and consequently enhancing the performance of the steam power plant.
Chuma Obiamaka Vivian, Oduwole Ayodele
In this research a topography indicator map was produced to show slope and landforms. The process involved the use of appropriate software in the Geographic Information System (GIS) environment. Different data was collected basically from Shuttle Radar Topography Mission (SRTM) and Global Positioning System(GPS) of the study area, these was integrated in the GIS environment using appropriate software. The Digital Elevation Model(DEMs) from STRM was preprocessed, data analyzed and then colors were assigned to relevant features to make the map readable. These type of maps produced can be used in Environmental Management, Urban planning, Agriculture and Hydrology.
Hiral Bhavsar, Nitin Raval
E-commerce platforms generate massive amounts of user interaction data in the form of reviews, ratings, and purchase history. Accurate prediction of customer satisfaction from this multi-modal data is critical for improving user experience, enhancing personalization, and driving business growth. However, existing solutions suffer from several challenges, including the cold-start problem for new users and items, data sparsity in user–item interaction matrices, and the inability to combine multiple data modalities effectively. This proposes a novel AI/ML-driven multi-modal framework that integrates three complementary components: BERT-based textual embeddings for capturing the semantic and sentiment information in customer reviews, LightGCN-based graph embeddings for modeling collaborative user–item relationships and mitigating sparsity issues, and metadata feature encoders for incorporating user demographics, product attributes, and contextual signals. The outputs from these components are fused in a joint feature space and passed through a neural prediction layer to estimate customer satisfaction scores. The expected outcome is a robust, scalable, and explainable prediction system that achieves higher accuracy, handles cold-start scenarios effectively, and can be deployed as a real-time decision-support tool for e-commerce platforms through a Streamlit-based interface.
Carifa Amouzou, Daniel Kumi Owusu, Wisdom Opare
The growing need for stable delivery of electrical energy in academic institutions requires innovative solutions for optimal utilization of these assets, especially in a resource constrained environment. This research aims to identify the problem of operational inefficiencies in the electrical distribution network of Takoradi Technical University (TTU) by proposing a parallel transformer optimization framework. Using a tri-phasic methodology, which includes bibliometric analysis, empirical field monitoring, and stochastic load forecasting, the research evaluates the transition from the independent operation of a 315 kVA and a 500 kVA unit to a synchronized parallel configuration. Technical evaluation results show that although there is a nameplate mismatch, the circulating currents are safe with 20.0 A. Implementation results prove a 30% load equity improvement with 8% technical losses reduction and 1.20% of voltage regulation. The economic analysis brings a quick payback of 1.48 years. Furthermore, the framework incorporates uncertainty quantification for load growth and sensitivity analysis for impedance-based load sharing to ensure system resilience. This study provides a scalable blueprint for institutions in developing economies to achieve energy security and operational reliability through cost-effective asset optimization. This research is responsible for offering a scaling approach for institutions with limited access to financial resources to implement an efficient use of existing assets while delaying costly capital growth.
Hajar Dewantara, Muhammad Rakib, Muhammad Rizky Fahrezi
This study aims to determine the effect of service robot utilization on the competitiveness of culinary businesses at Okinawa Sushi Trans Studio Mall (TSM) Makassar. Independent variables are service speed, service reliability, comfort and safety, and innovative value, the dependent variable is business competitiveness. The study used a quantitative associative approach with accidental sampling, involving customers who were incidentally encountered during fieldwork. A total of 98 respondents which has been served directly by a robot waiter. Validity, reliability, classical assumptions, multiple linear regression, and hypothesis tests were conducted on data collected through a Likert scale questionnaire. The study results show that service speed and service reliability have no significant effect on business competitiveness, while comfort and safety and innovative value have a positive and significant effect. Simultaneously, the four variables significantly affect business competitiveness. These findings suggest that service robots improve competitiveness mainly by enhancing comfort, safety, and innovation, strengthening Okinawa Sushi TSM Makassar’s competitive position.
Aisha Muhammad Gadanya, Daniel Abu Anyebe, Muhammed Atiku Kano1
Numerous studies have implicated vitamin D deficiency in the etiology of ulcerative colitis (UC). However, no research has determined whether vitamin D deficiency is a causative factor or a consequence of UC. The purpose of this study was to determine whether vitamin D deficiency is a risk factor for the development of UC. Methods: Mature male mice were grouped into control, ND+DSS group fed normal diet and 2.5% DSS in their water from day 21 to 28 to induce UC. DD+PC and DD+PC+DSS groups were given vitamin D deficient diet daily and paricalcitol intraperitoneally at 2 days interval for 12 days to induce vitamin D deficiency. They also received 2.5% DSS in their water from day 21 to 28 to induce UC. Disease activity index (DAI), vitamin D concentration, catalase (CAT), reduced glutathione (GSH) and superoxide dismutase (SOD), nitric oxide (NO), myeloperoxidase activity (MPO), tumor necrosis factor alpha (TNF α) and nuclear factor kappa b (NF-KB) were all estimated. Results:The results from this study indicates that vitamin D deficiency significantly increased the disease activity index in mice exposed to DSS compared to mice with normal levels of vitamin D. Antioxidant markers including CAT GSH and SOD were all significantly lower in vitamin D-deficient mice compared to mice with normal levels of vitamin D. Inflammatory markers, such as NO, MPO, TNF α, and NF-KB, were significantly higher in vitamin D-deficient UC mice compared to mice with normal levels of vitamin D. The exacerbation of UC progression, as evident by a significant increase in inflammation and oxidative stress, is a testament to the important role that vitamin D plays. Conclusions: The results show that the disease progression was faster in mice with vitamin D deficiency compared to mice with normal levels of vitamin D. It can be concluded that vitamin D deficiency may be a contributory predisposing factor to the development of UC.
Krishtel Joyce C. Clenuar, Rn
Purpose of the Study: This study explored the lived experiences of Filipino pediatric palliative care nurses working at the New Hope Foundation in China. It sought to understand how these nurses deliver holistic care to terminally ill children while managing emotional strain, ethical dilemmas, and cultural challenges in a multicultural setting. Research Method: A qualitative phenomenological approach was employed, using semi-structured interviews with six purposively selected nurses. Colizzi’s method guided the data analysis process.
Rakesh Kumar
For nearly three decades, Russia’s industrial regions, especially the far-flung and remote Arctic areas surrounding Norilsk and the vast industrialized Ural region, have been subject to significant and continuing environmental damage; this is true even though there have been periodic national and regional efforts to clean-up pollution and enforce pollution controls. These are areas of significant resource extraction and heavy industry, where the level of airborne toxic chemicals such as sulfur dioxide, the presence of heavy metals in soils and waterways, the effect of acid rain causing forest kill off, and the general destruction of the landscape due to mining and smelting activity all exist in varying degrees. This paper will provide a synthesis of some of the major geographical, historical and technological reasons why pollution continues to persist in these areas. In particular, it will examine how the enduring location-based advantage of having large mineral resource deposits (including large amounts of nickel, copper, palladium and iron ore) in combination with aging Soviet-era infrastructure, long-established patterns of industrial production and harsh climatic conditions have resulted in an almost insurmountable level of environmental harm. For example, the Arctic's low temperature and stable air layers trap pollutants close to their point of origin resulting in limited opportunity for natural dispersal; geographic remoteness also complicates the transportation of remedial materials and the removal of hazardous waste. In addition, the extreme winter weather in the Arctic makes the process of recovering contaminated soils and promoting plant growth and development difficult if not impossible, thus creating continuous cycles of contamination. The study highlights the combined effects of these elements, which explains why previous attempts at mitigating the environmental impacts of these areas through modernization and enforcement mechanisms have generally failed. Finally, the study identifies several important implications for environmental policy, including the need for multi-faceted approaches to addressing the economic reliance on these types of industries and developing adaptive strategies suitable to remote environments. Additionally, the study identifies potential avenues of research into sustainable industrial transition processes and climate-resilient remediation technologies.