20 Articles
Ajay Ramteke, Girish Katkar, Lekha Prajapati
The use of facial masks in the real-world setting has made the Masked Face Recognition (MFR) a critical research problem in Pattern Recognition. The classical face recognition technology is highly impaired in performance when the nose and mouth are covered as the main facial features. In this paper, a strong hybrid deep learning model will be suggested, which integrates the EfficientNet-B0 convolutional neural network with a self-attention mechanism to promote the learning of discriminative features on a partially visible part of the face. EfficientNet-B0 is also an efficient and scalable feature extractor, and the self-attention module allows global contextual reasoning and adaptable attention to unoccluded areas of the face, especially the periocular area. The suggested model is tested on the actual MFR2 dataset and has a recognition rate of 0.99, which proves to be better than the traditional CNN-based methods. The experimental test proves that the combination of self-attention can greatly enhance the resilience to the obstruction of the object by obstructing the features of the mask. The findings suggest that the hybrid architecture proposed is quite appropriate in real-time biometric authentication and surveillance and access control systems with masked environments.
Gopala Krishna Murthy H R, S. Shubhakar, Shubha. S
Artificial Intelligence (AI) has emerged as a transformative force in higher education assessment, particularly through automated grading and feedback systems. These AI-powered tools are reshaping higher education by addressing inefficiencies, subjectivity, and scalability limitations associated with traditional assessment methods. The rapid expansion of postgraduate programs, online learning environments, and large-scale digital classrooms has created an urgent need for assessment solutions that are scalable, consistent, and pedagogically effective. AI-based automated grading and feedback systems use machine learning, natural language processing, and deep learning techniques to evaluate student work and provide personalized feedback. This paper presents a comprehensive journal-level review of AI-driven grading systems, examining their historical development, methodological foundations, cross-disciplinary applications, educational benefits, ethical and technical challenges, and emerging research trends. The review finds that, when implemented responsibly with human oversight and transparent evaluation frameworks, AI-based assessment tools can significantly improve efficiency and support formative learning outcomes.
Guhan S, Milind Mallikarjun Kawachale, Sathish Kumar K
In this study, comparisons will be made between graduate students' reactions to semicircle seating and row seating, and an exploration will be made of the implications of each seating arrangement for participation, engagement, and democratic learning. This study will apply an evaluation of learning based on a 3D learning model that includes design, discipline, and participation. The primary data was collected from postgraduate students using a structured Google Forms survey. The research was descriptive in nature and centered on the meanings, experiences, and preferences of students in relation to seating arrangement configurations. In the questionnaire, students were asked questions concerning visibility of the teacher, interaction with other students, opportunity to speak with other students, degree of comfort to share ideas, and opportunity to work with other people. Most postgraduates reported a semicircular configuration of seating to be preferred to the conventional row setting. They indicated an improvement in the view of both the teacher and teaching aids, and thus understanding of a discussion topic was facilitated. Semi-circle seating made students talk more easily. It sparked more class discussion and made the room feel inclusive. It felt more democratic, one student said, and several others also said they now were more comfortable sharing their own thoughts in lectures and even classes. Some students mentioned limited classroom space for larger groups, yet they still preferred semi-circle seating. Participants said strong engagement and interaction outweighed the study’s limits. The study links semi-circle seating for postgraduates to stronger engagement, more participation, and better group learning. In grad seminars, flexible seating helps students join talks and decisions as equals. It helps instructors choose methods that boost participation and learning.
Dr. Anjani Bhartiam, Dr. Arvind Kumar Sah
This study applies the quasi-lattice model (QLM) incorporating temperature- and composition-dependent parameters to predict the thermodynamic properties of liquid binary alloys. By focusing on key alloys such as Cu-Pb and In-Tl, the model effectively captures temperature-induced variations in free energy, entropy, heat of mixing, and concentration fluctuations. Validation against experimental data demonstrates the model’s capability to describe non-ideal mixing and phase stability, offering valuable insights for alloy design and processing optimization.
Lee Zee Ang, Mohamad Noor Azeem Saini, Muhammad Aiman Haiqal Azizul, Nor Ain Nabihah Ahmad, Norhazren Izatie Mohd, Tan Yan Nee
Quotation management is a core pre-contract responsibility of quantity surveyors; however, in current construction practice, it remains predominantly manual, fragmented across emails, paper records, and isolated spreadsheets. Such practices contribute to processing delays, inconsistent data, limited cost transparency, and a higher risk of errors during the pre-contract stage. Despite the growing availability of digital tools in the construction industry, quotation management in quantity surveying continues to lack affordable, structured systems tailored to the operational needs of small- and medium-scale practices. To address this gap, this study proposes a centralized quotation management system in Microsoft Excel, integrated with Visual Basic for Applications (VBA). A system development methodology was adopted, comprising workflow mapping, identification of process weaknesses, system design, and prototype implementation. The proposed system design consolidates supplier quotations, material specifications, unit rates, and basic project indicators within a single platform, incorporating limited automation and data verification features to reduce duplication and improve record traceability. While the system was not empirically tested through live project deployment or formal user evaluation, the prototype demonstrates the practical feasibility of using spreadsheet-based tools to improve the organization, consistency, and accessibility of quotation data. The findings suggest that, although not a substitute for fully integrated commercial software, a structured Excel-VBA solution can offer a low-cost, transitional digital approach for enhancing quotation management in resource-constrained quantity surveying environments.
Bhawana Dahiya, M.Sc., Dr. J. R. Gaur, M.Sc., Ph.D., Shivani Surve, M.Sc.
An individual having two kinds of blood cell populations or two sets of deoxyribonucleic acid (DNA) types in different body tissues is called Chimeric and the situation is called as chimerism. This makes diagnosis and comparison of tissues for organ transplants and forensic investigations complicated and may lead to erroneous results. A few such cases, highly publicised in the past, have been mentioned in this paper. How chimerism affects DNA profiling results in forensic science and can lead to situations where the accused may be exonerated, and a genuine offspring of one’s parents may be denied parentage has been highlighted. How chimerism can be detected, and flawless reports can be given, the challenges faced in forensic cases have been mentioned in this paper.
Eferhi Adakpo
Digital transformation has become a major driver of competitiveness, efficiency, and sustainable economic growth across industries and national economies. This review paper examines the evolution of digital transformation and business systems innovation, with emphasis on the roles of business analysis, agile methodologies, data-driven decision-making, and enterprise system modernization. Drawing from existing literature and industry practices in telecommunications, fintech, and service sectors, the paper explores how technology-enabled solutions enhance organizational performance, promote financial inclusion, and support digital governance. Furthermore, the review discusses the importance of digital transformation to national economic interests in strengthening technological competitiveness and expanding the digital economy. Therefore, by incorporating theoretical frameworks and practical insights, this paper highlights success factors, challenges, and future directions for organizations and policymakers seeking to leverage digital transformation for inclusive and sustainable development.
Lisy M Thomas, Sonal Sahai
This analytical review explores the effectiveness of Artificial Intelligent Tutoring Systems (ITS) for academically underprepared learners in STEM higher education. The study synthesises recent advancements in generative AI and ITS, examining their potential to deliver personalised instruction, adaptive feedback, and scalable learning environments. It highlights the architectural components and operational mechanisms of ITS, evaluating their strengths in fostering academic improvement. The paper identifies key limitations including challenges in real-time adaptability, equitable access, and ethical data management, as well as concerns regarding the reliability of online assessments and the ability of ITS to replicate nuanced human guidance. The review calls for future research on emotion-aware computing, collaborative learning, explainable AI, emphasising the necessity for ethical, transparent, and accessible ITS solutions. Ultimately, the article argues that while ITS platforms hold significant promise for transforming STEM education and supporting underprepared learners, their success depend on continual innovation, robust evaluation, and a commitment to educational equity and excellence.
Aminah Awang, Mohamad Faizul Yahya, Suzaini Abdul Ghani
Plastic pollution is a critical environment issue, with significant plastic waste contaminating soil, oceans, and the atmosphere, posing serious health risks. Kibria et al., (2023) emphasize the need for innovative solutions to manage plastic waste. This research analyzes the relationship between textile structure and the physical and mechanical characteristics of plastic bottle yarn and kenaf yarn textiles. Three weave structures—plain weave, twill 2/2, and twill 3/1—were tested by their tensile strength, percentage crimp, weight, and thickness. A control sample made entirely of kenaf yarn was also subjected to analysis for comparison. The statistics were used to analyze the data in order to determine the correlations among these variables. The findings indicate that fabric structure significantly influences mechanical performance. The twill 3/1 weave had the highest thickness (3.84 mm) and weight (8.021 g/cm²) and is therefore the strongest, while the plain weave had the lowest thickness (2.85 mm) and weight (6.544 g/cm²) and is therefore most appropriate for lightweight applications. Tensile strength tests confirmed that twill 3/1 weaving structure was more effective than the other structures. Additionally, percentage crimp differed, with higher percentage crimp in the control sample (100% kenaf) and better dimension stability for fabrics with plastic bottle yarn. These results affirm the influence of fabric construction to determine performance as well as durability. The study also establishes the economic and environmental worth of plastic bottle yarn in sustainable textile production. For example, in Bangladesh, which is becoming a top manufacturer of recycled yarn and fabrics. Future research should examine larger populations and advanced testing methods to further optimize recycled textile applications.
Jitendra Kumar, Praveen Kumar
Molecular characterization using protein profiling is a crucial technique for understanding genetic variability among plant accessions. The present study investigates the genetic diversity among five Pisum sativum accessions using protein banding patterns, similarity indices, and statistical analyses. SDS-PAGE was employed to assess the presence or absence of protein bands with different molecular weights. The protein bands were analyzed based on their molecular weights (kDa), and the presence or absence of specific bands was recorded across different accessions. The similarity index was calculated to assess the genetic relationships among the accessions. Our findings provide valuable insights into the genetic diversity of Pisum sativum accessions, which may aid in breeding programs and disease resistance studies. A paired t-test and Pearson correlation matrix were used to analyze statistical significance and genetic relationships. The results indicate both conserved and polymorphic protein bands, highlighting genetic variation among the accessions. The paired t-test showed no statistically significant differences between accessions, while the Pearson correlation analysis suggested varying levels of genetic similarity. These findings provide valuable insights for breeding programs aimed at improving disease resistance and adaptability in Pisum sativum.
Dr. B. B. Baridam, Ndueso, Etukudo Ekefre, Prof. C. Ugwu
In studying Blockchain Technology, one of its predominant applications that have provided a massive growth in their recent global acceptance and market capitalization in the past few years is cryptocurrencies. Individual investors, notable institutions and corporate firms are readily investing in it. Predicting cryptocurrency prices owing to their volatile nature has been a challenging decision for researchers owing to social and psychological factors that affect price of cryptocurrency. Substantively, the crypto market is highly volatile when compared to the traditional commodity markets and may be affected by factors like sentimental, legal and other technical indicators. The uncertainty and unpredictable nature of cryptocurrency necessitated this study on Improved Intelligent Model for Cryptocurrency Trading in Blockchain Platform. The five cryptocurrencies utilized in this work were Bitcoin (BTC), Ethereum (ETH), XRP, Cadona (ADA), Solana (SOL). This study incorporated Bi-LSTM and Attention Mechanism techniques with trading strategies like buy, sell or hold depending on the choice of the investors. It is depicted that our model yields more accurate and reliable predictions when confirmed alongside with the Live price time-based model. This work provided with guarantee an interface that can be used by investors especially those in cryptocurrency trade for accurate predictions as it will go a long way in extenuating investment risk. This research adopts Object-Oriented Programming (OOP) methodology to design and implement an intelligent cryptocurrency trading system. This study was implemented using C# Programming Language with incorporation of python.NET. The system can be used in real-time scenarios as it is well trained and evaluated using standard data sets. The result depicted that this new system predicts cryptocurrency prices with high accuracy compared to the existing system. The outcome of this study assured us that our approach enhances the necessary assurance on the new system and offers customers a more reliable financial service in cryptocurrency trade.
Alexander Musa, Professor Bala G. Attah, Professor Thomas D. Bot
Insecurity as it affects education process caused by Boko Haram insurgency poses serious challenges to effective learning, particularly in mathematics. This study therefore, examined the effect of Boko Haram insurgency on senior secondary school students’ attitudes toward mathematics learning in Adamawa State, North East Nigeria. A comparative survey research design was used, with a population of 4828 and sample size of 660 involving students from insurgency-affected and non-affected areas in the state. Data were collected using a validated Mathematics Attitude Questionnaire and analyzed using descriptive statistics and independent samples t-tests. Results revealed a significant difference in attitudes toward mathematics between students in insurgency-affected and non-affected areas (t = 13.27, p < 0.05). Further analyses showed significant differences based on gender (t = 4.18, p < 0.05) and school location (t = 14.02, p < 0.05), with female students and those in rural schools exhibiting more negative attitudes. The findings highlight the need for psychosocial and context-sensitive educational interventions in conflict-affected regions.
Dr. B. B. Baridam, Ndueso, Etukudo Ekefre, Prof. C. Ugwu
The continuous operation of cryptocurrency markets generates massive data streams that challenge real-time trading systems. Traditional approaches process every price update equally, leading to substantial computational waste during routine market periods. This study introduces an attention-based mechanism that intelligently filters market activity, triggering predictions only when volatility exceeds dynamically adjusted thresholds. We implemented this approach within a Bidirectional Long Short-Term Memory framework and tested it across Bitcoin, Ethereum, XRP, Cardano, and Solana over a 24-hour monitoring period following extensive training on historical data from 2022 to 2025. Our findings demonstrate that selective processing reduces computational requirements by approximately 72% while maintaining prediction accuracy within 0.1 percentage points of continuous processing approaches. The system generated predictions during only 28% of monitored periods on average, yet achieved Root Mean Square Errors ranging from 0.8% to 2.1% across different cryptocurrencies. Confidence scoring proved well-calibrated, with predicted confidence levels matching actual accuracy within 1.2 percentage points. Notably, the system correctly identified stable market conditions, issuing "Hold" recommendations with 99% confidence when price movements fell within normal variance bands. Alert delivery consistently occurred within 10 seconds of significant market events, enabling timely trading decisions. The dynamic threshold adjustment successfully adapted to varying volatility regimes, preventing false triggers during high-volatility periods while maintaining sensitivity during stable conditions. These results suggest that attention-based filtering offers a practical solution for multi-cryptocurrency monitoring on standard computing hardware.
Aishwarya Gandhe, Dr. K. Meenarani, Nigama Neligi, Siri Chandana
In Indian philosophy, karma has two main uses: to encourage a person to act ethically and to provide a framework from which people view suffering/moral injustice. Essentially, karma defines an individual’s present and future experiences through good/bad actions or behaviors, due to the belief that whatever an individual does, whether on purpose or by accident (with no intention), will have consequences for that individual, not only in their current life but in future lives as well (by rebirth). "Karma" does not equate to "blind fate," rather karma promotes moral responsibility and agency (i.e., having the ability to choose), therefore all actions, not just physical, have some level of consequence. Karma also serves as a psychological and experiential tool in linking intent/action with consequence, enhancing moral consciousness through self-evaluation, controlling emotions, etc. As such, karmic principles provide an important context through which an individual develops resilience, discipline, and responsibility in daily living. This paper aims to explore karma from various contexts, including how different philosophies interpret karma (Hinduism, Buddhism, and Jainism), and how they relate psychologically and experientially.
Dada, Emmanuel Damilo, Dearsly, Emmanuel Markus, Eze, Kingsley Chijioke, Odiba John chubiojo, Ogidigo, Jane Chinwe, Oshatuyi Olukayode
Background: Sirtuin 1 (SIRT1) and sirtuin 3 (SIRT3) are NAD⁺-dependent deacylases that play critical roles in metabolic regulation, oxidative stress control, and aging. Natural products have emerged as promising sources of sirtuin-interacting molecules, yet systematic evaluation of Hyptis verticillata phytochemicals against these targets remains limited. Methods: Selected phytochemicals from Hyptis verticillata were subjected to molecular docking against SIRT1 (PDB ID: 4I5I) and SIRT3 (PDB ID: 3GLS) using AutoDock Vina. Binding affinities and interaction patterns were analyzed, followed by in silico pharmacokinetic and toxicity (ADMET) profiling using SwissADME and admetSAR to assess drug-likeness and safety. Results: Docking analysis revealed binding energies ranging from −5.0 to −9.5 kcal/mol across both targets. Squalene exhibited the strongest predicted affinity for SIRT1 (−9.5 kcal/mol), while oleanolic acid showed the highest affinity for SIRT3 (−8.5 kcal/mol). Several other compounds demonstrated moderate binding to both sirtuins. Interaction analysis indicated predominantly hydrophobic stabilization within the binding pockets. ADMET profiling suggested that while highly lipophilic compounds may face bioavailability limitations, oleanolic acid displayed a comparatively balanced pharmacokinetic and safety profile. Conclusion: The findings indicate that selected H. verticillata phytochemicals exhibit structural compatibility with SIRT1 and SIRT3, supporting their consideration as preliminary sirtuin-interacting candidates. However, the results represent structure-based predictions rather than functional evidence. Further molecular dynamics simulations and experimental validation are required to elucidate binding stability and biological activity.
Amoo Isiaka Adekunle*, Josephine Odunayo Ilade, Muraina Mutairu Omoniyi
The consumption of yoghurt is steadily increasing due to its recognized health benefits and versatility. However, the quality and nutritional content of yoghurt can vary depending on the source of milk used in its production. This study investigates the physicochemical, sensory, microbial, risk assessment and functional properties of fresh milk-based yoghurt and powdered milk-based yoghurt. 15 Samples of yoghurts coded: LZS, LZU, FFS, FFU, TYS, TYU, TSS, TJU, CYS, CYU, HYS, LZG, CGY, TJG and LZF were bought from supermarket in Nigeria. The ranges for physicochemical properties were pH (4.49 – 5.23), conductivity (3588 – 4988mS/cm), density (2.09 – 2.26g/cm³), viscosity (422 – 493mPa.S), titratable acidity (0.00011 – 0.00013%), moisture (70.40 – 89.30%), ash (1.37 – 5.70%), fat (4.3 – 4.81%), and protein (5.63 – 28.94%). The average concentration (mg/kg) of Na, Mg, K, Ca, Fe, Zn and Cu are 595.53, 280.00, 395.00, 1956.30, 12.14, 1.31 and 0.27 respectively, while Cd was not detected in all samples. Target Hazard Quotient, (THQ) for Fe, Zn and Cu in all samples were < 1 suggesting no potential health risk for consumers. B-glucan, inulin, pectin, gelatin and sodium alginate ranges from (5.48 – 65.6), (10.9 – 46.7), (8.70 – 28.3), (4.29 – 16.7), and (6.45 – 21.7) respectively. General acceptability ratings were between very good and excellent. Total viable bacterial count (TVBC) ranges from (6 – 79cfu/100mL), total viable lactic acid bacterial count (TVLBC) ranged between (12 – 18cfu/100mL) and total viable yeast and mold count (TVYMC) ranged between (2 – 31cfu/100mL). This study revealed that all yoghurt samples analyzed are safe and reliable sources of protein, minerals and nutrients.
Dr. Paul. K. Kurgat, Mr. Samson Kemboi Koech
Many countries around the world use economic diplomacy to exert their influence. Kenya is a major economy in Africa ranking currently among the biggest economies in East Africa Community. While many studies explore Kenya’s economic growth, few interrogate its efficiency as a tool for economic diplomacy in the EA. This study examined how regional trade stratagems influence Kenya’s diplomatic engagements within the EAC paradigm. The objective of this study was to investigate how regional trade stratagems influence Kenya’s diplomatic engagements within the EAC framework the literature was reviewed thematically guided by Interdependence theory and reinforced by Neoliberal theory. This study used a descriptive research design and its target population included Kenya’s Ministry of Trade and Industrialization. The study used purposive, stratified and random sampling methods to sample its respondents. Quantitative data was collected using questionnaires and coded using SPSS and analyzed using descriptive statistics (frequencies and percentages) and presented in tables. Qualitative data was collected by interviewing Key Informants (KIIs), analyzed using content and thematic analysis and presented in narrative and verbatim format. The study found that trade policies (b=0.245, p<0.001), economic partnerships (b=0.278, p<0.001), and strategic approaches (b=0.289, p<0.001) played a significant role in promoting Kenya in the EAC, whereas the persistent challenges (b=0.236, p=0.001) like tariffs and logistical barriers acted as a limit to progress. The study concludes that Kenya has its regional influence based on good policy alignment, economic integration, and strategic partnerships. The research recommends the harmonization of trade frameworks, investing in regional infrastructure and digital diplomacy as methods to enhance Kenya economic diplomacy in the EAC paradigm.
Dr. B. Umesh Kumar Sharma
Emotional well-being is fundamental to children’s academic engagement, social development, and long-term mental health. The present cross-sectional quantitative study examined the emotional well-being of 264 elementary school students from eight government schools in Bhoirymbong Block, Meghalaya. A validated, child-friendly questionnaire assessed multiple domains including overall emotional status, worry, happiness, loneliness, peer relationships, school perception, fear of teachers, and examination anxiety. Descriptive findings indicated that 74.9% of students demonstrated healthy emotional status; however, emotional vulnerability increased significantly across grade levels. Inferential analyses revealed significant grade differences in emotional vulnerability, F(2, 261) = 9.34, p < .001, and a gender difference in examination anxiety, with girls reporting higher anxiety. Multiple regression analysis showed that fear of teachers (β = .34), examination anxiety (β = .29), and loneliness (β = .21) significantly predicted emotional vulnerability, explaining 32% of the variance, while peer trust functioned as a protective factor. Findings highlight the critical role of school climate and assessment practices in shaping emotional outcomes and underscore the need for emotionally responsive pedagogy and structured social-emotional learning frameworks in elementary education.
Dr. Koushik Ray
Diagnostic healthcare—clinical laboratories, pathology services, imaging support units, and point-of-care testing—has become indispensable for modern medicine. Yet, the same systems that improve clinical outcomes can impose significant environmental burdens through biomedical waste generation, single-use plastics, chemical hazards, energy-intensive instrumentation, water consumption, and carbon emissions across supply chains. This paper examines the major sustainability challenges in diagnostic healthcare, focusing on environmental risks arising from laboratory operations and the policy responses required to mitigate them. Using a qualitative, policy-analytical approach grounded in environmental health governance, the paper maps key risk pathways: infectious and sharps waste, chemical and pharmaceutical residues, microplastics, wastewater contamination, and greenhouse gas emissions from energy use and logistics. The analysis highlights structural barriers including regulatory gaps, weak enforcement, fragmented waste infrastructure, limited green procurement, cost pressures, inadequate staff training, and insufficient environmental performance measurement. The paper proposes a multi-level policy framework: (i) strengthening regulation and compliance, (ii) implementing circular economy strategies such as extended producer responsibility and sustainable procurement, (iii) accelerating decarbonization through energy efficiency and renewables, (iv) improving segregation and treatment systems, (v) digitizing environmental monitoring, and (vi) building a culture of sustainability through training, accreditation incentives, and transparent reporting. The paper concludes with actionable recommendations and an implementation roadmap for low- and middle-income settings, where diagnostic expansion must be aligned with environmental protection and public health resilience.
Girly D. Serino, MBA, Leo C. Naparota, PhD
This study examined the innovative work environment and its relationship with the innovative work behavior of employees of the Community Rural Bank of Dapitan City, Inc. during calendar year 2024. A descriptive-correlational design was employed, involving 120 respondents. Weighted mean, standard deviation, and Spearman rank-order correlation (Spearman’s rho) were used for data analysis. Results indicated that employees perceived the innovative work environment as very high, while their innovative work behavior was assessed as high. Correlation analysis revealed a strong, positive, and statistically significant relationship between the innovative work environment and employees’ innovative work behavior. The findings may serve as a reference for the board of directors and managers in strengthening management strategies and workplace conditions that support innovative behavior among employees.