Asian Journal of Advanced Research and Reports

Geology of Energy Resources: A Comprehensive Scientific Review of Conventional Ore Systems and Emerging Energy Materials

Janmejaya Sahoo — Tue, 30 Jun 2026 00:00:00 +0000

Energy-resource geology is undergoing significant change as global energy systems shift from conventional fossil-fuel dependence towards low-carbon technologies and critical mineral supply. This review examines the geological controls, deposit types, exploration approaches, and sustainability challenges associated with both conventional energy resources and emerging energy materials. Conventional resources, including coal, petroleum, natural gas, and uranium, are discussed in relation to sedimentary basin evolution, organic matter preservation, hydrocarbon migration, structural trapping, redox processes, and hydrothermal activity. The review also considers the growing importance of lithium, cobalt, nickel, copper, graphite, vanadium, rare earth elements, and natural hydrogen in supporting batteries, electric vehicles, renewable-energy infrastructure, energy storage, and hydrogen-related technologies. The synthesis indicates that tectonic setting, sedimentary processes, magmatic differentiation, hydrothermal fluid flow, structural deformation, and supergene enrichment are major factors controlling the formation and distribution of energy-related resources. Modern exploration methods, including remote sensing, geographic information systems, geophysical surveys, geochemical analysis, artificial intelligence, machine learning, and three-dimensional geological modelling, are increasingly important for improving mineral targeting and resource assessment. However, expanded extraction of both conventional and critical resources is associated with environmental and social challenges, including land degradation, water contamination, mine waste generation, biodiversity loss, carbon emissions, and supply-chain vulnerability. The review emphasises that future energy security will require integrated geological understanding, responsible mining practices, circular economy approaches, recycling, environmental safeguards, and improved resource governance. Overall, the study highlights the continuing relevance of geological sciences in supporting sustainable energy-resource development during the global energy transition.

Improving Diagnostic Accuracy in Clinical Chemistry: Evaluation of Pre-Analytical and Analytical Errors in Resource-Limited Laboratories of Developing and Underdeveloped Countries

Ogonnaya, Chinemerem Cynthia — Tue, 30 Jun 2026 00:00:00 +0000

Clinical chemistry laboratories generate a large share of the data on which clinical decisions ultimately rest, so the accuracy of that data has a direct bearing on patient safety. In resource-limited settings—those typical of low- and middle-income countries (LMICs), including many developing and underdeveloped nations—the diagnostic value of laboratory results is frequently undermined by pre-analytical and analytical errors that occur at rates well above those documented in better-resourced health systems. This critical review draws together published evidence from January 2010 to February 2026 to examine the nature, distribution, and consequences of these errors, and to weigh the evidence behind strategies proposed to reduce them. Pre-analytical errors, encompassing failures in patient preparation, specimen collection, labelling, transport, and storage, consistently make up the largest share of laboratory errors worldwide, and they are especially common in LMIC laboratories, where standardised phlebotomy practice, reliable cold-chain infrastructure, and electronic patient identification remain the exception rather than the rule. Analytical errors, for their part, stem from weak internal quality control, infrequent participation in external quality assurance schemes, calibration failures, unstable reagents, and gaps in staff competency. Both categories of error carry well-documented consequences, including misdiagnosis, delayed or inappropriate treatment, antimicrobial misuse, and excess mortality. Underlying these problems are structural factors that are not easily fixed in the short term: chronic underfunding, workforce shortages, unreliable electricity and water supplies, and weak regulatory oversight. The strategies considered here—staged implementation of quality management systems aligned with ISO 15189, targeted deployment of point-of-care testing, adoption of laboratory information systems, and training programmes tailored to local constraints—offer a realistic, if incremental, path forward. The synthesis presented is intended to inform laboratory policy, quality improvement programmes, and future research priorities in low-resource settings.

Artificial Intelligence Competence among Higher Education Faculty: A Critical Review through the Lens of the DigCompEdu Framework

Leo Santiago III Arrabaca — Wed, 01 Jul 2026 00:00:00 +0000

The rapid diffusion of artificial intelligence (AI) tools across teaching, assessment and research has placed renewed pressure on universities to define what it means for academic staff to be digitally and technologically competent. The European Framework for the Digital Competence of Educators (DigCompEdu), originally conceived to describe generic digital competence, has increasingly been invoked, adapted and contested as a reference point for understanding faculty readiness to use, teach with, and critically evaluate AI. This article offers a critical narrative review of the empirical and conceptual literature examining AI competence among higher education faculty, situated explicitly within the DigCompEdu architecture and its emerging extensions. It traces the historical development of DigCompEdu and related frameworks such as Technological Pedagogical Content Knowledge (TPACK) and its AI-oriented derivatives, synthesises empirical evidence on faculty self-reported competence, usage patterns, attitudes and professional development needs, and identifies the institutional, disciplinary and ethical factors that shape competence development. The review further examines professional development strategies reported in the literature and concludes with a critical assessment of the conceptual and methodological gaps separating policy aspirations from classroom realities, including unresolved questions of equity, algorithmic literacy and academic integrity. The synthesis suggests that, while DigCompEdu remains a durable and adaptable scaffold, its AI-related extensions are conceptually fragmented, empirically uneven across world regions, and insufficiently attentive to questions of educator agency and structural inequality. The article closes by outlining implications for policy, institutional practice and future research, alongside an honest account of the review's limitations.

Inclusive Leadership: The New Normal Style for Diverse and Dynamic Organisations

P. Cyril Joy, Josheena Jose — Fri, 03 Jul 2026 00:00:00 +0000

Workforces have become more demographically varied, more geographically dispersed, and more frequently reorganised than at any point in the recent history of management scholarship. Against this backdrop, inclusive leadership has emerged as a leadership style oriented towards helping people feel that they belong while still being recognised for what makes them distinct. This review draws together the empirical and conceptual literature on inclusive leadership to clarify what the construct means, where it comes from, how it operates, and where it still falls short. The review traces the construct from its origins in healthcare team research through its development into a relational leadership style studied across hospitality, technology, nursing, higher education, and remote work settings. It examines the personal and organisational conditions that give rise to inclusive leadership, the psychological mechanisms — chiefly psychological safety, voice, and a sense of being valued — through which it shapes employee behaviour, and the measurement tools used to capture it empirically. The review also considers boundary conditions that complicate a simple "more is better" reading of the evidence, including curvilinear effects on team innovation and unresolved questions about cross-cultural transportability. The discussion closes by identifying gaps that limit confident application of the construct in practice, principally a continued reliance on cross-sectional, self-report designs and an underdeveloped account of how inclusive leadership functions in fully remote and artificial-intelligence-mediated work. The review is intended for organisational scholars, human resource practitioners, and leadership development specialists seeking an integrated account of a literature that has grown rapidly but unevenly.

Assessment of AI Technologies Applied to Food Image Calorie Estimation

C. Kalu, Bassey, C. Kalu-Ulu, Torty — Fri, 10 Jul 2026 00:00:00 +0000

Artificial intelligence methods are increasingly used to support food recognition, portion estimation and calorie assessment from digital images. This review examines selected AI technologies applied to food-image calorie estimation, with attention to food detection, segmentation, recognition, volume estimation and dataset availability. It discusses earlier mobile and cloud-based systems, machine-learning approaches such as support vector machines, and deep-learning methods, including convolutional neural networks, Faster R-CNN, semantic segmentation models and few-shot learning approaches. The review also summarises commonly used food-image datasets designed for food recognition, detection, segmentation and volume estimation. The analysis shows that AI-based calorie-estimation systems can improve convenience by reducing manual food logging and supporting automated nutritional assessment. However, accuracy depends on several linked tasks, including correct food classification, precise segmentation, reliable portion-size estimation and appropriate nutritional mapping. Food-recognition accuracy can be high under controlled conditions, but calorie estimation remains more difficult because food volume, density, preparation method, lighting, occlusion and mixed-food presentation introduce substantial uncertainty. Cloud-based processing can support computationally demanding models, while object-detection and segmentation methods can improve analysis of complex meals. Nevertheless, current systems still face important limitations in real-world food images. The review concludes that AI technologies provide a promising foundation for automated calorie estimation, but further work is required to improve dataset quality, depth estimation, contextual food information, local nutrition databases and validation under practical eating conditions.

The Four Pillars of Scientific Discovery and Their Role in Undergraduate Data Science Curricula

Ivaylo Donchev — Sat, 27 Jun 2026 00:00:00 +0000

The philosophy and practice of science have undergone profound methodological shifts over centuries. Although often viewed as a linear progression, the evolution of scientific discovery is better understood as an accretion of distinct paradigms: empirical, theoretical, computational, and data-driven. Each paradigm has unique epistemic foundations, tools, strengths, and limitations. The empirical paradigm grounds knowledge in direct observation and experimentation. The theoretical paradigm abstracts nature into mathematical laws and first principles. The computational paradigm uses simulation to explore systems that are intractable through pure theory alone. The data-driven paradigm extracts patterns from large datasets using machine learning, often without a priori hypotheses. This paper is a conceptual framework analysis rather than an empirical curriculum evaluation. It argues that contemporary scientific progress increasingly depends on the fluid integration of all four paradigms rather than on competition among them. We characterise each paradigm, illustrate their interplay through concrete examples (drug discovery and climate science), and extend the analysis to undergraduate data science education. Drawing on the Computing Competencies for Undergraduate Data Science Curricula report (ACM, 2021) as a primary curricular reference, we map each paradigm to specific knowledge areas and competencies. The mapping is illustrative rather than exhaustive; other frameworks (e.g., statistical education guidelines) may complement this view. We conclude that paradigm integration is not merely a methodological ideal but a core curricular requirement. To support implementation, we recommend: (1) explicit paradigm mapping of courses and learning outcomes; (2) integrative capstone projects that require the application of all four paradigms; (3) cross-paradigm assessment that evaluates not only technical execution but also justification of paradigm choice; and (4) embedding ethical responsibilities—bias, opacity, assumptions, and privacy—as intrinsic to each paradigm rather than as an add-on. Graduates who can navigate across all four paradigms will be better prepared for the interdisciplinary, ethical, and scalable demands of modern data science.

Assessment of Ambient PM₂.₅ and PM₁₀ Concentrations in Early Childhood Development Education (ECDE) Centres in High-Traffic and Industrial Areas of Nairobi City County, Kenya

Maurice Kavai, Esther Kitur — Sat, 20 Jun 2026 00:00:00 +0000

Air pollution in urban learning environments remains an important public health concern for young children, particularly where schools are located close to major roads and industrial activities. Early Childhood Development Education centres located near high-traffic roads, transport corridors, and industrial zones are especially exposed to elevated concentrations of particulate matter, which poses significant risks to respiratory and cardiovascular health. This study assessed ambient particulate matter with aerodynamic diameters of less than 2.5 micrometres (PM₂.₅) and less than 10 micrometres (PM₁₀) in Early Childhood Development Education (ECDE) centres located in high-traffic and industrial areas of Nairobi City County, Kenya. A descriptive cross-sectional research design was used. The study involved 120 ECDE children selected from six centres, with teachers, school administrators, environmental officers and public health personnel providing supplementary contextual information. Data were collected using structured questionnaires, observation checklists, key informant interviews and portable particulate matter monitors. Quantitative data were analysed using descriptive statistics, independent-samples t-tests, Pearson correlation analysis and multiple regression analysis in SPSS version 27. The findings showed that measured PM₂.₅ and PM₁₀ concentrations exceeded the World Health Organization guideline limits in the monitored ECDE environments. Traffic-dominated locations generally recorded higher PM₂.₅ concentrations, whereas industrial locations contributed substantially to PM₁₀ exposure. Reported health symptoms among learners included coughing, wheezing, shortness of breath, frequent colds and eye irritation. Particulate matter exposure showed positive and statistically significant associations with respiratory symptoms, with combined PM₂.₅ and PM₁₀ exposure showing the strongest relationship. Regression analysis further indicated that PM₂.₅ exposure, PM₁₀ exposure and proximity to pollution sources were significant predictors of reported respiratory illness among ECDE children. The study concludes that ECDE learners in high-exposure urban settings may face increased health risks from sustained particulate matter exposure and recommends routine air quality monitoring, enforcement of environmental regulations, buffer zones, urban greening and public awareness interventions.

Enhancing Student Engagement and Conceptual Understanding in Class IV Science Using the 5E Inquiry-Based Model

Namkha Wangdi — Mon, 22 Jun 2026 00:00:00 +0000

As 21st-century learners require more active and student-centred learning experiences, Inquiry-Based Learning (IBL), particularly the 5E model, has emerged as an effective approach for enhancing student engagement, critical thinking, problem-solving skills and conceptual understanding in primary science education. This study employed a quantitative method to examine the effectiveness of the 5E IBL model in enhancing engagement and conceptual understanding in science lessons. A 5E model intervention, consisting of Engage, Explore, Explain, Elaborate and Evaluate phases, was implemented over three weeks with 23 Class IV students based on low achievement in science class tests. The study also analysed pre- and post-test scores, together with a post-intervention Likert-scale survey.

The survey revealed a positive shift in student attitudes. All student participants expressed positive views towards hands-on activities, with 65.2% strongly agreeing and 34.8% agreeing that practical activities make science exciting. Students preferred self-discovery learning (95.7%) over lecturing, and 100% affirmed that group work improved lesson comprehension.

In addition, the post-test results revealed improvement in academic performance. The overall class pass rate increased from 78.3% to 91.3% (≥40), and the class mean score rose to 68.2. Moreover, the failure rate dropped from 21.7% to 8.7% in the post-test. Thus, this study concludes that Inquiry-Based Learning kept students interested and helped them think critically.

Neonatal Survival and Risk Factors for Retinopathy of Prematurity among Infants Admitted in Tertiary Hospitals, Northern Tanzania

Tue, 23 Jun 2026 00:00:00 +0000

Background: Retinopathy of prematurity is a preventable cause of childhood visual impairment, particularly among premature infants who survive after neonatal intensive care. Improved neonatal care in low- and middle-income settings may increase the number of infants at risk, making local data on survival, incidence, and associated factors important for planning screening services.

Aim: This study assessed neonatal survival, the incidence of retinopathy of prematurity, and associated neonatal and maternal factors among premature infants admitted to tertiary hospitals in Northern Tanzania.

Methods: A hospital-based prospective cohort study was conducted among premature infants with gestational age below 37 weeks and/or birth weight below 2500 g admitted to Kilimanjaro Christian Medical Centre and Arusha Lutheran Medical Centre. Eligible infants underwent retinal examination, and findings were classified according to the International Classification of Retinopathy of Prematurity. Data were analysed using descriptive statistics and Fisher’s exact test to assess associations between selected risk factors and retinopathy of prematurity.

Results: Seventy premature infants were enrolled. Eleven infants died before completing 28 days of life, and the reported neonatal survival proportion was 84.2%. Retinopathy of prematurity was reported in 11 infants, representing 20.0% of the analytic sample. All affected infants had bilateral disease; nine had stage 1 disease and two had stage 2 disease. No infant developed stage 3, 4, or 5 disease, and all cases regressed completely during follow-up. Lower birth weight, neonatal infection, more than one blood transfusion, and maternal pre-eclampsia were significantly associated with retinopathy of prematurity.

Conclusion: In this cohort, neonatal survival was high and one-fifth of assessed infants developed mild to moderate retinopathy of prematurity. The findings support continued retinal screening among premature infants and careful monitoring of infants with low birth weight, neonatal infection, repeated blood transfusion, or exposure to maternal pre-eclampsia.

A Design and Mathematical Modeling of the Novel L(LCL)₃ Hybrid Low-Pass Filter for Power Grid-Connected Solar PV Systems

Unogwu, Daniel Ogbu, Samson Dauda Yusuf, Abdulmumini Zubairu Loko — Thu, 25 Jun 2026 00:00:00 +0000

In the face of depleting natural resources, the efficient use of available energy sources is becoming increasingly important in reducing operational costs while satisfying ever-tighter pollution regulations. This paper presents the complete mathematical design framework and component-calculation methodology for the L(LCL)₃ filter, a novel high-order hybrid low-pass filter topology developed for the integration of utility-scale solar photovoltaic (PV) systems into high-voltage grid networks. The L(LCL)₃ filter extends the established LCL and LLCL filter architectures by incorporating three resonant trap circuits tuned to the inverter switching frequency (f_sw=10 kHz), its second harmonic (20 kHz), and a third attenuation band between four series inductors (L₁,L₂,L₃,L₄) distributed in a 0.40:0.25:0.20:0.15 ratio. The design employs a rigorous per-unit system analysis using HVDC-class constraints: total inductance ≤ 8% of base inductance, total capacitance ≤ 3% of base capacitance (limiting reactive power to ≤ 3 MVAr), and current ripple ≤ 20% of base current. For a 100 MVA, 33 kV reference system with f_grid= 50 Hz and f_sw=10 kHz, complete component calculations yield: L₁= 1.108 mH,L₂= 0.693 mH,L₃= 0.554 mH,L₄= 0.416 mH; C₁= 1.3 µF,C₂= 0.36 µF,C₃= 0.11 µF and damping network parameters R_d1= 6.0 Ω,R_d2= 4.0 Ω, R_d3= 3.0 Ω, R_d4= 2.0 Ω. The filter achieves a damping ratio ζ = 0.196 and quality factor Q = 2.55, ensuring well-damped operation with a primary resonance at 8,011 Hz. Resonance analysis confirms trap frequencies of 10,078 Hz (Trap 1) and 20,396 Hz (Trap 2) with less than 2% design error. MATLAB/Simulink simulation confirmed the analytical claims of grid-side THD 1.3% and primary resonance frequency within 3% of the analytically derived values, with filter efficiency exceeding 99.0% under rated operating conditions. The mathematical framework is presented in sufficient detail for direct replication and adaptation to other power ratings.

Assessment of PM₂.₅ and PM₁₀ Pollution Sources and Exposure in ECDE Centers in High-Traffic and Industrial Areas of Nairobi City County, Kenya

Maurice Kavai, Esther Kitur — Fri, 26 Jun 2026 00:00:00 +0000

Air pollution, particularly particulate matter (PM₂.₅ and PM₁₀), has emerged as a major environmental and public health challenge in rapidly urbanising cities, including Nairobi City County. Early Childhood Development Education (ECDE) centres located near high-traffic roads and industrial zones are especially vulnerable because of continuous exposure to emissions from vehicles, industrial activities, construction dust and other urban pollution sources. Young children are at higher risk because of their developing respiratory systems, higher breathing rates and prolonged exposure within school environments. This study aimed to assess and analyse the primary sources of PM₂.₅ and PM₁₀ pollution and determine their concentrations in selected ECDE centres in high-traffic and industrial areas. A descriptive cross-sectional research design was adopted, involving 120 ECDE children drawn from six ECDE centres. Data were collected using structured questionnaires, observation checklists, portable air-quality monitoring devices and key informant interviews with teachers and caregivers. Secondary data were obtained from the National Environment Management Authority (NEMA), Kenya Meteorological Department, World Health Organization, United Nations Environment Programme and peer-reviewed literature. Data were analysed using descriptive statistics, independent-samples t-tests, Pearson correlation and multiple regression analysis. The findings revealed that ECDE centres located near major highways recorded higher PM₂.₅ concentrations ranging from 38 to 82 µg/m³, while centres located in industrial zones recorded higher PM₁₀ concentrations ranging from 72 to 156 µg/m³. In both cases, the recorded levels exceeded the WHO recommended air-quality guidelines. The results further showed a strong positive correlation (r = 0.71, p < 0.01) between particulate matter exposure and the prevalence of respiratory symptoms among children aged 3–6 years, including coughing, wheezing and frequent colds. The study concludes that vehicular emissions and industrial activities are the primary contributors to elevated PM₂.₅ and PM₁₀ levels in ECDE environments and may pose respiratory health risks to children. It recommends stricter enforcement of air-quality regulations, strategic zoning of schools away from pollution hotspots and enhanced air-quality monitoring in urban educational settings to safeguard child health.

Effects of Sorghum Substitution on the Proximate Composition, Physicochemical Properties, Amino Acid Profile, and Sensory Quality of Millet-Based Kunun-Zaki

Samson Ugochukwu Alugwu — Tue, 30 Jun 2026 00:00:00 +0000

Kunun-zaki is a traditional non-alcoholic cereal-based fermented beverage commonly consumed in Northern Nigeria. It is traditionally produced from millet through spontaneous fermentation and is valued for its refreshing taste and nutritional attributes. Fermented foods are valued for their beneficial properties and contribution to healthy living. This study evaluated the effect of partial millet substitution with sorghum (0-80%) on the proximate composition, physicochemical characteristics, amino acid profile and sensory attributes of Kunun-zaki. Increasing sorghum substitution reduced ash and carbohydrate contents, whereas moisture, protein and fat contents increased, with the highest values recorded at 80% substitution. Sorghum substitution significantly influenced the physicochemical properties of the kunun-zaki samples, including pH, total soluble solids, total sugar, and total titrable acidity. However, the pH value of Sample C and the total titrable acidity of Sample D did not differ significantly (p > 0.05) from the corresponding values of the control sample. Amino acid concentrations generally increased as sorghum substitution increased. Essential amino acid content increased from 3.02 to 3.82 g/100 g, whereas non-essential amino acid content increased from 4.01 to 5.11 g/100 g. Exceptions occurred in Samples B and C for essential amino acids and in Sample B for non-essential amino acids. Sensory evaluation showed that the formulation containing 60% millet and 40% sorghum received the highest scores for appearance, mouthfeel, taste, aroma, consistency and overall acceptability. However, no significant differences (p > 0.05) were observed between the 60% millet-40% sorghum formulation and the 100% millet sample for mouthfeel, aroma and consistency. These findings demonstrate that sorghum can be utilised as a valuable ingredient in the production of nutritionally enriched Kunun-zaki, thereby promoting product diversification and reducing dependence on millet as the sole cereal substrate.

Integrated Aeromagnetic and Radiometric Investigation of Structural Controls on Pb-Zn Mineralization in the Ohinya Area, Abakaliki Basin, South-eastern Nigeria

Fri, 03 Jul 2026 00:00:00 +0000

This study assesses the lead-zinc (Pb–Zn) mineralization potential of the Ohinya license area in the Abakaliki Basin, southeastern Nigeria, through integrated interpretation of aeromagnetic and radiometric datasets. The primary objective is to delineate prospective structural and lithological zones that may host hydrothermal Pb–Zn mineralization and to establish a geophysical framework to guide further exploration. High-resolution airborne geophysical data, including Total Magnetic Intensity (TMI), First and Second Vertical Derivatives (1VD and 2VD), analytic signal, tilt derivative, Source Parameter Imaging (SPI), and radiometric data (K–Th–U) were processed and interpreted to define subsurface structures and estimate depths to magnetic sources. The results reveal that NE–SW and NW–SE trending faults predominate, with SPI depths ranging from 270 to 330 m. The central and southeastern zones, marked by high analytic signal amplitudes (0.006–0.008 nT) and Th–U enrichment, are interpreted as structurally controlled hydrothermal corridors prospective for Pb–Zn mineralization. The study, however, is subject to limitations inherent in airborne geophysical methods, such as the resolution of aeromagnetic data, approximate depth estimates from SPI, lack of ground validation, and the restriction of radiometric data to near-surface signatures, which may be affected by weathering and surface cover. Despite these constraints, the integrated approach offers a robust framework for reducing exploration uncertainty. The identified prospective zones merit follow-up investigations, including geological mapping, geochemical sampling, trenching, ground geophysical surveys, and ultimately drilling to confirm the presence and economic viability of Pb–Zn mineralization.

The Mediating Effect of Classroom Interaction to the Relationship of Instructional Feedback in Enhancing Writing Strategies

Fri, 03 Jul 2026 00:00:00 +0000

Background: Writing strategies in ESL play a significant role in enabling learners to communicate their ideas and higher-order understanding accurately. Classroom interaction is important in strengthening students’ writing strategies because it fosters collaborative learning and promotes active engagement in the learning process. Although previous studies have explored the roles of instructional feedback and classroom interaction in English as a Second Language (ESL) learning, limited research has investigated the mediating effect of classroom interaction on the relationship between instructional feedback and writing strategies.

Aims: This study examined the mediating effect of classroom interaction on the relationship between instructional feedback and writing strategies among learners in an ESL context. Grounded in social constructivist and self-regulated learning perspectives, the study investigated whether classroom interaction serves as a mechanism through which instructional feedback enhances students’ use of writing strategies.

Study Design: A quantitative correlational research design was employed.

Place and Duration of Study: The study was conducted at the College of Computing Education, College of Arts and Science Education, and College of Criminal Justice Education, University of Mindanao, Davao City Campus, between August 2024 and April 2026.

Methodology: The respondents were 300 second-year collegians from three colleges at the University of Mindanao who had completed UGE1 (Reading Comprehension). Data were collected using validated, adapted questionnaires that measured instructional feedback, classroom interaction, and writing strategies. All three variables were measured using 5-point Likert scales, and the data were analysed using descriptive statistics, Pearson correlation, and mediation analysis.

Results: Descriptive statistics revealed moderate to high levels of instructional feedback (M = 3.28, SD = 0.402), writing strategy use (M = 3.40, SD = 0.359), and classroom interaction (M = 3.53, SD = 0.396) among respondents. Correlation analysis showed significant positive relationships among all three variables. Mediation analysis further demonstrated that classroom interaction significantly mediated the relationship between instructional feedback and writing strategies, indicating that feedback contributes more effectively to students’ strategic writing development when supported by active and meaningful classroom engagement.

Conclusion: The findings highlight the importance of integrating responsive instructional feedback with interactive classroom practices to promote effective writing strategies. The study provides pedagogical implications for English language teachers in strengthening feedback practices and fostering collaborative learning environments that enhance students’ writing development.

The Interaction between Market Capitalization and Labor Dynamics in Today’s Digital Environment: A Perspective

Hayk Sargsyan A. — Fri, 03 Jul 2026 00:00:00 +0000

This study explores the relationship between market capitalisation and labour dynamics within the global digital banking sector. It examines how technological integration, including automation, cloud infrastructure and artificial intelligence, influences the operational structure of major banking institutions in the United States and Europe. By synthesising institutional data and industry reports, the analysis discusses how financial institutions adapt to compressed margins, changing interest-rate conditions and evolving regulatory pressures. The evidence suggests that institutional market valuation is increasingly associated with technological capacity, digital platform strength and operational scalability, reflecting a significant structural shift in banking business models. Rather than implying a simple reduction in labour demand, this transition indicates a restructuring of skills, functions and employment patterns. The study finds that digital banking may support new forms of service delivery while simultaneously creating adjustment pressures for workers in routine or branch-based roles. Overall, the paper argues that the interaction between market capitalisation and labour dynamics represents a structural transformation in financial services. These findings highlight the need for balanced regulatory oversight, proactive workforce adaptation and institutional strategies that prioritise both financial resilience and sustainable labour-market adjustment. The study provides a foundation for understanding the co-evolution of financial valuation and labour structures in the digital era.

Performance Evaluation of Corn Cob Ash and Wood Dust Ash in Fired Clay Bricks

Michael Olaniyi Ajadi — Fri, 03 Jul 2026 00:00:00 +0000

Background: The search for alternative approaches to utilising post-consumer waste has become a global priority in environmental management, particularly for reducing pollutants and minimising hazardous emissions from carbon-based materials used in sustainable construction. Among these waste materials, ash, often generated through industrial processes and the incineration of post-consumer wastes, remains an environmentally challenging by-product.

Aim: This study investigated the mechanical properties of corn cob ash (CCA) and wood dust ash (WDA) as eco-friendly admixture components in clay bricks fired at 750°C. Five proportions of each ash (5%, 10%, 15%, 20% and 25% by weight of clay) were used to produce fifteen groups of stabilised soil samples, with the aim of determining the optimum additive level for improved performance.

Methods: The oxide properties of the three constituent materials were analysed using Particle-Induced X-ray Emission (PIXE), and the mechanical properties of the test bricks were evaluated against a control sample (CS⁰) composed solely of clay.

Results: The results revealed that increasing the ash-to-clay ratio improved the porosity and water absorption characteristics of the bricks. However, compressive strength declined with higher ash content. Notably, bricks incorporating 5% and 10% WDA exhibited adequate strength relative to the control.

Conclusion: The findings suggest that incorporating CCA and WDA as admixtures is suitable for producing lightweight fired clay bricks with insulating potential and reduced thermal conductivity, thereby supporting sustainable construction applications.

Role of Student-Teacher Interaction on The Relationship between Work-Life Balance and Task Motivation among Early Childhood Education Teachers: A Mediation Analysis

Mary Rose M. Cangas — Fri, 03 Jul 2026 00:00:00 +0000

This study examined the mediating role of student–teacher interaction in the relationship between work–life balance and task motivation among early childhood education teachers. A cross-sectional correlational research design with mediation analysis was employed. The respondents comprised 300 kindergarten to Grade 3 teachers from the Central and South Districts of Tagum City, Davao del Norte, Philippines. Total enumeration sampling was used because the population was manageable. Data were collected using adapted instruments measuring student–teacher interaction, work–life balance, and task motivation. The instruments demonstrated acceptable to excellent reliability, with Cronbach’s alpha coefficients of 0.851, 0.949, and 0.981, respectively. Descriptive results indicated very high levels of student–teacher interaction, work–life balance, and task motivation. Correlation analysis revealed significant positive relationships between student–teacher interaction and task motivation, and between work–life balance and task motivation. Mediation analysis showed that work–life balance had significant direct and indirect associations with task motivation through student–teacher interaction. The mediated proportion was 0.40, indicating moderate partial mediation, with approximately 40% of the total effect operating indirectly through student–teacher interaction. These findings suggest that work–life balance is associated with task motivation both directly and through classroom relational processes. The study highlights the importance of supportive work conditions and positive student–teacher relationships in sustaining task motivation among early childhood education teachers.

A Moderator or Predictor? The Role of Risks in Kenya’s Mortgage-financed Construction Projects

Sat, 04 Jul 2026 00:00:00 +0000

Mortgage-financed construction projects (MFCPs) are important to housing delivery in Kenya, but they remain exposed to risks that can affect cost, time, quality, safety and cash flow. This study examined the role of risks in the relationship between critical success factors (CSFs) and the performance of MFCPs. A survey research design was adopted, targeting 1,063 mortgage-financed projects initiated during the 2021/2022 financial year. Data were collected from 122 respondents through structured questionnaires administered to site agents and clerks of works, and were analysed using descriptive statistics, ANOVA and multiple regression analysis. The findings show that project performance is influenced by several CSFs, with project management-related factors, consultant-related factors and project-related factors ranked highest. The risk assessment identified construction risks as the leading risk category, followed closely by financial and planning risks. Regression results indicated that the model explained 37.4% of the variance in project performance. The aggregate interaction term between risks and CSFs was not statistically significant, suggesting that risks did not function as a moderator in this sample. However, risks had a statistically significant negative direct effect on project performance. The study concludes that improving MFCP outcomes requires integrating strong project management, competent professional input and proactive risk management across project planning, financing and implementation.

Antibacterial Activity of Silver–Graphene Composites Against Escherichia coli

Anjum Farid Sayed, Manimegalai Ganesan, Ravibabu Ambadas Tayade — Tue, 07 Jul 2026 00:00:00 +0000

Silver nanoparticle-incorporated three-dimensional graphene (AgNP–3D graphene) composites were synthesised and evaluated for antibacterial activity against Escherichia coli. Graphene oxide was prepared using a modified Hummers’ method and subsequently reduced to form a three-dimensional graphene framework. Silver nanoparticles were incorporated in situ using silver nitrate as the precursor, while citrate or sodium bisulfite served as reducing agents in the presence or absence of ammonia. The structural and morphological characteristics of the prepared composites were assessed using X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDX). XRD confirmed the formation of crystalline metallic silver within the graphene framework, and SEM revealed differences in nanoparticle size and distribution according to the synthesis route. EDX analysis showed silver contents ranging from 17.25 to 20.22 wt.% among the silver-containing composites. Antibacterial performance was examined using batch and continuous-flow disinfection experiments with E. coli-contaminated water. In batch studies, the SC composite achieved a maximum log reduction value of 5.1 after 720 min, whereas comparative evaluation showed strong antibacterial activity for the SBA composite. Continuous-flow experiments indicated that the SCA composite had the highest apparent antibacterial activity rate constant under the tested conditions. Silver leaching remained low during the experimental period. These findings indicate that AgNP–3D graphene composites have measurable antibacterial potential against E. coli, with performance influenced by nanoparticle dispersion, graphene architecture, and synthesis conditions.

Flexural Behaviour of Locally Milled Steel Reinforced Concrete Beams with Tension Lap-Splices in Normal, Medium and High Strength Concrete

Wed, 08 Jul 2026 00:00:00 +0000

This study investigated the flexural behaviour of reinforced concrete beams reinforced with locally milled medium- to high-yield steel bars containing tension lap splices. The work examined the effects of lap length, concrete grade and bar diameter on tensile and flexural performance under four-point loading. Beam specimens were produced using concrete grades C25, C30 and C35 and reinforced with 12, 16 and 20 mm bars. Lap lengths of 10d, 15d and 20d were evaluated, together with control beams containing continuous reinforcement. The results showed that lap length was the main parameter governing splice performance. Beams with 10d lap splices exhibited marked reductions in strength and developed wider, more localised cracks, indicating premature bond failure. Increasing the splice length to 15d and 20d improved crack distribution, load-carrying capacity and ductility, although full theoretical strength was not always achieved. Higher concrete grades improved cracking resistance and enhanced bond behaviour, particularly when combined with adequate splice length. Larger bar diameters delayed crack initiation in some cases but reduced bond efficiency at ultimate load because of less favourable stress transfer conditions. Overall, the findings indicate that the flexural response of lap-spliced beams depends on the combined effects of splice length, concrete strength and reinforcement diameter.

Hybrid Machine Learning-driven Digital Twin Framework for Real-Time Traffic Optimization in Mega Cities

Thu, 09 Jul 2026 00:00:00 +0000

Traffic congestion remains a major challenge in Lagos, causing substantial delays, higher fuel consumption, pollution and reduced productivity. As the city continues to grow and vehicle ownership increases, the existing road infrastructure is insufficient to accommodate rapidly rising transport demand. Intelligent traffic management solutions are therefore required. This study proposes a hybrid machine learning-driven digital twin framework for Lagos. The framework integrates digital twin technology, hybrid machine learning and reinforcement learning to improve traffic prediction, reduce congestion and optimise traffic flow. It processes traffic data generated from simulated sensor, camera and connected-vehicle environments representing Lagos traffic conditions. The model combines temporal learning for identifying time-dependent traffic patterns with spatial learning for capturing road-network relationships. A reinforcement learning module is also integrated to optimise traffic-signal timing and routing decisions dynamically. The framework focuses on major transport corridors, including Third Mainland Bridge, Ikorodu Road and the Ozumba Mbadiwe corridor, where recurrent congestion occurs. Testing was conducted using the Simulation of Urban Mobility platform and Python-based tools. The system was evaluated under representative Lagos traffic scenarios, including peak-hour congestion, traffic incidents and routine weekday operations. The experimental results showed improved performance compared with traditional approaches and standalone machine learning models. The proposed framework reduced traffic prediction error by 61.6% compared with baseline approaches. Congestion decreased by up to 40.3% during peak periods, while waiting time was reduced by 49.8%. Digital and physical traffic states remained synchronised at 96.8% within real-time processing requirements.

Healthcare-Strain-Aware Epidemic Control Using State-Constrained Optimization

M. O. Durojaye, T. A. Ogunjemiyo, J. K. Odeyemi — Sat, 11 Jul 2026 00:00:00 +0000

A state-constrained optimal-control problem is formulated to prevent healthcare-system overload dynamically while balancing intervention costs. The formulation leads to an eight-compartment model (S, U, D, T, H, R, E, V), which includes a separate compartment for hospitalisation and a sigmoidal mortality rate \(\bar{\mu}_h(Q)\) that depends on the available capacity of the healthcare system. The objective is to minimise the combined disease burden and intervention costs while imposing the hard constraint H (t) \(\le\) Q. Using Pontryagin’s Maximum Principle extended to state-constrained problems, the adjoint equations and the characterisation of the optimal time-dependent controls for transmission reduction, testing/detection, clinical-treatment efficiency, and vaccination are obtained. Numerical simulations under different capacity levels and cost assumptions show that the optimal policies involve early scaling-up of non-pharmaceutical interventions and testing/detection to keep hospitalisations low, followed by a transition towards vaccination and efficient use of clinical resources. The inclusion of the state constraint leads to 15–30% higher implementation costs but avoids mortality peaks associated with violation of the healthcare-capacity constraint.