40 Articles
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Arturo B. Cunanan, Lucy M. Blanco, Marvin M. Abreu, Mary Jane F. Somao-I
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.
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.
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.
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.