Asian Journal of Advanced Research and Reports
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Impact of Differentiated Instruction on the Visualization Skills of Senior High School Students in Drafting Technology
No full textThis study aimed to examine the impact of Differentiated Instruction (DI) on the visualization skills of Senior High School students in Drafting Technology. Utilizing a pre-experimental one-group pre-test–post-test design, the research was conducted in selected public senior high schools in Laoag City during the School Year 2025–2026 (August 2025 to November 2025), involving 40 students enrolled in the Technical-Vocational-Livelihood (TVL) Drafting Technology track. Data were collected through a researcher-developed pre-test and post-test to measure students’ visualization skills and a structured feedback survey to assess perceived improvements in performance, accuracy and speed of drafting tasks, confidence in demonstrating techniques, ability to connect new lessons with prior knowledge, and the overall effectiveness of DI. Results revealed that DI significantly enhanced students’ drafting performance, with weighted means of 3.55 to 3.63 (Very Effective) for perceived improvement, accuracy, and speed, indicating that hands-on activities, visual demonstrations, and scaffolded exercises facilitated mastery of spatial concepts. Students’ confidence in demonstrating drafting techniques was rated 3.18 (Effective), while their ability to connect new lessons with prior knowledge received a mean of 3.75 (Very Effective), demonstrating that DI accommodated diverse learning styles and readiness levels while promoting meaningful learning connections. Overall, students rated the effectiveness of DI at 3.80 (Very Effective), reflecting strong engagement, skill acquisition, and motivation. These findings support the conclusion that Differentiated Instruction is a highly effective, learner-centered approach for improving visualization skills and overall performance in Drafting Technology, highlighting the value of flexible, inclusive instructional practices that foster technical competence, confidence, and independent learning. The study underscores the importance of adopting DI strategies in technical-vocational education to enhance both cognitive and practical skills essential for students’ academic and professional success
Role of Teacher and Student in the Context of STEM Education: A Review
No full textIntegrated STEM education is increasingly framed as practice-centered learning in which students investigate phenomena, design solutions, and communicate evidence-based reasoning across disciplinary boundaries. This review synthesizes recent peer-reviewed scholarship to clarify how the roles of teachers and students shift in such environments and why these shifts are pivotal to learning quality and equity. Drawing on practice-based, sociocultural, and identity/agency perspectives, the article conceptualizes “roles” as dynamic patterns of participation enacted through discourse, tool use, assessment routines, and classroom norms. The synthesis indicates that the teacher’s role extends beyond content delivery to include designing coherent integrative tasks; orchestrating inquiry and engineering design while sustaining productive uncertainty; cultivating discourse norms that support explanation, critique, and iteration; and implementing formative assessment practices aligned to processes as well as products. In parallel, the student’s role expands from procedural compliance to epistemic agency, including problem framing, evidence use, collaborative knowledge-building, and iterative refinement of ideas and designs. A central finding is that teacher and student roles are mutually constitutive: student agency depends on teacher-designed opportunities and positioning practices, while teacher facilitation depends on classroom cultures that legitimize student sense-making and distribute authority. The review also highlights the growing influence of digital technologies, including emerging AI supports, in reshaping participation and feedback loops, while cautioning that technology can widen inequities if access and norms are not intentionally structured. Implications are outlined for STEM teacher education, classroom design, and future research, emphasizing role-sensitive approaches to equity, assessment, and sustained professional agency
A Review on the Concept and Utilization of Sequence Stratigraphy in the Niger Delta
No full textSequence Stratigraphy incorporates comparative sea-level and time variation to analyze facie migration, and is conducted using well-logs, outcrops, cores and evaluations could be based on different set of data. Sequence Stratigraphy can be utilized to evaluate relationships between sedimentary formation within a periodic-stratigraphic system of repetitive, genetically linked layer bounded by surfaces of non-deposition or erosion, or their correlated conformities. In this study, a comprehensive review on the concept and utilization of sequence stratigraphy in the Niger-Delta was conducted. From the conceptual review study, accommodation space, depositional sequence analysis, surface, seismic and biostratigraphy were identified as key contributors to sequence stratigraphy evaluation. From the application review study, well-logs, seismic data, core-data and biostratigraphic data are very critical for effective sequence stratigraphic analysis, and are used for depositional arrangement, detection source rocks, oil & gas reservoir and seal-off section within sedimentary basins. Furthermore, Miocene sediment were observed to be predominant in the Niger-Delta formations
Why Smart Irrigation Alone Will Not be Enough to Fight Hunger and Food Insecurity in Nigeria
No full textBy the end of 2025, Nigeria is experiencing its worst food crisis in several decades. It is projected that 34.7 million Nigerians will be suffering from acute food insecurity in 2026, while food inflation is above 34 percent, with 129 million Nigerians living below the poverty line. Technologically advanced irrigation systems, including drip irrigation, soil moisture sensors, and automated water control, have the potential to increase agricultural production by 20-30 percent and irrigate an additional 1.2 million hectares. Currently, such technology is not available to small-scale farmers, who account for 70 percent of Nigeria’s food production. The major challenge is the prevailing sense of insecurity. In the first half of 2025 alone, over 6,800 people lost their lives in more than 4,672 violent attacks, representing a 19 percent escalation of violence compared to the previous year. Terrorists, armed robbers, and militias have rendered the North-East, North-West and Middle Belt regions unsafe. Farmers are kidnapped or killed, and farms destroyed and burnt down, as well as cattle rustled. Consequently, Nigeria loses 420,000 metric tons of wheat production every year in Borno State, while maize production in Zamfara and Katsina States declines by 50 percent, with post-harvest losses above 40 percent. A large portion of the violence is highly religious and ethnic in nature. Since 2009, Christian farming communities in states like Benue, Plateau, Kaduna and Taraba have been subjected to sustained attacks that include the destruction of churches, massacres, and displacement. These attacks systematically destroy food systems by destroying barns, poisoning water sources and isolating farmers in displacement camps. Even in the absence of violence, a lack of infrastructure makes smart irrigation systems unfeasible. Nigeria loses 76.9 million metric tons of food valued at $9.1 billion each year because of post-harvest loss, while increasing costs of fuel, fertilizer, and transportation make it even harder on farmers. Technological interventions simply cannot work when farmers cannot safely access their farmland. Simply investing in technology will have little impact without addressing issues of security, rebuilding trust between farmers and herdsmen and improving rural infrastructure and peacebuilding initiatives. Only a comprehensive strategy integrating security, governance and technology has a chance of reversing Nigeria’s worsening food crisis
Heavy Metal Pollution in the Ona River and Its Potential Human Health Consequences
No full textAims: This study investigates the levels, sources, and ecological implications of heavy metal contamination in River Ona within its residential catchment area.
Methodology: Water and sediment samples were analyzed for Fe, Cu, Ni, Zn, Mg, Mn, and As using standard analytical procedures. The degree of contamination was evaluated using multiple indices, including the Geo-accumulation Index (IGEO), Contamination Factor (CF), Enrichment Factor (EF), Pollution Load Index (PLI), and Ecological Risk Index (ERI).
Results: Results revealed moderate to high pollution levels, with arsenic and manganese posing the greatest ecological risks. Health risk assessment showed that the non-carcinogenic Hazard Index (HI) exceeded the safe threshold at all sites, primarily due to magnesium, arsenic, and manganese. The carcinogenic risk (CR) was dominated by arsenic, with total cancer risk (TCR) values far above the USEPA acceptable range, indicating a significant long-term risk of cancer for exposed populations.
Conclusion: The study concludes that the river system is under emerging heavy metal stress primarily from industrial effluents, domestic runoff, and agricultural inputs. Regular environmental surveillance and pollution control measures are essential to prevent further accumulation and protect human and ecological health
Epigenetics, Neuropharmacology, and Biochemical Modifications in Brain Disorders: Molecular Mechanisms and Therapeutic Perspectives
No full textBrain disorders represent a major global health burden, encompassing neurodegenerative, neuropsychiatric, and neurodevelopmental conditions. Increasing evidence highlights the crucial roles of epigenetic regulation, neuropharmacological mechanisms, and biochemical modifications in the onset and progression of these disorders. Epigenetic processes such as DNA methylation, histone modification, and non-coding RNA regulation dynamically modulate gene expression without altering DNA sequences and are profoundly influenced by environmental factors. Neuropharmacology provides insight into how drugs interact with neural systems to modify neurotransmission, behavior, and cognition. Furthermore, biochemical alterations including oxidative stress, neuroinflammation, mitochondrial dysfunction, protein aggregation, and neurotransmitter imbalance critically disrupt neuronal homeostasis. This review comprehensively discusses the interplay between epigenetics, neuropharmacology, and biochemical modifications in brain disorders, emphasizing molecular mechanisms, disease implications, current research techniques, and emerging therapeutic strategies. Understanding these interconnected pathways may facilitate the development of targeted, personalized, and disease-modifying treatments for neurological and psychiatric disorders
Analysing the Impact of Procurement Planning and Vendor Management on Health Policy Performance at The Eastern Regional Hospital, Koforidua, Ghana
No full textBackground: The effectiveness of health policies depends equally on the operational procedures that enable their execution and their design. Procurement planning and vendor management are critical components in the effective implementation of health policies, directly influencing the performance and outcomes of healthcare systems. Effective procurement planning ensures optimal resource allocation, minimising shortages and waste, while effective vendor management fosters strong supplier relationships, guaranteeing reliability and quality.
Aim: This study aims to examine the effect of procurement planning and vendor management on health policy performance.
Methods: This study utilised a mixed-methods approach, combining quantitative analysis of procurement data with qualitative interviews of hospital management, to elucidate the relationship between procurement procedures and enhanced health policy outcomes. This research utilised stratified and simple random sampling methods. Data were collected using a questionnaire and an interview protocol. Descriptive statistics were used to analyze the quantitative data, while content analysis was employed to analyze the qualitative data. A regression model was developed to demonstrate the relationship between variables.
Results: The study used construct reliability to determine Cronbach’s Alpha, which tests the internal consistency of items on a scale. The research demonstrates that procurement planning had the most substantial effect on ERH performance, with a value of 0.813, followed by inventory management at 0.789, and vendor management at 0.767. The study results indicated that if all independent variables are maintained at zero, the performance of ERH will be 0.986. The study indicated that a one-unit increase in procurement planning would result in a 0.813 rise in ERH performance. This variable was significant as p=0.037 is below 0.05. The findings suggest that integrating advanced procurement planning methods and fostering collaborative vendor partnerships can significantly boost the efficiency and effectiveness of health policies, hence improving healthcare delivery and patient outcomes.
Conclusion: This study provides practical insights that policymakers and hospital managers may utilise to enhance vendor management strategies and optimise procurement systems, ultimately advancing health policy objectives. The study concluded that a procurement plan is a tool for executing a budget and should be developed by the user departments to prevent or minimise excess expenditures in the entities\u27 budgets, ensuring that procurements occur only when sufficient funds are available for payment. The study also determined that vendor management favourably and significantly affects the performance of ERH
Comparison of Water Quality Index Values and Groundwater Quality Making Use of Machine Learning Methods: A Case Study
No full textThe variation in groundwater quality resulting from societal changes is a matter of concern, as groundwater is considered as one of the most vital sources of water supply among all available resources. Assessing water quality through systematic monitoring provides a basis for determining its suitability for various purposes, including effective water quality management. The present study focused on leveraging a hierarchical reconciliation algorithm (HCA) for forecasting water quality parameters and the Gradient Boosted Tree, Decision Tree and Random Forest models to predict Water Quality Index (WQI) values. This study was based on the experimental results conducted for monitoring and evaluating the quality of groundwater samples collected from two locations namely, Mudirajupalem and Lingayas Institute of Management and Technology (LIMAT), Vijayawada campus, Krishna district, Andhra Pradesh, India for the parameters, such as alkalinity, pH, total dissolved solids (TDS), total hardness (TH) and acidity using standard methods. The pH value was in the range of 8.5-10 for the Mudirajupalem samples, whereas pH value in the range of 6.5-8.0 was obtained in the LIMAT campus samples. It was reported that a range of 460-850 mg/L alkalinity was obtained in the Mudirajupalem samples, whereas a range between 550-750 mg/L of alkalinity was obtained in the LIMAT campus samples. Very high values of TDS were obtained in the Mudirajupalem samples with a range between 931-994 mg/L, whereas TDS values ranged between 199-273 mg/L in the LIMAT campus samples. It was reported that TH ranged between 20-246 mg/L for the Mudirajupalem samples, whereas for the LIMAT campus, TH values were in the range of 0-230 mg/L. Groundwater from LIMAT campus and Mudirajupalem samples showed the WQI values in between 39.59 to 41.01 and 396.73 to 397.09, respectively, which confirms that Mudirajupalem groundwater is not fit for public consumption. The foreseen WQI values were found to be similar with the results obtained via experiments, reinforcing the conclusion that the groundwater in Mudirajupalem is unsuitable for public consumption. Additionally, the HCA, which was employed for forecasting key water quality parameters proved to be effective. This study demonstrated promising results in predicting groundwater quality, opening up opportunities for further research into the use of advanced machine learning techniques to achieve even more accurate long-term groundwater quality predictions
Leveraging Machine Learning and Data Analytics to Predict Corporate Financial Distress and Bankruptcy in the United States
No full textPredictions about a company\u27s financial distress and potential bankruptcy are important for a business, investor, or even a regulator. Imagine scanning the horizon for financial issues and being able to nip them in the bud. This study looks into the possibilities that lie within machine learning and data analysis for predicting corporate bankruptcy in the United States. We build predictive models that depend on huge streams of data alongside elaborate algorithms to accurately assess the scope of financial turmoil a company may be facing. The research highlights the most effective data analytic techniques alongside financial indicators that are accurate predictors of sound decision making for businesses and investors, thus revealing the level of ruin they may face. The discovery equips stakeholders with the right guidance in need to deal with dangers and stumbles within the financial world, avoiding losses. Data-driven analytics can be leveraged to create a better business landscape that isn’t as brittle and can withstand future challenges. The justification behind this study lies in the growing scope of corporate failure and the necessity of more rapid, more precise, and more interpretable means to predict financial distress. Given past attempts with common statistical techniques, there is still a gap in research using and comparing state-of-the-art machine learning algorithms on an extensive, up-to-date dataset. To bridge this lacuna, the research employs comparative Random Forest, XGBoost, Support Vector Machines, and Neural Networks analysis of financial data between 2010 and 2024 for 1,000 U.S. companies. Employing supervised learning, the dataset was divided into training, validation, and test periods. The findings indicated the highest predictive accuracy being that of XGBoost at 93.2%, followed by Neural Networks (92.6%), followed by Random Forest (91.4%), and SVM (88.7%). These results demonstrate the superior performance of ensemble-based models for early warning signalling of financial distress, thereby achieving the purpose of this study to enhance financial decision-making via early, precise prediction
Trends in Solar Radiation to the Earth’s Surface: A Systematic Literature Review
No full textSolar radiation plays an important role in the Earth\u27s energy balance and climate system. This study uses a Systematic Literature Review (SLR) approach to analyze global trends in solar radiation to the Earth\u27s surface (Downward Surface Radiation/DSR) based on seven selected studies. The results of the study show a consistent increase in DSR values, indicating a significant shift in the global energy balance. This trend is associated with increased atmospheric transparency due to decreased aerosol concentrations, known as the global brightening phenomenon. This increase in surface radiation has broad implications, including accelerated surface warming, changes in the hydrological cycle, and increased risks to health and the resilience of the agricultural sector. These findings emphasize the importance of integrating DSR monitoring into climate policy, spatial planning, and cross-sectoral adaptation strategies to reduce risks and optimize the potential of solar energy for sustainable development