Asian Journal of Advanced Research and Reports

A Comprehensive Review of Functional Nanomaterial-based Catalysts for Enhanced Biodiesel Production and Environmental Sustainability

Sodiq Akinola Muraina — 2026-03-09

With the global shift to renewable energy sources, there has been growing interest in biodiesel, which is a more environmentally and economically sustainable alternative to fossil fuels. Nevertheless, conventional catalytic materials currently used in biodiesel production processes are faced with efficiency, stability, soap formation, and environmental problems. Thus, this paper gives a scientific review of functional nanomaterial catalysts which aim to improve biodiesel production and, at the same time, ensure sustainability in the environment. A broad array of nanomaterials and their hybrids has been discussed, such as metal oxide nanoparticles, carbon nanomaterials, magnetic nanocatalysts, mesoporous nanomaterials, and more advanced hybrid functional nanomaterials. These nanocatalysts possess high surface areas, optimal porosity, increased mass transfer, strong base and acid functionalities, and high catalytic recyclability, which allow faster and more eco-friendly transformation of raw materials to biodiesel. The review distils information gathered from cutting-edge experimental work, pointing to the efficacy afforded by functional nanomaterials to transesterification and esterification reaction efficiency, catalyst stability, and waste avoidance. Of paramount importance is placing the work against the background of environmentally sustainable methods of synthesis, catalyst recyclability and recovery, and the relevance of nanocatalysis to sustainable global initiatives. The study picks up essential scientific trends, efficiency deficits, toxicity considerations, and the prospects offered by cutting-edge nanostructures, functionalization, and sustainable nanotechnology to scale up biodiesel. it has been made clear by the review that nanomaterials-based catalysts demonstrate great potential to improve biodiesel production, and it is expected to significantly influence new innovative approaches on the global level to obtain clean and efficient biofuel.

Comparative Analysis of Fully Convolutional Networks (FCN), SegNet, and U-Net for Semantic Segmentation of Synthetic Wafer Map Defect Patterns

Balachandar Jeganathan — 2026-03-09

Wafer defect maps serve as a primary diagnostic tool for process engineers, enabling the visualization of spatial defect distributions and the identification of potential equipment or process issues. As technology nodes shrink and device densities increase, the volume of inspection data has grown exponentially, rendering manual inspection impractical and necessitating automated solutions for defect pattern recognition. Modern semiconductor manufacturing depends critically on automated wafer defect pattern detection to prevent yield loss excursions and maintain process control. Deep learning, particularly convolutional encoder–decoder architectures, has emerged as a transformative approach for anomaly detection and semantic segmentation of wafer map defect patterns. This review comprehensively examines the state of the art in applying deep convolutional neural networks, including Fully Convolutional Networks (FCN), SegNet, and U-Net to the detection and pixel-level segmentation of abnormal defect patterns on semiconductor wafers. The study analyzes methods for synthetic training data generation using Poisson point processes, evaluates the comparative performance of these architectures using intersection-over-union (IoU) metrics, and assesses their generalization capability to unseen defect patterns and real production wafer data. The review further discusses scalability considerations, the relationship between defect density and detection accuracy, and outlines future research directions, including vision transformers, transfer learning, and real-time deployment strategies. With approximately 25 recent references spanning 2015–2024, this article provides a thorough synthesis of methodologies, results, and open challenges for researchers and practitioners in semiconductor defect inspection. Future research should focus on bridging the synthetic-to-real domain gap through advanced data augmentation and domain adaptation techniques, incorporating attention mechanisms and transformer-based architectures, developing lightweight models for real-time edge deployment, and integrating explainable AI methods to enhance engineer trust and adoption.

Waste Segregation at Source for Rural Households: Towards Sustainable Development and Circular Economy – A Review

Dang Hoang Ha — 2026-03-17

Waste segregation at source is increasingly recognized as a pivotal component in achieving sustainable development and fostering a circular economy, particularly in rural areas where centralized waste management systems are limited. This paper critically reviews global and Vietnamese practices related to household waste segregation at source, emphasizing its environmental, economic, and social benefits. Internationally, countries such as Japan, Sweden, and South Korea have implemented robust policies and community based models that have successfully minimized landfill dependency and boosted recycling industries. In contrast, rural Vietnam continues to face significant challenges, including limited public awareness, inadequate infrastructure, and policy enforcement gaps. The study employs a qualitative synthesis of peer reviewed literature and case study analyses to assess current practices, explore limitations, and identify innovative policy and technological interventions. Findings suggest that successful source segregation requires a synergistic approach involving local community engagement, supportive policy frameworks, and investment in decentralized waste processing technologies. This paper concludes that implementing targeted waste segregation strategies can significantly contribute to a zero-waste future in rural areas while enabling inclusive growth within the circular economy model in Vietnam and beyond.

Ethosome-Based Nanocarrier Systems for Enhanced Topical Antifungal Drug Delivery: Recent Advances and Therapeutic Applications

Ruchita Raghuvanshi — 2026-03-18

Topical antifungal therapy remains the primary treatment for superficial fungal infections; however, conventional formulations often show limited therapeutic effectiveness due to poor skin penetration, low drug retention, and frequent dosing requirements. Ethosome-based nanocarrier systems have emerged as a promising strategy to enhance transdermal drug delivery due to their unique composition and improved skin permeation capabilities.

This review summarizes recent advances in ethosome-based nanocarriers for topical antifungal drug delivery, focusing on their structural characteristics, mechanisms of skin permeation, formulation strategies, and therapeutic applications. The literature for this review was collected from major scientific databases including PubMed, Scopus, and Google Scholar using relevant keywords such as ethosomes, nanocarriers, topical drug delivery, and antifungal therapy.

The findings from recent studies demonstrate that ethosomal formulations significantly improve drug permeation through the stratum corneum, enhance drug retention in deeper skin layers, and reduce systemic side effects compared with conventional topical formulations.

Overall, ethosome-based systems represent a promising strategy for improving antifungal therapy and may contribute to the development of more efficient and patient-friendly topical drug delivery systems in the future.

Biochemistry and Physiology of Lipid Metabolism in Pregnancy: A Clinical Review

Josiah Hart — 2026-03-26

The regulation of lipid pathways throughout gestation significantly impacts foetal maturation, neonatal health, and the future cardiovascular risks faced by the mother. Both fatty acids and cholesterol serve as essential building blocks for healthy foetal growth. During a typical pregnancy, women undergo a sequence of metabolic shifts that modify their lipid profiles. The early stages are primarily anabolic, focusing on increasing lipid production and building adipose reserves to prepare for the high energy requirements of the foetus later in the term.

By the third trimester, maternal physiology pivots to a catabolic state to break down these fat stores. This transition ensures a steady supply of nutrients for the growing foetus. In standard pregnancies, these adjustments typically manifest as a 50% rise in total cholesterol, a 30-40% increase in LDL, a 25% boost in HDL, and a doubling or tripling of triglyceride concentrations. However, disruptions in these lipid processes are closely linked to negative health outcomes for both mother and child. Research indicates a clear relationship between lipid imbalances (dyslipidaemia) and complications during the perinatal period or within the uterine environment.

Furthermore, the theory of "developmental programming" suggests that the lipid conditions encountered in utero can dictate a child’s lifelong vulnerability to chronic illness. Because of these significant risks, it is critical to screen for lipid irregularities before conception. If pre-pregnancy data is missing, clinicians should prioritize screening during the first prenatal visit. Managing abnormal profiles requires consistent tracking and a multi-pronged strategy centred on nutritional changes, physical activity, and weight management. Although medication carries potential foetal risks, current evidence supports the use of carefully monitored, risk-adjusted pharmacological therapies for severe cases.

Enhancing HealthCare Waste Management through Circular Economy Principles: A Systematic Review

Felix T. Michael — 2026-03-16

Objective: The growth and expansion of the healthcare industry is associated with the production of large amounts of waste. It is unclear whether healthcare facilities are applying sustainable waste management strategies that promote a circular economy. This study synthesizes evidence on the extent to which healthcare facilities have adopted sustainable strategies to support a circular economy in waste management.

Methods: A systematic review was conducted following PRISMA guidelines. A comprehensive literature search of PubMed, Scopus, Semantic Scholar, and ScienceDirect databases was performed for studies published between 2020 and 2024 using relevant keywords.

Results: A total of 32 studies (n = 32) were included in the review. The findings revealed significant deficiencies in healthcare waste management practices. Common issues included poor implementation of waste segregation using color-coding systems, inadequate storage and collection methods, limited on-site transport equipment, and manual offsite transport. Staff training and compliance with guidelines were also insufficient. Incineration often using substandard equipment and open burning were the predominant waste treatment methods. Only eight studies reported some integration of circular economy practices, such as reuse and recycling approaches to waste management.

Conclusion: This review identifies a significant gap in knowledge and practice regarding the application of circular economy principles in healthcare waste management, highlighting the urgent need for improved protocols and sustainable practices in healthcare settings.

Policy Credibility, Organizational Behaviour and Public Financial Management in U.S. Federal Institutions: Implications for Economic Outcomes and Global Leadership

Philip Williams Appiah-Agyei — 2026-03-28

Credibility of policy is very essential in institutional effectiveness, fiscal discipline and economic stability especially in advanced economies where the quality of governance has a strong impact on economic performance. In the United States, a growing discussion on policy inconsistency, political polarization and administrative fragmentation has escalated debate of the ability of the federal institutions to continue delivering effective management of the financial affairs of the people and global economic leadership. This paper examines the linkage between policy credibility, organizational behaviour in the U.S. federal institutions and the financial management of the populace, especially on its consequence to economic performance and international economic policy influence. The Preferred Reporting Items of Systematic Reviews and Meta-Analyses Extension of a Scoping Review (PRISMA-ScR) was used in order to conduct a systematic scoping review. The overall search in Scopus, Web of Science, ScienceDirect, JSTOR and Google Scholar helped to identify 1,320 publications, out of which 52 peer-reviewed articles satisfied the inclusion criteria and were incorporated into the synthesis. Three major themes are found. To begin with, effective and reliable policy structures go hand in hand with better public financial management results. Second, financial problems include institutional difficulties, like predicting delays, budgetary disputes, and coordination loopholes that undermine fiscal control and credibility of the policies. Third, organizational behaviour in the system of federal institutions, such as stability of leadership, bureaucratic professionalism, and coordination among agencies, has a critical mediating effect in the policy implementation and fiscal performance. The general observation of the study is that sustainable fiscal governance and sustained global economic leadership are not merely limited to the formulation of a formal policy framework but also on the institutional credibility and appropriate organizational behaviour of the public institutions.

Blockchain-artificial Intelligence Integration for Securing IoT Networks in Smart Cities and Critical Infrastructure

Innocent Junior Opara — 2026-03-31

The fast growth of Internet of Things (IoT) systems in smart cities and critical infrastructure has brought a great threat to cybersecurity, such as data breaches, distributed denial-of-service attacks, and centralised systems, which are a critical vulnerability. This work is a systematic review of the Blockchain-vitro artificially intelligent models of optimally securing IoT in smart infrastructure settings. This review will adhere to the PRISMA 2024 approach to locate, filter and synthesise peer-reviewed articles in large scientific databases since 2015. 1,384 records were found, and 43 studies were included in the final inclusion criteria. The architectural models, artificial intelligence methods, blockchain consensus mechanisms, and system performance metrics included in the analysis are able to detect scalability and interoperability. The results indicate that blockchain can be used to improve IoT security with the help of decentralisation, cryptographic integrity, and tamper-resistant data management, whereas artificial intelligence can be used to enhance adaptive threat detection by utilising anomaly detection, predictive analytics, and behavioural modelling. Nevertheless, difficulties exist such as computational overhead, lack of interoperability, regulatory compliance and inadequate real-world deployment validation. The review finds that blockchain-AI integrated systems have great potential to secure large-scale IoT infrastructures, but further studies are required to enhance scalability, alignment with regulations, and operational deployment. The research is relevant to the body of literature because it offers a synthesis of blockchain-AI cybersecurity frameworks in a comprehensive manner and establishes some of the research gaps that need to be filled in future smart infrastructure security systems.

A Multi-Dimensional Evaluation Framework for IoT Intrusion Detection: Balancing Accuracy, Efficiency and Real-World Deployment Constraints

Oluwapelunmi Bankole — 2026-03-06

The rapid proliferation of Internet of Things (IoT) devices has introduced unprecedented security vulnerabilities, with botnet attacks representing persistent threats to network infrastructure. While machine learning-based intrusion detection systems (IDS) show promise in laboratory settings, real-world deployment faces challenges including extreme class imbalance, resource constraints, and false alarm minimization. Traditional evaluation approaches focus primarily on accuracy, overlooking deployment factors such as execution time, memory consumption, and false alarm rates. This study introduces a comprehensive multi-dimensional evaluation
framework that assesses IDS performance across 14 distinct metrics, including two novel composite scores, Efficiency Score and Deployment Score, for quantifying deployment readiness and operational efficiency. Applied to the BoT-IoT dataset (3.6 million flows, 1:7,682 imbalance), we evaluated six algorithms using six feature selection methods and a novel two-stage balancing approach combining undersampling and SMOTE. Critically, all experiments were conducted within a 12GB RAM constraint to reflect realistic resource limitations in edge computing and IoT gateway deployments. Random Forest, XGBoost, and Decision Tree achieved 99.97% accuracy with zero false positives on 733,705 test samples, maintaining practical training times (0.06–1.98 s) and memory footprints (0.07–2.00 MB). Our framework provides use-case-specific recommendations: XGBoost for resource-constrained devices (0.07 MB), Decision Tree for real-time applications (0.04 s prediction), and Random Forest for balanced deployment (0.9999 deployment score). This framework enables IoT security practitioners to make informed, context-aware model selections aligned with specific deployment constraints—whether prioritizing minimal memory footprint for edge sensors, ultra-low latency for real-time prevention systems, or rapid retraining for adaptive security—thereby accelerating the transition from laboratory research to operational IoT security solutions.

Association of Socioeconomic Status, Dietary Pattern, and Lifestyle Behaviours with Obesity among Adults in Ngozi: A Case Control Approach

Jean Baptiste Bucumi — 2026-03-06

Introduction: Obesity is an emerging public health challenge in low-income countries undergoing rapid nutritional and socio-economic transition. Local analytical evidence remains limited in Burundi.

Methodology: An analytical case-control study was conducted by involving 90 participants, including 30 obese cases (BMI ≥ 30 kg/m²) and 60 normal-weight controls (BMI 18.5-24.9 kg/m²). Data were collected using structured questionnaires, anthropometric measurements (weight, height, BMI), blood pressure assessment and capillary blood glucose testing. Statistical analyses included bivariate tests (Chi-square, Student's t-test) and multivariate logistic regression. The significance threshold was set at p < 0.05.

Results and Conclusion: Findings revealed that non-consumption of legumes and nuts (ORa = 5.85; 95% CI: 1.34-25.53; p=0.019) and low dietary diversity (OR = 2.29; 95% CI: 1.09-5.14; p=0.030) were significantly associated with obesity. Female sex (ORa = 2.71; 95% CI: 1.04-7.09; p=0.042) and higher household income (>300,000 BIF) (ORa = 4.32; 95% CI: 1.45-12.89; p=0.009) were also significantly associated. Family history of obesity was identified as the strongest independent risk factor (ORa = 4.19; 95% CI: 1.51-11.61; p=0.006). Although hypertension and hyperglycemia were more frequent among obese participants, these associations were not statistically significant. However, elevated diastolic blood pressure at the second measurement showed a significant association (p=0.014), suggesting early cardiovascular alterations. Behavioral factors — including physical inactivity, sedentary lifestyle and stress — were not significantly associated with obesity. In conclusion, obesity in Ngozi Commune appears as a multifactorial condition driven by nutritional transition, socio-economic changes and familial susceptibility. These findings underscore the need for integrated prevention strategies focusing on dietary diversification, promotion of traditional diets and early risk screening.

Time Series Analysis of Petroleum Products Sales Using Autoregressive Model

O. Ihekuna, Stephen — 2026-03-12

Nigeria, Africa’s largest oil producer and a key member of the Organization of Petroleum Exporting Countries (OPEC), has an economy deeply rooted in petroleum production and exports. Since the discovery of oil at Oloibiri in 1956, the sector has driven national revenue, foreign exchange earnings, and infrastructural growth, while also contributing to economic volatility, environmental degradation, and regional unrest. This study examines the historical trajectory of oil exploration and production in Nigeria and applies a statistical time series approach to forecast petroleum product sales by the Nigerian National Petroleum Corporation (NNPC) as published in NNPC Annual Statistical Bulletin. Quarterly sales data from 2015- 2021 were analyzed using the Autocorrelation Function (ACF) and Autoregressive (AR) models to assess the dependency structure within the data and predict future sales trends. The results show significant short-term correlation in the series, with the partial autocorrelation indicating an AR(1) process as the best-fit model. The estimated autoregressive coefficient (ϕ₁ = 0.29) was used to forecast sales for the 2022 quarters, revealing a gradual decline compared to previous years—reflecting market instability and supply constraints. The findings highlight the persistent sensitivity of Nigeria’s petroleum sales to internal inefficiencies and external price shocks. This research underscores the importance of robust forecasting and diversification strategies in managing Nigeria’s oil-dependent economy amid global energy transitions.

Appropriate Model Identification Using Turn Around Time of Vessels at Tincan Island Port, Lagos, Nigeria

O. Ihekuna, Stephen — 2026-03-14

This study applies the Buys-Ballot procedure to decompose and analyze the monthly turnaround time of vessels at Tincan Island Port, Lagos, Nigeria, covering the period from 2015 to 2024. The primary objective is to identify the appropriate time series model—additive, multiplicative, or mixed that best represents the data structure and seasonal behavior of vessel turnaround operations at the port. Monthly data comprising 120 observations were arranged into a Buys-Ballot table, and both graphical and statistical analyses were conducted to estimate trend parameters and seasonal indices. The Levene’s test of equality of variances was employed to determine the correct decomposition model. Results indicated that the computed Levene’s test statistic (0.264) was less than the critical value (1.91) at the 5% level of significance, leading to the acceptance of the null hypothesis of equal variances. Consequently, the transformed data admit an additive model. The study concludes that the Buys-Ballot procedure is a simple and effective method for identifying suitable models in descriptive time series analysis.

Predictive Modeling of Digital Credit Risk in Commercial Banks Using Machine Learning Algorithms

Isaiah N. Barasa — 2026-03-16

Normally, financial institutions issue loans to customers aiming to recover the repayments within the scheduled time. However defaults, common with digital credit, cause them significant financial risks. Advancement in technology has created digital loan products. Unfortunately,in developing economies such as Kenya,the growth rate of such products has outpaced development of sound credit risk assessment systems. These factors, along with the limitations of conventional risk models, which are static, necessitate the adoption of dynamic models capable of capturing complex and non linear patterns among variables. This study addressed this gap by developing predictive models using machine learning algorithms. Using 6,000 simulated loan records, necessitated by restricted access to real world data, the study evaluated performance of the two models, among which, random forest emerged as the most robust. Even though random forests model initially achieved an Area Under Curve score of 1.0, under simulated conditions, a 10-fold cross validation model produced a more realistic. mean AUC of 0.90. This reflects strong discriminative ability and high predictive accuracy. These findings demonstrate that machine learning algorithms can enhance risk assessment framework in commercial banks thereby minimizing financial

Tuition as Strategic Communication: A Comparative Analysis of Framing and Signaling in Global and Philippine University Websites

Leo Santiago III Arrabaca — 2026-03-17

Tuition costs are a key factor in how universities compete and how students make choices, especially for private universities that rely on tuition for funding. Although tuition details are easy to find online, there has been little research on how this information is presented on specific tuition and financial aid webpages, rather than across whole university websites. This study uses a qualitative comparative document analysis to examine the English-language tuition and financial aid webpages of six private universities ranked 200–500 in the QS World University Rankings 2026, as well as those of one private university in Northern Mindanao, Philippines. Using Framing Theory and Signaling Theory, the study identifies seven dominant tuition frames: investment, accessibility, scholarship emphasis, prestige justification, transparency, employability outcomes, and calls to action. The global benchmark universities often combined investment, prestige, and employability messages with clear cost information, presenting tuition as a value tied to rankings, career prospects, and quality. In comparison, the Philippine university focused on transparency and accessibility but did not emphasize prestige, investment, or employability as much as it did on tuition details, which could limit its competitiveness and global profile. The results show that tuition webpages act as strategic communication tools, not just places for administrative information. Philippine universities aiming to boost their international reputation and rankings should consider adding more prestige signals, outcome data, and value-focused messages to their tuition pages while remaining transparent.

Synthesis of Y₂O₃ Nanoparticles in Water and Ethanol by Pulsed Laser Ablation for Y₂O₃/n-Si Heterojunction Photodetectors

Duaa Jabbar Hussain — 2026-03-19

Nanoparticles (NPs), nanotubes, rods, and sheets made of rare earth metals have recently become the material of choice for many device applications. This study aims to synthesise Y₂O₃ nanoparticles using the laser ablation technique in two different liquid media (deionized water and ethanol) and investigate their structural, morphological, optical, and chemical properties, as well as to evaluate the performance of Ag/Y₂O₃/n-Si/Ag heterojunction UV photo-detectors based on the prepared nanoparticles. Studies have been conducted on the structural, morphological, optical, and chemical characteristics of Yttrium oxide. XRD results verify the production of a polycrystalline Y₂O₃ film have cubic crystalline phase with crystallite sizes of 10.52 nm for Y₂O₃ in ethanol and 42.53 nm for Y₂O₃ in water. The SEM results of Y₂O₃ in ethanol show brittle and porous particles of different sizes ranging from 19.79 nm to 95 nm, while Y₂O₃ in water, the particles were of different shapes with some agglomerates with a particle size range of 40.27 nm to 3 μm. In the AFM image, the Y₂O₃ film's grains measured 49.25 nm in size for case ethanol, whereas in water, particles measured 52.97 nm. The energy gap of Y₂O₃ NPs (ethanol and water, respectively) was 4.9 eV and 4.8 eV. The bond Y–O stretching mode of the Y₂O₃ structure is responsible for the high intensity peaks detected by FTIR, which are concentrated around 500-696 cm−1. For Y₂O₃ in ethanol and water, the photo-detector's responsivity at the short wavelength region (350-400nm) was 0.16 and 0.17 A/W, respectively. However, for Y₂O₃ in ethanol and water, the photo-detector's responsivity at 850 nm was 0.42 nm and 0.51 A/W, respectively. The Y₂O₃ /n-Si heterojunction photo-detector's specific detectivity D* was determined to be 6.0×1012 and 7.3×1012 Jones, with corresponding quantum efficiencies of 59% and 62% at 350 nm for in case ethanol and water, respectively. It is recommended that the synthesis of Y₂O₃ by the method employed in this experiment would be beneficial for fabricating Y₂O₃ nanostructures with good electrical and optical properties to be used in UV optoelectronic devices.

Statistical Characterization of Climatic Trends in Delhi NCR (1990–2022) Using Regression, Dispersion Measures, and Correlation Analysis

Riyon Das — 2026-03-20

Delhi NCR-Region, India, is considered one of those cities which draws nation-wide attention because of its extreme climatic dynamics. Hence, a comprehensive study of this region is required for assessing its extreme climatic trends, environmental stress, and urban degradation. The core objective of this study is to understand the climatic dispersions, inter-relationship between climatic variables, seasonal study, and quantify linear trend detection in Delhi NCR during the period 1990-2022. The data was obtained from Kaggle, comprising data variables of Daily Maximum Temperature (TX), Daily Minimum Temperature (TN), Daily Mean Temperature (T), and Daily Precipitation (PRCP), recorded at the Safdarjung Meteorological Station operated by the IMD. Daily observations spanning 1990-2022 were aggregated into annual mean values to construct a 33-year dataset for statistical analysis; standard deviation (σ) was calculated for dispersion analysis; seasonal analysis was performed to compare variations in temperature and rainfall; Pearson’s correlation method was conducted for inter-parameter relationships among the data variables; and linear regression model was deployed for analysing temperature trends. Results revealed that annual precipitation exhibited the highest variance (CV = 34%), whereas temperature variables showed relatively low variability (CV = 2.23–2.85%). Pearson’s correlation analysis conveyed strong positive relationships among temperature variables (r = 0.76 - 0.94), whereas rainfall exhibited negligible correlation with temperature. Statistical tests indicated significant upward temperature trends (P < .05), although the regression models achieved modest explanatory power (R² = ≤ 0.44). Despite the variability, the statistically significant P-values indicate that the upward trajectory of the temperature trends is reliable. The identified warming trends emphasize the importance of continuous climate monitoring and adaptive strategies to mitigate rising heat stress and environmental risks in the study region. Overall, the research highlights significant long-term climatic trend analysis with potential implications for climate resilience strategies and government policy optimizations.

Visual Outcome of Cataract Surgery in a Tertiary Hospital, South East Nigeria: A Three-Year Retrospective Study

O. A. I. Otuka — 2026-03-21

The visual outcome of cataract surgery is a critical measure of its effectiveness and is an essential aspect of quality control in ophthalmic care. The burden of cataracts is especially pronounced in developing countries. This study investigated the characteristics of patients who had cataract surgery and their subsequent visual outcomes. Data was collected from a total of 117 patients, with a mean age of 65.23 years. The majority of the patients had mature cataracts (76.9%) and underwent Small Incision Cataract Surgery (SICS) (94.0%). Pre-operative visual acuity was poor in the majority of patients, with 88.0% having visual acuity better than 6/60 to LP. The visual outcome improved significantly on the first post-op day with 71.8% of patients having a visual acuity better than 6/18 to 6/60. This improvement was sustained at 6 weeks post-op. Pre-operative Intraocular Pressure (IOP) ranged between 10-19 mmHg for the majority (72.7%) of the patients. Post-operative IOP was largely reduced, with >90% % of patients having an IOP of 19 mmHg or less. Complications were relatively rare, with 94% of surgeries occurring without intra-op complications and 87.2% of patients having no post-op complications. High Intraocular Pressure and a hazy cornea were the most common post-op complications, occurring in 2.6% and 5.1% of cases, respectively. At 2 months post-op, 49.6% of patients had visual acuity of 6/18 or better with best correction. Without correction, 21.4% of patients had visual acuity of 6/18 or better. Patient characteristics (age and sex) and the type of cataract surgery performed did not show a statistically significant relationship with the visual outcome at 2 months post-op (P= 0.14,0.42). However, the nature of the cataract showed a significant association with the visual outcome, with mature cataracts having better visual acuity compared to hypermature cataracts (P=0.047). The study concludes that the majority of patients show significant improvement in visual acuity following surgery, but a considerable number of patients continue to exhibit suboptimal visual acuity and require additional corrective measures. The need for ongoing management and regular ophthalmological follow-up is recommended.

The Effects of Mould Materials on the Mechanical Properties of Aluminium Casting

A. A. Bankole — 2026-03-28

Centrifugal casting is a manufacturing process used to form cylindrical components with a central axis of rotatio. In continuous casting process, molten metal flows into the continuous casting mould, cools by cold water, and pulls out after solidification. In this research work, the effects of mold materials on mechanical properties of aluminium were investigated. Mould materials were prepared from sand, metal, and plaster of paris (POP). A steel rod of about 25mm in diameter was used as pattern to cast aluminium in these three mould materials. Test pieces were machined from the casting produced by each of the three mould materials for tensile, hardness and impact tests, as well as microstructure. It was observed from the tests that aluminium cast by sand has the highest value of ductility at the expense of its hardness, while those castings by metal and POP moulds have appreciable increase in hardness values but lesser values of ductility. The question as to which of the mould materials is the best depends on the engineering application requirements; sand mould would be preferred where ductility is the major requirement, while metal and POP moulds might be preferred when hardness is the priority.

Storytelling Through Video Presentations as a Strategy to Enhance English Comprehension Skills of Intermediate Grade Learners

Rafaela P. Acedera — 2026-03-31

Aims: This study examined the effectiveness of storytelling through video presentations as a pedagogical strategy to enhance English comprehension skills among intermediate-grade learners. It specifically investigated whether the integration of visual and auditory elements in storytelling could improve comprehension performance and learner engagement.

Study Design: A one-group pretest–posttest experimental design was employed to measure changes in comprehension before and after the intervention.

Place and Duration of Study: The research was conducted at Talisay Elementary School, Laoang, Northern Samar, Philippines, during the 2023–2024 academic year.

Methodology: Twenty Grade 4 learners participated in the study. A standardized comprehension test was administered before the intervention to establish baseline performance. Learners were then exposed to structured storytelling sessions delivered through video presentations that combined narrative flow, visual cues, and auditory reinforcement. The same test was administered after the intervention to measure comprehension gains. Frequency, perecentages and t-test were used in this study.

Results: The pre-test scores ranged from 2 to 12, with a mean of 6.15 (SD = 2.23), indicating average comprehension levels with moderate variability. After the intervention, post-test scores ranged from 5 to 14, with a mean of 10.90 (SD = 2.25), reflecting a substantial improvement. A paired-sample t-test revealed a highly significant difference between pre-test and post-test scores (t = -5.26, p < 0.001), confirming the effectiveness of the intervention. Most learners (55%) clustered between scores of 11–13 in the post-test, demonstrating strong comprehension gains compared to their baseline performance.

Conclusion: Storytelling through video presentations significantly improved learners’ English comprehension skills, validating its potential as an effective instructional strategy in elementary education. The integration of multimodal elements fostered learner engagement, accommodated diverse learning styles, and strengthened comprehension outcomes. Future studies with larger samples, varied grade levels, and longitudinal designs are recommended to further validate and extend these findings, contributing to sustainable innovations in language instruction.

Methane Fluxes in Seagrass Beds of Inner Ambon Bay: A Comparison of Tanjung Tiram and Halong Coast

Krisye Pasanea — 2026-03-31

This study evaluates the concentration and emission of methane gas (CH₄) in seagrass ecosystems in the Inner Ambon Bay, especially in the Tanjung Tiram and Halong Coast areas. Methane, as one of the greenhouse gases, plays an important role in the global carbon cycle. Gas sampling was conducted using chambers at two research locations from June to September 2023. The analysis showed that the concentration of CH₄ in Halong Coast was higher (1.6893 ± 0.2076 ppm) than in Tanjung Tiram (1.4967 ± 0.1101 ppm) (P < 0.05). The flux of CH₄ gas also showed similar results, with the flux value at Pantai Halong (0.1010 mg/m²/hour) higher than in Tanjung Tiram (0.0521 mg/m²/hour) (P < 0.05). This difference is associated with a higher variation in the physicochemical conditions of the waters, thus affecting the more intense methanogens microbial activity on Halong Coast. Although the methane flux in this seagrass ecosystem is relatively low compared to mangrove ecosystems, the results of this study provide important insights into the role of seagrass in greenhouse gas emissions and its implications for climate change. This finding is expected to support conservation efforts and sustainable management of coastal ecosystems in the Inner Ambon Bay.

Postural Habits and Its Association with Classroom Engagement among College Students in a Private College in Iloilo City

James Carvajal — 2026-03-31

Introduction: Postural habits play an important role in student engagement and attention during classroom activities. This study aimed to determine the relationship between postural habits and classroom engagement among 367 undergraduate students at a private college in Iloilo City.

Methods: Utilizing a quantitative descriptive-correlational design, respondents were selected using a simple random sampling to ensure equal representation across different programs. The data utilized two adopted questionnaires: Questionnaire on Body Awareness of Postural Habits in Young People and the Classroom Participation of College Students Scale. Analysis involved descriptive and inferential statistics through the Statistical Package for the Social Sciences. The Kolmogorov-Smirnov Test was used to determine data normality. Given the non - normal distribution, Kruskal-Wallis H Test and Spearman’s Rank-Order Correlation were employed to examine significant differences and relationships, respectively, at an alpha level of 0.05.

Results: The results revealed that undergraduate students generally maintain good postural habits (M = 2.73), and exhibit highly engaged classroom engagement (M = 3.78). Analysis showed no significant difference in postural habits across different colleges (p = .946). Similarly, the results showed no significant difference in classroom engagement across various colleges (p = .143). Furthermore, a significant positive correlation was found between postural habits and classroom engagement (r = .123; p = .018).

Discussion and Recommendations: The findings signify that proper postural habits lead to improved academic involvement among college students. Students should adopt positive postural habits, while educators and administrators should implement ergonomic awareness programs and posture reinforcement strategies. Future research should utilize objective screening tools and broader variables to further investigate the link between posture and learning outcomes.

Assessment of Nitrite Toxicity in Hydroponically Cultivated Lettuce (Lactuca sativa L.) during Post-Harvest Storage

Le Huy Hai — 2026-03-31

Lettuce (Lactuca sativa L.) is a common vegetable in daily meals and is widely grown hydroponically in Vietnam. The purpose of this study was to investigate the changes in Nitrite toxicity after harvest under different storage conditions. The study selected extraction methods, mainly grinding, crushing, and filtration, and chemical analysis methods suitable for the practical conditions of laboratories in vegetable farms. The results showed that at temperatures above 28°C after harvest, the nitrite content increased significantly, and the lettuce began to spoil after 24 hours. At temperatures below 25°C, the lettuce suffered less damage within 48 hours. When stored in a refrigerator at 4°C to 5°C, spoilage only began after 4 days. In conclusion, the Nitrite content in lettuce increased rapidly when stored at temperatures above 25°C. At temperatures below 5°C, intrinsic enzymes are inhibited and the growth of nitrate-reducing bacteria is almost completely halted.

Continuous Improvement Practices and Project Delivery Performance in Energy Projects

Christiana Augustine Okonko — 2026-04-01

Persistent cost overruns, schedule delays, and quality challenges continue to affect project delivery in the energy sector despite the adoption of advanced project management frameworks. While continuous improvement (CI) practices have been widely associated with operational performance in manufacturing and service contexts, limited empirical evidence explains how such practices influence project delivery outcomes in complex, project-based environments such as energy projects. Addressing this gap, this study examines the impact of continuous improvement practices on project delivery performance and investigates the mediating roles of process standardization and organizational learning. Drawing on the Resource-Based View and Organizational Learning Theory, the study employs a sequential mixed-methods design. Quantitative data were collected from 265 professionals involved in oil and gas, renewable energy, and power infrastructure projects and analyzed using structural equation modelling, complemented by qualitative insights from 20 semi-structured interviews with senior project practitioners. The results show that continuous improvement practices significantly enhance project delivery performance both directly and indirectly. Specifically, CI practices positively influence schedule adherence, cost efficiency, quality conformance, and safety performance, with process standardization and organizational learning acting as significant partial mediators of this relationship. These findings provide empirical evidence that continuous improvement functions not merely as a set of operational tools but as an organizational capability that strengthens execution discipline and learning processes within complex project environments. The study contributes to project management literature by clarifying the mechanisms through which CI practices translate into improved project delivery outcomes in the energy sector. Practically, the findings highlight the importance for project-based organizations to institutionalize improvement routines, standardized processes, and learning systems to enhance reliability and performance in high-risk energy projects.