Asian Research Journal of Current Science

The Paradox of Prevention: Why Measles and Rubella Persist in the Era of Effective Vaccines

2026-05-01T11:16:46+00:00

Measles and rubella remain major vaccine-preventable diseases that continue to cause significant morbidity and mortality, especially in Nigeria and other resource-limited settings of the Global South. This comprehensive review integrates molecular virology, epidemiology, immunology, and public health perspectives to assess the biology, transmission dynamics, and control strategies for these viruses. Measles, a highly contagious Morbillivirus with a basic reproduction number (R₀) of 12–18, and rubella, a teratogenic Rubivirus responsible for congenital rubella syndrome (CRS), both persist due to gaps in vaccination coverage, surveillance, and health system capacity. In Nigeria, suboptimal immunization—only about 54% first-dose measles vaccine coverage—combined with regional disparities and vaccine hesitancy, continues to fuel recurrent outbreaks. The introduction of the measles-rubella (MR) vaccine in 2023 represents a crucial milestone for CRS prevention. Molecular epidemiology shows that measles genotype B3 and rubella genotype 1 predominate in Africa, with molecular surveillance central to elimination verification.

The review highlights the economic benefits of vaccination, showing that measles-rubella immunization is highly cost-effective, averting millions of cases and disability-adjusted life years (DALYs) at minimal cost per DALY averted. Persistent immunosuppression following measles infection (“immune amnesia”) and rubella’s teratogenic impact underscore the urgency of sustained control efforts.

Policy recommendations emphasize strengthening routine and supplementary immunization, scaling up molecular surveillance, addressing vaccine hesitancy through community engagement, and bolstering health system resilience. Achieving measles and rubella elimination in Nigeria will require coordinated action across scientific, socioeconomic, and policy domains—linking advanced virological knowledge with equitable, community-driven public health delivery.

Beyond Detection: Reconciling Analytical Sensitivity with Biological Relevance in Toxicological Risk Assessment

2026-05-14T05:26:01+00:00

The modern era of analytical chemistry—propelled by advances in high-resolution mass spectrometry, tandem LC-MS/MS, and ambient ionization techniques—has ushered in an unprecedented capacity to detect synthetic chemical residues at picomolar and even femtomolar concentrations, often surpassing parts-per-trillion sensitivity. Yet this remarkable technological progress has precipitated a widening epistemic and regulatory chasm between what can be measured instrumentally and what constitutes biologically meaningful harm. This manuscript critically examines the systematic divergence between analytical detection capabilities and toxicological significance across three interconnected domains.

First, we interrogate the physiological mechanisms of endogenous detoxification, including Phase I functionalization (cytochrome P450-mediated oxidation, reduction, and hydrolysis), Phase II conjugation (glucuronidation, sulfation, glutathione conjugation, and N-acetylation), and Phase III transport processes (ATP-binding cassette efflux pumps and organic anion/cation transporters). We demonstrate that homeostatic resilience, adaptive stress responses, and hormetic dose–response relationships frequently render trace-level exposures physiologically inconsequential, even when analytically verifiable.

Second, we address the epistemological constraints of mixture toxicology, including the limitations of dose-addition and independent-action models, the challenges of identifying interaction thresholds for synergistic or antagonistic effects, and the statistical power constraints of high-dimensional mixture analyses. We critically evaluate the "cocktail effect" hypothesis, distinguishing documented synergistic interactions from speculative cumulative risk frameworks that lack empirical validation at environmentally relevant concentrations.

Third, we analyze the cognitive biases that distort public perception of chemical risk—including the affect heuristic, availability cascades, source confusion between natural and synthetic exposures, and the asymmetrical influence of precautionary framing on regulatory decision-making. We contextualize these biases within the broader sociology of scientific knowledge, examining how media amplification of single-study findings and the conflation of hazard identification with risk characterization drive disproportionate policy responses.

Building upon these analyses, we propose a three-tier risk-prioritization framework: Tier I comprises substances with robust epidemiological evidence of high-impact toxicity at environmentally relevant doses (e.g., certain organophosphates, legacy persistent organic pollutants, and confirmed endocrine-disrupting compounds); Tier II includes chemicals with moderate hazard profiles requiring targeted biomonitoring and exposure mitigation; and Tier III encompasses the vast majority of detectable synthetic contaminants whose trace-level presence, while analytically confirmable, lacks plausible mechanistic pathways to adverse health outcomes given endogenous detoxification capacity and realistic exposure scenarios. This framework is designed to redirect finite public health resources toward substances with demonstrated high-impact toxicity while contextualizing the actual risk posed by trace-level synthetic contaminants.

We further integrate insights from pharmacokinetic modeling (physiologically based pharmacokinetic [PBPK] approaches), toxicological epidemiology (including causal inference methods and exposure assessment validation), and risk psychology to argue for an evidence-based recalibration of regulatory thresholds and consumer priorities. We examine case studies including bisphenol A regulatory reversals, glyphosate hazard classification controversies, and per- and polyfluoroalkyl substances (PFAS) risk assessment evolution to illustrate how analytical detectability has been progressively decoupled from pathogenicity in both scientific discourse and policy formulation.

Finally, we consider the emerging dimension of the human microbiome as a metabolic interface between xenobiotic exposure and host physiology, evaluating whether microbiome-mediated biotransformation amplifies or attenuates toxicological risk. We conclude by emphasizing that detectability does not equate to pathogenicity, and that sustainable chemical safety governance requires explicit differentiation between analytical capability, hazard potential, and probabilistic risk—a distinction essential to preserving public trust in scientific institutions while optimizing the allocation of protective health resources.

Cancer Biology: Mechanisms, Hallmarks, and Therapeutic Insights

2026-05-16T07:37:08+00:00

Cancer represents a complex and highly heterogeneous group of diseases characterized by uncontrolled cellular proliferation, genomic instability, and the capacity for local invasion and distant metastasis. This review provides an integrated and contemporary overview of cancer biology, highlighting the intricate genetic, molecular, and environmental mechanisms that drive malignant transformation. At its core, carcinogenesis results from the progressive accumulation of mutations in two principal classes of genes: oncogenes, whose activation promotes uncontrolled cell growth and survival, and tumor suppressor genes, whose inactivation removes essential regulatory constraints on cell division and genomic integrity. The widely accepted Hallmarks of cancer framework serves as a conceptual foundation for understanding these processes, encompassing capabilities such as sustained proliferative signaling, evasion of growth suppressors, resistance to apoptosis, replicative immortality, induction of angiogenesis, activation of invasion and metastasis, and evasion of immune surveillance. Beyond genetic alterations, the tumor microenvironment including stromal cells, immune cells, and extracellular matrix components plays an important role in shaping tumor behavior, progression, and response to therapy. Additionally, nonmutational mechanisms such as epigenetic reprogramming and phenotypic plasticity further contribute to tumor heterogeneity and therapeutic resistance. Recent advances in molecular biology and genomics have significantly transformed cancer diagnosis and treatment, leading to the emergence of precision oncology. This includes targeted therapies directed at specific molecular alterations and innovative immunotherapeutic approaches, such as immune checkpoint inhibitors, which enhance anti-tumor immune responses. By linking molecular insights to clinical applications, this review emphasizes how evolving knowledge in cancer biology continues to drive the development of more effective and personalized strategies for cancer management.

Forecasting Annual Electricity Consumption in Myanmar: A Comparative Time Series Analysis for Strategic Energy Planning

2026-07-09T11:10:35+00:00

Electricity demand forecasting is essential for effective energy planning resource allocation, and policy formulation. This study analyzed comprehensive historical time series data spanning 63 years, from the 1961–1962 to the 2023–2024 fiscal years. The research extensively utilizes secondary time series data on annual electricity consumption sourced from the Myanmar Statistical Yearbooks, published by the Central Statistical Organization (CSO) under the Ministry of Planning and Finance. The Augmented Dickey-Fuller (ADF) unit root test was applied to determine the stationary of the data series. Three distinct univariate forecasting techniques the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) model, Brown’s Double Exponential Smoothing model, and Holt’s Double Exponential Smoothing mode were implemented to capture modeling trends. The best-performing forecasting model was determined through a comparative evaluation of standard residual fit statistics, specifically the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the Bayesian Information Criterion (BIC). The initial ADF test for stationary yielded a value of 3.68 with a p-value of 1.00, confirming that the original series was non-stationary and required a first-differencing transformation. Ljung-Box Q-statistic of 18.74 (df = 18, sig = 0.82) indicated no significant residual autocorrelation. In comparison, Holt’s model produced error accuracy values of 1285.32 (RMSE), 7.03% (MAPE), and 14.45 (BIC). Brown’s Double Exponential Smoothing model achieved an RMSE of 1277.84, a MAPE of 7.53%, and a BIC of 14.37. Based on the superior fitness parameters of the ARIMA model, the 95% confidence interval forecast estimates for electricity consumption (in kWh) are 33,977.3 for 2024–2025, 36,809.3 for 2025–2026, 39,877.3 for 2026–2027, and 43,201.0 for 2027–2028. Identifying precise time-series frameworks such as the ARIMA model provides highly accurate forecasts capable of capturing short-term fluctuations and data shifts within the temporal series. Regular implementation and monitoring of these refined statistical models, using continuous secondary updates, allow for the stable identification of long-term electricity demands. Consequently, these quantitative forecasting insights strategically empower energy planners and government policymakers to balance industrial expansion and economic growth with the long-term sustainability of the national grid infrastructure.

Fermented African Locust Bean (Parkia biglobosa) as a Potential Source of ACE-Inhibitory Peptides: Current Evidence, Limitations, and Research Priorities

2026-07-17T05:32:13+00:00

Hypertension remains a leading modifiable risk factor for cardiovascular and cerebrovascular disease worldwide, and food-derived bioactive peptides are increasingly being investigated as potential dietary adjuncts to, rather than replacements for, evidence-based antihypertensive therapy. Fermented African locust bean, produced from the seeds of Parkia biglobosa and known regionally as dawadawa, iru, soumbala or afitin, is a traditional West African condiment obtained through solid-state fermentation dominated by Bacillus species. During fermentation, microbial and endogenous proteolysis hydrolyses seed storage proteins into smaller peptides and free amino acids while reducing selected antinutritional factors, including phytate and tannin. This structured narrative review synthesised literature published from January 2000 to 29 March 2026, with evidence interpreted according to study design, from controlled human interventions and observational studies to animal, ex vivo, in vitro and in silico investigations. Preclinical evidence from crude fermented-seed extracts indicates reductions in blood pressure and heart rate at relatively high administered doses, although these findings do not establish a peptide-specific mechanism or relevance to customary dietary intake. Human evidence is limited to a single uncontrolled community comparison in Togo and remains susceptible to dietary, lifestyle and demographic confounding. Peptide-containing extracts from both fermented and nonfermented seeds showed ACE-inhibitory activity, but the nonfermented preparation was more active in the available direct comparison. Evidence from leaf phenolics and the congeneric species Parkia timoriana provides mechanistic context but does not directly demonstrate peptide-mediated antihypertensive activity in fermented P. biglobosa. No study identified in the review had isolated, sequenced and pharmacologically validated a specific antihypertensive peptide from the fermented seed product. Fermented locust bean should therefore be regarded as a biologically plausible but insufficiently characterised candidate source of ACE-inhibitory peptides. Peptide identification, digestive-stability testing, product standardisation, safety assessment and controlled human studies are required before its proposed antihypertensive potential or use as a functional-food ingredient can be substantiated.

Rodent-borne Viral Haemorrhagic Fevers in Sub-Saharan Africa: A Critical Narrative Review of the Epidemiology, Surveillance, Genomic Insights and Diagnostic Advances in Lassa Fever

2026-07-25T12:52:29+00:00

Lassa fever, caused by Lassa mammarenavirus, remains the most consequential rodent-borne viral haemorrhagic fever in sub-Saharan Africa, with recurrent seasonal epidemics concentrated in Nigeria, Sierra Leone, Liberia and Guinea and sporadic detections across a widening geographical range. Over the past decade, the field has been transformed by portable genomic sequencing, large-scale ecological modelling of the rodent reservoir, and the first randomised trials of candidate vaccines, yet no licensed vaccine or unequivocally effective antiviral therapy exists, and routine diagnosis still depends on centralised molecular assays that are inaccessible to most peripheral health facilities. This critical narrative review synthesises the contemporary evidence base on the epidemiology, reservoir ecology, molecular evolution, surveillance architecture and diagnostic landscape of Lassa fever, situating it within the broader category of African rodent-borne arenaviral haemorrhagic fevers that includes the rarer but more lethal Lujo virus. Evidence was drawn from peer-reviewed literature, systematic reviews and meta-analyses, and programmatic reports from national and international public health bodies. The review finds that the burden of disease remains poorly quantified because of substantial underascertainment, that genomic surveillance has repeatedly resolved uncertainty over whether case surges reflect novel strain emergence or intensified zoonotic spillover, and that promising point-of-care diagnostics have so far failed field validation despite encouraging laboratory performance. Ecological and climate-modelling studies converge on the conclusion that the geographical range suitable for virus circulation is likely to expand substantially by mid-century, while the evidence supporting ribavirin, the current standard of care, remains methodologically fragile. Pregnant women, healthcare workers and survivors experiencing long-term sequelae such as sensorineural hearing loss constitute under-served populations requiring dedicated research attention. The review concludes that an integrated One Health approach combining reservoir surveillance, decentralised diagnostics, and equitable vaccine deployment offers the most defensible pathway toward reducing the regional burden of this neglected but expanding threat.

Biotechnology for Aquatic Animal Health and Sustainable Aquaculture: Advances, Evidence Gaps and Responsible Translation

2026-08-03T11:10:41+00:00

Aquaculture is increasingly expected to supply nutritious aquatic foods while reducing disease losses, antimicrobial dependence, feed-resource pressure and environmental emissions. Biotechnology is central to this transition, but evidence is fragmented across genetics, diagnostics, immunology, microbial ecology, feed science and production-system engineering. This critical narrative review evaluates how contemporary biotechnologies influence aquatic animal health and sustainability, with emphasis on farmed finfish and shellfish and on interventions that connect biological mechanism to measurable production or environmental outcomes. Literature published from 1 January 2000 to 30 May 2026 was identified through accessible scholarly indexes, bibliographic databases, institutional sources and citation searching, then appraised for methodological quality, field relevance, reproducibility and evidence-claim alignment. Genomic selection has the strongest translational record among advanced genetic approaches because it can increase disease resistance cumulatively without introducing a novel allele, although its benefit depends on representative challenge phenotypes and maintenance of genetic diversity. Genome editing offers precise functional validation and potential improvements in sterility, growth and resistance, but commercial health claims remain less mature than laboratory proofs of concept and require stringent off-target, welfare and ecological assessment. Molecular diagnostics, environmental nucleic-acid surveillance and vaccines can shift disease control towards prevention, yet sensitivity, sampling design, pathogen viability and delivery logistics constrain field interpretation. Probiotics, biofloc communities and bacteriophages can reduce pathogen pressure and support host function, but inconsistent strain characterisation and context-dependent performance limit generalisation. Feed biotechnology can decouple production from marine ingredients through microbial, insect and engineered oil resources, although life-cycle benefits depend on substrates, processing, digestibility and effects on product quality. Recirculating, aquaponic and integrated multi-trophic systems can recover nutrients and improve biosecurity, but transfer impacts to energy demand and operational complexity. The evidence therefore supports an integrated, risk-proportionate portfolio rather than a single technological solution. Future progress requires harmonised field endpoints, multi-site validation, genomic and microbial stewardship, transparent life-cycle accounting and governance that makes innovation accessible beyond highly capitalised production sectors.

Web-Based Phishing Detection in the Era of Explainable and Generative AI: A Critical Narrative Review of Machine Learning, Deep Learning, Explainable AI and Large Language Models

2026-08-24T12:34:52+00:00

Web-based phishing detection has progressed from manually specified URL and webpage heuristics to machine learning, deep representation learning, explainable artificial intelligence, and, most recently, large language and multimodal models. This technical expansion has produced impressive benchmark results but has also made comparison increasingly difficult because models are evaluated on different datasets, temporal windows, feature sets, class balances, and operational assumptions. This critical narrative review evaluates the evidence for machine learning, deep learning, explainable artificial intelligence and large language model approaches to detecting phishing URLs and webpages, with emphasis on generalisability, adversarial robustness, interpretability, computational cost and deployment relevance. Literature published principally from 2010 to 9 June 2026 was identified through accessible scholarly indexes, digital-library records, DOI metadata, citation searching and authoritative proceedings. The evidence indicates that conventional machine-learning pipelines remain competitive when lexical, host, hyperlink and structural features are well engineered, particularly where latency and transparency matter. Deep learning reduces dependence on handcrafted features and can exploit sequential, visual and multimodal representations, but reported gains are often conditional on dataset construction and may weaken under temporal shift or adversarial manipulation. Explainable artificial intelligence improves model inspection and can reveal influential phishing cues, yet post-hoc explanations do not by themselves establish causal validity, stability or security robustness. Large language and multimodal models add semantic reasoning, brand identification and natural-language explanation, but the evidence base remains recent and operationally constrained by cost, latency, privacy, reproducibility and prompt- or content-mediated attack surfaces. Across paradigms, the dominant unresolved problem is not benchmark accuracy but reliable performance under changing, adversarial web conditions. The review therefore argues for temporally separated, cross-source and adversarial evaluation, calibrated uncertainty, explanation-quality testing and layered architectures in which lightweight detectors, visual or content models and language-model reasoning are invoked according to risk and resource constraints.

A Framework for Scalable API-First Development: Industry Trends and Enterprise Case Studies

2026-01-16T12:09:57+00:00

This paper examines API-first development, a cornerstone of modern software engineering, analyzing adoption trends and challenges in building scalable, interoperable systems. Scalability, versioning, documentation, and interoperability issues, such as 35% of GraphQL integrations facing delays, pose significant hurdles. We propose a framework for scalable API design, integrating contract-first design, scalability mechanisms, interoperability standards, developer experience enhancements, and governance practices. Validated through a survey of 50 software engineers and enterprise case studies, the framework demonstrates improved performance and integration efficiency. The study draws on industry reports and case studies from leading enterprises, illustrating benefits like enhanced modularity and reduced integration times. Future research directions, including hybrid API models and AI-driven design, are outlined to advance API-first systems. This work provides actionable guidelines for software engineers to build robust API-driven architectures, contributing to the evolving API economy.

Financial Fraud and Forensic Accounting on Corporate Performance of Deposit Money Banks (DMBs) in Nigeria

2026-01-21T13:07:17+00:00

This study examined the effect of financial fraud on the corporate performance of Deposit Money Banks (DMBs) in Nigeria, emphasizing the moderating role of forensic accounting and the causal relationships between fraud variables and bank performance. Panel data comprising 121 observations were analysed using descriptive statistics, Pearson correlation, fixed and random effects regression models, moderation analysis, and pairwise Granger causality tests. Corporate performance was measured by return on assets “(ROA), while financial fraud was captured through financial statement fraud, tax evasion fraud, and electronic fraud, including forensic accounting interaction terms. Results indicate that financial statement fraud positively and significantly affects reported performance in the absence of forensic accounting, suggesting earnings manipulation, while tax evasion fraud negatively influences performance. Electronic fraud showed no significant direct effect in the regression models; however, Granger causality tests revealed a unidirectional causal relationship from electronic fraud to corporate performance, indicating immediate financial consequences. Incorporating forensic accounting altered these relationships by weakening the artificially positive effect of financial statement fraud and intensifying the adverse impact of tax evasion fraud, thereby improving the model’s explanatory power. Diagnostic tests confirmed the absence of heteroskedasticity and specification errors, affirming model reliability. The study concludes that forensic accounting is crucial in mitigating the distortions caused by financial fraud, enhancing transparency, and sustaining performance in Nigerian DMBs. It recommends institutionalizing forensic accounting units, strengthening internal control systems, and enforcing regulatory oversight to reduce fraud risks and improve sector stability.

Effects of Seed Priming with Zn and N Solution on Improving Germination and Seedling Vigor of Rice

2026-01-29T07:37:08+00:00

The objective of this study was to investigate the effects of priming rice seeds with zinc (Zn) and nitrogen (N) solutions on germination and seedling vigor. The experiment was conducted from March to May 2024 at the Laboratory of the Department of Agronomy, Yezin Agricultural University, Myanmar, utilising a 4 × 3 factorial arrangement in a Completely Randomised Design (CRD) with four replications. In this experiment, factor A was four levels of nitrogen (N): N0 = 0% Urea (0 g N/100 mL), N1 = 0.10% Urea (0.046 g N/100 mL), N2 = 0.20% Urea (0.092 g N/100 mL), N3 = 0.30% Urea (0.138 g N/100 mL). Additionally, three levels of zinc (Zn) were applied: Zn0 = 0% ZnSO₄·7H₂O, Zn1 = 0.07% ZnSO₄·7H₂O, Zn2 = 0.14% ZnSO₄·7H₂O as factor B. The results indicated that the N2 treatment produced the highest germination percentage, vigor index, and shoot length, followed by N1 and N3 after 14 days of treatment. The N1 resulted in the greatest root length, root number, shoot dry weight, and root dry weight compared to N2 and N3. Conversely, N0 had the lowest performance in terms of seedling emergence and vigor. Among the Zn treatments, Zn1 achieved the highest germination percentage, vigor index, and shoot length compared to Zn2. However, Zn2 showed the highest root length, root number, shoot dry weight, and root dry weight among all the Zn treatments, while Zn0 resulted in the lowest seedling parameters across all treatments. The interaction of N and Zn treatments demonstrated enhanced germination, vigor, shoot length, root length, root number, shoot dry weight, and root dry weight. In the absence of priming with either Zn or N solutions, the germination and vigor of rice seedlings were significantly lower than those of others. The N0Zn0 treatment exhibited the poorest seedling parameters. In conclusion, priming rice seeds with N and Zn was effective in improving germination, seedling vigor, and overall seedling performance.

The Effect of Freshwater Quality Parameters on the Physiological Processes of Fish

2026-03-20T08:19:58+00:00

Water is essential for life and sustainable livelihoods. This study explores water quality parameters and their effect on fish abundance and physiological in Govind Sagar Dam Lalitpur. Fish are considered as water quality indicators, especially due to their sensitivity to pollution. Changes in water quality significantly alter fish behavior, an important index of fish growth and health. Given the importance of this relationship to aquaculture practices, it is essential to understand how water quality dynamics affect fish behavior. Water quality parameters are critical determinants of fish performance, as they directly or indirectly influence feed utilisation. The health and productivity of fish are strongly affected by the physicochemical characteristics of the aquatic environment, including key parameters such as dissolved oxygen (DO), total dissolved solids (TDS), turbidity, ammonia, salinity, pH, and electrical conductivity (EC). In the present study, the recorded water quality parameters were within the following ranges: temperature 26.40–28.80 °C, pH 7.23–9.34, turbidity 5.3–10.6 NTU, electrical conductivity 134.17–216 µS cm⁻¹, and dissolved oxygen 6.1–6.9 mg L⁻¹. All measured physicochemical parameters remained within the acceptable limits for the survival and optimal growth of freshwater fish species. This study therefore seeks to examine the influence of water quality on fish production, with particular emphasis on the role of sustainable water management practices.

Psychological Influence of Substance Abuse on Academic Performance among Students of College of Education Kastina-Ala, Benue State, Nigeria

2026-04-02T12:01:41+00:00

This study examines the psychological influence of substance abuse on academic performance among students of College of Education Kastina-Ala, Benue State, Nigeria. The study assesses the influence of substance abuse on academic performance, with specific objectives to examine its influence and determine gender differences. Using a cross-sectional survey design, the study sampled 110 students (71 males, 39 females) from College of Education Kastina-Ala. Results revealed a significant negative relationship between substance abuse and academic performance (r = -0.67, p < 0.01). Male students had higher academic performance (mean = 3.31, SD = 0.342) than female students (mean = 3.12, SD = 0.382). Recommendations include awareness programs, counseling services, and strengthened policies with comprehensive support to mitigate substance abuse's negative impact.

Effect of Different Rates of Phosphorus and Sulphur Fertilization on the Growth, Yield, and Protein Content of Mung Bean (Vigna radiata L.)

2026-04-03T12:46:53+00:00

This study aimed to evaluate the effects of different rates of phosphorus (P) and sulphur (S) on the growth, yield, and protein content of mung bean (Vigna radiata L.), to determine the combined effects, and to identify the optimal combination for maximum productivity and protein content of mung bean. The field experiment was conducted at the Department of Soil and Water Science Farm, Yezin Agricultural University, Myanmar, during the monsoon season from May to August in 2025, and post-monsoon season from October 2025 to January 2026. The experiment was laid out in a 4 × 3 factorial arrangement in a randomized complete block design (RCBD) with three replications. Four P levels (0, 30, 60, and 90 kg P₂O₅ ha⁻¹) and three S levels (0, 20, and 40 kg S ha⁻¹) were tested. Growth parameters (plant height, crop growth rate (CGR), and branches plant^-1), yield components (pods plant^-1, seeds pod^-1, and 100-grain weight), grain yield, and protein content were recorded. The tallest plants (71.60 and 60.40 cm), maximum CGR (15.56 and 13.08 g m^-2 day^-1), branches plant^-1 (2.20 and 2.0), pods plant^-1(30.73 and 27.10), seeds pod^-1(12.47 and 11.23), highest yield (1632.20 and 1712.20 kg ha^-1 ) and protein content (24.33 and 25.37 %) were recorded at the application of 90 kg P₂O₅ ha⁻¹ + 40 kg S ha⁻¹ (P₃S₂) during both the monsoon and post-monsoon seasons. These parameters were not significantly different from those of 60 kg P₂O₅ ha⁻¹ + 20 kg S ha⁻¹ (P₂S₁). Therefore, P₂S₁ is suggested as the most suitable nutrient combination for maximizing mung bean productivity and protein content in the study area.

Effects of Plant Growth Regulators and Organic Additives on Shoot Regeneration of Myanmar Royal Orchid, Bulbophyllum auricomum Lindl., from in vitro Seedlings

2026-04-04T09:15:37+00:00

Bulbophyllum auricomum is a royal orchid of Myanmar people due to its remarkable value and sweet fragrance. Because of notably limited in the growth and propagation, and also due to over exploitation and habitat destruction, it becomes now an endangered species. The aim of this study was to study the effect of plant growth regulators and organic additives on multiplication of B. auricomum. The research was conducted at tissue culture laboratory of Htone Bo Farm, DAR, Taunggyi, Shan State, Myanmar. In vitro germinated seedlings were used as the plant materials for shoot regeneration. The experimental design was two factors factorial arrangement in RCB design with factor A of different levels of 6-benzyl amino purine (BAP) and Kinetin (Kin) (1,3 mg L^-1) in combination with 0.50 mg L^-1NAA, and organic additives of potato, rice, banana each 100 g L^-1and factor B of Kayin, Dawei and Rakhine seedlings. Statistix version 8.0 and mean comparisons were performed using LSD at the 5% level. From this study, the organic additives fortified treatments shown the superior effects on shoot regeneration of selected B. auricomum cultivars. The medium each fortified with 100g L-¹ banana gave maximum number of shoots per explant and followed by 100 g L^-1 rice 15.33 shoots per explant and 13.00 on 100 g L^-1 potato. Twenty weeks after inoculation roots and bulbs were well developed. 100g L^-1 potato supplement medium produced the maximum number of roots (8.33) and (7.58) on 100 g L^-1 rice fortified medium, followed by that of banana accomplished medium (5.66). The incorporation of organic additives into culture media significantly enhanced in vitro shoot growth of B. auricomum, demonstrating its potential for commercial production. Extensive field trials are still required to assess the long-term survival and flowering consistency of the acclimatized plants.

A Machine Learning-Based Analysis of Prostate Cancer Cases in Delta State, Nigeria

2026-04-10T12:21:47+00:00

Prostate cancer is the leading cause of cancer-related mortality among Nigerian males, with most diagnosed cases presenting at an advanced, incurable stage. The incidence rate of prostate cancer in Nigeria is 32.8 per 100,000 while the mortality rate is 16.3 per 100,000. Despite advancements in early-stage detection and screening programs available in Delta State, the prevalence of late-stage diagnosis and lack of knowledge subsists. This study applied four supervised machine learning classifiers — Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine — to retrospective patient records from six healthcare institutions across Delta State, with the aim of identifying the clinical risk factors most strongly associated with advanced-stage diagnosis and building a predictive framework capable of distinguishing early- from late-stage disease. Secondary data was collected from 60 confirmed prostate cancer cases diagnosed between January 2015 and December 2023 from three tertiary referral centers and three general hospitals. The Models was trained on 80% of the pooled dataset and evaluated on the remaining 20% using accuracy, sensitivity, specificity, F1-score, and AUC-ROC. Results showed that 68.3% of cases were at Stage III or IV at the time of diagnosis. The mean age at presentation was 64.2 years, and three quarters of patients had PSA levels above 10 ng/m. The four strongest predictors of advanced-stage disease were PSA level (OR = 5.82), Gleason score 8–10 (OR = 4.37), age 65 years or above (OR = 3.14), and positive family history (OR = 2.41). Random Forest outperformed all three competing models, achieving 91.3% accuracy, 89.6% sensitivity, 92.8% specificity, and an AUC-ROC of 0.94. These findings show that supervised machine learning can effectively predict prostate cancer stage at diagnosis in Delta State using routinely collected clinical data, with Random Forest achieving the strongest classification performance. The results have direct implications for early detection policy and clinical triage in the region, and future research should prioritise prospective data collection, external model validation, and the development of deployable decision-support tools for primary healthcare settings.

Dividend Policy Metrics and Profitability of the Non-financial Firms Listed on the Nairobi Securities Exchange, Kenya

2026-04-16T12:10:11+00:00

Dividend policy is a critical determinant of a firm’s financial performance because it influences investor attraction, resource allocation, and reinvestment decisions. Non-financial companies listed on the Nairobi Securities Exchange play a significant role in Kenya’s economy; however, their dividend payments have often been inconsistent due to volatile market conditions and economic constraints affecting profitability. This study examined the relationship between dividend policy indicators Dividend Payout Ratio (DPR), Dividend Yield (DY), Dividend Coverage Ratio (DCR), and Dividend Changes (DC) and the earnings capacity of non-financial firms listed on the exchange. The analysis was grounded in the Dividend Irrelevance Theory, Signalling Theory, and Agency Theory to explain how dividend practices influence firm profitability. A descriptive research design was adopted, covering a five-year period and utilizing financial and market data from selected non-financial firms. Data were obtained from audited annual reports and the exchange database, and were verified through multiple checks to ensure reliability and accuracy. Analytical results were presented using tables, graphs, and summary statistics. The findings revealed that all four dividend policy variables positively and significantly influenced firm profitability. Firms with balanced dividend payout ratios recorded higher returns on assets, suggesting that consistent dividend yields strengthen market confidence and enhance firm performance. Similarly, higher dividend coverage ratios signaled financial stability, while positive dividend changes improved investor perception and overall corporate performance. The study recommends that corporate managers adopt sustainable dividend strategies that balance shareholder returns with reinvestment needs. Regulators should strengthen guidelines to ensure transparent dividend practices, while investors should consider dividend indicators when making investment decisions. Overall, the study contributes to understanding dividend policy dynamics in emerging markets and provides practical insights for improving profitability and competitiveness among non-financial firms in Kenya.

Predicting Growth and Dry Matter Production of Tomato in Response to Exogenous Proline Application under Drought Stress

2026-05-05T11:21:34+00:00

Aims: To evaluate the drought response of tomato under varying levels of proline application, to build a growth model for leaf area and shoot dry weight under drought and proline sprays, and to evaluate the model performance.

Study Design: Split-plot design: main factor (water supply: well-watered, drought-stressed), sub-factor (proline: 0, 20, 40, 60, 80 ppm) with four replications.

Place and Duration of Study: Polyhouse at the Department of Horticulture, Yezin Agricultural University (YAU), between October 2024 and May 2025.

Methodology: There were two pot experiments (Expt.): Expt. 1 for parameterization, Expt. 2 for model evaluation. Withholding of water and proline sprays was employed at five-leaf stage. Drought response of relative leaf expansion rate (RLER) to fraction of transpirable soil water (FTSW) was fitted by a linear–plateau regression (LPR) at each level of proline sprays and related parameters were generated for model simulation. Daily leaf expansion rate under well-watered condition (LER_w) was calculated from plant leaf area (PLA) estimated from measurements of individual leaf length and width. In model, LER under drought (LER_d) was described as the product of LER_w and RLER and PLA was the integral of LER. For each water regime, shoot dry weight (SDW) was the product of specific shoot mass and PLA.

Results: Moderate proline levels (20-40 ppm) delayed the drought response of RLER, exhibited by lower FTSW thresholds (0.49-0.5) compared to control (0.75). Predicted PLA under well-watered condition showed higher goodness of fit than droughted condition (R²=0.60 vs 0.55). Simulated SDW represented more under drought than the well-watered condition (R²=0.57 vs 0.42). Model performance across proline levels revealed RMSD ranges of 112-286 for PLA and 0.85-2.26 for SDW, with accuracies of 0.88-0.96.

Conclusion: Current growth model showed varying drought responses to proline sprays with some magnitudes of errors, which needed further calibration and validation.

Artificial Intelligence and Its Role in Forensic Accounting Investigations

2026-05-18T13:25:58+00:00

The integration of artificial intelligence (AI) into forensic accounting represents a significant transition from traditional manual auditing toward more efficient, accurate, and proactive fraud detection. This study examines the relationship between AI adoption and fraud detection effectiveness using survey data from 120 forensic accountants in Nigeria. Employing descriptive statistics, correlation, and multiple regression analyses, the study evaluates how AI techniques and organizational support influence forensic investigation outcomes. Findings reveal a strong positive relationship between AI usage and fraud detection effectiveness (β = 0.68, p < 0.001), with organizational support also contributing significantly (β = 0.22, p = 0.002). The model explains 70% of the variance in fraud detection effectiveness. Results support Fraud Triangle Theory (Cressey, 1953) and Agency Theory (Jensen & Meckling, 1976), demonstrating that AI reduces fraud opportunities and enhances monitoring mechanisms. The study highlights the need for ethical frameworks, regulatory oversight, and investment in AI capacity development in emerging economies.

Green Economics and Household Consumption in Maharashtra, India

2026-06-19T12:17:55+00:00

This study examines household consumption behaviour in Maharashtra, India, through the lens of Green Economics and sustainable development. Using secondary data from the Centre for Monitoring Indian Economy Consumer Pyramids Household Survey for March 2024 and March 2025, the study analyses the relationship between household income, total expenditure, food expenditure, health expenditure, leisure expenditure, recreation expenditure, restaurant expenditure and vacation expenditure. The analysis applies descriptive statistics, comparative analysis, a correlation matrix, Chi-square association tests and ordinary least squares regression. A Green Economics Sustainability Index was constructed to assess the sustainability orientation of household expenditure, assigning positive values to welfare-enhancing expenditure on food and health and negative values to discretionary expenditure on vacation-related consumption. The findings show that household income and total expenditure increased between March 2024 and March 2025. Food and health expenditure also increased, indicating continued attention to welfare-oriented consumption. However, the Green Economics Sustainability Index declined from 45.70 in 2024 to 43.40 in 2025, suggesting a slight weakening of sustainability orientation alongside higher lifestyle-related expenditure. Correlation results show a positive relationship between income and total expenditure, while total expenditure and leisure expenditure are negatively associated with the sustainability index. The OLS regression results indicate that food and health expenditure contribute positively to sustainability orientation, whereas income and leisure expenditure show negative associations. The study concludes that income growth alone does not ensure sustainable consumption. Balanced expenditure allocation, with attention to welfare-enhancing categories and moderation in discretionary consumption, is important for improving household sustainability orientation in Maharashtra households overall.

Pre-extension Demonstration of Improved Bread Wheat Variety (Kingbird) under Farmer Conditions in Atote Ule District, Halaba Zone, Ethiopia

2026-06-30T12:11:12+00:00

Wheat is one of the major staple crops in Ethiopia; however, productivity remains low, mainly because of limited access to improved varieties and low adoption of production technologies. To address this gap, a demonstration study was conducted in Atote Ule District to evaluate and promote the improved bread wheat variety Kingbird under farmer conditions. The objectives were to popularise Kingbird and evaluate farmers’ perceptions in comparison with the local standard check, Ogolicho. Training was provided to farmers, development agents, agricultural experts and other stakeholders. Inputs were collected and delivered through the research centre. A total of 10 farmers were purposively selected based on their interest in adopting new technologies, willingness to share experiences and readiness to provide feedback. The improved variety and local check were planted side by side on 0.25 ha plots in each farmer’s field. Results indicated that Kingbird produced a higher grain yield (3.61 t/ha) than Ogolicho (3.12 t/ha). Farmer preference ranking also showed that Kingbird was favoured because of its high yield potential, attractive seed colour, tolerance to major diseases (Septoria, spot blotch, yellow rust and stem rust), lodging resistance, suitability for fodder, desirable plant height and better marketability. Therefore, Kingbird is recommended for wider scaling-up under similar agro-ecological conditions to enhance wheat productivity and farmers’ income.

Cluster-aware Deep Sentiment Mining Framework for Real-time Public Opinion Monitoring

2026-06-30T12:18:10+00:00

The rapid growth of social media platforms, online discussion forums and digital communication channels has generated large volumes of opinion-rich textual data that can support real-time public opinion monitoring. However, existing sentiment analysis systems often face limitations related to contextual ambiguity, scalability, computational overhead and noisy social media expressions. This study proposes a Cluster-Aware Deep Sentiment Mining (CADSM) framework for real-time public opinion monitoring. The framework integrates adaptive semantic clustering, distributed stream preprocessing, cluster-aware contextual feature augmentation and hybrid CNN-LSTM sentiment inference within a unified architecture. Opinion streams collected from X (formerly Twitter), Reddit, online discussion forums and news comment platforms were semantically grouped before distributed preprocessing and deep sentiment classification. Structural cluster metadata, including semantic density, cluster similarity, contextual relationships and sentiment distribution, were combined with textual embeddings to improve sentiment representation. The framework was evaluated using multilingual social media datasets containing positive, negative and neutral sentiment classes, and its performance was compared with SVM, Random Forest, CNN and CNN-LSTM models. Experimental results showed that CADSM achieved 97.4% accuracy, 96.9% precision, 96.5% recall and a 96.7% F1-score, with a false positive rate of 2.2%. The framework also achieved an average processing latency of 1.4 seconds and maintained accuracy above 96% as the stream volume increased from 10,000 to 500,000 messages. Under noisy conditions, CADSM maintained 92.3% accuracy at 5 dB SNR and achieved an AUC score of 0.98. These findings indicate that combining semantic clustering with distributed deep sentiment inference can improve contextual consistency, scalability and robustness for real-time public opinion monitoring while supporting timely sentiment interpretation across dynamic online discussions.

Post-Remediation Soil Recovery in K-Dere, Ogoniland: Implications for Sustainable Environmental Restoration

2026-07-02T05:33:30+00:00

This study investigated the post-remediation effects on soil health in K-Dere, Ogoniland, Rivers State, to determine whether remediation activities have effectively restored soil physicochemical and biological functionality in a hydrocarbon-impacted environment. Composite soil samples were collected from remediated soil (RS) and unremediated soil (URS) at a depth of 0–15 cm, and laboratory analyses were conducted in triplicate using standard soil analytical methods to assess microbial biomass, enzyme activities, organic matter status, nutrient composition and key physicochemical properties. The results showed that total organic carbon decreased from 2.10% in URS to 0.55% in RS (74.0% reduction), whereas total organic nitrogen increased from 0.20% to 0.40% (100% increase). Sulphate concentration increased from 56.98 to 66.30 mg/kg (16.4% increase), nitrate from 4.6 to 10.7 mg/kg (132.6% increase), and phosphate from 5.88 to 23.4 mg/kg (298.0% increase). Bulk density declined from 7.59 to 5.24 g/cm³ (31.0% reduction), water retention capacity increased from 0.375 to 0.433 (15.5% increase), and cation exchange capacity rose from 5.1 to 18.5 cmol/kg (262.7% increase). In contrast, biological indicators declined markedly, as bacterial counts decreased from 4.0 × 10⁶ to 3.9 × 10⁵ cfu/g (90.3% reduction), fungal counts from 2.6 × 10⁶ to 3.0 × 10⁵ cfu/g (88.5% reduction), and dehydrogenase, urease and phosphatase activities decreased by 73.1%, 74.6% and 70.0%, respectively. The overall soil health measured by the Soil Quality Index (SQI) indicated better soil quality in the unremediated soil (SQI = 0.79 > 0.70) than in the remediated soil (SQI = 0.64 < 0.70). The study concludes that remediation activities in K-Dere have substantially improved soil nutrient availability and physicochemical conditions but have not yet achieved full biological recovery. It is therefore recommended that future remediation programmes integrate biological enhancement strategies, including organic amendments and microbial stimulation, supported by long-term post-remediation monitoring to ensure sustainable restoration of soil health.

Relationship between Internal Control Systems on Financial Performance of Non-Governmental Organisations in Kenya, Nairobi

2026-07-02T05:40:58+00:00

Non-governmental organisations (NGOs) play a critical role in supporting social service delivery and development initiatives in Nairobi County, Kenya. However, many organisations in this sector continue to experience financial sustainability challenges associated with weak governance structures, donor dependence, and inadequate internal control systems. This study examined the relationship between internal control systems and the financial performance of NGOs in Nairobi County. The study focused on four internal control dimensions: control environment, risk assessment, control procedures, and continuous monitoring. An explanatory cross-sectional research design was adopted. Structured questionnaires were administered to a stratified sample of 372 NGOs, and 275 valid responses were analysed using descriptive statistics, correlation analysis, and multiple regression analysis. Reliability testing confirmed acceptable internal consistency for all study constructs, with Cronbach’s alpha values exceeding the recommended threshold of 0.70. The correlation results indicated positive and statistically significant relationships between all internal control dimensions and financial performance (p < .05). Regression analysis revealed that the internal control variables jointly explained a substantial proportion of the variation in financial performance (R² = 0.755). Control procedures had the strongest effect on financial performance (β = 0.314, p < .001), followed by continuous monitoring (β = 0.268, p < .001), control environment (β = 0.231, p < .001), and risk assessment (β = 0.223, p < .001). The findings suggest that effective internal control systems enhance financial accountability, resource utilisation, and overall financial stability among NGOs. The study concludes that strengthening governance structures, risk assessment practices, formalised control procedures, and monitoring mechanisms is important for improving financial performance in the NGO sector.

Quality Evaluation of Smoked Sausage Produced from Goat Meat, Fish and Carrot Blends

2026-07-13T12:02:12+00:00

Smoked sausage is a popular meat product prepared from raw materials such as pork, goat and chicken. This study evaluated the quality of smoked sausage produced from goat meat, fish and carrot blends at ratios of 100:0:0, 50:50:0 and 50:25:25, coded as M, MF and MFC, respectively. Proximate, vitamin, mineral, physicochemical and microbiological qualities were analysed, and sensory attributes were evaluated. Data were subjected to ANOVA using SPSS 20.0, and means were compared using DMRT. Proximate analysis showed that sample M had the lowest moisture content (7.11%), while MFC had the highest (7.19%). Sample MF had the lowest ash content (2.22%), while MFC had the highest. Sample M recorded the lowest fat content (11.19%), whereas MF and MFC recorded the highest (11.20%). Protein content was lowest in M (48.30%) and highest in MFC (51.87%). Vitamin analysis showed that MFC had the highest vitamin A value (11.10 IU), while M had the lowest value (101.78 IU). Sample M had the highest vitamin C content (2.48 mg/100 g), while MFC had the lowest (1.91 mg/100 g). Mineral analysis showed that M had the lowest calcium (0.60 mg/100 g) and magnesium (0.38 mg/100 g), while MFC had the highest calcium (0.81 mg/100 g) and magnesium (0.44 mg/100 g). Physicochemical results showed that M and MF had higher total pigment contents (11.90 mg/L and 11.74 mg/L), while MFC had the lowest (11.47 mg/L). Sample M had the highest total lipid value (12.68 g), while MF had the lowest (12.27 g). Microbial analysis showed that MF had the highest microbial count. Sensory evaluation showed that MF and MFC had higher scores for colour, aroma and mouthfeel, while M had the highest general acceptability. Increasing carrot inclusion changed the technological and sensory properties of the sausage, indicating that fish and carrot can be successfully incorporated into goat meat sausage with satisfactory sensory attributes. The findings support further product development using these composite sausage formulations for consumers.

Advanced Metering Infrastructure (AMI): Technical Architecture, Functionalities and Implementation Outcomes of the AMI System in Yangon, Myanmar

2026-07-15T11:49:36+00:00

Myanmar’s electricity distribution sector continues to face significant operational and financial challenges, including widening gaps between electricity supply and consumer demand, high levels of technical and non-technical losses, and inefficient billing and revenue collection systems. These challenges have negatively affected the financial sustainability of the national power sector while reducing the quality, reliability, and efficiency of electricity services. In response, Advanced Metering Infrastructure (AMI) has emerged as a key technological solution for modernizing electricity distribution through real-time monitoring, automated metering, and improved operational transparency. This study evaluates the effectiveness of AMI deployment in enhancing electricity distribution performance and strengthening revenue management in Myanmar. A mixed-method approach combining analytical and case study methods was employed, focusing on AMI implementation across 22 townships in Yangon during the 2023–2024 fiscal year. The study examines the technical architecture of the AMI system, including smart meters, communication networks and the Meter Data Management System (MDMS). Statistical analysis was conducted to assess discrepancies among distributed energy, billed energy, collected revenue, and system losses. The findings indicate that large-scale AMI deployment significantly improves the accuracy, transparency, and efficiency of electricity metering, billing, and revenue collection. The implementation resulted in a total revenue recovery of approximately MMK 13.38 billion, demonstrating substantial financial gains from digital metering systems. However, notable variations in loss rates and collection efficiency were observed across townships, with industrial and peri-urban areas exhibiting higher levels of non-technical losses and revenue leakage. The results further show that AMI enables utilities to detect abnormal consumption patterns, identify electricity theft, and enhance data-driven operational decision-making. In summary, this study confirms that Advanced Metering Infrastructure (AMI) constitutes a critical technological foundation for transforming Myanmar’s electricity distribution sector into a modern digital system, with substantial potential to enhance operational efficiency, financial governance, service reliability, and revenue collection performance. The findings indicate that prioritizing AMI deployment in industrial zones and rapidly expanding urban areas characterized by high electricity consumption and elevated technical and non-technical losses can not only improve return on investment (ROI) but also reduce electricity losses, curb revenue leakage, and enable more transparent billing and collection processes. Moreover, the real-time data generated by AMI systems has been shown to strengthen data-driven decision-making in demand forecasting, load management, and electricity distribution policy formulation. Accordingly, Myanmar must pursue a phased nationwide expansion of AMI deployment to reduce Electric Power Losses as a technical and non-technical losses, while not relying solely on public funding but also mobilizing domestic and foreign investment through Public–Private Partnership (PPP) arrangements to extend AMI coverage across townships nationwide. If implemented effectively, such an approach would significantly reduce system-wide electricity losses and reinforce the efficiency, reliability, and financial sustainability of the distribution network. From a long-term perspective, successful nationwide AMI implementation would accelerate the development of Myanmar’s Smart Grid infrastructure, improve the balance between electricity generation and consumption, and create future opportunities to integrate surplus power into the regional ASEAN Power Grid for electricity trade. This, in turn, could contribute meaningfully to national energy security, economic development, and public revenue growth, thereby positioning AMI as a strategic instrument for the country’s broader energy transition.

Integrating Improved Poultry Production with School Feeding in the Sidama Region, Ethiopia: A Field Demonstration of the “One Egg for a Child” Initiative

2026-07-17T13:34:33+00:00

Malnutrition among school-age children remains a major public health challenge in Ethiopia, affecting physical growth, cognitive development and educational performance. This descriptive field demonstration documented the implementation and early operational performance of improved poultry production integrated with a Home-Grown School Feeding program through the “One Egg for a Child” initiative at Murancho Qutala Elementary School, Hawella Woreda, Sidama Region, Ethiopia, under the SAPLING Project of the Tropical Poultry Genetic Solutions Platform. Poultry-production and enterprise records were compiled from January to April 2024, while egg distribution to participating schoolchildren was documented separately following initiation of the school-feeding component. A poultry demonstration center using Bovans Brown layer chickens was established and managed under improved husbandry practices. A total of 1,200 two-month-old pullets were stocked. During the recorded production period, more than 61,000 eggs were produced, and surplus egg sales generated approximately ETB 500,000 in gross revenue. A total of 4,250 eggs were distributed to 100 nutritionally vulnerable schoolchildren, with each participating child receiving one boiled egg on designated school-feeding days. Gross revenue from surplus egg sales contributed to the purchase of poultry feed during the observation period, while the initiative directly engaged five staff members in poultry management and school-feeding activities. Awareness meetings, demonstration visits and experience-sharing activities were conducted for students, teachers, parents and community representatives, covering improved poultry management, egg consumption and local marketing practices. As the demonstration did not include a control group, baseline measurements or direct assessment of nutritional and educational outcomes, the findings primarily describe implementation processes and early operational outputs rather than intervention effectiveness. The findings demonstrate the short-term operational feasibility of linking a school-based poultry enterprise with egg provision for selected schoolchildren and provide a basis for longer-term evaluations of its nutritional, educational and economic effects.

Effect of Steaming Pretreatment and Extraction Conditions on the Quality of Roselle Seed Oil

2026-07-20T12:46:38+00:00

Roselle (Hibiscus sabdariffa L.) seeds are an underutilised by-product with considerable potential as a source of edible oil because of their high content of unsaturated fatty acids and bioactive compounds. This study aimed to optimise screw-press extraction conditions for roselle seed oil yield, residual oil content in the cake, and oil recovery, and to evaluate the effect of steaming pretreatment on the quality of the extracted oil. The study was conducted at the Department of Postharvest Technology, Advanced Center for Agricultural Research and Education (ACARE), Yezin Agricultural University (YAU), Myanmar. Steamed roselle seeds were screw-pressed at different temperatures and moisture contents. Response surface methodology (RSM), based on a central composite rotatable design, was used to optimise the extraction conditions. Pressing temperature and seed moisture content significantly influenced all response variables. The optimum extraction conditions were 160°C and 8% moisture content. The quadratic models were highly significant (p < 0.001) and showed close agreement between the experimental and predicted values (R² = 0.9902–0.9914). Quality evaluation showed that steaming pretreatment significantly reduced free fatty acid content and increased iodine value and antioxidant activity compared with the control (without steaming) (p < 0.05). However, peroxide value, saponification value, refractive index, and specific gravity were not significantly affected (p > 0.05). The combined use of steaming pretreatment and optimised screw-press extraction improved extraction performance and selected oil-quality characteristics, providing information for the development of efficient roselle seed oil processing conditions.

Effect of Mulching Type and Application Rate on Growth and Yield of Soybean (Glycine max (L.) Merr) during the Post-Monsoon Season

2026-07-25T12:58:59+00:00

Soybean production during the post-monsoon season in Myanmar is constrained by moisture stress associated with declining rainfall and fewer rainy days, which limits crop growth and grain yield. Therefore, this study evaluated the effects of different mulch types and application rates on the growth and grain yield of post-monsoon soybean (Glycine max) at the Yezin Agricultural University farm during the 2024–2025 and 2025–2026 growing seasons. The experiment was arranged in a randomised complete block design with four replications. The treatments comprised no mulch (M0), plastic mulch (M1), rice straw mulch at 3 t ha⁻¹ (M2), 6 t ha⁻¹ (M3), and 9 t ha⁻¹ (M4), and maize stover mulch at 3 t ha⁻¹ (M5), 6 t ha⁻¹ (M6), and 9 t ha⁻¹ (M7). Mulching significantly improved soil moisture retention, vegetative growth, yield components, and grain yield compared with no mulch. Plastic mulch promoted faster canopy development during the early growth stage, produced the highest leaf area index (LAI), and generally maintained the greatest soil moisture. Rice straw mulch at 9 t ha⁻¹ produced the highest grain yields of 2456.2 and 3273.1 kg ha⁻¹ in the 2024–2025 and 2025–2026 seasons, respectively. The yields obtained with plastic mulch, rice straw mulch at 6 t ha⁻¹, and maize stover mulch at 9 t ha⁻¹ were not significantly different. The findings indicate that locally available crop residues can serve as effective mulching materials for post-monsoon soybean production under the conditions of the study area.

Identification of Superior Forage Pearl Millet Genotypes Based on Growth and Fodder Yield Traits

2026-08-01T12:29:32+00:00

This study evaluated twelve forage pearl millet (Pennisetum glaucum (L.) R. Br.) genotypes under rainfed conditions during the kharif season of 2025 at the Rainfed Agriculture Farm, Narayan Bagh, Bundelkhand University, Jhansi, Uttar Pradesh, India. The experiment was conducted in a randomised block design with three replications. Data were recorded for days to 50% flowering, plant height, tillers per plant, leaves per plant, leaf length, leaf width, stem thickness, green forage yield per plant, dry matter content, and dry matter yield per plant. Analysis of variance indicated significant differences among the genotypes for the evaluated traits, demonstrating useful variability for selection. Giant Bajra recorded the highest green forage yield per plant (277.60 g), dry matter yield per plant (37.15 g), stem thickness (1.18 cm), and leaf width (2.81 cm). Raj Bajra also produced high green forage yield (206.67 g), while ICMV-1602 recorded a dry matter yield of 36.50 g per plant. GFB-1 produced the highest number of tillers per plant (5.07), and ICMR-15944 combined the earliest flowering (55.00 days) with the highest number of leaves per plant (30.20). JP-138 recorded the greatest plant height (178.47 cm). The findings identify Giant Bajra, Raj Bajra, ICMV-1602, GFB-1, and ICMR-15944 as promising genetic materials for forage pearl millet improvement. Their performance should be validated through multi-location and multi-season evaluation before wider recommendation.

Effects of Mulching and Combinations of Different Urea Levels with Rhizobium Inoculation on Growth and Yield of Groundnut (Arachis hypogaea L.)

2026-08-14T13:02:20+00:00

This study was conducted to investigate the effects of mulching and combinations of different urea levels with rhizobium inoculation on the growth and yield of groundnut (Arachis hypogaea L.) at the Oilseed Crops Research Section, Department of Agricultural Research (DAR), Myanmar, during the post-monsoon seasons from December 2024 to March 2025 and from November 2025 to March 2026. Field experiments were conducted using a split-plot design with three replications. Mulching, consisting of rice straw mulch at 6 t ha⁻¹ and no mulch, were assigned to the main-plot factor, while combinations of different urea levels and rhizobium (Rh) inoculation (100% U without Rh, 100% U with Rh, 75% U with Rh, 50% U with Rh, 25% U with Rh, and 0% U with Rh) were assigned to the subplot factor. Sinpadathar-11 was used as the test variety. In both experiments, mulching significantly enhanced SPAD values, growth parameters, and pod yield compared with no mulch. Reduced urea levels combined with Rh inoculation significantly improved plant height, SPAD values, leaf area index (LAI), crop growth rate (CGR), and pod yield. No significant interaction was observed between mulching and the combinations of urea levels with Rh inoculation. However, the highest pod yield was recorded with 50% U with Rh, followed by 25% U with Rh under mulching, whereas the lowest pod yield was observed with 0% U with Rh. Based on the improved growth performance and pod yield of groundnut, the application of 50% urea combined with Rh inoculation under mulching produced the highest pod yield. In addition, the application of 25% urea with Rh inoculation under mulching was also suitable for the study area, as it maintained pod yield without a significant reduction compared with 50% urea with Rh, reduced dependence on synthetic fertilisers, and promoted environmentally friendly and sustainable agricultural practices.

Quantifying Curricular Integration in Low-Cost Edge-AI Laboratory Instruction: A Formal Model and a Six-Week Pilot Evaluation in a Nigerian Computer Science Department

2026-08-18T12:01:36+00:00

Resource constraints can limit practical instruction in the Internet of Things (IoT) and embedded machine learning within undergraduate computer science programmes. This study evaluates a low-cost Edge-AI laboratory module centred on HydroSense-EI and aligned with five curriculum courses in a Nigerian university. A six-week pilot involved 30 third-year students, of whom 27 completed the pre-test and post-test assessment. Curricular integration was quantified using the proposed Integration Coupling Index (ICI), learning change was evaluated using normalised gain across six assessment domains, and affordability was examined through kit-amortisation and pedagogical cost-efficiency models. The module achieved an ICI of 0.696, corresponding to 3.07 effective courses per laboratory week relative to a single-course baseline of 1.00. Mean assessment performance increased from 20.7% to 76.8%, with a mean normalised gain of 0.711. During seven-day field deployment, the six student teams achieved mean classifier accuracy of 92.8% and mean water saving of 34.0%. The reported bill of materials was US$22.20 per team; under the stated reuse and breakage assumptions, amortised cost declined from US$4.44 per student in the first cohort to US$1.17 by the fifth. These pilot findings indicate that the proposed framework can support measurable cross-course integration and practical Edge-AI learning under constrained laboratory budgets, while requiring broader controlled and multi-site evaluation.

Tri-layered Hybrid Privacy Architectures for Deep Neural Networks: Synergizing Split Learning, Differential Privacy, and CKKS Homomorphic Encryption

2026-08-20T10:31:44+00:00

Deep learning is becoming increasingly collaborative and outsourced, with neural computation split between resource-constrained clients and capable servers. While split learning reduces the computational load on the client, the transferred intermediate activations are vulnerable to feature-space reconstruction and model inversion attacks, while gradient exchanges are vulnerable to membership inference attacks. In this paper, we design, formalise, and empirically evaluate a tri-layered hybrid privacy architecture that integrates split learning, differential privacy (DP), and Cheon-Kim-Kim-Song (CKKS) fully homomorphic encryption (FHE) into a single coherent pipeline. We demonstrate how each layer addresses a different threat class: split learning restricts exposure of raw data, DP bounds statistical leakage from gradients and activations, and CKKS enforces computational indistinguishability for data that must cross the network boundary. We provide a formal composition of the privacy layers, a reference architecture with client- and server-side responsibilities, and an experimental evaluation across three datasets, including a seven-class electrical fault-diagnosis dataset. Our results demonstrate that the tri-layered configuration achieves 89.9% accuracy at ϵ = 8 (only 4.7 percentage points below a non-private baseline of 94.6%), with computational security against an honest-but-curious server and a measured latency overhead of ≈10.4 × relative to plaintext split inference. We also quantify the marginal contribution of each layer to overall threat-model coverage and provide guidance on jointly tuning the noise multiplier, clipping norm, and CKKS multiplicative depth rather than treating them independently.

Effect of Different Refining Methods on Gossypol Content and Oil Quality of Pretreated Cottonseed (Gossypium hirsutum L.)

2026-08-24T10:02:37+00:00

The objective of this research was to apply the most effective pretreatment for the refining methods and determine its effect on the quality (gossypol content, free fatty acid (FFA), peroxide value, iodine value, saponification value, and colour) of cottonseed oil. This research was conducted at the Department of Postharvest Technology, Advanced Center for Agricultural Research and Education (ACARE), Yezin Agricultural University (YAU). Cottonseed of the Shwe Daung-10 variety was collected from Lune Kyaw Cotton Seed Farm, Department of Agriculture (DOA), Ministry of Agriculture, Livestock, and Irrigation. The experiment was conducted using a completely randomised design (CRD) with four replications for each refining method in the bleaching step. The treatments comprised activated carbon (AC) and bleaching earth (BE) at concentrations of 0.5, 1.0, and 1.5 g/100 g of oil, together with an untreated crude-oil control. Compared with AC, BE was more effective in decreasing the saponification value and increasing peroxide values. Both treatments were effective, but their efficiency varied depending on the quality parameters. However, gossypol content, FFA, peroxide value, and colour were all significantly decreased by both treatments, thereby improving the overall quality of cottonseed oil. As a result, the use of 1.0 g of bleaching earth and 1.0 g of activated carbon is advisable as appropriate adsorbents for the purification of cottonseed oil, depending on the quality attributes sought. Therefore, the use of 1.0 g of bleaching earth or 1.0 g of activated carbon is recommended as an effective bleaching treatment to improve the quality of refined cottonseed oil while minimising gossypol content

Allelopathic Effect of Rice Straw Extract and Its Potential Use as a Bioherbicide

2026-08-24T12:43:17+00:00

Rice straw contains allelochemicals that may contribute to sustainable weed management by suppressing weed growth and development. This study evaluated the allelopathic effect of rice straw extract from the Myanmar rice variety Paw San Yin (Oryza sativa L.) on lettuce (Lactuca sativa L.), four weed species, and two rice varieties under laboratory conditions at the Experiment and Lecture Building (II), Department of Agronomy, Yezin Agricultural University, Nay Pyi Taw, Myanmar, during May–September 2025. Two experiments were conducted in a randomised complete block design (RCBD) with four and five replications, respectively. Experiment I (May–July 2025) involved lettuce treated with aqueous rice straw extracts at 0, 10, 30, 50, and 100 g L⁻¹. Experiment II (July–September 2025) included barnyard grass (Echinochloa crus-galli (L.) P. Beauv.), red sprangletop (Leptochloa chinensis (L.) Nees), long-fruited primrose willow (Ludwigia octovalvis (Jacq.) P.H.Raven), sessile joyweed (Alternanthera sessilis (L.) R.Br. ex-DC.), and two rice varieties (Sin Thu Kha and Paw San Yin), treated with 0, 50, 100, and 150 g L⁻¹ extract concentrations. Germination percentage, shoot length (mm), root length (mm), fresh weight (mg), and dry weight (mg) were recorded. The results showed that rice straw extract significantly inhibited seed germination, seedling growth, fresh and dry weights of seedlings in a concentration-dependent manner. Complete inhibition of lettuce growth was observed at 100 g L⁻¹. In Experiment II, broadleaf weeds were more sensitive than grasses, with complete suppression of L. octovalvis and A. sessilis at higher concentrations. Root growth was more strongly inhibited by the allelochemicals in rice straw than shoot growth across all species. The extract also exhibited noticeable autotoxic effects on both rice varieties. Overall, the findings demonstrated that rice straw extract possesses strong allelopathic activity and has potential as a natural bioherbicide for weed management.

Workplace Belongingness and Its Impact on Clinical and Non-clinical Staff at the Federal Teaching Hospital, Gombe State, Nigeria

2026-08-26T11:21:38+00:00

Workplace belongingness is an important aspect of employees’ workplace experience because it reflects feeling accepted, valued, and included at work. This study examined workplace belongingness among clinical and non-clinical staff at the Federal Teaching Hospital, Gombe State, Nigeria, with emphasis on workplace challenges, their perceived effects, and strategies for improvement. A cross-sectional survey design was adopted. The study population comprised 13,536 workers, and a sample size of 400 was stated in the manuscript. Questionnaire data and ten in-depth interviews were used for data collection, while 393 questionnaire responses were represented in the reported tables. Quantitative data were analysed using frequencies, percentages, and chi-square testing, while qualitative data were analysed manually to complement the survey findings. Most respondents reported a positive sense of belonging at work; however, important challenges remained. The most frequently reported challenges included lack of support from management and lack of recognition for contributions, followed by limited opportunities for social interaction and cultural or values misalignment. Reported effects included reduced engagement or motivation, lower productivity, decreased job satisfaction, isolation, and increased stress or burnout. Respondents most frequently identified enhanced communication and feedback, recognition and appreciation programmes, career development resources, mentorship, and improved management support as strategies for strengthening belongingness. The findings indicate that supportive organisational practices, recognition, communication, and opportunities for development are important considerations for strengthening workplace belongingness in the hospital.