CALL FOR PAPER - Vol 10 - Issue 1

This study examined the barriers and opportunities for women’s participation in Islamic religious practices and institutions in Kakamega County, Western Kenya, where Muslims form a minority within a predominantly Christian population. A qualitative design was used. Data were gathered through 24 key informant interviews and four focus group discussions involving 32 participants, and were analysed thematically. Seven barrier themes were identified: domestic role expectations and time poverty, honour culture and restricted mobility, the silencing of women in mixed religious discourse, inadequate mosque infrastructure, exclusion from institutional governance, poverty and economic trade-offs, and limited knowledge of women’s rights in Islam. Four opportunity themes were identified: self-organised women’s groups, progressive interpretation and male allies, digital connectivity, and community-based re-entry pathways for marginalised women. The barriers were found to intersect, so that rural, poor and widowed women faced compounded exclusion, while urban, educated and connected women were better placed to use the available opportunities. The evidence shows that the constraints stem mainly from patriarchal culture rather than Islamic theology, and that women’s self-organised groups and digital access are reshaping participation from below. The study recommends flexible programming, investment in women’s Islamic rights education, improved mosque facilities for women, and support for women’s groups and their digital networks.

Background: Childhood obesity is rising fastest in low- and middle-income countries, yet sub-national evidence from Nigeria remains scarce. We examined the distribution and predictors of body mass index (BMI) among primary-school children aged 5–10 years in Kogi State and compared the performance of ordinary least squares (OLS) and three regularized regression estimators. Methods: In a descriptive cross-sectional design, anthropometric measurements (weight, height, BMI) and questionnaire data (age, sex, physical activity, diet type) were obtained from primary-school pupils selected by multi-stage stratified, cluster and simple random sampling. BMI was modelled with OLS, Lasso (L₁), Ridge (L₂) and Elastic Net (combined L₁–L₂) regression. Penalty parameters were tuned by cross-validation; models were compared using R², AIC, BIC, MSE and RMSE, with residual diagnostics for normality, homoscedasticity and autocorrelation. Results: Weight, height and BMI category were strong and statistically robust predictors of BMI across all four estimators (p < 0.001). Age and sex were not significant; the significance of physical activity and diet type was not supported once test statistics were recomputed from the reported coefficients and standard errors (see Note to Table 5). Elastic Net returned the most favourable fit metrics (R² = 0.987, AIC = 131.0, BIC = 150.0, MSE = 2.25, RMSE = 1.50) and the lowest variance-inflation factors among the models. All models satisfied residual assumptions (Shapiro–Wilk p > 0.05; Breusch–Pagan p > 0.05; Durbin–Watson ≈ 1.98). Conclusions: Regularized regression, particularly Elastic Net, controls multicollinearity more effectively than OLS for this anthropometric data structure. Because BMI is a deterministic function of weight and height, the very high R² should be interpreted with caution. The findings nonetheless support continued investment in school-based physical-activity and nutrition programmes, consistent with the global evidence base.

This paper presents a PLC-based hardware implementation for transmission line fault analysis, transformer protection, and overload sharing in power stations. The proposed system integrates programmable logic controller technology with protective relays, sensing devices, and embedded controllers to detect abnormal operating conditions and enhance system stability. Several fault conditions, including single line-to-ground, line-to-line, double line-to-ground, and three-phase faults, are analyzed. In addition, an intelligent load-sharing scheme for parallel transformers is developed to mitigate overload conditions, improve reliability, and reduce thermal stress. Experimental validation of the hardware prototype demonstrates accurate fault detection, selective tripping, fast relay response, and effective load redistribution. The results confirm that the proposed architecture offers a practical and cost-effective solution for power system automation and protection applications.

When it comes to the law, crime has not ever been legal. All offenses have a mind in them that thought or felt or has otherwise failed to function properly to commit the offense, and all reactions to the offense have a system of investigators and lawyers and judges and correctional officers trying to decipher that mind. The area in which criminal psychology falls lies between these two realities. It considers the nature of offending, their thinking and how to use their thinking to help prevent offending and to serve them and the wider community justice. This paper discusses the development of the discipline from the "positivist" criminology of the 19th century to the present day of neuroscience and AI-based risk assessment tools, and how these tools are applied in four areas: criminal investigation and profiling; risk assessment and prevention; courtroom and sentencing decisions; and offender rehabilitation. Recommends changing the approach to justice from one of merely "what" to "who" and "why" and cautions, yet, that criminal psychological tools are probabilistic, culturally specific, and can be misused if not used with safeguards. It concludes with an examination of the Indian criminal justice system, where forensic psychology is slowly becoming institutionalized, and suggestions for its further institutionalization, strengthening and ethical incorporation into police, judicial and corrections systems.

There are many systematic reviews on predicting stock. However, each reveals a different portion of the hybrid AI analysis and stock prediction puzzle. The principal objective of this research was to systematically review the existing systematic reviews on Artificial Intelligence (AI) models applied to stock market prediction to provide valuable inputs for the development of strategies in stock market investments. Keywords that would fall under the broad headings of AI and stock prediction were looked up in Scopus and Web of Science databases. We screened 69 titles and read 43 systematic reviews, including more than 379 studies, before retaining 10 for the final dataset. This work revealed that support vector machines (SVM), long short-term memory (LSTM), and artificial neural networks (ANN) are the most popular AI methods for stock market prediction. In addition, the time series of historical closing stock prices are the most commonly used data source, and accuracy is the most employed performance metric of the predictive models. We also identified several research gaps and directions for future studies. Specifically, we indicate that future research could benefit from exploring different data sources and combinations, while we also suggest comparing different AI methods and techniques, as each may have specific advantages and applicable scenarios. Lastly, we recommend better evaluating different prediction indicators and standards to reflect prediction models’ actual value and impact.

The Nigerian telecommunications industry has experienced remarkable growth over the past two decades, becoming one of the largest mobile markets in Africa. However, persistent challenges such as poor network quality, frequent call drops, congestion, and unresolved customer complaints continue to undermine service delivery and erode customer satisfaction. This study evaluates the impact of optimizing key performance metrics—including Call Setup Success Rate (CSSR), Call Drop Rate (CDR), Handover Success Rate (HOSR), and signal strength—on both service quality and customer satisfaction within Nigeria’s mobile sector. A mixed-methods research design was employed, relying on secondary data drawn from the Nigerian Communications Commission (NCC) Quality of Service reports (2022–2023), GSMA publications, and a dataset of 4,327 customer complaints lodged with consumer protection agencies. Data analysis was carried out using Microsoft Excel for KPI computation and SPSS for statistical testing, including ANOVA to compare operator performance. Results indicate that MTN and Airtel consistently outperformed Glo and 9mobile in CSSR and HOSR, yet none of the operators met NCC’s benchmark for CDR. Analysis of complaints further revealed that billing irregularities and poor customer service responsiveness were the dominant drivers of dissatisfaction, surpassing even technical network failures. The findings demonstrate that optimizing technical KPIs alone does not guarantee improved customer satisfaction; effective complaint resolution and transparent customer engagement mechanisms are equally critical. The study concludes that sustainable satisfaction requires a dual strategy combining network quality enhancement with robust customer relationship management. Limitations of this study include reliance on secondary data and the exclusion of real-time proprietary operator datasets, which may provide deeper insights. Future research should incorporate predictive analytics and machine learning approaches to monitor QoS trends and forecast customer churn, offering more proactive strategies for improving service quality in Nigeria’s telecommunications sector