Adamawa State University

This study examined the impact of retirement benefits administration on the welfare of retirees in state tertiary institutions in Adamawa State, Nigeria. The study adopted a descriptive research design and targeted retirees and officials of the Adamawa State Pension Board across five selected institutions, with a population of 205 and a sample size of 132 determined using the Krejcie and Morgan sample size table. Data were collected through structured questionnaires and analyzed using descriptive statistics. From the 132 questionnaires administered, 127 were retrieved, representing a response rate of 96.2%. The findings revealed that the retirement benefits administration system is moderately functional but characterized by significant gaps in transparency, communication, and accessibility. While some components such as contribution modalities and record management recorded average performance, critical areas like awareness of payment timelines and benefit tracking were weak. The study also identified key challenges, including inadequate funding, poor record management, bureaucratic bottlenecks, corruption, and policy inconsistencies, which collectively undermine the efficiency of the system. The study concluded that although the administrative structure exists, its operational inefficiencies significantly impair retirees’ welfare. It recommended improved transparency through digital platforms, enhanced pre-retirement education, streamlined administrative processes, and strengthened funding and accountability mechanisms to ensure effective service delivery.

Abstract The integration of spatial Artificial Intelligence (AI) with aero-magnetic and satellite remote sensing data is redefining subsurface resource exploration across mineral-rich, data-constrained regions. This review synthesizes recent advances in geospatial artificial intelligence, including machine learning, deep learning, and hybrid approaches, with particular emphasis on their applications in mineral prospectivity and groundwater potential mapping. Using a comparative framework, the study categorizes AI models based on learning architecture, data source fusion, and deployment context, highlighting emerging techniques such as Federated Learning, Graph Convolutional Networks, Autoencoders, and Vision Transformers. A novel taxonomy is proposed to evaluate model maturity and field readiness across four African case contexts: Nigeria, Ghana, South Africa, and the Democratic Republic of Congo. The review highlights how these techniques improve spatial resolution, predictive accuracy, and automation in subsurface characterization, while addressing regional challenges such as geological heterogeneity, limited labeled data, and infrastructure constraints. In addition to summarizing the state of AI adoption, this review identifies systemic gaps in policy alignment, data governance, and cross-border collaboration. By drawing cross-regional comparisons and proposing a roadmap for scalable AI-driven geoscientific exploration, this work supports the development of resilient and inclusive technologies for sustainable resource management across sub-Saharan Africa and other developing regions.

Accurate crop yield forecasting under fluctuating agro-climatic conditions is critical for sustainable agriculture and food security. This study presents a climate-resilient, data-driven framework for soybean (Glycine max L.) yield prediction using Artificial Neural Networks (ANNs), to capture delayed and nonlinear agro-climatic effects relevant to smallholder farming systems. The model synthesizes a diverse set of agro-meteorological and environmental variables, including temperature, rainfall, humidity, wind speed, fertilizer inputs, soil characteristics, and atmospheric pressure, captured between June and October 2022 to support robust predictive analysis. The ANN architecture was trained and evaluated using standardized agro-climatic datasets, achieving R2 of 0.9439, MSE of 0.3253 t2/ha2, and MAE of 0.1432 t/ha, RSME of 0.1804 t/ha, demonstrating strong predictive capability under variable environmental conditions. Comparative analysis showed that the ANN outperformed Multiple Linear Regression and Random Forest models, particularly in representing nonlinear rainfall-temperature-fertility interactions. Lag-time sensitivity analysis further revealed that rainfall and humidity shifts exert the most influential delayed effects on soybean yield. Principal Component Analysis and feature ranking identified fertilizer input and rainfall as primary yield drivers, with relative humidity and temperature playing secondary roles. These findings are consistent with established agronomic patterns in legumes and highlight the ANN's ability to capture complex environmental interactions. The findings highlight the ANN model's applicability for resource-limited, smallholder contexts, providing a practical decision-support tool for climate-smart agronomy and adaptive planning in semi-arid regions.

Using panel data from 46 Sub-Saharan Countries, this study employed Descriptives statistics, Correlation, Bound test for cointegration, Panel ARDL analysis to analysed the data by examining how health outcomes affect economic performance in Sub-Saharan. The study was motivated by the growing recognition that health is an important component of human capital development and a critical determinant of productivity, labor efficiency, and sustainable economic growth. Despite various healthcare reforms and increased investments in the health sector across the region, many countries in Sub-Saharan Africa continue to experience poor health outcomes, including low life expectancy and high under-five mortality rates, which may hinder economic performance. Against this background, the study investigated the extent to which selected health outcome indicators influence economic growth in the region. The findings indicates that life expectancy, under-five mortality, health expenditure and Population have significant impact on economic growth. The study recommends among others that Authorities in Sub-Saharan Africa should place more emphasis on measurable improvements in life expectancy and reductions in under-five mortality by adopting targeted, high-impact health interventions.

This study examined the effect of work facilities and work environment on the performance of the Nigeria Police Force, Adamawa State Command. Specifically, the study investigated the effect of vehicles and office accommodation on the performance of police personnel in Adamawa State Command. The study adopted an explanatory survey research design and was conducted in Adamawa State, Northeastern Nigeria, with its capital at Yola. The population of the study comprised personnel of Adamawa State Command Headquarters and its component commands, including Karewa Area Command, Mubi Area Command, Yola Area Command, and Numan Area Command, totaling 6,400 personnel based on the staff nominal roll of 2025. The sample size of 376 respondents was determined using Taro Yamane’s formula, while an additional 10% was added to account for non-response, resulting in a total sample size of 414 respondents. Data for the study were collected through self-administered questionnaires. The reliability of the research instrument was tested using Cronbach’s Alpha reliability coefficient, and all variables recorded coefficients above the acceptable threshold of 0.70. Data collected were analyzed using multiple regression analysis with the aid of Statistical Package for Social Sciences (SPSS) version 24.0 at 5% level of significance. Out of the 414 questionnaires distributed, 376 were successfully returned and found valid for analysis, representing a response rate of 90.8%, while 38 questionnaires representing 9.2% were not returned. The findings revealed that vehicles have a negative but significant effect on the performance of Adamawa State Command of the Nigeria Police Force (β = -0.798, t = -8.583, p = 0.000), indicating that vehicle-related challenges hinder operational effectiveness. The findings also showed that office accommodation has a moderate positive and significant effect on the performance of Adamawa State Command of the Nigeria Police Force (β = 0.206, t = 3.582, p = 0.000). Based on the findings, the study concluded that inadequate management and maintenance of work facilities negatively affect police performance, while conducive office accommodation enhances staff morale, satisfaction, and productivity. The study recommended that police authorities should invest in adequate patrol vehicles, establish regular maintenance schedules, and ensure proper allocation and monitoring of vehicles to improve operational efficiency. The study also recommended improvement in office accommodation through the provision of adequate workspace, proper infrastructure, and conducive working facilities to enhance employee productivity and effectiveness.