The Journal of Science Research and Reviews (JOSRAR) is the official scientific publication of the ERDEL Scientific Research Group (ESRG), Federal University Dutsin-Ma, Katsina State, Nigeria. Publishing Schedule The Journal of Science Research and Reviews (JOSRAR) shall publish issues bimonthly. Covering January-February, March-April, May-June, July-August, September-October & November-December Issues. Indexing: Crossref, Google Scholar, Researchgate, etc. DOI: https://doi.org/10.70882/josrar
Federal University Dutsin-Ma, Katsina State, Nigeria
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Systematic Review of Learning Algorithms for Large-Scale Fuzzy Cognitive Maps
<span lang="EN-US" style="font-size: 12.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-ascii-theme-font: major-bidi; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: major-bidi; mso-bidi-theme-font: major-bidi; color: black; mso-themecolor: text1; mso-font-kerning: 0pt; mso-ligatures: none; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Fuzzy Cognitive Maps (FCMs) are an important and versatile tool for modeling systems with complex dynamics in various domains like medicine, engineering, environmental monitoring, financial systems, among others to aid in decision-making. These domains usually datasets with large number of nodes and connections leading to large-scale maps. This paper reviews learning algorithms for FCMs under Hebbian, Population-based and hybrid classifications. While Hebbian-based algorithms suffer from local optima and generalization issues, population-based algorithms are generally computationally prohibitive from large-scale exploration, which also affects the global search component of hybrid algorithms. A number of algorithms have been developed specifically for large-scale FCMs by adopting problem decomposition, parallelization, sparsity inducement, and multi-agent-based techniques. Considering the great potential of FCMs as a modeling tool in domains with large, complex systems, research on algorithms tailored to large-scale systems remain limited and needs to be explored further.
Copyright (c) 2025 Faith O. Echobu, Oyenike M. Olanrewaju, Sani Zaharaddeen (Author) https://creativecommons.org/licenses/by-nc/4.0
Potentials of Adansonia digitata linn. Seed and Leaf: Proofs from Two Provenances and Implications for Human Diet
This study aims to assess the nutritional contents of Adansonia digitata (Linn) seeds and leaves from two provenances. The prevalence of malnutrition and nutrient deficiencies, particularly in developing regions, and the underutilization of baobab's nutritional potential. The study seeks to explore the nutritional value of baobab seeds and leaves and their contribution to human diet. Study was conducted at the Nutrition Laboratory of Biochemistry complex, Federal University Dutse, Jigawa State. Samples of Adansonia digitata leaf and seed were collected from two locations (Dutse, Jigawa and Ilorin, Kwara). Purposive sampling was used to select the communities and mother trees. Parameters such as moisture, ash, fat, protein, carbohydrate, and crude fibre were assessed. Data was analyzed using descriptive statistics and t-test to compare the nutritional content of seeds and leaves. The results showed significant variations in moisture, ash, fat, protein, carbohydrate, and crude fibre content among the samples. Results revealed that Ilorin seeds and leave had the highest moisture (9.217±0.617), (6.159±0.541), protein content (20.235±0.946), (14.219±0.675) Fat (13.846±1.078), (2.797±0.619), while Dutse seeds and leave had the highest carbohydrate content (59.299±1.295), (77.641±0.910) and crude fibre respectively. Adansonia digitata seeds and leaves from Dutse and Ilorin exhibit notable nutritional variations influenced by provenance. Ilorin samples showed higher moisture, protein, and fat contents, while Dutse samples were richer in carbohydrates and crude fibre. These findings highlight the plant’s nutritional versatility. It is recommended that both provenances be promoted for dietary use, while further studies should explore environmental factors affecting nutrient composition and support their inclusion in food and nutrition programs.
Copyright (c) 2026 Journal of Science Research and Reviews https://creativecommons.org/licenses/by-nc/4.0
Play Beyond Recreation: Integrating Informal Learning in Children’s Recreational Environments
Play serves as a fundamental mechanism for children’s cognitive, social, emotional, and physical development. Traditionally viewed primarily as recreation, contemporary research positions play as a powerful vehicle for informal learning. This manuscript explores the theoretical foundations of play and learning, drawing on Piaget’s constructivist theory and Vygotsky’s sociocultural perspective, while integrating modern playful learning frameworks. It examines the characteristics of effective child-conscious recreational environments and analyzes case studies from Abuja, Nigeria (Maitama and Magicland Amusement Parks), Cairo’s Cultural Park for Children, and Rome’s Corviale Recreation Center. Findings reveal a significant gap in many urban African facilities, where design prioritizes commercial amusement over enriched learning opportunities. The paper proposes evidence-based design principles, including safety and comfort, flexibility with loose parts, rich learning affordances, inclusivity, and cultural-natural integration so as to create environments that seamlessly blend play with informal learning while accommodating parents at a secondary level. Recommendations for a proposed Children’s Recreation Center in Abuja emphasize child-conscious architecture that supports imagination, fantasy roles, and holistic development. This work advocates for a paradigm shift in recreational design, particularly in rapidly urbanizing contexts, and highlights the built environment’s role as a “third teacher” in fostering creative, independent, and confident future generations.
Copyright (c) 2024 Journal of Science Research and Reviews https://creativecommons.org/licenses/by-nc/4.0
Quality Assessment of Sweetpotato Roots (Ipomoea batatas L.) as Influenced by Ridge Height, Vine Cutting and Vine Orientation
Sweetpotato in sub-saharan Africa is presently receiving attention as it is a cheap source of calories and helps in the fight against malnutrition. In view of these, two field trials were conducted during the 2016 and 2017 rainy seasons at the Research Farm Federal University Dutse (11°70' N, 9°34' E, and 460 m above sea level, in the Sudan savannah zone of Nigeria to determine effect of ridge height, vine cutting and vine orientation on nutritional quality of sweetpotato. The treatments consisted of ridge heights (0 cm (flat ground), 30 cm, 40 cm, 50 cm and 60 cm), vine cuttings (apical, mid and basal) and vine orientations (45°, 90° and 180°) laid out in a split plot design, with the ridge heights and vine cuttings in the main plot and vine orientations in the sub plot and replicated three times.. Ridging had significant effects on quality attributes with higher quantities of nutrient elements in higher ridges. Carbohydrate content was higher in the apical vine cutting, calcium and crude fibre contents were higher in the base than in other vine cuttings. In conclusion it can be recommended that for better plant establishment and subsequent root yield and better quality attributes ridging from 30cm, apical vine cuttings should be used. For higher fibre content the basal vine cutting should be used.
Building Design Impacts and Climate Resilience in Katsina State: A Systematic Review of Sustainable Architectural Practices in Hot-Dry Climates
Katsina State in northern Nigeria faces significant challenges from its hot-dry Sahelian climate, intensified by climate change. This systematic review synthesizes literature on how building design choices influence environmental, social, economic, and resilience outcomes. Traditional Hausa architecture, with features like thick mud walls and courtyards, provides effective passive cooling, while modern concrete designs often exacerbate overheating and energy demands. Barriers to sustainable practices include limited awareness and weak policy enforcement. Flood vulnerability further compounds risk in many areas. The review highlights research gaps in localized empirical studies and recommends hybrid vernacular-modern approaches, updated building codes, and capacity building to enhance climate resilience.
Copyright (c) 2025 Journal of Science Research and Reviews https://creativecommons.org/licenses/by-nc/4.0
Conceptual Evaluation of a Small-Scale Gravity-Based Pumped Hydro Storage System with Permanent Magnet Generators (PMG) for Rural Renewable Energy Stabilization in Nigeria
Intermittency remains a critical bottleneck for renewable energy adoption in developing regions, as supply fluctuations disrupt grid stability. This study evaluates a hybrid energy system designed to improve power delivery continuity by integrating gravity-fed energy storage (GES) with high-efficiency recovery mechanisms. To address the cradle-to-grave challenges of chemical batteries, a mechanical-hydraulic synergy was modeled to capture surplus energy and convert it into gravitational potential. The system's operational logic utilizes surplus power to drive a hydraulic pump, lifting water to an elevated reservoir; during periods of low supply, this water descends to drive a micro-hydro turbine integrated with a Permanent Magnet Generator (PMG) for electrical conversion. The research utilized a configuration consisting of an elevated water tower (10–20 meters), a micro-hydro recovery unit, and precision instrumentation including flow meters and pressure sensors to track energy movement. Performance was mathematically modeled using gravitational energy equations and verified through semi-urban wind-yield simulations, with the recovery subsystem demonstrating a modeled round-trip efficiency in the range of 72–81%. Results suggest that the hybrid approach could potentially achieve substantially higher reliability compared to standalone intermittent systems, which exhibited approximately 45% reliability under equivalent conditions. The system's projected 50-year service life and favorable levelized cost of storage relative to lithium-ion alternatives further support its viability. This battery-free design represents a potentially scalable, low-maintenance pathway toward rural electrification, offering meaningful mechanical inertia to help stabilize weak-grid infrastructure while mitigating the environmental risks associated with electrochemical disposal.
Copyright (c) 2026 Bashar S. Qasim, Yusuf M. Ahijjo, Muazu Musa, Sadiya Umar, Samira A. Hassan, Muhammad B. Abdullahi, Tinke I. Peni (Author) https://creativecommons.org/licenses/by-nc/4.0
A Review of Illegal, Unreported and Unregulated (IUU) Fishing: Global Trends, Drivers, Impacts and Management Responses
Fish and other aquatic resources are critical to global food security, economic development, and livelihoods, especially in developing countries. However, illegal, unreported, and unregulated (IUU) fishing continues to jeopardise fisheries' viability and undercut global conservation and management efforts. This article investigates the idea and forms of IUU fishing, identifies the primary causes, and assesses the environmental, economic, and social consequences. It also examines current management and policy responses on the national and international levels. Furthermore, the study identifies rising global trends in IUU fishing, such as its concentration in weakly governed countries like West Africa and the growing sophistication of illegal fishing operations. The study highlights the importance of stronger governance frameworks, better monitoring and enforcement systems, improved traceability in seafood supply chains, and expanded international cooperation. Strengthening these measures is crucial for ensuring sustainable fisheries management and protecting marine biodiversity.
GC-MS Analysis of Hexane Extracts from the Roots, Stems, and Leaves of Hyptis suaveolens: Organ-Specific Phytochemical Profiling
Phytochemical characterization of medicinal plants is essential for elucidating their bioactive constituents and supporting their ethnopharmacological applications. Hyptis suaveolens (L.) Poit. (Family Lamiaceae) is widely utilized in traditional medicine for the management of infectious, inflammatory, and febrile conditions. However, comparative organ-specific chemical profiling of its non-polar extracts remains limited. This study aimed to characterize and compare the volatile and semi-volatile constituents of hexane extracts obtained from the roots, stems, and leaves of Hyptis suaveolens collected in Kano, Nigeria using Gas Chromatography–Mass Spectrometry. Plant organs were air-dried, pulverized, and macerated in hexane, and the extracts were analyzed using Agilent 7890A GC system coupled to a 5975C mass selective detector. Compound identification was performed by comparison with the NIST spectral library using a similarity index ≥ 80%. GC–MS analysis revealed distinct organ-specific phytochemical distributions. The root extract was dominated by bicyclic and heterocyclic compounds, notably cis-7-oxabicyclo[4.3.0]nonan-8-one (24.535%) and 2-butynedioic acid, di-2-propenyl ester (21.555%). The stem extract was characterized predominantly by fatty acid methyl esters, with hexadecanoic acid methyl ester (27.757%) and 10-octadecenoic acid methyl ester (20.790%) as the major constituents. Similarly, the leaves extract was rich in fatty acid derivatives, particularly pentadecanoic acid, 14-methyl-, methyl ester (29.898%) and 9-octadecenoic acid methyl ester (21.847%), alongside the diterpenoid phytol (6.989%). These findings demonstrate pronounced organ-specific variation in lipophilic secondary metabolites of H. suaveolens, highlighting the chemical diversity of this species. The study provides phytochemical data for H. suaveolens from Kano Nigeria and establishes a basis for future bioassay-guided fractionation and pharmacological investigations.
Meat Quality and Storage Stability of Turkeys Fed Diets Containing Synthetic and Non-Synthetic Additives
Feed additives are groups of non-nutritive products added to rations to improve the efficiency of animal production and have shown antimicrobial, antifungal, antiviral, antioxidant, and sedative properties. The study investigated the performance, meat quality, and storage stability of turkeys fed diets containing different additives. A total of 150 day-old turkeys were purchased and brooded. After two weeks of brooding, the birds were balanced for weight and assigned to five (5) treatments, with each treatment containing 30 turkeys, with 10 turkeys in replicates of 3. Five diets consisting of basal diet with no additives (control), basal diet + 4 g/kg of cloves, basal diet + 4 g/kg of duam palm, basal diet + 2.0 mg/kg of vitamin C, and basal diet + 0.25 mg/kg of butylated hydroxytoluene (BHT) were used. The feeding trial lasted a period of 12 weeks. Data collected showed that the pH values recorded across dietary treatments ranged from approximately 6.5 to 7.0, with birds fed diets containing BHT and vitamin C showing slightly higher pH values compared to those fed cloves, duam palm, and the control diet. Potassium differed significantly (P<0.05). Cloves had the highest potassium, followed by intermediate values in BHT and Vitamin C, while control and duam palm were lower. The result of the microbial load demonstrated that aerobic plate count (APC), total coliform count (TCC), and total fungal count (TFC) increased progressively with storage duration. The results of this study demonstrate that dietary additives influence sensory quality of turkey meat in different ways. Vitamin C was most effective in enhancing flavour, while cloves maintained acceptable aroma and tenderness.
Forests as Natural Barrier or Facilitators? The Role of Vegetation Structure in Mosquito Dispersal Patterns
Mosquito-borne diseases such as malaria, dengue, Zika, and chikungunya continue to pose major global health challenges, particularly across tropical and subtropical regions. While the role of climate, human mobility, and land use change in shaping disease transmission has been widely studied, the influence of forest vegetation structure on mosquito dispersal remains underexplored. Forests can simultaneously act as natural barriers, through dense canopy cover, lower temperatures, and reduced light, and as facilitators when fragmented or disturbed, creating transitional ecotones conducive to vector survival and flight. Recent research integrating landscape ecology, entomology, and remote sensing has revealed that vegetation heterogeneity, canopy height, and microclimate gradients directly mediate mosquito flight range, host-seeking behavior, and pathogen transmission potential. For example, Anopheles species often exploit partially cleared forest edges, while Aedes mosquitoes thrive in peri-urban forest mosaics that combine vegetation cover with artificial breeding habitats. Using Light Detection and Ranging (LiDAR)-based canopy measurements, Normalized Difference Vegetation Index (NDVI) modeling, and mark–release–recapture experiments, scientists are quantifying how vegetation structure predicts vector mobility across landscapes. This review synthesizes current knowledge (2021–2025) on how forest attributes modulate mosquito dispersal and vector-borne disease risks, emphasizing tropical ecosystems in Africa, South America, and Southeast Asia. We discuss mechanistic pathways linking vegetation and vector ecology, evaluate forest management implications for disease prevention, and identify future research priorities integrating remote sensing and ecological modeling.
Fractional-Order Mathematical Modeling and Analysis of Malaria Incorporating Control Measures via the Adams–Bashforth–Moulton Method
The epidemiological features of malaria infection are taken into consideration in this paper as a fractional-order mathematical model in Caputo derivative. The activities that the model uses to manage the disease are treatment and vaccination to study the effects of the controls on the disease dynamics. The theory of Lyapunov functions determines and verifies the existence and uniqueness of solutions within the frame of the fractional order and the stability of the endemic equilibrium point. The model is numerically obtained with the help of the fractional Adams-Bashforth-Moulton algorithm that will indicate the alteration of the model parameters, and the fractional orders of the model parameters to the impact of each of the mentioned parameters on the course of the disease. It has been established through the application of simulation that the more the disease is treated and vaccinated the less the prevalence of malaria and that the fractional-order models have high level of flexibility and realism than the classical integer order equations. The paper identifies the importance of fractional modeling in the description of the interactions between the effects of memory and nonlocal interaction between the biological systems and this enhances the understanding and control of infectious diseases. The model does however assume that the population is homogeneous mixed and hypothetical values of the parameters thus preventing the empirical validation. To make the model more predictive and practical to use in the formulation of effective control schemes against malaria, then the future research must be capable of addressing the spatial heterogeneity, stochasticity.
Assessing Housing Affordability and Accessibility Issues in Nigerian Cities: Evidence from an Empirical Study in Warri, Delta State
This research evaluates the issues surrounding urban residential housing in Nigeria, with Warri in Delta State serving as the case study location. Its objectives included investigating the consequences of housing shortages and overcrowding, examining the influence of deficient basic infrastructure, analyzing problems of housing affordability, exploring the significance of informal settlements, and assessing the involvement of government and private sector actors in tackling these challenges. The investigation adopted a descriptive research approach, focusing on a population comprising 805 landlords, tenants, and government officials residing in Warri. Application of Taro Yamane’s formula yielded a sample size of 267 respondents. Information was gathered through a structured questionnaire administered via a multi-stage sampling procedure that integrated stratified and convenience techniques, then examined using descriptive statistics (means and standard deviations). Results indicate that housing shortages and overcrowding increase rental prices, worsen living standards, create health hazards, and overburden infrastructure systems. Deficient infrastructure including inadequate roads, drainage, water provision, electricity, and waste disposal compromises housing quality and long-term viability. Housing affordability suffers from elevated construction expenses and limited government initiatives, resulting in disproportionately high rent payments. Informal settlements intensify difficulties via inadequate planning, sanitation shortcomings, and restricted access to services. Collectively, these problems are interlinked and require immediate action. The research concludes that strengthened government policies, greater investment in infrastructure, stricter regulatory oversight, and collaboration with the private sector are vital to advancing affordable housing, regularizing informal settlements, and enhancing overall urban livability in Warri.
Assessment of Pesticide and Herbicide Residues and Associated Risks in Yam (Dioscorea rotundata) Tubers in Kwande Local Government Area, Benue State, Nigeria
Yam (Dioscorea rotundata) is a major staple crop in Nigeria, yet its production increasingly relies on herbicides and pesticides to control weeds and pests. This study assessed agrochemical use patterns, perceived health and environmental effects, and residue levels in yam tubers from Kwande Local Government Area. A stratified random sampling design was employed across three production zones (North, South, and West). Thirty farmers were interviewed, and thirty yam tuber samples were analyzed using the QuEChERS extraction method coupled with GC-MS/MS and LC-MS/MS. Method validation showed good linearity (R² ≥ 0.995), recoveries of 80–110%, relative standard deviations below 15%, and limits of quantification of 0.01 mg/kg. Herbicide use was reported by 91.2% of farmers and pesticide use by 73.5%, with metazachlor, atrazine, cypermethrin, chlorpyrifos, glyphosate, and paraquat most frequently detected. Mean herbicide residues ranged from 0.042 ± 0.008 mg/kg (North) to 0.091 ± 0.012 mg/kg (West), while pesticide residues ranged from 0.035 ± 0.009 mg/kg (North) to 0.124 ± 0.018 mg/kg (West). Most values were below or close to international maximum residue limits, though higher concentrations in the West zone suggest more intensive chemical use. Farmers commonly associated agrochemical exposure with skin irritation, respiratory problems, and water and soil contamination. The findings indicate spatial variability in residue burden and highlight potential food-safety and environmental risks. Strengthened regulation, routine residue monitoring, and promotion of integrated pest management are recommended to ensure safer and more sustainable yam production.
Copyright (c) 2026 Avese Winifred Lakpa, Simon Alyegba Shomkegh, David Oriabure Ekhumelo, Tavershima Charles Shabu (Author) https://creativecommons.org/licenses/by-nc/4.0
Parametric Distribution Fitting and Extreme Value Inference for Rainfall in Maiduguri
The paper takes a stringent approach of statistical characterization and existence of extreme values of monthly rainfall in Maiduguri capital city of Borno State, Nigeria and applies a 4-decade dataset (1981-2023). Descriptive statistics show that there is a highly skewed, discontinuous rainfall regimen as indicated by the wide gap between the value of mean (53.86 mm) and the median (1.60 mm). As this aims to determine the best probabilistic model that describes the non-zero monthly rainfall, Gamma, Weibull, and Lognormal distributions were both fit using Maximum Likelihood Estimation (MLE) and compared using Akaike (AIC) and Bayesian (BIC) Information Criteria. The Gamma distribution proved to be the best model to choose since it gave the smallest AIC (3007.99) and BIC (3018.78) values and its estimation value was strong though in the lower and middle quantile ranges. To select the Gumbel distribution (as opposed to the Generalized Extreme Value (GEV) distribution) to model annual maxima, the shape parameter <!-- [if gte msEquation 12]><m:oMath><m:r>ξ</m:r></m:oMath><![endif]--> <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; position: relative; top: 3.0pt; mso-text-raise: -3.0pt; mso-font-kerning: 0pt; mso-ligatures: none; mso-ansi-language: EN-GB; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><!-- [if gte vml 1]><v:shapetype id="_x0000_t75" coordsize="21600,21600" o:spt="75" o:preferrelative="t" path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f"> <v:stroke joinstyle="miter"/> <v:formulas> <v:f eqn="if lineDrawn pixelLineWidth 0"/> <v:f eqn="sum @0 1 0"/> <v:f eqn="sum 0 0 @1"/> <v:f eqn="prod @2 1 2"/> <v:f eqn="prod @3 21600 pixelWidth"/> <v:f eqn="prod @3 21600 pixelHeight"/> <v:f eqn="sum @0 0 1"/> <v:f eqn="prod @6 1 2"/> <v:f eqn="prod @7 21600 pixelWidth"/> <v:f eqn="sum @8 21600 0"/> <v:f eqn="prod @7 21600 pixelHeight"/> <v:f eqn="sum @10 21600 0"/> </v:formulas> <v:path o:extrusionok="f" gradientshapeok="t" o:connecttype="rect"/> <o:lock v:ext="edit" aspectratio="t"/> </v:shapetype><v:shape id="_x0000_i1025" type="#_x0000_t75" style='width:5pt; height:12.5pt'> <v:imagedata src="file:///C:/Users/User/AppData/Local/Temp/msohtmlclip1/01/clip_image001.png" o:title="" chromakey="white"/> </v:shape><![endif]--> closes to zero <!-- [if gte msEquation 12]><m:oMath><m:r>(</m:r><m:r>ξ</m:r><m:r>≈ 0.14)</m:r></m:oMath><![endif]--> <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; position: relative; top: 3.0pt; mso-text-raise: -3.0pt; mso-font-kerning: 0pt; mso-ligatures: none; mso-ansi-language: EN-GB; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><!-- [if gte vml 1]><v:shape id="_x0000_i1025" type="#_x0000_t75" style='width:47.5pt;height:12.5pt'> <v:imagedata src="file:///C:/Users/User/AppData/Local/Temp/msohtmlclip1/01/clip_image003.png" o:title="" chromakey="white"/> </v:shape><![endif]--> and the principle of parsimony were used. The estimation of the level at returns describes the large flood events indicating a 469.44 mm and 516.21 mm of the significant floods 50-year and 100-year returns respectively. The results offer crucially designed site values critical to improving the urban drainage-related infrastructure and flood resilience mechanisms of the semi-arid Sudano-Sahelian region.
Development of an IoT-Based Fish Pond Water Quality Monitoring System
Aquaculture has remained a critical component of global food security, especially in developing countries where fish farming serves as a key source of nutrition and income. The productivity and sustainability of fish farms are largely dependent on maintaining optimal water quality. Traditional methods of monitoring pond water quality involve manual sampling and chemical test kits, which are time-consuming, labor-intensive, and often reactive rather than proactive. To address these challenges, this study presents the development of a low-cost Internet of Things (IoT)-based system designed to monitor essential water quality parameters in real time. The developed system integrates multiple sensors—namely, temperature, dissolved oxygen (DO), ammonia, pH, and electrical conductivity (EC)—connected to a microcontroller (ESP32), which processes the data and displays it on a local LCD screen. This design eliminates the need for continuous manual testing and provides immediate, on-site access to water quality information. The device is enclosed in a waterproof housing and powered by a solar-rechargeable battery, ensuring its suitability for rural and remote aquaculture environments with limited infrastructure. Unlike many existing solutions that rely on internet connectivity for cloud-based dashboards and remote notifications, this system operates entirely offline. It is particularly beneficial for small- and medium-scale fish farmers who require affordable, standalone monitoring tools. The system enables timely intervention when parameters deviate from acceptable levels, improving fish health and farm efficiency. This work contributes a practical and scalable solution to water quality management in aquaculture and lays a foundation for future enhancements such as data logging and alert systems.
Application of an STL–ARIMA Hybrid Framework for Monthly Rainfall Forecasting in Maiduguri, Nigeria
The problems of climate variability in Sahel regions with severe rainfall anomalies bring serious challenges to the water resource management and mitigation of the flood disasters. This paper analyzes the effectiveness of a hybrid STL-ARIMA model to predict monthly rainfall in Maiduguri, Nigeria, which is a town that was affected by the 2024 floods. The article compares the hybrid, traditional Seasonal ARIMA (SARIMA) and Exponential Smoothing by Holt-Winter to assess previous rainfall data of 1981-2023. The suggested method employs Seasonal-Trend Decomposition by Loess (STL) to single out non-linear and seasonal trends that are too complicated then subjecting the time series to ARIMA modeling. With regards to performance, it can be seen that the STL-ARIMA model is far ahead of the conventional approach with a Root mean square error of 29.54mm, as opposed to 43.94mm and 43.01mm using the SARIMA and Holt-Winter models respectively. The hybrid model minimized the Mean Squared Error (MSE) by nearly 55% and it was more effective in terms of capturing the sharp variance variation and extreme wet-season peaks, which are characteristic of the area. These results provide a strong scientific foundation in enhancing flood early warning systems directly related to SDG 13 (Climate Action) goals in Northeastern Nigeria.
Health Risk Assessment of Organochlorine Pesticide Residues in Yam Samples in Ibaji Local Government of Kogi State, Nigeria
Organochorine pesticide residues remain a global public health concern due to their presence, bioaccumulation, and long-term toxicity in food chain. This study evaluates the human health risks associated with organochlorine pesticides (OCPs) residues detected in yam (Dioscorea spp.) samples collected from selected agricultural sites over two consecutive years. Residue extraction was performed using the QuEChERS method, and quantification was achieved through gas chromatography-mass spectroscopy (GC-MS). The targeted OCPs included alpha-BHC, beta-BHC, gamma-BHC, endosulfan sulfate, and gamma-chlordane. Concentrations were assessed against international regulatory standards and acceptable daily daily intake (ADI) thresholds. Results revealed vary levels of contamination, with some residues, particularly Aldrin and heptachlor epoxide, exceeding permissible risk level. Estimated cancer risk (CR) values for both adults and children indicated significant potential for chronic health impacts, especially due to Aldrin (CR-Children: 3.64 × 10⁻³ – 5.12 × 10⁻⁴; CR-adult: 1.01 × 10⁻³ – 1.43 × 10⁻⁴) and heptachlor epoxide (CR-Children: 9.95 × 10⁻⁴ – 3.66 × 10⁻⁴; CR-adult: 2.77 × 10⁻⁴ – 1.02 × 10⁻⁴). Even at lower concentrations, residues such as p,p'-DDT and gamma-BHC contributed to the cumulative risks burden. The findings highlight the urgent need for continuous environmental monitoring of pesticides residues in food crops. Strengthening regulatory enforcement and promoting sustainable agricultural practices are critical steps toward minimizing long-term dietary exposure and safeguarding public health.
Advanced Environmental Biotechnology for Pollution Control and Sustainable Development: A Review
Environmental biotechnology has emerged as a critical tool for addressing the growing challenges of environmental degradation and pollution. This review is aimed to explore the advances in environmental biotechnology at effective pollution control, highlighting sustainable and eco-friendly approaches to mitigate environmental contaminants. Emphasis is placed on the role of biotechnological processes in wastewater treatment, which utilize microbial consortia and engineered biological systems to degrade organic pollutants and remove heavy metals. Recycling and bio-waste conversion technologies are also discussed, showcasing how agricultural, municipal, and industrial waste can be transformed into value-added products such as biofertilizers, bioplastics, and bioenergy. The review delves into the various application areas of environmental biotechnology, including soil restoration, air pollution mitigation, and water purification. Bioremediation one of the cornerstone technologies is examined in detail, focusing on its mechanisms, microbial agents, and real-world applications in detoxifying hazardous environments. Furthermore, the integration of renewable energy technologies, such as biofuel production from biomass, highlights the synergy between waste management and clean energy generation. These developments underscore a paradigm shift towards sustainable environmental management, where biotechnology not only aids in pollution reduction but also promotes circular resource utilization. The review concludes by identifying challenges and opportunities for advancing the field, calling for interdisciplinary innovation to realize its full environmental and socio-economic potential.
Biotechnology, Bioremediation, Renewable energy, Pollution, Degradation
Sensory Evaluation and Shelflife of Smoke-Cured Clarias Gariepinus Preserved with Different Sources of Fuel Energy
The impact of three smoking media (firewood, charcoal, and sawdust) on the sensory qualities and nutritional composition, lipid oxidation products, and microbial quality of smoked Clarias gariepinus during 28 days storage at room temperature was investigated in this study. Fish samples were tested on Days 0, 7, 14, 21 and at day 28. According to the sensory analysis of results, at Day 0 all samples were very well accepted were taste (8.03-8.58), aroma (8.23–8.45), texture (8.43–9) and color (7.83­-9). However, scores decreased over time (p < 0.05) to 1.09–1.82 on Day 28. The firewood-smoked fish had the best sensory scores throughout storage (taste: 5.00). Proximate composition indicated a gradual decline in crude protein from Day 0 to Day 28. Firewood-smoked fish decreased from 44.71% to 19.92%, charcoal from 39.42% to 16.03%, and sawdust from 37.54% to 10.02%. Lipid oxidation (TBARS) increased steadily, with sawdust attaining the highest value (10.74 mg MDA/kg) at Day 28, compared to charcoal (10.56 mg MDA/kg) and firewood (10.10 mg MDA/kg). Microbial analysis showed substantial bacterial and fungal loads from the start, ranging between 2.8×10⁶–9.1×10⁶ CFU/ml for bacteria and 1.7×10⁶–6.1×10⁶ CFU/ml for fungi on Day 0. Microbial counts rose consistently during storage, with sawdust-smoked samples recording the highest loads (>10⁷ CFU/ml by Day 28), while firewood-treated samples showed comparatively lower microbial growth. Overall, firewood emerged as the most effective smoking medium, producing fish with superior sensory quality, higher protein retention, lower lipid oxidation, and better microbial stability. The study demonstrates that smoking medium significantly influences both the shelf life and safety of smoked catfish, underscoring the need for improved smoking technologies and post-processing hygiene to minimize spoilage and enhance product quality.
Assessment of Petroleum Source Rocks of Yola Basin, North-Eastern Nigeria: From the Viewpoint of Bulk Geochemistry and Organic Petrography
The Yola Basin is a hydrocarbon exploration frontier basin in Nigeria where to date; minimal subsurface data is available for adequate assessment of its petroleum source rocks. The Bima Formation is the oldest lithogenetic unit in the basin followed by Yolde, Dukul, Jessu, Sekuliye formations, Numanha Shales and Lamja Formation. Source rock geochemical characterisation was conducted on the Cretaceous sequences of Yola Basin with the aim to determine the organic matter type, richness, thermal maturity and hydrocarbon generation potential. The organic matters within the shales of Bima Formation have reached peak oil window to high thermal maturity stage of oil generation. They possess fair source generative potential based on the present-day kerogen Types I, Type III and Type IV, showing minor oil and major gas prospect. The shales of Yolde, Dukul, Jessu, Sekuliye formations and Numanha Shales have reached peak-oil window to mature stage of oil generation and possess fair source rock generative potential based on their present-day kerogens Type II-III, Type III and Type IV and are likely to generate mainly gas although, minor oil could be expected owing to the presence of small amount of Type II kerogens. The Lamja Formation shales and coals are at early to peak oil generation window and possess poor to very good source generative potential based on the present-day Type III and Type II-III kerogens, expected to generate major gas and liquid components of hydrocarbons. The high thermal maturity has effect on the original nature of the organic matter and any oil-prone organic matter if present would have been cracked into thermogenic gas probably due to the intense thermal event attributed by the Tertiary volcanic intrusion known to be present in the basin.
An Intelligent Hybrid Learning Framework Integrating ANFIS, Genetic Algorithms, and Reinforcement Learning for Traffic Signal Control
Rapid urbanization and increasing vehicular demand have intensified traffic congestion, exposing the limitations of conventional static traffic signal control systems. This study proposes a novel hybrid intelligent traffic control framework that integrates an Adaptive Neuro-Fuzzy Inference System, Genetic Algorithm, and Reinforcement Learning (ANFIS–GA–RL) to achieve real-time adaptive signal optimization. The proposed approach uniquely combines interpretable fuzzy reasoning for managing uncertainty, genetic algorithms for global parameter optimization, and reinforcement learning for closed-loop, real-time decision-making, distinguishing it from existing standalone and partially hybrid methods. Performance is evaluated in a MATLAB-based urban traffic simulation using eight performance indicators, travel time, average vehicle speed, throughput, traffic density, queue length, delay time, intersection delay, and computational time. Comparative results against conventional Fuzzy Logic, standalone Genetic Algorithm, Artificial Neural Network, ANFIS, and ANFIS–GA controllers demonstrate consistent and measurable performance gains. Relative to the baseline fuzzy logic controller, the proposed ANFIS–GA–RL model achieves an overall improvement of 64.9%, characterized by substantial reductions in travel time delay, intersection delay, and computational overhead, alongside enhanced throughput and traffic flow stability. These findings confirm the robustness, scalability, and real-time applicability of the proposed framework for intelligent urban traffic signal control, with future work focusing on IoT-enabled deployment and field validation.
Adaptive Portfolio Optimization using Deep Reinforcement Learning and Generative Models
Cryptocurrency financial markets are characterized by high volatility and non-stationary price dynamics, posing significant challenges to traditional portfolio optimization techniques that rely on static risk–return assumptions. In such environments, existing methods often struggle to generalize and adapt effectively, leading to suboptimal performance and increased downside risk. To address these limitations, this paper proposes a novel adaptive portfolio optimization framework that integrates Generative Adversarial Networks (GANs) for synthetic data augmentation with a state-of-the-art Soft Actor-Critic (SAC) deep reinforcement learning (DRL) agent. By augmenting real historical OHLC data with realistic TimeGAN-generated price sequences, the proposed approach exposes the DRL agent to a broader range of market scenarios, thereby improving generalization and mitigating overfitting. A convolutional neural network (CNN) feature extractor captures deep temporal dependencies, while causal and dilated convolutions model complex inter-asset correlations. Empirical results demonstrate that the proposed GAN–SAC hybrid consistently outperforms conventional strategies and the baseline Deep Portfolio Optimization (DPO) model, achieving a higher Accumulative Portfolio Value (APV) of 53.72, an improved Sharpe Ratio of 0.0980, and a reduced Maximum Drawdown (MDD) of 28.5%. These findings confirm the effectiveness of combining generative models and DRL to develop robust, adaptive portfolio strategies capable of navigating highly volatile cryptocurrency markets, with practical implications for next-generation algorithmic trading systems requiring enhanced resilience and dynamic risk control.
A Density Functional Theory Study on Effect of Cations and Anions Co-Alloyed Cu2ZnSnS4 Kieserite on Structural and Formation energy as possible Photovoltaic Material
Cu2ZnSnS4 (CZTS) has the potential to be applied as an earth-relatively abundant and non-toxic material in thin-film solar cells, based on its suitable electrical and optical properties. However, many challenges have prevented the achievable efficiencies from exceeding 12.6%, which is well below desirable efficiencies compared to other competing solar cell technologies. One of the problems with the development of Cu2ZnSnS4 solar cells is the number of defects leading to severe potential fluctuations. This research work investigates the effect of alloying CZTS with Silver (Ag) (Cation) and Selenium (Se) (Anion) theoretically using Density Functional Theory (DFT). The optimized lattice parameters were found to increase from 5.26 to 5.85 and 10.60 to 12.32 respectively due to the introduction of Ag and Se. The volume of the crystal unit cell increased from 162.747 3 to 219.472 3 respectively. The formation energy was found to be reduced from 0.663eV to 0.476eV. From the obtained results, it can be seen that the alloyed Ag2ZnSnSe4 compound has a low-range energy structure and its stable within a low temperature range. This result is expected to enhance the properties of kesterite due to the alloying.
Isolation and Characterization of Flavonol-rich Fractions of Jatropha curcus and Evaluation of their in vivo Wound-healing Potential
Jatropha curcus (J. curcus) (Euphorbiaceae) is a drought resistant shrub whose leaves, latex, bark, and seed cake employed in traditional medicine to treat cuts, burns, and chronic ulcer. This study reports the isolation and structural characterization of bioactive compound(s) from the methanolic leaf extract of Jatropha curcas. Methanolic extraction of air-dried leaves was followed by silica gel column chromatography with gradient elution (hexane-ethyl acetate-methanol). Fractions were monitored by thin-layer chromatography (TLC), pooled according to similar Rf values, and crystallized to yield isolates. Purified compounds were characterized using FTIR, UV-Vis, 1H/13C NMR and LC-MS. One major yellow crystalline compound (designated TBF-19) was obtained (210.0 mg), and HRMS gave an [M]+ at m/z 198.0917 consistent with C13H11NO; the data supported the proposed structure (2-aminobenzophone as recorded in the primary notes). Wound-healing studies employed excision models in albino rats with three treatment groups (petroleum jelly, povidone iodine, and TBF-19 obtained from Jatropha Curcas). Tissue analyses included hydroxyproline and total protein assays to assess collagen deposition and tissue regeneration. The isolation workflow and spectroscopic evidence indicate that flavonoid- and phenolic-type constituents are major components responsible for the antioxidant and wound-healing properties observed in crude extracts. These findings validate the ethnomedicinal use of J. curcas and provide a basis for further bioassay-guided fractionation and detailed structure elucidation using 2D-NMR and high-resolution MS techniques.
Assessment of Physicochemical Parameters of Underground Water (Well Water) in Ijora - Badia Area of Lagos- State, Southwestern-Nigeria
This study presents reports on the Assessment of Physicochemical Parameters of Undergroundwater (wellwater) in Ijora - Badia Area of Lagos- State, Southwestern- Nigeria. Ten wellwater samples were randomly collected from ten locations, four times per month, between August 2024 and January 2025. Samples were obtained with pre-washed, labelled plastic bottles, digested, and analyzed using standard procedures to measure physicochemical parameters such as pH, temperature, electrical conductivity (EC), acidity, alkalinity, turbidity, total suspended solids (TSS), and total dissolved solids (TDS). Results of Physicochemical parameters shows: pH (5.44 -7.05), temperature (29.3 - 29.8°C), EC (1417.5 - 2312.5 μS/cm), acidity (35.0 - 202.7 mg CaCO3/L), alkalinity (103.5 - 384.7 mg CaCO3/L), turbidity (7.8 - 337.5 NTU), TSS (30.5 -51.5 mg/L), and TDS (0.799 - 2.4123 mg/L). Principal Component Analysis identified three factors -PC1, PC2, and PC3 - which accounted for 70.25% of the total variance indicating predominantly anthropogenic sources from industrial, municipal, and domestic waste, dissolved and suspended solids, industrial effluents, and surface runoff, contributing to salinity and mineral-related pollution, thermal pollution, mineral dissolution, chemical weathering and natural geochemical processes. Correlation analyses (p > 0.5) further supports the high pollution levels. Physicochemical parameters varied significantly, except temperature.The physicochemical parameters values exceeded Nigeria Industrial Standards (NIS) and World Health Organization (WHO) limits, indicating that groundwater in the area is significantly polluted and unsuitable for domestic use.
A Deep Convolutional Neural Network and SDN-based Closed-Loop System for Real-Time Network Attack Detection and Mitigation
The increasing sophistication of cyber threats demands more intelligent and adaptive network defenses than traditional architectures can provide. This research bridges this gap by designing and implementing an integrated system that leverages a Deep Convolutional Neural Network (DCNN) for real-time traffic classification within a Software-Defined Networking (SDN) control loop, enabling automated threat mitigation. Evaluated in an emulated environment, the system demonstrated a perfect 100% detection rate with zero false positives against a range of common attacks while introducing minimal operational overhead, with only a 3.1% throughput reduction. Crucially, it preserved legitimate throughput by a factor of 4.2x during attacks, proving the viability of a closed-loop, DCNN-SDN framework for achieving robust, self-defending network security without compromising performance.
Deep Convolutional Neural Network (DCNN)
Development of a Past Questions Repository System
Limited access to past examination question papers and the slow response time of existing question paper repositories have contributed significantly to poor academic preparation, leading to increased course failure rates among university students. Many traditional repositories suffer from inefficient search mechanisms, delayed retrieval, and restricted availability, making it difficult for students to obtain relevant past questions when needed. This paper presents the implementation of an Artificial Neural Network (ANN) within an electronic repository of past examination question papers in a university setting to address these challenges. The repository represents a significant advancement in academic resource management by providing a streamlined, user-friendly, and highly accessible platform for students and staff. Through an intuitive interface and efficient search tools, users can easily navigate extensive collections of question papers spanning multiple disciplines, courses, and semesters. The integration of ANN technology enhances accessibility and system performance by reducing search time by up to 5 ms through automated classification of student queries, thereby minimizing delays associated with complex database operations. The system also incorporates security measures to ensure the confidentiality and integrity of stored question papers while promoting environmental sustainability by reducing reliance on printed materials.
Foraminiferal Biostratigraphy and Paleoenvironment of Ovia-1 Well, Northern Depobelt of Niger Delta Basin, Nigeria
The Ovia-1 well located in the Northern Depobelt, Niger Delta Basin was investigated based on Gamma Ray log, Lithostratigraphy, foraminifera and nanno fossil biostratigraphy. This is aimed to determine biostratigraphic distribution, age and paleodepositional environment of the studied intervals. Based on the foraminfera, the marker species identified are Quinqueloculina microcostata, Amphistegina lessonii, Heterostegina sp, Lenticulina inornata, ex gr. costiferum, Heterolepa pseudoungeriana, Hanzawaia stratoni, Spirosigmoilina oligocaenica, and Cibicorbis inflata. Twelve (12) biozones identified are N15 – N14 (middle Miocene middle Miocene 12.26 – 13.12Ma), ( N13 – N12/N11 middle Miocene 13.12 – 13.68Ma ) N9 – N10/N11 middle Miocene 14.20 – 15.6Ma) ( N4 - P22 early Miocene 20.94 –23.2Ma ) ( P17-P18 early Oligocene 33.0 – 36.50Ma ) ( P14 middle Eocene 36.50 – 43.2Ma ) P12 middle Eocene 41.0 – 43.2Ma ) ( P4 late Paleocene 56.8 – 57.3Ma) ( P1 – M18 early Paleocene 64.6 – 65.0Ma respectively ). The paleoecological interpretation, based on the identified plantic foraminiferal species, indicates Inner Neritic to Outer Neritic zones. Lithostratigraphic criteria including Gamma Ray log responses and sand/shale ratios supported by paleobathymetric data indicate that the Ovia-1 have well, have two broad lithofacies units -the Marine Paralic and the Marine Units, which are ascribed to the Agbada Formation, Lithological analysis revealed alternating layers of shale, and sand, reflecting different paleoenvironmental conditions during deposition. The age of the well was established as Middle to Late Miocene, based on the bioevents of key marker species. Additionally, the limited presence of calcareous benthic and planktonic foraminifera supports the interpretation of a shallow marine depositional setting.
Assessment of Environmental Control Policy Elements’ Somersaults in Ilupeju - Ekiti, Ekiti State, Nigeria
Environmental policy elements are crucial for safeguarding natural resources, reducing pollution, and promoting sustainable development in Nigeria, particularly in Ilupeju Ekiti, Ekiti State. Despite national frameworks like the National Environmental Standards and Regulations Enforcement Agency (NESREA) Act and state-level statutes, environmental degradation persists. A study assessed the existing Environmental Control Policy Elements (ECPE), implementation extent, strategies, and factors influencing ECPE in the area. Researchers surveyed 124 stakeholders, including farmers, traders, artisans, teachers, commercial drivers, and students, using simple random sampling. Results showed that respondents, mostly females (50.8%) with tertiary education (57.5%), were aware of ECPE guiding environmental use (83.3%) and enacted laws (71.7%). However, 73.3% rated implementation strategies as ineffective, and 99.2% were unaware of specific government strategies. Political factors (70.8%) were major contributors to ECPE somersaults, with negative impacts on waste management (65.0%) and overall environmental quality. Logit regression analysis revealed that age (-4.09), educational status (0.000), and awareness of policy enforcement agencies (0.256) influenced ECPE implementation. The study confirms robust ECPE existence but highlights ineffective implementation and enforcement. The study concludes that prohibition laws are a step in the right direction, and recommends government support for enforcement agencies to ensure effective compliance monitoring and ECPE implementation.
Assessment of Climate Change Mitigation Policy Elements in Oye-Ekiti, Ekiti State, Nigeria
The implementation of effective Climate Change Mitigation Policies (CCMPs) is vital for protecting local communities from the adverse effects of climate variability. However, in rural settings like Oye-Ekiti, Nigeria, the enforcement of CCMPs is often lacking, and institutional coordination is inadequate to meet national climate objectives. This study assessed the Climate Change Mitigation Policy Elements (CCMPE) in Oye-Ekiti, identifying existing policies, awareness levels, and challenges to implementation. A simple random sampling method selected 120 stakeholders in environmental use, including farmers, artisans, traders, and forestry staff. Data were collected through secondary sources and a structured questionnaire, analyzing existing CCMPE, awareness of climate change, effectiveness in reducing greenhouse gas emissions, stakeholder participation, and enforcement levels. The findings indicate that respondents were aware of climate change (68.3%) but lacked understanding of greenhouse gases (71.7%). About 32.5% were unsure of CCMPE effectiveness in reducing emissions. Many were unaware of government programs (53.3%) and enforcement agents (56.7%), with only 43.3% occasionally involved in policy implementation. Identified CCMPE included tree planting and efficient waste management. Educational status, communication methods, government program awareness, and presence of enforcement agents significantly influenced stakeholders' participation. The study concludes that CCMPs are not yielding desired results due to low awareness and lack of strict measures for effective implementation. The government must address these gaps to achieve desired results, including increasing awareness, improving enforcement, and ensuring stakeholder participation.