JOURNAL OF BASICS AND APPLIED SCIENCES RESEARCH
The aim of Journal of Basics and Applied Sciences Research (JOBASR) is to be one of the foremost sources of scholarly articles and research papers through the support of research publication at reasonable or at no cost in long run. This is to make research publication hassle free for financially constrained researchers and scholars. The published article will always be an open access, free under Creative Commons License 4.0 and archived for future generations. The Objectives of Journal of Basics and Applied Sciences Research (JOBASR) are: 1. To supply publishing platform to scholars and researchers from different science and technology-related fields 2. To grant research and scholarly articles available free of cost to all users without any subscription or login ID. 3. To give scholars a chance to be part of the scholars community who assists and helps others in publication and review. 4. To create research publication hassle free to ensure sharing of knowledge in due time. Publication Scheduling This journal publishes 6 issues in 1 volume a year.
Faculty of Physical Sciences, Federal University Dutsin-Ma
Accurate derivation and efficient implementation of higher order logic programming in Hopfield network
represented and dealt effectively within artiﬁcial neural networks. This paper describes the implementation of higher order logic programming in Hopfield network by carrying out computer simulation using NetLogo and running the relaxation for several trials and combination of neurons. In Hopfield network, optimization of logical inconsistency is carried out by the network after the connection strengths are defined from the logic program; the network relaxes to neural states which are models (i.e. viable logical interpretations) for the corresponding logic program. A solution of an optimization problem is obtained after the network is relaxed to an equilibrium state. Applications that benefitted on Hopfield network include solving NP-complete optimization problem such as travelling salesman problem; non-linear and discontinuous data in larger field and connections, and it is able to detect all possible interactions between predictor variables such as detect complex nonlinear relationships of dependent and independent variables.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0
Isolation, identification and antimicrobial susceptibility profilling of Escherichia coli from clinically non-mastitis animals in Sokoto metropolis
Mastitis, which is the inflammation of the mammary gland, is among the most common and very significantly devastating potential dairy disease occurring in all dairy herds worldwide. This study investigated the prevalence of environmental pathogens specifically Escherichia coli in clinically non mastitis animals. A total of 200 samples from cows and goats were cultured on nutrient agar. From the result after culturing, 178 samples showed colonial growth which were then sub cultured on selective media (EMB) and characterized by Gram staining in which the prevalence was found to be 18.5% and the result of Gram staining revealed short to medium sized rod organisms staining pinkish. Representatives of positive isolates were then subjected to antimicrobial susceptibility profiling where all were found to be resistant to Amoxicillin/clavulanic acid, Ampicillin, and tetracycline but susceptible to chloramphenicol and gentamycin. The current study has shown a little decrease in the prevalence of Escherichia coli causing mastitis which may be because of regular treatment, and improvement of sanitary condition which must be maintained. Also, sensitivity of the test employed and the period during which the work was conducted might be some of the reasons for the low prevalence. Enhanced sanitary measures during milking and education on hygienic practices are recommended to further reduce contamination risks.
Physicochemical and characteristic properties of activated carbon prepared from Mango seed shells via H3PO4 and ZnCl2 activation
Highly porous and surface area activated carbons was prepared from mango seed shells using H3PO4 and ZnCl2 as an activating agent. The activated carbon was prepared firstly by carbonisation followed by activation process. Physicochemical properties of activated carbons namely ash content was found to be 9.21±0.03% for AC1 (activated carbon prepared using H3PO4 activation) and 8.12±0.03% for AC2 (activated carbon prepared using ZnCl2 activation). ; low moisture content of 2.03±0.01% for AC1 and 2.02±0.13% for AC2; volatile matter of 20.11±0.05% was recorded for AC1 and 20.02±0.03% for AC2 while high fixed carbon values was recorded for AC1 as 68.65±0.05% and 69.84±0.14% for AC2. The findings also revealed BET surface area values of 227.40 m2/g and 375.14 m2/g for AC1 and AC2; total pore value of 0.35 cm3/g and 0.48 cm3/g for AC1 and AC2 while pore diameter values was found to be 2.12 nm for AC1 and 2.46 nm for AC2 which indicated that both activated carbons are mesoporous making them crucial in facilitating the access of the adsorbate molecules to the interior of the carbon particles. FTIR analysis in activated carbon revealed stretching vibration C=O bond, C C bond, C N bond and C-H bond for AC1 and AC2. TGA was used to assess how the material responds to increasing temperatures. The prepared activated carbon has good fixed carbon content, better surface area and good pore diameter for better adsorption application.
Geospatial assessment of soil physicochemical properties in Kaita local government area of Katsina state-Northern Nigeria
This study investigated the spatial distribution and variations of soil physicochemical properties in Kaita Local Government Area, Katsina State-Northern Nigeria. Soil samples were collected, air-dried, sieved to two (2) mm, and analyzed for pH, particle size distribution, total nitrogen, available phosphorus, organic carbon, and exchangeable potassium using standard laboratory techniques. Soil pH was measured potentiometrically in soil suspensions, particle size distribution was determined by hydrometer analysis following dispersion, and nutrient contents were quantified via Macro-Kjeldahl digestion, spectrophotometry, and flame photometry. Descriptive statistical parameters and spatial interpolation using Inverse Distance Weighted (IDW) techniques with ArcGIS 10.8 were used enabled the visualization of soil physicochemical property distributions, with convergence indicated by a texture correlation coefficient (R²) of 0.53. The results revealed that a predominance of sandy soils (80–88%), moderate clay (6–12%) and silt contents (2–9%), and mildly acidic to neutral pH (6.4–7.3) respectively. While organic carbon levels were low (0.02–0.24%), phosphorus moderately distributed (14–35 ppm), and nitrogen markedly deficient in 97.5% of samples in the study area. The heterogeneity of physiochemical properties of the soil suggests that there is need for spatially targeted soil management practices. Recommendations include integrated soil management through organic amendments, fertilization, acidity correction, and conservation practices is significant for improving soil fertility and productivity; regular monitoring and geospatial tools are important for managing variability and ensuring sustainable agriculture in the study area.
Extending the shelf life of Tiger-nut milk drinks using Amaranthus Caudatus (l.) (African Spinach) and Syzygium Aromaticum (l.) Merr. & l.m. Perry (clove) as natural preservatives
Tiger-nut (Cyperus esculentus L.) is a perennial monocotyledonous plant with a tough, erect fibrous root system. Tiger-nut tubers possess a slightly sweet flavour, and their texture is comparable to coconut. Fresh, semi-dried, or dried tubers are commonly consumed as snacks due to their nutty taste. However, many consumers are not well informed about the wide range of products that can be derived from tiger-nut and its nutritional benefits. This contributes to its continued status as an under utilised crop despite widespread consumption in regions where it is cultivated. The high cost of animal milk in developing countries has increased the demand for plant-based milk alternatives. An inexpensive substitute in the form of beverages produced from locally available plant sources, rich in nutrients, can help reduce protein-energy malnutrition. This study evaluates microorganisms associated with tiger-nut milk spoilage, the methods of preparation, and their effects on nutritional composition and shelf stability. It also assesses the effectiveness of Amaranthus caudatus and Syzygium aromaticum as natural preservatives in extending the shelf life of tiger-nut milk.
Improving generalizability of smart gate ticketing system using plate number detection through image analysis
This study presents an improved generalizability of a smart gate ticketing system for robust license plate recognition across heterogeneous national plate formats. Unlike conventional country-specific approaches, the proposed framework integrates a Faster R-CNN–based detection model with an Optical Character Recognition (OCR)-driven recognition stage, enhanced through adaptive image preprocessing and data augmentation to address variations in font styles, illumination, and plate geometry. A multi-country dataset comprising plates from Nigeria, Niger, France, and the United Nations was employed to evaluate cross-jurisdictional performance under real-world conditions. Experimental results demonstrate an overall accuracy of 96.9%, with precision and recall of 96.3% and 97.5%, respectively, while maintaining near real-time inference latency of approximately 89 ms. Comparative analysis of the proposed system against existing License Plate Recognition (LPR) models confirms superior generalizability and computational efficiency. The findings validate the suitability of the proposed system for scalable intelligent gate and access-control environments where interoperability across diverse regulatory contexts is critical.
Contraceptive effect on Serum Glucose and Lipid profile in Alloxan-induced diabetic female Wistar Rat
Diabetes mellitus is a metabolic disorder that affects many women of reproductive age. Oral contraceptive pills are now commonly used in millions of women worldwide. The study aim to evaluate the effect of a combined oral contraceptive on the glucose levels and lipid profile in alloxan-induced hyperglycemic rats. Twenty-five female albino Wistar rats weighing 150-200g were divided into five groups(A-E) of five rats each. Animals in group A (control) received distilled water, and Groups B-E were induced into hyperglycemia by intraperitoneal injection of alloxan monohydrate (150mg/kg body weight). Groups (B-D) received distilled water, COCs 0.6mg/kg BW, glibenclamide reference antidiabetic drug) 25mg/kg body weight, for 14 days, 18days, and 22 days, respectively, while those in group E received glibenclamide 25mg/kg BW for 22 days. At the end of the study, all rats were sacrificed, blood sample was collected via cardiac puncture and placed in plane bottles for biochemical analysis, Administration of a COCS significantly (P<0.05) increased blood glucose, a well as lipid profile. This result suggests that diabetic rats administered oral contraceptive pills may be more susceptible to oxidative stress by enhanced depletion of antioxidant and increased lipid peroxidation.
Effect of dehydration methods on the nutritional composition of onion and garlic
Allium cepa (onion) and Allium sativum (garlic) are important spices with notable nutritional and medicinal properties. However, their high perishability causes post-harvest losses and seasonal scarcity. This study explored preservation techniques to extend shelf life while retaining nutritional quality. Fresh bulbs were procured from Wadata market, Makurdi, Nigeria and taxonomically authenticated. It was then taken to the laboratory for preparation and analysis. Forty bulbs each of onion and garlic were sliced and divided into four parts. One part was meshed and stored in airtight containers as fresh samples; others were processed via sun drying, freeze drying, and microwave irradiation. Samples were subjected to proximate, mineral, vitamin, and amino acid analyses. Fresh onion and garlic had high moisture contents (85.47±0.55 % and 66.82±0.10 %), while dried samples ranged from 7.73±0.01 % to 18.80±0.25 %, supporting microbiological stability. As moisture decreased, ash, fat, protein, fibre, and carbohydrate levels increased. Mineral content remained stable, though potassium in onion rose from 16.99±0.01 mg/100 g (fresh) to 163.37±0.00 mg/100 g (freeze-dried). Vitamins A, B1, and B2 were consistently low. Vitamin C declined, especially in microwave-dried garlic (1.25±0.01 to 0.04±0.00 mg/100 g). Amino acid levels increased with drying; microwave-treated garlic had high lysine (5.83±0.01 mg/100 g). Freeze drying proved most effective, best preserving nutrients and bioactivity in onion and garlic.
Anti-microbial and anti-inflammatory studies of Zinc-Salen complex
There is much traction about Schiff base metal complexes because of their structural versatility, potentials in bioactivities etc. Thus, N,N′-bis (salicylidene) ethylenediamine - a salen ligand- was synthesized from salicylaldehyde and ethylenediamine; then followed by coordination with zinc(II) to form a Zn–salen complex. The yields of the salen and its zinc complex were 81 ± 2% and 73 ± 2%, respectively. These samples were characterized by melting/decomposition point analyses, solubility tests, Fourier-transform infrared (FTIR) and UV–Visible absorptions. From the results of FTIR, there were evidence for azomethine linkage and Zn complexation because of the characteristic shift of the C=N stretching vibration and deprotonation of the phenolic O–H group. UV–Visible analyses showed π→π* and n→π* transitions of the salen, along with a salen-to-metal charge transfer band in the Zn–salen complex at 350 – 420 nm. These are reasonable features implying coordination between the Zn ion and the base. Melting point of 128 ± 2 for the salen ligand was obtained, indicating high purity. On the other hand, the Zn–salen complex showed lower melting point as compared to other reported analogues. The Zn-salen was lipophilic than the free salen based on the solubility results. The Zn–salen complex gave generic antibacterial activities against Gram-positive, Gram-negative, and fungal strains. In vitro anti-inflammatory assessment showed moderate COX-1 and COX-2 inhibition. And Zn-salen gave higher COX-2 selectivity compared to ibuprofen. These findings implied Zn–salen has potential for the development of novel antimicrobial and anti-inflammatory agents, necessitating detail pharmacological investigations.
A self-starting block methods (SSBMs) for the solution of ordinary differential equations
In this paper, self-starting block numerical methods for the solution of stiff initial value problems ordinary differential equations were developed. The Backward Differentiation Formulas and Generalized Backward Differentiation Formulas are used in the derivations. The E-transformation is applied to the triples and self-starting methods are obtained. The numerical implementation of the methods on ordinary differential equations are reported to show the effectiveness and efficiency of the methods. The computational of convergence of absolute stability and consistent tends to the theoretical order as h tends to zero.
Data Pre-processing challenges with generic and domain specific solutions: a critical review
Data pre-processing is a critical phase in the machine learning and data analysis lifecycle, significantly influencing model accuracy, efficiency, and reliability. While numerous standard techniques such as normalization, encoding, and missing value imputation are widely used, existing literature provides limited guidance on how to address complex, context-dependent challenges that require non-generic solutions. This gap creates uncertainty for practitioners when selecting appropriate preprocessing strategies across diverse data scenarios. This study aims to critically review and systematically categorize data pre-processing challenges by distinguishing between those effectively addressed using generic techniques and those requiring domain-specific or context-aware approaches. A systematic literature review methodology was adopted, synthesizing findings from academic research and industry practices across multiple data modalities, including tabular, textual, image, and time-series data. The findings reveal that generic techniques are effective for routine data issues but are insufficient for handling semantic inconsistencies, complex feature interactions, and context-driven anomalies. To address this, the study proposes a structured, decision-oriented framework that guides practitioners in evaluating data characteristics, identifying preprocessing challenges, and selecting appropriate strategies. This work contributes a practical and unified approach that enhances decision-making in data pre-processing, ultimately improving the robustness, interpretability, and performance of machine learning models.
The out-of-plane libration points in ER3BP with radiation of the smaller primary, triaxiality of both primaries surrounded by a BELT
The paper investigates the motion of an infinitesimal particle near the out-of-plane equilibrium points in the elliptic restricted three body problem (ER3BP) when the primaries are triaxial rigid bodies with the smaller primary a source of radiation and surrounded by a belt. It is observed that there exist two out-of-plane equilibria which lie in the ξζ- plane in symmetrical positions with respect to the orbital plane. The parameters involved in the system affect their positions. The position changes with an increase in triaxiality, radiation and belt. The position and stability of the out-of-plane equilibrium points are investigated numerically using two binary systems (Xi-Bootis and Kruger 60) and they are found to be unstable.
A multi-task learning framework for sentiment analysis and news classification for low-resource language
Despite the growing progress in Natural Language Processing (NLP), low-resource languages such as Hausa remain underrepresented in model development and evaluation. This paper presents a shared-encoder multi-task learning (MTL) framework based on AfriBERTa for joint Hausa sentiment analysis and news topic classification. The model learns both tasks simultaneously using Hausa subsets of the NaijaSenti and MasakhaNEWS datasets. Seven structured experiments were conducted, systematically evaluating optimization strategies including class weighting, encoder freezing, gradual unfreezing, and dynamic loss weighting. The best sentiment result was achieved in Experiment 5 (F1 = 80.7%) through encoder freezing and class rebalancing, while the best news classification result was achieved in Experiment 7 (F1 = 89.0%) through combined optimization with seed variation. The final shared-encoder model achieves a Macro-F1 of 74.0% and a Harmonic-F1 of 72.3%, substantially outperforming the FonMTL dual-encoder baseline in task balance (+27.2 Macro-F1 points; +64.1 Harmonic-F1 points). Although task interference was observed, the results confirm the viability of shared-encoder MTL for heterogeneous tasks in low-resource African language NLP and provide practical guidance for adapting multilingual models in such settings.
The effect of health information systems management on healthcare delivery in Northern Nigerian Tertiary Hospitals
This study investigated the influence of Health Information Systems (HIS) management on healthcare delivery within tertiary hospitals in Northern Nigeria. The research specifically examined how HIS management—conceptualized through data quality, system reliability, and staff competence—affects service efficiency, clinical decision-making, and overall quality of care. Adopting a descriptive cross-sectional survey design, primary data were elicited from a sample of 300 healthcare professionals, yielding 284 valid responses for analysis. Data were analyzed using descriptive statistics, Pearson Product-Moment Correlation, and Multiple Regression analysis. Findings revealed a strong significant positive relationship between HIS management and healthcare delivery ($r = 0.68$, $p < 0.05$). Regression results further indicated that HIS management is a significant predictor of service quality improvements, accounting for $54\%$ of the variance ($R^2 = 0.54$). Despite these benefits, the study identified critical bottlenecks, including dilapidated ICT infrastructure, erratic power supply, chronic underfunding, and specialized training gaps. The study concludes that robust HIS management is a non-negotiable catalyst for optimizing healthcare outcomes in Northern Nigeria. It recommends targeted investment in resilient power solutions and continuous professional ICT development for medical personnel.
Identifying factors influencing implementation of coding and indexing in secondary facilities: case study of general hospital Daura, Katsina state
Coding and indexing are essential components of health information management, ensuring the accurate classification, storage, and retrieval of patient data. However, secondary healthcare facilities often face implementation gaps that compromise data integrity. This study examined the factors influencing the implementation of coding and indexing at General Hospital Daura, Katsina State. Using a descriptive survey research design, data were collected via structured questionnaires administered to health information management personnel and administrative staff. The data were analyzed using descriptive statistics, including frequencies and percentages. The findings revealed that while staff possess moderate foundational knowledge, effective implementation is significantly hindered by a lack of continuous professional training, a severe shortage of physical coding materials (ICD-10 manuals), and limited technological infrastructure. Furthermore, weak administrative commitment and inadequate supervision were identified as key systemic barriers. These factors contribute to delays in record retrieval and inaccurate health statistics. The study concludes that improving coding practices requires a multi-faceted approach involving resource provision and management support. It is recommended that the hospital management invests in digital health record systems, provides updated coding manuals, and establishes mandatory periodic training workshops for personnel to ensure adherence to international classification standards and enhance overall healthcare delivery.
Enhancement of obstacle detection and avoidance in smart cars using microcontrollers
autonomous obstacle-avoiding robotic car designed for collision-free navigation. Several researches were conducted to address gaps such as distance covered by the sensor, power consumption, and mobility of the robot but gaps still exist. The aim of this research is to implement a robot car that, while moving, has the ability to detect obstacles in its path and change direction, the primary objective was to engineer a mobile platform capable of detecting environmental barriers and recalculating its trajectory without external human intervention. By utilizing an HC-SR04 ultrasonic sensor for distance measurement and an Arduino UNO microcontroller as the primary processing unit, the system continuously monitors its path through 40KHz sound wave pulses. Upon detecting an obstacle within a predefined threshold, the robot halts, scans its immediate surroundings via a servo-mounted sensor, and identifies the safest direction based on the maximum clear distance available. Experimental evaluations demonstrate a 90% success rate in navigating controlled environments containing various objects such as desks, chairs, and walls; it also utilized the HC-SR04 sensor to improve detection accuracy and minimise power consumption. This implementation serves as a scalable prototype for enhancing automotive safety and industrial automation, offering a cost-effective alternative to expensive LiDAR-based systems.
An enhanced system design of IoT-based smart office
This study presents an enhanced system design for an Internet of Things (IoT)-based smart office system aimed at improving workplace efficiency, security, and energy management. The proposed system integrates intelligent sensors, automated controls, and real-time data processing to monitor office conditions such as lighting, temperature, device usage, and access control. By using interconnected IoT devices and cloud- and edge-computing architectures, the system enables automatic adjustments, remote monitoring, and timely decision-making. The design also focuses on reducing energy waste, improving employee comfort, and strengthening office security through advanced smart authentication and alert mechanisms. Results obtained from system evaluation show that the enhanced IoT-based smart office design provides faster response times, better resource management, and more reliable automation compared to conventional office systems. This study presents an enhanced system design for an IoT-based smart office, aimed at improving energy efficiency, security, and user comfort through automated monitoring and control of devices. A prototype implementation was developed using IoT sensors, actuators, and a centralized control platform, with experiments conducted to evaluate system performance in a real office environment. Results indicate a 20% reduction in energy consumption, a 15% improvement in response time for automated controls, and enhanced security through real-time access monitoring. The study contributes by proposing an integrated smart office framework that combines environmental monitoring, automated device management, and user-centric control, offering a scalable and cost-effective solution for modern workplaces. These findings demonstrate the potential of IoT integration to optimize operational efficiency and inform future research on intelligent office environments.
An enhanced federated machine learning for secure DDOS detection in IOT network
The rapid growth of Internet of Things (IoT) devices has created new opportunities for automation and connectivity, but it has also increased exposure to cyber-attacks especially Distributed Denial of Service (DDoS) attacks. Traditional centralized security systems struggle to protect IoT networks because they require collecting large amounts of data in one place, which raises privacy concerns and slows down detection. This research proposes an enhanced Federated Machine Learning (FML) approach for secure and efficient DDoS detection in IoT environments. Instead of sending raw data to a central server, each IoT device trains a local model and only shares learned model updates, keeping sensitive information private. The enhanced system combines lightweight learning algorithms, secure communication techniques, and improved aggregation methods to boost accuracy and resistance against malicious interference. Experimental results show that the proposed approach achieves higher detection accuracy and lower latency compared to traditional centralized methods, reduces data exposure, and perform well even in resource-limited IoT devices. Overall, the enhanced FML-based solution provides a stronger, more privacy-preserving, and scalable defence mechanism for securing modern IoT networks against DDoS threats. This study addresses a critical gap in existing research, where many federated learning (FL)-based intrusion detection systems for IoT environments fail to adequately handle data heterogeneity, communication overhead, and robustness against sophisticated distributed denial-of-service (DDoS) attacks. To overcome these limitations, the proposed approach introduces an enhanced federated machine learning framework that integrates adaptive model aggregation and lightweight anomaly detection mechanisms to improve detection accuracy while preserving data privacy. The main contributions of this study include the development of an optimized FL-based detection model, improved resilience against diverse DDoS attack patterns, and reduced communication costs suitable for resource-constrained IoT devices. The remainder of the paper is structured as follows: Section 2 reviews related literature, Section 3 presents the proposed methodology, Section 4 discusses experimental results, and Section 5 concludes the study with recommendations for future research.
Cybersecurity and data protection: challenges and proposed solutions
In today’s digital era, cyber threats are becoming increasingly sophisticated, posing serious risks to individuals, organizations, and governments. The rapid growth of online services, cloud computing, and internet-connected devices has made sensitive data more vulnerable to breaches, theft, and unauthorized access. This research explores the main challenges in cybersecurity and data protection, including weak authentication systems, malware attacks, phishing, insider threats, and inadequate data privacy regulations. It also examines the consequences of these vulnerabilities, such as financial losses, reputational damage, and legal liabilities. To address these challenges, the study proposes a set of solutions, including advanced encryption techniques, multifactor authentication, continuous monitoring, employee awareness programs, and the adoption of robust cybersecurity frameworks. This study adopts a qualitative and exploratory research methodology, combining an extensive review of existing literature with selected case studies to examine contemporary cybersecurity and data protection issues. The analysis focuses on global trends with reference to developing economies, drawing on recent reports, academic publications, and documented security incidents from the past decade. The study contributes by synthesizing fragmented insights into a structured framework that highlights emerging threats, evaluates current mitigation strategies, and proposes practical, context-aware solutions aimed at strengthening data protection practices and improving cybersecurity resilience.
Artificial intelligence–driven remote depression detection as a socioeconomic intervention: implications for food and job security in North-West Nigeria
Depression remains a major public health challenge with substantial socioeconomic implications, particularly in low-resource settings where early detection and treatment gaps persist. This study developed and evaluated an artificial intelligence (AI)–driven remote depression detection system integrated with a structured mental health intervention framework and socioeconomic impact assessment in North-West Nigeria. A total of 412 adults aged 18–60 from six rural and peri-urban communities participated in the study. Using a quasi-experimental design with treatment and control groups, data were collected over a 16-week period comprising baseline assessment, AI-driven monitoring, targeted intervention, and follow-up evaluation. Ethical approval was obtained from the Nigerian National Health Research Ethics Committee (NHREC), and all participants provided informed consent. A machine-learning model trained on multi-modal behavioural and survey data demonstrated strong predictive performance, achieving 88.4% accuracy (95% CI: 85.2–91.1), 0.86 precision, 0.91 recall, 0.88 F1-score, and an ROC-AUC of 0.92. Following AI-triggered intervention, mean depression scores decreased by 37% (p < 0.001), with 64% of high-risk participants transitioning to lower-risk categories. Socioeconomic outcomes improved significantly, including a 21% increase in productivity, 18% improvement in employment stability, 24% reduction in absenteeism, 14% rise in monthly income (p < 0.05), and 19% reduction in moderate-to-severe food insecurity. Difference-in-Differences analysis confirmed that the treatment group experienced a 32% greater reduction in depression and a 26% improvement in composite welfare relative to controls. These findings validate the impact pathway linking mental health improvement to enhanced productivity, income growth, and household welfare. The study demonstrates that AI-driven remote depression detection, when combined with timely intervention, can yield significant psychological and economic benefits. The results position AI-enabled mental health screening as a scalable, cost-effective policy tool for strengthening human capital and reducing poverty in underserved regions.
Immunomodulatory and microbial effect of Lactobacillus Casei against Escherichia coli infection in Wistar Albino rats
Microorganisms such as Lactobacillus casei has been extensively studied for its probiotic effects. This bacterium produces lactic acid, antimicrobial peptides, such as bacteriocins, which inhibit the colonization and growth of pathogenic bacteria such as Escherichia coli. Twenty (20) Wistar albino rats which were used and shared into five groups: The Control, infected (Escherichia coli-infected), Probiotic (Escherichia coli-treated), Prophylactic (exposed to Lactobacillus casei, next infected with Escherichia coli), Antibiotic (ciprofloxacin-treated). At 0, 7, 14 and 21 days, body temperature, weight and faecal samples were collected. While at day 21 (the end of the experiment), blood samples and organs (liver and colon) were collected for hematological analysis and microbial analysis of organ samples respectively. All the groups had temperature range from 36 - 36.3 oC at day 0. At day 14 and 21, all the groups had an increase in temperature, though, there was no statistical difference among the groups with respect to temperature (p>0.05). At day zero (0), the body weight of the experimental animals was within the ranged 150.7 - 191.4 g. The experimental animals in group B had a decline in body weight from 191.4 g at day 0 to 170 g at day 21. While group C wistar rats had an average body weight of 150.7 g at day 0, 169 g at day 7, 156 g at day 14 and a sharp body weight increase of 198 g at day 21 (the end of the experiment). The White blood cells (WBC) range from 8.8±0.7-14.7±4.6 103/µL and there was statistical difference in WBC counts within the groups (p = 0.02). The red blood cells (RBC) ranged from 5.2±2.4106/µL for group D to 7.0±0.3 106/µL for group A. For hemoglobulin (HGB), group C and D had the lowest of 14.0±1.6 g/dL and group A recorded the highest of 15.8±0.7 g/dL (p = 0.08). This results highlighted that probiotic (Lactobacillus casei) did not only exhibited antagonistic activities against E. coli but also positively have effect on hematological indices which possibly reflect and also enhanced immune response.
Ratio-product cum regression variance estimators in the presence of random Nonresponse
The variability of a population under study can be estimated using variance estimators so that better policies can be devised. This study aimed to propose a new approach for estimating the variance of a finite population using auxiliary information, while accounting for two common scenarios of random non-response in simple random sampling is suggested. The proposed estimator was derived using linear combination approach. The power series and exponential series were used in deriving the properties of the estimator (bias and mean square error). The properties were compared theoretically with existing estimators, up to the first level of approximation. The theoretical conditions for efficiency under the two scenarios of random non-response were established. The criteria of mean square error and percentage relative efficiency were used in assessing the performance of the estimator. The empirical findings using real datasets revealed that the proposed estimators performed better than the existing variance estimators with minimum mean square error and higher percentage relative efficiency values. Thus, the proposed estimator can be utilized to estimate population variability in real-world scenarios where random non-response occurs.
Optimized deep learning and KNN Models with PCA feature selection for forecasting Cowpea yeild in Nigeria
Reliable prediction of crop yield plays a critical role in improving agricultural decision-making and promoting sustainable farming practices. Conventional approaches are often limited in their ability to model the nonlinear relationships that exist among plant growth dynamics. In this study, three machine learning techniques namely; Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks and K-Nearest Neighbors (KNN) were designed and assessed for their effectiveness in forecasting cowpea (Vigna unguiculata) yield based on data acquired from IoT-enabled sensors, the dataset was obtained from a controlled cultivation experiment, with yield quantified by the number of pods produced per plant. Principal Component Analysis reduced dimensionality while preserving over 95% of the variance. Hyperparameter optimization was performed using GridSearchCV for KNN and Keras Tuner RandomSearch for CNN and LSTM. The optimized achieved the highest predictive accuracy with an R² of 0.8754, MAE of 0.0492, and RMSE of 0.0639, outperforming the optimized LSTM (96 units, additional dense layer of 64 units, RMSprop optimizer; R² = 0.8334) and optimized KNN (n_neighbors = 3; R² = 0.7566). However, both CNN and LSTM showed systematic under-prediction bias at higher yield values, with LSTM exhibiting the largest negative residuals. The findings demonstrate that deep learning approaches, particularly CNN, can effectively model crop yield with relatively small datasets when combined with appropriate feature selection and hyperparameter optimization. The integration of statistical feature selection with agronomic domain knowledge enhances model robustness and biological interpretability. These results support improved decision-making in precision agriculture, enabling more accurate yield forecasting for sustainable cowpea production.
Development of odd Rayleigh-Frechet distribution: properties and applications
Accurate modelling of rainfall patterns is crucial for hydrological forecasting, water resource management, and climate analysis. In this study, we propose the Odd Rayleigh Fréchet Distribution (ORFD) as a novel probability model for analysing rainfall at the Piracicaba River and the average annual rainfall across Nigeria. The performance of ORFD is assessed against two competing models: Fréchet Distribution (FD) and Weibull Distribution (WD) using log-likelihood, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) across five months (May-September). The results show that ORFD consistently provides the best fit, exhibiting the lowest AIC and BIC values, whereas FD and WD perform less effectively and indicating numerical instability. A simulation study is conducted to evaluate the Bias and Root Mean Square Error (RMSE) of the parameter estimates for ORFD. The results demonstrate that bias and RMSE decrease as the sample size increases, confirming the consistency and efficiency of the proposed model. These findings highlight the potential of ORFD as a robust statistical tool for modelling heavy-tailed rainfall data, thereby improving hydrological predictions and decision-making processes.
An integrated aeromagnetic and resistivity investigation of groundwater in Tarauni local government area, Kano state, Nigeria
The study assess the intense issue of groundwater potential in Tarauni, Kano state Nigeria with a focus on indicators of groundwater that show it presence. The research deploys two geophysical techniques; Aeromagnetic method and Vertical Electrical Sounding [VES] Techniques; Sheet 81 of Aeromagnetic data was acquired from Nigerian Geological Survey Agency [NGSA]. The Vertical Electrical Sounding was taken at 30 different location of the study area. The aeromagnetic data is processed and interpreted using Oasis Montaj and ARCGIS softwares, and the results of the Aeromagnetic data analysis were used to outline six maps: Total Magnetic Intensity, Residual, First Vertical Derivative, Lineament, Rose diagram, Source Parameter Imaging map, in which Six major lineament orientations were revealed. The major lineaments trends directions are: N-S, NE-SW, ENE-WSW, WNW-ESE directions. Minor trends directions were also revealed which are: NW-SE and NNE-SSW. The lineament which shows fault or fold were used to indicate groundwater presence in the study area. The resistivity data from Vertical Electrical Sounding were interpreted using IP2WIN software which indicate three-four layers with distinct resistivity value, depth and thickness. The first layer is sandy clay soil, second layer is mostly clayey and third layer is a weathered basement, and in some VES points fourth layer is the weathered basement which has good groundwater potentials. VES4, VES16, VES17, VES18, VES19, VES20, VES21, VES25, VES26, VES27, VES29, VES30 have the best aquifer and hence recommended as suitable point for groundwater exploration. The study shows a good relations between aeromagnetic indicators of groundwater and vertical electrical sounding indicators which underscores the significance of the geological structures as groundwater pathways.
Modeling long-term memory in climate systems through fractional calculus: advancing resilient urban futures at the nexus of innovation and sustainability in Plateau North Central, Plateau state, Nigeria
This study introduces a fractional energy balance model (FEBM) utilizing the Caputo fractional derivative to better capture the long-term, cumulative impact of historical climate drivers like greenhouse gas concentrations and radiative forcing. By incorporating memory kernels, the FEBM addresses the limitations of traditional integer-order models that lack memory and fail to accurately model long-term climate persistence. Analytical solutions are derived using Laplace transforms and Mittag-Leffler functions, demonstrating superior ability to simulate delayed and aggregated temperature anomalies. The FEBM provides enhanced predictive power for global surface temperature anomalies, directly supporting robust climate risk management and the development of sustainable, evidence-based urban and health strategies. By leveraging fractional calculus to account for memory and hereditary properties in climate processes, this research advances climate science and exemplifies the synergy between mathematical innovation and sustainability initiatives.
Numerical study of magnetohydrodynamic Blasius and Sakiadis boundary-layer flow with thermal radiation, Buoyancy effects, and convective heating over a vertical plate
This study presents a numerical investigation of two-dimensional magnetohydrodynamic (MHD) boundary-layer flow over a vertical plate, considering both Blasius (stationary) and Sakiadis (moving) configurations. The study examines the influence of thermal radiation, buoyancy forces, and convective heating on the velocity and temperature distributions within the boundary layer. The flow of a cold fluid at a uniform velocity pass over the vertical plate, where the left surface is exposed to convective heating from a hot fluid. The governing boundary value problems are transformed into initial value problems using the shooting method and integrated numerically with a fifth-order Runge–Kutta technique for enhanced accuracy. The influence of key controlling parameters, including magnetic field strength, Prandtl number, buoyancy parameter, and thermal radiation, is analyzed through graphical and tabular representations. The results provide insight to evaluate the velocity and temperature profiles within the boundary layer for both Blasius (stationary plate) and Sakiadis (moving plate) flow configurations and can be applied to engineering processes involving heat transfer in magnetohydrodynamic (MHD) boundary-layer flows over vertical plates.
Data sovereignty and the leaky pipe: charting a course for data governance and information security in Nigeria and Africa
The enactment of the Nigeria Data Protection Act (NDPA) 2023 marks a significant milestone in Africa's pursuit of digital sovereignty. However, the presence of strong legislation does not automatically guarantee effective data protection in practice. This study aims to examine the pressure between Nigeria's evolving data protection framework and the real-world challenges of information security, aiming to determine whether legislative progress alone can achieve meaningful data sovereignty and whether institutional, technical, and infrastructural capacities are equally important. To achieve this, the study adopts a qualitative doctrinal legal analysis of the Malabo Convention and the Nigeria Data Protection Act (NDPA) 2023, combined with a thematic review of peer-reviewed articles, institutional reports, and industry publications from 2020-2026. Data were synthesized narratively to identify patterns, challenges, and actionable recommendations. The study finds that the NDPA 2023 marks a significant milestone, aligning Nigeria with global data protection standards. However, implementation remains constrained by weak enforcement, institutional capacity gaps, a shortage of skilled professionals, inadequate infrastructure, and dependency on foreign-owned cloud services. Key friction points include the tension between legal authority and technical capacity, disproportionate compliance burdens on SMEs, and erosion of public trust due to inconsistent enforcement. It concludes that transitioning from a passive data colony to a digital powerhouse requires sustained investment in people, institutions, and infrastructure to ensure effective implementation and enforcement of data protection laws. The study contributes theoretically by integrating legal, security, and implementation science perspectives on the data governance gap, and practically by offering a policy framework with lessons for Africa.
Antibiosis of Salicylic acid pre-hardening treatments of Cowpea on development of pod sucking bug, Clavigralla tomentosicollis Stal. (Hemiptera: Coreidae)
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Assessment of plants Alkaloids isolated from calotropis Procera for managing pod sucking bug, Clavigralla tomentosicollis Stal (Hemiptera: Coreidae) infestation on Cowpea