MULTIDISCIPLINARY JOURNAL OF ENGINEERING, TECHNOLOGY AND SCIENCES
The Multidisciplinary Journal of Engineering, Technology and Sciences (ISSN: 2636-4999; E-ISSN: 2616-1060) is a peer-reviewed open-access journal committed to advancing the frontiers of knowledge across a broad spectrum of disciplines. With a focus on interdisciplinary collaboration and innovation, MJETS serves as a platform for researchers, engineers, technologists, and scientists to disseminate original research, review articles, case studies, and technical reports that address complex problems and emerging challenges in the fields of engineering, technology, and the applied sciences. It is a blind peer review journal domiciled in Nnamdi Azikiwe University, Awka, Nigeria. The journal is indexed on Google Scholar.
ANTIBACTERIAL ACTIVITY OF ANNONA MURICATA LEAF BIOFRACTIONS AGAINST PSEUDOMONAS AERUGINOSA ISOLATED FROM WOUND INFECTIONS
Wound infections caused by drug-resistant Gram-negative bacteria constitute a major global health concern, with Pseudomonas aeruginosa recognized as a leading etiological agent due to its intrinsic and acquired resistance mechanisms. The burden of antimicrobial resistance (AMR) associated with this pathogen exceeds 300,000 deaths annually, highlighting the urgent need for alternative therapeutic approaches. This study investigated the antibacterial activity of solvent-partitioned biofractions of Annona muricata (soursop) leaves against clinical isolates of P. aeruginosa obtained from wound infections. Fresh leaves were pulverized, extracted with methanol, and subsequently partitioned sequentially using n-hexane, ethyl acetate, and methanol. Ten isolates were confirmed using standard biochemical tests, while antibiotic susceptibility was determined using the disc diffusion method following Clinical and Laboratory Standards Institute (CLSI) guidelines. The antibacterial activity of the biofractions was evaluated at concentrations ranging from 100–400 μg/mL, and minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were determined using the broth microdilution method. Ethyl acetate yielded the highest extract (49.64%), followed by methanol (46.52%) and n-hexane (3.84%). All isolates exhibited multidrug resistance, although ciprofloxacin and gentamicin showed comparatively higher effectiveness. Both ethyl acetate and methanol fractions demonstrated concentration dependent antibacterial activity, with the ethyl acetate fraction showing slightly superior efficacy. MIC values ranged from 300–400 μg/mL, while MBC values were predominantly 400 μg/mL, indicating bactericidal effects. These findings support the potential of A. muricata leaf biofractions as promising candidates for the development of plant-based antimicrobial agents against resistant wound pathogens.
Annona muricata; antimicrobial resistance; biofraction; disc diffusion; minimum inhibitory concentration; phytotherapy; Pseudomonas aeruginosa; wound infection
Ibukunoluwa, M.R.; Omobolade, O.; Omobolade, E.O.; Abadariki, O.O.; Amubieya, E.O.
EFFECT OF FINANCIAL INCLUSION ON FOOD SECURITY AMONG RURAL WOMEN FARMERS IN SOUTH EAST NIGERIA: A STYLED NESTED MODELLING APPROACH
The study assesses the impact of financial inclusion on food security among rural women farmers in Southeast Nigeria. A total of 266 rural women farmers participated in the research, which relied on primary data collected through a semi-structured questionnaire. The study spanned from August 2021 to August 2023. Various statistical methods, including Ordinary Least Squares (OLS) multiple regression, binary logistic regression, and ordinal probit regression, were employed to achieve the study’s objectives. The findings reveal that 41.7% of the participants were food secure, while 58.3% were food insecure. There were significant differences in calorie consumption between the two groups, with food-secure households consuming significantly more calories than food-insecure households. The mean calorie consumption was 3,776.6 Kcal for food-secure households and 1,378 kcal for food-insecure households. In the Ordinary Least Squares multiple regression analysis, factors such as ownership of commercial bank accounts, savings within the past 12 months, farm income, and the proportion of credit utilized for its intended purpose were found to have a significant positive effect on the food security index of rural women farmers. The binary logistic regression model indicated significant associations between savings, access to professional financial advice, and credit utilization with food security levels. Similarly, the ordinal probit regression model showed that the explanatory variables jointly explained variations in food security levels, with mobile banking usage and ownership of commercial bank accounts emerging as significant factors. Overall, the study highlights the need for targeted government intervention—such as the provision of soft loans, grants, and incentives—alongside strengthened financial inclusion policies to alleviate food insecurity among rural women farmers.
Financial Inclusion, Food Security Levels, Rural, Women, Farmers
Umeh, Onyebuchi Jonathan; Okpukpara, B.C.; Ude, Kingsley David
ANTIPLASMODIAL EFFICACY, PHYTOCHEMICAL COMPOSITION, AND SAFETY PROFILE OF TEN MEDICINAL PLANT EXTRACTS AGAINST PLASMODIUM BERGHEI IN SWISS ALBINO MICE: IMPLICATIONS FOR HERBAL MALARIA THERAPY
Malaria remains a catastrophic public health burden in sub-Saharan Africa, with Nigeria accounting for approximately 30.9% of global malaria-related deaths (WHO, 2024). The emergence of partial artemisinin resistance across African countries has intensified the search for novel antiplasmodial agents from medicinal plant sources. This study evaluated the phytochemical composition, proximate and mineral content, antiplasmodial activity, acute toxicity, and haematological, biochemical, and histopathological safety profiles of aqueous and ethanolic extracts of ten medicinal plants used in traditional malaria management in Ondo State, Nigeria, against Plasmodium berghei (NK 65) in Swiss albino mice. The plants studied were Anthocleista djalonensis, Azadirachta indica, Cajanus cajan, Crescentia cujete, Lawsonia inermis, Lophira alata, Myrianthus pruessii, Nauclea latifolia, Olax subscorpioidea, and Terminalia glaucescens. Phytochemical screening confirmed alkaloids, saponins, tannins, and cardiac glycosides across all ten plants, with highest alkaloid and saponin content in T. glaucescens stem bark. All aqueous extracts significantly suppressed parasitaemia from Day 1–4, with gradual recrudescence observed thereafter. The polyherbal combination demonstrated activity comparable to chloroquine. Histopathological examination revealed no serious lesions at standard doses, though dose-dependent tissue changes were observed at 300 mg/kg and above. These plants, individually and in combination, exhibit significant antiplasmodial activity underpinned by diverse bioactive constituents. While their safety profile at standard doses supports continued ethnomedicinal use, standardisation, dose optimisation, and mechanistic studies are warranted before clinical translation. These formulations represent a promising strategy amid escalating antimalarial drug resistance.
Plasmodium berghei; antiplasmodial; medicinal plants; phytochemistry; Swiss albino mice; herbal malaria therapy; Ondo State; Nigeria
Ibukunoluwa, M.R., Awe, O.O., Adediran M.B & Daniel L.N
Copyright (c) 2025 MULTIDISCIPLINARY JOURNAL OF ENGINEERING, TECHNOLOGY AND SCIENCES
EFFECT OF FINANCIAL INCLUSION ON LEVELS OF COMMERCIALIZATION AMONG RURAL WOMEN FARMERS IN SOUTH EAST, NIGERIA
This study evaluated the effect of financial inclusion on the level of agricultural commercialization among rural women farmers in the South-East region of Nigeria. The research was conducted in the South-East region of Nigeria, specifically targeting a sample size of 266 rural women farmers. The study utilized primary data collected through a semi-structured questionnaire. The objectives were achieved using various statistical techniques, including the Commercialization Index and the Ordinal Probit Regression model. Financial inclusion in this study encompassed the availability, adequacy, awareness, affordability, and accessibility of financial services. The findings revealed that the indices for availability, adequacy, awareness, affordability, and accessibility were 0.25, 0.44, 0.18, 0.40, and 0.37, respectively. The mean financial inclusion index was 0.33. A significant proportion (73.7%) of rural women farmers were commercially oriented, while 26.3% were not. The Ordinal Probit Regression model produced a log-likelihood value of −237.0175, which was statistically significant at the 1% level. The pseudo-R-squared value of 0.34025 indicates that the model provides a satisfactory fit. Out of the ten explanatory variables included in the model, two were found to be statistically significant at different probability levels. These variables are: expert advice on financial management (coefficient = 0.277963) and access to insurance policy (coefficient = 0.491930). The study recommends that the National Root Crops Research Institute, the International Institute of Tropical Agriculture, and local development groups should provide support in the form of production inputs, financial advisory services, and awareness creation on insurance policies to enhance the commercialization of rural women farmers. Furthermore, rural women farmers should be supported through training and extension programmes. They should also receive assistance in contract negotiations to improve market access and increase their financial returns.
Effect, Financial inclusion, levels, Commercialization, rural, Women Farmers
STRENGTHENING PUBLIC HEALTH EMERGENCY PREPAREDNESS THROUGH HEALTH INFORMATION, EDUCATION AND COMMUNICATION PRINCIPLES IN NIGERIA
The health of the population in Nigeria remains at risk due to recurrent public health emergencies, which reveal persistent gaps in preparedness systems, particularly in the areas of risk communication and community engagement. This study examined how the principles of Health Information, Education, and Communication (IEC) can enhance public health emergency preparedness in Nigeria. The paper draws on established theories of behaviour and communication, as well as existing empirical evidence and the current structure of preparedness in the country, to synthesize how IEC can contribute to increasing risk awareness, reducing misinformation, building public trust, and promoting protective behaviours before, during, and after emergencies. The analysis identified the existence of institutional frameworks alongside significant policy, capacity, and coordination gaps, as well as limitations in current action plans for integrating IEC into emergency preparedness and response frameworks. Overall, the study demonstrates that systematic, theory-driven, and community-centred IEC is essential for strengthening resilient preparedness systems and improving population health outcomes in Nigeria.
Public Health Emergency, Health Information, Education and Communication Principles, Nigeria
Enyinnaya Blessing O. Ph.D; Sampson Judith Chinwenmeri; Eric Ajoku, Kenlin Sarima
KNOWLEDGE, ATTITUDES AND PRACTICE OF MODERN FAMILY PLANNING AMONG MARRIED MEN: A CASE STUDY OF UMUAHIA NORTH LOCAL GOVERNMENT AREA, ABIA STATE, NIGERIA
This study evaluated the knowledge, attitudes, and practices of contemporary family planning among married men in Umuahia North Local Government Area, Abia State, Nigeria. A community-based cross-sectional descriptive research design was adopted, involving a sample of 308 married men aged 25 to 59 years, selected using a multi-stage sampling technique. Data were collected using a structured, interviewer-administered questionnaire and analyzed using both descriptive and inferential statistics at a 5% level of significance. The level of awareness of contemporary family planning methods was high (96.8%), with condoms (98.1%) and injectables (74.0%) being the most commonly known methods. However, awareness of vasectomy was relatively low (34.4%). Most respondents demonstrated positive attitudes, with 85.7% acknowledging the benefits of family planning for child spacing and 75.3% supporting the use of contraceptives by their spouses. Despite this, the level of practice was lower than that of knowledge and attitudes. About 59.1% reported that their partners were currently using a modern contraceptive method, while only 33.8% had accompanied their spouses to a family planning clinic. The findings reveal a persistent knowledge–practice gap, highlighting the need for targeted male-focused reproductive health interventions in Southeastern Nigeria. Active male involvement in family planning can be enhanced through improved spousal communication, the provision of male-friendly services, and the elimination of sociocultural myths surrounding contraceptive use.
Knowledge, Attitudes, Practice, Modern Family Planning, Married Men, Umuahia North Local Government Area
AWARENESS, ACCEPTANCE AND AND UPTAKE OF HEPATITIS B VACCINE AMONG WOMEN OF REPRODUCTIVE AGE IN ILARA COMMUNITY, ONDO STATE, NIGERIA
Hepatitis B virus (HBV) infection remains a major public health burden in sub-Saharan Africa, with Nigeria reporting an estimated 8–12% chronic infection prevalence. Despite an efficacious vaccine, uptake among women of reproductive age in community settings remains suboptimal due to knowledge gaps, financial barriers, and sociocultural factors. This study assessed HBV vaccine awareness; acceptance and uptake among women aged 15–49 years in Ilara Community, Ondo State, Nigeria, and identified determinants of uptake. A descriptive cross-sectional design was employed. Using multi-stage sampling, 384 women were recruited across Ilara Community. A structured Likert-scale questionnaire was administered and data analysed using SPSS version 25 with descriptive statistics. Respondents were predominantly aged 18–25 years (34.9%), Yoruba (74.2%), Christian (59.4%), and single (55.7%). Knowledge scores were moderate (mean: 3.47–3.57). Perceptions were largely positive, with strong belief in vaccine effectiveness (M = 4.14) and importance for women of childbearing age (M = 4.18). Despite this, uptake was critically low, with over 80% disagreeing with statements reflecting vaccination behaviour (mean scores: 3.89–4.23). Principal barriers included healthcare cost (M = 3.45), limited accessibility (M = 3.56), fear of side effects (M = 3.54), cultural and religious beliefs (M = 3.41), and inadequate provider recommendations (M = 3.40). Although women in Ilara Community demonstrate moderate knowledge and positive perceptions of the HBV vaccine, actual uptake remains critically low. The gap between attitudes and behaviour underscores the need for multi-level interventions targeting structural, economic, and sociocultural barriers. Enhanced health education, subsidised vaccine access, and provider-initiated counselling are urgently recommended.
hepatitis B virus, vaccine uptake, awareness, acceptance, women of reproductive age
Awe, Omolara Oluwafunmilola & Ogunmolawa, Janet Taiwo
KNOWLEDGE OF THE IMPACT OF MATERNAL ANXIETY AND STRESS ON PREGNANCY OUTCOMES AMONG PREGNANT WOMEN ATTENDING A PRIMARY HEALTH FACILITY IN AKURE, NIGERIA
Pregnancy constitutes one of the most physiologically and psychologically demanding periods in a woman’s life. Maternal anxiety and stress during this period have been increasingly associated with adverse pregnancy outcomes, yet knowledge of these associations among pregnant women in low- and middle-income countries remains poorly documented. This study evaluated the knowledge of the impact of maternal anxiety and stress on pregnancy outcomes among pregnant women attending the comprehensive health center, Arakale, Akure, Ondo State. A descriptive cross-sectional design was employed. A systematic random sample of 171 pregnant women was recruited over four weeks. Data were collected using a structured, researcher-administered questionnaire comprising five sections. Descriptive statistics (frequencies, percentages) and inferential statistics (Pearson’s chi-square) were used for analysis via SPSS version 27.0. The majority, (83.0%) of respondents was aware of maternal anxiety and stress; 54.4% demonstrated good overall knowledge. A high prevalence of maternal anxiety and stress was reported by 60.2% of respondents. Over 62.6% had good knowledge of the effects of anxiety and stress on pregnancy outcomes. Key associated factors included fear of childbirth (54.4%), financial difficulties (48.5%), relationship conflict (48.5%), and significant life changes (78.4%). Chi-square analysis revealed a statistically significant relationship between knowledge level and prevalence of maternal anxiety and stress (χ2 = 2.00, df = 2, p = 0.004). Pregnant women in this setting exhibit a significant yet imperfect understanding of maternal anxiety and stress and their consequences. Structured integration of mental health education into routine antenatal care is recommended to improve knowledge and mitigate adverse outcomes.
Maternal anxiety, maternal stress, pregnancy outcomes, antenatal care, knowledge, Nigeria
Awe, Omolara Oluwafunmilola & Oricha, Oluwabusayo Oluwayimika
PERCEPTIONS OF DATA PROTECTION COMPLIANCE AMONG SOFTWARE ENGINEERS IN NIGERIA FINANCIAL TECH
Data protection compliance has emerged as a central issue in Nigeria’s growing financial technology (FinTech) ecosystem, where vast amounts of sensitive personal data are processed daily. However, concerns remain regarding how well software engineers who create and manage FinTech systems understand and implement regulatory frameworks such as the Nigerian Data Protection Regulation (NDPR) and the European Union’s General Data Protection Regulation (GDPR). The study therefore, examined the perceptions of data protection compliance among software engineers in Nigerian FinTech companies. Specifically, the objectives of the study were to examine the level of familiarity of software engineers in Nigerian FinTech companies with data protection laws; assess how software engineers perceive their role in protecting user data and the extent to which they implement data protection practices; and identify the challenges software engineers face in complying with data protection regulations. The study adopted a mixed-methods design comprising a quantitative survey of 113 engineers and qualitative Key Informant Interviews with compliance managers across leading Lagos-based FinTech firms. The analysis revealed a significant positive correlation between awareness of data protection laws and responsible compliance behaviour (r = .582, p < .01). However, practical compliance remains constrained by inadequate training, limited managerial support, and the absence of localized compliance tools. The study concludes that awareness alone is insufficient to ensure compliance unless it is supported by strong institutional frameworks and the integration of privacy-by-design principles into software development processes. It recommends, among other measures, the provision of hands-on data protection training for engineers, the integration of privacy considerations into all stages of software development, and investment in modern security tools.
Data Protection, Fintech, Software Engineers, Nigeria Data Protection Regulation (NDPR), Compliance
A GEOSPATIAL ASSESSMENT OF THE VARIATION AND DISTRIBUTION PATTERNS OF BITUMEN DEPOSITS IN SOUTHERN ONDO STATE, NIGERIA
This study examines the spatial variation and distribution patterns of bitumen deposits in Southern Ondo State, Nigeria, using advanced geospatial techniques. The research aims to delineate the areal extent, spatial structure, and locational variability of bituminous formations across Odigbo, Irele, and Okitipupa Local Government Areas (LGAs). Spatial datasets, comprising geological maps, satellite imagery, and bitumen block coordinates, were analyzed using Geographic Information System (GIS) tools to quantify coverage, detect overlaps, and model distribution dynamics. Results reveal that bitumen deposits are unevenly distributed, with Irele LGA possessing the highest concentration (97.6% land coverage), followed by Okitipupa (73.8%) and Odigbo (33.4%). Blocks IV, V, and VI emerge as the most spatially extensive and economically promising zones. The observed distribution aligns with the sedimentary framework of the Dahomey Basin, indicating structural continuity and favorable depositional conditions for hydrocarbon accumulation. While the spatial abundance signifies substantial economic potential, it also presents ecological risks, including deforestation, soil degradation, and water contamination. The study emphasizes the necessity of sustainable exploration strategies that incorporate geospatial monitoring and environmental management frameworks. By integrating spatial analytics with resource evaluation, this research provides a data-driven foundation for policy formulation, concession planning, and the sustainable exploitation of Nigeria’s bitumen belt. The findings reaffirm the strategic significance of Southern Ondo State as a vital frontier for bitumen development and contribute to advancing geospatial applications in mineral resource management.
Bitumen deposits, Geospatial assessment, spatial variation, Dahomey Basin, GIS
ADEYEMI B.A. PhD, OLALEYE O.M. PhD, OGUNBOYE A.A., AGUN O.F. PhD, BABATUNDE H.K.
AI-DRIVEN ROOT CAUSE ANALYSIS FRAMEWORK FOR DISTRIBUTED MICROSERVICES ARCHITECTURES
Multi-layered IT systems are characterized by persistent system downtime, which creates significant operational burdens due to high costs and prolonged troubleshooting processes. Traditional root cause analysis (RCA) techniques are largely ineffective because they are predominantly reactive and manual, making them unsuitable for the scale and complex interdependencies of modern microservices architectures. This study addresses the critical “observability–complexity gap,” a condition in which contemporary monitoring tools generate vast amounts of correlated data but fail to provide true causal resolution. To address this challenge, the study introduces RCASage, a novel AI-augmented inference engine designed for autonomous fault identification. The core innovation of RCASage lies in its hybrid architecture, which comprises a three-stage pipeline: (1) multi-modal telemetry data ingestion and dynamic dependency graph construction; (2) unsupervised anomaly detection using LSTM autoencoders combined with Natural Language Processing (NLP) classifiers; and (3) an Autonomous Inference Engine (AIE). This engine uniquely integrates Graph Neural Networks (GNNs) with the Neural Granger Causal Discovery algorithm to distinguish true root causes from downstream symptomatic effects. By shifting RCA from symptom correlation to causal inference, RCASage surpasses existing approaches such as beta-binomial inference and event-graph-based systems (e.g., GROOT). Furthermore, it bridges the gap between development and operations by incorporating Just-in-Time (JIT) defect prediction into the CI/CD pipeline. Empirical evaluation demonstrates that the proposed AI-augmented framework reduces Mean Time to Resolution (MTTR) by over 90% compared to traditional manual approaches. In addition, RCASage advances the RCA paradigm by embedding Explainable AI (XAI) principles, providing transparent causal explanations alongside visual dependency graphs. This positions RCASage as an intelligent, evidence-based, and autonomous solution for root cause analysis in contemporary digital infrastructures.
Root Cause Analysis (RCA), RCASage, AI-Driven, AI-Augmented, Causal Inference, Distributed Systems, Graph Neural Networks, Graph-based Deep Learning, Anomaly Detection, Software Defect Prediction, Continuous Integration/Continuous Deployment (CI/CD), Microservices Architecture, Explainable AI (XAI), CI/CD Proactive Monitoring
Awodele S. O.; Faruna, J. O.; Mustapha M. M.; Ojuawo O. O.; Olorunyomi O. B.; Chukwulobe I.;Fayemi T. A.
ETHICAL PERSPECTIVES IN MITIGATING HUMAN FACTORS IN CYBERSECURITY
The issue of cybersecurity is of high importance in the digital age, and human factors remain the most vulnerable element despite significant technological progress. Even the strongest security systems can be compromised by human error, negligence, insider threats, and ethical breaches. This proposed study examines the ethical challenges associated with mitigating human factors in cybersecurity, with particular emphasis on the impact of ethical awareness, training, and policy compliance on safe behavior among users and professionals. The primary aim of the study is to examine how ethical practices contribute to addressing human-related cybersecurity threats and to propose strategies for fostering ethical responsibility within organizations. The research relies on secondary quantitative data drawn from existing empirical studies and institutional reports on the relationship between ethical interventions and the reduction of human-related cybersecurity breaches between 2020 and 2024. The findings reveal a strong negative relationship between ethics training and the frequency of cybersecurity breaches (R² = 0.67), highlighting the significance of institutionalized ethics governance. Based on these findings, the study recommends mandatory periodic ethics training, transparent accountability frameworks, and the integration of ethics into cybersecurity education and professional development programs.
human factors, cybersecurity, ethics, privacy, insider threat, Training
Awodele S. O; Ojuawo O. O.; Fayemi T. A.; Faruna J. O.; Chukwulobe I.; Olorunyomi O. B.; Mustapha M. M.
Design and Implementation of an Intelligent Business to Business Stock Control System Using Machine Learning Technique
Stock control is a requirement for automatic monitoring of merchandise within a stock database and predicts when it is due for restocking in real-time. To achieve this the methodology used are data collection, data analysis, feature selection, feature transformation, machine learning, and the Internet of Things (IoT) to develop a stock control system. The data used is a 3-year Out of Stock(OOS) records in Shoprite Enugu State shopping mall, was collected and subjected to a series of processing steps. The processed data features were selected with Chi-square and then transformed into a compact feature vector using Principal Component Analysis (PCA) to train a Feed-Forward Neural Network (FFNN) algorithm and generate a model for OOS prediction. Upon achieving this, an IoT algorithm that utilized Simple Mail Transfer Protocol (SMTP) was used to notify the stock admin of the need for restocking of the identified OOS product. The system was implemented using MATLAB and JavaScript programming language. The results of the evaluation process showed that the proposed model recorded tolerable error during the training process with a Mean Square Error (MSE) of 0.17168 and a Regression (R) of 0.96907, which suggested a very good prediction model. To validate the model, a k-fold cross-validation approach was applied, and the results recorded an MSE average of 0.09686, while the R reported 0.97168. Comparative analysis with other state-of-the art algorithms was performed, considering the MSE results of the new and existing OOS prediction models, and the results showed that the new model was among the best three performing models compared. However, the new model, due to its IoT features, was the most reliable as it was capable of notifying the stock admin in real time of the stock status and the need for restocking of products. Experimental validation of the model as a software considering several products which are running out of stock showed the ability of the system to monitor in real time and notify the admin through email on the need to restock the products
Stock, shopping mall, FFNN, OOS, SMTP, IoT, Chi-square, PCA
Ifeyinwa Chidum Dimson; Onyia Tochukwu Cyprian; Ezekwem Chigozie ThankGod
A COMPARATIVE ANALYSIS OF EXT4 AND NTFS: PERFORMANCE, SECURITY, AND RELIABILITY FEATURES
This paper presents a comprehensive comparative analysis of the EXT4 and NTFS file systems, focusing on their performance, security, and reliability features. EXT4 is a widely used file system for Linux-based systems, while NTFS is the default file system for Windows environments. Both file systems are designed to handle large storage capacities and provide robust solutions for different operating environments. The study contrasts the two in terms of read/write performance, journaling, data integrity, encryption, access control mechanisms, and their ability to recover from system crashes or power failures. This paper draws upon prior research and experimental analysis to highlight the strengths and limitations of both file systems under various use cases.
EXT4, NTFS, File systems, Performance, Security
Ojuawo Olutayo Oyewole; Jiboku Folahan Joseph
INVESTIGATING THE EFFECTS OF SOIL EROSION ON RESIDENTIAL HOUSING IN OGERE REMO, OGUN STATE, NIGERIA
The aim of the study is to examine the effect of soil erosion on housing in Ogere Remo area of Ogun State. The main objectives are to assess the physical impact of the erosion on existing housing and identify the existing management strategies place in the study area. One hundred and forty-two (142) questionnaires were distributed to Odugbesan, Ayegbami, and Akuro with the used photograph to identify most affected buildings shows that Survey method was adopted in data collection for this research. Data collected in the course of this study was analysed using both descriptive and inferential methods. The descriptive part will consist of frequency, percentages and measures of central tendencies while Kruskal Wallis non-parametric method of inference was used to infer on the physical impacts of soil erosion on existing housing in the study area. Therefore, there should be construction of drainages with specifications to drain and redirect rainfall water to the appropriate channels and planning regulations should be adhere strictly especially in the areas of development control and penalties on noncompliance need to enforce.
Effects, Erosion, Housing, Residential, Soil
Oladimeji Olusola OJO; Abimbola Adedayo OGUNSOLA
INFLUENCE OF SENSORY ATTRIBUTES ON THE ACCEPTABILITY OF INDIGENOUS SOUPS PREPARED WITH SELECTED GREEN-LEAFY VEGETABLES IN FOOD SERVICE ESTABLISHMENT IN KOGI STATE, NIGERIA
This study evaluated the influence of sensory attributes on the acceptability of indigenous soups prepared with selected green-leafy vegetables in food service establishment in Kogi State, Nigeria. The soups were prepared with Vitex doniana, Ipomea batatas and Talinum triangulare leaves. The objectives of the study were to evaluate the sensory attributes of soups prepared with the selected green-leafy vegetables and to determine the influence of these sensory attributes on the overall acceptability the soup samples in food service establishment in Kogi State. The study adopted a sensory evaluation research design. Sensory evaluation was conducted using 35 customers as panelists, who were randomly selected from Niger Restaurant located in Federal Polytechnic Idah. The instrument for data collection was a sensory evaluation form based on a 9-Point hedonic scale. Data collected were analysed using one-way ANOVA and multiple linear regression. The sensory evaluation result showed that none of the soups was rated as most acceptable above the others, as all three soups - Ejiji, Emi-uchaomu and Gbogroguin – recorded high acceptability scores of 8.49±1.04, 8.31±1.02, and 8.57±0.81, respectively. The mean scores for the sensory attributes of taste, aroma, appearance and colour indicated that the soups were liked very much by the panelists. The results on the influences of sensory attributes revealed that the taste, texture, and aroma significantly influenced the overall acceptability of Ejiji soup (R2 = 0.811). The overall acceptability of Emi-uchaomu soup was influenced mainly by sensory aroma (R2 = 0.748). Appearance, colour, taste, and texture influenced the overall acceptability of Gbogroguin soup (R2 = 0.700). In conclusion, given the remarkable medicinal and nutritional benefits associated with the consumption of green leafy-vegetables, soups prepared from indigenous leafy vegetables could complement exotic vegetables in menus featured in food establishments in Kogi State. It is therefore recommended that these indigenous soups be promoted and featured in the menus of various hospitality outlets across Kogi State to increase culinary varieties for consumers and to the preservation of cultural heritage.
Sensory attributes, Acceptability, Indigenous soups, Green-leafy vegetables, Food service establishments
Akpan, F. U.; Ngoka, P. C.; Enemou, O. B.; Anyanwu, I. B.; Obiwanne, C. O
LEVERAGING CIVIL ENGINEERING AND DATA ANALYTICS FOR ECONOMIC GROWTH: A CASE STUDY ON SUPPLY CHAIN OPTIMIZATION IN SPORTS FACILITY RENOVATIONS
Sports facilities are crucial in regard to the economy as they enhance tourism, employment opportunities, and business revenue in local communities. This paper analyses how the combination of civil engineering and analytics can improve the supply chain processes in sports facility renovations to maximize economic benefits. A moderately sized sports stadium is modeled as a case study for its renovation in region using a predictive analytics multidisciplinary engineering approach to manage costs, mitigate delays, and regionally stimulate economies. Its data, tables, and references balance modern sports and economic activities with infrastructural development in order to illustrate an optimized, or potential, level of efficient infrastructure development posited to evaluation.
ESTIMATING URBAN FARM HOUSEHOLDS' WILLINGNESS TO PAY FOR SUSTAINABLE WASTEWATER MANAGEMENT AND RENEWABLE ENERGY SERVICES IN SOUTHEAST NIGERIA: A CONTINGENT VALUATION APPROACH
The study evaluated urban farm households' willingness to pay (WTP) for sustainable wastewater management and renewable energy services in Southeast Nigeria. It focused on urban farm households in five states within the region. A multi-stage random sampling technique was used to select 600 households, and data collection was conducted through face-to-face structured interviews using a pre-tested questionnaire. The Contingent Valuation Method (CVM) was employed to estimate households' WTP for government-proposed wastewater treatment, renewable energy production, and public health monitoring services. The hypothetical scenario presented a government-led initiative that would treat wastewater for irrigation, produce biogas for energy, generate bio-based fertilizers, and implement public health monitoring through wastewater-based epidemiology (WBE). Findings revealed that, in terms of willingness to pay, 36.83% of urban farm households were willing to pay for sustainable wastewater management and renewable energy services, while 80% expressed unwillingness. (Note: These figures seem inconsistent and may require clarification; see below.) Regarding the maximum amount households were willing to pay, the majority (23.98%) indicated a willingness to pay between ₦12,001 and ₦15,000, followed by 19.46% who were willing to pay between ₦3,001 and ₦6,000. The Tobit regression model identified significant socio-economic determinants of WTP. Variables such as gender, age, marital status, household size, years of schooling, and access to credit significantly influenced the amount households were willing to pay for these services. Additionally, results from the multivariate probit regression model showed that access to credit, market distance, awareness of sustainable practices, and off-farm income were key factors influencing households' choices among the proposed services. Specifically, access to credit significantly increased the likelihood of choosing wastewater treatment but decreased the probability of adopting bio-based fertilizers. Awareness of sustainable practices had contrasting effects—positively influencing fertilizer adoption while reducing the likelihood of adopting bioenergy and wastewater services. Furthermore, land tenure insecurity consistently reduced the probability of adoption across all service types. These findings underscore the multidimensional factors shaping household behavior and highlight the need for integrated policy approaches. Based on the results, it is recommended that the Federal Government of Nigeria collaborate with financial institutions to improve access to affordable credit facilities for urban farm households.
Umeh, Onyebuchi Jonathan; Ude Kingsley David; Ikechukwu Ebere Tina & Agianpuye Rose
SECURING AI-DRIVEN HEALTHCARE SYSTEMS IN CLIMATE-RESILIENT INFRASTRUCTURE: A FRAMEWORK FOR SUSTAINABLE PUBLIC HEALTH IN NIGERIA
This study explores the intersection of artificial intelligence (AI), climate-resilient infrastructure, and cybersecurity within Nigeria's public healthcare system. It proposes an integrated policy framework to ensure sustainable health outcomes amid technological and environmental challenges. As Nigeria adopts AI to enhance diagnostics, disease surveillance, and healthcare delivery, it also faces mounting cybersecurity threats. These threats are compounded by infrastructural vulnerabilities, particularly in climate-sensitive regions. Employing a qualitative research design and an interdisciplinary analysis of health systems, digital risks, and climate adaptation strategies, the study identifies critical policy and implementation gaps that expose AI-driven health technologies to cyberattacks and operational failures during climate-induced disruptions. It advocates for a unified cybersecurity policy that aligns digital health innovation with resilient infrastructure planning, ethical AI use, and robust regulatory oversight. The study concludes by asserting that securing Nigeria's AI-enabled healthcare system requires urgent, coordinated action across sectors to safeguard data integrity, ensure service continuity, and protect the most vulnerable populations in an increasingly digital and climate-volatile era.
PATTERNS AND DRIVERS OF LAND USE CONVERSION ALONG ADIGBE-PANSEKE ROAD, ABEOKUTA, OGUN STATE
The study examines the patterns and drivers of land use conversion along Adigbe-Panseke Road, Ogun State, Nigeria. The research employs a survey of 88 buildings out of 112 identified using Taro Yamane sample size calculator as being converted in the study area, to assess the nature of land use changes and identify the key factors driving conversion in the study area. Structured questionnaire was used to collect data out of which 89.8% of the research instrument was duly filled and returned. Findings reveal that the conversion of residential and agricultural land to commercial and industrial use is driven primarily by population growth (mean = 3.84, t-test = 2.928, p < 0.05), economic development (mean = 3.90, t-test = 3.391, p < 0.05), and inadequate urban planning (mean = 3.94, t-test = 2.351, p < 0.05). The other factors driving land use conversion within the study area was found to be cultural and social factors. Inadequate urban planning, zoning regulations, economic development and industrial growth were the key drivers. It was concluded that, patterns of land use were driven by rapid urbanization, growing of commercial activities, and changes in population density. While economic opportunities are expanding, it was found that, weak enforcement of zoning laws and planning regulations has allowed unplanned conversions to emerged causing physical and environmental challenges. Therefore, Government needs to involve in provision and management of the essential infrastructure to prevent congestion and deterioration of the study area.
Oladimeji Olusola Ojo & Toluwalase G. Oluwole
INDIGENOUS CROP-BASED BREAD AS A STRATEGY FOR REDUCING MALNUTRITION AND IMPROVING PUBLIC HEALTH
This study assessed the potential of soy okara, an underutilized by-product of soybean processing, as a functional and nutritional enhancer in wheat bread formulation for affordable and health-oriented consumption. Four bread formulations were developed: WSB1 (100 % wheat flour), WSB2 (90 % wheat:10 % soy okara), WSB3 (80 % wheat:20 % soy okara), and WSB4 (70 % wheat:30 % soy okara). The samples were evaluated for proximate composition, functional properties of the flour blends, physical bread characteristics, and sensory attributes. Statistical analysis was performed using ANOVA and Duncan’s Multiple Range Test at p < 0.05 and the experimental design employed for the study was a Completely Randomized Design. Results showed a progressive increase in protein (from 9.38 % in WSB1 to 13.76 % in WSB4), fat (3.81 % to 7.21 %), and fibre content (3.0 % to 4.60 %) with increasing okara inclusion, while carbohydrate content decreased and Moisture content also increased. Functional properties of composite flours such as bulk density, swelling index, water and oil absorption capacities improved in WSB2 and WSB3 but dropped significantly at 30% substitution (WSB4). Physical properties showed that loaf volume and specific volume were highest in WSB2 (327 mL, 1.52 mL/g). Sensory evaluation revealed that WSB1 and WSB2 were most preferred in terms of appearance, taste, texture, and overall acceptability, with WSB2 achieving the highest general acceptance among the composite samples. Incorporating soy okara at 10 % – 20 % improved the nutritional and functional properties of bread without adversely affecting sensory and physical quality. WSB2 (90 % wheat, 10 % okara) emerged as the most preferred formulation, balancing enhanced nutrition with consumer acceptability. This study supports the use of soy okara as a cost-effective, functional ingredient for developing nutrient-enriched bread suited to low-income, health-conscious populations and addressing malnutrition.
SENSORY ASSESSMENT OF COOKIES PRODUCED FROM BLENDS OF ORANGE-FLESHED SWEET POTATO, SOYBEAN, AND DATE FRUIT FLOURS
This study assessed the sensory characteristics of cookies made from blends of orange-fleshed sweet potatoes, soybean, and date fruit flour. Four formulations were tested: Control (OSD1) with 100% wheat flour, and four test groups—OSD2 (80% orange-fleshed sweet potatoes, 15% soybean, 5% date flour), OSD3 (70% orange-fleshed sweet potatoes, 20% soybean, 10% date flour), and OSD4 (60% orange-fleshed sweet potatoes, 25% soybean, 15% date flour). OSD5 (50% orange-fleshed sweet potatoes, 30% soybean, 20% date flour). Sensory attributes evaluated included appearance, taste, texture, aroma, and acceptability, using a completely randomized design (CRD). ANOVA and Duncan's multiple range test (DMRT) were used for statistical analysis at p < 0.05. The control (OSD1) was rated highest across all attributes, with appearance, taste, and overall acceptability scores of 7.40, 7.20, and 7.30, respectively. Incorporating soybean and date flour (OSD2, OSD3, OSD4, OSD5) significantly (p < 0.05) reduced these scores, particularly for taste and aroma. While the test blends had slightly lower overall acceptance, they were still close to the control. The study suggests that adding soybean and date flour can enhance nutrient density, but careful attention is needed to maintain desirable sensory qualities.
EVALUATING THE EFFICACY OF WATER GUARD AND AQUA TABS IN TREATING MICROBIALLY CONTAMINATED WATER FOR SAFE DRINKING PURPOSES
This study was carried out to discover the efficacy of Water Guard and Aqua Tabs in treating microbially contaminated water for safe drinking purposes in remote areas. Uchu river located in Umuchu, Aguata local Government Area of Anambra State was used for the study. The main source of the river is located between Ugwuakwu and Osete communities. The water samples were collected randomly from the source of Uchu River on 22nd February 2021, at the peak of dry season to ensure that, the River was not contaminated by flood and run-off water during the season. The water samples were taken to Project Development Institution (PRODA) at Emene Industrial layout Enugu Nigeria within twenty four hours (24 hrs) to test the efficacy of the two disinfectants in treating microbially contaminated water for safe drinking purposes. The experimental water was divided into three groups; sample A, B and C. Sample A and B represent bacterial contaminated water treated with Water Guard and Aqua Tabs disinfectant while sample C represents control. Results of experiment A and B showed no presence of bacteria growth after treating the bacterial contaminated water with Water Guard and Aqua Tabs disinfectants. Water sample C which represents control experiment (untreated water) the following micro organisms were isolated; 6 Bacteria subtrillus, 62 Staphylococcus Aureus, 97 Chromobacterium violaceum, 12 Pseudomoni Spp, 22 Klebsiell species and 44 Enterococcus species respectively. The study discovered that Uchu River is loaded with micro organisms. Thus, the water from Uchu River can be used for other domestic activities but not safe for human drinking without proper treatment. Recommendations: Water Guard and Aqua Tabs substances are very good for water treatment for safety.
*Samuel Uchechukwu Ezenwosu1*., Philomena C. Chukwuemeka, Helen Ngozi Ebeh., Dialoke Christian Enyi
RF POWER PERFORMANCE OF AN LDMOSFET ON FM BROADCAST BAND II POWER AMPLIFIER APPLICATIONS
The performance, analysis, design, and evaluation of medium-voltage laterally diffused metal oxide semiconductor (LDMOS) transistors for band II applications up to 108 MHz is presented. Using an optimized N-LDMOS transistor, the current state of laterally diffused metal-oxide-semiconductor (LDMOS) technology, which for over ten years has been the preferred tool for RF power applications. LDMOS satisfies the needs for numerous Class AB applications. The performance per unit gate width has been found to decline with increasing width, even when RF transmitter power amplifiers employ larger transistors to deliver higher power. A field solver and circuit simulator are used in this work to identify the mutual inductance in the system as a primary reason for the performance deterioration in RF LDMOSFET power amplifiers. For RF power applications, laterally diffused metal-oxide-semiconductor (LDMOS) technology has been the preferred device for more than ten years. LDMOS meets the specifications for a variety of Class AB and pulsed applications, including broadcast, microwave, and base station. We demonstrate the LDMOS transistor's cutting-edge RF performance using a load-pull test system, attaining class-AB drain efficiencies of 80% at FM broadcast band II , 88-108 MHz for both packaged and on-wafer devices. Additionally, the outcomes of a number of Class AB amplifier implementations built using this method are displayed.
Ayeoribe Olarewaju Peter, Olaitan Akinsanmi, Ayeoribe Iyiola Victor
SMART WEARABLE DEVICES FOR HEALTH MONITORING: A FOCUS ON RURAL HEALTHCARE
Smart wearable devices are transforming health monitoring, particularly in rural areas with limited healthcare access. Equipped with sensors and real-time analytics, these devices monitor vital signs such as heart rate, blood pressure, and glucose levels, enabling continuous remote monitoring. This reduces the need for frequent facility visits and allows early detection of chronic conditions like diabetes and cardiovascular diseases. Wearables also empower patients by providing direct access to their health data, improving health literacy, and encouraging proactive self-care. Integration with AI enhances predictive capabilities, detecting health anomalies such as arrhythmias or stroke symptoms for timely intervention. However, data privacy, affordability, and internet connectivity remain barriers to widespread adoption. This study will adopt a mixed-methods approach, combining quantitative data collection through pilot deployments of smart wearables in selected rural communities with qualitative interviews of patients and healthcare providers. Health outcome metrics and user feedback will be analyzed to assess the effectiveness, usability, and barriers to the implementation of wearable technologies in rural settings. With targeted investment and collaborative efforts, smart wearables can bridge healthcare gaps and significantly improve outcomes in underserved populations.
Stephen Vure Gbaraba, Ashiata Yetunde Mustapha, Busayo Olamide Tomoh, Akachukwu Obianuju Mbata, and Adelaide Yeboah Forkuo
A REVIEW OF STOCHASTIC MODELING TECHNIQUES IN PUBLIC HEALTH RISK ASSESSMENT AND POLICY DEVELOPMENT
Stochastic modeling has emerged as a vital tool in public health risk assessment and policy development, enabling decision-makers to account for uncertainty and variability in epidemiological and environmental health studies. This review explores the application of stochastic modeling techniques in assessing public health risks and shaping evidence-based policies. It discusses key methodologies, including Monte Carlo simulations, Bayesian networks, Markov models, and agent-based modeling, highlighting their strengths and limitations in addressing complex health challenges. Monte Carlo simulations are widely used to quantify uncertainty in disease spread and exposure assessment, allowing policymakers to evaluate intervention strategies under different probabilistic scenarios. Bayesian networks facilitate probabilistic reasoning by integrating prior knowledge with real-time data, improving the accuracy of disease prediction models. Markov models, particularly in chronic disease progression studies, provide insights into long-term health outcomes and cost-effectiveness of interventions. Agent-based modeling is instrumental in understanding individual and population-level behaviors, particularly in the context of infectious disease transmission and health policy compliance. The review also examines real-world applications of stochastic models in epidemiological surveillance, vaccination strategies, and environmental exposure risk assessment. Notable case studies include their use in modeling COVID-19 transmission dynamics, optimizing influenza vaccination policies, and assessing the impact of air pollution on respiratory diseases. Additionally, the integration of stochastic approaches with machine learning and artificial intelligence is discussed, emphasizing their role in enhancing predictive analytics for public health. Despite their advantages, stochastic models face challenges such as computational complexity, data availability, and model validation. Addressing these limitations requires interdisciplinary collaboration, robust data collection frameworks, and advancements in computational power. The review underscores the importance of stochastic modeling in public health risk assessment and emphasizes the need for continued innovation to refine predictive accuracy and policy relevance. Future research directions should focus on improving model interpretability, incorporating real-time data streams, and developing hybrid models that combine stochastic and deterministic approaches. By providing a comprehensive review of stochastic modeling techniques, this study contributes to a deeper understanding of their applications in public health. It advocates for their broader adoption in policy formulation to enhance health system resilience and risk mitigation strategies.
Tunde Victor Nihi; Adelaide Yeboah Forkuo, Opeyemi Olaoluawa Ojo, Collins Nwannebuike Nwokedi, Olakunle Saheed Soyege
A CONCEPTUAL FRAMEWORK FOR AI-DRIVEN HEALTHCARE OPTIMIZATION AND PREDICTIVE ANALYTICS
The integration of Artificial Intelligence (AI) into healthcare has revolutionized patient care, operational efficiency, and decision-making. However, existing healthcare models often lack a cohesive framework that effectively utilizes AI-driven predictive analytics for optimizing healthcare delivery. This paper proposes a conceptual model for AI-driven healthcare optimization and predictive analytics, addressing critical challenges such as early disease detection, personalized treatment planning, and resource allocation. The model leverages machine learning algorithms, big data analytics, and real-time data processing to enhance predictive capabilities in diagnosing and managing diseases. A key component of this model is the integration of electronic health records (EHRs) with AI-driven diagnostic tools, enabling proactive and personalized healthcare interventions. Predictive analytics techniques, such as deep learning and neural networks, facilitate early identification of chronic diseases, improving patient outcomes and reducing hospital readmissions. Furthermore, the model incorporates natural language processing (NLP) for analyzing unstructured medical data, ensuring comprehensive insights into patient health trends. The proposed framework also addresses healthcare resource optimization by employing AI-driven decision support systems to enhance hospital workflow management, reduce waiting times, and allocate resources efficiently. By leveraging federated learning, the model ensures data privacy and security while facilitating cross-institutional collaboration in medical research and diagnostics. Additionally, explainable AI (XAI) techniques improve transparency and trust in AI-driven clinical decisions, mitigating ethical concerns and bias in medical predictions. The conceptual model is evaluated against key performance indicators such as diagnostic accuracy, patient outcome improvement, and operational efficiency. Case studies from AI applications in oncology, cardiology, and infectious disease management demonstrate the model's effectiveness in real-world healthcare settings. Future research will focus on integrating blockchain technology for secure data exchange and developing robust AI governance frameworks to ensure compliance with regulatory standards. By adopting this AI-driven healthcare optimization model, healthcare systems can improve decision-making, enhance patient care, and optimize resource utilization, ultimately transforming the landscape of modern medicine. This study contributes to the growing body of research on AI in healthcare, offering a scalable, data-driven solution to address contemporary healthcare challenges.