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The aim of this project is to carry out a comparative evaluation of foreign and homebased manufactured hybrid 3.5kva inverter system. Conventional non-hybrid inverter systems are characterized by their dependency on the grid, low efficiency in solar charging, limited energy management capabilities, and ineffective communication between components. Therefore, this endeavor is designed to integrate hybrid features to overcome these shortcomings. The process entailed comparing a hybrid inverter system to address the limitations of non-hybrid inverters and to do this, we incorporated an alternative power source, i.e. solar energy, to charge the battery. This involved designing an MPPT (Maximum Power Point Tracking) charge controller and seamlessly integrating its circuitry with that of the inverter in the non-hybrid system. Additionally, we established effective communication between the DSPIC30F2010 microcontroller on the inverter and the DSPIC30F2010 microcontroller on the MPPT circuitry using serial communication, which we integrated into the inverter. All communication protocols were outlined in the source code. To ensure organization and tidiness, we housed all these components within a single enclosure. The project successfully achieved its intended objectives by comparing the hybrid features of the homebased and foreign manufactured inverter systems. Through meticulous design and implementation, all identified limitations were effectively addressed, leading to significant improvements in system performance and functionality. Relevant tests such as output voltage and frequency test, load and no load test, as well as power efficiency tests were carried out to compare the performances of the foreign and home based manufactured hybrid inverter systems. The performance of the home based hybrid inverter was 219.8V for output voltage versus 230V for the foreign. Frequency for home based was 50.04Hz versus 50.0Hz for the foreign. Both inverters displayed a comparable sine wave output. Power efficiency for home based was 90.64 percent while for foreign, it was 94.5 percent, these results show that there was no remarkable difference between the output of the home based compared to the foreign inverter. Furthermore, the locally assembled inverter cost far less than the foreign counterpart. Hence, this study proves that cost efficient inverter systems can be manufactured locally.

BACKGROUND: Complementary feeding is a component of infant and young child nutrition, particularly between 6 and 23 months of age when nutritional needs increase beyond what breast milk alone can provide. Despite global and national recommendations, poor complementary feeding practices remain prevalent in many low- and middle-income settings, including Nigeria. OBJECTIVES: This study assessed the knowledge and practices of complementary feeding among mothers of children aged 6–23 months in Benin city, Edo State, and factors associated with these outcomes.
METHODS: A descriptive cross-sectional study was conducted among 442 mothers of children aged 6–23 months selected using a multistage sampling technique. Data were collected using a structured, pretested, interviewer-administered questionnaire. Minimum meal frequency (MMF), minimum dietary diversity (MDD) and minimum acceptable diet (MAD) were determined using WHO criteria. Dietary diversity was assessed using a 24-hour recall method. Data were analyzed using IBM SPSS version 27. Associations were tested using Chi-square and Fischer’s exact test, and logistic regression was performed to identify predictors of good knowledge, practice and dietary diversity at p < 0.05 (95% CI).
RESULTS: The mean age of the mothers and index children were 32.3±5.6 years and 14.6 ± 5.9 months respectively. Mothers’ knowledge assessment revealed that 32 (29.9%) of the mothers demonstrated good knowledge of complementary feeding. Knowledge was significantly higher among married women (p < 0.001), mothers with higher level of education (p < 0.001), higher xvi skill level (p < 0.001), higher household income (p < 0.001), more educated spouses (p < 0.001), spouses with higher skill level (p < 0.001), older index child (p = 0.012), and those with more children (p = 0.034). Being married and having higher education were significant predictors of good knowledge (OR = 20.767, p = 0.004; OR = 4.438, p < 0.001). Mothers who demonstrated good practice were 41 (10.1%), MMF was 48.2%, and MAD was 19.0%. Good practice was significantly higher among married mothers (p = 0.009), mothers with higher education (p < 0.001), higher skill levels (p < 0.001), higher household income (p = 0.008), more educated spouses (p < 0.001), spouses with higher skill level (p < 0.001), older index child (p < 0.001), and higher complementary feeding knowledge level (p < 0.001). Having an older index child and higher education were significant predictors of good practice (OR = 0.335, p = 0.021; OR = 3.767, p = 0.001). A total of 167 (41.3%) of respondents’ children consumed five or more of the recommended food group in the last 24 hours, and MDD was 37.8%. Dietary diversity was significantly higher in older mothers (p = 0.033), those married (p = 0.034), with more educated spouses (p = 0.002), higher household income (p = 0.003), better socioeconomic status (p = 0.033) and older index child (p < 0.001). Mothers with lower socioeconomic status were less likely to have good practice (OR = 0.402, p = 0.018) and those with an older index child were more likely (OR = 4.075, p < 0.001).
CONCLUSION: Majority of the mothers had poor knowledge and practice of complementary feeding and a majority of children had poor dietary diversity. Targeted nutrition education focusing on simplifying complementary feeding guidelines and interventions to support local food production in order to improve dietary diversity are recommended.

Maize (Zea mays L.) is a key global staple, particularly in Sub-Saharan Africa., but its productivity is often constrained by the high cost and documented environmental risks of conventional inorganic fertilizers. This study investigated the use of Panicum maximum leaf powder, a locally available organic nutrient source, application on the agronomic response of maize. The field trial was conducted at the Teaching and Research Farm, University of Benin, Benin City. The experiment utilized a Randomized Complete Block Design (RCBD) with five replications. The treatments consisted of four rates of P. maximum application: 0kg/ha, 60kg/ha, 120kg/ha and 180kg/ha. The variables measured included percentage emergence, percentage establishment, days to tasselling, plant height, number of ears, and stover yield. Results showed that the control exhibited significantly poorer establishment, delayed tasselling, shorter plants and reduced stover yield compared to all fertilized plots. The 60kg/ha rate was sufficient to maximize benefits for establishment and maturity, though the total number of ears was not significantly affected by treatment. It is concluded that P. maximum leaf powder is an effective, sustainable organic fertilizer for enhancing maize vegetative growth and stand establishment.

Alcohol consumption is a globally prevalent practice with well-documented systemic effects. However, its specific impact on visual performance, a critical component for tasks like driving, remains relatively under-investigated. This study aimed to investigate the effects of a standardized dose of alcohol on visual performance parameters. A single-blind, randomized study design was employed, and data were analyzed using the IBM Statistical Package for Social Sciences (SPSS) version 22.0, incorporating descriptive statistics and repeated measures ANOVA. The study comprised sixty participants (mean age 24.1 ± 2.8 years), randomly assigned to an Alcohol group (n = 30) that consumed 250ml of red wine (12% alcohol) or a Control group (n = 30) that consumed an equal volume of a matched placebo. Visual performance parameters, which includes visual acuity (distance and near), contrast sensitivity, amplitude of accommodation, and near point of convergence, were measured at baseline, 30 minutes, and 60 minutes post-consumption. The findings of this study showed that the consumption of alcohol induced a significant deterioration in visual function: distance visual acuity (LogMAR) worsened from a baseline of -0.06±0.14 to 4.29±1.50 at 30 minutes (p = 0.002), near visual acuity (N-notation) worsened from 0.02±0.05 to 3.40±0.67 (p = 0.003), amplitude of accommodation decreased from 13.20±3.40D to 9.83±3.01D (p = 0.001), and near point of convergence (break) receded from 5.50±3.93cm to 8.50±4.38cm (p = 0.003). In contrast, alcohol consumption did not produce a statistically significant effect on contrast sensitivity at any time point (p > 0.05). In conclusion, this study showed that acute, moderate alcohol intake significantly impairs key aspects of visual performance essential for depth perception and focusing. These findings hold clinical significance for optometry, providing evidence-based data to counsel patients on the specific visual risks of alcohol consumption, particularly for activities requiring precise oculomotor control and sharp vision, such as driving at night.

Iron dysregulation remains a critical global health challenge, affecting billions through conditions ranging from iron deficiency anemia to hereditary hemochromatosis. This study investigated the effects of Piper guineense (Uziza) leaf extract on HFE gene, TFRC gene and survival rate of Drosophila melanogaster. Fresh Uziza leaves were collected, authenticated and extracted using 70% ethanol maceration. Five experimental groups (n=60 flies per replicate, three replicates each) were established: Control, 100 mg/mL, 200 mg/mL, 300 mg/mL, and 400 mg/mL Piper guineense extract treatments. Survival was monitored over 21 days, followed by molecular analysis using conventional PCR, agarose gel electrophoresis, and quantitative realtime PCR with Gpdh as the housekeeping gene. Results revealed a striking hormetic response pattern, with 100 mg/mL treatment significantly enhancing survival (66.67% ± 4.67%) compared to controls (48.33% ± 3.06%), representing 30.3% mortality reduction. Higher concentrations (300-400 mg/mL) demonstrated dose-dependent toxicity. HFE gene expression showed significant dose-dependent upregulation, peaking at 300 mg/mL (2.65-fold increase, p < 0.001), with progressive increases at 100 mg/mL (1.42-fold), 200 mg/mL (2.08-fold), and 400 mg/mL (1.98-fold) compared to controls. Statistical analysis (one-way ANOVA, F(4,10) = 6.82, p < 0.05) confirmed significant inter-group differences. The dissociation between optimal survival (100 mg/mL) and maximal gene expression (300 mg/mL) indicates that moderate transcriptional activation represents beneficial adaptation, while robust upregulation signals cellular distress. These findings demonstrate that Piper guineense bioactive compounds modulate iron metabolism gene expression through mechanisms likely involving iron chelation and oxidative stress signaling. The narrow therapeutic window emphasizes the critical importance of dose optimization for potential therapeutic applications in iron-related disorders.

Cardiovascular Diseases (CVDs) remain the paramount global health challenge, responsible for approximately 17.9 million deaths annually, with cardiac arrest representing a particularly critical and sudden event. Despite advancements in machine learning, accurate prediction is often hindered by highdimensional clinical data ("the curse of dimensionality") and the performance limitations of standalone classifiers. Consequently, this study aimed to develop a framework that integrates a novel Hybrid Recursive Feature Elimination-Firefly (RFE-Firefly) feature selection technique with a stacked ensemble classifier to enhance predictive accuracy. The study utilized a structured heart disease dataset from the Kaggle repository, subjecting it to rigorous pre-processing, including normalization and class balancing via the Synthetic Minority Over-sampling Technique (SMOTE). The proposed Hybrid RFEFirefly algorithm was implemented to optimize the feature space, successfully reducing the data to a parsimonious subset of seven critical features. For classification, a stacking ensemble model was constructed using five diverse base learners which are Decision Tree, Support Vector Machine, Logistic Regression, Random Forest, and XGBoost and unified by Random Forest as meta-learner to synthesize their predictive strengths. Experimental results demonstrated that the proposed Hybrid RFE-FA Stack Ensemble achieved a superior accuracy of 90.57% and an F1-score of 0.7619. This performance significantly outperformed individual base classifiers as well as ensembles using standalone feature selection methods. The study concludes that integrating hybrid feature selection strategy with an advance stack ensemble classifier effectively mitigates overfitting and provides a robust, highperformance tool for clinical decision-making in cardiac arrest detection.

This research examined the extent of A Quasi-Experimental Study on the Effect of Informatics in Teaching and Learning in Universities in Edo State, with particular emphasis on differences across federal, state, and private institutions. The study adopted a descriptive survey research design and was guided by the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). Data were collected from 345 lecturers and students using a structured questionnaire designed around key constructs such as ICT infrastructure availability, utilization, digital competency, and perceived challenges. Data analysis involved descriptive statistics, Analysis of Variance (ANOVA), correlation, and regression analysis using SPSS. Findings revealed that ICT infrastructure and utilization levels were generally high, with private universities demonstrating significantly higher adoption compared to public institutions. Gender did not significantly influence ICT utilization, while institutional type and academic role (lecturer or student) were significant determinants. Regression results further confirmed that ICT utilization positively and significantly enhances teaching and learning effectiveness. The study concludes that institutional readiness, infrastructural support, and digital competency are critical to effective ICT integration in higher education. It recommends increased investment in ICT infrastructure, continuous capacity building, and supportive institutional policies to enhance technology driven teaching and learning.

Fertilizer application influences plant health and susceptibility to pests. Optimal levels of nitrogen, phosphorus, and potassium foster robust plant growth, which can deter pests and mitigate yield losses. Therefore, this research aimed to examine the impact of fertilizer application on Fall armyworm (FAW) infestation in maize. The study was conducted during the late maize cropping season of 2023 at the teaching and research farm of the Department of Crop Science, Faculty of Agriculture, University of Benin. The experiment was fitted in a Randomized Complete Block Design (RCBD) with three treatments viz: no fertilizer (T1), inorganic fertilizer (T2), and organic fertilizer (T3), each replicated three times. Treatment plots measured 3x2m and spaced by 1m. The data were analysed using one-way analysis of variance (ANOVA). The results revealed no significant differences (p > 0.05) in FAW abundance, damage, or severity among the treatments at 3WAS, 4WAS, 5WAS, 6WAS, and 7WAS. However, the highest mean abundance at 3WAS was recorded in the organic treatment (4.25), while at 4WAS, 5WAS, and 7WAS, the highest mean abundances were observed in the organic treatment (3.26, 4.93, and 3.95, respectively). In terms of damage, the highest mean damaged plants were recorded in the organic treatment at 3WAS (1.93) and 5WAS (3.30). Similarly, the highest mean severity was observed in the organic treatment at 3WAS (3.99), 5WAS (6.36), and 6WAS (5.85). In conclusion, both organic and inorganic fertilizers tested did not influence FAW larval abundance, damage, or severity on maize.

Fisheries and aquaculture are vital to global food security but face critical challenges, including security threats from unauthorized access, the limitations of reactive manual monitoring, and uncertainty in long-term production forecasting. Existing management tools often lack a unified approach to integrate real-time operational surveillance with predictive decision-support analytics. This study aims to address these gaps by designing and developing a hybrid methodological framework that integrates computer vision for security and advanced machine learning and time-series forecasting for production analysis and stock management. The study employed a dual-component methodology. For security, an OpenCV-based Histogram of Oriented Gradients (HOG) descriptor combined with a Support Vector Machine (SVM) was implemented to achieve automated, real-time human intrusion detection and alarm triggering. For predictive analytics, a hybrid approach was utilized, applying Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors (SARIMAX) for long-term production forecasting and various ensemble classifiers (Random Forest, Gradient Boosting, K-Nearest Neighbors) to analyze fish survivability and production trends. Results demonstrate that this integrated framework is highly effective across operational and strategic levels. The computer vision system successfully provided continuous, automated surveillance with verifiable digital evidence. The ensemble classifiers achieved near-perfect performance in survivability prediction (e.g., Random Forest Accuracy = 0.98, F1-score = 0.99, AUC ≈ 0.999). Furthermore, the SARIMAX model effectively projected a steady increase in global fish production, forecasting growth from 232.19 million tonnes in 2024 to 272.12 million tonnes by 2043—a 17.19% increase—capturing long-term temporal dynamics that static regressors fail to model. Based on these findings, it is recommended that fisheries stakeholders prioritize the deployment of computer vision-based surveillance to reduce security risks and labor costs. Additionally, management authorities should integrate ensemble classifiers into stock-management systems to optimize survivability and adopt SARIMAX-based forecasting for robust, evidence-based policy planning. The implementation of these data-driven platforms will significantly enhance sustainable fisheries governance and support proactive resource allocation.

The procurement of motor-vehicle spare parts through traditional physical markets presents challenges such as difficulty in identifying compatible parts, lack of price transparency, geographic and time constraints, and absence of centralized vendor platforms. This study focused on the design and development of a web-based online marketplace for motor-vehicle spare parts to address these limitations by providing a centralized, secure, and user-friendly platform connecting vendors and customers. The research adopted the Agile methodology using the Scrum framework and implemented a layered system architecture consisting of presentation, application, data access, and data storage layers. The system was developed using PHP for server-side processing, MySQL for database management, and HTML5, CSS3, JavaScript, and the Bootstrap framework for responsive user interface design. Selected spare-parts markets in Benin City, Edo State—Evbareke Market, Uwelu Market, and Igun Market were used as case studies to understand vendor operations and customer needs. The study objectives included designing a marketplace that supports product listing and searching based on vehicle make and model, implementing secure user authentication for buyers and vendors, enabling price comparison and vendor rating features, and testing the system to ensure reliable operation. The implemented system successfully delivered a functional multi-vendor marketplace with advanced search and filtering, vehicle compatibility matching, secure shopping cart and checkout processes, order management, inventory control, and role-based access control. Results demonstrated that the system effectively addresses problems associated with traditional spare- parts procurement by improving accessibility, transparency, and efficiency. Challenges such as database optimization, security implementation, responsive design, and system integration were encountered and resolved. The study concludes that web-based marketplace technologies can effectively transform spare-parts trading and support digital transformation in the automotive industry.