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Phenotypic diversity and performance for some agronomic characters in sesame (Sesamum indicum L.) germplasm collection from Nasarawa state, Nigeria
Get PDFSesame is one of the most important oilseed crops in the tropical and subtropical regions of the world. Despite its importance, sesame yield in Nigeria is poor due to lack of improved and high yielding varieties. The aim of the work was to study genetic diversity in some agro-morphological traits of sesame germplasm. Germplasm exploration was carried out using systematic survey from October 2022 to September 2023 in different sites across Nasasrawa State. One hundred and twenty (122) germplasm was collected and evaluated in an incomplete randomized block design with 3 replications at the Faculty of Agriculture, Federal University Lafia, during October 2023 under irrigation. Data was collected on plant height (cm), number of primary branches (NBR), number of Capsules per plant and seed yield per plant (g), Analysis of variance revealed significant (p < 0.01) variation among the 122 genotypes for all the traits studied. Partitioning the significant variations into variance components showed that genotypic variance range from 0.92 for SYP to 287.34 for PHT. The genotypic effects have significant contribution to the phenotypic variation in all traits. The magnitude of the genotypic variance component resulted in high heritability estimates for PHT and CAP. Biplot PCA on agro-morphological traits displayed three principal components that contributed for 71.38% variations. Cluster analysis grouped the collected germplasm into three main clusters. The phenotypic diversity observed in this study may be exploited in the selection of parental lines for hybridization when breeding sesame for high seed yield
Prevalence and Environmental Determinants of Aflatoxin Contamination in Maize Sold in Open Markets Across Nasarawa State, Nigeria
Get PDFThis study evaluated the prevalence of aflatoxins (AFB1, AFB2, AFG1, and AFG2) in maize sold across 13 Local Government Areas (LGAs) in Nasarawa State, Nigeria, and examined how environmental factors contribute to fungal growth and mycotoxin production. Maize samples (130) were subjected to total heterotrophic fungal count (THFC) analysis using standard microbiological methods, while aflatoxin levels were measured using thin-layer chromatography (TLC) combined with densitometry. Statistical methods, including analysis of variance (ANOVA) and regression modeling, were utilized to clarify spatial contamination trends and identify environmental variables that could predict contamination. The findings revealed significant aflatoxin contamination, with 85.4% of samples surpassing the 20-ppb safety limit set by the Standards Organisation of Nigeria (SON). Aspergillus flavus, the main producer of aflatoxin B1, was found in 94% of the samples, followed by Fusarium verticillioides (71%) and Aspergillus niger (59%). Regression analysis showed a strong correlation (r = 0.710, P<0.001) between fungal load and AFB1 levels, with humidity explaining 41.1% of the variability in THFC. The highest AFB1 concentration (137.10 ± 15.10 ppb) was found in Doma, while Lafia showed consistently lower contamination levels, likely due to better post-harvest handling practices. This study underscores the urgent need for targeted interventions to reduce aflatoxin contamination, such as rapid drying, hermetic storage systems, and educating farmers on preventing fungal growth. It also recommends implementing aflatoxin surveillance programs and researching resistant maize varieties to improve food safety and public health in Nasarawa State
Assessment of Physicochemical and Bacteriological Quality of Government Intervention Borehole Water in Ipetu-Ijesa, Osun State
Get PDFAccess to clean and safe drinking water remains a significant public health concern in Nigeria, where millions lack improved water sources. This study assessed the physicochemical and bacteriological quality of water from selected government intervention boreholes in Ipetu-Ijesa, Osun State. Water samples were collected from five boreholes and analysed for parameters including pH, electrical conductivity, turbidity, total dissolved solids (TDS), dissolved oxygen (DO), nitrate levels, salinity, and microbial contamination (total coliform and Escherichia coli counts). The results indicated that most physicochemical parameters fell within the permissible limits set by the World Health Organisation (WHO) and Nigerian Standards for Drinking Water Quality (NSDWQ). However, turbidity and total coliform counts exceeded safe limits in some locations. Total coliform counts ranged from 2.0 to 14.0 cfu/100 mL with a mean value of 7.0 cfu/100 mL. Borehole D (Oko Owo) exhibited the lowest contamination levels, while Borehole C (Bamikemo) recorded the highest total coliform count. No faecal coliforms were detected in the examined samples. Boreholes A, B, and C exhibited elevated conductivity, TDS, and temperature, suggesting potential contamination from environmental and anthropogenic sources. Borehole D had slightly acidic water, indicating possible metal leaching. Principal Component Analysis (PCA) revealed strong correlations between conductivity, TDS, and temperature, highlighting potential underground contamination sources. Although the physicochemical characteristics and total coliform counts were mostly within permissible limits, deviations in some boreholes suggest the need for continuous monitoring and quality assessment to ensure safe drinking water
Improved Zeta DC – DC with Hybrid Multilevel Converter Topology for Single –Phase Induction Motor Control
Get PDFThis paper presents an improved Zeta DC-DC converter integrated with a hybrid multilevel converter topology for efficient single-phase induction motor (SPIM) control. The proposed system aims to enhance power quality, reduce total harmonic distortion (THD), and improve the dynamic performance of the motor drive. The improved Zeta converter provides a stable and regulated DC link voltage with reduced ripple, ensuring efficient power conversion and better motor control. The hybrid multilevel inverter configuration minimizes switching losses and enhances output voltage quality by generating stepped waveforms with lower harmonic content. This combination enables smooth speed control and high efficiency operation of the SPIM, making it suitable for low-power industrial and residential applications. Simulation results validate the effectiveness of the proposal topology in terms of the reduced harmonics, improved voltage regulation, and enhanced motor performance compared to conventional converter-inverter topologies
Habitat Characterization of Culicine Mosquitoes in Two Local Government Areas of Nasarawa State, Nigeria
No full textMosquitoes are major vectors of disease, with their abundance influenced by ecological and environmental factors. This study assessed the habitat characterization and abundance of mosquito species in Awe and Nasarawa Eggon LGAs, Nasarawa State, Nigeria. Larvae were sampled monthly from July 2023 to June 2024 using standard dipping techniques, and physicochemical parameters of breeding sites were analyzed. A total of 2,158 mosquito larvae were collected, with culicines significantly (P < 0.001) accounting for 90.08% while anophelines accounted for 9.92% of the population. Among the emerged adult culicines, Culex quinquefasciatus (76.67%) was significantly (P < 0.001) more abundant followed by Anopheles gambiae (13.40%), while An. funestus had the lowest emergence rate at 1.97%. Puddle habitats (47.21%) supported the highest mosquito abundance, followed by rice fields (34.99%), while animal hoof prints (0.79%) had the least mosquito abundance. Therefore, habitat type significantly (P = 0.037) influenced mosquito abundance in both LGAs. The highest larval abundance was recorded at a 351–400 m (36.52%) gradient from human dwellings, but distance significantly (P = 0.625) influence larval abundance. Physicochemical parameters such as temperature, pH, total dissolved solids, and dissolved oxygen exhibited varied correlations with mosquito abundance, with significant effects in some habitats. These findings highlight the need for targeted vector control strategies, particularly habitat modification and public health interventions, to reducemosquito proliferation and mitigate disease transmission risks in the study area
Abundance and Diversity of Fruit-Feeding Butterflies in Federal University of Lafia
Get PDFFruit-feeding butterflies, an ecologically significant group, play a key role in maintaining biodiversity and ecosystem functions, particularly in tropical and subtropical regions. This study aimed at comparing the abundance and diversity of fruit-feeding butterflies in two habitats in Federal University of Lafia Permanent Site, Nasarawa State from July to August, 2024. Butterflies were trapped using rotten banana fermented in palm wine which was placed in a dish and suspended in the butterfly trap and allowed to stand between the hours of 7:00 am and 6:00 pm at each survey day. Temperature and relative humidity were recorded when trap was set-up and as at the time traps were removed. A total of 35 individual fruit-feeding butterflies was recorded in this study which belong to the family of Nymphalidae spread across three species namely; Charaxes epijasius (48.6%), C.varenes vologeses (28.6%), and C. boueti boueti (22.8%). The species Charaxes epijasius accounted for the highest butterfly population in both gallery forest and savannah woodland habitats and differences between species was significant ((2 = 10.993, df = 2, P = 0.004102). There was a significant difference ((2 = 4, df = 1, P = 0.0455) in butterfly abundance between the two habitat types. Temperature and humidity had a positive influence on butterfly abundance across the two habitat types. In conclusion, this research contributes to a better understanding of the ecological dynamics of fruit-feeding butterflies in Federal University of Lafia Permanent Site in wet season period. Hence, felling of trees and cattle grazing within the premises of the University should be discouraged.
Synthesis, Compositional, Morphological, Structural, and Thermal Properties of Eggshell Calcium Oxide for Solar Photovoltaic Glass
Get PDFThe increasing demand for sustainable materials in engineering applications has prompted the exploration of eco-friendly alternatives. Conventional calcium oxide (CaO) production from limestone is energy-intensive and environmentally detrimental, prompting interest in biogenic alternatives such as poultry eggshell waste, predominantly composed of calcium carbonate. Despite extensive research into eggshell-derived CaO for catalytic and biomedical applications, its potential for photovoltaic glass applications, particularly considering region-specific variations in material properties, has not been adequately investigated. This study comprehensively evaluates CaO extracted from chicken eggshells sourced from Lafia Local Government Area, Nasarawa State, Nigeria, to establish its suitability for photovoltaic glass manufacturing. The extraction process involved thermal calcination at 900 °C for two hours, followed by detailed compositional, morphological, structural, and thermal characterizations using XRF, SEM-EDS, XRD, and TGA techniques. The results demonstrated brilliant white powder with high compositional purity (98.58% CaO), nanoscale spherical morphology conducive to uniform integration in glass matrices, robust crystallinity beneficial for structural stability, and strong thermal resilience crucial for high-temperature processing. These findings highlight eggshell-derived CaO as an economically viable, sustainable, and high-performance alternative for PV glass manufacturing. Further studies are recommended to assess practical integration in PV glass formulations, long-term durability, study of farming systems, and lifecycle economic viability.
Low-Cost Weather Station Assessment of Urban Heat Island Compared with ERA5 Reanalysis Data
Get PDFRapidly urbanizing regions usually experience urban heat islands with the attendant environmental and public health challenges. This study has used a low-cost LILYGO T-SIM7000G board with BME280 as a sensor to assess the UHI Index and compare such results with those obtained from the Copernicus ERA5-Land reanalysis data. The result shows a bias of about 1.36 in the UHI Index against the ERA5-Land data; this bias can be mitigated by applying a corrective offset. It was also observed that the ambient temperature in Lafia city has been consistently increasing between the years 1980 and 2024 by about 0.017 oC per year, with the highest increase recorded in the year 2024. The highest diurnal value of the UHI Index was observed around 1500 hours, while the lowest nighttime value was observed at around 0600 hours; this finding agrees with results as observed in other studies. This study has shown the importance of adopting low-cost components in monitoring environmental variables such as the UHI Index in low-income areas of the world, where funding may pose problems
LIVELIHOOD SUSTAINABILITY AND LAND DEGRADATION: SOCIO-ECONOMIC ASSESSMENT OF CHARCOAL TRADE IN YEWA DIVISION, OGUN STATE
Get PDFCharcoal merchandise is a thriving business in Yewa Division of Ogun State. Despite the livelihood support from charcoal trading, significant negative impacts on land and environment have been widely reported. There is the need to balance economic gains and land degradation. A survey of purposively sampled five hundred charcoal stakeholders was conducted through the use of well-structured questionnaire and interviews. Data were collected on socio-economic characteristics, perceptions on livelihoods and land degradation. Data were analysed using descriptive statistics, logistic and ordinal regression analyses. The study found that 56% of the respondents were male and 55% were married. Most of the respondents (63%) had secondary level education, involved as producers (60%), and earning income of between ₦50,000 and ₦120,000 monthly (48%) from charcoal activities. The research revealed that marital status (χ22,498 = 7.43; p = 0.02) and occupation (χ21,499 = 0.001; p = 0.01) significantly influenced the perception of respondents with respect to charcoal merchandise for livelihood support. Percieved impact of charcoal production on land degradation was significantly influenced by marital status (χ22,498 =7.43; p=0.02), education level (χ22,498 =4.68; p=0.01) and income level (χ22,498 =11.53; p=0.01). Charcoal trading appears to be a lucrative business that supports livelihoods. Married couple, well educated people and high income earners appeared to be involved in this business, and they seemed fully aware of the consequenes of charcoal activities on land degradation. However, the study recommends that charcoal merchandise should be strictly regulated to forestall adverse consequences to the land and environment.
Document Classification in HEIs Using Deep Learning: A CNN, RNN, and Hybrid CNN-RNN Approach
No full text Higher Education Institutions (HEIs) are increasingly confronted with the complexities of evolving rules and requirements, necessitating innovative technology solutions to streamline document handling processes. Traditional paperwork methods are often inefficient and error-prone, leading to potential non-compliance. This research addresses these challenges by developing an AI-powered electronic document management system designed to automate compliance checks and simplify document handling as HEIs grow. The primary objective is to create a document classification model utilizing deep learning techniques, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and a hybrid CNN-RNN approach, to enhance document accuracy and compliance. The study involves collecting and preprocessing a substantial dataset of documents, designing and evaluating various deep learning models, and optimizing hyperparameters. Performance comparisons among the models indicate that the hybrid CNN-RNN architecture outperforms individual models, achieving superior accuracy, recall, and F1-score, alongside a significantly lower mean squared error (MSE). Initial evaluations revealed the CNN, RNN, and CNN-RNN models achieved accuracies of 73%, 44%, and 27%, respectively, on the raw dataset. However, with an upgraded dataset, these models improved to 76%, 48%, and 79% accuracy, respectively, highlighting the hybrid model\u27s enhanced capability in accurately classifying documents. The findings revealed the effectiveness of integrating advanced deep learning techniques to improve document verification processes in HEIs, ultimately facilitating better compliance and operational efficiency