Journal of Science Research and Reviews

Formulation and Characterization of Black Medicated Soap using Waste Agricultural Products with Blended Oils

2025-04-17T23:16:54+00:00

The saponification technique, which involves reacting triglyceride-containing oil/fat with caustic soda (NaOH) are used to formulate soaps. However, the fatty acid makeup of various oils varies, which accounts for the various qualities of the soaps made from them. The X-ray fluorescence examination performed on the agricultural wastes ashes revealed the existence of potassium oxide (K₂O) and sodium oxide (Na₂O) as the primary components in the ashes. The plantain peel, cocoa pod, and palm tree bunch ashes were also analyzed using a flame photometer, and the results showed that the K: Na ratio was 2:1. KOH and NaOH were combined in a 2:1 ratio to serve as the real lye utilized in the saponification of the blended oils. The sequence of PKO >HPO >BTO were determined by analyzing the oils for saponification number (SN), iodine value (IV), unsaponifiable matter (UM), and acid value (AV). Nine distinct soap samples were formulated by blending three different oils in varying ratios. The combination of 150 cm³ of palm kernel oil, 90 cm³ of hump oil, and 60 cm³ of beef tallow oil was shown to be the best formulation. This oil blend was discovered to have an iodine number of 77.96±0.72 and a saponification number of 249.57±0.78, both of which are greater than the individual values. As a result, soap made with a combination of these oils has superior qualities than soap made from individual oils. Based on SON indices for evaluating soap quality, the evaluation of the formulated soaps revealed that the soap's quality was in the following order: soap made from plantain peel extract was superior to soap made from cocoa pod extract, and soap made from cocoa pod extract was superior to soap made from palm tree bunch extract. Given that both the control and soap solution's, antimicrobial activity rises with concentration, the antimicrobial screening results of the soaps were largely good. Agricultural waste of plantain peel, cocoa pod, and palm tree bunch ashes, can be used to make good, biodegradable, and ecologically friendly organic soaps. For the first time in the history of soap technology globally, hump fat blend was used to formulate excellent organic medicated soap

Assessment of Bacterial Contamination in Farmed Catfish and Wild Caught (Clarias gariepinus) (Burchell, 1822) and Water Sources in Zaria, Nigeria

2025-04-15T21:43:39+00:00

Catfish (Clarias gariepinus), obtained from both cultivated farms and natural environments serves as an important source of protein in Nigeria. Nevertheless, the presence of bacterial contamination in both fish and aquatic habitats presents considerable risks to public health. Evaluating the microbial quality of both farmed and wild catfish, as well as their water sources in Zaria is crucial for ensuring food safety and mitigating the threat of waterborne diseases. This study therefore, aimed at assessing the bacterial contamination levels in water and fish (Clarias gariepinus) from selected dams and fish farms in Zaria, Nigeria, and identified the bacterial species present. Water and fish organ samples (kidney and liver) were analyzed for bacterial and coliform counts over 12 months using standard culture methods. Results revealed significant variations in bacterial (0.35x10⁵- 10.65x10⁵) and coliform (0.8x10⁴-9.0x10⁴) counts across months and sampling sites, with farm water samples exhibiting higher contamination levels than dam water. For the fish organs, however, the bacterial contamination rate of dam fish samples was higher (12.62x10⁵) than the farm fish samples (8.75x10⁵). Pathogenic bacteria were isolated from water and fish samples, including Staphylococcus aureus, Escherichia coli, Salmonella typhi, and Klebsiella pneumoniae. The findings reveal the public health risks associated with consuming contaminated fish and emphasize the need for improved water quality management and good food safety practices in the region.

Investigation of Groundwater Pollution: A Case Study of Potiskum, Yobe State

2025-04-15T21:43:40+00:00

In recent years, the increasing trend in health risks associated with water pollution in Yobe State has encourage researchers to investigate the source of the groundwater contamination. This research examined groundwater pollution in Yobe State, focusing on Potiskum as a case study. The study utilized Electrical Resistivity and Hydrochemical methods. The electrical resistivity survey identified two aquifers in the research area, which comprises a semi-confined aquifer and a confined aquifer. The semi-confined aquifer is the first aquifer in the research area, and its closeness to the surface facilitated the contamination of groundwater within the study area. The Hydrochemical assessment of groundwater samples in the area, using Atomic Absorption Spectrometer, indicated that the groundwater possesses trace metals at high concentrations. The spatial distribution of these trace metals in the groundwater indicated that their levels are greater in the southeastern quadrant of the research area due to heightened human activities and metal works in the region. The inconsistent amounts of trace metals like Chromium, Cadmium, Nickel, Lead, and Arsenic in the groundwater were thought to be contributing factors to the rising water-related risks within the area and Yobe State overall. To mitigate the escalating problem of drinking water contamination in the study area, effective waste management practices must be implemented to safeguard water resources from pollution caused by leachate from dumpsites. Affected boreholes and wells in the region should be sealed, while new ones should be drilled into second aquifer that is confined in the area. Most of the second aquifers in the research area are artesian and suitable for drinking water supply. Based on the outcomes of this study, it is advised that routine groundwater monitoring be promoted in the study area

Geophysical Investigation of Groundwater Potential in Gauta Buzu, Nigeria

2025-04-15T21:43:40+00:00

The increase in human activities, population and climate change in Gauta Buzu area of Keffi has put enormous pressure on the existing water resources in the area. There is a high demand for quality drinking water in the area. In order to facilitate the provision of adequate potable water in Gauta Buzu area of Keffi Local Government, Nasarawa State, this study was carried out to evaluate the groundwater potential in Gauta Buzu, using electrical resistivity survey method, with a view to providing useful information that will help stakeholders in the area in adequate groundwater resources management. In this study, vertical electrical sounding data were obtained, using Schlumberger electrode configuration. Four geologic layers were delineated which includes, topsoil, weathered basement, fractured basement and the fresh bedrock. The second and the third geologic layers of the subsurface in the study area constitute the aquiferous layer. The results of the evaluated aquifer parameters showed that the groundwater potential of the study area ranged from intermediate to good categories. The evaluated aquifer characteristics of the study area indicated that the southwestern part of the study area has good groundwater potential and it’s the most appropriate location for municipal borehole site. The other parts of the study area have intermediate or moderate groundwater potential, mainly suitable for domestic water provision. The results of this study will serve as a basis for inform decision making on groundwater resources management in the study area.

Production and Analysis of Laundry Soaps from Blended Oils

2025-04-15T21:43:40+00:00

Stearic acids or any other fatty acids' sodium or potassium salts are what soaps are. The saponification method, which involves reacting triglyceride-containing oil with caustic soda (NaOH) to produce soap, is how they are made. However, the fatty acid makeup of various oils varies, which accounts for the various qualities of the soaps that are created from them. The aim of this study is to produce and analyze laundry soaps made from blended oils. Four different kinds of oils were used in this study. Four distinct soap samples were created by blending them in varying proportions. To determine which soap was the best, all soap samples were compared for their cleansing and lathering qualities, and the blend of palm kernel oil, palm stearin, beef tallow, and cotton seed oil at 3:1:3:3 ratio was determined to be the best with 76.4% total fatty matter (TFM) and 98.30% yield. The blends were examined for a variety of properties and compared with those found in the literature. The saponification and iodine values of the individual oils were also examined, and the results showed that soap made with the four oils in the 3:1:3:3:3 ratio had superior qualities to soaps made with other blends, was most cost-effective and ideal for laundry. Various characteristics of these samples were examined.

An Overview of the Chemistry and Utilization of Detergents, both Soap and Non-Soap

2025-04-24T05:33:08+00:00

This analysis explores the fundamental contrasts between detergent soaps and non-soap detergents, concentrating on their chemical makeup, cleaning methods, environmental impact, and applicability for diverse uses. Traditional soaps are made from natural fats and oils, which results in a biodegradable cleaning agent. Non-soap detergents, on the other hand, are made synthetically and frequently have better cleaning capabilities, but their limited biodegradability may cause environmental issues. In order to determine the best option depending on particular cleaning requirements, this study examines the trade-offs between these two cleaning chemicals, taking into account variables including water hardness, skin sensitivity, and sustainability consequences. According to this overview, the dynamic development, formulation, and use of soaps and soapless detergents are primarily driven by chemistry and chemical principles. The aim of this study therefore, is to conduct extensive survey on the chemistry and utilization of detergents, both soap and non-soap.

Evaluating Heavy Metal Pollution in Nigerian Coal: Enrichment Factors, Pollution Indices and Environmental Implications

2025-04-15T21:43:41+00:00

Heavy metal pollution is a significant environmental concern in Nigeria, particularly in coal deposits. This study evaluates the level of heavy metal pollution in Nigerian coal deposits using enrichment factors and pollution indices. Coal samples were collected from Maganga, Gboko, Onyeama, Okobo, Opoko-Obido, Odagbo and Ofugo coal fields, and the concentrations of lead, arsenic, chromium, manganese, nickel, cobalt, strontium, antimony and barium were determined. The results showed significant levels of heavy metal pollution, with enrichment factors indicating anthropogenic sources. Pollution indices reveal moderate to high levels of pollution. The study highlights the need for effective environmental management and pollution control measures to mitigate the environmental and health risks associated with the heavy metal pollution in Nigerian coal deposits.

A Spectral Conjugate Gradient Method via Hybridization Approach for System of Nonlinear Equations

2025-03-19T11:41:37+00:00

This paper present an effective conjugate gradient method via hybridization approach of classical Newton direction and conjugate gradient search direction, the method scheme satisfies the sufficient decent condition. Under mild condition, the global convergence result for the method is established. Preliminary numerical results for some large-scale benchmark test problems reported in this work, demonstrate that, the method is practically effective and competitive to some existing methods.

Green Synthesis, Characterization and In Vitro Antioxidant Activity of Silver Nanoparticles from Aqueous Candle Bush (Senna alata) Leaf Extract

2025-03-18T22:30:13+00:00

Green synthesis of AgNPs has become a preferred method over chemical and physical approaches because it is eco-friendly, cost-effective, and avoids the use of toxic chemicals. Senna alata, commonly known as Candle Bush, is a medicinal plant widely used in traditional medicine for treating skin infections, diabetes, and inflammatory diseases. This study assesses the phytochemical content and antioxidant activity of Senna alata leaf extract, which is used in the environmentally friendly manufacture of silver nanoparticles. Both qualitative and quantitative phytochemical screening revealed a high concentration of bioactive chemicals, with the main ingredients being flavonoids (638.88 ± 4.45 mg/100 g), tannins (665.45 ± 10.29 mg/100 g), and phenols (941.32 ± 10.56 mg/100 g). The biosynthesized AgNPs have been characterized by various analytical techniques such as TGA/DTA, TEM, SEM, and EDS. TEM analysis revealed spherical nanoparticles within the size range of 2.70–6.37 nm. TGA confirmed good thermal stability up to a temperature of 400°C. SEM showed the structures to be of irregular porosity with sizes varying between 100 nm and 9 μm, whereas EDS showed silver as the major constituent (50.58 wt %). Antioxidant activity was evaluated by DPPH and ABTS assays. The nanoparticles demonstrated higher DPPH radical scavenging activity (IC₅₀: 0.129 mg/mL) than that of the crude extract (IC₅₀: 0.134 mg/mL), while showing similar ABTS radical scavenging activities (IC₅₀: 0.943 and 0.954 mg/mL, respectively). These results indicated that S. alata-mediated synthesis of AgNPs is a very promising eco-friendly approach toward developing nanoparticles with improved antioxidant properties that can be useful in pharmaceutical and biomedical applications.

Effect of some Processing Methods on the Proximate Composition of some Frozen Marine Fishes

2025-03-15T21:05:35+00:00

Fish being a significant protein contributor to our daily diet, is cheap to afford but is highly perishable due to change in climatic conditions and a favourable medium for the growth of microorganisms and hence processing method is important to extend the shelf life and add value. Boiling, smoking, salting and sun-drying methods were used to assess proximate composition of Atlantic mackerel (Scomber japonicus), Round Sardinella (Sardinella aurita), and Horse mackerel (Trachurus trachurus). The steaming method was used for the boiling, cold method smoking was used for the smoking, salt sprinkling and open sun-drying methods were used for the salting and sun-drying. Each of the processed fish species were analyzed for the moisture content, dry matter, lipid content, crude protein content, ash content, and the nitrogen free extract. The moisture content, dry matter, crude protein, ash content, lipid and nitrogen free extract of salting and sun-drying method ranged between 36.23-60.98%, 34.02-63.77%, 35.08-56.71%, 5.00-12.20%, 5.24-5.43% and 26.72-53.42% respectively. The smoking method ranged between 46.30-60.49% of moisture content, 39.51-53.70% of dry matter, 52.73-59.87% of crude protein, 3.07-13.15% of the ash content, 6.10-7.95% of lipid and 22.52-36.25% of nitrogen free extract. The boiling method ranged between 47.47-57.27% of moisture content, 42.73-52.53% of dry matter, 35.93-65.60% of crude protein, 6.23-8.35% of the ash content, 5.09-6.23% of lipid and 26.77-50.69% of nitrogen free extract all against the raw fish species which also ranged between 51.50-62.89% of moisture content, 39.11-48.50% of dry matter, 48.55-55.67% of crude protein, 2.21-6.67% of ash content, 6.50-8.05% of lipid content and 30.55-38.85% of nitrogen free extract. The processing methods used (smoking, boiling, salting and sun-drying) showed no negative effect on the proximate composition after analysis. Boiling, smoking, salting and sun-drying processing method has more positive effect on the proximate composition of Trachurus trachurus than Sardinella aurita and Scomber japonicus.

Sunlight-Assisted Green Synthesis of Silver Nanoparticles using Musa accuminata Peel and their Antimicrobial Potential

2025-03-15T20:51:55+00:00

Green nanotechnology has acquired high demand due to its cost-effective and eco-friendly approach for the synthesis of nanoparticles. Silver nanoparticles (AgNPs), currently, are among the most widely used artificial nanomaterials present in a range of consumer products. The silver nanoparticles were synthesized from the water extract of banana peels using a green technique that is eco friendly. The plant's secondary metabolites served as capping and reducing agents. The synthesized silver nanoparticles (MAP-AgNPs) were characterized using Fourier Transform Infrared (FTIR) Spectroscopy, Ultraviolet-visible Spectroscopy (UV-vis), X-Ray Diffraction (XRD), Scanning Electron Microscope (SEM) and Energy-dispersive X-ray Spectroscopy (EDS). The plant extract contained bioactive components that were responsible for biogenic synthesis and the capping and stabilizing properties. These compounds could be the source of the vibration frequencies noticed in the spectra of the MAP AgNPs and that of the plant extract, such as those seen at 3272 cm^-1, 3280 cm^-1, 2918 cm^-1, 2851 cm^-1, 1736 cm^-1, 1636 cm^-1 etc. The 400–500 nm absorption peak was visible in the UV–Vis spectra, showing the adsorption of silver nanoparticles. XRD studies confirmed the crystalline nature of the synthesized material showing five distinctive diffraction peaks at 2θ degrees of 38.37^◦(111), 44.54^◦(200), 64.75^◦(220), 77.89^◦(311) and 81.87^◦(222), which evidently indicated the formation of the face-centered cubic (fcc) crystalline structure of the AgNPs. The SEM image revealed the shape of the synthesized MAP AgNPs as being spherical. EDS result showed that the materials are primarily composed of silver, 65.20 wt%. Other minor visible elements like carbon, oxygen, silicon, iron, potassium, calcium, and aluminum may have come from the phytochemicals from the plant part used to bioreduce AgNO₃ solution. Thermogravimetric analysis shows that the material is stable, since no loss in mass was recorded until at a very high temperature of about 300 ^oC. The nanoparticles exhibited antibacterial activity against all tested organisms, indicating broad-spectrum efficacy.

Intelligent Traffic Management System Using Ant Colony and Deep Learning Algorithms for Real-Time Traffic Flow Optimization

2025-03-19T11:42:17+00:00

Urban traffic congestion presents a formidable global challenge that necessitates innovative and adaptive solutions, surpassing the capabilities of traditional traffic management systems. This research introduces an Intelligent Traffic Management System (ITMS) that synergistically integrates Ant Colony Optimization (ACO) and Deep Learning (DL) methodologies, effectively optimizing real-time traffic flow. To dynamically adapt to complex urban environments, the ITMS leverages ACO for agile routing and DL for precise traffic prediction, enabled by a novel Long-Short-Combination (LSC) framework designed to accommodate both congested and uncongested traffic attributes. Real-time data acquisition is achieved using a computer vision model, which detects and classifies vehicles into four categories (cars, bikes, buses, and trucks) with updates every 15 minutes. Data preprocessing addresses inconsistencies to ensure integrity. The ITMS employs ACO to optimize vehicle routing dynamically by simulating artificial "ants" that evaluate routes based on pheromone levels representing congestion and distance, thus adapting to real-time fluctuations. Reinforcement learning dynamically adjusts traffic signal timings, minimizing congestion and optimizing overall traffic flow. Six Machine Learning models were tested, finding a weighted average precision, recall, and f1-score of 0.95. More specifically, for traffic situation classification, a detailed model performance analysis was conducted, revealing that Class 0 achieved a precision of 0.99, recall of 0.98, and F1-score of 0.99. Class 1 achieved a precision of 0.90, recall of 0.87, and F1-score of 0.88. Class 2 achieved a precision of 0.93, recall of 0.96, and F1-score of 0.95, and Class 3 had a precision of 0.96, recall of 0.96, and F1-score of 0.96. These results highlight the transformative potential of AI-driven traffic optimization.

Leveraging Machine Learning for Personalized Dietary Recommendations, Nutritional Patterns, and Health Outcome Predictions

2025-02-21T11:08:59+00:00

Unhealthy dietary patterns are key contributors to chronic diseases such as obesity, diabetes, and cardiovascular conditions. This study employs machine learning (ML) techniques to analyze dietary intake, identify patterns, and assess their relationships with health outcomes. The aim is to provide personalized dietary recommendations and insights to promote healthier eating habits.

Data for this research were sourced from a Kaggle dataset on foods and nutrients and the National Health and Nutrition Examination Survey (NHANES) on health outcomes. Preprocessing steps included data cleaning, feature selection, and transformation using one-hot encoding and scaling techniques. Machine learning algorithms were applied to build a food recommendation system and a diet health check system. Visualizations such as correlation heatmaps, scatter plots, and dashboards further illustrated the relationships between demographic factors, nutrient intake, and health outcomes. The food recommendation system effectively identified foods with similar nutritional profiles to user preferences. For instance, it suggested nutrient-rich alternatives like rice noodles and kale, achieving similarity scores above 0.99 in multiple test cases. The diet health check system analyzed nutrient intake against predefined thresholds and provided tailored recommendations. Excessive carbohydrate, protein, fat, and cholesterol consumption were linked to conditions such as diabetes, coronary heart disease, and cancer, with specific dietary adjustments suggested for improvement. This study demonstrates the power of machine learning in personalizing dietary advice and enhancing health outcomes. By leveraging advanced algorithms and diverse datasets, the developed systems present a scalable solution for promoting balanced diets and mitigating chronic disease risks. Further refinement and broader implementation of these tools are recommended to maximize their impact on public health.

Linear Regression Approach to Solving Multicollinearity and Overfitting in Predictive Analysis

2025-03-14T11:54:15+00:00

Multicollinearity and overfitting are ubiquitous problems in predictive analysis, especially in linear regression models, which significantly hinder the precision and interpretability of predicted results providing critical insights for data-driven decision-making in diverse industries. This research examines a linear regression approach to address the dual challenges of multicollinearity and overfitting in predictive analysis. The dataset, sourced from the National Center for Disease Control (NCDC), was analyzed using multiple regression techniques, including Linear Regression, Ridge Regression, LASSO Regression, and Elastic Net Regression. The study aimed to assess and compare the efficacy of these methods in mitigating multicollinearity (measured by Variance Inflation Factor) and reducing overfitting through Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) metrics. Data was analyzed both with all features and after applying feature selection. Results demonstrated that while all models effectively addressed multicollinearity and overfitting, Elastic Net Regression exhibited superior performance, offering the best generalization capabilities with minimal MSE and RMSE discrepancies between internal and external data. These findings highlight the potential of advanced regularization techniques in improving predictive accuracy and interpretability, particularly in high-dimensional data contexts such as those involving COVID-19 outcomes. The study underscores the importance of further research into enhanced machine learning techniques and the inclusion of broader datasets to refine predictive models for practical decision-making across sectors.

Comparative Studies of the Antifungal Properties of Mango and Orange Leaves in the Prevention of Gummy Stem Blight in both Water Melon and Cucumber

2025-02-14T05:35:51+00:00

Gummy stem blight, caused by the fungal pathogen Didymella bryoniae, is a significant disease of watermelon (Citrullus lanatus) and cucumber (Cucumis sativus), leading to reduced yield and economic losses. Fungicides are commonly used to manage this disease; however, they pose risks such as environmental pollution, fungal-resistance and food safety concerns. There is need to explor plant-based alternatives due to their biodegradability, safety and eco-friendliness. This study evaluated phytochemical composition and in vitro antimicrobial potential of ethanolic and methanolic leaf extracts of orange (Citrus sinensis) and mango (Mangifera indica) against Didymella bryoniae, the causal agent of gummy stem blight in watermelon and cucumber. Leaf extracts were prepared using Soxhlet extraction with methanol and water as solvents, and the agar well diffusion method was employed for the susceptibility assay. Preliminary fungal identification was based on conidial and colony morphology, Pathogenicity was conducted on fresh test plants to ensure the organism caused the disease. Phytochemical analysis revealed the presence of tannins, flavonoids, carbohydrates, steroids, and alkaloids in both extracts, with orange leaf extract exhibiting a high concentration of alkaloids and cardiac glycosides (+++). Saponins and anthraquinones were absent in both extracts. Methanolic orange leaf extract demonstrated the highest antifungal activity, with zones of inhibition measuring 88.00 mm and 80.00 mm at concentrations of 200 mg/mL and 150 mg/mL, respectively. In contrast, methanolic mango leaf extract exhibited inhibition zones of 62.23 mm and 57.89 mm at the same concentrations. The lowest concentration (12.50 mg/mL) showed minimal activity (20.21 mm). Fluconazole, used as a control, exhibited inhibition zones of 100 mm at similar concentrations. Statistical analysis indicated significant differences (P < 0.05) in the inhibition zones among the various concentrations and between the extracts and the control. The results suggest that orange leaf extract is more effective than mango leaf extract in inhibiting Didymella bryoniae.

Bio-Stimulant and Nitrogen Fixating Efficacy of Leaf Extract on the Early Growth of Adansonia digitata Linneus for Nursery Development

2025-02-14T04:48:56+00:00

Slow initial growth of baobab seedlings presents a significant challenge for widespread cultivation and productive plantation establishment. Synthetic growth promoters often carry environmental risks, prompting the exploration of eco-friendly alternatives. This study evaluates the bio-stimulant and nitrogen-fixing efficacy of leaf powders derived from locally available plants to enhance the growth of Adansonia digitata seedlings during nursery development. This research focused on the application of Moringa (Moringa oleifera), Bitter Leaf (Vernonia amygdalina), Senna (Senna semia), and Tamarind (Tamarindus indica) leaf powders as growth promoter. A Completely Randomized Design (CRD) with three replications were used to assess the impact of these treatments on shoot height, stem diameter, leaf number, and leaf area. Results revealed that Moringa leaf powder significantly enhanced all growth parameters. Seedlings treated with Moringa showed the highest increase in shoot height, reaching 6.23 cm, compared to 3.6 cm in the control group. Similarly, Moringa-treated seedlings exhibited the most substantial leaf production, with up to five leaves per seedling. Bitter Leaf and Tamarind treatments also demonstrated moderate effects with Bitter Leaf improving shoot height and leaf number, while Tamarind slightly increased leaf area and stem diameter 2.43 cm and 20.27 mm respectively. The findings emphasize the potential of plant-derived bio-stimulants as sustainable alternative to synthetic growth enhancers. The use of these bio-stimulants not only improves seedling vigor but also aligns with environmental sustainability goals by reducing dependency on chemical fertilizers.

Assessing the Impact of Spent Engine Oil Contamination Soil on the Growth of Adansonia digitata Linneous Seedlings Compared to Top Soil

2025-02-14T03:54:39+00:00

Some landscapes are known with soil pollutants and it is important to establish indigenous tree species to acclaim this substance. Therefore, this study was carried out to determine the comparative effect of top soil and spent engine contaminated soil on the growth pattern of Adansonia digitata seedlings in the nursery which was done in year 2023. Complete Randomized Design was used for the study with two treatments and fifteen replicates. Independent sample T- test and descriptive statistics were used for the analysis. Experiment was conducted for three months while shoot height, stem diameter and number of leaves were measured. Top soil produced better morphological characteristics of the species. Adansonia digitata can grow and survive in both top and contaminated soil but better with top soil. Therefore, this study recommended that Adansonia digtata should be planted in top soil. However, this species performed closely to the top soil. Therefore, further study should be conducted to determine on the ability of the species to absorbed ion and cation for remediation of the pollutants in the savannah landscape.

Assessment of Computer Studies Teachers’ Job Satisfaction in Private Secondary Schools in New Bussa Metropolis, Niger State

2025-02-14T02:15:08+00:00

A teacher that is satisfied with his/her job is equipped with collection of best teaching practices and it could also be noted that improvement of classroom instruction is largely dependent upon teachers’ satisfaction with his/her job. The study assesses “computer studies teachers’ job satisfaction in private secondary schools in New Bussa metropolis, Niger state”. The design for the study was qualitative wherein participants were extensively interviewed. Four (4) research questions guided the study. The sample selected for the study comprises of ten (10) computer studies teachers from seven (7) private schools in New Bussa metropolis. The interview questions were written in interview protocol proforma to guide the researcher in interviewing each teacher and the important observations were recorded in detail on the last pages given in the same proforma. The interview process of each teacher was recorded using mobile phone sound recorder and each recording was transcribed after the completion of every interview. An inductive thematic analysis conducted on the transcripts were used to generate codes which were converted into potential themes, then those potential themes and relevant data were merged and all transcripts themes and data were reviewed and analysed descriptively. The findings of the study showed that computer studies teachers were satisfied with the support and collaboration, recognition and feedback, ability to balance work and personal life that the computer teaching job accorded them. However, the computer studies teachers were not satisfied with their work environment, professional development, career growth and salary/pay. Specifically, the teachers’ job satisfaction level in the study area was found to be relatively above average. Furthermore, it was found that computer teachers’ job satisfaction can be improved by providing necessary facilities which help them bring out better academic performances in their students

Sentiment-Driven and Economic Indicators for Bitcoin Price Forecasting: A Hybrid Time Series Model

2025-03-13T14:59:50+00:00

Bitcoin, the leading cryptocurrency, has gained significant attention due to its high volatility and potential economic impact. Traditional financial forecasting models struggle to accurately predict Bitcoin prices due to its sensitivity to various factors, including market sentiment and macroeconomic conditions. Existing models primarily rely on historical price data, often neglecting external influences such as public sentiment and economic indicators like Gross Domestic Product (GDP). To address these limitations, this study explores a hybrid approach that integrates Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models with sentiment analysis and GDP data to enhance Bitcoin price prediction accuracy. The study evaluates the predictive capabilities of these models under different scenarios. When trained on Bitcoin price data combined with sentiment analysis and GDP data, the ARIMA model achieved a Mean Absolute Error (MAE) of 2081.66, Root Mean Square Error (RMSE) of 2518.35, and an R-squared value of 0.9143. In comparison, when trained on Bitcoin data alone, it exhibited lower accuracy. The LSTM model demonstrated superior performance, achieving an MAE of 1253.24, RMSE of 1717.65, and an R-squared value of 0.9602 when incorporating sentiment and GDP data, significantly outperforming its standalone counterpart. The results highlight the effectiveness of integrating sentiment analysis and GDP data in cryptocurrency price prediction, demonstrating that hybrid models provide greater forecasting accuracy than traditional approaches. This study offers a robust framework for financial time series forecasting, aiding investors, analysts, and policymakers in making more informed decisions in the cryptocurrency market.

PIXE Analysis of Vegetation Samples from Mining Site, Dange-Shuni LGA, Sokoto State

2025-02-20T21:58:18+00:00

This study has used Proton induced X-ray Emission (PIXE) to analyze fifteen vegetation samples collected from mining sites in Dange-Shuni LGA, Sokoto State. It is in a bid to ascertain trace elemental concentration in the vegetation samples as a result of their exposure to unearthed soil from deeply excavated mining activities in the mining communities of Dange-Shuni LGA. The elements; Al, Mn, Zn, Pb, Ti, Cr, Ni, Co, Cu, Fe, and As, were identified via the PIXE analysis of the vegetation samples from fifteen mining sites in a stratified random sampling by proportions. The results of PIXE through intrinsic efficiency of the detector indicates fair elevations of the elemental concentrations from the samples. The results are a justification of potential toxicity relevance in the samples as a response to the unabated mining activities in the localities. Caution must be applied on open grazing of animals and usage of vegetation of these localities for medicinal and other domestic applications to avert toxicity in human especially children of tender ages.

Isolation and Identification of Airborne Pathogen from a General Hospital Wards in Kaduna Metropolis, Kaduna State, Nigeria

2025-02-20T20:57:10+00:00

Nosocomial infection poses a significant and pervasive threat to human health, thereby remains a significant concern globally, with airborne pathogens contributing substantially to their transmission. This study was conducted to isolate and identify bacteria and fungi airborne pathogens of some selected wards at Yusuf Danstoho Memorial Hospital, Tudun Wada, Kaduna. The microbial quality of indoor air of five wards which include; Accident and Emergency (A and E) unit, Male Medical Ward (MMW), Male Surgical Ward (MSW), Female Medical Ward (FMW), and Female Surgical Ward (FSW) was conducted. Sedimentation technique using open Petri-dishes containing different culture media was employed, isolates were identified according to standard methods. The isolated bacterial species were identified as Staphylococcus aureus, Pseudomonas aeruginosa, Klebsiella pneumonia, Escherichia coli, and Micrococcus sp. from the study. Pseudomonas aeruginosa has the highest percentage occurrence of 28.89%, followed by Staphylococcus aureus (24.24%), then Klebsiella pneumoniae (20.00%), while Escherichia coli (15.56%) and Micrococcus sp recorded the least (11.11%). Fungi isolates obtained were Aspergillus spp, Penicellium spp and Candida sp. with Aspergillus spp. having the highest occurrence of 52.94%, followed by Penicellium spp and Candida spp both with 23.52%. The accident and emergency ward (A & E) recorded the highest airborne bacterial and fungal population with 24.44% and 26.47% respectively. The results also showed that airborne bacterial pathogens were present in all the sampled hospital wards. These findings emphasize the need for stringent cleaning and ventilation measures in our hospitals to prevent nosocomial infections.

Digital Solution for Seamless Water Supply Management in Wukari Metropolis

2025-02-14T03:01:34+00:00

Access to safe and reliable water sources is a critical challenge faced by many urban areas, including Wukari Metropolis, Nigeria, where residents encounter significant difficulties obtaining clean water. Water vending is an ancient practice globally, especially in developing countries like Nigeria, where vendors deliver water by hand, donkey carts, pushcarts, or tank trucks. People patronize these informal water supplies despite lacking safety and quality assurance. Residents struggle with limited access to safe water sources and lack efficient water vendors. As such, this study addresses these pressing issues by proposing a web-based water supply management system designed to enhance accessibility and efficiency. The proposed system will allow users to conveniently order water through their smartphones, featuring functionalities such as user registration, order placement, order tracking, and flexible payment options. Utilizing an object-oriented design methodology, specifically the Unified Modelling Language (UML), the system development was guided by best practices in software engineering. This innovative solution addresses the limitations of the existing water supply system, providing a more convenient and effective means for residents to access clean water in Wukari metropolis.

Perception of Socioeconomic Effect and Constraints of Artificial Intelligence (Agricultural Technology) Performance of Agricultural Extension Agent in Delta State, Nigeria

2025-01-27T21:58:43+00:00

Artificial intelligence (AI) is taking over the different strata of life and industries, such as agriculture, to higher levels of productivity, efficiency, and decision-making with the use of smart technologies. This study evaluated the perceived socioeconomic impacts and challenges of AI on the productivity of agricultural extension agents in Delta State, Nigeria. The data was collected from 51 respondents through use of stratified random sampling technique and analyzed using descriptive statistics. The findings indicated that the majority of the extension agents saw AI as having both economic benefits and limitations. Perceived economic impacts formed the largest means of 2.89 where the respondents were most concerned with affordability with a mean of 3.14 and the redundancies that are expected to be witnessed with a mean of 2.55. Perceived barriers to AI integration mainly concerned restricted access to the internet (mean = 3.14) and lack of technical skills (mean = 3.12) with a grand mean of 2.86. From the study, it suggested that infrastructure, technical training, and policy intervention should be put in place to support AI usage in agricultural extension services.

Exploring Accelerated Failure Time Models for Tuberculosis Survival: Loglogistic and Weibull Survival Regression Model

2025-01-15T06:41:36+00:00

Tuberculosis (TB) remains a significant global health burden, necessitating robust statistical models to understand survival dynamics and inform interventions. Most survival analyses rely on the Cox Proportional Hazards (Cox PH) model, which may not adequately capture the survival time distribution. This study focuses on data from the National Tuberculosis and Leprosy Center (NTLC), Zaria, Kaduna State, Nigeria, to identify factors influencing TB survival and assess alternative parametric survival models. The study aims to identify factors associated with TB mortality, assess their impact on survival outcomes, and compare the performance of Weibull, and Log-Logistic Accelerated Failure Time (AFT) models to determine the most suitable model for TB survival data from NTLC Zaria. This study compares the performance of two Accelerated Failure Time (AFT) models, the Weibull, and the Log-Logistic in analyzing TB survival data. The analysis evaluates model fit using p-values, log-likelihood, and Akaike Information Criterion (AIC). Results indicate that the Weibull AFT model outperforms the Log-logistic, with the highest log-likelihood (-228.6) and the lowest AIC (485.11), and the Log-Logistic AFT model (AIC: 492.02, log-likelihood: -232.0). The p-values for both models demonstrate statistical significance, highlighting their effectiveness in modelling TB survival data. However, the Weibull model's higher performance suggests it better captures survival time variability in TB patients. These findings emphasize the importance of selecting appropriate survival models for TB data analysis and support the application of the Weibull AFT model for future studies. Further research should explore integrating advanced statistical techniques and machine learning approaches to enhance predictive accuracy and improve TB management strategies. This study contributes to this growing field by applying parametric survival models, to analyse TB survival data from the National Tuberculosis and Leprosy Centre (NTLC) in Zaria, Nigeria.

Determination of the Distribution of Gamma Emitting Radionuclides in Abeokuta using an In-Situ Gamma Spectrometric System

2025-01-15T06:41:36+00:00

In all ground formations, naturally occurring radionuclides, such as ⁴⁰K, ²²⁶Ra and ²³²Th and their decay products exist as trace levels. In this study, in-situ gamma spectrometry, which consists of a NaI(Tl) detector, a portable shield 4 cm thick and 17.5 cm deep which was filled with lead shots and a mobile stand has been used to measure different components of environmental radioactivity around Abeokuta, capital of Ogun State, Southwestern Nigeria. Results from the study shows that ⁴⁰K, ²²⁶Ra and ²³²Th has a mean of 469.28 ± 4.09, 65.11± 0.27 and 558.93 ± 6.61 Bq/kg correspondingly. Calculated values for absorbed dose rate are as follows: 451.27 nGy/h for the mean, 86.76 nGy/h for the lowest, and 1635.92 nGy/h for the highest. The mean value of the yearly effective dosage is 0.99 mSv/y, with low value of 0.22 mSv/y and high value of 4.22 mSv/y. The minimum value exceeds the maximum value. Based on the findings of this investigation, one may draw the conclusion that the comparatively high activity concentration level of the city can be attributed to the unexpectedly high concentrations that were found at some sites.

Intelligent Waste Management Optimization Through Machine Learning Analytics

2025-01-27T13:28:03+00:00

Waste management presents serious obstacles to making metropolitan regions more habitable. Conventional waste management techniques are not usually optimized, resulting in overflowing bins, wasteful waste collection trips, and various negative environmental effects. This study addresses these challenges by developing an intelligent system integrating the Internet of Things (IoT) and machine learning technologies. This study aims to develop an intelligent waste management system that optimizes waste collection routes and schedules through machine learning. (ML) models and Internet of Things (IoT) powered smart bins. The system utilized Support Vector Machines (SVM) and Artificial Neural Networks (ANN) for data analysis, complemented by dynamic route optimization algorithms. Data collection over 90 days across 47 sites encompassed bin fill levels, battery status, and environmental parameters such as temperature and humidity. Results demonstrated significant operational improvements, with the system achieving 89% accuracy in fill-level prediction and enabling a 35% reduction in collection frequency. Implementation led to a 42% decreased fuel consumption and a 2.4-hour reduction in daily collection times. Commercial zones exhibited 1.8 times higher fill rates than residential areas, while weekend waste generation peaked at 2.1 times weekday. The findings indicate that IoT-ML technology integration substantially enhances urban waste management efficiency through data-driven decision-making. Phased implementation, prioritizing high-waste-volume areas, integrating with existing metropolitan systems, and developing standardized data protocols are recommended. This research contributes to the growing body of evidence supporting smart technology adoption in urban waste management, offering a scalable solution for improved operational efficiency and environmental sustainability.

Comparative Effect of Single and Mixed Organic Manure on Cucumber Growth Parameters in Girei Local Government Area of Adamawa State

2025-01-31T06:54:40+00:00

The abiotic challenge facing crop production in the Tropics is the inherent low concentration of essential nutrients in the soil for crop growth and development. The objective of this research is to determine the effect of single and mixed organic manure (Cowdung, Leafcompost, and mixture of Cowdung and leaf compost) on Vine length, number of leaves, and number of branches of cucumber. the experiment was laid in a randomized complete block design (RCBD) with 3 treatments replicated 3 times, one as a control measure. Materials used include: meter rule, Soil, organic manure, Polythene bag, string, water, etc. Results were expressed as mean and standard deviation. The comparative effect was analyzed using a two-way ANOVA. The significant differences of treatments were separated at 95% LSD level. The Results revealed that the application of cow dung manure gave the highest effect in increasing vine length, number of leaves and number of branches of cucumber with the highest mean of 79.33cm, 18 leaves and 8.66 branches respectively and the control gave the least Effect on cucumber vine length, number of leaves and number of branches with the lowest mean of 17cm, 4.66 leaves and 2 branches respectively. It therefore showed that application of cow dung manure should be used to observe More and Most effective growing medium for Cucumber.

Real-Time Infection Detection System in Broiler Farm using MobileNetSSD Model

2024-12-29T21:48:26+00:00

The early detection of diseases in poultry farms is very important in safeguarding flock health and reducing economic losses. Outdated method of monitoring poultry health involves manual examinations, which are time consuming, labor-intensive and prone to inaccuracies. To curtail these challenges, this study presents a real-time infection detection system using lightweight object detection model called MobileNetSSD model for efficient and automated health monitoring. The system consists of deep learning techniques with affordable hardware that support real-time detection, tracking and analysis of broilers movement patterns in farm A, that consist of untagged healthy broilers and Red tagged sick broilers. The exercise was repeated three times to obtain movement threshold 84.9cm for sick broilers and 213.03cm healthy broilers, the outcome produced from reference farm A was used to analyze farm B and farm C. The model achieved 87% average accuracy, 93% average precision 78% average recall and 84.9% average F1 score. The integration of this system into existing farms, can lead to prompt interventions, curb the spread of infections and overall improvement in health management of broilers. This also research highlights the potential of computer vision in modifying poultry health monitoring practices, contributing to more sustainable and efficient poultry farming.

Hydrocarbon Movability Properties of Sandstone Reservoirs of Sapele Shallow Field, Niger Delta, Southern Nigeria

2025-01-02T23:36:15+00:00

Sapele shallow field is a prolific oil field which forms the proximal portion of Sapele field in the Niger Delta oil province, Nigeria. The Sapele field itself is an onshore field of OML 41 located in the North-western part (Greater Ughelli deposit) of the Niger Delta oil province. The Greater Ughelli deposit is characterized by paralic interbedded sandstone and shale with a thickness of over 3000 m. Well logs from six wells, well 21, well 22, well 29, well 30, well 31 and well 32 were integrated to study the hydrocarbon movability potential of the field. The hydrocarbon movability potential of the field was delineated by looking at the various hydrocarbon movability factors such as the flushed zone obtained from water saturation parameter, movable oil saturation which is arrived at by subtracting water saturation from flushed zone, residual oil saturation evaluated from the difference between hydrocarbon saturation and movable oil saturation, and hydrocarbon movability index which is the ratio of water saturation and flushed zone. The study area on an average has flushed zone water saturation () of 0.88, an average hydrocarbon saturation of 0.47, an average movable hydrocarbon saturation of 0.35, residual hydrocarbon saturation of 0.12 and an average movable hydrocarbon index of 0.6. This study has shown that Sapele shallow field just like Sapele deep field is very viable with good hydrocarbon storage and transmission ability.

Impact of Abattoir Waste on Water Quality and Public Health around Slaughterhouses in Jos Metropolis, Plateau State, Nigeria

2025-02-10T21:28:43+00:00

Water pollution occurs when harmful elements enter rivers, lakes, wells, streams, boreholes, or reserved freshwater sources in homes and industries. This study assesses the impact of abattoir waste on water quality in Jos metropolis, Nigeria. Two slaughterhouses were selected and data was collected through field surveys, interviews, and questionnaire administration to abattoir workers and residents living around the Abattoirs. Water samples were also collected from six wells near the abattoirs for physicochemical and bacteriological analysis. Simple descriptive statistics, the Chi-Square test and the Spearman Correlation Coefficient were applied to analyse the data collected. Results of water analysis from the laboratory show that all water samples collected do not fall within World Health Organization (WHO) permissible limits which is very critical to the wellbeing of the people living within the vicinity. Also, interviews and questionnaire results showed that abattoir wastes were discharged directly into the environment without proper treatment, thereby contaminating both surface and groundwater quality which residents depend on for domestic consumption. About 62% of the respondents depend on underground water source for their use. About 72% of respondents had observed changes in their water quality over the years, leading to diseases with typhoid at the fore. The Spearman correlation result reveals a strong and positive value of 0.664 at a p-value of 0.05 level of significance, which indicates a significant and positive relationship between abattoir waste and water quality. The X² value of 91.654^aand Asymptotic significance (2-sided) was less than 0.05 (0.011) also giving reason to retain the alternative hypothesis and conclude that there is a significant relationship between abattoir waste and water quality in Jos Metropolis slaughterhouses. The research suggests addressing abattoir waste and water contamination in Jos metropolis through stricter environmental regulations, water treatment facilities, operator training, and sustainable waste management practices.