International Journal of Science for Global Sustainability
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The Beneficial Roles of Microbes in Food Production and Preservation: A Review
Get PDFMicroorganisms play a fundamental role in food production, contributing to the transformation of raw ingredients into a variety of nutritious and flavorful foods through processes such as fermentation and enzyme production. Beneficial microbes, including bacteria, yeasts, and molds, are responsible for the creation of staple food products like: yogurt, cheese, bread, wine and soy-based foods. Lactobacillus and Bifidobacterium species, for example, are widely used in dairy fermentation, enhancing not only taste and texture but also nutritional value by producing probiotics that promote gut health. In addition to their role in fermentation, microbes produce essential enzymes such as: amylases, proteases, and lipases, which aid in food processing by breaking down starches, proteins, and fats, respectively. These enzymes enhance food texture, improve digestibility and extend shelf life, making microbial involvement indispensable in modern food production. Beyond food production, microbes are equally significant in food preservation, helping to prevent spoilage and extend shelf life while maintaining food safety. Traditional preservation methods, such as: lactic acid fermentation in sauerkraut, kimchi, and pickles, rely on beneficial bacteria to create acidic environments that inhibit the growth of harmful pathogens. Similarly, acetic acid bacteria like Acetobacter species are responsible for vinegar production, which acts as a natural preservative in many foods. Some microbes also produce antimicrobial compounds, such as bacteriocins and organic acids that prevent the growth of spoilage organisms and foodborne pathogens. As food safety and sustainability become global priorities, harnessing microbial processes in food preservation for reducing food waste, enhancing shelf stability and food supply
Implications of Students’ Access to Computer and Academic Performance in Selected Public Secondary Schools in Tarauni and Kano Municipal Local Government Areas, Kano State, Nigeria
Get PDFRelevance of computer based education cannot be overemphasized in the 21st century. The use of computers in education impacts academic performance of students. In Nigeria, the use of computers especially in secondary schools is on the rise. A focus on North-western Nigeria, a region considered as educationally disadvantaged compared to other regions of the country, is vital to the nation. Although there is an affirmative national policy on the integration of Information and Communication Technology (ICT) in the secondary school curriculum, implementation of the policy at the level of school management is called to question. This research investigated the implications of student’s access to computer and academic performance in some selected public secondary schools in Kano State, Northwest Nigeria. The research adopted a survey design that used randomly selected but purposive sample of students from four schools in Kano metropolis with a view to investigating the level of availability of computers and the impact of the outcome on students’ performance. Results showed that some schools suffer non-existence of computers, most schools exhibit low level of computer availability but with students who indicate the capacity of to use computers. Results also indicated that students still suffer restricted access to computer at school, with the students having more access mostly from homes and cyber cafes. This has serious implications as most of the students fear that they cannot handle a computer based test (CBT). Provision of more computers by the school and a non-restricting computer access to students should be promoted
Mathematical Modeling of Prostate Cancer in Uganda Using the Atangana-Baleanu Caputo Derivative
Get PDFThis paper introduces a mathematical model to better understand how prostate cancer progresses in a population, using the Atangana-Baleanu Caputo fractional derivative. The model divides the population into different groups: susceptible individuals, those exposed to cancer-causing factors, individuals at various stages of infection, and those who have recovered. The exposed and infected groups represent the different stages of cancer, while the recovered group accounts for people who have overcome the disease through treatment or natural remission. By using fractional derivatives, the model captures the idea that cancer progression is influenced by past events, making it a more accurate reflection of how the disease unfolds over time. The paper develops a set of equations to describe how people move between these groups, considering factors like exposure, cancer progression, and recovery. Simulations show how the disease spreads and how various treatment strategies can influence the outcome. This model offers a valuable tool for better understanding prostate cancer dynamics and can help guide
A New Method for the Direct Solution of Second, Third and Fourth Order Differential Equations with Oscillatory Behavior
Get PDFThis paper presents a new block hybrid method for the direct numerical solution of second, third, and fourth-order ordinary differential equations (ODEs) with oscillatory characteristics. Unlike traditional approaches that rely on transforming higher-order equations into systems of first-order equations, the proposed method tackles the equations in their original forms, thereby reducing computational complexity and preserving the physical structure of the models. The method is formulated using power series approximations, collocation, and interpolation techniques to derive continuous implicit schemes and their discrete counterparts. A rigorous analysis of the method's basic properties such asrder, consistency, zero-stability, convergence, and absolute stability is conducted to establish its reliability. To validate the method, several numerical examples are presented. The results demonstrate that the new method offers high accuracy and stability compared to existing techniques, making it a robust and efficient tool for solving higher-order oscillatory differential equations directly
A Comparative Analysis of Stacked Ensemble and Neural Network Models in Enhancing Customer Retention in Banking
Get PDFCustomer churn is a major problem for the banking sector, as it affects the profitability and sustainability of the business. Therefore, it is important to identify the customers who are likely to leave to take appropriate actions towards retaining them. The study aims to apply machine learning models to predict churn and identify the most effective model based on performance metrics such as log loss, ROC AUC, and accuracy using a dataset of 10,000 customers with 14 features. The study also utilizes domain feature engineering to get better predictive signals. we performed data analysis, and tested models including a neural network and stacked ensemble models comprising Random Forest, XGBoost, LightGBM, and Gradient Boosting with logistic regression as the meta-learner. The study performed a comparative analysis of the performance of a neural network with stacked ensemble model of all four tree models. The stacked ensemble model outperformed others, achieving a log loss of 0.2497, ROC AUC of 0.9609, and accuracy of 89.11% on the test set. This indicates that the ensemble model, by combining the strengths of individual learners, effectively handles complex patterns in the data, thereby providing a tool for reducing churn and improving customer engagement strategies in the banking sector
Physiological Effects of Rapidus-50 On Chlorophyll, Protein and Lipid Content in Wolffia sp.: Implications for Aquatic Plant Health
Get PDFThis study aimed to investigate the physiological effects of varying concentrations of Rapidus-50, a diclofenac-based non-steroidal anti-inflammatory drug (NSAID), on Wolffia sp. Over a five-day exposure period, chlorophyll and lipid contents were measured in treatment groups receiving 10 µL, 100 µL, 1000 µL, and 10000 µL of Rapidus-50. The results demonstrated a clear concentration-dependent decrease in both chlorophyll and lipid levels, with the most pronounced reductions observed at higher concentrations. Statistical analysis using ANOVA confirmed the significance of these changes (p < 0.05), suggesting that the chemical exposure led to photosynthetic disruption and impaired lipid biosynthesis. These findings highlight the ecological risks posed by pharmaceutical pollutants in aquatic ecosystems
Transient Electro-Magneto Hydrodynamic (EMHD) Control of Hybrid Nanofluid Flow over 2D Riga Plate: Entropy Generation and Stability Analysis
Get PDFThis study investigates the transient Electro-Magneto Hydrodynamic (EMHD) control of hybrid nanofluid flow over a two-dimensional Riga plate, focusing on entropy generation minimization and hydrodynamic stability analysis. The Riga plate, composed of alternating electrodes and permanent magnets, generates an exponentially decaying Lorentz force, which enhances boundary layer flow control. While previous research has primarily examined steady-state EMHD flows, this work addresses the transient behavior critical for real-world dynamic systems. The governing equations for momentum, energy, and nanoparticle concentration are derived and solved analytically using perturbation methods for steady-state conditions and Laplace transforms for transient solutions. A finite difference numerical scheme is employed to validate the results numerically, ensuring accuracy through convergence criteria. Key dimensionless parameters, including the modified Hartmann number (Z), Richardson number (λ), and suction parameter (s), are analyzed to assess their impact on flow, heat transfer, and stability. Results demonstrate that EMHD effects significantly enhance velocity profiles, while Prandtl number (Pr) variations influence thermal boundary layers. Entropy generation analysis reveals that thermal irreversibility dominates near the plate, whereas viscous and Joule heating effects prevail farther away. Stability studies confirm that strong suction (s) stabilizes the flow, suppressing perturbations. Additionally, Nusselt and Sherwood numbers increase with higher Z and s, indicating improved heat and mass transfer efficiency. This study provides critical insights into optimizing EMHD-based thermal systems by minimizing entropy generation and ensuring hydrodynamic stability. The findings have significant implications for microfluidics, nuclear cooling, and aerospace propulsion, where precise thermal management is essential
Asymmetric Modeling of NairaYuan Exchange Rate with Structural Breaks
Get PDFThis study investigates the impact of structural breaks on the Naira–Yuan askrate and assesses the stability and volatility (asymmetry) of the series using daily data spanning January 2015 to July 2025. Stationarity of the series was examined using the Augmented Dickey–Fuller (ADF) test, while symmetric models incorporating five structural breaks were employed to estimate the stabilized series and conditional variations in the Naira–Yuan exchange rate. Model performance was evaluated using model selection criteria. The study also provides an indepth discussion of exchange rate dynamics from the perspectives of persistence and leverage effects during the period under review. The results demonstrate that the Glosten–Jagannathan–Runkle GARCH (GJRARCH) model with five structural breaks outperforms earlier asymmetric models, achieving lower values for the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and standard error
A Deep Learning Approach to Smishing Detection in Mobile Apps Using Convolutional Neural Network and Long ShortTerm Memory
Get PDFSmishing, or SMS phishing, poses a significant cybersecurity threat to smartphone users. These attacks exploit the brevity and symbolic nature of SMS messages, making detection challenging. Existing methods, such as DSmishSMS, employ traditional machine learning techniques but require further enhancement. To address this rising and concerning issue, this thesis offers a model for an improved detection. The experiment demonstrate that the developed CNN model outperformed traditional algorithms, achieving an accuracy of 98.97% which is better than the 97.93% from the benchmark paper. By integrating deep learning techniques, this contributes to safeguarding users from fraudulent smishing attack
Serum Inflammatory Markers [(Interleukin (Il)-6, Il-8, And Il-10)] Relative to Group B Streptococcus Colonisation in Women At Delivery
Get PDFNewborns with Group B Streptococcus (GBS) infections have a significant rate of morbidity and mortality. One of the main risk factors for newborn GBS illness is maternal GBS colonisation during pregnancy. Previous investigations reported that inflammatory indicators produced by immune cells as a result of bacterial infection could also aid in identifying bacterial infections, in addition to other conventional methods. Thus, the study examined the potential utility of maternal serum interleukin (IL)-6, -8, and -10 as markers for predicting GBS colonisation at birth. The study included 136 HIV negative pregnant women who were not taking any antibiotics and had no other medical conditions. At the time of delivery, venous blood and vaginal swabs were taken. Using the culture approach, GBS was isolated from swabs. If a mother's culture was positive, she was considered GBS colonised. Maternal serum was used to analyse inflammatory markers (cytokines) using the R&D pre-mixed magnetic Luminex assay. A logarithmic transformation was used to normalise the cytokine values. Forty-seven (47) of the 136 participants had GBS colonisation. The logged concentrations of IL-6 (P=0.8), IL-8 (P=0.5) or IL-10 (P=0.9) did not differ significantly upon delivery according to colonisation status. According to our findings, maternal serum levels of IL-6, IL-8, and IL-10 are not accurate indicators of GBS colonisation at delivery