International Journal of Science for Global Sustainability

Exploration of Shallow Basement Aquifers for Cost-Effective Groundwater Exploitation Using Electrical Resistivity Profiling and Vertical Electrical Sounding in Minna, Nigeria

2026-07-01T04:54:46+00:00

Geoscientific exploration is a prerequisite for groundwater development through borehole drilling in the Basement Complex. With advances in model drilling technologies that increase drilling depth, accompanied by uncertainties, the cost of drilling boreholes has increased, especially where shallow groundwater zones are poorly explored. Horizontal Electrical Resistivity Profiling (HERP) and Vertical Electrical Sounding (VES), employing the Schlumberger configuration, were used to investigate the lateral and vertical resistivity variation of the study area. 5 profile lines, each consisting of 5 to 7 sounding stations, were established. Low resistivity points on the profiles were selected for further prospecting employing the VES with 50 m maximum current electrode half separation (AB/2). The HERP reveals lateral variation of the resistivity distribution associated with the subsurface weathered and fractured Basement rock units. Relatively low resistivity values (0.12 Ω to 0.17 Ω and 30.0 Ωm to 41.9 Ωm) were delineated in the north-western and south-western portions of the study area. These portions are interpreted as weathered, fractured, and water-saturated lithologies with shallow groundwater potential. In contrast to low resistivity zones, high resistivity zones, which represent relatively unfractured, fresh Basement lithology, observed along parts of profile 3 are considered unsuitable zones for shallow groundwater exploitation. VES interpretation reveals predominantly H-model curve type comprising 3 geoelectric layers (ρ₁ > ρ₂ < ρ₃). These layers represent the unsaturated topsoil, the weathered materials, and the fractured/fresh Basement rock units. The fractured Basement rock layer defines the shallow aquiferous zone within the depths of 30 m to 50 m.

Post-COVID-19 Schistosomiasis Burden in School Pupils: Evidence from Thomas Dam, Kano State, Nigeria.

2026-07-01T05:13:38+00:00

Schistosomiasis remains a major global public health problem, affecting approximately 250 million people worldwide, with about 90% of cases occurring in sub-Saharan Africa. Nigeria bears the highest burden globally, and recent evidence indicates that primary school children in resource-poor communities are disproportionately affected. In line with World Health Organization recommendations for integrated control including improved access to safe water, sanitation, health education, and mass drug administration. This study investigated the post-COVID-19 prevalence and socio-demographic factors of schistosomiasis among primary school pupils (PSP) in communities surrounding Thomas Dam, Dambatta Local Government Area, Kano State, Nigeria. A cross-sectional study was conducted among 864 PSP aged 5–15 years across eight primary schools. Schistosoma mansoni eggs were detected by formol-ether concentration technique from stool samples, Schistosoma haematobium eggs were detected by sedimentation technique from urine samples. Socio-demographic and risk factor data were obtained through pre-tested questionnaires, and descriptive statistics alongside Pearson’s chi-square tests were used to assess associations between variables and infection status. The findings revealed a predominance of males (66.7%), with older pupils aged 10–15 years forming the majority. Frequent water contact activities such as swimming, bathing, and washing at the dam indicated substantial exposure to potential sources of infection, while awareness and knowledge of the disease remained low among the pupils. These results highlight significant behavioral risk factors and poor health education, underscoring the need for targeted school-based interventions and improved awareness programs.

The overall prevalence of schistosomiasis among PSP was 14.6%, comprising 9.0% S. haematobium and 5.6% S. mansoni. Males had a higher prevalence (15.9%) than females (12.2%), although the difference was not statistically significant (p = 0.164). Infection was significantly associated with age (p = 0.002), with the highest prevalence observed among children aged 10–15 years. The findings indicate persistent transmission and low awareness in the study area, emphasizing the need for strengthened integrated control strategies to reduce morbidity and achieve elimination.

In Silico Screening of Natural Bioactive Compounds Targeting RXLR Effector Protein 25 In Plasmopara Viticola, The Causal Agent of Grapefruit Downy Mildew

2026-07-01T05:28:28+00:00

Downy mildew caused by the obligate biotrophic oomycete Plasmopara viticola remains one of the most destructive diseases of grapevine globally, leading to significant yield losses and a heavy reliance on synthetic fungicides, which are increasingly compromised by resistance development. This study utilized structure-based in silico screening to identify natural bioactive compounds targeting RXLR Effector Protein 25 (PvRXLR25), a critical virulence factor that suppresses grapevine immune responses. The three-dimensional structure of PvRXLR25 was predicted using AlphaFold 3, followed by virtual screening of diverse natural compound libraries from specialized databases, including Seaweed Metabolites 2024, Phenol-Explorer 2024, Lichen Database 2024, and others. Molecular docking with PyRx/AutoDock Vina revealed Strychnine Acid (CNP0260135.6) as the most promising candidate with the highest binding affinity of -6.7 kcal/mol, followed by thelephoric Acid (-6.2 kcal/mol) and variolaric Acid (-6.1 kcal/mol). Detailed interaction analysis demonstrated stable binding via a strong conventional hydrogen bond to PHE242 (2.0191 Å), a π–π T-shaped interaction with PHE241, and multiple hydrophobic contacts. These results indicate that lichen-derived secondary metabolites, particularly Strychnine Acid, possess strong potential as inhibitors of PvRXLR25 and offer promising scaffolds for the development of novel, eco-friendly strategies to manage grape downy mildew.

Serological Profiling of Epstein Barr Virus Among Blood Donors Attending Hajia Gambo Sawaba General Hospital in Zaria, Kaduna, Nigeria

2026-07-01T07:39:08+00:00

Epstein-Barr virus (EBV) is a ubiquitous human herpesvirus, and its serological profiling provides important insights into infection stages, immune response, and potential transmission risks. The aim of the research was to determine the serological profile of EBV blood donors attending Hajia Gambo Sawaba General Hospital in Zaria, Nigeria. Ninety (90) blood samples were collected and analyzed for anti-EBV antibodies (VCA-IgM, VCA-IgG, EBNA-IgG) using enzyme-linked immunosorbent assay (ELISA). Serological analysis showed antibody distributions of 4.4% for VCA-IgM, 90.0% for VCA-IgG, and 91.1% for EBNA-IgG. Based on antibody profiles, infection stages were classified as 8.8% primary infection, 4.4% recent active infection, 76.6% past infection, and 10% not classifiable. Real-time PCR with SYBR green analysis was carried out to confirm the presence of the EBNA-1 gene in recent active infections. PCR analysis confirmed the presence of the EBNA-1 gene in cases identified as recent active infections. The study demonstrates that EBV infection is widely distributed among apparently healthy blood donors in Zaria, with the majority presenting antibody profiles consistent with past infection and a small proportion showing asymptomatic recent infections, posing potential transfusion-related risks.

Geospatial Assessment of Heavy Metal Concentration in Groundwater of Kaduna South Local Government, Nigeria

2026-07-01T07:53:26+00:00

Water is an essential natural resource that supports human life, economic activities, and ecosystem functioning. However, groundwater quality is increasingly threatened by heavy metal contamination arising from anthropogenic activities. This study presents a geospatial assessment of heavy metal concentrations in groundwater within Kaduna South Local Government Area (LGA), north-western Nigeria. A total of thirty (30) groundwater samples were collected from boreholes and hand-dug wells and analysed for Zinc (Zn), Chromium (Cr), Cadmium (Cd), Lead (Pb), Iron (Fe), and Copper (Cu) using Atomic Absorption Spectrophotometry (AAS). Geographic Information System (GIS) techniques, specifically Ordinary Kriging interpolation, were employed to model spatial distribution patterns. Results revealed significant spatial variability, with elevated concentrations of Cr (0.23-0.25 mg/L), Cd (0.18-0.23 mg/L), Pb (3.65-4.73 mg/L), and Fe (3.92-7.04 mg/L) forming contamination hotspots in industrialised wards such as: Kakuri, Makera, Sabo, and Tudun Wada. In contrast, Zn (0.29-0.46 mg/L) and Cu (0.36-0.53 mg/L) remained within WHO and SON permissible limits. However, Cd, Pb, and Fe exceeded recommended thresholds in a substantial proportion of samples, indicating potential public health risks. The spatial patterns suggest strong anthropogenic influence, including industrial discharge, poor waste management, and infrastructure deterioration. This study demonstrates the effectiveness of geospatial techniques in groundwater quality assessment and provides critical data for sustainable groundwater management and public health protection.

Hybrid Deep Learning with Explainable AI for Breast Cancer Prediction Using the WBCD Dataset

2026-07-01T08:05:30+00:00

Breast cancer remains a leading cause of morbidity and mortality among women worldwide, creating demand for diagnostic models that are both accurate and interpretable. While machine learning and deep learning methods have achieved strong results on benchmark datasets, such as the Wisconsin Breast Cancer Diagnostic (WBCD) dataset, limitations including class imbalance, overfitting, and lack of interpretability hinder their clinical use. This study proposes a hybrid deep learning framework that integrates Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU), supported by class weighting and stratified sampling to address class imbalance, and SHAP (SHapley Additive exPlanations) to enhance model transparency. Preprocessing involved KNN imputation for missing values, label encoding, and Min-Max normalization. The model was evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. On the WBCD dataset, the CNN–GRU achieved 95.61% accuracy, 100.0% precision, 88.10% recall, 93.67% F1-score, and 0.9944 AUC-ROC. The absence of false positives underscores the model’s conservative boundary, though five false negatives highlight sensitivity trade-offs. SHAP-based global and local explanations identified concave points, perimeter, and radius as the most influential features, consistent with clinical knowledge and providing insight into both correct and incorrect classifications. Unlike prior studies that reported higher accuracies but offered limited interpretability, the proposed framework strikes a balance between predictive strength and clinical trustworthiness. These findings position CNN–GRU with explainable AI as a promising tool for decision support in breast cancer diagnosis and point to future directions in multimodal integration, federated learning, and prospective clinical validation.

Impact Assessment of Electromagnetic Radiation from Second and Fourth Generation Mobile Phones: Findings from GSM Village Kurudu, Abuja- Nigeria

2026-07-01T08:15:11+00:00

Due to overall surge from the use of handset close to the body tissues and the widespread use of these devices has created an impending health crisis regarding their emissions. Twenty four (24) different models of commonly used mobile phones were selected for the study at Kurudu, Abuja municipal area comprising of twelve brands of 2G and twelve 4G phones for comparison on their mode of exposure. The handsets include: Alcatel XL 535, Philips D1311, Gigaset C430, AT& TCL 2940, VTech CS6114, Panasonic KX-TS500, Xenios ACD 010, Telematrix (9700 series), Nokia 2600, Sony Ericson j230i, Samsung SGH-X490, Bontel, Tecno spark 20, Samsung Galaxy (SN-A1157/B3), Infinix hot (X682B), Iphone MGFP34/A, Redmi A3X, Opo A57, Itel A80, Villoan, Poco C61, Vivo X300Pro, Huawei P60Pro and Xiaomi 14.Electromagnetic field levels of the mobile phones were measured using an electromagnetic field meter with measurements piloted at close proximity (0cm to 5cm) from each device to mimic real-world exposure. A diverse and realistic head model was used to estimate the effects of electromagnetic radiation coupling to the human head. The potential hazards posed by mobile phone handsets on the human head were also addressed by evaluating the specific absorption rate (SAR) in human tissue for 2G and 4G phones. The results showed the highest SAR value of 1.66±0.05 obtained from 2G phone model type-Sony Ericson j230i and 0.23±0.01 was obtained for Samsung Galaxy (A11-SN-A1157/B3) for 4G phone respectively. The lowest SAR for 2G and 4G phones were as well obtained from Sony Ericson j230i with a value of 0.04±0.05 and 0.04±0.01 for Iphone MGFP34/A. However, comparing the various results acquired, the 2G phone of Sony Ericson j230i had above the threshold boundary of 1.6 set by the Federal Communications Commission (FCC) but falls below the 2.0 value of the International Commission on Non-Ionizing Radiation Protection (ICNIRP) limits.

A Secure AI-Enabled Data Governance Framework for Digital Economy Platforms

2026-07-01T08:25:07+00:00

The rapid expansion of digital economy platforms has created an increasing demand for secure, scalable and intelligent data governance systems capable of managing large volumes of digital transactions and sensitive information. Despite the growing adoption of digital technologies, many conventional data governance frameworks still experience major challenges such as; cybersecurity threats, poor scalability, inadequate privacy protection, inefficient governance processes and limited real-time decision-making capabilities. To address these challenges, this study presented a Secure AI-enabled data governance framework for digital economy platforms by integrating artificial intelligence, blockchain technology, cloud computing and intelligent automation mechanisms. A design-oriented and experimental system development methodology was adopted for the development and evaluation of the framework. The system was evaluated using key performance metrics including response time efficiency, precision, recall, Scalability, throughput, system availability, privacy preservation effectiveness, security performance, user satisfaction and governance efficiency. The evaluation results revealed that the framework significantly outperformed the conventional framework across all metrics. Specifically, the framework achieved an overall average performance rate of 96.68%, compared to 60.8% recorded by the conventional system. The findings indicate substantial improvements in operational efficiency, scalability, cybersecurity resilience, privacy preservation, governance effectiveness and system reliability. The study demonstrates that integrating AI-driven governance mechanisms with blockchain-supported security architectures can significantly improve transparency, trust, compliance monitoring and intelligent decision-making within digital economy platforms. Therefore, the framework provides a robust, reliable and sustainable solution for secure digital governance across sectors such as finance, healthcare, e-commerce, education and smart government systems.

Valorization of Low Quality Waste Cooking Oil (LQWCO) Via Catalytic Transesterification

2026-07-01T08:40:16+00:00

The large volume of waste cooking oil (WCO) produced globally (~ 5.0 to 10.0 billion gallons/yr) and the associated market size of $11.6 to $12.8 billion USD inferred that the development of more valorization strategies is essential. One of the feasible upgrading options currently considered is the transesterification into biodiesel. However, developing catalyst that is resistant to FFAs and other impurities in the waste cooking oil (WCO) is still being investigated. We have herein demonstrated the successful development of a red mud (RM) material with good potential to catalyze very low quality waste cooking oil (LQWCO) upgrading. The LQWCO feed contains 37.50% of free fatty acids (FFAs) in addition to 4.5% of other impurities. Interestingly, for the transesterification reaction performed at 60^oC using 0.3g of RM catalyst and 1:5 (oil: methanol) volume ratio for 1.0h, the RM catalyst produced 95.05% yield of biodiesel that was stable for five (5) repeated cycles of reaction. Additionally, the final biodiesel product contains negligible concentrations of FFAs, showing the ability of the RM material to catalyze combined in-situ esterification-transesterification process. The overall activity of this catalyst was much higher than that observed with each of the acid-modified charcoal systems (i.e. HSMC and HPMC). The RM catalyst was characterized to exhibit the oxides: Fe₂O₃ (42.0%), Al₂O₃ (22.5%), SiO₂ (15.5%), Na₂O (12.0%) and CaO (8.0%). Thus, the overall activity could be associated to the synergic effects of the various active sites in these oxide systems. The RM catalyst has strong industrial potentials for upgrading of WCO of low quality.

Heavy Oil Valorization into Lighter Viscous Fuel Using Low-Cost Catalysts

2026-07-01T08:50:31+00:00

Sequel to the escalations in global energy requirements, different catalysis-based options are under development for the enhanced recovery (i.e. EOR) of the huge heavy and extra-heavy crude oil reserves strategically distributed in key global regions. In line with this trend, the current work investigated the roles of different local catalysts in the aquathermolysis of a heavy crude oil sourced from Ondo State, Nigeria. For the aquathermolysis reactions performed at the conditions 150 cm³ (total oil-water content, 70/30), 120^oC and 0.2g catalyst for 1.5 h, the activity of the various catalytic systems obeyed the pattern C1 (pure carbon) < C2 (CaO-modified) < C3 (MgO-modified < C4 (CaO-MgO modified) < RM (Fe₂O₃-rich Red Mud). The results showed the RM material with the highest concentration of basic sites (i.e. 0.50 mmol/g) the best aquathermolysis catalyst followed by the CaO-MgO-modified carbon material formed under constant reaction conditions. The study therefore demonstrated that adequate amount of the basic sites is necessary to actualize the C-S, C-O, C-N and C-C ruptures in bulky heavy oil compounds for successful transformation into lighter more desirable hydrocarbon fuel products. Additionally, the RM catalyst is very prospective for field applications.

An Assessment of Environmental Determinants of Tourism Sustainability in Kaduna Metropolis

2026-07-01T11:17:53+00:00

Tourism in Kaduna Metropolis is increasingly impacted by environmental challenges that threaten its growth and sustainability. This study assessed environmental factors affecting the sustainability of the tourism industry in Kaduna Metropolis. Data were acquired using a standardized questionnaire delivered to 367 respondents and key informant interviews with relevant stakeholders. Quantitative data were analyzed using descriptive and inferential statistics, including regression and principal component analysis (PCA), while qualitative data were coded and reviewed with NVivo software. The results revealed that environmental factors exert a considerable influence on tourist behavior. Security and safety were the most decisive considerations, with 99.2% of respondents underlining their importance. Similarly, cleanliness, proper waste management, and absence of pollution influenced site choice, with over 85% agreement, while accessibility and infrastructure were also significant (88.1%). PCA confirmed three key clusters of factors: environmental–safety (security, pollution, climate, accessibility), natural endowments (scenic beauty, biodiversity), and cleanliness–waste management. Environmental issues inhibited tourism significantly: 39% of respondents were often deterred from visiting destinations owing to negative conditions, while 63.7% changed travel plans because of seasonal or environmental factors. Natural hazards had significantly stronger effects, with 91.8% saying that flooding, poor drainage, or pollution shaped their site preferences. Results of the regression analysis revealed inadequate site maintenance as the most critical challenge (Odds Ratios up to 2.77, p < 0.001), followed by flooding, deforestation, and climate variability. The study concluded that environmental quality directly influences the viability of tourism in Kaduna Metropolis. The study recommended increased policy enforcement, improved waste management and drainage infrastructure, biodiversity conservation, and active community participation to ensure long-term tourism growth and its contribution to economic development.

Herb–Drug Interaction and Antidiabetic Drug Development: A Spectroscopic and Pharmacokinetics Investigation of Polyalthia Longifolia Versus Metformin

2026-07-01T11:34:26+00:00

Diabetes mellitus is a chronic metabolic condition characterized by consistently high blood sugar levels, resulting from problems with insulin production, insulin function, or both. Metformin remains the most widely recommended first-line treatment for type 2 diabetes because of its proven effectiveness, good safety profile, and ability to improve the body’s response to insulin. Despite its benefits, many diabetic patients now combine metformin with herbal remedies, such as Polyalthia longifolia, in hopes of gaining additional health advantages. While this practice is increasingly common, it also raises important concerns about possible herb–drug interactions, especially since there is limited scientific evidence on how Polyalthia longifolia may affect the pharmaceutical performance of metformin. Understanding these interactions is essential to ensure safe co-administration, preserve therapeutic effectiveness, and prevent unwanted changes in drug absorption or availability. In this study, the effect of methanolic extract of Polyalthia longifolia on the in-vitro dissolution profile of different brands of metformin tablets was investigated. The selected metformin brands were first evaluated for key physicochemical properties according to pharmacopeia standards. Findings showed that all brands met the required quality specifications, including friability values below 1%, weight variation within ±5%, disintegration times under 15 minutes, and hardness values ranging from 4–10 kg/F. These results indicate that the tablets possessed satisfactory quality, strength, and formulation stability. Further phytochemical screening of the plant extract revealed the presence of important bioactive compounds such as alkaloids, flavonoids, saponins, terpenoids, steroids, and cardiac glycosides, all of which may influence drug dissolution and absorption. To assess potential interactions, dissolution studies were conducted in phosphate buffer (pH 6.8) to simulate intestinal conditions, both in the absence and presence of the plant extract. The results demonstrated that Polyalthia longifolia extract significantly increased the apparent dissolution rate of metformin, with some tablet brands achieving complete drug release of up to 100%. This increase may be due to physicochemical interactions between the extract and the drug, enhanced solubilization, or possible analytical interference from phytochemical constituents present in the extract. In summary, methanolic extract of Polyalthia longifolia significantly altered the dissolution behavior of metformin tablets, suggesting a potential herb–drug interaction that may influence the drug’s bioavailability and therapeutic response. These findings highlight the importance of exercising caution when Polyalthia longifolia is used alongside metformin and reinforce the need for further in-vivo studies to determine the real clinical significance of this interaction.

Isolation and Identification of Aspergillus fumigatus From Fresh Cow Milk Within Sokoto Metropolis

2026-07-01T11:45:26+00:00

Fresh cow milk serves as an important source of nutrients and is widely consumed within Sokoto metropolis, yet it is highly susceptible to microbial contamination, including fungal organisms. This study was conducted to isolate and identify Aspergillus fumigatus from fresh cow milk and to assess the prevalence of fungal organisms in clinically non-mastitis milk from bovines within Sokoto metropolis. A total of eighty fresh cow milk samples were collected from different associated farms and selected households across the study area. The samples were collected aseptically and transported to the laboratory for mycological examination using standard cultural and morphological identification methods. Each sample was cultured on appropriate fungal media and incubated under suitable conditions to allow fungal growth and identification. Out of the eighty milk samples processed, fifteen samples were found positive for fungal isolates, indicating the presence of fungal contamination in fresh cow milk within Sokoto metropolis. The most common fungal isolates identified included Rhizopus species, Candida species, and Aspergillus fumigatus. The prevalence rates of these isolates were 18.5%, 5%, and 3.75% respectively, with Rhizopus species being the most predominant. The presence of Aspergillus fumigatus in fresh cow milk is of particular concern due to its potential to cause opportunistic infections and allergic reactions, especially among immunocompromised individuals. These findings suggest that fungal organisms play a significant role in bovine milk contamination within the study area. The study highlights the importance of proper hygiene and sanitation practices during milking and milk handling. Improved farm management and judicious use of antibiotics may help reduce the incidence of fungal contamination. Regular monitoring and quality control measures are recommended to ensure safe milk consumption.

Investigating the Influence of Sociodemographic, Lifestyle and Clinical Factors on Vesicovaginal Fistula (VVF) Severity in Sokoto, Nigeria

2026-07-01T11:55:28+00:00

Vesicovaginal fistula (VVF) remains a major public health challenge in developing countries, particularly in sub-Saharan Africa, where limited access to quality maternal healthcare contributes to its persistence. Despite extensive studies on VVF in Nigeria, limited research has examined how sociodemographic, lifestyle, and clinical factors jointly influence the severity of the condition in Sokoto State using a multilevel modelling approach. This study therefore investigated these factors among women in Sokoto State, Nigeria. Primary data were collected from 402 respondents at the Gynaecology Department of Maryam Abacha Women and Children Hospital using a structured questionnaire covering sociodemographic characteristics, lifestyle behaviours, reproductive history, and clinical factors. The outcome variable (VVF severity) was coded as a binary response. Data were analysed using descriptive statistics, chi-square tests, and multilevel logistic regression. Bivariate analysis showed significant associations between VVF severity and age, education, socioeconomic status, smoking, illicit drug use, place of delivery, prolonged labour, and parity. Multilevel logistic regression identified age (OR = 1.83, 95% CI: 1.05–2.12), socioeconomic status (OR = 1.51, 95% CI: 1.03–1.95), illicit drug use (OR = 2.34, 95% CI: 1.76–2.77), place of delivery (OR = 2.03, 95% CI: 1.73–2.54), prolonged labour (OR = 1.64, 95% CI: 1.06–1.97), and parity (OR = 1.54, 95% CI: 1.04–1.88) as significant predictors of severe VVF. The final model showed the best fit compared to alternative models based on AIC and BIC values.

The study concludes that VVF severity is driven by a combination of sociodemographic, lifestyle, and clinical factors. Strengthening maternal healthcare services and promoting institutional delivery are essential for reducing VVF severity in the region.

A Bayesian Predictive Framework for Early Detection of Water Quality Degradation in Urban Distribution Network

2026-07-01T12:08:33+00:00

In recent times, the scenarios where the modern quality monitoring systems are used have been changing rapidly, with the quality being changed over time, the situation being uncertain and the amount of data being extremely limited, thus fundamentally challenging the assumptions of the classical Statistical Process Control (SPC) methods. This article proposes quality monitoring within the Bayesian framework and Predictive Posterior Error (PPE), which facilitates shifting the emphasis in quality control from the gathering of historical data to the delivering of forward, looking predictive evaluations. The framework integrates latent regime, switching dynamics with robust observation models to explain structural changes, heavy, tailed noise, and low event, rate processes. Control charts based on PPE are formed from the posterior predictive mean, which results in monitoring signals that are interpretable and probabilistically calibrated. The proposed approach reliably controls the false alarm rate and detects the changes effectively in both stationary and non, stationary situations as demonstrated by comprehensive simulation studies, including outlier and intentional model misspecification cases. Further findings reveal favourable accuracy, efficiency trade, offs among the different exact and approximate Bayesian inference regimes. The paper presents a practical data application that demonstrates the efficacy of the developed tool, where PPE, based alarms correspond closely to the known process insults but at the same time these alarms are smoother and earlier than the traditional charts. In summary, the outcomes denote the posterior predictive expectation as a lasagna, laying and operationally relevant concept for modern Bayesian quality surveillance systems.

An Improved Convolutional Neural Network Framework For Efficient Motor Imegary Electroencepholography (EEG) Classification Using Common Spatial Pattern (CSP)

2026-07-04T12:56:06+00:00

This study develops an optimized Convolutional Neural Network (CNN) architecture based on the Common Spatial Pattern (CSP) method to improve the classification performance of motor-imagery-based brain-computer interface (BCI) systems. The use of brain activity is to communicate and control external devices by people with motor limitations has demonstrated encouraging outcomes for motor-imagery-based BCIs. However, the electroencephalography (EEG) signals can be difficult to extract pertinent information from, which limits the classification accuracy of these systems. The CSP algorithm, which successfully extracts discriminative features from multi-channel EEG signals, is combined in this study's unique approach with a CNN framework that makes better use of the learnt spatial filters. The preprocessing of the EEG data, use of the CSP algorithm to extract pertinent features, and training of the CNN with an optimized architecture make up the technique. Using publicly accessible benchmark datasets, the performance of the proposed strategy was assessed and compared to current state-of-the-art techniques (DNN). Improvements in classification accuracy (i.e., 98.80% was achieved against the 88.10%) of the existing approach (DNN-FBCSP), decreased computing complexity, and greater usability of motor-imagery-based BCIs are predicted consequences of this research, which was ultimately developed assistive technologies for people with motor disorders.

Effects of Solid Waste Disposal on the Occurrence of Common Diseases in Sabon Gari Local Government Area of Kaduna State, Nigeria

2026-07-04T13:16:40+00:00

The problem of environmental sanitation has remained unresolved over the years particularly in the few urban centers arising from increased population density, commercial and industrial activities. The study examined the effect of solid waste disposal on common health diseases in Sabon Gari local government area (LGA) of Kaduna State. Questionnaire is a major research instrument and was administered to 418 purposively selected samples in the six metropolitan areas having solid waste dump sites. The areas include: Samaru, Basawa, Sabon Gari Town, Palladan, Dogarawa and Bomo. Descriptive statistics, including frequencies, percentages, tables, and charts, were used to characterize and summarize the study data. The result shows that papers (26.1%) and broken bottles (21.3%) were the solid waste types generated mostly in the study area of which paper waste was mostly present in Bomo (38.5%). Weekly (43.3%) removal of solid waste was mostly practiced in the study area. The commonest diseases were malaria (30.4%) and Diarrhea (24.4%) in the study area. The periodic evacuation of solid waste (43.5%) and regular sanitation (25.8%) recorded the most measures used to combat the occurrence of common diseases which was practiced most in Bomo (82.1%) and Sabon Gari town (52.4%) respectively. The government (59%) plays the key role in managing domestic solid waste in the study area. The study recommends that regulatory laws governing waste disposal and management should be strengthened through public enlightenment campaign which will minimize indiscriminate dumping of waste and reduce environmental related health problems.