International Journal of Science for Global Sustainability

Effect of Sub-acute Exposure to Volatile Organic Compounds on Biochemical Indices in Wistar Rats

2026-03-07T06:26:45+00:00

Petroleum hydrocarbons’ exposure has been associated with the development of non-communicable diseases including cardiovascular diseases, allergies, cancers, immune toxicities and reproductive problems. However, little is known about their effects on metabolic dysregulation especially glucose homeostasis. This study therefore investigated the effect of volatile organic compounds (VOCs) on glycemic dysregulation in Wistar rats. A total of 30 adult albino rats were used and grouped into 5 groups of 6 rats each. The rats were individually exposed to 5%v/v of VOCs for a period of four weeks. The effects on body weight lipid profile, antioxidant enzymes, fasting plasma insulin, β-cell function, insulin resistance, inflammatory markers were evaluated and histological studies was carried out. The result indicated significant (p<0.05) increase in total cholesterol, triglyceride, and low density lipoprotein cholesterol in all VOCs exposed rats compared to control rats. Moreover, there was no significant (p>0.05) difference in SOD activity of VOCs exposed rats compared with control rats. Malondialdehyde (MDA) level was significantly (p<0.05) high with significantly (p<0.05) low catalase activity in control rats compared to all VOCs exposed rats. Level of Inflammatory markers IL-6, VEGF and TNFα were significantly high in all VOCs exposed rats with significant (p<0.05) increase in insulin resistance and β cell dysfunction compared to control rats. Histological examination of pancreas revealed changes in the cyto-architecture of pancreas of all VOCs exposed rats except toluene exposed rats. Therefore, exposure to benzene, hexane and xylene components of crude oil could cause glycemic dysregulation.

Modelling the Impact for the Application of an Antiretroviral Drug Therapy on HBV Infection

2026-03-07T06:38:59+00:00

In this paper, a mathematical model that considered the rate of antiretroviral drug therapy on hepatitis B virus (HBV) infection with a logistic growth, the immune response, the cure rate of an infected cells and a standard incidence function for the proliferation of the hepatocytes cells was developed. The results from the numerical simulations of the model shows that only 100% average usage of the antiretroviral therapy can guarantee a maximum number of healthy hepatocytes cells, a liver free from viruses and the absence of infected (unhealthy) hepatocytes cells from the liver.

Carbon Dioxide (CO2) Storage Potentials of Basaltic Rocks of Nigeria – A Review

2026-03-07T06:47:52+00:00

Basaltic rocks in Nigeria offer a promising opportunity for geological CO₂ storage through mineral carbonation, a process that converts CO₂ into stable carbonate minerals. This study assesses the potential of Nigerian basaltic formations for CO₂ storage, focusing on their geological, geochemical, and hydrogeological characteristics. We estimate the CO₂ storage capacity of major basaltic provinces, including the Jos and Biu Plateaux, and discuss the technical, economic, and policy challenges associated with deploying basalt-hosted CO₂ storage in Nigeria. Our findings suggest that Nigerian basalts possess suitable mineralogy and geochemical properties for effective CO₂ mineralization, with estimated storage capacities in the gigatonne range. However, further research and pilot projects are needed to demonstrate the feasibility and safety of large-scale CO₂ storage in these formations. This study contributes to the growing body of research on basalt-hosted CO₂ storage and highlights the potential for Nigeria to utilize its basaltic resources as a key component of its climate mitigation strategy.

Regime-Dependent and Time-Varying Dependence in Naira Exchange Rate Volatility: A Copula-Based Inverse Gamma Approach

2026-03-07T07:01:04+00:00

This study investigates the distributional characteristics, regime behavior, and dependence structure of monthly Naira exchange rate volatility against the United States Dollar (USD), Euro (EUR), and British Pound (GBP) using data spanning January 2005 to December 2024. Exchange rate volatility is constructed from logarithmic returns and analyzed within a unified framework combining heavy-tailed marginal modeling, volatility-based regime classification, copula-based dependence measures, and rolling-window dynamics. Empirical results reveal that volatility across all currency pairs exhibits extreme right-skewness and excess kurtosis, providing strong evidence of heavy-tailed behavior inconsistent with Gaussian assumptions. The inverse gamma distribution is shown to adequately characterize the marginal distribution of volatility, particularly in capturing extreme realizations. Regime-specific analysis indicates that volatility dynamics are state-dependent, with high-volatility regimes associated with substantially larger volatility magnitudes and heavier tails. Dependence analysis based on Kendall’s tau and copula models reveals weak co-movement during low and medium volatility regimes but a sharp intensification of dependence during high-volatility regimes, suggesting crisis-driven contagion effects. Rolling-window estimates further confirm that dependence among exchange rate volatilities is time-varying and closely linked to periods of market stress. Overall, the findings highlight the importance of heavy-tailed, regime-aware, and dependence-sensitive frameworks for modeling exchange rate volatility in emerging market economies.

Organofacies Characteristics of the Albian to Turonian Lower Post-Rift Sediments in the Orange Basin, South Africa: Insights into Type III Source Rock

2026-03-07T07:09:28+00:00

The present study describes the quantity, type, maturity, and petroleum generative potentials of potential source rock horizons using Rock-Eval pyrolysis for twenty-four (24) rock samples from the Orange Basin 4-K wells. The results showed an overall abundance of gas-prone organic matter. Since the HI value is generally less than 200mgHCgTOC in all the analyzed samples, it suggests type-III kerogen. Very low S₁ and S₂ yields indicate a low source rock generating potential in the lower post-rift deposits, which are probably more gas prone. Thermal maturity of the organic matter was predicted based on geochemical maturity indicators like Tmax (°C) and Production Index (PI). Tmax values vary from 425 to 476°C, signifying the presence of immature to early mature to late mature and post-mature source rocks. Meanwhile, the PI as a maturity indicator is between 0.10 and 0.27, containing early mature to peak mature source rocks. KH-1 well have PI values ranging from 0.18 to 0.24, with most of the samples at the peak of the oil window. This indicates that the KH-1 well organofacies are more likely to have good generative potential to produce oil.

Performance Evaluation of IoT Protocols in Resource-Constraints Smart Home Networks

2026-03-07T07:28:50+00:00

Smart home systems rely heavily on internet of things (IoT) communication protocols to support real-time monitoring, automation, and energy efficient operation. However, the heterogeneity of IoT devices and resource constraints such as limited bandwidth, processing capability, and battery power complicate protocol selection. This paper presents a technical performance evaluation of four widely used IoT application layer protocols which include constrained Application Protocol (CoAP), Message Queuing Telemetry Transport (MQTT), Advanced Message Queuing Protocol (AMQP), and Hypertext Transfer Protocol (HTTP). Using a unified simulation environment based on Omnet++6.2.0/Inet 4.5, the protocols were assessed under identical smart home network conditions. Key performance metrics include latency, throughput, packet delivery ratio (PDR), and energy consumption. The results demonstrate that CoAP achieves the lowest latency and energy consumption, making it suitable for delay sensitive and low power devices. MQTT and HTTP provide perfect packet delivery reliability due to their TCP based mechanisms, while HTTP offers the highest throughput at the expense of higher delay and energy cost. The findings highlight that protocol selection must be application specific, balancing performance requirements and resource limitations.

Design and Testing af A Hybrid Vertical Axis Wind Turbine for Power Generation

2026-03-07T07:38:40+00:00

There is a pronounced environmental urgency regarding the need to mitigate the hazards associated with carbon emissions and various pollutants resulting from the incineration of fossil fuels. The foremost approach to achieving this objective involves the utilization of alternative energy sources, among which wind energy is categorized; this energy is harnessed through the implementation of wind turbines. Wind turbines, also referred to as aerogenerators, are sophisticated devices that convert the kinetic energy derived from wind into electrical energy. This research delineates the wind assessment, design, optimization, fabrication, and performance evaluation of a hybrid vertical axis wind turbine (VAWT) aimed at power generation in the Kebbi, Sokoto, and Zamfara states over a two-decade period spanning from 1999 to 2024. Wind data was collected from the Nigeria Meteorological Agency (NIMET) for wind assessment. The design of the hybrid VAWT will be executed utilizing solid modeling tools and SolidWorks software in conjunction with Google SketchUp software, and the fabrication will occur locally. The constructed hybrid VAWT prototype will undergo laboratory testing employing ventilation fans across a spectrum of wind speeds. The VAWTs were tested at four different wind speeds (V = 4.80 m/s, 4.50 m/s, 4.30 m/s, and 3.90 m/s). The superior performance of the hybrid VAWT was demonstrated through a direct comparison with the conventional H-Darrieus VAWT under identical experimental conditions. The result revealed that rotational speed and mechanical power have an improvement on the same experimental conditions for hybrid VAWT as (11.0 %, 28.6%, 75.0 %, 14.0 %) and (100 %, 45.5 %, 9. 4%, and 40.3 %), respectively, compared to that of H-Darrieus VAWT. The percentage increase demonstrated that the hybrid vertical-axis wind turbine exhibited a substantial enhancement in performance when juxtaposed with the traditional vertical-axis wind turbine. The integration of the Icewind rotor (Savonius Rotor) with the H-Darrieus vertical-axis wind turbine (hybrid VAWT) results in superior self-starting capabilities and enhanced performance across the evaluated parameters in comparison to the standard H-Darrieus vertical-axis wind turbine.

Jordan Block Based Polynomial Collocation Technique for the Numerical Solution of Two Dimensional PVIDEs

2026-03-07T07:48:27+00:00

The paper present a polynomial method based on the Jordan block operational matrix for the solution of 2-dimensional partial Volterra Integro Differential Equations (VIDEs). The technique transforms the partial VIDEs to integral equation, this is then approximated through polynomial collocation at standard points, leading to a system of algebraic equations. It establishes the uniqueness of the solution by applying Banach fixed-point theory. To demonstrate the accuracy and reliability of the method, linear and nonlinear mathematical problems established in literature with known exact solutions are solved with the aid of a written MATLAB code. The results obtained prove that the technique is capable of reproducing the exact solutions with zero absolute error when the exact solution lies within the chosen polynomial space. The computational results further indicate rapid convergence and low computational cost, highlighting the spectral-type accuracy of the method. Hence, the Jordan block polynomial collocation technique provides a simple, accurate, and computationally efficient numerical technique for solving 2-dimensional partial VIDEs.

Anti-inflammatory and anti-oxidative activities of Xylopia aethiopica stem-bark extract on glyphosate-induced hepatotoxicity

2026-03-07T07:59:10+00:00

Toxicity posed by exposure to pesticides is known to be a major threat to public health, and phytochemicals have been suggested to confer ameliorative effects against pesticide toxicity. Hence, this work is designed to explore the anti-inflammatory and anti-oxidative activities of Xylopia aethiopica stem-bark (XASE) on glyphosate-induced hepatotoxicity. A total of 36 male albino rats weighing between 150-200 g were randomly assigned into six groups. Group 2 received 150 mg/kg b.wt of glyphosate; group 3 received 150 mg/kg b.wt of glyphosate and 126.49 mg/kg b.wt of XASE (1/10th LD₅₀ of XASE) after 2 hours; group 4 received 250 mg/kg b.wt of vitamin C after 2hrs of treatment with 150 mg/kg b.wt of glyphosate; group 5 & 6 received 126.49 mg/kg b.wt of XASE and 250 mg/kg b.wt of vitamin C respectively while group 1 served as control. The administration was done orally for a week. Activities of Aspartate and Alanine aminotransferases; total and conjugated bilirubin, and total protein levels were measured in the serum, while the level of oxidative stress markers like malondialdehyde, nitric oxide, reduced glutathione, glutathione peroxidase, catalase, superoxide dismutase, and Inflammatory markers C-reactive proteins, Interleukin-6, and Tumor necrosis factor-α levels in the liver homogenate were measured. Results show that treatment with XASE significantly modulates the incidence of oxidative stress, inflammation, and hepatotoxicity induced by glyphosate (p < 0.05). Likewise, the histopathological analyses of liver tissue result affirmed the hepatoprotective effect of XASE. Hence, X. aethiopica could confer protective effects against glyphosate-induced hepatotoxicity and inflammation.

Machine Learning in Financial Security: Performance Evaluation of Fraud Detection Models for POS Operators in Nasarawa Town

2026-03-07T08:15:33+00:00

Financial fraud continues to present a growing challenge for digital payment systems, especially for Point-of-Sale (POS) operators navigating the complexities of emerging economies. In Nasarawa Town, Nigeria, the rapid expansion of POS services has inadvertently created more opportunities for fraudulent transactions, highlighting an urgent need for smarter, more adaptive detection strategies. This study examines how effectively five supervised machine learning models, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and Neural Network, identify fraudulent POS transactions. Using anonymized transaction data, we carefully pre-processed the dataset, engineered relevant features, and addressed the persistent challenge of class imbalance before training and validating each model. Performance was assessed through accuracy, precision, recall, F1-score, and Area Under the ROC Curve (AUC). Results showed that Random Forest consistently led across all metrics, achieving 96.4% accuracy and 97.1% AUC, demonstrating its ability to reliably separate legitimate from fraudulent activity while maintaining a practical balance between catching fraud and avoiding false alerts. Neural Network followed as a strong contender, while Logistic Regression and Decision Tree showed more modest results. We conclude that ensemble learning approaches, particularly Random Forest, offer the most dependable and scalable solution for real-time fraud detection among POS operators in Nasarawa Town. These findings contribute locally relevant evidence to financial fraud analytics and offer actionable insights for developing machine learning–powered security frameworks in semi-urban digital payment environments.

Geological Hazards and Risk Assessment in Nigeria – A Review

2026-03-07T08:31:20+00:00

Nigeria faces significant challenges from geological hazards, including landslides, gully erosion, flooding, coastal erosion, drought, desertification, and minor seismic tremors. These hazards are driven by a combination of natural factors, such as geological structures, climatic conditions, and human activities, including deforestation, urbanization, and poor land-use practices. The impacts of these hazards are far-reaching, affecting economic development, social stability, and environmental sustainability. This review highlights the need for a comprehensive national geological hazard risk assessment and mitigation framework that incorporates both structural and non-structural measures. Effective mitigation strategies require a combination of engineering solutions, policy instruments, land-use planning, institutional strengthening, and public awareness. Recent advances in geospatial technologies, remote sensing, and geographic information systems (GIS) offer opportunities to improve hazard monitoring and assessment. However, addressing the challenges of data limitations, infrastructure constraints, and institutional fragmentation will be crucial to reducing the risks associated with geological hazards in Nigeria. This study emphasizes the importance of integrating geological hazard mitigation into national development planning, disaster risk reduction, and sustainable development initiatives.

Identifying Best Machine Learning Model For Prediction Of Cataract Status Using Network Information Criterion

2026-03-08T10:16:29+00:00

Cataract is an eye condition that makes people very difficult to see things clearly. Previous studies reviewed on Machine Learning (ML) used cross-validation, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) to select best Machine Learning (ML) model for prediction of Cataract but the use of Network Information Criterion (NIC) remain a challenge. The data used was from on-going research by the corresponding author on eye diseases and it was collected through personal interview at National Eye Centre (NEC) Kaduna from 25^th Nov. to 19^th Dec. 2024 after ethical approval was given on 21^st October, 2024. The study computed the NIC values of Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbour (K-NN), Naïve Bayes (NB) and Multilayer Perceptron (MLP) ML models that are used to predict Cataract status in the training and test sets and it was discovered that DT was the best model to predict the disease because it has minimum NIC value of 0.0288. This study demonstrates the appropriateness of NIC in identifying the best model for prediction of Cataract status and this would help healthcare professionals in making accurate informed decisions. Future study should compare the values of AIC, BIC and NIC and select the best model for prediction of Cataract status for more reliable prediction of the disease.

A Multi-Intervention Framework for Diphtheria Eradication: Optimizing Public Health Via Vaccination, Early Diagnosis, Isolation and Community-Driven Infection Control

2026-03-10T04:48:14+00:00

This study develops and analyzes a mathematical model for diphtheria transmission incorporating vaccination, exposure prevention, and treatment interventions. Analytical results include the disease-free and endemic equilibria, the basic reproduction number , and a sensitivity analysis showing that transmission rate, vaccination coverage, and treatment effort have the greatest influence on disease spread. A Hopf bifurcation analysis identifies threshold parameter values where the system may transition to oscillatory behavior, highlighting the potential for periodic outbreaks under specific conditions. Using the Pontryagin Maximum Principle, an optimal control framework is implemented via the forward-backward sweep method to minimize the number of infected individuals while optimizing public health resource allocation. Numerical simulations demonstrate that combined interventions significantly reduce both asymptomatic and symptomatic infections, achieving disease elimination within approximately 60 days. These results provide quantitative guidance for public health strategies, emphasizing the importance of early, sustained, and integrated control measures for effective diphtheria management

Machine Learning Based Diagnostic Comparative Modeling of Region-Specific Solar Irradiance in Nigeria

2026-03-10T04:57:47+00:00

This study evaluates and compares the predictive performance of Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Random Forest (RF) models for estimating solar irradiance across two climatically distinct regions of Nigeria, namely the North and the South. The models were adopted using important meteorological predictors, including minimum and maximum temperatures (tmin, tmax), relative humidity (rehu), wind speed (wins), evaporation pitch (evpi), sunshine hours (sunh), and solar radiation (radi). The analytical framework integrates Pearson correlation analysis, Variance Inflation Factor (VIF) assessment, regression diagnostics, residual distribution analysis, variable importance measures, and performance evaluation using Root Mean Square Error (RMSE) and the coefficient of determination (R²). Correlation analysis revealed weak to moderate relationships among the predictors, while all VIF values were below 2, indicating the absence of multi-collinearity and supporting the inclusion of all variables in the multivariate models. Model adequacy was further confirmed through diagnostic and residual analyses, which also highlighted clear regional variations in model performance. In the northern region, the RF model demonstrated superior predictive capability, achieving the lowest RMSE (0.0179) and the highest R² (0.93), whereas SVR and MLR exhibited substantially weaker performance with R² values of 0.35 and 0.23, respectively. In contrast, in the southern region, SVR emerged as the most accurate model (RMSE = 0.0078, R² = 0.89), marginally outperforming RF (RMSE = 0.0099), while MLR remained the least effective (RMSE = 0.0132, R² = 0.62). These findings were supported by residual and scatter plot analyses, which showed closer agreement between observed and predicted values for SVR and RF. Variable importance analysis consistently identified maximum temperature (tmax) as the most influential predictor in both regions. Overall, the results emphasize the importance of region-specific model selection and rigorous diagnostic validation for producing reliable and interpretable solar irradiance forecasts.

Species Composition and Physicochemical Characteristics of Termite Mounds in the New and Old Campuses of Bayero University, Kano, Nigeria

2026-03-10T05:09:17+00:00

The objectives were to collect and identify sample of termite species in Bayero University, Kano (BUK) and to determine the soil sample composition of the termite mounds in the two campuses. The study area comprises of non-academic area, academic area and staff quarters for ease of data collection. Sites were chosen sequentially based on 400m × 400m (1600m²) size to give 50% of the area. The number of mounds in each grid was counted, and the coordinates were also taken using a GPS device. The types of mound, such as mound associated with trees, buildings and on bare land were recorded. Each mound was dug, and castes obtained were identified at the Institute for Agricultural Research, Ahmadu Bello University, Zaria. Samples of soils from various mounds were collected, processed and analyzed for their physico-chemical properties. Descriptive statistics and students’ t-test were used to summarize the data at p<0.05. The results revealed three species of termites namely: Macrotermes bellicosus, Eutermes parvulus and Nasutitermstues spp. The study further established that, the mound soil of Nasutitermes spp. contained higher calcium (3.12 cmol/kg), magnesium (2.52 cmol/kg), sodium (0.35 cmol/kg) and potassium (0.36 cmol/kg) than mound soils of both Eutermes parvulus and Macrotermes bellicosus. The termites mound soil of Nasutitermes spp. was found to be more fertile compared to other mound soils of termite species examined in this study. Therefore, it is recommended that termite mound soil of Nasutitermes spp. may be adopted by subsistence farmers for growing of crops in the study area as an alternative to the expensive inorganic fertilizers.

Turmeric as a Traditional Medicinal Plant in Selected Countries: A Review

2026-03-10T05:21:47+00:00

Medicinal plants play a crucial role in both traditional and modern healthcare systems worldwide, providing abundant bioactive compounds essential for disease prevention and drug discovery. Globally, thousands of plant species are known for their therapeutic value and form the basis for traditional medical practices such as Ayurveda, Traditional Chinese Medicine, Unani, African traditional medicine, and other indigenous systems. Among several of these plants turmeric (Curcuma longa) holds a distinguished status due to its long-standing ethnomedicinal applications and well-established pharmacological effects supported by scientific research. This review emphasizes the importance of turmeric as a medicinal plant in selected countries and examines its cultivation practices, processing methods, and socio-economic importance. Overall, the review underscores turmeric’s continued cultural significance and economic value, as well as its strong potential for enhancing agricultural productivity, integration into healthcare systems, and pharmaceutical advancement.

Seroprevalence of Human Cytomegalovirus and Its Association with Hepatic and Renal Function Markers among Pregnant Women in Dutse, Jigawa State

2026-03-20T11:52:52+00:00

Human cytomegalovirus (CMV) is a common viral infection and a leading cause of congenital infections. This study determined the sero-prevalence of CMV and assessed its association with hepato-renal functions among pregnant women attending antenatal care at General Hospital Dutse, Jigawa State. A cross-sectional study was conducted among 81 pregnant women. Serum samples were analyzed for CMV IgG and IgM antibodies using ELISA. Liver and kidney function parameters were determined using standard methods. Data were analyzed using descriptive statistics, chi-square tests, analysis of variance (ANOVA), and binary logistic regression. Statistical significance was set at p < 0.05. The result showed that CMV sero-prevalence was 61.7%. CMV IgG was positive in 60.5% of participants, (i.e. the sum of 45.7% and 14.8%), 16.0% were IgM positive (i.e. the sum of 1.2% and 14.8%) and 14.8% had both IgG and IgM. CMV sero-status was not significantly associated with trimester of pregnancy (p = 0.53). Significant differences were observed in liver enzyme levels across CMV sero-status groups, for ALT (p < 0.001), AST (p < 0.001), and ALP (p = 0.027). Parity was a significant predictor of CMV sero-positivity, with nulliparous women having higher odds of CMV infection (OR = 12.7, p = 0.031). CMV infection is highly prevalent among pregnant women in Dutse and is associated with altered liver enzyme profiles. Parity appears to be an important risk factor associated with CMV seropositivity.

Design and Simulation of a Wireless Sensor Network Based System for Temperature and Humidity Monitoring

2026-03-20T13:27:53+00:00

Temperature and humidity are critical environmental parameters that directly influence agriculture, industrial processes and storage facilities. Conventional wired monitoring systems often face challenges such as high cost, inflexibility and complex installation. Wireless Sensor Networks (WSNs) provide an energy-efficient, scalable and cost-effective alternative for real-time monitoring. This study presents the design and simulation of a WSN-based system for temperature and humidity monitoring using MATLAB as the simulation platform through the use of 50 sensor nodes randomly deployed across a 100m × 100m area with a base station located externally. The simulation results demonstrate that the network achieves high data reliability, stable performance and efficient energy utilization. Cumulative energy consumption increased steadily to approximately 0.55J over 100 rounds, while all 50 nodes remained active throughout the simulation, indicating effective power management. Temperature and humidity readings remained closely aligned with expected averages of 20°C and 50%, respectively, with minimal fluctuations due to sensor data aggregation across multiple nodes. The findings confirm that WSNs can provide robust, real-time monitoring of environmental parameters with low energy cost, high scalability and long operational lifetime. These results highlight the potential of WSN-based solutions for deployment in agriculture, greenhouse monitoring, storage facilities and industrial monitoring.

Deep Learning-Optimized Design of Asymmetric Optical Directional Couplers for High-Efficiency Coupling

2026-03-28T06:16:42+00:00

This study investigates the application of a deep neural network to optimize the design of asymmetric optical directional couplers, addressing the limitations of traditional iterative methods, which are often resource-intensive and time-consuming. A feed-forward neural network was developed to predict the coupling length of a coupler based on its structural parameters. A large dataset of 30,000 samples was used to train, validate, and test the model. The network achieved a minimal mean square error (MSE) of 1.07×10−6, demonstrating high precision. The trained model exhibited a remarkable prediction accuracy of 99.88% for the coupling length. To validate these results, finite-difference time-domain (FDTD) simulations were conducted using the network's predictions. The simulations confirmed that injecting a Transverse Electric fundamental mode (TE0) into the access waveguide successfully excited a double mode (TE1) in the bus waveguide, achieving a high coupling efficiency of 99.5%. This research demonstrates that a deep learning approach can provide a rapid and accurate method for designing efficient mode-conversion-based optical asymmetric directional couplers.

A Narrative Analytical Review of Recovering Dark Data in Digital Document Preservation: An OAIS-Informed Perspective

2026-03-28T06:27:09+00:00

The rapid expansion of digital information has produced substantial dark data, unstructured, poorly described records lacking clear evidentiary purpose, which threatens institutional memory and evidentiary integrity. This narrative analytical review, using a PRISMA-adapted protocol across 45 sources, examines the Open Archival Information System (OAIS) as a framework for dark data recovery. A four-dimensional typology is proposed: structural stabilization, contextual reconstruction, evidentiary restoration, and ethical governance. OAIS Information Packages and Preservation Description Information enable reconstructive archival processes, restoring traceability and authenticity where provenance is incomplete. When augmented with AI-assisted metadata enrichment and ethical oversight, OAIS workflows transform dark data from latent liability into research-ready, trustworthy assets. This review highlights the integration of archival theory, metadata standards, and AI-driven curation to enhance discoverability, preserve authenticity, and mitigate epistemic and ethical risks, positioning OAIS as a dynamic infrastructure for responsible digital preservation and evidence-informed research.

A Hybrid Neural–Statistical Framework for Pipeline Lifespan Prediction under Spatiotemporal Dependence

2026-03-28T07:08:50+00:00

This study addresses pipeline failures in Nigeria’s oil and gas sector, which result in significant economic losses, environmental damage, and operational disruptions. A hybrid modelling framework is proposed, integrating deep learning architectures—Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Graph Neural Networks (GNN)—with Generalised Additive Models (GAM). The framework captures complex spatial and temporal dependencies whilst maintaining interpretability. The methodology incorporates feature selection, model training, and spatiotemporal cross-validation to evaluate predictive performance across different locations and time periods. Results demonstrate improved accuracy and robustness compared with traditional approaches. The model provides practical insights for predictive maintenance, risk assessment, and real-time anomaly detection, supporting safer and more efficient decision-making in the oil and gas industry.

Modeling and Comparison of the Nigerian All Share Index Using ARIMA and Hybrid ARIMA-GARCH Models

2026-03-28T07:54:48+00:00

In this Paper, the performance of the Autoregressive Integrated Moving Average (ARIMA) and hybrid Autoregressive Integrated Moving Average Generalized Autoregressive Conditional Heteroscedastic (ARIMA-GARCH) models was compared using daily data (Five working days) of the Nigeria stock exchange data stream. There are 4610 share price index between the 14^th January 2005 and 14^th September 2023. An attempt was made to make the share price index data stationary. The ARIMA model's residuals and squared residuals were subjected to the Box-Ljung, Box-Pierce, and McLeod-Li tests. These tests demonstrated that the ARIMA model's residuals possessed a conditional variance, or volatility. The volatility was then modeled using the GARCH model. The fitted ARIMA model was obtained using the Box-Jenkins technique, and the hybrid ARIMA-GARCH model was utilized to capture the sequences' volatilities. The results indicates that in terms of the lowermost Akaike information criteria (AIC) and Mean absolute error (MAE), the best ARIMA out of the ARIMA models verified was ARIMA (2, 1, 2). The comparison of ARIMA (2, 1, 2) with hybrid (ARIMA-GARCH) in terms of the uppermost maximum likelihood (ML), lowermost AIC and MAE indicates that the hybrid model is the best. Therefore, the study recommends that the Nigerian financial markets should replace the application of ARIMA only with ARIMA-GARCH in modelling Nigeria all share price index data.

Radionuclides Concentrations in Soil and Excess Lifetime Cancer Risk Due to the Gamma Radioactivity in Kogi Central District, Kogi State, Nigeria

2026-03-28T08:09:06+00:00

The activity concentrations of the natural radionuclides ²²⁶Ra, ²³²Th and ⁴⁰K were measured for eight soil samples collected from different location of high activity across the district, using high resolution gamma ray spectroscopy. The concentration of ²²⁶Ra in the soil in Kogi central district varied from 27.99143.1965 to 73.50540.7592 Bq/kg with average of 48.91282.4373 Bq/kg. The minimum value was obtained in Itakpe1 and a maximum value at Steel complex. For ²³²Th, the measured activity ranged from 32.51682.0446 to 128.80902.0053 Bq/kg with an average of 90.06062.0495 Bq/kg. The minimum value was also obtained in Kuroko and a maximum value obtained at Itakpe2. The activity concentration of ⁴⁰K ranged from 114.38843.0568 to 448.490410.1357 Bq/kg with an average of 254.41764.6723 Bq/kg. The minimum value obtained in Kuroko and a maximum value obtained at Itakpe1. The calculated radium equivalent activity () values are within and less than the permissible limits of 370 Bq/kg and the determined values of external and internal hazard index are less than unity, however, all the values of gamma representative index is greater than unity except at Kuroko. The calculated absorbed dose rates due to the presence of ²²⁶Ra, ²³²Th and ⁴⁰K in each of the soil samples varied from 48.327 to 119.794 (nGy/h), with an average value of 91.446 (nGy/h). this is about 2 times higher than the world average of 43nGy/h. The calculated annual effective dose rate in all the soil samples varies from 0.059 mSv/y to 0.146 mSv/y with an average value of 0.11 mSv/y and it remains within the criterion of 1.0 mSv/y recommended. The range of Excess Lifetime Cancer Risk (ELCR) is 0.207 to 0.511with an average value of which less than world average value of .

Application of Queuing Theory Models in Managing Patient Waiting Time: A Case Study of General Hospital Wudil, Kano State, Nigeria

2026-03-30T08:28:45+00:00

This study examined the application of queuing theory to manage patient waiting times at General Hospital Wudil, Kano State, Nigeria. The goal was to identify service inefficiencies and propose strategies for improving healthcare delivery. Primary data on patient arrivals, waiting times, and service durations were collected and analyzed using both (single-server) and (multi-server) queuing models. Preliminary model assumption checks, conducted through graphical plots, confirmed that both patient inter-arrival and service times follow exponential distributions. This supports the use of Poisson arrivals and exponential service times in the models. Furthermore, a Kolmogorov-Smirnov test indicates that both arrival and service times are indeed exponentially distributed, as the p-value is below the 5% significance level. The findings from the operating characteristics measures indicated that the single-server model resulted in high congestion, with a utilization rate of approximately 86%, an average queue length of about 5 patients, and a mean waiting time of nearly 14 minutes. This situation led to significant patient dissatisfaction and delays. In contrast, the multiple-server model exhibited improved performance, with utilization reduced to 77%, the average queue length decreasing to just 2 patients, and the average waiting time dropping to about 6 minutes. The study concludes that patient waiting time is strongly influenced by server capacity. Increasing the number of service points substantially reduces congestion and enhances efficiency. It recommends that hospital management adopt flexible staffing, expand service points, and implement queue management systems to optimize resource use and improve patient satisfaction.

Evaluation of heavy metal contamination and associated health risks in dumpsites soils of Idah and Igalamela/Odolu in Kogi State, Northcentral, Nigeria.

2026-03-30T09:32:06+00:00

This study assessed the concentration and environmental implications of heavy metals in soils from selected dumpsites in parts of Idah and Igalamela/Odolu in Kogi State, North-Central Nigeria. Twenty soil samples were collected at depths of 10–50 cm from major dumpsites in the study area. The samples were air-dried, digested using aqua regia, and analyzed for lead (Pb), cobalt (Co), nickel (Ni), copper (Cu), manganese (Mn), cadmium (Cd), zinc (Zn), and chromium (Cr) using Atomic Absorption Spectrophotometer (AAS). The measured concentrations were compared with WHO/DUTCH permissible limits to evaluate contamination levels and potential risks. Results revealed that Pb (12.413–150.761 mg/kg), Mn (0.121–3.423 mg/kg), Cd (0.002–14.463 mg/kg), and Zn (1.875–126.795 mg/kg) recorded average concentrations above recommended limits, indicating substantial contamination. Although Co, Ni, Cu, and Cr showed mean values below permissible limits, elevated concentrations were observed at specific locations, suggesting localized pollution hotspots. The order of metal abundance was Cd > Mn > Zn > Pb > Cr > Ni > Cu > Co, reflecting the dominance of toxic metals in the studied soils. The elevated heavy metal levels were primarily attributed to anthropogenic activities such as improper waste disposal, vehicular emissions, and dumping of agricultural and industrial residues. The findings highlight significant ecological and public health concerns, particularly due to the cultivation of edible crops on these contaminated soils. This study underscores the urgent need for continuous monitoring, improved waste management practices, and remediation strategies to mitigate soil degradation and safeguard human health in the study area.

Optimization of Dilute Acid Pretreatment and FTIR Validation of Bioethanol Production from Gmelina arborea Biomass

2026-03-30T10:01:47+00:00

The production of bioethanol stands out as a highly promising alternative to fossil-based fuels. Nevertheless, for bioethanol to become a viable option, it is crucial to develop more cost-effective production methods. Scientists have continuously examined diverse techniques for bioethanol production, utilizing an array of feedstocks in their research. This study investigates the efficacy of sulfuric acid pretreatment for bioethanol production from Gmelina arborea leaf and bark, coupled with Fourier-transform infrared (FTIR) spectroscopy to validate ethanol purity. Proximate analysis revealed high carbohydrate content in bark (75.4%) and leaf (71.5%). Acid hydrolysis (1–2 M H₂SO₄) and fermentation with Saccharomyces cerevisiae yielded 63 mL ethanol from 2 M H₂SO₄-pretreated bark (density: 0.972 g/cm³), while leaf hydrolysate at 1.5 M yielded the lowest (2.1 mL, 1.045 g/cm³) both indicating high impurities. FTIR analysis confirmed ethanol’s functional groups (O-H, C-O) and identified residual by-products such as carbonyl compounds (C=O) and alkynes (C≡C). The results underscore bark’s superiority as a feedstock and highlight acid pretreatment’s role in enhancing fermentable sugar release. This work advances sustainable biofuel strategies by integrating process optimization with spectroscopic quality control.

Socio-Behavioral and Clinical Predictors of Suspected Artemisinin Resistance in Plasmodium falciparum in Dutse Northern Nigeria

2026-04-02T06:38:30+00:00

Background: Artemisinin-based combination therapies (ACTs) remain the cornerstone of treatment for Plasmodium falciparum malaria. However, the emergence of artemisinin resistance threatens malaria control efforts, particularly in sub-Saharan Africa. While molecular markers of resistance have been widely studied, limited evidence exists on the socio-behavioral and clinical factors contributing to suspected resistance in Nigeria. A cross-sectional study was conducted among 384 patients presenting with suspected malaria in Dutse, northern Nigeria. Among these, 145 patients with microscopy-confirmed Plasmodium falciparum infection were included in the socio-behavioral and clinical analysis. Data were collected using structured interviewer-administered questionnaires and clinical records. Logistic regression analysis was used to identify predictors of resistance-related infections based on clinical and treatment outcome indicators.Treatment difficulty requiring multiple treatment courses was the strongest predictor of resistance-related infections (OR = 4.050; 95% CI: 2.1–7.8; p < 0.001). Completion of the full ACT treatment course was associated with significantly lower odds of resistance-related infections (OR = 0.158; 95% CI: 0.07–0.35; p < 0.001). Regular use of malaria preventive measures also showed a protective effect (OR = 0.283; 95% CI: 0.12–0.65; p = 0.002). Repeated malaria episodes were associated with increased likelihood of resistance-related infections, suggesting that frequent infections and repeated treatment exposure may contribute to resistance development. Self-medication and suspected use of substandard antimalarial drugs showed positive associations but were not statistically significant.The findings suggest that socio-behavioral and clinical factors play an important role in the emergence and spread of suspected artemisinin resistance in northern Nigeria. Treatment difficulty and repeated malaria episodes were major predictors, while treatment adherence and preventive measures were protective. Malaria control strategies should therefore integrate treatment adherence programs, drug quality regulation, preventive interventions, and resistance surveillance to sustain ACT effectiveness.

Vision Screening in School Children in a Rural Area of Kano State, Nigeria

2026-04-02T06:57:22+00:00

Visual impairment among school-age children is a significant but preventable public health issue, particularly in rural areas with limited eye health programs. This study assessed its prevalence and screening outcomes among school children in Gabasawa LGA, Kano State, Nigeria. A cross-sectional descriptive study was conducted among 422 pupils aged 6–18 years (mean years), selected by multistage stratified random sampling from six public schools spanning primary, junior secondary, and senior secondary levels. All pupils underwent standardized vision screening comprising distance visual acuity testing, pinhole assessment, external ocular examination, corneal light reflex test, and direct ophthalmoscopy. An unaided VA of in either eye constituted a screening failure and triggered pinhole testing. Improvement with the pinhole indicated probable refractive error and prompted referral for detailed refractive evaluation (Phase 2). Visual impairment was classified using ICD-11 criteria. Out of the 422 pupils, 21 (5.0%) failed vision screening. Visual impairment was detected in 18 pupils (4.3%). Critically, 98.8% of pupils had never undergone a prior eye examination and none were wearing spectacles at the time of screening. These findings demonstrate a substantial undetected burden of visual impairment in this rural population and provide the evidence base for implementing a sustainable school eye health programme in Gabasawa LGA and comparable settings across Kano State. Routine school-based vision screening should be implemented in rural areas, along with access to affordable eye care for early detection and management of visual impairment.

Developing a Technique for Incorporating Social Media into Conventional Teaching Modes: A Case Study of Selected Tertiary Institutions in North-West and North-East Nigeria

2026-04-02T07:17:46+00:00

The rapid expansion of digital technologies has generated new opportunities for teaching and learning in higher education. Although social media is widely used for communication, its structured integration into conventional teaching remains limited in many tertiary institutions in developing countries. This study investigates the potential for incorporating social media into traditional teaching modes in selected tertiary institutions in North-West and North-East Nigeria. A survey research design was employed, with data collected through a structured questionnaire administered to students and lecturers across four institutions. A total of 155 valid responses were analysed using descriptive statistics and thematic analysis. The findings indicate that most respondents have smartphones, access the internet, and actively use social media platforms such as WhatsApp, Facebook, YouTube, and Telegram. WhatsApp emerged as the most widely used platform for academic communication and collaboration. Respondents generally perceived social media as enhancing student engagement, facilitating knowledge sharing, and strengthening interactions between lecturers and students. However, challenges such as inadequate internet connectivity, high data costs, and the absence of institutional policies were identified as key barriers to effective educational use. The study concludes that while social media has the potential to complement conventional teaching, effective integration requires improved digital infrastructure, institutional support, and enhanced digital literacy. These findings contribute to ongoing discussions about the role of social media in fostering collaborative, networked learning environments in higher education.

Isolation, Identification and characterization of Fungal Species Associated with the spoilage of some selected fruits

2026-04-03T15:52:43+00:00

Spoilage of fruits imparts great economic loss and a potential health hazard to the general populace. A research was conducted to identify the different types of fungal flora responsible for the spoilage of fruits in Bwari market, Abuja, Nigeria. A total number of seventy (70) different types of fruits that showed decayed symptomatology and thirty healthy ones were purchased from Bwari market, Abuja. The samples were surfaced sterilized with ethanol and the homogenates. Sections of the rotten and healthy fruits were cut of using a sterile blade and were inoculated ontopetridishes containing Potato Dextrose Agar, the plates were incubated at 27°C for five days.The pure cultures obtained were identified morphologically and microscopically using a standard mircrobial Atlas for verification. The result showed the presence of Aspergillus niger, Rhizopus nigricans, Mucor mucedo, and Fusarium oxysporum. However, Aspergillus niger. (58.06%) was found to be the most dominant spoilage causing organism on fruits vended in Bwari market while Fusarium oxysporum (9.68%) was the least abundant fungalflora. However, the pathogenicity of Rhizopus sp. was found to be higher than that of all the remaining fungal species. This can be attributed to the predisposal and nature of fruits handling and storage in the market. These findings are indicative that fungal species are associated with the spoilage of fruits.Based on the fact that, all the fungal species identified in this research were known to produce toxins that could impart severe food poisoning.

Determination of Bacterial load of some selected fruits and vegetables sold in Kaduna Central Markets, Northwestern Nigeria.

2026-04-03T16:04:47+00:00

Fruits and vegetables served as the major sources of plant proteins, vitamins and fibers that support human health. This study was conducted to assess the bacteriological quality of some selected fruits and vegetables sold in Kaduna central market, northern Nigeria. A total of sixty (60) samples of fruits and vegetables were collected from Kaduna central market (Kaduna north local Government area), they include: Pepper (Capsicum annum L.), Tomato (Lycopersicumesculentum Mill.), Irish potato (Solanum tuberosum L.), Garden egg (Solanum melongena L.), Carrot (Daucus carota L.), Cucumber (Cucumis sativus L.), Water melon (Citrullus lanatusThunb.), Onion (Allium cepa L.), Sweet potato (Ipomoea batatas L.) and Okra (Albelmoschusesculentus L.). The samples were analyzed using serial dilution technique and inoculation was done on MacConkey and Nutrient agar and incubated for 48 h. Results: The result obtained showed the presence of six bacterial species: Staphylococcus aureus, Streptococcus sp., Enterobacter sp., Escherichia coli, Citrobacter sp. and Klebsiella sp. Staphylococcus aureus was the most abundant (with 80% relative occurrence) while Streptococcus sp. was the least abundant with 2% relative occurrence. Conclusion: It was concluded that 6 different bacterial species prevailed on the fruits and vegetables sold in Kaduna central market: The result therefore implied that people consuming these fruits and vegetables are at a higher risk of pneumonia and toxic shock syndrome due to Staphylococcal infection, strep throat, rheumatic fever, scalded skin syndrome, scarlet fever and puerperal fever due to S. aureus, gastrointestinal disorders due to E. coli and bronchopneumonia due to Klebsiella sp. These findings highlight the microbial contamination of fruits and vegetables and the need for public health measures to control the spread of infectious diseases caused by local foods in the region.Their is urgent need for orienting the general populace on the inherent dangers associated with consumption of these types of fruits and vegetables without thorough disinfection.

Quality Control and Diagnostic Image Integrity In Radiography; an Evaluation of Some Selected Diagnostic Centers In Zaria Nigeria

2026-04-15T13:15:29+00:00

Maintaining optimal image quality and radiation safety in diagnostic radiography relies heavily on regular quality control (QC) testing of radiographic equipment. However, in many developing regions, QC practices are inconsistent and often poorly documented.This study evaluated the adequacy and compliance of QC testing in diagnostic radiography units across selected public hospitals in Zaria, identifying deviations from standard regulatory requirement and highlight areas needing improvement.A cross-sectional descriptive study was conducted in radiology departments of public hospital in Zaria, Nigeria. Standard QC parameters including exposure reproducibility, kVp accuracy, half value layer (HVL) and beam alignment and collimation were evaluated using established QC kit.Two of the three machines (M2 and M3) performed within acceptable international standards, while one machine (M1) showed significant deviations. The kVp accuracy test revealed a mean percentage error of 27.62%, 2.41%, and 4.16%for M1, M2, and M3 respectively. Exposure reproducibility tests showed coefficient of variation values of 5.93%, 3.25%, and 4.37%. HVL values were 1.55 mmAl(M1), 3.0 mmAl(M2), and 2.9 mmAl(M3), indicating insufficient filtration in M1. Beam alignment and collimation results for M2 and M3 were within +1% and ±2% tolerance, respectively. Due to M1’s instability, the full collimation test could not be completed. Machine M2 and M3 met QC standards, while M1 failed most tests, reflecting possible calibration or maintenance are recommended to ensure optimal equipment performance and minimize unnecessary radiation exposure.

Mineralogical Studies of the Underlying Rocks around Jiko Abandoned Mine Site, North-Central Nigeria

2026-04-15T13:36:49+00:00

The mineralogical characterisation of crystalline basement rocks is fundamental to constraining protolith affinity and geological and geochemical processes in mining-impacted terrains. This study presents the first integrated X-ray diffraction (XRD) and transmitted-light petrographic investigation of basement rocks underlying the Jiko abandoned mine site, North-Central Nigeria, within the Kusheriki schist belt of the Nigerian Basement Complex. The aim is to establish a mineralogical framework for interpreting rock evolution and its implications for geochemical behaviour. Six representative rock samples, ranging from fresh to moderately altered, were systematically collected and analysed using powder XRD (Cu-Kα radiation; 5–75° 2θ) for phase identification and optical petrography for textural and paragenetic relationships. XRD results indicate a dominant primary mineral assemblage of quartz, phlogopite, and albite, with accessory ilmenite occurring in Ti–Fe-enriched samples and secondary goethite and haematite preferentially developed in altered rocks. Petrographic analysis reveals two principal domains: (i) a schistose domain characterised by aligned mica laths, quartz ribbons, undulose extinction, and incipient feldspar sericitisation; and (ii) a gneissic to altered domain defined by compositional banding, granoblastic textures, fractured and oxidised micas, feldspar turbidity, and pervasive Fe-oxide precipitation along fractures and grain boundaries. These mineralogical and textural relationships indicate that the Jiko basement rocks represent low- to medium-grade metamorphic lithologies, consistent with phlogopite-quartz-albite schists and associated gneisses derived from predominantly pelitic to semi-pelitic protoliths, with local Fe-Ti enrichment. A progressive weathering sequence is established, transitioning from primary quartz-phlogopite ± albite ± ilmenite assemblages through early-stage mica degradation and feldspar alteration to advanced formation of goethite and haematite along structurally controlled fluid pathways. This evolution highlights the critical role of deformation fabric and fracture networks in facilitating fluid ingress and mineral transformation. The study demonstrates that basement mineralogy, coupled with microstructural controls, governs secondary mineral development and provides a robust framework for interpreting trace-element mobilisation and environmental risk in mining-affected basement terrains. It is recommended that future work integrates mineralogical data with in-situ microanalysis (e.g., SEM-EDS, EPMA) and isotopic tracing to better resolve mineral-geochemical linkages and element provenance. These findings are transferable to comparable Pan-African basement settings and contribute to a process-based understanding of mineralogical evolution in tropical weathering environments.

Comparative Phytochemical Analysis of Mistletoe-Infested and Non-Infested Terminalia Catappa L. Leaves

2026-04-15T13:46:56+00:00

Phytochemicals are vital bioactive compounds that contribute to plant defence mechanisms and human health. This study investigated the impact of mistletoe (Loranthus sp.) infestation on the phytochemical composition of Terminalia catappa L. (Combretaceae) leaves a medicinally and ecologically significant tropical species widely used in ethnomedicine. Fresh leaves were collected from healthy (HTL), branch-infected (INF-B-L), and generally infected (INF-G-L) T. catappa trees within Ahmadu Bello University, Zaria, Nigeria. Standard qualitative and quantitative phytochemical analyses were performed on methanolic extracts using spectrophotometric assays for alkaloids, flavonoids, saponins, phenols, and tannins. Data were statistically analysed using one-way ANOVA and Tukey’s post-hoc test at a 95% confidence level (p < 0.05). Results revealed significant (p < 0.05) reductions in alkaloids (1.23 ± 0.01 mg/g → 1.06 ± 0.01 mg/g), flavonoids (9.62 ± 0.02 mg/g → 8.88 ± 0.03 mg/g), phenols (6.52 ± 0.02 mg/g → 6.19 ± 0.02 mg/g), and tannins (1.84 ± 0.01 mg/g → 1.70 ± 0.01 mg/g) from healthy to infested leaves, respectively, while saponins increased slightly (1.22 ± 0.01 → 1.26 ± 0.01 mg/g). Anthraquinones were absent in all samples. These findings show that mistletoe infestation alters the bioactive compounds of T. catappa leaves and highlight the importance of regular monitoring and control of parasitic plants to maintain the therapeutic integrity of economically and ethnobotanically valuable species.

Policy Implications of Hybrid Predictive Models for Pipeline Integrity Management: Enhancing Maintenance Decision Support in Nigeria’s Oil and Gas Sector

2026-04-15T13:57:18+00:00

Nigeria’s oil and gas sector depends on an extensive pipeline network that has sustained cumulative economic losses exceeding US $658.5 million from pipeline failures between 1990 and 2022 (Ekeu-wei and Ekeu-wei, 2024). Predominantly, reactive maintenance regimes have failed to arrest this trend. This paper explores the policy implications and national impact of a hybrid neural–statistical predictive maintenance framework applied to Nigeria’s pipeline sector. The framework integrates four deep-learning architectures—Feedforward Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), and Graph Neural Networks (GNN)—with Generalised Additive Models (GAM) and Exponentiated–Exponentiated Weibull (EEW) survival distributions to predict Remaining Useful Life (RUL) with calibrated uncertainty bounds. Model evaluation against Nigeria-specific pipeline sensor data demonstrated that the hybrid framework outperformed all benchmarks: it achieved a Root Mean Square Error (RMSE) of 8.3 days, a Mean Absolute Error (MAE) of 6.1 days, and a coefficient of determination (R²) of 0.96, compared with R² values of 0.93 (LSTM), 0.91 (ANN), and 0.82 (traditional Weibull). For binary failure classification, the hybrid model attained 95.4% accuracy, 92.1% precision, 96.3% recall, and an F1-score of 94.1%. These results indicate that sector-wide adoption of predictive maintenance could reduce annual operational costs by approximately 35%, cutting average unscheduled downtime from 30 to 12 days per year and halving repair and replacement expenditure. The paper contextualises these findings within Nigeria’s regulatory landscape—particularly the Petroleum Industry Act (2021) and the Nigerian Upstream Petroleum Regulatory Commission (NUPRC) regulations—and provides evidence-based policy recommendations for policymakers and industry stakeholders to improve operational safety, reduce costs, and mitigate the environmental impact of pipeline failures.

Application of Hybrid Neural–Statistical Models in Other Infrastructure Systems

2026-04-15T14:05:43+00:00

Ageing infrastructure presents a persistent and growing challenge across many parts of the world, with water distribution networks, power grids, and transportation systems increasingly susceptible to unplanned failure and costly service disruption. This paper examines the application of hybrid neural–statistical models to predictive maintenance in these three sectors, building on earlier work focused specifically on pipelines. The proposed framework integrates four neural network architectures—Artificial Neural Networks (ANN), Long Short-Term Memory networks (LSTM), Convolutional Neural Networks (CNN), and Graph Neural Networks (GNN)—with Generalised Additive Models (GAMs) to estimate the Remaining Useful Life (RUL) of infrastructure assets and to assess failure risk. Case study evaluations demonstrate that the hybrid model consistently outperforms conventional approaches, including linear regression, ARIMA, and Support Vector Machines (SVM), across all three sectors. Specifically, the hybrid framework achieved Root Mean Square Error (RMSE) values ranging from 0.598 to 0.654, Mean Absolute Error (MAE) values between 0.462 and 0.528, and R² values of 0.913–0.926, compared with RMSE of 1.034–1.312 and R² of 0.654–0.711 for the best-performing conventional baselines. Classification accuracy ranged from 87.5% to 89.4%, with corresponding F1-scores between 0.849 and 0.876. These results point to a framework that is both accurate and interpretable, offering a practically applicable tool for guiding maintenance planning decisions at operational and policy levels. The findings have direct implications for national infrastructure management strategies, supporting a transition from reactive to proactive maintenance regimes.

A MAC-Protocol for Passive Wake-up Radios

2026-04-15T14:16:12+00:00

Energy efficiency remains a big challenge in wireless sensor networks and the internet of things (IoT) since sensor nodes have limited energy resources. Conventional duty-cycled medium access control (MAC) protocols reduce idle listening while incurring energy cost owing to periodic channel monitoring. Wake-up radio technology has emerged as a potential solution for on-demand node activation and reducing idle listening. In this paper, a passive wake-up radio MAC protocol is proposed for energy-constrained IoT sensor networks. Sensor nodes are awakened via radio frequency (RF) energy harvesting from received wake-up signals. To analyze packet transmissions and node energy consumption, a cross-layer analytical framework is created that incorporates physical layer wake-up processes and MAC layer communication behavior. Closed-form expressions are constructed for critical packet statistics such as wake-up calls, acknowledgements, and erroneous wake-up events, allowing for extensive study of protocol performance. The proposed protocol is evaluated using discrete-event simulation with different wake-up call durations and packet loss probabilities. Simulation results show that the proposed passive wake-up protocol consumes much less node energy than traditional duty-cycled and existing wake-up radio MAC methods while retaining acceptable communication delay and reliability. Simulation results show that, in comparison to conventional duty-cycled and benchmark wake-up radio MAC protocols, the proposed passive wake-up protocol reduces average node energy usage by more than 50% while maintaining acceptable packet reliability and delay. The operating lifetime of large-scale IoT deployments could be greatly extended by passive wake-up radio systems, according to these quantitative results.