Water is essential to life but many people do not have access to clean and safe drinking water while many even die of water borne bacterial infections. The sanitary quality of stream water cannot be predicted due to behavioral differences of the users and their living condition. This investigation was carried out on the seasonal evaluation of the microbiological quality of Agangwu Stream in Enugu State, Nigeria. A total of eighteen (18) stream water samples collected at wet and dry seasons comprised of six (6) water samples each from the upstream, midstream and downstream locations. They were subjected to a wide range of analytical assessments like the multiple tube fermentation method according to APHA, 2016. Then, the bacteria isolates were characterized and identified morphologically and biochemically using standard microbiological techniques. The dilution pattern showed varying coliform concentration especially for MS¹ (3-0-2) and DS¹ (3-1-2). E. coli, the most common fecal coliform appeared in both seasons with the highest (most probable number) MPN index of 75mpn/100ml (95% confidence limits: 14-230) in dry season and the least MPN index of 1.5mpn/100ml (95% confidence limits: 3 - 44) in wet season. Even though the values were statistically insignificant (P=>0.5). The molecular identification of some of the isolates from the stream water samples confirmed various pathogens, including Salmonella enterica subsp. Enterica, Escherichia coli, etc. The MPN index for both seasons reveal a clear gradient of increasing fecal contamination from upstream to downstream sections of Agangwu Stream. These findings demonstrate that Agangwu stream is definitely not safe for direct human consumption or recreational use without adequate treatment. Rather, there is need for regular monitoring, effective water treatment, and public health interventions to mitigate the risks associated with waterborne diseases.

This system presents the classical Geometric Brownian Motion (GBM) benchmark with constant initial price. The Stochastic Differential Equation (SDE) assumes no deterministic trend, seasonality or regime effects. The simulation results for Fidelity Bank share prices were presented using a constant GBM model with varying sample paths. Four graphical solutions were analyzed to examine path behavior, expected growth, and sensitivity to drift and volatility. The results highlight the interaction between deterministic trend and stochastic noise in shaping Fidelity’s price dynamics. The model serves as a benchmark for market efficiency with no deterministic trend. Also, the existence and uniqueness of the strong solution is proved under standard Lipschitz conditions. This system serves as the null model for comparing the other stochastic systems.

Miniature hearing aids operate under severe constraints in volume, mass, battery capacity, acoustic performance, moisture resistance, and user comfort. This paper investigates whether otherwise dissipated mechanical motion associated with the ear canal and temporomandibular joint can be converted into supplementary electrical energy by piezoelectric transduction. The study uses a physics-based contemporary review and conceptual engineering analysis. Direct piezoelectric constitutive relations, transducer capacitance, open-circuit voltage, time-varying power, mechanical strain energy, resonance, acoustic intensity, storage energy, and end-to-end efficiency are used to establish a quantitative framework. Published in-ear studies demonstrate that jaw-related ear-canal deformation is a measurable mechanical energy source and that flexible PVDF structures can generate electrical output in the microwatt range under defined conditions. A free-field acoustic calculation also shows why ordinary sound alone is a weak source for a hearing-aid-sized capture area. Literature from 2024-2026 emphasizes flexible PVDF-based harvesters, mechanically amplified structures, MEMS integration, and ultra-low-power power-management circuits. The proposed architecture combines a compliant piezoelectric transducer, low-loss rectification, energy buffering, an ultra-low-power power-management unit, and micro-storage or an auxiliary load. The analysis does not support continuous full self-powering of the hearing aid; the more defensible near-term application is battery support, gradual energy accumulation, or intermittent operation of low-power auxiliary electronics. The governing physics establishes plausibility, while experimental validation remains necessary to determine the net energy benefit in a real device.

Many institution-level credential-verification systems generate a random-looking reference code and treat it as secure by virtue of being unique, without examining whether that code is actually resistant to guessing. This paper re-examines a deployed reference-number scheme built for the Academic Document Verification System at Northwest University (NWU), Kano, and shows that its uniqueness suffix - four hexadecimal characters drawn from a UUID4 - carries only 16 bits of entropy (65,536 possible values). We show analytically and by Monte Carlo simulation that, while the system's retry-on-collision loop guarantees no reference is ever issued twice, the same 16-bit space can be exhaustively enumerated against the public verification endpoint in under 11 minutes at a modest 100 requests per second, since no rate limiting is applied. We then design, specify, and analyse a drop-in replacement: an HMAC-SHA256-derived tag computed over the year, document-type, department, and a monotonic serial number, keyed by a server-side secret. At a 48-bit truncation this raises brute-force exhaustion time to an estimated 89,000 years under the same attack rate, at a measured computational cost of 3.2 microseconds per generation or verification - statistically negligible against the system's sub-second query latency. The two schemes are compared against manual, QR-code-based, blockchain-anchored, and zero-knowledge credential-verification approaches reported in the literature on a five-dimension feature matrix, supplemented by a quantitative verification-time comparison. The system was validated through 34 functional test cases (100% pass rate) and an exploratory, formative user-acceptance evaluation (n=8; overall satisfaction 4.5-4.8/5.0, not powered for statistical inference), in which security confidence was the lowest-rated criterion - a finding consistent with, though not statistically proof of, the entropy weakness this paper identifies and addresses. The contribution of this paper is therefore not the verification system as an artefact, but a transferable method for quantifying and closing the guessability gap in identifier-based credential-verification schemes generally.

Vehicular Ad Hoc Networks (VANETs) provide communication between vehicles and roadside infrastructure for safety, traffic management, and intelligent transportation applications. However, the highly dynamic nature of VANETs exposes vehicular communication to security threats including unauthorized access, spoofing, Sybil attacks, data tampering, and privacy violations. This study presents a blockchain-based security model for enhancing data integrity, privacy, authentication, and communication reliability in VANETs. The proposed framework integrates a blockchain layer with a Modified Stable Weighted Clustering (m-SWC) algorithm, cryptographic authentication, pseudonymous identities, and a consensus mechanism. The model was evaluated through simulation under different vehicle densities and attack conditions. Performance was assessed using packet delivery ratio, end-to-end delay, authentication success rate, attack rejection, communication overhead, and scalability. The results show that the proposed blockchain-enabled VANET achieves higher and more stable packet delivery performance than conventional approaches. Authentication performance also improved, with the Blockchain VANET achieving a 97% authentication success rate and 95% attack rejection compared with 86% and 78%, respectively, for the PKI system. The results further show that blockchain introduces moderate additional latency because of consensus and verification processes, but the delay remains within acceptable limits for real-time vehicular applications. The system also demonstrates controlled communication overhead and robust scalability as the number of vehicles increases from 50 to 200. The study therefore demonstrates that blockchain integration provides an effective balance between security, reliability, and scalability in VANET environments.

The growing reliance on telehealth and Internet of Medical Things (IoMT) technologies has improved healthcare accessibility but exposed patient data and clinical operations to cybersecurity threats, including unauthorized access, spoofing, and data alteration attacks. Conventional cloud-based intrusion detection systems (IDS) are unsuitable for telehealth environments because they introduce latency, bandwidth overhead, and privacy risk. This study proposes an Edge-Optimized Deep Neural Network integrated with Marine Predators Algorithm feature selection (EO-DNN-MPA) for real-time intrusion detection in telehealth systems. The model was trained and evaluated on the WUSTL-EHMS-2020 healthcare cybersecurity dataset following preprocessing, class balancing, and feature optimization that reduced dimensionality from 36 to 16 features. Three model configurations were evaluated: the original EO-DNN-MPA, a pruned version, and a pruned-and-quantized version. The original model achieved 95.33% accuracy, 93.15% precision, 97.85% recall, 95.44% F1-score, 99.29% AUC, and an inference time of 1.1101 ms. The pruned-and-quantized model maintained comparable detection performance, 95.12% accuracy, 93.20% precision, 97.34% recall, 95.22% F1-score, and 99.25% AUC, while reducing inference time to 0.6679 ms, a 39.8% latency reduction. The proposed model outperformed comparable state-of-the-art approaches evaluated on the same dataset, including federated DNN, ensemble boosting, and explainable XGBoost methods. The results demonstrate that combining edge-aware architectural design, pruning, and quantization with deep learning yields an efficient, privacy-preserving, and low-latency intrusion detection solution suitable for real-time telehealth security applications.

Community infrastructure management remains largely based on current-condition assessment and corrective or preventive interventions. This approach has a major limitation: it provides insufficient information about the likely deterioration trajectory of assets and the time at which their sustainability may cross a critical threshold. Research on the Global Sustainability Index (IGD) has introduced a multidimensional infrastructure assessment integrating physical, functional, environmental, economic, social and institutional dimensions. However, when calculated at a single point in time, the IGD remains essentially a state indicator.

This paper proposes a methodological protocol for developing and validating an Artificial Intelligence-based Predictive Global Durability Index (IGDₚ). The approach relates to a longitudinal database combining repeated IGD assessments with technical characteristics, usage intensity, environmental exposure, maintenance history, spatial information, and economic, social and institutional variables. Several model families are proposed for comparison, including statistical baselines, Random Forest, Gradient Boosting, XGBoost and, where justified by data availability, neural and temporal models.

The validation framework combines five dimensions: predictive performance, temporal validation, robustness and generalization, explainability, and decision usefulness. Proposed performance metrics include mean absolute error, root mean square error and coefficient of determination. Explainability is integrated through feature-contribution approaches, including SHAP. Procedures for uncertainty quantification, sensitivity analysis of IGD weights and external validation are also proposed.

The methodological architecture predicts the future values of individual durability dimensions before aggregating them into the IGD ₚ. This strategy preserves the multidimensional interpretability of the index while adding a prognostic capability. The proposed protocol therefore constitutes an intermediate step between static sustainability assessment and prescriptive community infrastructure management.

This study analyzes changes in forest cover in the Acacia forest of Khartoum (2021–2026) using GIS, remote sensing, Sentinel-2 imagery, and an artificial intelligence-based Random Forest classification model. Random Forest is often an appropriate baseline for tabular data and for traditional pixel-based change detection (e.g., comparing multi-temporal bands, NDVI, or SAVI), and it is used in this study.

Spectral indices (NDVI and SAVI) derived from bands B2, B3, B4, and B8 were used as model inputs. The classifier was trained on 2023 baseline data, using NDVI thresholding to distinguish forest from non-forest areas. The results indicate substantial forest degradation over the five-year monitoring period, primarily caused by unauthorized logging. According to the NDVI values used to assess deforestation in the forest, the NDVI values for Year 2021 (0.4151), Year 2022 (0.5480), Year 2023 (0.5962), Year 2024 (0.5864), Year 2025 (0.56226), and Year 2026 (0.35181) indicate deforestation across the study years.

This work proposes the implementation of an intelligent system based on Wireless Sensor Networks (WSN) and the Internet of Things (IoT) for real-time water consumption monitoring using a digital water meter. The research is framed within a qualitative, empirical, and descriptive approach, aiming to analyze the potential of emerging technologies in improving water resource management, reducing losses, and increasing the efficiency of measurement processes. The proposed system uses the Arduino Uno microcontroller as a processing unit, due to its accessibility, flexibility, low cost, and compatibility with different computing platforms, including Windows and Linux operating systems. The solution aims to overcome limitations of traditional measurement models, such as reading errors, malfunctions, and equipment inaccuracies, allowing for the continuous collection, processing, and transmission of data on water consumption. The integration between IoT, smart sensors, and digital water meters represents an innovative alternative to support decision-making, promote the sustainable use of water, and contribute to the digital transformation of water supply systems.