Benue State University

A mathematical model with time delay is considered to describe the dynamics of hearing loss resulting from noise exposure. The analysis illustrates that the solutions consistently maintain positivity, and the model has at most two steady states: noise-free steady-state and noise-endemic. The stability of the noise-free steady-state and noise-endemic is shown analytically, demonstrating that the noise-endemic steady-state is stable below a critical value of time delay; beyond this threshold, it becomes unstable and undergoes a Hopf bifurcation when the time delay equals the critical value. The paper also examines the bifurcation and estimates the delay needed to keep the system stable. Furthermore, an assessment of parameter elasticity is performed to measure its influence on hearing loss control. Numerical bifurcation is conducted using DDE-BIFTOOL and traceDDE to explore various dynamical behaviors of the model via numerical continuation for varying values of system parameters. Analytical findings are supported by numerical simulations of the model to show different types of dynamical behavior.

A hernia is the abnormal protrusion of a viscus through a defect in the surrounding wall. Laparoscopic repair is increasingly favored over open surgery for ventral hernias due to its minimally invasive approach. However, consolidated evidence comparing their outcomes remains limited. This study aimed to compare outcomes including surgical site infection, hospital stay, operative time, and hernia recurrence between laparoscopic and open ventral hernia repair in adult patients, using evidence from randomized controlled trials and cohort studies. We systematically searched PubMed, Embase, and the Cochrane Library from 2000 to the current year. Eligible studies included randomized controlled trials and observational (prospective or retrospective cohort) studies comparing laparoscopic and open ventral hernia repair in adults (≥18 years) and reporting at least one of the outcomes of interest. Studies were excluded if they focused on pediatric populations, assessed only one outcome, or were case reports, meta-analyses, reviews, editorials, or studies addressing only inguinal/incisional hernias without specifying inclusion under ventral hernias. Risk of bias was assessed using the Cochrane RoB 2 tool and Newcastle–Ottawa Scale. Considering the included studies differed in clinical characteristics such as hernia type, defect size, mesh materials, fixation techniques, and surgeon experience a random effects model was selected before analysis as the most appropriate statistical approach, A random-effects meta-analysis was conducted and results presented narratively and using forest plots.hig. Nineteen studies (3 RCTs and 16 cohort studies; 7,826 patients) were included. Laparoscopic repair significantly reduced the risk of surgical site infection (RR = 0.36, 95

Microplastics (MPs) are increasingly recognized as ubiquitous contaminants, with freshwater systems acting both as sinks and conduits for their transport to marine environments. Yet, data on their occurrence in African freshwater systems—particularly in Nigeria—remain limited. This study assessed their presence in sediments from the River Benue. Samples were collected in December 2023 and processed using NaCl/NaI density separation and H₂O₂ oxidation, while suspected MPs were characterized using FTIR and SEM–EDS. A total of 70 suspected MPs ranging between 0.5–5 mm were recovered, with the southern bank exhibiting higher abundance (43 items kg⁻1) than the northern bank (27 items kg⁻1). Fragments and films were the dominant morphotypes. Surface sediments from the southern bank recorded mean particle and mass concentrations of 2.8 ± 1.9 items kg⁻1 and 20.3 ± 10.8 mg kg⁻1 d.w, decreasing to 0.8 ± 1.0 items kg⁻1 and 3.0 ± 4.3 mg kg⁻1 d.w in deeper layers. On the northern bank, surface layers contained 1.9 ± 2.6 items kg⁻1 and 17.1 ± 20.7 mg kg⁻1 d.w, dropping to 0.3 ± 0.58 items kg⁻1 and 4.2 ± 9.9 mg kg⁻1 d.w at depth. Tentatively identified polymers included polystyrene, polyethylene terephthalate, and polyamide. Preliminary risk assessment revealed contamination factor values of 1–19 and pollution load index values of 1–4.36, with a PLIzone of 2.28, indicating polluted conditions. The findings highlight anthropogenic influence on MP contamination and provide critical data on Nigeria’s freshwater systems, underscoring the need for expanded monitoring and implementation of abatement strategies.

This study aims to prepare an aluminum-citrate ion cell from waste aluminum foils and citric acid extracted from Dovyalis caffra fruits (Kei apples) and further demonstrate their electrochemical performance at varying impedances, electrode thicknesses and discharge times. Aluminum oxide ions were prepared from discarded aluminum foils, and citric acid were produced by co-precipitating macerated Dovyalis caffra extracts by acid infusion. Aluminum foil wastes were utilized in acid co-digestion to produce aluminum oxide nanoparticles. The prepared aluminum oxide was then characterized for surface morphology, composition and phases present. The prepared particles revealed Al2O3 boehmite moieties and ranged between 66.3 and 106.1 nm in size. The synthesized citric acid depicted desirable morphological and electrolytes traits similar to those of commercial citric acid. The cells open and closed current-voltages were directly proportional to the electrode diameter. While the cells were found to be quite Ohmic in nature with increased impedance with electrode distances, discharge times were also proportional to electrode diameters. The cell's energy balance was 477.6–346.3 J/s, with a density of 47.1–53.7 Wh/kg. The results showed that the cells could successfully produce portable energy storage devices from waste materials.

The increasing proliferation of Internet of Things (IoT) devices has introduced significant cybersecurity vulnerabilities due to huge data these devices generate, and the growing sophistication of cyberattacks. Traditional signature-based intrusion detection systems are often ineffective against zero-day and evolving threats, particularly in dynamic IoT environments where labeled datasets are scarce. This paper proposes an explainable hybrid unsupervised deep learning framework that integrates Isolation Forest and Bidirectional Long Short-Term Memory (BiLSTM) autoencoder models for intelligent network intrusion detection. The framework combines statistical anomaly isolation with deep temporal sequence learning to enhance the detection of abnormal network behaviors without relying on labeled data. Network traffic data obtained from the Kaggle network traffic dataset were preprocessed through data cleaning, feature engineering, normalization, and temporal sequence generation. A weighted ensemble mechanism was employed to combine anomaly scores from both models, while SHapley Additive exPlanations (SHAP) and LIME (Local Interpretable Model-Agnostic Explanations) techniques were integrated to improve interpretability and transparency of detection decisions. Experimental results demonstrated strong convergence of the BiLSTM Autoencoder with extremely low reconstruction losses and effective discrimination between normal and malicious traffic patterns. The hybrid framework successfully detected anomalous traffic bursts and suspicious communication behaviors, with “packets_per_time_unit” identified as the most influential anomaly indicator. The proposed framework provides an efficient, scalable, and explainable solution for adaptive IoT cybersecurity and intrusion detection in heterogeneous network environments.