Search Results (3,593)

Depth-based probabilistic routing (DPR) is an efficient underwater acoustic network (UAN) routing protocol which resists the depth-spoofing attack. DPR’s optimal value of the unqualified forwarding probability depends on the UAN topology, condition, and threat state, which are highly dynamic. If the static forwarding probability used in DPR is set too low for the current state, packet delivery ratio (PDR) drops. If it is set too high, unnecessary forwarding occurs when the network is not under attack, thus wasting valuable energy. In this paper, we propose a novel routing protocol, which uses a feedback mechanism that allows the sink to continuously adapt the unqualified forwarding probability according to the current network state. The protocol aims to achieve an application-controlled desired delivery ratio using one of three proposed update algorithms developed in this work. We analyze the performance of the proposed algorithms through simulation. Results demonstrate that the proposed adaptive routing protocol achieves resilience to depth-spoofing attacks by successfully delivering more than 80% of generated packets in more than 95% of simulated networks, while avoiding unnecessary unqualified forwarding in normal conditions. Full article

The Internet of Medical Things (IoMT) improves healthcare delivery through many medical applications. Because of medical data sensitivity and limited resources of wearable technology, privacy and security are significant challenges. Traditional encryption does not provide secure computation on encrypted data, and many blockchain-based IoMT solutions partially rely on centralized structures. IoMT with dynamic encryption is an innovative privacy-preserving system that combines sensitivity-based classification and advanced encryption to address these issues. The study proposes privacy-preserving IoMT framework that dynamically adapts its cryptographic strategy based on data sensitivity. The proposed approach uses a hybrid SDAIPA (SDAIA-HIPAA) classification model that integrates Saudi Data and Artificial Intelligence Authority (SDAIA) and Health Insurance Portability and Accountability Act (HIPAA) guidelines. This classification directly governs the selection of encryption mechanisms, where Advanced Encryption Standard (AES) is used for low-sensitivity data, and Fully Homomorphic Encryption (FHE) is used for high-sensitivity data. The Whale Optimization Algorithm (WOA) is used to maximize cryptographic entropy of FHE keys and improves security against attacks, resulting in an Optimized FHE that is conditionally used based on SDAIPA outputs. This proposed approach provides a novel scheme to dynamically align cryptographic intensity with data risk and avoids the overhead of uniform FHE use while ensuring strong privacy for critical records. Two datasets are used to assess the proposed approach with up to 806 samples. The results show that the hybrid OHE-WOA outperforms in the percentage of sensitivity of privacy index with dataset 1 by 78.3% and 12.5% and with dataset 2 by 89% and 19.7% compared to AES and RSA, respectively, which ensures its superior ability to preserve privacy. Full article

Alzheimer’s disease (AD) affects over 50 million people worldwide, making early and accurate diagnosis essential for effective treatment and care planning. Diagnosing AD through neuroimaging continues to face challenges, including reliance on subjective clinical evaluations, the need for manual feature extraction, and limited generalisability across diverse populations. Recent advances in deep learning, especially convolutional neural networks (CNNs) and vision transformers, have improved diagnostic performance, but many models still depend on large labelled datasets and high computational resources. This study introduces an attention-enhanced CNN with a multi-activation fusion (MAF) module and evaluates it using the Alzheimer’s Disease Neuroimaging Initiative dataset. The channel attention mechanism helps the model focus on the most important brain regions in 3D MRI scans, while the MAF module, inspired by multi-head attention, uses parallel fully connected layers with different activation functions to capture varied and complementary feature patterns. This design improves feature representation and increases robustness across heterogeneous patient groups. The proposed model achieved 92.1% accuracy and 0.99 AUC, with precision, recall, and F1-scores of 91.3%, 89.3%, and 92%, respectively. Ten-fold cross-validation confirmed its reliability, showing consistent performance with 91.23% accuracy, 0.93 AUC, 90.29% precision, and 88.30% recall. Comparative analysis also shows that the model outperforms several state-of-the-art deep learning approaches for AD classification. Overall, these findings highlight the potential of combining attention mechanisms with multi-activation modules to improve automated AD diagnosis and enhance diagnostic reliability. Full article

This paper introduces a novel control framework for prescribed-time synchronization of higher-order nonlinear multi-agent systems (MAS) subject to parametric uncertainties and external disturbances. The proposed method integrates a fuzzy neural network (FNN) with a robust non-singular terminal sliding mode controller (NTSMC) to ensure leader–follower consensus within a user-defined time horizon, regardless of the initial conditions. The FNN is employed to approximate unknown nonlinearities online, while an adaptive update law ensures accurate compensation for uncertainty. A terminal sliding manifold is designed to enforce finite-time convergence, and Lyapunov-based analysis rigorously proves prescribed-time stability and boundedness of all closed-loop signals. Simulation studies on a leader–follower MAS with four nonlinear agents under directed communication topology demonstrate the superiority of the proposed approach over conventional sliding mode control, achieving faster convergence, enhanced robustness, and improved adaptability against system uncertainties and external perturbations. Full article

This study provides a systematic mechanical characterization and manufacturing analysis of stereolithography-printed microneedle arrays across six geometric designs (300–400 μm diameter and three aspect ratios: 2:1, 3:1, and 4:1) and three array configurations (1 × 1, 5 × 5, 10 × 10). Compression testing to 50 N revealed geometry-dependent optimization: low-aspect-ratio designs (Designs 1, 4, 5) exhibited superior performance in high-density arrays (10 × 10), while high-aspect-ratio designs (Designs 2, 3) performed better as single needles. Manufacturing success rates increased significantly with array density: from 44.2% (95% CI: 41.1–47.3%) for single needles to 67.3% (95% CI: 63.2–71.4%) for 10 × 10 arrays, with 400 μm diameter designs showing higher reliability. Two-way ANOVA confirmed significant effects of both geometric design [F(5, 72) = 145.3, p < 0.001, η² = 0.91] and array configuration [F(2, 72) = 78.2, p < 0.001, η² = 0.68] on compressive displacement. Design 5 (400 μm diameter, 3:1 aspect ratio) in a 10 × 10 format exhibited optimal mechanical characteristics, including controlled displacement (0.578 ± 0.036 mm), a high safety factor (SF = 13.32), and a superior manufacturing yield. These findings provide quantitative design guidelines for optimizing 3D-printed microneedle arrays. Full article

Air pollution, a critical environmental threat, has worsened alongside urbanization and industrialization, particularly in rapidly developing economies like India. Despite efforts to curb emissions, the concurrent rise in energy consumption, industrial activity, and digitalization complicates the fight against air pollution. This study examines the interplay between air pollution, economic growth, clean energy transition, digitalization, and urbanization in India from 1990Q1 to 2020Q4. Using advanced econometric techniques, including multivariate quantile-on-quantile regression (MQQR) and the quantile ADF and quantile KPSS tests, we investigate the complex, non-linear relationships across these factors. Our findings suggest that while economic growth exacerbates air pollution, the clean energy transition can mitigate its impact, especially when integrated with digitalization. However, the effects of digitalization are nuanced, potentially increasing pollution unless paired with green energy policies. The study demonstrates that the combined strategies of promoting clean energy and digitalization can provide a sustainable pathway for reducing air pollution in India. This work offers novel insights into the role of digital technologies in enhancing environmental sustainability and highlights the need for policy interventions that balance economic growth with climate resilience. The results present a roadmap for India’s sustainable development, emphasizing the integration of clean energy, digital innovation, and urban planning. Full article

Artificial intelligence (AI) is increasingly positioned as a transformative force in healthcare. The translation of AI from technical validation to real-world clinical impact remains a critical challenge. This systematic review aims to synthesize the evidence on the AI translational pathway in healthcare, focusing on the systemic barriers and facilitators to integration. Following PRISMA 2020 guidelines, we searched PubMed, Scopus, Web of Science, and IEEE Xplore for studies published between 2000 and 2025. We included peer-reviewed original research, clinical trials, observational studies, and reviews reporting on AI technical validation, clinical deployment, implementation outcomes, or ethical governance. While AI models consistently demonstrate high diagnostic accuracy (92–98% in radiology) and robust predictive performance (AUC 0.76–0.82 in readmission forecasting), clinical adoption remains limited, with only 15–25% of departments integrating AI tools and approximately 60% of projects failing beyond pilot testing. Key barriers include interoperability limitations affecting over half of implementations, lack of clinician trust in unsupervised systems (35%), and regulatory immaturity, with only 27% of countries establishing AI governance frameworks. Moreover, performance disparities exceeding 10% were identified in 28% of models, alongside a pronounced global divide, as 73% of low-resource health systems lack enabling infrastructure. These findings underscore the need for systemic, trustworthy, and equity-driven AI integration strategies. Full article

Milk contains wide microbial diversity, composed mainly of lactic acid bacteria (LAB), which are used as probiotics for both humans and livestock. We isolated, characterized, and evaluated LAB from indigenous Saudi Arabian camel milk to assess its probiotic potential, including antagonistic activity (against Methicillin-Resistant Staphylococcus aureus (MRSA) and Klebsiella pneumoniae), survivability in simulated gastric juice, tolerance to bile salts, cell surface hydrophobicity, auto- and co-aggregation, and antibiotic susceptibility tests. The two most promising LAB strains showed probiotic potential and were identified as Leuconostoc mesenteroides based on 16S rRNA gene sequences. These strains inhibited all pathogens tested to varying degrees and were resistant to kanamycin and vancomycin. None of the LAB cultures demonstrated hemolytic or gelatinase activity. Overall, the current data suggests that camel milk has substantial potential for introducing probiotics/LAB strains into the human food chain, making camel milk a potentially sustainable food. Full article

Background/Objectives: Upper extremity deep vein thrombosis (UEDVT) is a harmful complication of elective upper limb surgeries. Different strategies are employed to prevent this condition. The aim of the review is to quantify the effectiveness of various preventive interventions and investigate correlated factors that affect the incidence of UEDVT (upper extremity deep vein thrombosis). Methods: We performed a systematic search using the PubMed, EBSCO, Ovid, EMBASE, Cochrane, and Google Scholar databases. Randomized controlled trials (RCTs), prospective or retrospective cohort studies, or case–control studies were examined. We included adult patients over 18 years old undergoing elective upper limb surgery and receiving Prophylactic measures for Upper Extremity Deep Venous Thrombosis. Results: After a literature search and quality assessment, 6 studies were included. All the studies were of good quality but significantly heterogeneous in terms of sample size, population size, treatment modalities, and baseline characteristics. In these studies, the reported incidence of symptomatic venous thromboembolism (VTE) varied widely, ranging from 0.41% to 13%. However, thromboprophylaxis did not have a significant impact on the rates of deep vein thrombosis (DVT). Certain factors such as older age and trauma as the cause of surgery were identified as notable risk factors for symptomatic VTE. Conclusions: This systematic review highlights the complexity of preventing upper extremity deep vein thrombosis (UEDVT) following elective upper limb surgeries. The reported incidence of symptomatic VTE varies considerably across studies, and thromboprophylaxis was not associated with a significant reduction in its rates. The evidence is characterized by substantial heterogeneity in patient populations and surgical contexts. More research is needed to better understand the role of thromboprophylaxis in preventing DVT. Full article

Most traditional estimators assume normality and remain sensitive to extreme observations, which limits their usefulness in practical applications. To improve accuracy, we introduce quintile-based median estimators using transformation methods in a stratified two-phase sampling technique. The design allows for efficient use of auxiliary data and enhances robustness across heterogeneous strata. Stratified sampling further reduces variability by ensuring representation from all subgroups within the population. Bias and mean squared error expressions are obtained through first-order approximations. The efficiency of the proposed estimators is evaluated using the mean squared error (MSE) as the benchmark criterion. The effectiveness of the proposed estimators is examined by conducting simulations under various skewed distributions. To strengthen the conclusions, additional analysis is performed on real population datasets. Simulation and empirical studies confirm the superior performance of the proposed methods. The findings show that the suggested estimators perform well in practical situations involving median estimation as well as achieving higher precision and effectiveness than existing estimators. Full article

Background/Objectives: The CD4⁺ T-cell count is a primary indicator of immune status in HIV-positive patients. Rapid identification of immune suppression severity may improve clinical decision-making and triage. The relationship between CD4⁺ counts and other immunologic and hematologic markers, however, is not well characterized, especially in resource-limited settings. The objectives of this study were to classify HIV-positive patients by CD4⁺ T-cell count, compare hematologic and immunologic markers across severity groups, assess correlations between CD4⁺ and other variables, and evaluate routine blood tests’ potential to serve as surrogate indicators of immune status. Methods: A retrospective cohort of 229 HIV-positive patients from the Regional Laboratory in Makkah, Saudi Arabia, was stratified into three groups: severe (<200 cells/mm³), moderate (200–500 cells/mm³), and preserved (>500 cells/mm³). Hematologic (RBC, Hb, Hct, ESR, WBC, and lymphocytes) and immunologic (CD3, CD4, CD8, B, NK cells, and CD4/CD8 ratio) data were analyzed using ANOVA and Pearson correlation. Results: Significant group differences were observed in RBC, Hb, Hct, ESR, and lymphocyte counts (p < 0.001). CD4⁺ counts correlated positively with CD3 (r = 0.76), B cells (r = 0.63), and CD8⁺ (r = 0.41) and negatively with ESR (r = –0.37). Over 75% of the patients had disrupted CD4/CD8 ratios, and one-third of the severely immunosuppressed patients showed abnormal B- and NK-cell counts. Conclusions: Routine hematologic markers reflect immune suppression severity and can serve as accessible, low-cost tools for monitoring HIV-positive patients in resource-limited settings. Integrating these parameters into immune monitoring may enhance early assessment and provide regional benchmarks for clinical evaluation. Full article

Wind shear-induced aviation turbulence (WSAT) remains a major safety concern during approach and takeoff phases at complex terrain airports. This study develops an interpretable Explainable Boosting Machine (EBM) framework to classify WSAT events at Hong Kong International Airport (HKIA). The framework integrates Differential Evolution with HyperBand (DEHB) for hyperparameter tuning and applies multiple data balance methods such as SMOTE, Borderline SMOTE, Safe-Level SMOTE, and G-SMOTE. The dataset consists of Pilot Reports (PIREPs) collected between 1 January 2007 and 31 July 2023, with 6838 wind shear events that include variables that relate to wind shear magnitude, altitude, runway distance, rainfall condition, and causal factors. Among all configurations, the EBM tuned via DEHB and trained with SMOTE-treated data achieved the highest predictive performance with BA = 0.710, MCC = 0.321, and G-Mean = 0.708, higher than untreated and other balance variants. EBM-based interpretation showed that wind shear altitude and wind shear magnitude were key predictors, and their interaction reflected a nonlinear pattern where WSAT probability rose under moderate-to-high shear conditions (wind shear altitude ≈ 0.5–2.5 and magnitude ≈ 30–35 knots). The DEHB-optimized EBM–SMOTE framework provides a transparent interpretive foundation for WSAT risk assessment and advances quantitative evaluation in aviation meteorology. Full article

In this study, silver-decorated magnetite nanoparticles (Ag@Fe₃O₄) were synthesized via a green method using Brachychiton populneus leaf extract and employed as an efficient support matrix for immobilization of horseradish peroxidase (HRP). The biosynthesized nanocomposite exhibited magnetic properties that facilitated easy separation and reuse, while the silver loading imparted enhanced stability and potential antimicrobial activity. Comprehensive physicochemical characterizations, including XRD, FTIR, FESEM, EDX, BET, and VSM, confirmed the successful formation of Ag@Fe₃O₄ and effective enzyme loading. The immobilization yield of HRP on Ag@Fe₃O₄ reached 93%, and the immobilized enzyme showed improved tolerance toward temperature and pH variations, with an optimal pH of 7.5 and optimal temperature of 60 °C, compared to 7.0 and 50 °C for the free enzyme. Kinetic studies revealed a moderate increase in Km but maintained or slightly increased Vmax, indicating preserved catalytic efficiency. The immobilized enzyme demonstrated excellent reusability over 15 cycles (66% residual activity) and long-term storage stability (81% activity after 60 days at 4 °C). These enhancements are attributed to the protective microenvironment provided by the Ag@Fe₃O₄ matrix, which mitigates denaturation and leaching. This work highlights the potential of Ag@Fe₃O₄ as a sustainable and reusable platform for enzyme immobilization in biocatalytic applications, particularly in environmental remediation and industrial bioprocessing. Full article

The aim of this paper is to find formulas for the solutions of the nonlinear system of difference equations related to symmetry $P_{n+1}={\displaystyle \frac{T_n{T}_{n-2}}{-P_{n-3}-T_n}},T_{n+1}={\displaystyle \frac{P_n{P}_{n-2}}{\pm T_{n-3}\pm P_n}},$ where the initial conditions $P_{-3},P_{-2},P_{-1},P_0,T_{-3},T_{-2},T_{-1}$, and $T_0$ are arbitrary real numbers. Moreover, the theoretical results are verified through several numerical examples, which are simulated and graphically illustrated using mathematical programs. Full article

In the present study, the endophytic fungus Fusarium fujikuroi was isolated from the medicinal plant Debregeasia salicifolia and cultivated for the extraction of bioactive metabolites. The crude extract was fractionated via gravity column chromatography using solvents of increasing polarity (n-hexane, n-hexane/chloroform 1:1 v/v, chloroform, ethyl acetate, and methanol) to isolate bioactive compounds. The antimicrobial activity of these fractions was evaluated against pathogenic bacteria (Bacillus subtilis, Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli). Most extracts exhibited significant antimicrobial activity, with the n-hexane/chloroform fraction (HCF) showing the highest efficacy (18 mm inhibition zone), followed by the n-hexane fraction while Ciprofloxacin was used as a positive control. Fractions were tested in triplicate; antibacterial activities (p < 0.05) were highest in the HCF. Bioactive compounds from the most potent fractions were further purified and analyzed using gas chromatography-mass spectrometry (GC-MS). The GC-MS profiling revealed the presence of diverse bioactive metabolites, including polycyclic aromatic hydrocarbons (PAHs), phenols, and fatty acids. Notably, several of these compounds have not been previously reported in Fusarium fujikuroi, highlighting the potential for novel antimicrobial agents from this endophytic strain. In silico toxicity prediction using the ProTox-II tool indicated that the major compounds possess low to moderate toxicity profiles, supporting their potential safety for further biological evaluation. Full article