Search Results (373)

Edge computing (EC) plays a critical role in advancing the next-generation Industrial Internet of Things (IIoT) by enhancing production, maintenance, and operational outcomes across heterogeneous network boundaries. This study builds upon EC intelligence and integrates graph-based learning to propose a Cross-Layer Controller Tasking Scheme (CLCTS). The scheme operates through two primary phases: task grouping assignment and cross-layer control. In the first phase, controller nodes executing similar tasks are grouped based on task timing to achieve monotonic and synchronized completions. The second phase governs controller re-tasking both within and across these groups. Graph structures connect the groups to facilitate concurrent tasking and completion. A learning model is trained on inverse outcomes from the first phase to mitigate task acceptance errors (TAEs), while the second phase focuses on task migration learning to reduce task prolongation. Edge nodes interlink the groups and synchronize tasking, migration, and re-tasking operations across IIoT layers within unified completion periods. Departing from simulation-based approaches, this study presents a fully implemented framework that combines learning-driven scheduling with coordinated cross-layer control. The proposed CLCTS achieves an 8.67% reduction in overhead, a 7.36% decrease in task processing time, and a 17.41% reduction in TAEs while enhancing the completion ratio by 13.19% under maximum edge node deployment. Full article

Bemisia tabaci (Aleyrodidae family) is one of the most damaging pests of numerous crops worldwide. Insecticides, namely pyrethroids and organophosphates, have long been the primary control tools against this pest, resulting in several resistance cases. In Tunisia, the two most damaging biotypes of B. tabaci, MEAM1-B and MED-Q, are sympatric, and more concerns about developing resistance keep rising due to the extensive use of insecticides. Here, we aimed to elucidate the molecular mechanism of resistance to pyrethroids and organophosphorus insecticides in two Tunisian populations of B. tabaci, collected respectively on Capsicum annuum and Lantana camara, and then determine the bacterial community associated with insecticide resistance and susceptible biotypes based on 16S rRNA Illumina sequencing. The results showed that the population collected on Capsicum annuum belonged to the MEAM1-B biotype with an insecticide resistance profile. In contrast, the population collected on the Lantana camara belonged to the MED-Q biotype with a sensitive profile. The bacterial communities of the two biotypes were predominantly structured by the Proteobacteria phylum and three genera, including Candidatus Portiera, the secondary facultative symbiont, and Hamiltonella, which were unevenly distributed between the two biotopes. Our results provide the first evidence for insecticide resistance alleles in Tunisian MEAM1-B populations and suggest an association between bacterial community composition within susceptible biotypes and insecticide resistance. Full article

Microbial extracellular polymeric substances (EPSs) are emerging as sustainable alternatives to conventional corrosion inhibitors due to their eco-friendly nature, biodegradability, and functional versatility. Secreted by diverse microorganisms including bacteria, fungi, archaea, and algae, EPSs are composed mainly of polysaccharides, proteins, lipids, and nucleic acids. These biopolymers, chiefly polysaccharides and proteins, are accountable for surface corrosion prevention through biofilm formation, allowing microbial survival and promoting their environmental adaptation. Usually, EPS-mediated corrosion inhibitions can take place via different mechanisms: protective film formation, metal ions chelation, electrochemical property alteration, and synergy with inorganic inhibitors. Even though efficacious EPS corrosion prevention has been demonstrated in several former studies, the application of such microbial inhibitors remains, so far, a controversial topic due to the variability in their composition and compatibility toward diverse metal surfaces. Thus, this review outlines the microbial origins, biochemical properties, and inhibition mechanisms of EPSs, emphasizing their advantages and challenges in industrial applications. Advances in synthetic biology, nanotechnology, and machine learning are also highlighted and could provide new opportunities to enhance EPS production and functionality. Therefore, the adoption of EPS-based corrosion inhibitors represents a promising strategy for environmentally sustainable corrosion control. Full article

Lung adenocarcinoma is a prevalent, aggressive cancer with a poor prognosis due to early metastasis and resistance to treatment. LncRNA AFAP1-AS1 has been shown to be associated with the development of multiple carcinomas. This study investigates the functional role of AFAP1-AS1 in lung adenocarcinoma cell proliferation via miR-508-3p and ZWINT. Human lung adenocarcinoma A549 cells were transfected with siRNA constructs against AFAP1-AS1 (si-AFAP1-AS1) to silence its expression. Cell proliferation was evaluated via CCK-8 and colony-forming assays. Apoptosis was assessed using AO/EB staining, and invasion was determined via Transwell assay. The interaction between AFAP1-AS1, miR-508-3p, and ZWINT was confirmed via dual luciferase reporter assay and qRT-PCR analysis. Data were analysed using appropriate statistical tests. AFAP1-AS1 was significantly upregulated in lung adenocarcinoma cells compared to normal BEAS-2B cells. Silencing of AFAP1-AS1 resulted in a marked reduction in A549 cell proliferation and colony development, as observed in CCK-8 and colony formation assays. The AO/EB assay showed a significant increase in apoptosis (30 ± 4.4%) in si-AFAP1-AS1 transfected cells compared to control si-NC (3 ± 1.2%). In addition, knockdown of AFAP1-AS1 led to an upsurge of pro-apoptotic Bax and decline of anti-apoptotic Bcl-2 expression. The dual luciferase assay established the interaction between AFAP1-AS1 and miR-508-3p. Furthermore, ZWINT, identified as a target of miR-508-3p, was significantly upregulated in lung adenocarcinoma tissues. Overexpression of ZWINT rescued the inhibitory effects of AFAP1-AS1 silencing on cell proliferation, colony formation, and apoptosis, while also reversing the reduction in cell invasion. AFAP1-AS1 accelerates the development of lung adenocarcinoma by cell proliferation, apoptosis, and invasion via the miR-508-3p/ZWINT axis. Thus, targeting AFAP1-AS1 or its downstream regulatory axis could offer novel therapeutic approaches in lung adenocarcinoma treatment. Full article

This study empirically examines the impact of 30% alternative fuel (AF) adoption on the emission of CO₂ to the environment in the UAE cement industry. The researchers employed a quantitative method to robustly analyze secondary data obtained from the 12 cement manufacturing units of the UAE, the International Energy Agency (IEA), the United States Geological Survey (USGS), and peer-reviewed published papers. The researcher’s main focus was on data from 2018 to 2024 and aligned that with the UAE Green Agenda 2030. The data analysis was conducted through a well-known software, the Statistical Package for Social Sciences (SPSS), and tests like descriptive statistics, correlation, and regression were employed. The correlation analysis showed that there is a strong negative relationship between AF adoption and CO₂ emissions. The test also showed that the relationship is inverse, that is, increasing the adoption rate of AF lowers CO₂ emissions and thus positively impacts the environment. The Pearson correlation analysis (r = −0.82) showed a strong inverse relationship between the independent and dependent variables. This strong relationship was further revealed and confirmed by the regression analysis, and AF as an individual independent variable explained a 67% reduction in CO₂ emission (R² = 0.67), while a combination with mediating variables, such as economic incentives and the integration of advanced technologies, further increased the impact to 83%, where the explanatory power jumped to R² = 0.83 (p < 0.001). As the relationship is strongly inverse between the independent and dependent variables, this reinforces the hypothesis that AF adoption is a good strategy to decarbonize the production of cement and make the operations sustainable. Full article

Background/Objectives: Artificial intelligence (AI) is revolutionising healthcare for people with disabilities, including those with autism spectrum disorder (ASD), in the era of advanced technology. This work explicitly addresses the challenges posed by inconsistent data from various sources by developing and evaluating a robust deep ensemble learning system for the accurate and reliable classification of autism spectrum disorder (ASD) based on facial images. Methods: We created a system that learns from two publicly accessible datasets of ASD images (Kaggle and YTUIA), each with unique demographics and image characteristics. Utilising a weighted ensemble strategy (FPPR), our innovative ASD-UANet ensemble combines the Xception and ResNet50V2 models to maximise model contributions. This methodology underwent extensive testing on a range of groups stratified by age and gender, including a critical assessment of an unseen, real-time dataset (UIFID) to determine how well it generalised to new domains. Results: The performance of the ASD-UANet ensemble was consistently better. It significantly outperformed individual transfer learning models (e.g., Xception alone on T1+T2 yielded an accuracy of 83%), achieving an impressive 96.0% accuracy and an AUC of 0.990 on the combined-domain dataset (T1+T2). Notably, the ASD-UANet ensemble demonstrated strong generalisation on the unseen real-time dataset (T3), achieving 90.6% accuracy and an AUC of 0.930. This demonstrates how well it generalises to new data distributions. Conclusions: Our findings demonstrate significant potential for widespread, equitable, and clinically beneficial ASD screening using this promising, reasonably priced, and non-invasive method. This study establishes the foundation for more precise diagnoses and greater inclusion for people with autism spectrum disorder (ASD) by integrating methods for diverse data and combining deep learning models. Full article

One-dimensional TiO₂ nanotube arrays (TNAs) were vertically aligned and obtained via the electrochemical anodization method. In this study, Bi₂S₃-TiO₂-SiO₂/TNA heterojunction photocatalysts were successfully prepared with different amounts of Bismuth(III) sulfide (Bi₂S₃) loading on the TNAs by the successive ionic layer adsorption and reaction (SILAR) method and characterized by X-ray diffraction (XRD) patterns, field-emission scanning electron microscope–energy-dispersive spectroscopy (FESEM-EDS), Fourier transform infrared (FTIR) spectra, ultraviolet-visible diffuse reflectance spectra (UV–Vis/DRS), and electrochemical impedance spectroscopy (EIS) techniques. The photocatalytic performances of the samples were investigated by degrading Basic Yellow 28 (BY 28) under visible-light irradiation. Optimization of the condition using the response surface methodology (RSM) and central composite rotatable design (CCRD) technique resulted in the degradation of BY 28 dye, showing that the catalyst with 9.6 mg/cm² (designated as Bi₂S_3(9.6)-TiO₂-SiO₂/TNA) showed the maximum yield in the degradation process. The crystallite size of about 17.03 nm was estimated using the Williamson–Hall method. The band gap energies of TiO₂-SiO₂/TNA and Bi₂S_3(9.6)-TiO₂-SiO₂/TNA were determined at 3.27 and 1.87 eV for the direct electronic transitions, respectively. The EIS of the ternary system exhibited the smallest arc diameter, indicating an accelerated charge transfer rate that favors photocatalytic activity. Full article

Background/Objectives: Accurate and efficient diagnosis of heart disease through electrocardiogram (ECG) analysis remains a critical challenge in clinical practice due to noise interference, morphological variability, and the complexity of overlapping cardiac signals. Methods: This study presents a comprehensive deep learning (DL) framework that integrates advanced ECG signal segmentation with transfer learning-based classification, aimed at improving diagnostic performance. The proposed ECG segmentation algorithm introduces a distinct and original approach compared to prior research by integrating adaptive preprocessing, histogram-based lead separation, and robust point-tracking techniques into a unified framework. While most earlier studies have addressed ECG image processing using basic filtering, fixed-region cropping, or template matching, our method uniquely focuses on automated and precise reconstruction of individual ECG leads from noisy and overlapping multi-lead images—a challenge often overlooked in previous work. This innovative segmentation strategy significantly enhances signal clarity and enables the extraction of richer and more localized features, boosting the performance of DL classifiers. The dataset utilized in this work of 12 lead-based standard ECG images consists of four primary classes. Results: Experiments conducted using various DL models—such as VGG16, VGG19, ResNet50, InceptionNetV2, and GoogleNet—reveal that segmentation notably enhances model performance in terms of recall, precision, and F1 score. The hybrid VGG19 + SVM model achieved 98.01% and 100% accuracy in multi-class classification, along with average accuracies of 99% and 97.95% in binary classification tasks using the original and reconstructed datasets, respectively. Conclusions: The results highlight the superiority of deep, feature-rich models in handling reconstructed ECG signals and confirm the value of segmentation as a critical preprocessing step. These findings underscore the importance of effective ECG segmentation in DL applications for automated heart disease diagnosis, offering a more reliable and accurate solution. Full article

Acute Myeloid Leukemia (LAML) is a life-threatening hematological malignancy, and the DEAD-box helicase 3 X-linked (DDX3X) gene is a potential yet underexplored target gene for LAML. Biomolecules derived from medicinal plants like Aerva javanica offer a great source of therapeutic candidates. This study aimed to investigate the role of DDX3X in LAML and identify plant-derived biomolecules that could inhibit DDX3X using computational approaches. Pan-cancer mutational profiling, a transcriptomic analysis, survival, protein–protein interaction networks, and a principal component analysis (PCA) were employed to elucidate functional associations and transcriptomic divergence. Subsequently, biomolecules from A. javanica were subjected to in silico screening using molecular docking and ADMET profiling. The docking protocol was validated using RK-33, a known DDX3X inhibitor. DDX3X was found to be linked to key leukemogenic pathways, including Wnt/β-catenin and MAPK signaling, indicating it to be a potential target. Molecular docking of A. javanica compounds revealed CIDs 15559724, 5490003, and 74819331 as potent DDX3X inhibitors with strong binding affinity and favorable pharmacokinetic and toxicity profiles compared to RK-33. This study highlights the importance of DDX3X in LAML pathogenesis and suggests targeting it using plant-derived inhibitors, which may require further in vitro and in vivo validation. Full article

This study establishes a fractional-order model (FOM) to describe the rumor spreading process. Members of society in this FOM are classified into three categories that change with time—the population that is ignorant of the rumors and does not know them, the population that is aware of the truth of the rumors but does not believe them, and the spreaders of rumors—taking into consideration awareness programs (APs) through media reports as a subcategory that changes over time where paying attention to these APs makes ignorant individuals avoid believing rumors and become better-informed individuals. We prove the positivity and boundedness of the FOM solutions. The feasible equilibrium points (EPs) and their local asymptotical stability (LAS) are analyzed based on the control reproduction number (CRN). Then, we examine the influence of model parameters that emerge with the CRN through a sensitivity analysis.A fractional optimal control problem (FOCP) is formulated by considering three time-dependent control measures in the suggested FOM to capture the spread of rumors; $u_1$, $u_2$, and $u_3$ represent the contact control between rumor spreaders and ignorant people, control media reports, and control rumor spreaders, respectively. We derive the necessary optimality conditions (NOCs) by applying Pontryagin’s maximum principle (PMP). Different optimal control strategies are proposed to reduce the negative effects of rumor spreading and achieve the maximum social benefit. Numerical simulation is implemented using a forward–backward sweep (FBS) approach based on the predictor–corrector method (PCM) to clarify the efficiency of the proposed strategies in order to decrease the number of rumor spreaders and increase the number of aware populations. Full article

Background: Various methods for preventing pressure injury have been developed across the globe, particularly in Saudi Arabia. Current available research has investigated the knowledge, attitudes, and practices regarding pressure injury prevention. However, no systematic review and meta-analysis have yet examined the associations among knowledge, attitudes, and practices regarding pressure injury prevention based on the perspectives of registered nurses. Objective: This study examines and summarizes the reported relationships among knowledge, attitudes, and practices regarding pressure injury prevention on the basis of the perceptions of registered nurses. Methods: The CINAHL, ProQuest, PubMed, ScienceDirect, and Web of Science databases were searched for quantitative evidence published in English between 2019 and 2025. The systematic review and meta-analysis adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (2021) guidelines. Results: Out of the 1986 records that were initially examined, a total of 10 quantitative, cross-sectional, and correlational studies were included in the final systematic review and meta-analysis. In the context of the meta-analysis, 10 studies were included for the association between knowledge and attitudes, whereas only 3 studies were available for the association between knowledge and practice, and similarly, only 3 studies addressed the association between attitudes and practice of pressure injury prevention. Collectively, 2457 registered nurses were involved in these studies, mostly working in intensive care units. The studies were conducted in various countries across Asia and the Middle East, mostly in Turkiye, within the last five years. The registered nurses in the 10 studies reported associations among knowledge, attitudes, and practices toward pressure injury prevention that ranged from insignificant to weak, indirect, and strong direct. Conclusions: Relationships among knowledge, attitudes, and practices toward pressure injury prevention are both positive and negative from the global perspective and are shaped by various confounding and mediating factors, including socio-demographic, nursing-related, and hospital-related factors. Improving the knowledge base of registered nurses and promoting a favorable attitude toward pressure ulcer prevention would provide healthcare organizations with the potential to enhance the already commendable levels of practice and prevention noted in this review. Full article

This systematic review examines the critical intersection of digital transformation, supply chain resilience, and sustainability within manufacturing contexts, with specific implications for Saudi Arabian industries. Through a comprehensive analysis of 124 peer-reviewed articles published between 2018 and 2024, we identify how emerging technologies—including Internet of Things (IoT), artificial intelligence, blockchain, and big data analytics—transform traditional supply chains into dynamic ecosystems capable of withstanding disruptions while advancing sustainability goals. Our findings reveal that digital transformation positively influences both resilience and sustainability outcomes. Still, these relationships are significantly moderated by three key factors: supply chain dynamism, regulatory uncertainty, and integration of innovative technologies. The study demonstrates that while high supply chain dynamism amplifies the positive effects of digital technologies on resilience capabilities, regulatory uncertainty creates implementation barriers that potentially diminish these benefits. Moreover, successfully integrating innovative technologies is a critical mediating mechanism translating digital initiatives into tangible sustainability improvements. The review synthesises these findings into an integrated conceptual framework that captures the complex interrelationships between these domains and provides specific strategic recommendations for Saudi Arabian manufacturing organisations. By addressing the identified research gaps—particularly the lack of industry-specific investigations in emerging economies—this review offers valuable insights for researchers and practitioners seeking to leverage digital transformation for simultaneously efficient, resilient, and sustainable supply chain operations in rapidly evolving business environments. Full article

Background: Sensory processing difficulties can interfere with daily functioning and participation across adulthood. While standardized assessment tools exist, culturally validated instruments for Arabic-speaking adults remain limited. Objectives: This study aimed to validate the Arabic version of the Sensory Processing Measure—Second Edition (SPM-2) Adult Self-Report form in a Saudi population and evaluate its utility for the early detection of sensory processing challenges in at-risk individuals. Methods: A total of 399 Saudi adults (205 females and 194 males), aged 21 to 87 years (M = 44.1; SD = 16.2), completed the Arabic SPM-2 online. The scale consists of eight subscales, six of which form the Sensory Total score—Vision, Hearing, Touch, Taste and Smell, Body Awareness, and Balance and Motion—representing core sensory processing abilities (i.e., Sensory Total (ST)). The remaining two—Planning and Ideas and Social Participation—capture higher-order integrative functions and do not contribute to the ST. Results: The overall scale demonstrated strong internal consistency (α = 0.89), with subscale alphas ranging from 0.43 (Hearing) to 0.70 (Body Awareness). Confirmatory factor analysis (CFA) (χ² [3052] = 4147.4; p < 0.001) showed good absolute fit (RMSEA = 0.030) and moderate incremental fit (CFI = 0.74; TLI = 0.73), values that are typical for large-df models. Descriptive and cluster analyses identified distinct participant subgroups with elevated frequency ratings (scores of 2 or 3) suggestive of sensory risk. Significant age-related differences were observed across multiple sensory domains, while no significant sex-related effects were found. Conclusions: Although Social Participation and Hearing showed lower reliability, the Arabic SPM-2 exhibits sound internal structure and therefore shows promise for future clinical application once criterion validity is established. The findings support its application in culturally responsive screening, early risk identification, and intervention planning in Arabic-speaking contexts. Full article

Background: Heart failure (HF) contributes to a poor physical and emotional health-related Quality of Life (HRQoL) and poor health outcomes. Coping strategies have been identified as essential in enhancing HRQoL. The study’s purpose was to examine the relationships between the factors that influence coping (i.e., age, sex, education, income, HF duration), HF severity, coping strategies (i.e., problem-focused, active emotion-focused, avoidant emotion-focused), and physical and emotional HRQoL. Methods: A cross-sectional study was conducted using online surveys. Descriptives, Pearson’s correlation, and one-way ANOVA analyses were used to analyze the data. Results: A total of 108 participants completed the study, with the majority being Black men. The result showed significant negative relationships (p < 0.05) between problem-focused and active emotion-focused coping and HF severity. Lower age was significantly related to the use of problem-focused and active emotion-focused coping (p < 0.05); females showed higher use of all coping strategies as compared with males (p < 0.05). A better physical HRQoL was significantly associated with active emotion-focused coping (r = −0.283, p = 0.005), whereas a better emotional HRQoL was significantly associated with problem-focused coping (r = −0.265, p = 0.005) and active emotion-focused coping (r = −0.373, p < 0.001). Conclusions: Findings showed that individuals with a lower HF severity, a younger age, and a higher income and education tended to predominantly utilize adaptive coping strategies. Individuals with HF who use problem-focused and active emotion-focused coping may experience better physical and emotional HRQoL, whereas those using primarily avoidant emotional-focused coping may need guidance in their coping strategies. Healthcare professionals may take factors such as HF severity into account to tailor interventions that promote adaptive coping and enhance HRQoL outcomes. Full article