Search Results (5,257)

Kuwait has reported a problematic increase in the prevalence of Antimicrobial Resistance (AMR). However, the absence of studies that analyze AMR from combined human, agricultural (animal), and environmental domains limits our ability to assess the extent of the problem in Kuwait. Therefore, this systematic review provides a comprehensive insight into the AMR status in Kuwait regarding humans, agriculture (animals), and the environment from the perspective of the One Health approach. A systematic search was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to identify the relevant literature on AMR in Kuwait. Multiple online electronic databases, including the Cochrane Library, Google Scholar, Web of Science, PubMed, BioMed Central (BMC), and Scopus, were searched to perform a narrative synthesis and meta-analysis. Twenty-eight studies published between 2009 and 2024 were included in this study. Domain-wise distribution varied, with 11 studies related to clinical settings, 11 to the environment, 4 to agricultural (animal), and 2 to both clinical and community settings. The narrative synthesis indicated a high occurrence of AMR bacteria in human, agriculture (animal), and environmental domains. In human domains, the dominant AMR isolates belonged to four Gram-negative species: E. coli, K. pneumoniae, P. aeruginosa, and Acinetobacter baumannii. In agriculture (animals), Salmonella isolates from poultry display high resistance to cefotaxime, ampicillin, and amoxicillin. Camel milk analyses revealed that 80% of bacterial isolates are resistant to antibiotics such as penicillin, tetracyclines, and carbapenems. An environmental analysis of sewage, seawater, sediment, and aerosol samples documented widespread antibiotic resistance genes (ARGs) with resistance mechanisms such as extended-spectrum beta-lactamase, carbapenemases, and colistin. The cross-domain analysis identified the overlapping of ARGs. Regarding the One Health approach, none of the studies used this approach to interlink these sectors. Meanwhile, the meta-analysis indicated a high resistance rate in humans (34.05%, 95% CI (22.81 to 46.27, p < 0.0001, I² = 98.94%)), agriculture (animals) (67.42%, 95% CI (30.30 to 94.93, p < 0.0001, I² = 97.40%)), and environment (69.86%, 95% CI (48.80 to 87.26, p < 0.0001, I² = 98.78%)). The reported spread of AMR and the overlap of resistance genes among isolates across the domains demonstrate the interconnected nature of AMR in Kuwait. These findings underscore the need to adopt the One Health approach to strengthen surveillance, implement control measures, and enhance public education strategies to address the complexity of AMR challenges in Kuwait. Full article

The swift growth of the low-altitude economic ecosystem and Unmanned Aerial Vehicle (UAV) swarm applications across diverse sectors presents significant challenges for engineering managers in terms of effective performance evaluation and operational optimization. Traditional evaluation methods often struggle with the inherent complexities, dynamic nature, and multi-faceted performance criteria of UAV swarms. This study introduces a novel Bayesian Network (BN)-based multicriteria decision-making framework that systematically integrates expert intuition with real-time data. By employing variance decomposition, the framework establishes theoretically grounded, bidirectional mapping between expert-assigned weights and the network’s probabilistic parameters, creating a unified model of subjective expertise and objective data. Comprehensive validation demonstrates the framework’s efficacy in identifying critical performance drivers, including environmental awareness, communication ability, and a collaborative decision. Ultimately, our work provides engineering managers with a transparent and adaptive tool, offering actionable insights to inform resource allocation, guide technology adoption, and enhance the overall operational effectiveness of complex UAV swarm systems. Full article

Emotion vocabulary is essential for recognising, expressing, and regulating emotions, playing a critical role in language proficiency and emotional competence. However, traditional vocabulary assessments have largely overlooked emotion-specific lexicons, limiting their ability to identify learners’ gaps in this area. Therefore, this study addresses this gap by developing and validating the Productive Emotion Vocabulary Size Test (PEVST), a tool designed to evaluate productive emotion vocabulary knowledge in ESL/EFL contexts. The PEVST incorporates low-, mid-, and high-frequency emotion words, assessed through context-rich vignettes, offering a comprehensive tool for measuring productive emotion vocabulary knowledge. The study recruited 156 adult participants with varying language proficiency levels. Findings revealed that word frequency significantly influenced production accuracy: higher frequency words were more easily retrieved, while lower frequency words often elicited higher frequency synonyms. Rasch analysis provided validity evidence for the test’s scoring, highlighting the effectiveness of a granular scoring system that considers nuanced responses. However, some limitations arose from misfitting items and the homogeneity of participants’ language proficiency, calling for further evidence with a more linguaculturally diverse target group and careful control for individual differences. Future iterations should address these challenges by incorporating cultural adaptations and accounting for individual differences. The PEVST offers a robust foundation for advancing emotion vocabulary assessment, deepening our understanding of the interplay between language, emotions, and cognition, and informing emotion-focused language pedagogy. Full article

Background: Helcococcus kunzii, a facultative anaerobe and Gram-positive coccus, has been documented as a cunning pathogen, mainly in immunocompromised individuals, as evidenced by recent clinical and microbiological reports. It has been associated with a variety of polymicrobial infections, comprising diabetic foot ulcers, prosthetic joint infections, osteomyelitis, endocarditis, and bloodstream infections. Despite its emerging clinical relevance, no licensed vaccine or targeted immunotherapy currently exists for H. kunzii, and its rising resistance to conventional antibiotics presents a growing public health concern. Objectives: In this study, we employed an integrated subtractive proteomics and immunoinformatics pipeline to design a multi-epitope subunit vaccine (MEV) candidate against H. kunzii. Initially, pan-proteome analysis identified non-redundant, essential, non-homologous, and virulent proteins suitable for therapeutic targeting. Methods/Results: From these, two highly conserved and surface-accessible proteins, cell division protein FtsZ and peptidoglycan glycosyltransferase FtsW, were selected as promising vaccine targets. Comprehensive epitope prediction identified nine cytotoxic T-lymphocyte (CTL), five helper T-lymphocyte (HTL), and two linear B-cell (LBL) epitopes, which were rationally assembled into a 397-amino-acid-long chimeric construct. The construct was designed using appropriate linkers and adjuvanted with the cholera toxin B (CTB) subunit (NCBI accession: AND74811.1) to enhance immunogenicity. Molecular docking and dynamics simulations revealed persistent and high-affinity ties amongst the MEV and essential immune receptors, indicating a durable ability to elicit an immune reaction. In silico immune dynamic simulations predicted vigorous B- and T-cell-mediated immune responses. Codon optimization and computer-aided cloning into the E. coli K12 host employing the pET-28a(+) vector suggested high translational efficiency and suitability for bacterial expression. Conclusions: Overall, this computationally designed MEV demonstrates favorable immunological and physicochemical properties, and presents a durable candidate for subsequent in vitro and in vivo validation against H. kunzii-associated infections. Full article

Plant-based diets are increasingly attracting attention as they play a significant role in human health and environmental sustainability and are believed to be key components of sustainable food systems. In the present study, both pros and cons of the adoption of plant-based diets are analyzed using a bibliometric method integrated with a qualitative examination of the scientific literature. For the bibliometric study, Bibliometrix software was utilized, examining 3245 scientific articles, downloaded from the Scopus database, and printed between the years 1957 and 2025. The analyses were conducted using R software, version 4.4.1, with access to the Bibliometrix package, version 4.1. The results indicate a remarkable rise, in the last two decades, in the scholarly focus on the influence of plant-based diets on the individual’s health condition as well as the environment. Keyword co-occurrence studies and international collaborations demonstrate a dominance of research focus in both the United States and Europe, with significant contributions from the Asia–Pacific region. Furthermore, the current work offers qualitative identification of the benefits of plant diets from various perspectives like nutritional, economic, ecological, and cultural. It also explores the main dissuaders from adhering to these diets, including perceived nutritional hazards, cost perceptions, low availability, and social prohibitions. Findings emphasize that, in spite of all the barriers, plant food-based diets have a wide-ranging ability to provide tangible benefits at both the individual and population levels, and documented in the scientific literature are recommendations of expert-led education programs, economic incentives, and judiciously framed public policies to overcome these barriers and to make this transition possible towards sustainable food choices. Findings provide a comprehensive understanding of the current lines of inquiry and stage the subsequent work on how to motivate sustainability among the general population. Full article

Silicon carbide (SiC) ceramics have gained significant attention in advanced engineering applications because of their superior mechanical properties, resistance to wear and corrosion, and thermal stability. However, the precision machining of these materials is extremely challenging because of their intrinsic hardness and brittleness. Wire Electrical Discharge Machining (WEDM) has become increasingly popular as a viable technique for processing SiC ceramics because of its ability to produce intricate geometries and high-quality surface finishes. In this review paper, a comprehensive overview of WEDM technology applied to SiC ceramics is presented, emphasizing the influence of process parameters, wire materials, and dielectric fluids on cutting efficiency and quality. This research explores recent experimental findings related to Wire Electrical Discharge Machining (WEDM) and highlights the challenges in reducing material damage. It also presents strategies to improve machining performance. Additionally, potential future directions are discussed, providing a roadmap for further research and the application of WEDM in processing silicon carbide (SiC) and its variants, including solid silicon carbide (SSiC) and silicon-infiltrated silicon carbide (SiSiC). Full article

Ultrasound imaging is one of the most widespread biomedical imaging techniques thanks to its advantages such as being non-invasive, portable, non-ionizing, and cost-effective. Ultrasound imaging generally provides two-dimensional cross-sectional images, but the quality and interpretative ability vary based on the experience of the examiner, leading to a lack of objectivity and accuracy. To address these issues, there is a growing demand for three-dimensional ultrasound imaging. Among the various types of transducers used to obtain three-dimensional ultrasound images, this paper focuses on the most standardized probe, the linear array transducer, and provides an overview of the system implementations, imaging results, and applications of volumetric ultrasound imaging from the perspective of scanning methods. Through this comprehensive review, future researchers will gain insights into the advantages and disadvantages of various approaches to three-dimensional imaging systems using linear arrays, providing direction and applicability for system configuration and application. Full article

Introduction: Pain is highly prevalent among community-dwelling older adults and can undermine their ability to perform Instrumental Activities of Daily Living (IADL), which are essential for independent living. This systematic review aimed to summarize existing research to clarify the relationship between pain and IADL disability in community-dwelling older adults. Methods: We conducted a search of PubMed on 27 July 2025. Eligible studies met the following criteria: (1) assessed the association between pain and IADL disability; (2) included community-dwelling older adults aged 60 and older; and (3) were published in English. Results: Of the 400 records screened, 29 studies met the inclusion criteria. Of these, 23 studies (18 cross-sectional and 5 cohort studies) reported a significant association between pain and IADL disability, while 6 cross-sectional studies did not. Pain was assessed using diverse instruments across varying recall periods and thresholds, and IADL disability was measured using multiple scales. Such methodological heterogeneity precluded quantitative synthesis. Conclusions: In community-dwelling older adults, pain consistently predicts IADL disability across designs and settings. However, the lack of standardized, multidimensional measures and incomplete adjustment for treatment, multimorbidity, and polypharmacy limits precise effect estimation. Future research should adopt harmonized assessment tools, control comprehensively for relevant confounders, and perform meta-analyses where data permit to clarify pain’s true impact on functional independence. Full article

Chronic pain management is constantly evolving, and our literature review aims to describe the general innovations happening within the field. The need for advancements in chronic pain is a necessity, as debilitating back pain and other forms of chronic pain are significant issues in the United States. Traditionally, medications have been the initial treatment options in cases of chronic pain; however, the advancement in pharmacogenetics has led to an increased ability to create more personalized medication plans. Additionally, neuromodulation in spinal cord stimulation, transcranial magnetic stimulation, transcranial direct-current stimulation, and dorsal root ganglion stimulation continue to see increased usage in mainstream chronic pain management. These techniques have continued to prove successful in many chronic pain management cases. They are allowing practicing physicians more confidence in the variety of treatment options. Lastly, great strides have also been made in stem cell and regenerative therapies, such as platelet-rich plasma injections, and artificial intelligence, further advancing the various treatment options and overall efficiency of pain management. This review aims to critically analyze and review the most up-to-date literature within each section mentioned and comprehensively discuss the future of innovation in chronic pain management. Full article

In maritime blurred image recognition, ship collision accidents frequently result from three primary blur types: (1) motion blur from vessel movement in complex sea conditions, (2) defocus blur due to water vapor refraction, and (3) scattering blur caused by sea fog interference. This paper proposes a dual-branch recognition method specifically designed for motion blur, which represents the most prevalent blur type in maritime scenarios. Conventional approaches exhibit constrained computational efficiency and limited adaptability across different modalities. To overcome these limitations, we propose a hybrid CNN–Transformer architecture: the CNN branch captures local blur characteristics, while the enhanced Transformer module models long-range dependencies via attention mechanisms. The CNN branch employs a lightweight ResNet variant, in which conventional residual blocks are substituted with Multi-Scale Gradient-Aware Residual Block (MSG-ARB). This architecture employs learnable gradient convolution for explicit local gradient feature extraction and utilizes gradient content gating to strengthen blur-sensitive region representation, significantly improving computational efficiency compared to conventional CNNs. The Transformer branch incorporates a Hierarchical Swin Transformer (HST) framework with Shifted Window-based Multi-head Self-Attention for global context modeling. The proposed method incorporates blur invariant Positional Encoding (PE) to enhance blur spectrum modeling capability, while employing DyT (Dynamic Tanh) module with learnable α parameters to replace traditional normalization layers. This architecture achieves a significant reduction in computational costs while preserving feature representation quality. Moreover, it efficiently computes long-range image dependencies using a compact 16 × 16 window configuration. The proposed feature fusion module synergistically integrates CNN-based local feature extraction with Transformer-enabled global representation learning, achieving comprehensive feature modeling across different scales. To evaluate the model’s performance and generalization ability, we conducted comprehensive experiments on four benchmark datasets: VAIS, GoPro, Mini-ImageNet, and Open Images V4. Experimental results show that our method achieves superior classification accuracy compared to state-of-the-art approaches, while simultaneously enhancing inference speed and reducing GPU memory consumption. Ablation studies confirm that the DyT module effectively suppresses outliers and improves computational efficiency, particularly when processing low-quality input data. Full article

Long-term and accurate prediction of air pollutant concentrations can serve as a foundation for air pollution warning and prevention, which is crucial for social development and human health. In this study, we provide a model for predicting the concentration of air pollutants based on big data spatiotemporal correlation analysis and deep learning methods. Based on an improved temporal convolutional network (TCN) and a bi-directional gated recurrent unit (BiGRU) as the fundamental architecture, the model adds two attention mechanisms to improve performance: Squeeze and Excitation Networks (SENet) and Convolutional Block Attention Module (CBAM). The improved TCN moves the residual connection layer to the network’s front end as a preprocessing procedure, improving the model’s performance and operating efficiency, particularly for big data jobs like air pollution concentration prediction. The use of SENet improves the model’s comprehension and extraction of long-term dependent features from pollutants and meteorological data. The incorporation of CBAM enhances the model’s perception ability towards key local regions through an attention mechanism in the spatial dimension of the feature map. The TCN-SENet-BiGRU-CBAM model successfully realizes the prediction of air pollutant concentrations by extracting the spatiotemporal features of the data. Compared with previous advanced deep learning models, the model has higher prediction accuracy and generalization ability. The model is suitable for prediction tasks from 1 to 12 h in the future, with root mean square error (RMSE) and mean absolute error (MAE) ranging from 5.309~14.043 and 3.507~9.200, respectively. Full article

The study compares the overall sustainability of two organic and two conventional rice farming systems during the 2022 drought. The research aimed to develop an experiment exploring the ability of an integrated methodological approach to identify tradeoffs and provide actionable insights for a sustainable agricultural transition under extreme climate stress. To this aim, the study employed economic analysis, Life Cycle Assessment (LCA) for environmental impact, and the OASIS framework for broader social and resilience indicators. The study revealed tradeoffs between the economic efficiency of conventional rice farming and the ecological resilience of organic systems, a conclusion made possible only through its integrated assessment methodology. By combining different methods, the research suggested that while conventional farms achieved clear financial superiority and greater efficiency per ton of rice, organic systems showcased superior ecological performance per hectare, greater biodiversity, and enhanced resilience. This highlights a crucial research frontier focused on designing hybrid systems or new economic models that can translate the environmental resilience of organic methods into tangible market value, effectively resolving the very tradeoffs this comprehensive assessment suggested. Full article

Background/Objectives: No Arabic-language version of the Observable Social Cognition Rating Scale (OSCARS) is available that allows to properly and specifically assess social cognition (SC) in Arabic-speaking populations. This study aimed to examine the preliminary psychometric characteristics of the Arabic translated version of the OSCARS, including factor structure, reliability, concurrent validity, and measurement invariance across sex. Methods: This cross-sectional study has been conducted during February and March 2024 and included 113 chronic, remitted, and clinically stable patients with schizophrenia. Results: The originally proposed two-factor model (Social Cognitive Bias and Social Cognitive Ability) showed acceptable model fit after removal of two items that yielded low factor loadings (items 2 and 3). Total and factor scores showed good internal consistency, with Cronbach’s alpha of 0.85–0.94. Measurement invariance was established across sex groups at the configural, metric, and scalar levels. No significant differences emerged between male and female patients for latent mean scores of the OSCARS. Finally, concurrent validity was supported by appropriate patterns of correlations with functioning, recovery, and emotional intelligence measures. Conclusions: The Arabic OSCARS stands out as a brief, valid, reliable, and comprehensive assessment tool to evaluate SC in Arabic-speaking patients with schizophrenia based on the perspectives of interviewers. Offering this measure to clinicians and researchers who work in Arab settings may close the existing gap in the assessment of SC in schizophrenia. Due to its easy and fast application, the Arabic OSCARS is believed to be highly valuable in clinical and research practices. Full article

Accurate assessment of histological remission is a critical goal in the management of ulcerative colitis (UC); however, routine evaluation is hindered by the invasiveness of endoscopy and biopsy. Non-invasive alternatives like intestinal ultrasound (IUS) and faecal calprotectin (FC) show promise for monitoring mucosal inflammation, though their ability to predict histological healing remains underexplored. This systematic review and meta-analysis aimed to evaluate the diagnostic accuracy of IUS, FC, and their combined use for detecting histologic remission in patients with UC. A comprehensive literature search identified two eligible studies comprising 72 patients. Pooled estimates for IUS demonstrated high sensitivity (0.84, 95% CI: 0.35–0.98) but variable specificity (0.78, 95% CI: 0.08–0.99), while FC alone exhibited high sensitivity (0.85, 95% CI: 0.72–0.92) with moderate specificity (0.60, 95% CI: 0.38–0.79). Notably, only one study assessed the combined diagnostic approach, reporting superior performance with sensitivity and specificity of 0.88 and 0.80, respectively. The certainty of the evidence was rated as moderate. These exploratory findings suggest that a multimodal, non-invasive approach combining IUS and FC may improve diagnostic accuracy in detecting histological remission in UC, potentially reducing reliance on invasive procedures. However, given the limited number of studies included and the high degree of heterogeneity, these results should be interpreted with caution. Further large-scale, methodologically robust studies are needed to validate these preliminary findings and establish standardized diagnostic protocols. Full article

MXenes, a novel class of two-dimensional (2D) transition metal carbides and nitrides, have garnered significant attention due to their exceptional thermal conductivity, electrical properties, and mechanical strength. This review offers a comprehensive overview of MXenes, focusing on their synthesis methods, material properties, tribological performance, and potential challenges and opportunities. Typically synthesized through the selective etching of layered precursors, MXenes offer highly tunable structures, allowing for precise tailoring for specific functionalities. Their outstanding properties, such as high electrical conductivity, chemical versatility, mechanical durability, and intrinsic lubricity, make them promising candidates for various applications, including energy storage, electromagnetic shielding, water purification, biosensing, biomedicine, and advanced tribological systems. While many of these applications are briefly acknowledged, this review primarily emphasizes MXenes’ potential in tribological applications, where recent studies have highlighted their promise as solid lubricants and tribological additives due to their low shear strength, layered structure, and ability to form protective tribofilms under sliding contact. However, challenges such as oxidation resistance, long-term stability, and performance under extreme environments continue to impede their full potential. With less than a decade of focused research, the field is still evolving, but MXenes hold tremendous promise for revolutionizing modern material science, especially in next-generation lubrication and wear-resistant systems. This review explores both the opportunities and challenges associated with MXenes, emphasizing their emerging role in tribology alongside their broader engineering applications. Full article