Search Results (24,467)

Objective: Radiotherapy remodels the tumor microenvironment (TME) and may enhance the efficacy of immunotherapy in cancer treatment, particularly in patients with large, unresectable hepatocellular carcinoma (HCC) complicated by portal vein tumor thrombus (PVTT). Because of these unique effects, a growing body of research has found that stereotactic body radiation therapy (SBRT) combined with transcatheter arterial chemoembolization (TACE) or programmed death protein 1 (PD-1) inhibitors has a synergistic impact on unresectable advanced hepatocellular carcinomas (HCCs) larger than 5 cm in diameter. We aim to explore the efficacy of these treatment modalities through a network meta-analysis (NMA). Methods and Analysis: We evaluated the efficacy and safety of different SBRT-based treatment modalities for large advanced HCCs with PVTT (tumor diameter ≥ 5 cm), with primary endpoints including overall survival (OS), progression-free survival (PFS), and grade 3–4 severe adverse events (SAEs). Results: Eighteen studies comprising 2303 patients were included. SBRT combined with transcatheter arterial chemoembolization (SBRT + TACE) demonstrated significantly superior overall survival compared with other monotherapy or combination strategies. Most other treatment regimens showed comparable PFS outcomes. Notably, SBRT alone and SBRT combined with PD 1 inhibitors (SBRT + PD 1) were associated with significantly lower incidences of severe adverse events compared with other treatment modalities; all of these reported SAEs were manageable with appropriate clinical intervention. Conclusions: For patients with large (≥5 cm) advanced HCC with PVTT, SBRT combined with TACE was associated with superior OS and PFS compared with other treatment strategies. These findings suggest potential synergistic interactions between SBRT and TACE or immunotherapy. Further high-quality prospective trials are warranted to validate these observations and clarify the underlying molecular mechanisms. Our results provide evidence to inform therapeutic decision-making in advanced HCC. Full article

Multimodal fusion methods leveraging various sensors provide strong support for 3D object detection. However, under adverse weather conditions such as rain, fog, snow, and intense glare, complex environmental factors can degrade sensor data quality, leading to increased false positives and missed detections. In addition, sensor modalities (e.g., LiDAR and cameras) inherently vary in information density, and directly fusing them can cause critical details in high-density data to be diluted by low-density data, thereby increasing errors. To address these issues, we propose a Semantic-Enhanced Bidirectional Multimodal Fusion (SeBFusion) framework. By introducing a semantic enhancement mechanism and a bidirectional fusion strategy, SeBFusion mitigates the impact of noise under adverse weather and alleviates information dilution in multimodal fusion. Specifically, SeBFusion first employs a virtual point generation and camera semantic injection module to selectively map image semantic features into 3D space, producing semantically enhanced LiDAR features to compensate for the sparsity of the raw LiDAR point cloud. Then, during cross-modal interaction, we design a bidirectional cross-attention fusion module. This module estimates the confidence of each modality and adaptively reweights the bidirectional information flow, thereby reducing the risk of noise propagation across modalities and improving the robustness and accuracy of 3D object detection in complex environments. Experiments on adverse-weather versions of datasets such as KITTI-C and nuScenes-C validate the effectiveness and superiority of the proposed method. On the nuScenes-C dataset, it achieves 66.2% mAP and 66.6% mAP under fog and snow conditions, respectively. Full article

Background: Healthy dietary behaviours are essential for maintaining health, functional independence, and quality of life in later life. Family members are a key source of social support for community-dwelling older adults, yet the ways in which family support shapes older adults’ dietary behaviours, particularly among those who retain autonomy, remain insufficiently synthesized. Therefore, this review aims to map how family support influences dietary behaviours among community-dwelling older adults by examining the forms, roles, and contextual influences of family support within a Social Support Theory framework. Methods: Following Joanna Briggs Institute guidance and PRISMA-ScR reporting standards, we conducted a scoping review of empirical studies published in English or Chinese. Searches were conducted across PubMed, CINAHL, PsycINFO, Web of Science, and Scopus from inception to 2025. Quantitative and qualitative evidence was synthesised using a convergent–segregated mixed-methods approach. Qualitative findings were deductively mapped to instrumental, informational, emotional, and esteem support domains. Results: Nineteen studies were included. Quantitative evidence indicated that family support, particularly shared meal preparation, joint dietary adherence, and autonomy-supportive encouragement, was generally associated with better diet quality, dietary adherence, and nutritional outcomes. Qualitative findings showed that the influence of family support depended on relationship dynamics and contextual factors, including communication patterns, autonomy negotiation, shared responsibility, and cultural expectations. Conclusions: Family support plays a multifaceted and context-dependent role in shaping dietary behaviours among community-dwelling older adults. These findings can inform the development of family-inclusive strategies and interventions that promote healthy dietary behaviours while respecting older adults’ autonomy and relational contexts. Full article

Incretin-based pharmacotherapy has rapidly transformed obesity management. However, despite its efficacy, gastrointestinal (GI) adverse events (AEs) are common and represent a major driver of treatment discontinuation. Symptoms such as nausea, vomiting, acid reflux, diarrhea, and constipation, not only impair the quality of life, but also compromise adherence, thereby limiting the real-world effectiveness of these agents. Targeted nutritional strategies may play a pivotal role in mitigating these symptoms and supporting sustained treatment. However, most clinical trials have relied on generalized lifestyle advice combined with hypocaloric dietary prescriptions, with limited integration of structured, mechanism-based nutritional counseling tailored to the physiological actions of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and dual glucose-dependent insulinotropic polypeptide (GIP)/GLP-1 RAs. Consequently, practical guidance for clinicians and dietitians remains fragmented. The present review synthesizes the available evidence on GI AEs associated with incretin-based therapies and examines whether structured, targeted nutritional management can meaningfully reduce symptom burden. We also outline key monitoring strategies and focus on important clinical aspects for physicians and dietitians, aiming to optimize patient outcomes. In addition, we provide detailed information on the spectrum of GI AEs to guide effective management and limit intolerance. By bridging pharmacology with applied clinical nutrition, we aim to provide a pragmatic framework for improving tolerability, sustaining adherence, and translating trial efficacy into durable real-world effectiveness. Full article

The antimicrobial effect of Chios mastic gum essential oil (mastic EO) was evaluated in vitro in a variety of spoilage and pathogenic bacteria and yeast strains isolated from spoiled cheeses with concentrations ranging from 0.006 to 2% (Minimum Inhibitory Concentration (MIC)) and in situ (cheese slices). The mastic EO (2%) was incorporated in sodium alginate edible gel films (Mastic Edible Films (MEFs)), and then the films were applied between the cheese slices that had been previously inoculated with a cocktail of three strains of Listeria monocytogenes (on both sides of the slices) and subjected or not to high-pressure processing (HPP). Cheese samples were vacuum-packaged and cold stored (4 °C), and microbiological, pH and organoleptic (in pathogen-free slices) analyses were employed, while Fourier Transform Infrared (FTIR) spectroscopy was applied as a rapid technique to monitor the biochemical changes present on the slices. Samples without MEF, without the pathogen and with or without HPP were employed as controls. Results showed that the MIC of the mastic EO varied from 0.01% to 1.8% depending on the species and/or strains. Pathogen’s growth was suppressed by HPP, MEF or their combination, which showed the highest efficacy. These results could provide useful data to support risk assessment studies on ready-to-eat foods. Finally, FTIR implementation with data analytics was found to be satisfactory, indicating FTIR's potential as a reliable information source for cheese quality control. Full article

With the rapid development of intensive marine aquaculture, water quality has become a key factor affecting both economic benefits and ecological safety in marine aquaculture. In the process of actual water quality evaluation, due to the great uncertainty and ambiguity of evaluation indicators, experts find it difficult to evaluate in real number form and are more inclined to use linguistic variables to evaluate indicators, which poses challenges for the construction of water quality evaluation models. An intuitionistic fuzzy set (IFS) is an effective tool for dealing with uncertainty and fuzziness in complex problems. Based on a detailed analysis of existing distance measures for IFS, this study proposes a new distance measure that not only considers membership and non-membership information, but also constructs an allocation function for membership and non-membership, introducing hesitation information into distance metrics. We proposed the definitions and proved the properties. The comparative experiments show that the new distance measure can overcome the shortcomings of existing distance measures. Furthermore, based on the newly proposed distance measure, the IFS TOPSIS method is improved in multi-attribute decision-making applications. Finally, a practical application of marine aquaculture water quality evaluation is used. The results illustrate that when α = 1 the closeness declines from 0.741 to 0.432, when =2 the closeness declines from 0.662 to 0.46, and when =6 the closeness declines from 0.566 to 0.82. The convenience and effectiveness of the new method is demonstrated. Full article

Sustainable manufacturing performance is increasingly influenced by the quality of decisions embedded in Quality Management System (QMS) activities, particularly those related to problem analysis and preventive action. In industrial environments such as welded bus chassis production, recurring quality defects—although involving small components—can generate sustainability impacts through rework, inspection effort, and energy consumption. Although artificial intelligence (AI) is increasingly adopted to support quality-related tasks, its contribution is often assessed in terms of automation rather than its effect on decision quality. This study presents an AI-supported, prompt-driven decision framework designed to strengthen preventive performance within QMS. The framework is implemented through a deterministic software application that formalizes prompt engineering as a rule-based process, transforming informal human problem descriptions into structured prompts suitable for external AI reasoning tools. The application itself does not embed AI and does not generate decisions; instead, it functions as a transparent decision interface that reduces variability in problem formulation and supports methodological consistency. The framework was validated through an industrial case study conducted in a bus chassis manufacturing plant experiencing recurring defects related to missing or incorrectly positioned welded brackets. Quantitative evaluation using Key Performance Indicators demonstrates reduced analysis cycle time, improved completeness of problem definitions, higher corrective action implementation rates, and lower defect recurrence. Full article

Camellia oleifera Abel, recognized as a woody oil-producing tree species, possesses considerable economic significance. To improve the breeding efficiency of C. oleifera, it is crucial to elucidate the genetic foundation underlying the mechanisms regulating fruit traits. In this study, a total of 6,252,197 high-quality single-nucleotide polymorphisms (SNPs) were identified from 109 germplasm accessions. Through genetic structure analysis, these accessions were categorized into two distinct populations. The average fixation index (Fst) was found to be 0.0153, indicating weak population differentiation. The genome-wide association analysis (GWAS) identified 157 significant loci. From these loci, 110 candidate genes were selected, which were associated with disease resistance, reproduction, development, and RNA biosynthesis. Twenty-three genes were involved in metabolic pathways, including genetic information-processing protein families, metabolic protein families, terpenoids, and polyketides. The identification of gene loci closely related to fruit traits not only provides genetic data for studying the molecular mechanisms of fruit traits but also offers new research avenues for molecular breeding of C. oleifera. Full article

As a proactive response, reserving a certain amount of relief materials in advance is crucial for responding to potential disasters. Different from public tendering and bidding, this study proposes the purchasing mode of authorization, under which a nonprofit organization (NPO), as a buyer, wholly authorizes the procurement of relief materials to a professional agent. The relief material procurement system under the purchasing mode of authorization is regarded as a bilevel relief supply chain consisting of one buyer, one agent, and two suppliers with private information about the quality levels of relief materials. For the disclosure of private information, the quality-related procurement strategy is designed in the form of a menu based on the suppliers’ private information. A bilevel optimization model is developed based on agency theory to derive the optimal strategic decisions, and the impacts of the main influencing factors on the optimal procurement strategy and the buyer’s minimum expected cost are discussed via numerical analysis. Then, the study is extended by exploring supplier’s alternative cost functions and supply availability, as well as proposing future research directions. This paper presents an optimal quality-related procurement strategy, which provides rules for quickly responding to the changes in influencing factors during the material procurement process, as well as the minimum expected cost for the buyer to purchase relief materials, which serves as a threshold for screening a reliable retail enterprise as the agent. Finally, three managerial implications with practical significance, drawn from our findings, are presented to facilitate cooperation between NPO and large retail enterprises in order to achieve effective procurement of relief materials at the pre-disaster preparation stage. Full article

Background/Objectives: Despite a wide range of international studies examining antibiotic prescribing practices among physicians, research from Central Asia remains scarce. To address this gap, the present study aimed to investigate antibiotic resistance awareness and prescribing practices among general practitioners (GPs) in Kazakhstan, Kyrgyzstan, Uzbekistan, and Tajikistan. Methods: The online questionnaire was completed by 1231 GPs, including 469 from Kazakhstan, 274 from Kyrgyzstan, 369 from Uzbekistan, and 119 from Tajikistan. Results: Most physicians (71.1%) acknowledged that their antibiotic prescribing behavior influences the development of antibiotic resistance in their regions. More than half reported discussing antibiotic resistance with their patients often or very often. However, the strategy of delayed antibiotic prescribing was unknown to 27.1% of GPs. Factors associated with good knowledge of indications for antibiotic prescribing included female sex, older age, working in Uzbekistan, practicing in urban areas, seeing 20 or more patients per day, and use of practice guidelines. Clinical practice guidelines were the most frequently reported source of current information on antibiotic therapy and resistance (20.4%), followed by continuing professional education (15.9%) and textbooks (14.1%). The vast majority of GPs (94.4%) indicated a need for additional information resources to support more rational antibiotic prescribing. The most commonly cited needs were higher-quality clinical practice guidelines (22.5%) and better access to existing guidelines (17.7%). Conclusions: These findings suggest that, despite generally high awareness of antibiotic resistance, important knowledge gaps remain among GPs in Central Asia. Strengthening access to clinical guidelines and continuing professional education may support more rational antibiotic prescribing. Full article

Managing supply chain risks is a core pillar of operational and supply chain resilience building in the global industrial chain system, which is essential for the high-quality and sustainable development of manufacturing firms. Against the backdrop of escalating global economic uncertainties and interconnected supply chain vulnerabilities, mitigating the adverse impact of supply chain risks on firms’ overseas market expansion has become a critical research and practical issue in the field of operational and supply chain risk management. Based on the textual analysis of annual reports of listed firms, this study constructs a systematic supply chain risk measurement indicator system through standardized text preprocessing, multi-dimensional feature keyword lexicon construction, context co-occurrence frequency calculation and so on. We further validate the effectiveness of the indicator system by comparing its trend with the global economic uncertainty index, confirming that it can capture firm-specific supply chain risk information effectively. Employing text analysis, this study constructs a systematic supply chain risk measurement indicator system for A-share manufacturing firms and empirically verifies that elevated supply chain risks significantly constrain their overseas market expansion. Three interrelated operational mechanisms, namely surging operating costs, tightened financing constraints, and slumping R&D investments, drive this inhibitory effect. Notably, firms can effectively offset this negative effect by broadening overseas operational scope and intensifying overseas digital and technological innovation. Heterogeneity analyses further reveal that the inhibitory effect is more pronounced for five types of firms: those with lower overseas revenue, located in less market-oriented regions, operating in upstream value chain sectors, with lower current liabilities, and with a lower degree of digital transformation. Full article

Accurate and interpretable air quality prediction remains a critical challenge for environmental health management due to complex, nonlinear interactions among emissions, meteorology, and atmospheric chemistry. This study presents a hybrid physics informed and multimodal deep learning framework for city-scale air quality and health risk prediction. The framework combines a Gaussian plume dispersion model with a residual CNN-LSTM network that learns data driven corrections while preserving physical consistency. Multimodal open datasets, including ground based pollutant sensors, meteorological records, and satellite derived aerosol and temperature features, are jointly fused to improve spatiotemporal fidelity. An Exposure Health Index module further links predicted pollutant fields with respiratory morbidity indicators, providing a quantitative bridge between atmospheric variability and health outcomes. Using open source datasets from Riyadh, Jeddah, and Dammam, the proposed approach achieves up to 25% lower mean absolute error and $R^2$ values above 0.85 compared with physics only and purely data driven baselines. Explainability analyses using SHAP and spatial attention highlight physically plausible drivers and confirm feature relevance. The results demonstrate that physics guided residual learning can unify deterministic dispersion modeling and multimodal inference, providing a transparent, scalable, and reproducible foundation for air quality forecasting and health risk assessment. Full article

This study used machine learning methods and spatial analysis to examine groundwater quality in Winkler County, Texas, focusing on nitrate pollution. By analyzing 85 years of groundwater data from six aquifers, the study uses advanced machine learning models Random Forest, Decision Tree, Linear Regression, and XGBoost to predict contamination levels and explore spatial and temporal trends. These models were chosen because of their ability to handle larger and more complex datasets and their ability to capture nonlinear relationships between water quality parameters and environmental variables. These machine learning algorithms are particularly effective at identifying patterns and interactions that may not be obvious with traditional analytical methods, and get more reliable and accurate results. Our decadal analysis specifically identified systematic fluctuations in nitrate levels, with a notable increase since the early 2000s, driven by the synergistic effects of rising temperatures and intensified agricultural land use. Climate change, pressured by rising temperatures and lessened precipitation, along with natural factors such as the formation of sinkholes, has been identified as a key driver of groundwater quality fluctuations. Elevated nitrate levels were mostly related to agricultural irrigation and excessive use of synthetic fertilizers. The machine learning model also highlights how land cover changes and human activities are contributing to groundwater quality deterioration. This research reinforces the value of integrating machine learning and spatial analysis for groundwater management. This is especially true in areas affected by sinkholes. It provides important information to reduce man-made impacts to water quality in West Texas. Full article

Corporate governance reforms in emerging and frontier markets frequently assume that strengthening board oversight, audit committees, and ownership monitoring will improve audit quality and enhance firm value. Yet, in weak institutional environments, these mechanisms often function symbolically rather than substantively. This study rethinks the governance–audit–value nexus by integrating Agency Theory, Institutional Theory, and the concept of symbolic governance to explain why governance may appear structurally robust while failing to constrain managerial discretion. Using panel data from Ghanaian listed firms between 2015 and 2023, the analysis shows that audit committee independence and board independence are negatively associated with both audit quality and firm value, indicating that formal independence without expertise, authority, or enforcement capacity does not translate into meaningful oversight. By contrast, institutional and managerial ownership positively influence both outcomes, suggesting that incentive alignment and informed monitoring can substitute for weak formal governance. Foreign ownership improves firm value but does not consistently enhance audit quality, while macroeconomic conditions such as inflation and GDP growth further shape firm performance. The study advances the literature by reconceptualising governance effectiveness in weak institutional environments, demonstrating that governance mechanisms may exist in form without functioning in substance. The findings underscore the need for governance reforms that prioritise enforcement capacity, board expertise, and audit committee competence rather than structural compliance alone. Full article

Underwater images suffer from quality degradation such as haze, detail blurring, color distortion, and low contrast due to factors like light scattering and wavelength-dependent attenuation in water. This severely hinders the high-quality completion of target detection tasks for Autonomous Underwater Vehicles (AUV) relying on image information. Although deep learning-based methods have gained widespread attention, existing approaches still face challenges such as insufficient feature extraction and limited generalization in complex real-world scenes. Methods based on physical models, on the other hand, heavily rely on depth information which is difficult to obtain accurately. To address these issues, this paper proposes a novel underwater image restoration method that integrates depth estimation with the Akkaynak-Treibitz physical imaging model. In the depth estimation stage, efficient and robust feature extraction is achieved through a lightweight encoder–decoder architecture combined with a channel–spatial hybrid attention mechanism. To overcome the inherent scale ambiguity problem in monocular depth estimation, which prevents direct output of absolute depth consistent with the real scene, sparse depth priors are introduced. Subsequently, adaptive depth binning and depth map optimization are realized via m-Vision Transformer and convolutional regression. In the image restoration stage, the acquired high-quality depth map is combined with the Akkaynak-Treibitz physical imaging model for inverse solving, achieving high-quality restoration from degraded to clear images. Experimental results demonstrate that the proposed method outperforms mainstream depth estimation methods (LapDepth, UDepth, etc.) and mainstream image restoration methods (CLAHE, FUnIE-GAN, etc.) in terms of evaluation metrics and visual perceptual quality. When processing the extremely degraded UIEB-S dataset, the proposed method achieves evaluation metrics of SSIM = 0.8954, UCIQE = 0.6107, and PSNR = 23.35 dB. Compared to the CLAHE and FUnIE-GAN methods, SSIM improved by 2.8% and 16.7%, UCIQE improved by 9.6% and 14.3%, and PSNR improved by 22.5% and 13.9%, respectively. Comprehensive subjective and objective evaluation results validate the effectiveness of the proposed method in addressing image quality degradation, particularly demonstrating outstanding capability in severe color cast correction and detail recovery. Full article