Search Results (1,080)

Digital transformation has been widely studied for its impact on corporate performance, innovation, and efficiency, yet its effect on ESG imbalance has received little attention. We address this gap by constructing a text-based digital empowerment index from corporate annual reports of Chinese listed firms in 2010–2020, while ESG imbalance is measured using the standardized absolute difference between environmental and social responsibilities. The results reveal that digital empowerment significantly exacerbates ESG imbalance, with the effect being more pronounced in mid-central cities, competitive industries, and heavily polluting sectors. Strong governance, state ownership, and balanced resource allocation are found to mitigate this imbalance, while dynamic analysis confirms its persistence over time. These findings highlight the need for targeted policies that enhance governance capacity, promote equitable resource allocation, and address sustainability risks associated with digital transformation. Full article

Automatically generated chains-of-thought (gCoTs) have become common as large language models adopt deliberative behaviors. Prior work emphasizes fidelity to internal processes, leaving explanatory properties underexplored. Our central hypothesis is that these traces, produced by highly capable reasoning models, are not arbitrary by-products of decoding but exhibit stable and practically valuable textual properties beyond answer fidelity. We apply a multidimensional text-evaluation framework that quantifies four axes—structural coherence, logical–factual consistency, linguistic clarity, and coverage/informativeness—that are standard dimensions for assessing textual quality, and use it to evaluate five reasoning models on the GSM8K arithmetic word-problem benchmark (~1.3 k–1.4 k items) with reproducible, normalized metrics. Logical verification shows near-ceiling self-consistency, measured by the Aggregate Consistency Score (ACS ≈ 0.95–1.00), and high final-answer entailment, measured by Final Answer Soundness (FAS0 ≈ 0.85–1.00); when sound, justifications are compact, with Justification Set Size (JSS ≈ 0.51–0.57) and moderate redundancy, measured by the Redundant Constraint Ratio (RCR ≈ 0.62–0.70). Results also show consistent coherence and clarity; from gCoT to answer implication is stricter than from question to gCoT support, indicating chains anchored to the prompt. We find no systematic trade-off between clarity and informativeness (within-model slopes ≈ 0). In addition to these automatic and logic-based metrics, we include an exploratory expert rating of a subset (four raters; 50 items × five models) to contextualize model differences; these human judgments are not intended to support dataset-wide generalization. Overall, gCoTs display explanatory value beyond fidelity, primarily supported by the automated and logic-based analyses, motivating hybrid evaluation (automatic + exploratory human) to map convergence/divergence zones for user-facing applications. Full article

Introduction/Object: Gastrointestinal cancers are among the most common types of neoplasms and are often associated with malnutrition, which affects physical performance, treatment tolerance and prognosis. This paper aims to synthesize, through a systematic search, the evidence on the impact of nutritional interventions on nutritional status in patients with digestive cancers prone to malnutrition. Methods: A systematic search was performed in PubMed, MDPI, Web of Science and ScienceDirect, for articles published between 2009 and 2025. Overall, 14,503 records were identified, and after screening of titles, abstracts and full-text evaluation, 80 studies (cross-sectional and cohort) were included. Data extraction was performed by a single researcher, using pre-established criteria and a standardized table, and the assessment of study quality was performed qualitatively, taking into account study design, sample size, nutritional assessment methods and clarity of reporting of results. Results: Evidence suggests that individualized and early applied nutritional interventions contribute to maintaining weight and protein status, improve tolerance to oncological treatments and may positively influence patient survival. Conclusions: Nutritional therapy plays a crucial role in preventing complications and supporting the body during oncological treatment, optimizing patients’ quality of life. This review provides a clear synthesis of the current evidence and recognizes methodological limitations related to the qualitative assessment of the included studies. Full article

Background and Objectives: Early recognition of life-threatening organ dysfunction is central to modern sepsis frameworks. We systematically reviewed the prognostic performance and clinical utility of the Neonatal Sequential Organ Failure Assessment (nSOFA) for mortality and major morbidity in NICU populations. The search identified 939 records across databases; after screening and full-text assessment, 16 studies met the inclusion criteria. Methods: Following PRISMA guidance, we searched major databases (2019–2025) for observational or interventional studies reporting discrimination or risk stratification using nSOFA in neonates. Populations included suspected/proven infection and condition-specific cohorts. Heterogeneity in timing, thresholds, and outcomes precluded meta-analysis. Results: A cumulative sample exceeding 25,000 neonates was identified across late- and early-onset infection, all-NICU admissions, necrotizing enterocolitis, respiratory distress, and very preterm screening cohorts. Across settings and timepoints, nSOFA demonstrated consistent, good-to-excellent mortality discrimination, with reported AUROCs ≥ 0.80 and upper ranges near 0.90–0.92; serial scoring within the first 6–12 h generally improved risk classification. Disease-specific applications (NEC, early-onset infection) showed similar discrimination for death or composite adverse outcomes. Conclusions: Evidence from diverse NICU contexts indicates that nSOFA is a pragmatic, EHR-ready organ dysfunction score with robust discrimination for mortality and serious morbidity, supporting routine, serial use for risk stratification and standardized endpoints in neonatal sepsis pathways, aligned with contemporary organ dysfunction–based pediatric criteria. Full article

Background and Objectives: Recent advances in dentistry include microbiological and metabolomic analyses, which have the potential to improve the understanding of oral microbiome–host imbalances during orthodontic treatment. Fixed appliances, functional devices and, more recently, clear aligners have been associated with several oral health conditions, including enamel demineralization, dental caries, gingivitis, periodontitis and root and bone resorption. In this context, metabolomic approaches may enable the identification of metabolites in biological samples that could potentially serve as biomarkers and reflect functional biological changes within the oral ecosystem. Investigating orthodontic appliances and associated metabolomic alterations may therefore contribute to advancing current knowledge in orthodontics. This systematic review aimed to describe the available evidence on oral metabolomic changes during orthodontic treatment. Materials and Methods: A systematic literature search was conducted in PubMed, Web of Science, Scopus and the Cochrane Library. A total of 1632 records were identified. After duplicate removal and screening, 18 full-text articles were assessed for eligibility. Of these, 15 studies were excluded, and three studies met the inclusion criteria. Risk of bias was assessed using the ROBINS-I and RoB 2 tools, and the GRADE approach was applied to evaluate the certainty of evidence. The review protocol was registered in PROSPERO (CRD420251141544). Results: Three studies met the inclusion criteria. Overall, the available evidence was limited and heterogeneous. The included studies suggested potential differences in oral microbiome composition and metabolomic profiles between patients treated with fixed appliances and those treated with clear aligners. Reported metabolomic findings were exploratory and involved amino acid-related, immune-associated, and acidic metabolic pathways. Limitations: Only three studies were included, all conducted in a single country. The small sample size and methodological heterogeneity limit the generalizability of the findings. In addition, potential confounding variables highlight the need for further standardized longitudinal studies. Full article

Background and Objectives: Acute and obstructive pyelonephritis (AOP) management, despite advancements in diagnostic imaging and antimicrobial therapy, is characterized by delayed recognition and increasing antimicrobial resistance. This review aimed to summarize current evidence regarding the clinical characteristics, microbiological spectrum, biomarkers, and imaging findings associated with AOP. Materials and Methods: A systematic review was conducted according to PRISMA guidelines and registered in PROSPERO (CRD420251162736). Literature searches were performed across the PubMed, Scopus, and Web of Science databases for articles published between January 2014 and 31 March 2025 using the term “acute obstructive pyelonephritis”. Inclusion criteria comprised original full-text English-language studies, published in the last 10 years and conducted in adults, reporting clinical, laboratory, microbiological, and imaging characteristics. Exclusion criteria are letters to the editor, expert opinions, case reports, conference or meeting abstracts, reviews, and redundant publications; having unclear or incomplete data; and being performed on cell cultures or on mammals. The quality of included studies was assessed using the Newcastle–Ottawa Scale. Results: Twenty-three studies met the inclusion criteria. AOP predominantly affected elderly patients with comorbidities, especially diabetes mellitus and urinary tract obstruction. Predictors of septic shock included thrombocytopenia, hypoalbuminemia, elevated procalcitonin (>1.12 µg/L), presepsin, and a neutrophil-to-lymphocyte ratio ≥ 8.7. Escherichia coli remained the leading pathogen (60–95%) with extended-spectrum β-lactamase (ESBL) rates between 20 and 70%, followed by Klebsiella pneumoniae. CT demonstrated 71–100% sensitivity for detecting obstructive complications, confirming its superiority over ultrasound, while MRI provided comparable diagnostic accuracy in selected cases. Source control through double-J stenting or percutaneous drainage significantly improved survival. Conclusions: AOP requires prompt recognition and early decompression to prevent sepsis-related mortality. Biomarkers such as procalcitonin, presepsin, and neutrophil to lymphocyte ratio enhance risk stratification, while CT remains the gold-standard imaging modality. The increasing prevalence of ESBL-producing pathogens underscores the need for antimicrobial stewardship and individualized therapeutic strategies guided by local resistance data. Full article

Interest in plant-based diets has grown expressively in different regions of the world. However, the missing regulation for meat analogs may mislead consumers by suggesting that these products are the same as the meat they are replacing. Therefore, this study aims to analyze the current global scenario of meat analogs, discuss consumption changes and their regulation, as well as pointing out future perspectives for the sector. A narrative literature review was performed using scientific papers from the Virtual Health Library (BVS), LILACS, PubMed (NIH), Embase, Web of Science, Scopus, and official documents. Included studies were aligned with the research theme, concentrating on countries with regulations for plant-based analog products and those lacking or pursuing such regulations. Additionally, studies were selected based on the following criteria: original or review studies from different countries, papers discussing meat analogs in terms of consumption, sensory attributes, market dynamics, sustainability, regulation, food safety; availability of full text; and publication dates ranging from 2015 to 2025. The data reveals that most of the assessed nations still lack specific regulations for meat analog products, adopting general labeling and naming standards that range from flexible approaches to strict restrictions. To conclude, the article highlights that meat substitutes are emerging as promising and sustainable options; however, their true consolidation is conditioned on the existence of more defined regulatory frameworks, increased consumer confidence, and market conditions that favor their large-scale adoption. Full article

An algorithm has been implemented and it is provided in this article as an executable program to extract the five solar cell parameters within the one-diode solar cell model. Boole’s integration rule has been put into practice to integrate the current minus the short-circuit current, yielding a more accurate Co-Content function. Afterwards, the Co-Content function is fitted to a second-degree polynomial in two variables, namely, the voltage and the current minus the short-circuit current, providing six fitting constants. The five solar cells are deduced from these six fitting constants. This algorithm has been implemented in an automatic program that performs the calculations. The program also obtains the standard deviations of the fitting errors, which are used to obtain the standard deviations of the five solar cell parameters. The program reports to the user the results in three text files, from which the user can easily copy-paste the results into softwares like Origin, Word, or Excel. A program to smooth the current voltage curves is also provided. Two videos are also available, one explaining how to profit from this executable program, and the other one how to use the smoothing program. Full article

Background: Vitamin E has been studied for its role in reducing the growth of colorectal cancer (CRC). CRC is a worldwide health concern. A meta-analysis reported that CRC patients have a lower concentration of serum vitamin E, suggesting it to be a risk factor. Although rodent models are widely used in disease research, their application in studying vitamin E as a preventive or therapeutic agent in CRC is not well characterized. To address this gap, we conducted a scoping review to examine the available evidence, adhering to the PRISMA-ScR checklist. Methods: We searched PubMed, Google Scholar, Scopus, and Web of Science (WoS) for full-text English original articles published before May 2024, using Medical Subject Headings (MeSH) terms and free text. The following search string strategy was applied: (Vitamin E OR tocopherol$ OR tocotrienol$) AND (Colo$ cancer OR colo$ carcinoma) AND (Rodentia OR mouse OR Rodent$ OR mice OR murine OR rats OR guinea OR rabbit OR hamsters OR Animal model OR Animal testing OR animals) AND (neoplasm$ OR “tumor mass” OR tumor volume OR tumor weight OR tumor burden). Data were charted into five categories using a standardized, pretested form. The charted data were synthesized using descriptive and narrative methods. Conclusions: This study highlights that γ- and δ-tocopherols, as well as δ-tocotrienol and its metabolites, were reported to reduce tumor volume and formation in various rodent models. While these results are promising, this scoping review identifies a need for further research to address translational barriers such as dosing, bioavailability, and long-term safety before clinical application. Full article

Background/Objective: Systematic reviews (SRs) with network meta-analysis (NMA) support evidence-based decision-making by enabling both direct and indirect comparisons across multiple interventions. Given the expanding use of Janus kinase (JAK) inhibitors in rheumatoid arthritis (RA), the methodological rigor of SRs with NMA is essential for trustworthy conclusions. This study is aimed at evaluating the methodological quality of SRs with NMA assessing the efficacy and/or safety of JAK inhibitors in RA. Methods: PubMed and Embase were searched for full-text SRs with NMAs evaluating JAK inhibitors as a therapeutic class in RA. Eligible publications were English-language articles reporting efficacy and/or safety outcomes. Narrative reviews, letters, duplicates, reviews focused on a single JAK inhibitor, and reviews without quantitative synthesis were excluded. Three independent reviewers assessed methodological quality using AMSTAR 2. Descriptive statistics were used to summarize findings. Results: Of the 222 records identified, 18 SRs with NMA met the inclusion criteria: 5 focused on efficacy, 5 on safety, and 8 assessed both. The most consistently fulfilled AMSTAR 2 items were a clearly defined PICO question (100%), duplicate study selection (100%), and reporting of conflicts of interest (100%). Common shortcomings included lack of protocol registration (44%), incomplete reporting of the search strategy (39%), and absence of publication bias assessment (50%). Risk-of-bias assessment varied by review focus: all safety reviews complied (100%), compared with 20% of efficacy reviews and 37% of mixed reviews. Conclusions: Most SRs with NMA of JAK inhibitors in RA present relevant methodological limitations, particularly in protocol registration, search reporting, and risk-of-bias assessment. Methodological standards were generally higher in safety-focused reviews, underscoring the need for more consistent and rigorous conduct and reporting, especially in efficacy and mixed reviews, to strengthen confidence in NMA-derived conclusions. Full article

The COVID-19 pandemic exacerbated structural, social, economic, and racial inequalities affecting immigrant, refugee, and asylum-seeking men—vulnerable populations often overlooked in men’s health research. This study investigated the health conditions of immigrant, refugee, and asylum-seeking men during the COVID-19 pandemic. A scoping review was conducted following Joanna Briggs Institute guidance, and a qualitative lexical analysis (text-mining of standardized study syntheses) was performed in IRaMuTeQ using similarity analysis, descending hierarchical classification, and factorial correspondence analysis. We identified 93 studies published between 2020 and 2023 across 35 countries. The evidence highlighted vaccine hesitancy, high epidemiological risks (infection, hospitalization, and mortality), barriers to accessing services and information, socioeconomic vulnerabilities, psychological distress (e.g., anxiety and depression), and structural inequalities. Findings were synthesized into four integrated thematic categories emphasizing the role of gender constructs in help-seeking and gaps in governmental responses. Most studies focused on immigrants, with limited evidence on refugees and especially asylum seekers; therefore, conclusions should be interpreted cautiously for these groups. Overall, the review underscores the urgency of multisectoral interventions, universal access to healthcare regardless of migration status, culturally and linguistically appropriate outreach, and gender-sensitive primary care strategies to support inclusive and resilient health systems. Full article

Phishing emails continue to evade conventional detection systems due to their increasingly sophisticated, multi-faceted social engineering tactics. To address the limitations of single-modality or rule-based approaches, we propose SAHF-PD, a novel phishing detection framework that integrates multi-modal feature extraction with semantic-aware hierarchical fusion, based on large language models (LLMs). Our method leverages modality-specialized large models, each guided by domain-specific prompts and constrained to a standardized output schema, to extract structured feature representations from four complementary sources associated with each phishing email: email body text; open-source intelligence (OSINT) derived from the key embedded URL; screenshot of the landing page; and the corresponding HTML/JavaScript source code. This design mitigates the unstructured and stochastic nature of raw generative outputs, yielding consistent, interpretable, and machine-readable features. These features are then integrated through our Semantic-Aware Hierarchical Fusion (SAHF) mechanism, which organizes them into core, auxiliary, and weakly associated layers according to their semantic relevance to phishing intent. This layered architecture enables dynamic weighting and redundancy reduction based on semantic relevance, which in turn highlights the most discriminative signals across modalities and enhances model interpretability. We also introduce PhishMMF, a publicly released multimodal feature dataset for phishing detection, comprising 11,672 human-verified samples with meticulously extracted structured features from all four modalities. Experiments with eight diverse classifiers demonstrate that the SAHF-PD framework enables exceptional performance. For instance, XGBoost equipped with SAHF attains an AUC of 0.99927 and an F1-score of 0.98728, outperforming the same model using the original feature representation. Moreover, SAHF compresses the original 228-dimensional feature space into a compact 56-dimensional representation (a 75.4% reduction), reducing the average training time across all eight classifiers by 43.7% while maintaining comparable detection accuracy. Ablation studies confirm the unique contribution of each modality. Our work establishes a transparent, efficient, and high-performance foundation for next-generation anti-phishing systems. Full article

As generative speech synthesis produces near-human synthetic voices and reliance on online media grows, robust audio-deepfake detection is essential to fight misuse and misinformation. In this study, we introduce the Arabic Fake Audio Dataset for Modern Standard Arabic (AFAD-MSA), a curated corpus of authentic and synthetic Arabic speech designed to advance research on Arabic deepfake and spoofed-speech detection. The synthetic subset is generated with four state-of-the-art proprietary text-to-speech and voice-conversion models. Rich metadata—covering speaker attributes and generation information—is provided to support reproducibility and benchmarking. To establish reference performance, we trained three AASIST models and compared their performance to two baseline transformer detectors (Wav2Vec 2.0 and Whisper). On the AFAD-MSA test split, AASIST-2 achieved perfect accuracy, surpassing the baseline models. However, its performance declined under cross-dataset evaluation. These results underscore the importance of data construction. Detectors generalize best when exposed to diverse attack types. In addition, continual or contrastive training that interleaves bona fide speech with large, heterogeneous spoofed corpora will further improve detectors’ robustness. Full article

The deployment of Vision-Language Models (VLMs) in Satellite IoT scenarios is critical for real-time disaster assessment but is often hindered by the substantial memory and compute requirements of state-of-the-art models. While parameter-efficient fine-tuning (PEFT) enables adaptation, with minimal computational overhead, standard supervised methods often fail to ensure robustness and reliability on resource-constrained edge devices. To address this, we propose EdgeV-SE, a self-reflective fine-tuning framework that significantly enhances the performance of VLM without introducing any inference-time overhead. Our framework incorporates an uncertainty-aware self-reflection mechanism with asymmetric dual pathways: a generative linguistic pathway and an auxiliary discriminative visual pathway. By estimating uncertainty from the linguistic pathway using a log-likelihood margin between class verbalizers, EdgeV-SE identifies ambiguous samples and refines its decision boundaries via consistency regularization and cross-pathway mutual learning. Experimental results on hurricane damage assessment demonstrate that our approach improves image classification accuracy, enhances image–text semantic alignment, and achieves superior caption quality. Notably, our work achieves these gains while maintaining practical deployment on a commercial off-the-shelf edge device such as NVIDIA Jetson Orin Nano, preserving the inference latency and memory footprint. Overall, our work contributes a unified self-reflective fine-tuning framework that improves robustness, calibration, and deployability of VLMs on edge devices. Full article

Retrieval-Augmented Generation (RAG) is widely used for long-text summarization due to its efficiency and scalability. However, standard RAG methods flatten documents into independent chunks, disrupting sequential flow and thematic structure, resulting in significant loss of contextual information. This paper presents MOEGAT, a novel graph-enhanced retrieval framework that addresses this limitation by explicitly modeling document structure. MOEGAT constructs an Orthogonal Context Graph to capture sequential discourse and global semantic relationships—long-range dependencies between non-adjacent text spans that reflect topical similarity and logical associations beyond local context. It then employs a query-aware Mixture-of-Experts Graph Attention Network to dynamically activate specialized reasoning pathways. Experiments conducted on three public long-text summarization datasets demonstrate that MOEGAT achieves state-of-the-art performance. Notably, on the WCEP dataset, it outperforms the previous state-of-the-art Graph of Records (GOR) baseline by 14.9%, 18.1%, and 18.4% on ROUGE-L, ROUGE-1, and ROUGE-2, respectively. These substantial gains, especially the 14.9% improvement in ROUGE-L, reflect significantly better capture of long-range coherence and thematic integrity in summaries. Ablation studies confirm the effectiveness of the orthogonal graph and Mixture-of-Experts components. Overall, this work introduces a novel structure-aware approach to RAG that explicitly models and leverages document structure through an orthogonal graph representation and query-aware Mixture-of-Experts reasoning. Full article