Search Results (20,622)

In recent decades, forest fires have become one of the most disruptive and complex natural hazards from both environmental and territorial perspectives. The Canary Islands represent a particularly suitable setting for analysing wildfire risk. This study aims to characterise the Large Forest Fires (LFFs) that occurred across the archipelago between 2012 and 2024 through an integrative approach combining geospatial, meteorological, and socio-environmental information. A total of 13 LFFs were identified in Tenerife, Gran Canaria, La Palma, and La Gomera, affecting 55,167 hectares—equivalent to 7.4% of the islands’ total land area. The results indicate a temporal concentration during the summer months and an altitudinal range between 750 and 1500 m, corresponding to transitional zones between laurel forest and Canary pine woodland. Meteorological conditions showed average temperatures of 24.3 °C, minimum relative humidity of 23.7%, and thermal inversion layers at around 270 m a.s.l., creating an environment conducive to fire spread. Approximately 81% of the affected area lies within protected natural spaces, highlighting a high level of ecological vulnerability. Analysis of the Normalized Burn Ratio (NBR) index reveals a growing trend in fire severity, while social impacts include the evacuation of more than 43,000 people. These findings underscore the urgency of moving towards proactive territorial management that integrates prevention, ecological restoration, and climate change adaptation as fundamental pillars of any disaster risk reduction strategy. Full article

Rice blast, caused by Magnaporthe oryzae, is a devastating global disease. Its control through the deployment of host resistance genes relies on a detailed knowledge of the pathogen’s race structure and the corresponding avirulence (Avr) genes. To guide effective rice breeding for blast resistance, this study investigated the population dynamics of M. oryzae in Jilin Province from 2022 to 2024. The distribution frequencies of seven Avr genes were detected using PCR and avirulence gene-specific primers, and the physiological race structure of 193 isolates was characterized using a set of Chinese differential cultivars, which contains seven cultivars. The results revealed a high prevalence and stability of specific Avr genes, with Avr-Pi9, Avr-Pias, Avr-Piz-t, and Avr-Pib all exhibiting detection frequencies exceeding 80%. In particular, Avr-Pib showed a high frequency (80.83%) and a very low disease incidence (0.64%) on the differential variety Sifeng 43 (which carries Pib), confirming its low mutation rate and the ongoing effectiveness of the corresponding resistance gene. Conversely, the significant decline in Avr-co39 suggests that its corresponding resistance gene should be avoided. Race diversity increased over the three-year period, characterized by a shift toward a more complex structure dominated by ZG1, ZA17, ZA43, and ZB31. Based on the gene-for-gene interactions and pathogen population structure, we recommend a breeding strategy that prioritizes the incorporation of the highly effective Pib, Pi54, and Pik genes, utilizing resistant donors like Sifeng 43. These results can help inform the design of sustainable management strategies adapted to the changing pathogen population. Full article

Background/Objectives: The rapid evolution of digital technologies has significantly transformed prosthodontic workflows, improving clinical precision, communication, and patient satisfaction. However, the extent to which dental professionals perceive, integrate, and evaluate these technologies remains insufficiently standardized. This study aimed to develop and validate a questionnaire for assessing perceptions, attitudes, perceived advantages, barriers, and future intentions regarding the use of digital technologies in prosthodontic practice. Methods: A cross-sectional survey was conducted among 420 dental professionals (305 dentists and 115 dental technicians) from Northeastern Romania. The 27-item questionnaire, structured on five theoretical dimensions, was distributed online via the Survio platform. Internal consistency was assessed using Cronbach’s Alpha, and construct validity was analyzed through Exploratory Factor Analysis (Principal Component Analysis with Varimax rotation). Conclusions: Cronbach’s Alpha coefficients ranged from 0.700 to 0.799 across the five dimensions, indicating acceptable to very good internal reliability. The Kaiser–Meyer–Olkin value (0.646) and Bartlett’s Test of Sphericity (p < 0.001) confirmed data suitability for factor analysis. The validated questionnaire represents a reliable and conceptually coherent tool for evaluating professional perspectives on digitalization in prosthodontics. Its application can inform educational strategies, guide institutional investments, and support a balanced transition toward integrated digital workflows in clinical and laboratory settings. Full article

Speech is one of the earliest-affected modalities in Parkinson’s disease (PD). For more reliable PD evaluation, speech-based telediagnosis studies often use multiple samples from the same subject to capture variability in speech recordings. However, many existing studies split samples—rather than subjects—between training and testing, creating a biased experimental setup that allows data (samples) from the same subject to appear in both sets. This raises concerns for reliable PD evaluation due to data leakage, which results in over-optimistic performance (often close to 100%). In addition, detecting subtle vocal impairments from speech recordings using multiple feature extraction techniques often increases data dimensionality, although only some features are discriminative while others are redundant or non-informative. To address this and build a reliable speech-based PD telediagnosis system, the key contributions of this work are two-fold: (1) a uniform (fair) experimental setup employing subject-wise symmetric (stratified) splitting in 5-fold cross-validation to ensure better generalization in PD prediction, and (2) a novel hybrid data complexity-aware (HDC) feature selection method that improves class separability. This work further contributes to the research community by releasing a publicly accessible five-fold benchmark version of the Parkinson’s speech dataset for consistent and reproducible evaluation. The proposed HDC method analyzes multiple aspects of class separability to select discriminative features, resulting in reduced data complexity in the feature space. In particular, it uses data complexity measures (F4, F1, F3) to assess minimal feature overlap and ReliefF to evaluate the separation of borderline points. Experimental results show that the top-50 discriminative features selected by the proposed HDC outperform existing feature selection algorithms on five out of six classifiers, achieving the highest performance with 0.86 accuracy, 0.70 G-mean, 0.91 F1-score, and 0.58 MCC using an SVM (RBF) classifier. Full article

In an increasingly secular and pluralistic age marked by declining religious affiliation and rising individualized spiritual pursuits, accompanied by soaring mental health issues, the need for psychologically grounded perspectives on spiritual health is urgent. Drawing on developmental psychology, positive psychology, and psychology of religion and spirituality, this article introduces the Thrive Spiritual Health Framework. Spiritual health involves experiencing and responding to a loving source of transcendence in cognitive, affective, behavioral, and relational ways, and integrating those responses into narrative identities that inform who we are and who we belong to, shape our ethical ideals, inform virtues, and orient purpose—allowing us to sustain lives of love. The framework synthesizes six interrelated facets—transcendence, habits and rhythms, relationships and community, identity and narrative, vocation and purpose, and ethics and virtues (THRIVE)—through which spirituality nurtures thriving. Each facet is contextualizable across cultural and secular settings, highlighting both opportunities and vulnerabilities of contemporary spirituality. While individualized spiritual pathways may empower autonomy and innovation, they also risk fragmentation without relational and communal support. The framework provides an empirically grounded resource for research and practice, clarifying when spirituality promotes thriving and offering guidance for spiritual innovation in pluralistic contexts. Full article

Improving alignment between timber sawmilling and prefabrication, defined as the coordination of information, materials, and decision-making across the supply chain, is critical for sustainable construction. This study examined integration through semi-structured interviews with 15 industry practitioners. Using framework analysis supported by NVivo, eight interlinked themes were identified: supply chain fragmentation and market cycles; data-driven forecasting; inventory and moisture management; digital integration; smart planning and production; quality assurance and workforce capability; circular economy and residue utilisation; and systemic enablers and constraints. The findings show that technical capabilities such as optimisation, grading, and QR-based traceability are often undermined by organisational and policy barriers, including distributor-mediated purchasing, limited interoperability, outdated standards, and uneven skills pathways. Integration was considered more feasible for mass timber prefabrication, where batch planning, tighter quality assurance, and vertical integration align with mill operations, compared with frame-and-truss networks that rely on just-in-time project workflows. The study provides empirical evidence of practitioner perspectives and identifies priorities for action that translate into sustainability gains through improved material efficiency, waste reduction, higher-value residue pathways, and supportive policy settings. Full article

Accurate environmental monitoring in outdoor and indoor settings is critical for exposure assessment in environmental and public health research. Conventional methods, predominantly relying on high-end instruments or laboratory analyses, face limitations in real-world applications due to their high cost and inflexibility. Recent advances in low-cost sensor technologies have enabled more adaptable monitoring. This study systematically reviews research utilizing low-cost sensors for environmental monitoring in real-world settings across China. A literature search was performed using the Web of Science database, resulting in the inclusion of 43 eligible studies out of 31,003 initially identified records. These studies primarily investigated air pollution (17 studies), noise (14), light (7), and water pollution (5). Results reveal that air and noise pollution were the most extensively examined factors. Nevertheless, the reviewed studies exhibited notable shortcomings, including limited geographical/thematic coverage, inadequate reliability validation, small sample sizes (typically under 100 participants), and short durations (often under one month). This review discusses these challenges and suggests future research directions. By synthesizing current practices and identifying gaps, this work offers valuable insights to guide the design of future sensor-based environmental monitoring projects and inform the selection of suitable sensors. Full article

Cardiovascular–kidney–metabolic syndrome (CKMS) represents the intricate interconnection of cardiovascular, kidney, and metabolic disorders, with systemic inflammation now recognized as a key driver of both pathogenesis and prognosis. This systematic review aimed to synthesize current evidence on the prognostic value of inflammatory biomarkers in individuals with CKMS. A systematic search of PubMed, Embase, CINAHL, Web of Science, and Scopus were conducted to identify studies published between 1 January 2024 and 30 June 2025, following the recognition of CKMS as a distinct syndrome in December 2023. Eligible studies included adults (aged ≥ 18 years) with CKMS, that assesses one or more inflammatory markers and reported prognostic outcomes such as mortality or disease progression. Data extracted included study characteristics, biomarker types, outcome measures, and key findings. In addition to longitudinal cohorts, we included a small number of cross-sectional studies and treated them as association (non-prognostic) evidence analyzed in a separate stream from prognostic cohorts. Risk of bias was evaluated using the Quality in Prognostic Studies (QUIPS) tool. Due to considerable variability in prognostic outcomes, follow-up durations, and inflammatory indices, a meta-analysis was not feasible. Instead, a narrative synthesis was undertaken to summarize the evidence, identify consistent associations, and emphasize the need for standardized approaches and biomarker validation in future CKMS research. Analysis was conducted in line with the SWiM guidelines. Thirteen studies (n = 13) comprising 282,016 participants (100,590 males; 97,295 females) were included from 1404 initial records. Five of the studies were cross-sectional, providing information on associations rather than prognostic outcomes. Most were large-scale cohort studies conducted in the USA and China. Frequently assessed biomarkers included systemic inflammatory response index (SIRI), systemic immune-inflammation index (SII), high-sensitivity C-reactive protein to high-density lipoprotein cholesterol ratio (hs-CRP/HDL-C), dietary inflammatory index (DII), and triglyceride–glucose (TyG) index. Elevated levels of these biomarkers were consistently associated with higher risk of all-cause and cardiovascular mortality, CKMS progression, and adverse metabolic outcomes. This review highlights systemic inflammation as a critical and associated marker of CKMS prognosis. Inflammatory biomarkers may assist in hypothesis generation, but clinical utility remains to be established pending standardized adjustment and external validation. Because CKMS has only recently been operationalized, we limited inclusion to studies published from 1 January 2024 onward, enhancing definitional comparability but narrowing the evidence base and potentially emphasizing early-adopter regions (predominantly the U.S. and China). Accordingly, these findings should be interpreted as early signals that require replication in diverse settings and confirmation through longitudinal and interventional studies to inform integrative CKMS management strategies. Across observational studies, the certainty of evidence is low to moderate due to indirectness and imprecision; findings should be treated as associational signals pending external validation. Full article

Immersive methods and biometric tools provide a rigorous, context-rich way to study how people perceive and choose food. Immersive methods use extended reality, including virtual, augmented, mixed, and augmented virtual environments, to recreate settings such as homes, shops, and restaurants. They increase participants’ sense of presence and the ecological validity (realism of conditions) of experiments, while still tightly controlling sensory and social cues like lighting, sound, and surroundings. Biometric tools record objective signals linked to attention, emotion, and cognitive load via sensors such as eye-tracking, galvanic skin response (GSR), heart rate (and variability), facial electromyography, electroencephalography, and functional near-infrared spectroscopy. Researchers align stimuli presentation, gaze, and physiology on a common temporal reference and link these data to outcomes like liking, choice, or willingness-to-buy. This approach reveals implicit responses that self-reports may miss, clarifies how changes in context shift perception, and improves predictive power. It enables faster, lower-risk product and packaging development, better-informed labeling and retail design, and more targeted nutrition and health communication. Good practices emphasize careful system calibration, adequate statistical power, participant comfort and safety, robust data protection, and transparent analysis. In food science and consumer behavior, combining immersive environments with biometrics yields valid, reproducible evidence about what captures attention, creates value, and drives food choice. Full article

This study investigates whether incorporating highlighted information in discharge notes improves the quality of the summaries generated by Large Language Models (LLMs). Specifically, it evaluates the effect of using highlighted versus unhighlighted inputs for fine-tuning LLaMA2-13B model for summarization tasks. We fine-tuned LlaMA2-13B in two variants using MIMIC-IV-Ext-BHC dataset: one variant fine-tuned with the highlighted discharge notes (H-LLaMA), and the other on the same set of notes without highlighting (U-LLaMA). Highlighting was performed automatically using a Cardiology Interface Terminology (CIT) presented in our previous work. H-LLaMA and U-LLaMA were evaluated on a randomly selected test set of 100 discharge notes using multiple metrics (including BERTScore, ROUGE-L, BLEU, and SummaC_CONV). Additionally, LLM-based judgment via ChatGPT-4o rated coherence, fluency, conciseness, and correctness, alongside a manual completeness evaluation on a random sample of 40 notes. H-LLaMA consistently outperformed U-LLaMA across all metrics. H-summaries, generated using H-LLaMA, in comparison to U-summaries, generated using U-LLaMA, achieved higher BERTScore (63.75 vs. 59.61), ROUGE-L (23.43 vs. 21.82), BLEU (10.4 vs. 8.41), and SummaC_CONV (67.7 vs. 40.2). Manual review also showed improved completeness for H-summaries (54.8% vs. 47.6%). All improvements were statistically significant (p < 0.05). Moreover, LLM-based evaluation indicated higher average ratings across coherence, correctness, and conciseness. Full article

This paper presents OpS-EWMA-LLM (Operational State Shifts Detection using Exponential Weighted Moving Average and Labeling using Large Language Model), a hybrid framework that combines fleet-normalized statistical shift detection with LLM-assisted diagnostics to identify and interpret operational state changes across heterogeneous fleets. First, we introduce a residual-based EWMA control chart methodology that uses deviations of each component’s sensor reading from its fleet-wide expected value to detect anomalies. This statistical approach yields near-zero false negatives and flags incipient faults earlier than conventional methods, without requiring component-specific tuning. Second, we implement a pipeline that integrates an LLM with retrieval-augmented generation (RAG) architecture. Through a three-phase prompting strategy, the LLM ingests time-series anomalies, domain knowledge, and contextual information to generate human-interpretable diagnostic insights. Finaly, unlike existing approaches that treat anomaly detection and diagnosis as separate steps, we assign to each detected event a criticality label based on both statistical score of the anomaly and semantic score from the LLM analysis. These labels are stored in the OpS-Vector to extend the knowledge base of cases for future retrieval. We demonstrate the framework on SCADA data from a fleet of wind turbines: OpS-EWMA successfully identifies critical temperature deviations in various components that standard alarms missed, and the LLM (augmented with relevant documents) provides rationalized explanations for each anomaly. The framework demonstrated robust performance and outperformed baseline methods in a realistic zero-tuning deployment across thousands of heterogeneous equipment units operating under diverse conditions, without component-specific calibration. By fusing lightweight statistical process control with generative AI, the proposed solution offers a scalable, interpretable tool for condition monitoring and asset management in Industry 4.0/5.0 settings. Beyond its technical contributions, the outcome of this research is aligned with the UN Sustainable Development Goals SDG 7, SDG 9, SDG 12, SDG 13. Full article

Background: Optimal nutritional care is essential to improving outcomes in hemodialysis, yet translation of evidence into routine practice remains uneven across settings. To inform health system planning and implementation priorities, we mapped global research on hemodialysis-related nutrition. Methods: We searched the Web of Science Core Collection for English-language original articles on nutrition and hemodialysis from 1 January 2006 to 13 October 2025. Publication trends, productivity by country and institution, influential journals and authors, citation impact, and conceptual structure via Keyword Plus co-occurrence, trend, and thematic evolution analyses were assessed using the bibliometrix package (version 5.0) in R. Results: A total of 332 articles from 115 journals were identified, with substantial growth and multidisciplinary authorship, though international collaboration remains limited. The United States contributed 21.4% of publications and achieved the highest citation impact, while China, Japan, Iran, and Brazil formed the next tier of contributors. The Journal of Renal Nutrition accounted for 16.6% of papers. Highly cited studies established links between dietary intake, mineral and electrolyte management, and survival, while supporting the use of intradialytic oral nutritional supplements. Thematic evolution showed a shift from biochemical markers toward patient-centered priorities, including diet quality, adherence, body composition, mental health, and quality of life. Emerging directions point to whole-diet approaches and microbiome-modulating strategies. Conclusions: Global research on diet and hemodialysis has progressed from foundational nutrient studies to multidimensional, patient-focused approaches. Our findings suggest opportunities for health systems to strengthen dietitian-led models of care, integrate patient-reported outcomes, and prioritize scalable nutrition interventions within routine dialysis services. Full article

Single-cell RNA sequencing (scRNA-seq) provides a high-resolution view of cellular heterogeneity, yet accurate cell-type annotation remains challenging due to data sparsity, technical noise, and variability across tissues, platforms, and species. Many existing annotation tools depend on a single form of prior knowledge, such as marker genes or reference profiles, which can limit performance when these resources are incomplete or inconsistent. Here, we present scANMF, a prior- and graph-regularized non-negative matrix factorization framework that integrates marker-gene information, partial label supervision, and the local manifold structure into a unified annotation model. scANMF factorizes the expression matrix into interpretable gene–factor and cell–factor representations, enabling accurate annotation in settings with limited or noisy prior information. Across multiple real scRNA-seq collections, scANMF achieved a high annotation accuracy in within-dataset, cross-platform, and cross-species evaluations. The method remained stable under varying levels of label sparsity and marker-gene noise and showed a broad robustness to hyperparameter choices. Ablation analyses indicated that marker priors, label supervision, and graph regularization contribute complementary information to the overall performance. These results support scANMF as a practical and robust framework for single-cell annotation, particularly in applications where high-quality prior knowledge is restricted. Full article

Consider a formal context (G, M, I) as the basic mechanism to capture information about a
set G of objects, a set M of attributes and the relation I ∈ G × M between them. Traditional
use of Formal Concept Analysis has some shortcomings in its information-eliciting capabilities,
which were expanded by the related processes of Formal Independence Analysis
and Formal Equivalence Analysis, which analyse different information types. The core
of these three approaches can be seen as different instantiations of a theorem by Birkhoff
when applied to different concept-forming operators (technically, some types of Galois
connection). In this paper, we propose the notion of context transform as a way to elicit
new information types from contexts that we call information qualia. We apply this notion
of context transform to explain how we may expect other formal analyses of different
information qualia to arise from a formal context. We also use the concept of formal quale
across the board to provide the affordances of many of the choices needed for practitioners
to make effective use of data analysis techniques. Full article

Poor coping among university students is widespread globally, yet few studies examine whether modifiable lifestyle risk factors are associated with this phenomenon. This study aims to assess the frequency of physical activity, chronotype, and disordered eating attitudes among students, and to determine whether these factors are associated with effective functioning in academic settings and subjective cognitive well-being. A cross-sectional survey was conducted among Omani undergraduate students (n = 408) using a questionnaire covering sociodemographic characteristics and instruments, including the International Physical Activity Questionnaire, the Morningness–Eveningness Chronotype Scale, the Eating Attitudes Test, and a measure of subjective cognitive well-being. Participants’ mean age was 20.21  years (female = 74.3%). In total, 28.4% showed disordered eating attitudes, and half were physically active. 34.1% were classified as evening type. Independent regression analysis showed that chronotype was positively associated with physical activity (β = 0.06, p = 0.004). Disordered eating behavior did not significantly associate with physical activity (β = 0.1, p = 0.16). Moreover, physical activity was positively associated with cognitive function (β = 0.11, p = 0.039). However, the effect sizes were small, suggesting additional factors may contribute to these associations. This study is among the first to explore the influence of lifestyle factors on cognitive well-being in university students and may inform future studies and interventions targeting modifiable lifestyle behaviors to improve coping and academic functioning. Full article