Search Results (4,713)

Based on hourly reanalysis data during 2010–2020, the Self-Organizing Map method is used to objectively classify convective precipitation events in the Chengdu terminal area. Combined with circulation background characteristics, the results are further grouped into three typical synoptic types. Among these three types, Type 1, characterized by a pattern with strong high pressure and abundant water vapor, yields the most intense precipitation. Type 2, a pattern with moderately strong high pressure and water vapor convergence, produces the second-highest precipitation. Type 3, associated with a low trough and weak water vapor conditions, has the weakest precipitation. Two indicators of the Weather Severity Index (WSI) and Node Coverage Index (NCI), respectively describing the coverage extent of heavy precipitation over the terminal area and over key arrival and departure nodes, are established and calculated based on heavy precipitation samples. The results show that Type 1 exhibits the highest WSI and NCI values, indicating the greatest potential impact. Type 2 displays a lower WSI than Type 1 but retains a relatively higher NCI, suggesting a more directionally biased impact, whereas Type 3 records the lowest values for both indicators, indicating a relatively weak impact. The integration of synoptic weather classification and spatial impact indicators offers a reference for weather-impact identification and scenario-based operational assessment in terminal areas. However, some limitations remain in the current study. The weather classification is primarily based on reanalysis data, and the correspondence between the WSI/NCI and actual airport operational constraints requires further validation. Full article

Due to the recent rapid development of artificial intelligence (AI) and its expanding impact on the planet, green and sustainable AI research has increasingly gained attention. This systematic literature review searches main databases, including Scopus, Web of Science, and Google Scholar, using an organized methodological approach. Following a thorough screening process, 49 final studies published between 2016 and 2026 are selected from an initial identification of 325 original records. We identify and analyze four key categories of sustainable AI practices: (1) model-level algorithmic efficiency, (2) hardware- and system-level optimization, (3) lifecycle- and data-centric approaches, and (4) operational and policy-level sustainability. We also highlight and explain four dimensions at the intersection of AI and environmentally responsible behavior: AI for sustainable applications’ development in industries, ethical considerations and accountability in using AI, and opportunities enabled by generative AI. We then combine existing taxonomies, evaluation metrics, and challenges to identify areas for improvement and suggest future research directions. Based on our analysis, we emphasize the need for interdisciplinary cooperation to facilitate responsible AI innovation and match it with global sustainable development goals (SDGs). We also highlight the importance of developing adequate frameworks along with precisely defined and standardized metrics to assess the environmental impact of AI. This review aims to encourage more responsible and environmentally friendly AI practices by providing a structured framework for researchers, educators, and professionals engaged in sustainable AI. Full article

This study comprehensively analyzed the effects of gamma irradiation (GI) on the flavor profile of aged light-flavor tartary buckwheat Baijiu (LTB) using E-nose, E-tongue, and high-sensitivity headspace–gas chromatography–ion mobility spectrometry (HS-GC-IMS). A total of 30 volatile organic compounds (VOCs) were identified, with concentrations showing significant dose-dependent correlations with GI treatment. Aging alone reduced harsh and pungent VOCs (e.g., 1-propanol, 2-methyl butanoic acid ethyl ester), while GI followed by aging further decreased undesirable compounds (e.g., butanal-D, pyrrolidine) and enhanced beneficial flavor components, such as 1,1-diethoxy ethane-D and butanoic acid propyl ester. Notably, this treatment partially restored 1-propanol, triethylamine, and 2-butanone-M, though their levels remained significantly lower than in newly brewed LTB, achieving a more balanced purity and flavor complexity. The significantly elevated levels of tetrahydrofuran-M/D, 1,1-diethoxy ethane-D, and cyclohexane in GI-treated aged LTB, along with their dose-dependent accumulation patterns, suggest their potential as reliable markers. Multivariate analysis confirmed that all three techniques (E-nose, E-tongue, and HS-GC-IMS) effectively differentiated LTB samples, with strong correlations between E-nose and HS-GC-IMS data, as well as between E-tongue and HS-GC-IMS results. This work provides flavor fingerprints and potential markers for gamma-irradiated LTB identification, while proposing an innovative technical approach for rapid flavor assessment of light-flavor Baijiu. Full article

The rapid development of metropolitan areas means that their spatial patterns must be reconstructed and brings a series of urban problems such as traffic congestion and imbalance among transportation facilities. As the skeleton of the comprehensive transportation network, the planning of passenger corridors in metropolitan areas has a positive impact on the integrative development of urban spaces and transportation systems. The identification of passenger corridors is the basis for the optimization of the configuration and organization of transportation facilities. In this paper, passenger transportation modes were distinguished through a multilayer network. Considering the technological and economic characteristics of each mode synthetically, an improved method for identifying passenger corridors was proposed. First, a multilayer network was constructed based on the passenger transportation facilities network in a metropolitan area to distinguish between different transportation modes. Based on the traditional importance degree model of nodes, an importance degree model of routes was constructed by considering transportation modes, passenger demand, and transportation costs. Through qualitative judging using regional planning, supported by quantification according to the importance degree of routes, passenger corridors in the chosen metropolitan area were identified and divided into primary and secondary corridors. Suzhou metropolitan area was studied as an example. Identification results for three transverse corridors and two longitudinal corridors were obtained after analysis and calculation, verifying the availability of the method. The study can contribute to the balance of transportation supply and demand, realize the intensive use of transportation facilities, and promote the sustainable development of metropolitan transportation systems. In particular, the proposed method provides a reference for the rational optimization of transportation facility configuration within passenger corridors in metropolitan development areas, facilitating the formation of efficient passenger transport organization systems and compact, transit-oriented land use patterns by improving the coordination between passenger corridors and ecological spaces. Full article

The effective control of foodborne salmonellosis relies on the rapid and reliable detection and identification of the pathogen. Reliable detection tools for identifying the most common Salmonella serovars should translate to a considerable alleviation of the health burden attributed to Salmonella. We have developed a PCR assay for the rapid identification of colonies of Salmonella enterica serovar Thompson, a common serovar. Genomic analyses of publicly available sequences of Salmonella Thompson revealed the presence of a unique, Thompson-specific fragment, which we have used to design a pair of oligonucleotides, STho-F and STho-R, for the PCR amplification of an 808 bp DNA fragment. Using crude DNA extracts, the 808 bp fragment was detected in 77 out of 78 isolates of S. Thompson (sensitivity = 98.7% n = 78 isolates) but not in any of the non-Salmonella organisms tested (n = 100; 100% specificity) nor in non-Thompson Salmonella serovars (n = 100; 100% specificity). The sensitivity (inclusivity) and specificity (exclusivity) indices of the PCR assay for S. Thompson met the standard regulatory requirements. The Thompson primer pair was compatible with other primers pairs in a multiplex PCR designed for three other common Salmonella serovars. Colonies belonging to the Enteritidis serovar (n = 100), Heidelberg serovar (n = 100), Typhimurium serovar (n = 100), and Thompson serovar (n = 77) were correctly designated, indicating excellent inclusivity and exclusivity scores for all four Salmonella serovars tested in a single multiplex PCR. Full article

Accurately identifying urban functional zones and revealing their spatial interaction characteristics is crucial for understanding urban operational mechanisms and optimizing spatial layouts. Addressing the limitations of traditional research in simultaneously capturing static functional attributes and dynamic resident travel behaviors, this study takes the central urban area of Lhasa as the research object, integrating ride-hailing trajectory data with Point of Interest (POI) data to conduct research on urban functional zone identification and spatial interaction characteristics. First, Thiessen polygons were used to quantify the spatial influence range of POIs, and an address matching algorithm was employed to associate ride-hailing origins and destinations (ODs) with POIs. A weighted land use intensity index was constructed, and functional zones were precisely identified using information entropy and K-Means clustering. Secondly, with basic research units as nodes and OD flows as edges, a directed weighted spatial interaction network was constructed. Complex-network indicators and the Infomap community detection algorithm were utilized to analyze network characteristics, node importance, and community interaction patterns. The results show that: (1) The functional mixing degree in the study area exhibits a pattern of “highly composite core, relatively differentiated periphery.” Eight functional zone types, including commercial–residential mixed, science–education–culture, and transportation service zones, were ultimately identified. Residential areas form the base, while the core area features multi-functional agglomeration. (2) The spatial interaction network exhibits typical small-world effects, while its degree distribution is better characterized by a lognormal distribution rather than a power law. Node importance is dominated by betweenness centrality, with Lhasa Station, the Potala Palace, and core commercial areas constituting key hubs. (3) The network can be divided into four functionally coupled communities: the core multi-functional area, the western industry–residence integrated area, the eastern science–education-dominated area, and the southern transportation hub area, forming a “core leading, two wings supporting” center–subcenter spatial organization pattern. This study verifies the effectiveness of integrating trajectory and POI data for identifying urban functional zones and provides a new perspective for understanding the spatial structure and planning of plateau cities. Full article

Decision-making is a central activity in agile software engineering (SE), yet research on how decisions are made and supported in agile contexts remains fragmented across models, methods, roles, and lifecycle stages. While prior studies have examined isolated aspects such as prioritization or planning, a comprehensive synthesis of decision-making as a phenomenon in agile SE is lacking. This systematic literature review addresses this gap by consolidating and structuring existing research on agile decision-making and to identify dominant patterns, gaps, and future research directions. A systematic search was conducted in IEEE Xplore, ACM Digital Library, Scopus, and Web of Science, complemented by backward and forward snowballing, covering publications from 2014 to 2024. In total, 42 studies were included and analyzed using a structured coding scheme covering decision models, methods, actors, lifecycle contexts, and research methodologies. The results reveal a strong concentration of analytical and hybrid decision-making models in planning and requirements activities, while decision-making in coding, testing, and operations remains underexplored. Software developers are the most frequently studied decision-making actors, whereas managers are mainly discussed as external stakeholders rather than active decision-makers within agile workflows. The main contributions of this study are the following: a structured synthesis of agile decision-making research over multiple analytical dimensions, the identification of key research gaps in lifecycle coverage and actor perspectives, and the proposal of a coherent nomenclature for decision-making in agile SE. These contributions provide a foundation for future empirical studies and support the development of more comprehensive theories of decision-making in agile software engineering organizations. Full article

Nipah virus (NiV) is a highly pathogenic zoonotic paramyxovirus responsible for sporadic outbreaks of severe disease with high case fatality rates in South and Southeast Asia. Human infection occurs through spillover from natural reservoirs, primarily fruit bats, or via human-to-human transmission, and is characterized by a broad clinical spectrum ranging from asymptomatic infection to acute respiratory disease and fatal encephalitis. Following entry via ephrin-B2 and ephrin-B3 receptors, NiV exhibits marked endothelial and neuronal tropism, leading to systemic vasculitis, disruption of the blood–brain barrier, and direct infection of the central nervous system. Disease progression is driven by a complex interplay between viral replication strategies and host immune responses. NiV effectively counteracts innate immunity through multiple viral proteins that inhibit interferon signaling, while simultaneously inducing dysregulated inflammatory responses that contribute to tissue damage and multi-organ failure. Neurological involvement represents the most severe manifestation, often resulting in acute or relapsing encephalitis with long-term sequelae among survivors. Despite the severity of the disease, no licensed antiviral therapies or human vaccines are currently available. Therapeutic development has focused on neutralizing monoclonal antibodies targeting viral glycoproteins and small-molecule antivirals that inhibit viral RNA synthesis, both of which show promising results in preclinical models, but remain limited by timing and translational challenges. In parallel, several vaccine platforms—including viral vectors, mRNA-based constructs, and recombinant protein subunits—have advanced to early-phase clinical trials, demonstrating encouraging immunogenicity. Beyond biomedical interventions, effective outbreak containment relies on integrated public health strategies. The “Kerala model” highlights the importance of rapid case identification, isolation, contact tracing, and community engagement within a One Health framework to mitigate transmission and reduce mortality. This review synthesizes the current knowledge on NiV pathogenesis, immune evasion, clinical manifestations, and emerging therapeutic and vaccine strategies, while highlighting critical gaps and future directions for improving the preparedness and response to this high-consequence emerging pathogen. Full article

With increasing environmental pollution and a high incidence of respiratory infections, pulmonary nodules (PN) are being detected more frequently. Although most are benign, they are often accompanied by chronic inflammation and localized fibrosis, which may predispose patients to progression toward idiopathic pulmonary fibrosis (IPF). However, the biological relationship between benign pulmonary nodules (BPNs) and IPF remains poorly understood. Therefore, this study aims to investigate the shared molecular mechanisms and identify potential biomarkers linking BPN and IPF, with the goal of elucidating the pathogenic transition from BPN to IPF. In this study, microarray data from GEO datasets were systematically analyzed to explore shared molecular mechanisms, immune infiltration characteristics, and potential early intervention strategies linking BPN and IPF. Differential expression analysis, protein–protein interaction (PPI) networks, weighted gene co-expression network analysis (WGCNA), and integrative machine learning approaches identified MME and ANKRD23 as key hub genes associated with the transition from BPN to IPF. Both genes demonstrated strong diagnostic performance, with Area Under the Curve (AUC) values exceeding 0.7, and were significantly correlated with immune cell infiltration, particularly effector memory CD8⁺ T cells. Functional enrichment and gene set enrichment analyses indicated that these genes were mainly involved in immune-related processes in BPN, while in IPF, ANKRD23 was linked to cytoskeletal organization and genomic stability, and MME was enriched in profibrotic pathways such as TGF-β signaling. The diagnostic value of these biomarkers was further validated in a bleomycin-induced IPF mouse model using quantitative polymerase chain reaction (qPCR). In addition, drug–gene interaction prediction and molecular docking analyses highlighted several naturally derived compounds with favorable binding affinity and anti-inflammatory properties, among which folic acid, curcumin, and arbutin emerged as promising candidates for safe early intervention. Collectively, these findings identify MME and ANKRD23 as potential biomarkers for early identification of BPN patients at risk of developing IPF and provide a theoretical basis for early diagnosis and targeted preventive strategies. Full article

This study presents a comprehensive flavour profile analysis of 12 Morchella samples (5 cultivated and 7 wild species) collected from diverse regions across China. The contents of free amino acids and volatile organic compounds were determined using UHPLC-QE-HRMS and HS-SPME-GC-MS. Flavour contribution was assessed by calculating taste activity values (TAVs) and relative odor activity values (rOAVs), and the influence of environmental factors on flavour compound accumulation was further explored. The findings indicated that cultivated Morchella exhibited pronounced fruity, floral, sweet, and mushroom-like notes (e.g., 1-octen-3-one, beta-damascone, and 1-(2-aminophenyl)ethanone), rendering them suitable for fresh consumption. In contrast, wild Morchella exhibited higher levels of herbaceous and smoky aroma compounds (e.g., (E,Z)-2,6-nonadienal, benzenemethanethiol, and non-8-enal), suggesting potential for premium product development. Correlation analysis revealed metabolic associations between taste-active amino acids and key volatile organic compounds via intermediates of the lipoxygenase pathway and the tricarboxylic acid cycle. Furthermore, environmental parameters including elevation, annual precipitation, and solar radiation were found to significantly influence the accumulation of flavour-related metabolites. These findings provide insights into the chemical basis underlying the flavour diversity of Morchella and offer a theoretical foundation for species identification, flavour-directed breeding, and differentiated product development. Full article

Background: The systematic identification of transcriptional repressors remains challenging, as current inference frameworks are predominantly optimized for accessible chromatin, leaving regulatory signals embedded within repressive domains undercharacterized. Methods: Here, we present NuRepress, a computational framework that predicts candidate transcriptional repressors by integrating repressive chromatin architecture, functional signatures, and transcriptional outcomes. NuRepress first identifies well-phased nucleosome arrays within repressive chromatin. These arrays are treated as discrete structural units that capture characteristic local chromatin organization associated with regulatory activity. Since distinct Tn5 cut signal patterns often imply divergent regulatory functions, the framework stratifies these arrays into potential functional subtypes. By synthesizing the quantified repressive efficacy of each subtype with spatial motif enrichment and observed transcriptional dynamics, NuRepress systematically prioritizes and ranks candidate repressors. Results: Our analysis indicated that well-phased nucleosome arrays exhibited accessibility-defined organizational patterns with distinct repressive efficacies, and that these patterns were also observed across species, suggesting that the structural principles captured by NuRepress might extend beyond one specific biological system. Positional motif analysis revealed that distinct TFs exhibited different spatial preferences relative to well-phased nucleosome arrays, suggesting scale-specific preferences for their interactions with these organized chromatin structures. When applied to pancreatic cancer progression, NuRepress identified changes in nucleosome organization associated with stage-specific transcriptional remodeling, highlighting candidate repressors of key oncogenic drivers. Conclusions: NuRepress establishes a structure-aware strategy for repressor inference that extends regulatory genomics beyond accessibility-centered paradigms. By linking well-phased nucleosome organization to transcriptional outcomes, it provides a principled framework for dissecting transcriptional repression across diverse biological settings. Full article

Background/Objectives: Wound infections cause significant morbidity, yet current diagnostics rely on time-consuming microbial culture. Volatile organic compounds (VOCs) from bacterial metabolism offer potential for early diagnosis. This study aimed to validate the volatile metabolites profiled by gas chromatography–ion mobility spectrometry (GC-IMS) combined with machine learning for rapid identification of wound infections and certain bacterial infections. Methods: Headspace of clinical wound samples were analyzed using GC-IMS. Volatile metabolite profiles were compared between infected and non-infected groups and between Escherichia coli (E. coli)-positive and negative samples. Partial least squares discriminant analysis (PLS-DA) and Mann–Whitney U test were used for preliminary screening with variable importance in projection (VIP) > 1 and p-value < 0.05. Three machine learning algorithms, namely support vector machine (SVM), logistic regression (LR), and random forest (RF), were trained on the selected features for classification, using 5-fold cross-validation with 10 repeated runs. Model performance was assessed using key evaluation metrics, including accuracy, sensitivity, specificity, the area under the curve (AUC) and feature importance ranking to identify the most relevant biomarkers. Results: A total of 19 volatile metabolites associated with clinical wound samples were identified. The RF model achieved 90.15% sensitivity and 0.91 AUC for bacterial infection detection. For E. coli identification, LR reached 85.35% sensitivity and 0.89 AUC. Potential volatile metabolic biomarkers including elevated 3-methyl-1-butanol, 2-methyl-1-butanol, and ethyl hexanoate for identifying bacterial infection were selected through the cross-validation results of the three algorithms. Conclusions: Untargeted metabolomics by GC-IMS effectively captures infection-specific volatile metabolic signatures in complex wound samples. Integration with machine learning enables rapid, high-accuracy diagnosis of bacterial infections and E. coli identification at point of care. This approach addresses clinical metabolomics translational challenges by providing a portable and cost-effective method, potentially reducing antibiotic misuse through more timely and targeted therapy. Full article

Urban soil contamination poses persistent risks to redevelopment, public health, and ecological restoration, yet actionable evidence is scattered across site investigation reports, monitoring databases, and regulatory documents. Existing decision-support tools often depend on manual searches and provide limited structured reasoning. This study develops a domain knowledge graph (KG) and a KG-powered question-answering (KBQA) system for urban soil management to organize multi-source evidence and deliver precise, auditable answers to parcel- and pollutant-specific queries. The approach (1) defines an urban soil ontology covering parcels, land uses, pollutants, measurements, pathways, and regulatory thresholds; (2) extracts and links entities and relations from textual and tabular sources; (3) constructs a graph database with provenance; and (4) implements a KBQA pipeline that maps natural-language questions to constrained graph queries and verbalizes results with citations. The resulting system supports source identification, land-use-specific exceedance checks, affected-parcel listing, and remediation reference retrieval. Experiments on a curated QA set and a South China case study show higher answer accuracy and lower latency than text-only baselines, while consistently returning traceable evidence and reducing cross-document lookup effort. Compared to text-only RAG baselines, the KG-powered system achieved a 0.14 improvement in Exact Match scores (e.g., 0.81 vs. 0.58 for Threshold tasks) and maintained a competitive median latency of 0.75 s. The pipeline utilizes a 13B-parameter instruction-tuned LLM. The ontology, schema, benchmark QA sets, and sample queries are publicly released to support transfer to other regions. Full article

Background: Dengue fever is one of the most common mosquito-borne viral infections, with severe cases characterized by plasma leakage, hemorrhage, and multi-organ involvement. Identification of dengue serotypes and reliable biomarkers is essential for predicting disease progression and guiding timely interventions. Methods: This prospective cohort study was conducted at a super-speciality tertiary care hospital in southern India from July 2024 to July 2025. A total of 69 patients presenting with dengue warning signs were included in the study. Patients were categorized into the severe dengue group (n = 25) and non severe dengue group (n = 44). Clinical data, laboratory findings, dengue serotype, and serial serum samples collected on Days 1, 4, and 8 were analyzed to evaluate the predictive and monitoring efficacy of Interleukin-6 (IL-6) and Interleukin-8 (IL-8), and followed up till discharge. Results: Out of 69 dengue patients with warning signs, 32 dengue-positive patients were serotyped, which included DEN V-1 (31.3%), DEN V-2 (31.3%), DEN V-3 (15.6%), DEN V-4 (18.8%), and mixed DEN V-(2 + 3) (3.1%). Severe dengue patients exhibited a higher frequency of secondary dengue infection (IgG) than primary dengue infection (88% vs. 12%), with statistically significantly higher packed cell volume, hemoglobin levels, high AST levels, and prolonged activated partial thromboplastin time, as well as lower platelet counts and albumin levels. Platelet transfusion was given to 35 dengue patients, which had also resulted in significant length of stay in hospital in comparison to non-transfused patients. IL-6 and IL-8 levels were significantly elevated in severe dengue patients when compared to non-severe dengue patients on Day 1 and Day 4, followed by a decline on Day 8, corresponding with clinical recovery. However, the elevated IL-8 levels were observed to be significantly associated with longer hospital stays, indicating its potential role as an early predictor of disease progression. Conclusions: The observed co-circulation of multiple serotypes reflects the hyper-endemic pattern reported across India. Early measurement of these cytokines IL-6 and IL-8 helps distinguish severe from non-severe dengue among patients presenting with warning signs. IL-6 and IL-8 may have potential as biomarkers for disease severity. However their role in guiding platelet transfusion requires further investigation in non-severe cases and prioritizing timely management for those at higher risk of severe disease. Full article

Systemic sclerosis (SSc) is a complex autoimmune connective tissue disease characterized by microvascular dysfunction, immune activation, and progressive fibrosis affecting multiple organs, including the heart. Myocardial involvement represents an important but frequently underrecognized manifestation of SSc and may develop even in the absence of overt clinical symptoms. Cardiac manifestations include ventricular dysfunction, arrhythmias, conduction abnormalities, and heart failure, contributing substantially to morbidity and mortality. The underlying pathophysiology involves coronary microvascular dysfunction, immune-mediated myocardial inflammation, and progressive myocardial fibrosis, which often precede clinically apparent cardiac disease. This review aims to summarize the current understanding of myocardial involvement in SSc and to provide a comprehensive overview of contemporary multimodality cardiovascular imaging techniques for its detection, characterization, and risk stratification. A comprehensive overview of the current literature was conducted focusing on established and emerging cardiovascular imaging modalities for the evaluation of myocardial involvement in SSc. Particular attention was given to echocardiography, cardiac magnetic resonance (CMR), nuclear imaging techniques including positron emission tomography (PET) and single-photon emission computed tomography (SPECT), and cardiac computed tomography (CT). Recent advances in imaging biomarkers, parametric mapping, myocardial strain analysis, and emerging technologies such as artificial intelligence (AI), radiomics, and molecular imaging were also considered. Multimodality cardiovascular imaging plays a central role in the early detection and comprehensive assessment of myocardial involvement in SSc. Advanced imaging techniques enable improved identification of subclinical myocardial dysfunction, microvascular impairment, inflammation, and fibrosis. An integrated imaging approach combining echocardiography, CMR, nuclear imaging, and CT may facilitate earlier diagnosis, enhance risk stratification, and ultimately improve cardiovascular outcomes in patients with SSc. Full article