Search Results (51)

The frequent occurrence of fires has prompted China to accelerate the development of community fire prevention and emergency management systems. Language, serving both communicative and affective functions by facilitating the flow of information and fostering mutual understanding, runs through the entire process of community fire emergency management. In response to the early-stage nature of this field and the lack of a systematic framework, this study constructs a dynamic capability evaluation system for urban community fire-related emergency language services (FELS) by integrating multi-source and heterogeneous data. First, by adopting a hybrid approach combining dynamic capability theory and text mining, a three-level indicator system is established. Second, based on domain knowledge, quantitative methods and scoring rules are designed for the third-level qualitative indicators to provide standardized input for the model. Third, a weighting and integration framework is developed that simultaneously considers the internal mechanism characteristics and statistical properties of indicators. Specifically, a knowledge-driven weighting approach combining FAHP and fuzzy DEMATEL is employed to characterize indicator importance and interrelationships, while the CRITIC method is used to extract Data-Driven weights based on data dispersion and information content. These knowledge-driven and Data-Driven weights are then integrated through a multi-feature fusion weighting approach. Finally, a linear weighting model is applied to combine the normalized indicator values with the integrated weights, enabling a systematic evaluation of the dynamic capabilities of community FELS. To validate the proposed framework, application tests were conducted in four representative types of urban communities, including internationally developed, aging and vulnerable, newly developed, and economically diverse communities, using fire emergency scenarios as the entry point. The external validity and internal robustness of the proposed model were verified through these tests. The results indicate that the evaluation system provides accurate, objective, and adaptive assessments of dynamic capabilities in FELS across different community contexts, offering a governance-oriented quantitative tool to support grassroots fire prevention and to enhance community resilience. Full article

Background/Objectives: GuaB, which is known as inosine 5′-phosphate dehydrogenase (IMPDH), is an enzymatic target involved in the de novo guanine biosynthetic pathway of the multidrug-resistant (MDR) Acinetobacter baumannii. GuaB has emerged as a potential therapeutic target to cope with increasing antibiotic resistance. Here, we used machine learning-based virtual screening as a verification technique to find potential inhibitors possessing different chemical scaffolds, using structure-based drug design as a discovery platform. Methods: Four machine learning models, built based on chemical fingerprint data, were trained, and the best models were used for virtual screening of the ChEMBL library, which covers 153 active molecules. Molecular dynamics (MD) simulations of 200 ns were carried out for all three compounds in order to explain conformational changes, evaluate stability, and provide validation of the docking results. Post-simulation analyses include principal component analysis (PCA), bond analysis, free-energy landscape (FEL), dynamic cross-correlation matrix (DCCM), radial distribution function (RDF), salt-bridge identification, and secondary-structure profiling, etc. Results: For molecular docking, the screened compounds were used against the GuaB protein to achieve proper docked conformation. Upon visual examination of the best-docked compounds, three leads (lead-1, lead-2, and lead-3) were found to have better interaction with the GuaB protein in comparison to the control. The mean RMSD scores between the three leads and the control were between 2.54 and 2.89 Å. In addition, the three leads as well as the control were characterized for pharmacokinetic features. All three leads met Lipinski’s Rule 5 and were thus drug-like. PCA and FEL analyses showed that lead-2 exhibited improved conformational stability, identified as deeper energy minima, whereas RDF and DCCM analyses revealed that lead-2 and lead-3 exhibited strong local structuring and concerted dynamics. In addition, lead-2 displayed a very rich hydrogen-bonding network with a total of 460 frames possessing such interactions, which is the highest among the complexes investigated here. Based on entropy calculations and the maximum entropy method of gamma–gram, lead-1 proved to be the most stable one with the lowest binding free-energy. Conclusions: This study provides an integrated machine learning-based virtual screening pipeline for the identification of new scaffolds to moderate infections associated with AMR; however, in vitro validation is still required to assess the efficacy of such compounds. Full article

This study examined technology adaptation and collaborative pedagogical practices within Flexible Emergency Learning Spaces (FELS) established during a military emergency to ensure sustainable and resilient education for displaced children and adolescents. A mixed-methods design combined 43 semi-structured interviews with teachers and principals across diverse FELS and 13 classroom observations, analyzed at the statement level. Data were analyzed through the e-CSAMR framework, assessing levels of technology integration and collaboration, and the Mindtools framework, which considers theoretical foundations of technology use. Quantitative comparisons complemented the qualitative analysis. Findings indicate that FELS may support advanced technology use when appropriate tools and pedagogical knowledge exist, while highlighting the need for teacher training and technological adaptation responsive to learners’ needs in emergencies. Furthermore, FELS enabled collaborative teaching, supporting educators’ pedagogical and emotional needs. Teachers adopted diverse strategies—including advisory collaboration, assistance, parallel teamwork, enrichment, and simultaneous teaching—reflecting flexible cooperation. FELS also enabled peer collaboration among learners, offering emotional, social, and cognitive support essential for resilience. This study highlights the importance of analyzing collaboration across cognitive, social, and emotional dimensions and proposes a conceptual framework for defining types of teacher teamwork and understanding how adaptive pedagogy can enhance educational resilience. Full article

Ultrashort bunch length measurements are crucial for characterizing electron beams in short-pulse accelerators, including novel accelerators like EuPRAXIA and those used for free-electron lasers (FELs). This work provides an overview of the design process and the current status of a single-shot bunch length monitor prototype based on a broadband spatial imaging system for coherent transition radiation (CTR), which was recently installed at the MAX IV short-pulse facility (SPF). The THz-based imaging system was designed using optical system simulation software for full bunch simulation. CTR images were captured experimentally, followed by image analysis for comparison with reference bunch length data from the transverse deflecting cavity (TDC). This paper presents the conceptualization and design choices for the optical system of the bunch length monitor, the current experimental set-up, the installation details, and preliminary positive observations confirming the potential of this method as a novel approach to bunch length monitoring using spatial CTR images and a scalar technique, with potential for future bunch profile measurements. Full article

Feline leishmaniosis (FeL) is still considered an emerging and neglected disease. Cats, once considered accidental hosts, are now recognized as adjunctive reservoirs of the disease, especially in areas where canine (CanL) and human (HumL) leishmaniosis are widespread. Although often asymptomatic, infected cats could contribute to the transmission cycle of the parasite. Recent studies in Campania (Italy) have found a significant prevalence of feline infection, indicating the need to implement diagnostic and surveillance protocols to prevent the spread of the disease. The aim of the study was to outline the current scenario by studying the prevalence of FeL in Campania to identifying the potential zoonotic risk and in addition to validate the Immunofluorescence Antibody Test (IFAT) method for the diagnosis of leishmaniosis in cats. The study involved initially 702 cats; for each cat, a clinical record was compiled, including identification data, anamnesis, and clinical findings. Due to incomplete information, statistical analysis was performed only on a subset of 601 cats. A blood sample was collected to obtain serum/plasma specimens. When feasible, a lymph node fine-needle aspiration was performed. The observed seroprevalence rate was 32.1% (193/601), with a higher seroprevalence in outdoor cats and the presence of asymptomatic seropositive animals (28.0%;54/193), suggesting that felines may act as silent reservoirs of Leishmania infantum. An excellent result was obtained for the validation and standardization of the analytical IFAT method for the diagnosis of feline leishmaniasis; therefore, an inter-laboratory test has been carried out to establish the dilution cut-off at ≥1:80 as compatible with infection. Furthermore, a xenodiagnosis examination was conducted on a cat that was infected to more accurately evaluate the possibility of asymptomatic cats acting as carriers of the infection; however, this test resulted negative. Full article

Background/Objectives: Qatar and other Gulf Cooperation Council (GCC) countries are experiencing a growing incidence of diet-related non-communicable diseases (NCDs). The lack of a culturally relevant food exchange list (FEL) for commonly consumed foods in Qatar and the GCC limits the application of cultural preferences in medical nutrition therapy (MNT) for managing diet-related NCDs, thereby reducing patient adherence and metabolic outcomes. Therefore, the primary objective of this study was to develop a culturally tailored FEL for 50 main course dishes widely consumed in the region. Methods: A four-phase approach was followed in this developmental study. First, common Qatari and GCC dishes were identified based on cultural practices and market availability. Second, nutrient composition was compiled from regional food composition tables and validated using dietary analysis software. Pearson correlation was conducted to compare macronutrient values, with significance set at p < 0.05. Third, standard serving sizes were determined using Wheeler et al.’s methodology and converted into household measures using a kitchen scale. Finally, we developed a macronutrient exchange list for the dishes based on the established Wheeler rounding-off criteria. Results: A culturally tailored FEL for 50 frequently consumed Qatari and GCC dishes was successfully developed. Significant correlations were observed between laboratory-derived and software-derived values for carbohydrates (r = 0.7) and protein (r = 0.9), with a weaker correlation for fat (r = 0.5). Macronutrient exchange analysis revealed substantial variation across dishes, with several carbohydrate-based dishes also contributing meaningful protein and fat exchanges. Findings indicated that visual assumptions about nutrient composition may not accurately reflect exchange values, highlighting the need for systematic analysis in diet planning. Conclusions: This study developed a novel culturally relevant FEL for commonly consumed composite dishes in Qatar and the GCC. The exchange list provides a practical tool for dietitians and healthcare professionals to support culturally tailored MNT and public health interventions in the region. It also serves as a valuable resource for researchers in nutritional epidemiology, enabling the analysis of dietary data by converting raw food intake information. Full article

In this work, our goal is to develop a privacy-preserving distributed weed detection and management system. The proposed work integrates FEL (Federated Learning) and deep learning with multi-modal sensor fusion to enhance the model’s performance while minimising data transfer, latency, and energy consumption. In this study, we used multimodal sensors, such as LiDAR (Light Detection and Ranging), RGB (Red–Green–Blue) cameras, multispectral imaging devices, and soil moisture sensors placed in controlled agricultural plots. Deep learning models, such as Convolutional Neural Networks (CNNs), LSTM–CNN hybrids, and Vision Transformers, were trained using standardized parameters. A proposed Federated CNN (FedCNN) was deployed across multiple edge devices, each locally trained on sensor data without exchanging raw data, using FedAvg and FedProx algorithms. The experimental work revealed that the model FedCNN performed well in comparison to other models and achieved the highest accuracy of 94.1%, precision of 94.3%, recall of 93.9%, F1-score of 94.1%, and AUC of 94.1% during hybrid fusion strategies. We compared the centralized and federated learning performance. Full article

The impacts of land use transition on ecological environment quality (EEQ) during China’s rapid urbanization have attracted growing concern. However, existing studies predominantly focus on single-scale analyses, neglecting scale effects and driving mechanisms of EEQ changes under the coupling of administrative units and grid scales. Therefore, this study selects Zhejiang Province—a representative rapidly transforming region in China—to establish a “type-process-ecological effect” analytical framework. Utilizing four-period (2005–2020) 30 m resolution land use data alongside natural and socio-economic factors, four spatial scales (city, county, township, and 5 km grid) were selected to systematically evaluate multi-scale impacts of land use transition on EEQ and their driving mechanisms. The research reveals that the spatial distribution, changing trends, and driving factors of EEQ all exhibit significant scale dependence. The county scale demonstrates the strongest spatial agglomeration and heterogeneity, making it the most appropriate core unit for EEQ management and planning. City and county scales generally show degradation trends, while township and grid scales reveal heterogeneous patterns of local improvement, reflecting micro-scale changes obscured at coarse resolutions. Expansive land transition including conversions of forest ecological land (FEL), water ecological land (WEL), and agricultural production land (APL) to industrial and mining land (IML) primarily drove EEQ degradation, whereas restorative ecological transition such as transformation of WEL and IML to grassland ecological land (GEL) significantly enhanced EEQ. Regarding driving mechanisms, natural factors (particularly NDVI and precipitation) dominate across all scales with significant interactive effects, while socio-economic factors primarily operate at macro scales. This study elucidates the scale complexity of land use transition impacts on ecological environments, providing theoretical and empirical support for developing scale-specific, typology-differentiated ecological governance and spatial planning policies. Full article

To genetically characterise an epidemic isolate of feline panleukopenia virus (FPLV) harbouring the Ala91Ser mutation in China, a clinical strain (accession number: OR921195.1), named FPLV-CC19-02, was isolated from a PCR-positive faecal swab sample. Phylogenetic analysis revealed that it is far removed from all current commercial vaccine strains and differs from the FPLV prototype strain Cu-4 (M38246.1), specifically the vaccine strain of Fel-O-Vax^® PCT, at positions 91 (Ala91Ser) and 101 (Ile101Thr) within the VP2 protein. This virus can induce the typical cytopathic effect seen in parvovirus infection in feline kidney cells, resulting in severe clinical symptoms in cats, including haematochezia and hyperthermia. Furthermore, infected cats died of virus infection within 5–10 days post-infection (dpi) (100% morbidity and 83% mortality), indicating that FPLV-CC19-02 is a strain with increased virulence. Additionally, it demonstrated good immunogenicity in cats. Overall, these findings may help us to better understand the molecular prevalence of feline panleukopenia virus in cats and provide valuable basic data for the development of effective, locally adapted feline panleukopenia virus vaccines in China. Full article

Federated edge learning (FEL) unites the decentralized training capabilities of multiple edge nodes to allow model gradient sharing and parameter aggregation across a peer-to-peer network. However, many intrinsic policy conflicts still exist in FEL, for example, the open accessibility of gradients will lead to the privacy leakage risk during the federal aggregation process. In this paper, we first identify that malicious users weaponized with generative artificial intelligence (GenAI) can generate fake samples that are almost identical to FEL participants’ training data. By analyzing how different configurations of GenAI affect attack effectiveness, we find that an adversary with strong patchwork and reconstruction capabilities can stealthily steal diverse training data from nearly all FEL participants. To thwart such a generative data reconstruction attack (GenDRA) scheme, we propose a novel target semantic dissolution (TSD) mechanism for enhancing the privacy-preserving ability of FEL, which encrypts only a very small number (≤10%) of gradient values in each training round that have a significant impact on human visual formation using format-preserving encryption. With TSD, the speculator cannot obtain a fake sample that is visually similar to the training sample because real gradients are actively concealed. Extensive experiments based on four benchmark datasets are performed to demonstrate the huge threat of GenAI and the effectiveness of TSD in all aspects: compelling accuracy performance, strong visual privacy guarantee, and low computing overhead. Full article

The ubiquitous presence of marine litter has brought huge environmental pressure. A wide range of measures have been developed to address this problem. This paper focuses on the removal measure—Fishing for Litter (FEL). It aims to identify the potential factors affecting fishers’ participation in the FFL program. A two-step approach, including interviews and questionnaire surveys, was employed. A total of 10 fishers participated in the interviews, and 8 factors were initially identified using thematic analysis and utilized in the questionnaire design. A total of 412 valid samples were collected. Descriptive statistics and binary logit regression were used for data analysis. The results showed that rewards, the participation of other friends, and inconveniences or troubles incurred from handling trash feature most in fishers’ decision-making on the participation. Furthermore, fishers’ views toward marine environments also had a behavioral impact on their participation in the program. Potential management measures were proposed, including reducing inconveniences incurred from handling trash on board as well as at ports, providing rewards, encouraging environmental education for fishers, and distributing information regarding the program. It is hoped that fishers will eventually make it a normal onboard practice to collect trash found at sea and develop a sense of marine environmental stewardship. Full article

Accurate estimation of landslide depth is essential for practical hazard assessment and risk mitigation. This work addresses the problem of determining landslide depth from satellite-derived elevation data. Using the principle of mass conservation, this problem can be formulated as a linear inverse problem. To solve the inverse problem, we present a regularization approach that computes approximate solutions and regularization parameters using the Balancing Principle. Synthetic data were carefully designed and generated to evaluate the method under controlled conditions, allowing for precise validation of its performance. Through comprehensive testing with this synthetic dataset, we demonstrate the method’s robustness across varying noise levels. When applied to real-world data from the Fels landslide in Alaska, the proposed method proved its practical value in reconstructing landslide thickness patterns. These reconstructions showed good agreement with existing geological interpretations, validating the method’s effectiveness in real-world scenarios. Full article

In macromolecular crystallography (MX), a complete diffraction dataset is essential for determining the three-dimensional structure. However, collecting a complete experimental dataset using a single crystal is frequently unsuccessful due to poor crystal quality or radiation damage, resulting in the collection of multiple incomplete datasets. This issue can be solved by merging incomplete diffraction datasets to generate a complete dataset. This study introduced a new approach for merging incomplete datasets from MX to generate a complete dataset using serial crystallography (SX). Six incomplete diffraction datasets of β-glucosidase from Thermoanaerobacterium saccharolyticum (TsaBgl) were processed using CrystFEL, an SX program. The statistics of the merged data, such as completeness, CC, CC*, R_split, R_work, and R_free, demonstrated a complete dataset, indicating improved quality compared with the incomplete datasets and enabling structural determination. Also, the merging of the incomplete datasets was processed using four different indexing algorithms, and their statistics were compared. In conclusion, this approach for generating a complete dataset using SX will provide a new opportunity for determining the crystal structure of macromolecules using multiple incomplete MX datasets. Full article

Background: As one of the four most valuable animal medicines, Fel Ursi, named Xiong Dan (XD) in China, has the effect of clearing heat, calming the liver, and brightening the eyes. However, due to the special source of XD and its high price, other animals’ bile is often sold as XD or mixed with XD on the market, seriously affecting its clinical efficacy and consumers’ rights and interests. In order to realize identification and adulteration analysis of XD, UHPLC-QTOF-MS^E and multivariate statistical analysis were used to explore the differences in XD and six other animals’ bile. Methods: XD, pig gall (Zhu Dan, ZD), cow gall (Niu Dan, ND), rabbit gallbladder (Tu Dan, TD), duck gall (Yan Dan, YD), sheep gall (Yang Dan, YND), and chicken gall (Ji Dan, JD) were analyzed by UHPLC-QTOF-MS^E, and the MS data, combined with multivariate analysis methods, were used to distinguish between them. Meanwhile, the potential chemical composition markers that contribute to their differences were further explored. Results: The results showed that XD and six other animals’ bile can be distinguished from each other obviously, with 27 ions with VIP > 1.0. We preliminarily identified 10 different bile acid-like components in XD and the other animals’ bile with significant differences (p < 0.01) and VIP > 1.0, such as tauroursodeoxycholic acid, Glycohyodeoxycholic acid, and Glycodeoxycholic acid. Conclusions: The developed method was efficient and rapid in accurately distinguishing between XD and six other animals’ bile. Based on the obtained chemical composition markers, it is beneficial to strengthen quality control for bile medicines. Full article

In contemporary evacuation systems, the evacuation sign typically points fixedly towards the nearest emergency exit, providing guidance to evacuees. However, this static approach may not effectively respond to the dynamic nature of a rapidly evolving fire situation, in particular if the closest emergency exit is compromised by fire. This paper introduces an intelligent evacuation sign control mechanism that leverages smoke and temperature sensors to dynamically adjust the direction of evacuation signs, ensuring evacuees are guided to the quickest and safest emergency exit. The proposed mechanism is outlined through a rigorous mathematical formulation, and an ESP heuristic is devised to determine temperature-safe, smoke-safe, and congestion-aware evacuation paths for each sign. This algorithm then adjusts the direction light on the evacuation sign to align with the identified evacuation path. To validate the effectiveness of this approach, fire simulations using FDS software 6.7.1 were conducted in the Taipei 101 shopping mall. Temperature and smoke data from sensor nodes were utilized by the ESP algorithm, demonstrating superior performance compared to that of the existing FEL algorithm. Specifically, the ESP algorithm exhibited a notable increase in the probability of evacuation success, surpassing the FEL algorithm by up to 34% in methane fire scenarios and 14% in PVC fire scenarios. The significance of this improvement is more pronounced in densely congested evacuation scenarios. Full article