Search Results (8,223)

The impacts of flooding on people’s livelihoods are profound. Therefore, the rapid restoration of safe conditions in existing buildings post-flood, through rational and effective emergency strengthening, constitutes a most urgent priority. Focusing on the specific challenges of flood-induced damage to buildings, coupled with the constraints of limited resources and time-sensitive conditions after a disaster, this study established an indicator system for prioritizing emergency strengthening schemes for existing buildings after floods. A dedicated prioritization model is developed by integrating Prospect Theory and a combination weighting method. The application of this model to a practical engineering case verifies its feasibility and effectiveness. The results demonstrate that the proposed model can rationally and efficiently select the optimal scheme, thereby providing new insights for the quantitative selection of optimal emergency strengthening schemes for existing buildings after floods. This study also highlights the model’s transferability to different disaster scenarios, while its limitations were discussed and future research directions outlined. Full article

Background: The Republic of Korean humidifier disinfectant disaster, involving toxic chemical exposure, constitutes a major social disaster causing severe trauma. While physical and psychological difficulties are documented, this study investigated the relationship between posttraumatic stress disorder (PTSD) symptoms and survivors’ daily life adaptation across children, adolescents, and adults, examining PTSD’s mediating role. Methods: The sample included 834 participants (417 exposed survivors and 417 unaffected individuals), divided into three age groups. PTSD symptoms and life adaptation were measured via self-reports. Multigroup Structural Equation Modeling (SEM) was utilized to test the indirect associations among exposure, PTSD symptoms, and life adaptation, and to examine age-group comparisons. Results: Survivors in all age groups reported higher PTSD symptoms and lower adaptive functioning compared to unaffected individuals, with the largest PTSD mean difference found in children and adults. Multigroup SEM confirmed that exposure positively impacted PTSD symptoms, and PTSD symptoms negatively impacted life adaptation across all ages. PTSD symptoms significantly mediated the exposure-life adaptation link in all groups. Critically, the direct effect of exposure on life adaptation was significant only in adults, indicating a full mediation via PTSD symptoms in children and adolescents. Conclusions: Exposure to toxic humidifier disinfectants is linked to life adaptation difficulties through elevated PTSD symptoms. These findings emphasize addressing trauma-related symptoms and suggest the utility of developmentally sensitive psychological interventions. Limitations include reliance on self- and parent-reported measures rather than clinical diagnoses, and the lack of control for external contextual factors (e.g., policy changes, media exposure). Full article

The Urban Heat Island (UHI) phenomenon constitutes one of the most significant climate-related challenges for contemporary cities, intensifying thermal stress, energy demand, and social vulnerability. This study proposes a methodological framework that integrates multi-source data with explainable machine learning techniques in order to both analyse and support the refinement of climate adaptation policies. The approach combines satellite-derived land surface temperature from Sentinel-3, meteorological and air quality indicators, and biophysical and anthropogenic variables. After a preprocessing stage, clustering and classification models (Logistic Regression, Support Vector Classifier) were trained for the city of Madrid, with inference applied to Athens as a reference case. The evaluation of model performance was complemented by explainability techniques (Feature Importance and SHAP), which highlighted temporality, soil moisture, and urban morphology as the most decisive factors for UHI intensity, while atmospheric pollutants were found to play a secondary role. These insights were systematically compared with existing international, European, and national policy frameworks, including the Sustainable Development Goals, the European Green Deal, and Spain’s National Energy and Climate Plan. The findings demonstrate how interpretable, data-driven analysis can bridge the gap between predictive modelling and governance, providing a transparent basis for targeted and evidence-based urban climate adaptation strategies. Full article

Maintaining the stability of the mine roadway is of paramount importance, as it is critical in ensuring the daily operational continuity, personnel safety, long-term economic viability, and sustainability of the entire mining operation. Significant instability can trigger serious disruptions—such as production stoppages, equipment damage, and severe safety incidents—which ultimately compromise the project’s financial returns and future prospects. Therefore, the proactive assessment and rigorous control of roadway stability constitute a foundational element of successful and sustainable resource extraction. In China, thick and extra-thick coal seams constitute over 44% of the total recoverable coal reserves. Consequently, their safe and efficient extraction is considered vital in guaranteeing energy security and enhancing the efficiency of resource utilization. The surrounding rock of gob-side roadways in typical coal seams is often fractured due to high ground stress, intensive mining disturbances, and overhanging goaf roofs. Consequently, asymmetric failure patterns such as bolt failure, steel belt tearing, anchor cable fracture, and shoulder corner convergence are common in these entries, which pose a serious threat to mine safety and sustainable mining operations. This deformation and failure process is associated with several parameters, including the coal seam thickness, mining technology, and surrounding rock properties, and can lead to engineering hazards such as roof subsidence, rib spalling, and floor heave. This study proposes countermeasures against asymmetric deformation affecting gob-side entries under intensive mining pressure during the fully mechanized caving of extra-thick coal seams. This research selects the 8110 working face of a representative coal mine as the case study. Through integrated field investigation and engineering analysis, the principal factors governing entry stability are identified, and effective control strategies are subsequently proposed. An elastic foundation beam model is developed, and the corresponding deflection differential equation is formulated. The deflection and stress distributions of the immediate roof beam are thereby determined. A systematic analysis of the asymmetric deformation mechanism and its principal influencing factors is conducted using the control variable method. A support approach employing a mechanical constant-resistance single prop (MCRSP) has been developed and validated through practical application. The findings demonstrate that the frequently observed asymmetric deformation in gob-side entries is primarily induced by the combined effect of the working face’s front abutment pressure and the lateral pressure originating from the neighboring goaf area. It is found that parameters including the immediate roof thickness, roadway span, and its peak stress have a significant influence on entry convergence. Under both primary and secondary mining conditions, the maximum subsidence shows an inverse relationship with the immediate roof thickness, while exhibiting a positive correlation with both the roadway span and the peak stress. Based on the theoretical analysis, an advanced support scheme, which centers on the application of an MCRSP, is designed. Field monitoring data confirm that the peak roof subsidence and two-side closure are successfully limited to 663 mm and 428 mm, respectively. This support method leads to a notable reduction in roof separation and surrounding rock deformation, thereby establishing a theoretical and technical foundation for the green and safe mining of deep extra-thick coal seams. Full article

Transposable elements (TEs) constitute a significant portion of eukaryotic genomes and play crucial roles in genome evolution, yet their diverse and complex sequences pose challenges for accurate classification. Existing tools often lack reliability in TE classification, limiting genomic analyses. Here, we present TEclass2, a software employing a deep learning approach based on a linear transformer architecture with k-mer tokenization and sequence-specific adaptations to classify TE consensus sequences into sixteen superfamilies. TEclass2 demonstrates improved classification performance and offers flexible model training on custom datasets. Accessible via a web interface with pre-trained models, TEclass2 facilitates rapid and reliable TE classification. These advancements provide a foundation for enhanced genomic annotation and support further bioinformatics research involving transposable elements. Full article

Objective crime risk and perceived safety constitute distinct yet interrelated dimensions of urban security, whose spatial discrepancies may lead to misaligned policy interventions. This study develops a street-level analytical framework to examine the (mis)match between perceived safety and crime risk in Chaoyang District, Beijing. An enhanced Street-view imagery (SVI) segmentation model with object detection was applied to extract streetscape elements and estimate perceived safety scores, which were then standardized and compared with street-level crime data, yielding two types of matches and two types of mismatches. Three conditions were analyzed using multinomial logit regression: (1) objective unsafety with low perceived safety, (2) objective safety with low perceived safety, and (3) objective unsafety with high perceived safety. Findings demonstrate how visual and social environmental factors jointly shape discrepancies between perceived and actual safety and identify potential determinants to mitigate such (mis)matches. Full article

The aryl hydrocarbon receptor (AhR) plays a crucial role in mediating xenobiotic responses, as well as regulating broader metabolic, differentiation, and stress response programs. In this study, we present a comprehensive long-read RNA sequencing dataset that examines transcriptional changes in the HepaRG human cell line during differentiation induced by dimethyl sulfoxide (DMSO) and acute activation of the AhR with 3-methylcholanthrene (3-MC). We identified 946 genes that were differentially expressed between the NonDiff and Diff conditions (303 genes upregulated and 643 genes downregulated), and 1786 genes that showed differential expression between Diff and Ind conditions (961 genes upregulated and 825 genes downregulated). The acute induction of 3-MC produced a robust AhR signature, characterized by the robust induction of CYP1A1 and CYP1B1, along with a coordinated downregulation of several constitutive hepatic genes involved in drug metabolism (e.g., CYP3A4 and CYP2C8). To facilitate further analysis and reuse of our data, we have provided processed gene-level count matrices, transcript per million (TPM) tables, and detailed differential expression results, as well as analysis scripts. This resource supports research into AhR biology, pharmacogene regulation, and the development of methods for long-read transcriptomics in liver models. Full article

Accurate colorimetric quantification presents a significant challenge, as traditional imaging technologies fail to resolve metamerism and even hyperspectral imaging (HSI) is compromised by nonlinearities and specular reflections. This study introduces a high-fidelity colorimetric system using cross-polarized HSI to suppress specular reflections, integrated with a Support Vector Regression (SVR) model to correct the system’s nonlinear response. The system’s performance was rigorously validated, demonstrating exceptional stability and repeatability (average $\Delta E_{00}<0.1$). The SVR calibration significantly enhanced accuracy, reducing the mean color error from $\Delta E_{00}=4.36$ to $0.43$. Furthermore, when coupled with a Random Forest classifier, the system achieved 99.0% accuracy in discriminating visually indistinguishable (metameric) samples. In application-specific validation, it successfully quantified cosmetic color shifts and achieved high-precision skin-tone matching with a fidelity as low as $\Delta E_{00}=0.82$. This study demonstrates that the proposed system, by synergistically combining cross-polarization and machine learning, constitutes a robust tool for high-precision colorimetry, addressing long-standing challenges and showing significant potential in fields like cosmetic science. Full article

Earthquake-induced soil liquefaction poses significant geotechnical hazards, including sand boiling, loss of foundation bearing capacity, lateral spreading, pipeline flotation, uneven settlement, and slope instability. While cyclic liquefaction constitutive models can effectively simulate and predict site liquefaction behavior, their reliability hinges on the accurate calibration of constitutive parameters. Traditional calibration methods often fail to capture the comprehensive material response, are labor-intensive, time-consuming, and susceptible to subjective judgment. To overcome these limitations, this study develops an intelligent calibration framework for a cyclic liquefaction constitutive model by integrating a numerical solver for unit tests with the genetic algorithm (GA)-based optimization framework. The proposed method is rigorously evaluated in terms of calibration accuracy, convergence, repeatability, uncertainty, and computational efficiency. Validation via a series of laboratory unit tests on materials from an extremely high earth-rock dam project confirms the method’s effectiveness. Results demonstrate that the intelligent calibration approach achieves a high accuracy of 91.84%, offering a reliable, efficient, and robust solution for parameter determination. Full article

This study examines how organizational models are evolving beyond post-Fordism in the context of digitalization, platformization, and new forms of labor governance. Using a bibliometric analysis of 1573 Web of Science publications, the article maps the intellectual genealogy, disciplinary foundations, and global collaborative patterns of research on the platform economy. The field has consolidated around three core concepts—platform economy, gig economy, and sharing economy—anchored in clusters focused on business models, labor precarity, and regulatory and governance debates. The analysis reveals a temporal shift from early narratives centered on sharing and collaborative consumption to contemporary concerns with algorithmic management, precarious work, and worker resistance. Parallel discussions of Industry 4.0 and 5.0 expose tensions between human-centered aspirations and the continued expansion of platform capitalism. The global landscape shows both vitality and asymmetry: China leads in empirical output, while the USA and England dominate theoretical agenda-setting and international collaboration. Overall, the findings demonstrate that platform research constitutes a mature, interdisciplinary field bridging labor sociology and management studies. The study calls for stronger integration of Global South perspectives and further examination of whether human-centered organizational visions can meaningfully counteract the structural inequalities embedded in platform-mediated work. Full article

Privacy-preserving machine learning (PPML) constitutes a core element of responsible AI by supporting model training and inference without exposing sensitive information. This survey presents a comprehensive examination of the major cryptographic PPML techniques and introduces a unified taxonomy covering technical models, verification criteria, and evaluation dimensions. The study consolidates findings from both survey and experimental works using structured comparison tables and emphasizes that recent research increasingly adopts hybrid and verifiable PPML designs. In addition, we map PPML applications across domains such as healthcare, finance, Internet of Things (IoT), and edge systems, indicating that cryptographic approaches are progressively transitioning from theoretical constructs to deployable solutions. Finally, the survey outlines emerging trends—including the growth of zero-knowledge proofs (ZKPs)-based verification and domain-specific hybrid architectures—and identifies practical considerations that shape PPML adoption in real systems. Full article

The pervasive deployment of large language models (LLMs) has given rise to mounting concerns regarding the safety and security of the content generated by these models. Nevertheless, the absence of comprehensive evaluation methods constitutes a substantial obstacle to the effective assessment and enhancement of the safety and security of LLMs. In this paper, we develop the Safety and Security (S&S) Benchmark, integrating multi-source data to ensure comprehensive evaluation. The benchmark comprises 44,872 questions covering ten major risk categories and 76 fine-grained risk points, including high-risk dimensions such as malicious content generation and jailbreak attacks. In addition, this paper introduces an automated evaluation framework based on multi-model judgment. Experimental results demonstrate that this mechanism significantly improves both accuracy and reliability: compared with single-model judgment (GPT-4o, 0.973 accuracy), the proposed multi-model framework achieves 0.986 accuracy while maintaining a similar evaluation time (~1 h) and exhibits strong consistency with expert annotations. Furthermore, adversarial robustness experiments show that our synthesized attack data effectively increases the attack success rate across multiple LLMs, such as from 14.76% to 27.60% on GPT-4o and from 18.24% to 30.35% on Qwen-2.5-7B-Instruct, indicating improved sensitivity to security risks. The proposed unified scoring metric system enables comprehensive model comparison; summarized ranking results show that GPT-4o achieves consistently high scores across ten safety and security dimensions (e.g., 96.26 in ELR, 97.63 in PSI), while competitive open-source models such as Qwen2.5-72B-Instruct and DeepSeek-V3 also achieve strong performance (e.g., 96.70 and 97.63 in PSI, respectively). Although all models demonstrate strong alignment in the safety dimension, they exhibit pronounced weaknesses in security—particularly against jailbreak and adversarial attacks—highlighting critical vulnerabilities and providing actionable direction for future model hardening. This work provides a comprehensive, scalable solution and high-quality data support for automated evaluation of LLMs. Full article

To understand the hot deformation behavior of 7085 aluminum alloy, compression tests were performed under varied conditions (593–743 K/0.001–1 s⁻¹). While the true stress–strain curves predominantly display the features of dynamic recovery, the softening mechanism shifts towards dynamic recrystallization when deforming at higher temperatures and lower strain rates. The validity of the constructed strain-compensated Zener–Hollomon model is confirmed by its exceptional precision in forecasting the flow stress, achieving an R² value of 0.992. The instability areas are concentrated in the high-strain-rate regions, and the optimal deformation processing for 7085 aluminum alloy is 693–743 K/0.01–0.001 s⁻¹. The alloy’s softening mechanism undergoes a transition from solely dynamic recovery to a progressively more significant coordinated role of dynamic recovery and dynamic recrystallization as the temperature rises and the strain rate drops. Full article

The urban heat island (UHI) effect, intensified by rapid urbanization, necessitates the precise identification and mitigation of thermal sources and sinks. However, existing studies often overlook landscape connectivity and rarely analyze integrated source–sink networks within a unified framework. To address this gap, this research combines source–sink theory with the local climate zone classification to examine the spatiotemporal patterns of thermal characteristics in Fuzhou, China, from 2016 to 2023. Using morphological spatial pattern analysis, the minimum cumulative resistance model, and a gravity model, we identified key thermal source and sink landscapes, their connecting corridors, and barrier points. Results indicate that among built-type local climate zones, low-rise buildings exhibited the highest land surface temperature, while LCZ E and LCZ F were the warmest among natural types. Core heat sources were primarily LCZ 4, LCZ 7, and LCZ D, accounting for 19.71%, 13.66%, and 21.72% respectively, whereas LCZ A dominated the heat sinks, contributing to over 86%. We identified 75 heat source corridors, mainly composed of LCZ 7 and LCZ 4, along with 40 barrier points, largely located in LCZ G and LCZ D. Additionally, 70 heat sink corridors were identified, with LCZ A constituting 96.39% of them, alongside 84 barrier points. The location of these key structures implies that intervention efforts—such as implementing green roofs on high-intensity source buildings, enhancing the connectivity of cooling corridors, and performing ecological restoration at pinpointed barrier locations—can be deployed with maximum efficiency to foster sustainable urban thermal environments and support climate-resilient city planning. Full article

Public ecological products constitute the most fundamental public goods supporting human well-being. Enhancing the high-quality supply of public ecological products is critical for maintaining ecological safety, ensuring the ecological regulation function, and promoting the harmonious coexistence of humans and nature. To deeply investigate the supply process and behavioral mechanisms of public ecological products, this study constructs a simulation model based on Agent-Based Modeling (ABM) to simulate the behavior rules and dynamic processes of four main subjects involved in the supply of public ecological products: government, enterprises, the public, and environmental non-governmental organizations (ENGOs). After calibrating the model parameters with relevant data from the water production and supply industry in Beijing, the good fit of the model output results verifies the effectiveness of the model. This study reveals the operating mechanism of multi-subject collaborative supply of public ecological products, providing a basic model for investigating the mechanism and evolution process of ecological product supply under more complex conditions, and also providing a powerful tool for the ex-ante evaluation of the implementation effect of public ecological product supply policies. Full article