Search Results (14,017)

Tsunami fragility modeling plays a central role in probabilistic coastal risk assessment; however, representing structural vulnerability under near-field tsunami conditions remains challenging due to complex hydrodynamic loading, strong spatial variability, and the presence of pre-existing earthquake damage. This paper advances near-field tsunami fragility modeling through three specific contributions, each bridging simulation-based methods and empirical damage survey observations. First, it demonstrates how a successive earthquake–tsunami simulation framework can generate conditional fragility surfaces that explicitly account for pre-existing seismic damage without relying on statistically intractable probabilistic decompositions. Second, it develops and validates a distance-dependent intensity-shifting approach—derived from analysis of the 2011 Great East Japan tsunami survey dataset—that adapts baseline fragility curves to near-field and near-coast conditions in a physically interpretable and practically deployable manner. Third, it establishes an explicit cross-validation pathway between simulation-derived fragility surfaces and empirical damage observations through machine-learning-assisted feature importance analysis, a connection largely absent from prior literature. Together, these contributions provide a physically consistent and data-informed foundation for near-field tsunami fragility modeling that is directly applicable—as a methodological framework—to loss and resilience estimation platforms such as IN-CORE and HAZUS and to risk-informed coastal infrastructure design in subduction-zone regions, subject to typology-specific calibration; the simulation results are demonstrated for a US Reinforced Concrete (RC) moment-frame archetype and the empirical results for Japanese wood-frame construction, so direct quantitative application to other structural typologies requires recalibration of the respective model components. Full article

Background: Virtual reality (VR) is a promising technology for the relief of physical and psychosocial burdens. We found that individualised VR videos were well tolerated and accepted and seemed to have a stronger effect on well-being and emotional connection than standardised VR in cancer inpatients under palliative care. For implementation, it is important to actively involve patients, as their input helps to ensure that the VR intervention meets their needs, thus making it more likely to be accepted and effective in practice, while balancing the needs of healthcare professionals. Aim: Exploration of patients’ and healthcare professionals’ perspectives on best practice VR intervention implementation. Design: Workshop-based 360° focus group using a strengths–weaknesses–opportunities–threats (SWOT) model and deductive/inductive qualitative analysis with a ‘framework’ approach. Setting/participants: The focus group took place at the National Centre for Tumour Therapy of a German university hospital. Participants were a local doctor (1) and nurses (3) with VR experience, the cooperating patient advisory board of the study (2), and members of a regional self-help group (3). Results: Eighteen subthemes were identified in the SWOT model. While there was agreement on the ‘strength of distraction’ and ‘opportunities of individualised VR’, concerns remained regarding data protection when using private VR content. There was an argument about gatekeeping by relatives worried about mental distress in patients immersing in home or family VR scenes. In contrast, many ideas were discussed regarding how to overcome rejectionist staff attitudes. However, the high organisational time and staff deployment were addressed as major weaknesses. Conclusions: Involving patient stakeholders and healthcare professionals in the planning of the implementation strategy revealed several issues that require attention. In particular, information needs to be provided not only to patients but also to relatives and hospital staff, alongside ensuring data protection and adequate staffing. Trial registration: Registered at German Clinical Trials Register (Deutsches Register Klinischer Studien; DRKS); registration number: DRKS00032172; registration date: 11 July 2023. Full article

Semantic segmentation technology based on Inverse Synthetic Aperture Radar (ISAR) images can provide crucial perception and analytical capabilities for intelligent safety maintenance of on-orbit spacecraft. However, conventional semantic segmentation methods suffer from three main limitations: firstly, the lack of modeling for radar physical characteristics in the “image first, segment later” pipeline leads to loss of scattering information and phase details; secondly, reliance on extensive pixel-level manual annotation increases application costs; thirdly, ineffective utilization of spacecraft structural priors fails to guide networks to focus on the main body and edges of spacecraft segmentation. To address these issues, this paper proposes a complex-domain semantic segmentation framework named One-Stop Segmentation (OSS) based on ISAR echoes. The framework incorporates two innovative modules: an Automatic ISAR Labeling (AIL) method designed based on ISAR scattering characteristics to generate labels corresponding to ISAR echoes, and a complex-domain semantic segmentation network named One-Stop Segmentation Network (OSSNet) that performs semantic segmentation directly on echoes, avoiding information loss from imaging while shortening the data processing chain. Core contributions of OSSNet include: (1) a Domain Alignment Module (DAM) to effectively mitigate domain mismatch caused by data distribution differences between raw echo signals and labels; (2) a Multi-Perspective Attention (MPA) framework incorporating a Sliding Correlation Attention (SCA) module and a Subdomain Balanced Attention (SBA) module, lever-aging spacecraft structural priors to guide the network’s focus on main structures and edge details from complementary perspectives, significantly improving segmentation ac-curacy. Experimental results on a simulated ground-based radar dataset demonstrate that the proposed OSS framework achieves a mean Intersection over Union (mIoU) of 92.13% and a mean F1-score of 95.75% in ISAR spacecraft semantic segmentation tasks, outperforming existing methods. Full article

Target speaker extraction (TSE) aims to isolate speech spoken by a target speaker out of a mixture using speaker information in an enrollment utterance. Recently, several methods have been proposed that exploit the relationship between the enrollment utterance and the input mixture using cross-attention, without extracting speaker embeddings from the enrollment. Previous approaches applied the cross-attention to the encoded representations or to the real and imaginary parts of the compressed spectrograms separately, which may not have a physical meaning. In this paper, we propose a two-stage TSE method with a physically interpretable modified cross-attention block and a dual post-refinement structure. In the first stage, the attention weights to fuse the enrollment and mixture are derived from the cross-correlation between the complex spectra for the two signals in a form analogous to the phase-sensitive mask. The fused features along with the mixture features were subsequently fed into a speech extraction network to obtain a coarsely extracted target speech. The second stage consists of two parallel branches, where one branch refines the first-stage output using the enrollment in a similar way to the first stage, and the other utilizes the mixture to complement possibly attenuated target speech. In addition, the low-dimensional speaker embeddings extracted from the enrollment and the first-stage output are incorporated into the second stage to exploit the speaker discriminability. Experimental results show that the proposed method consistently outperformed existing TSE methods on the Libri2Mix dataset under both clean and noisy conditions, in terms of speech quality, speech intelligibility, and signal distortion measures. Full article

Modeling thermal environments across scales is crucial for climate-adaptive design and energy management. Traditional numerical methods (e.g., CFD) offer high accuracy and physical consistency, but they are computationally expensive. In contrast, purely data-driven models, though efficient, lack physical consistency and generalization capability. This review systematically examines Physics-Informed Neural Networks (PINNs), a hybrid paradigm in which physical prior knowledge is embedded directly into the neural network training process. A structured keyword search of the Web of Science Core Collection was performed, and 94 peer-reviewed journal articles were analyzed. The evolution from numerical simulations and data-driven surrogate models to PINNs is outlined. PINN methods are classified according to the stage at which physical prior information is integrated (i.e., dataset development, model construction, or loss function formulation). Current research remains heavily focused on loss function constraints, whereas systematic integration into data augmentation and model construction remains limited. Application domains span indoor environments, outdoor environments, and building systems, with each domain exhibiting unique prior integration strategies tailored to specific problems. Future PINN modeling should evolve toward multi-physics coupling, adaptive loss balancing, cross-scenario transfer learning, and unified evaluation benchmarks. PINNs in this field are promising but remain at an early stage, especially for complex urban-scale deployment. This review synthesizes existing research around the three stages of dataset development, model construction, and loss function formulation, summarizes the prior integration strategies adopted in the domain of building thermal environments, and provides a practical workflow for embedding physical prior knowledge at different stages of model development. Full article

In modern cloud-native systems, a single root cause can trigger cascading anomalies across multiple entities (e.g., microservices, databases, and hosts), generating alert storms with hundreds or thousands of heterogeneous alerts. Alert convergence (automatically grouping these alerts into actionable incident tickets) is critical for reducing operator burden and recovery time. Existing graph-based methods construct a topological graph from known entity dependencies and then leverage Graph Neural Networks (GNNs) for information propagation, but they rely on static physical topologies that fail to capture implicit fault propagation paths. Large Language Model (LLM)-based methods focus on reasoning about the textual information of alerts, yet they do not incorporate global topological structure and struggle with consistency at scale. Motivated by these limitations, we propose LLM-Guided Graph Structure Learning (LLM-GSL), a novel framework that combines the semantic reasoning ability of LLMs with the structural modeling power of GNNs for alert convergence. Specifically, LLM-GSL first leverages an LLM to evaluate pairwise entity relationships and discover implicit fault propagation paths that are absent from static topologies, thereby enhancing the physical-topology graph into a more complete structure. A Graph Attention Network (GAT) then refines alert representations over this enhanced graph via graph message passing, guided by a self-supervised graph affinity loss with continuous multi-modal supervision targets that fuse adjacency structure, textual affinity, and temporal affinity. Finally, density-based clustering groups the learned representations into incident tickets. Experiments on five public datasets, including four LogHub-derived datasets and one RCAEval microservice fault-injection subset, demonstrate that LLM-GSL achieves an average F1-score of 96.2%, outperforming six baselines including both traditional clustering and LLM-based methods by at least 14.0 percentage points. Full article

Roadside LiDAR is a key sensing technology for intelligent transportation systems (ITSs) due to its high-precision spatial information and reliable monitoring of traffic environments. However, extracting traffic information from LiDAR point cloud data remains challenging because measurements are produced through angular sampling, causing the spacing between adjacent points to depend on radius and beam distribution. This study proposes a geometry-aware framework that incorporates LiDAR sampling geometry into the neighborhood criterion used to determine point-to-point association. The formulation defines neighborhood tolerance as a function of radial distance and vertical angular separation, enabling clustering decisions that are consistent with the sensing mechanism. In addition, the approach integrates deployment constraints based on sensor mounting height and region-of-interest limits to maintain physically meaningful connectivity under roadside sensing conditions. A systematic calibration procedure is conducted to estimate the scaling factor and radial spacing parameters and evaluate the method using both controlled and real-world datasets. Experimental results reveal that the proposed approach improves clustering accuracy and stability by reducing false negatives in sparse regions while avoiding excessive cluster merging in dense areas. The method demonstrates robust performance across varying sensing conditions and achieves higher accuracy than baseline approaches without parameter retuning, while introducing negligible computational overhead. Full article

Databases serve as critical repositories of digital evidence in criminal investigations, and the recoverability of deleted data is a key determinant of forensic success. Microsoft SQL Server, one of the most widely deployed relational database management systems, has been the subject of multiple forensic studies examining how deleted records persist in physical database files across different acquisition methods. A previous study established a reference baseline using SQL Server 2008 and 2017, demonstrating that the Database Shrink operation causes version-specific and method-specific behavior: under logical collection with Shrink applied in SQL Server 2017, unallocated deleted data becomes fully initialized, rendering recovery impossible—a pattern not observed in SQL Server 2008 or under physical collection in either version. With the release of SQL Server 2025, the most significant architectural update to the platform in a decade, it remained unknown whether these forensic behaviors persist in the latest version. This study replicates the experimental design of in a controlled SQL Server 2025 environment, applying the same deletion scenario (DELETE command without conditions), the same two acquisition methods (logical and physical collection), and the same Shrink condition. The results demonstrate that SQL Server 2025 does not reproduce the version-specific initialization behavior observed in SQL Server 2017: across all four experimental conditions, deleted data residue in unallocated page space remains recoverable, indicating a fundamental change in the interaction between the Shrink operation and the logical collection mechanism. This recoverability is a double-edged property: while it benefits forensic investigators by preserving deleted evidence, it simultaneously represents a data-sanitization risk from a security and privacy standpoint, as deleted records are not reliably erased. These findings provide updated forensic guidance for digital investigators operating in contemporary SQL Server environments. Specifically, the results inform acquisition-method selection in real-world investigations where a suspect may have deleted records and where only a logical backup (.bak) is available to investigators. Full article

Particle accelerators are complex facilities that collide particles at extreme high speed, aiming to discover new physics. In this context, high-energy calorimeter systems play a crucial role, as they provide the particle energy quantity, which is important information for the potential new discoveries. Therefore, this work evaluates the performance of the commonly used Optimal Filter (OF) method and several Convolutional Neural Network (CNN) architectures in reconstructing the amplitude and phase of simulated signals that represent the response pulses produced by high-energy calorimeters. The comparison is conducted using quantitative metrics—including RMS, MAE, MedAE, and Coefficient of Determination. The results show that different CNN architectures exhibit varying performances depending on the calorimeter cell occupancy rate but generally outperform the typical linear OF method, providing more accurate signal reconstructions. Considering all evaluated occupancy levels (10%, 50%, 80%, and 100%), the CNN-based approaches achieved an average improvement of approximately 79% in amplitude RMS and 62% in amplitude standard deviation when compared to the OF method. For phase estimation, the CNNs achieved improvements of approximately 26% for both RMS and standard deviation metrics. Although the proposed strategy requires a large execution time due to the training process across multiple folds, these findings indicate that CNNs are promising alternatives for calorimeter energy reconstruction, particularly in high-occupancy conditions such as those expected for high-luminosity experiments. Full article

Flood-induced scour remains one of the leading causes of bridge failure, yet the chain linking flood hazard to bridge decisions is still commonly treated as a sequence of disconnected tasks. This review examines that chain using uncertainty as a unifying interpretive framework, synthesizing the recent literature on non-stationary flood hazard assessment, bridge-scale hydraulics, scour processes and predictive models, scour monitoring, monitoring-informed forecasting, structural vulnerability, and risk-informed decision-making. The review synthesizes the state of the art across all these stages of the chain, highlighting how the dominant uncertainty changes along it: climate and hydrologic variability upstream; model-form, sediment, and parameter uncertainty in scour prediction; measurement noise and inverse-inference uncertainty in monitoring; and threshold and consequence uncertainty in closure, retrofit, and network-level decisions. Although major advances have been achieved in probabilistic modelling, machine learning, hybrid physics-informed methods, and multimodal sensing, most published frameworks still transfer deterministic outputs from one stage to the next. As a result, uncertainty is rarely propagated consistently to the decision level. The main value of this review lies in making the chain’s weak interfaces explicit, in showing how uncertainty propagation can serve as a unifying framework across otherwise disconnected literatures, and in identifying which methodological directions are most promising for connecting prediction, monitoring, and decision support into a coherent end-to-end probabilistic chain supporting climate-resilient bridge management. Full article

Solar insecticidal lamps are important physical control devices for green pest management, but faults in their photovoltaic power supply units can reduce trapping efficiency and shorten service life. To improve fault identification under complex agricultural environments, this study proposes a signal-image-level multimodal fusion network (SIL-MMFN) for detecting and classifying photovoltaic panel operating states in solar insecticidal lamps. The method combines time-series measurements with short-time Fourier transform (STFT)-based time–frequency images. A convolutional image branch extracts spatial features from time–frequency representations, whereas a bidirectional GRU branch with attention models temporal dependencies in the original signals. In addition, physics-informed features based on the illumination–current residual and output power are introduced to enhance discriminative fault information. Field data collected from four agricultural deployment nodes were used to classify normal, open-circuit, and mismatch states. Experimental results show that the proposed method achieved an accuracy of 97.5%, precision of 96.7%, recall of 97.8%, and macro-F1 score of 97.3%, outperforming single-modality and representative comparison models. The results indicate that multimodal fusion helps reduce confusion between open-circuit and mismatch faults and provides a potential approach for operating-state monitoring and maintenance of agricultural photovoltaic equipment. In this study, fault diagnosis refers to the detection and classification of photovoltaic panel operating states, including normal, open-circuit, and mismatch conditions. Full article

Lockdown drills have become standard practice in K-12 schools across the United States, but there are concerns about the psychological health impact, quality of implementation, and equity implications of current practices. This scoping review compiles the empirical literature on lockdown and active-threat drills to provide insight into how drills are defined and conducted, what outcomes are measured, and the remaining gaps. In accordance with well-researched scoping review methodologies, 27 peer-reviewed U.S.-based studies were aggregated from six primary areas: drill definitions and typologies, implementation practices, reported outcomes, trauma-informed and developmental considerations, equity and disability inclusion, and evidence gaps. Findings reveal wide variability among drill terminology and protocol categorization and most studies emphasize advance warning and low-realism practices. Psychological outcomes are measured much more often than objective measures of implementation fidelity or physical preparedness. Educator and staff experiences, caregiver perceptions, and longitudinal outcomes are underrepresented. Although a number of studies report developmental adaptations and disability accommodations, comprehensive equity analyses remain rare. Overall, potential psychological harms are more clearly documented than protective effects in the literature. This review emphasizes the importance of standardized drill taxonomies, fidelity measurement methods, trauma-informed mental health integration, and inclusive designs to inform school safety policy and practice. Full article

Bike-sharing systems (BSS) are a key component of sustainable urban mobility. However, their performance is strongly influenced by urban topography and socio-economic conditions. This study analyzes Barcelona’s public BSS, Bicing, to examine how altitude and neighborhood income affect bicycle availability, departures, and electric bicycle adoption. The main objective is to determine whether the observed “electric divide” is driven by income or by topographical necessity. The analysis uses 2023 data from 511 Bicing stations and income information from 62 neighborhoods obtained from Open Data Barcelona and the Spanish National Statistics Institute. Three indicators were constructed: bike availability ratio, departures ratio, and electric bicycle ratio. Results show a strong negative correlation between altitude and bike availability (r = −0.71) and a strong positive correlation between altitude and electric bicycle use (r = 0.78). High-altitude stations show lower availability and fewer departures, while electric bicycles dominate uphill trips. Although high-income neighborhoods initially appear to use more electric bicycles, regression results show that income becomes insignificant once altitude is controlled for. Therefore, electric bicycle adoption is driven mainly by physical necessity rather than socio-economic preference. Full article

In Underwater Acoustic Target Recognition (UATR), accurately extracting spectral lines from time–frequency spectra in complex ocean environments faces three critical challenges: low-frequency spectral confusion, line spectrum and noise mixture, and a computational efficiency vs. performance trade-off. To address these, we propose a deep learning-integrated framework based on application-oriented integration and adaptation of established techniques tailored to the underwater acoustic domain. The framework consists of the following: (1) the Line Spectrum Separation Network (LSS-Net), which integrates a Time–Frequency Joint LSTM and a Temporal Gated Cross-Attention (TGCA) module within an encoder–decoder architecture adapted for high-resolution underwater acoustic time–frequency spectra; (2) a physics-informed signal simulation approach that realistically models Doppler frequency drift and intensity fluctuations; and (3) a Peak-Tracking Line Extractor (PTLE) algorithm that leverages underwater acoustic-specific temporal constraints. The proposed framework achieves an MOTA of 0.89 on simulated data and 0.52 on real sea trial data, outperforming existing methods by 0.06-2.14 in MOTA and significantly suppressing high-resolution background noise. Full article

Wearable electrocardiogram (ECG) monitoring enables continuous cardiovascular assessment, yet signals acquired in ambulatory environments are inevitably corrupted by baseline wander, electrode motion artifacts, and muscle interference, which obscure diagnostically critical waveform features. Existing deep learning denoisers rely on heuristic attention mechanisms and time-domain-only losses, lacking principled control over what information the network retains or discards. To address this limitation, we propose EFIB-Net, an information bottleneck-guided multi-resolution network for robust ECG denoising. The framework introduces two complementary components: an efficient frequency-guided attention module that derives temporal attention weights directly from the energy distribution of parallel multi-resolution convolutional branches, requiring only four learnable parameters while providing physically interpretable feature selection that naturally highlights QRS complexes, and a variational information bottleneck constraint at the encoder–decoder bottleneck that forces the latent representation to retain only reconstruction-relevant information and discard noise, guided by a spectral–temporal composite loss. To the best of our knowledge, we are among the first to explicitly introduce the information bottleneck principle into deep-learning-based ECG signal denoising. Experiments on the MIT-BIH Arrhythmia Database show that EFIB-Net outperforms ten traditional and deep learning baselines across four standard metrics—signal-to-noise ratio (SNR), root mean square error, percentage root-mean-square difference, and correlation coefficient; at an input SNR of −5 dB it reaches $8.12$ dB output SNR, surpassing the strongest attention-based competitor by 1.77 dB ($p<0.01$) while using only 0.45 M parameters and 10.8 ms inference latency per segment; downstream evaluation further demonstrates that the denoised signals achieve 99.18% R-peak detection sensitivity and 91.26% heartbeat classification F1-score, both within approximately one percentage point of the clean-signal upper bound, making it practical for real-time cardiac monitoring on resource-constrained wearable devices. Zero-shot cross-database evaluation on the QT Database further confirms generalizability, with only 0.54 dB degradation without retraining. Full article