Search Results (19,264)

Real-time seismic reservoir monitoring of geologic reservoirs requires both large-scale data management and efficient computational workflows. Addressing these challenges is facilitated by developing techniques capable of selectively capturing critical geologic features, thereby increasing computational efficiency and reducing data storage requirements. This paper proposes a double-compression framework integrating Haar wavelet transforms with machine learning (ML) for efficient multiparameter seismic inversion. First, Haar wavelet compression significantly reduces the dimensionality of the input elastic models, preserving essential geologic structures while limiting data volumes. Next, a convolutional neural network with the long short-term memory (CNN-LSTM) architecture, including dual encoders and multi-decoders, compresses seismic data into a latent space to generate a multi-scale P-wave velocity estimate. By leveraging transfer learning to speed up convergence and enhance prediction accuracy, we fine-tune the latent representation to estimate the P-to-S-wave velocity ratio and acoustic impedance at multiple resolution scales. Tests on the synthetic ${\mathrm{CO}}_2$-injection Kimberlina model show that wavelet-based compression—including detuning large-scale trends—minimizes artifacts in simulated wavefields and accelerates neural-network training. The results demonstrate that combining wavelet-based pre-compression for reservoir models with data-driven latent encodings for seismic data achieves high compression ratios, reduces computational costs, and maintains the fidelity of subsurface imaging. Compared with a redundant-decimation baseline, the proposed framework reduces network training time by approximately 70% and GPU memory usage by 33–73%, achieves a wavefield energy loss below 0.1% at a 16:1 model-dimension reduction, and produces multi-resolution predictions of $V_{\mathrm{P}}$, $V_{\mathrm{P}}/V_{\mathrm{S}}$, and acoustic impedance with normalized errors below 0.04 across all six wavelet decomposition levels. Thus, the double-compression framework enables robust and scalable seismic monitoring of elastic reservoir parameters. Full article

Urban street canyons exert a critical influence on local microclimates; however, the dynamics of mixed convective airflow under unsteady wind and thermal forcing remain poorly quantified. This study systematically investigates the spatiotemporal characteristics of airflow within symmetric and asymmetric street canyons through integrated long-term field measurements and complementary CFD simulations. Field data collected over 120 monitoring days at the Weishui Campus of Chang’an University were analyzed using the Levenberg–Marquardt nonlinear curve-fitting algorithm. The analysis demonstrates that sine functions accurately represent diurnal surface temperature variations during consecutive clear sky periods, whereas polynomial functions of varying orders are required to characterize meteorologically complex episodes, including cold-wave cooling and seasonal transitions. Ambient wind patterns outside the canyon were further classified into two characteristic variation modes: stepwise and gradual. Complementary unsteady RANS simulations, with wall boundary conditions derived directly from the fitted field data, reveal that canyon geometry and meteorological forcing jointly govern the evolution of airflow structures and thermal distributions across seasons. In the symmetric canyon, the flow transitions from complex multi-vortex activity in spring and summer to a more stable regime in autumn, with two well-defined counter-rotating vortices emerging during winter cold-wave events. In the asymmetric canyon, strong summer solar heating sustains a dominant leeward vortex with a strengthening secondary structure, whereas winter cold wave intrusion generates a hierarchically nested vortex system in which secondary and tertiary vortices progressively develop and detach. By coupling empirical surface temperature functions with CFD boundary conditions, this study advances the precision of predictive microclimate models and provides an evidence-based framework for optimizing street canyon geometry to enhance ventilation performance, energy efficiency, and outdoor thermal comfort. Full article

Fatigue driving causes 20–30% of global traffic accidents. To address limitations in feature fusion and real-time performance, this study proposes an improved You Only Look Once version 8 (YOLOv8)-Long Short-Term Memory (LSTM) model with symmetry-aware spatiotemporal feature learning. In the spatial phase, Group Shuffle Convolution (GSConv) and Slim Neck structures are introduced to enhance facial feature detection while reducing parameters by 32.3%. In the temporal phase, an improved Inverted Transformer(iTransformer) with differential attention is integrated with an LSTM-Feed-Forward Network (FFN) architecture, achieving a 90.1% prediction accuracy and an 84.6% noise suppression rate. A standardized dataset of 13,200 images was constructed using a four-level classification system. By implementing TensorRT acceleration and multi-process parallel frameworks, the system optimizes single-frame latency to 38 ms—a 9.5× efficiency gain—while maintaining an overall detection accuracy of 92.4%. These results demonstrate that the proposed framework effectively balances model lightweighting with high precision, providing a robust and efficient solution for real-time driver monitoring in complex driving scenarios. Full article

Against the backdrop of the expansion of sustainable finance and the growing relevance of ESG-related information, disclosure and regulation, this paper examines the dynamic relationship between sustainability-related uncertainty and ESG equity market volatility in a global framework. Sustainability-related uncertainty is proxied by the Global GDP-Weighted ESG-Based Sustainability Uncertainty Index (ESGUI), while ESG market volatility is measured through a monthly proxy constructed from estimated daily conditional variances obtained from GJR-GARCH(1,1) models with Student-t innovations. The paper explicitly distinguishes sustainability-related uncertainty, understood as ambiguity in the ESG information environment, from ESG market volatility, understood as market-pricing instability in ESG equity benchmarks. Empirically, the study combines bootstrap full-sample Granger-causality tests, parameter-stability diagnostics, and rolling-window bootstrap analysis. Robustness and extended analyses use an EGARCH-based volatility proxy, alternative rolling-window lengths, macro-financial controls, an emerging-market ESG benchmark, impulse-response analysis, forecast-error variance decomposition, and out-of-sample forecasting tests. The full-sample results indicate an asymmetric predictive pattern: ESG market volatility contains Granger-causal predictive information for changes in sustainability-related uncertainty, whereas the reverse direction is not supported on average. However, parameter-stability tests reject constancy, and rolling-window evidence shows that predictive effects arise episodically in both directions, with changes in sign, magnitude and significance. The uncertainty-to-volatility channel becomes statistically relevant and locally stronger during stress episodes, especially around 2019–2021, while macro-control results show that broader market stress absorbs part of the volatility-to-uncertainty linkage. The findings indicate a regime-dependent uncertainty–volatility nexus and support dynamic approaches to ESG risk monitoring, portfolio management and regulatory communication. All results are interpreted as predictive evidence, not structural causality. Full article

This study evaluates the impact of Dynamic Soil–Structure Interaction (DSSI) on the efficiency of an algorithm based on the existing literature on Vibration-Based Structural Health Monitoring (VBSHM). The algorithm is designed for Level 3 detection, that is, to accurately estimate the presence, location in height, and extent of structural damage simultaneously. Using computer simulations of a hypothetical two-dimensional six-story symmetrical reinforced concrete building, the study analyzes the algorithm’s performance under increasing soil flexibility. Efficiency is measured through four key metrics: the number of false positives and negatives, a weighted stress index, the iterations required for damage intensity estimation, and the accuracy of the identified versus simulated stiffness reduction. Results indicate that the algorithm remains effective even when input motions correspond to actual soft-soil ambient vibration recordings modified by kinematic DSSI effects, despite frequency contents differing from white-noise conditions. Conversely, inertial DSSI negatively impacts performance, leading the VBSHM algorithm to underestimate damage as soil deposits become softer. Full article

Background: Patients described as high-need, high-cost (HNHC) represent a subset of individuals with complex multimorbidity whose healthcare trajectories are characterised by recurrent instability and intensive use of acute care services. Concepts such as trajectory disruption, resilience, and complex adaptive behaviour are widely discussed in health systems research, yet linking these ideas to longitudinal patient care remains limited. The PaJR (Patient Journey Record) relational system was designed using principles from complex adaptive systems theory, enabling longitudinal observation of patient trajectories in real-world care. Objective: This study develops a middle-range theory grounded in longitudinal relational monitoring data. Methods: Two datasets (MonashWatch and Irish cohorts) provide empirical grounding through descriptive analysis of signal clustering, distribution, and multi-domain patterns. Monitoring calls capture structured patient-reported signals across multiple domains, including illness, medication, healthcare utilisation, social support, environmental factors, and self-care. Results: Results demonstrate long-tail signal distributions, temporal clustering, and multi-domain instability preceding admission. Alerts frequently occurred in clusters across consecutive monitoring calls 88% of alert calls were part of a consecutive alert sequence, with approximately 64% of alert calls occurring immediately after a previous alert. Alerts were also commonly multi-domain, with approximately 64% involving disturbances across more than one domain simultaneously.Conclusions: Longitudinal relational monitoring reveals instability patterns in patient journeys that are not visible in episodic health-system data. Recognising these instability phases may enable earlier, more adaptive responses for patients with complex healthcare needs and provides empirical grounding for emerging theories of healthcare trajectories within complex adaptive systems. Although grounded in relational monitoring data, the instability–plasticity framework may extend to inform interpretation across physiological and connected health monitoring systems. Full article

Fiber Bragg Grating (FBG) sensors are widely used for strain and temperature monitoring due to their high sensitivity, compact size, electromagnetic immunity, and multiplexing capability. While conventional FBG interrogators remain bulky and costly, Photonic Integrated Circuit (PIC) platforms provide a promising route toward compact, scalable, and low-power FBG interrogation. However, the choice of architecture strongly determines the achievable resolution, bandwidth, multiplexing capacity, and robustness. This review compares on-chip demodulation architectures, evaluating their performance in resolution, bandwidth, and interrogation speed. We show that the optimal architecture depends strongly on the application: AWG-based schemes excel in compact, multi-FBG readout; ring-resonator systems are highly effective for tunable filtering; and interferometric phase-domain schemes offer the highest sensitivity for dynamic strain sensing. Despite these architectural advances, practical deployment remains constrained by system-level bottlenecks. These challenges primarily include source/detector integration, fiber–chip coupling, packaging robustness, and thermal drift. Overcoming these barriers requires a shift in future development from isolated photonic-device optimization toward comprehensive, system-level co-design. Full article

Structural damage classification in composite UAV wings is a key challenge in Structural Health Monitoring (SHM), particularly under barely visible impact damage conditions. Fiber Bragg Grating (FBG) sensor networks provide high-resolution strain data; however, systematic experimental benchmarking of lightweight neural architectures trained on real FBG datasets remains limited, especially under sensor degradation scenarios. This work presents a four-phase benchmarking study of Multilayer Perceptron (MLP) configurations using strain measurements from a composite UAV wing instrumented with 32 FBG sensors across five damage states and 210 loading experiments. The framework evaluates optimization strategies, hyperparameter sensitivity, architectural depth, and robustness under controlled sensor dropout, Gaussian noise, and wavelength drift perturbations. Results indicate that compact architectures with progressive dimensional reduction (256–128–64) trained using adaptive optimizers (AdamW and Nadam) achieve the best balance between macro-F1 performance (up to 0.85 during validation), stability, and computational efficiency. Robustness analysis shows gradual performance degradation under sensor loss, suggesting distributed strain-field learning. These findings provide practical guidelines for selecting computationally efficient and robust neural models for deployable FBG-based SHM systems in aerospace applications. Full article

The criminal law assessment of consensual sexting between minors requires interpretation within a child-rights framework that accounts for children’s evolving capacities and the ultima ratio principle of criminal law. Although child self-generated sexual images and videos (CSGIV) may, in many jurisdictions, conceptually fall within the scope of offences relating to child pornography or child sexual abuse material (CSAM), consensual peer-to-peer sharing typically lacks the classical elements of sexual exploitation. This article provides a structured comparative overview of how the criminal law systems of the twenty-seven European Union (EU) Member States regulate consensual minor-to-minor sexting, identifying three regulatory models and assessing their compatibility with child-rights standards. The research is based on a structured comparative legal analysis drawing on the report and country reports of the second monitoring round of the Lanzarote Committee, complemented by a primary analysis of the relevant criminal law provisions of the Member States. The analytical framework relies on a coding manual developed by the authors along thematic dimensions. The findings identify three regulatory models: systems that provide explicit differentiation and safeguards; systems that formally criminalise the conduct but operate with implicit mitigation; and systems that entail a broad risk of criminalisation. The analysis reveals considerable normative fragmentation and demonstrates that the absence of explicit differentiation may expose forms of adolescent self-expression to criminal liability. The article concludes that, to comply with child-rights standards, explicit normative safeguards and a consistent application of the exceptional character of criminal law are required. Full article

This article revisits the debate on whether the Brussels Agreement and the Ohrid Agreement, including its Implementation Annex, are legally binding. It develops a three-test framework that separates international-law binding force from EU-law legal effects. Tests A and B adapt the International Court of Justice (ICJ) indicators of animus contrahendi and acceptance through subsequent conduct, acquiescence and silence. Test C examines whether the agreements produce legal effects through EU enlargement conditionality, monitoring and reporting. The analysis finds that the treaty status of both instruments remains contestable. The Brussels Agreement is textually specific, yet intent signals are mixed, practice remains reversible and treaty-type obligation structures are weak. The Ohrid Agreement is drafted in a more treaty-like register, but references to a future “legally binding agreement” and the politics of non-signature leave inter se binding force unsettled. Nonetheless, both agreements can produce EU legal effects. They operate as enlargement benchmarks that shape assessments of Serbia’s and Kosovo’s progress, while Commission reporting and standardised compliance indicators may indirectly bind EU institutions through consistency, equal treatment and legitimate expectations. Full article

Galloping of overhead transmission lines is a low-frequency, large-amplitude vibration hazard that poses a severe threat to power grid safety, yet existing monitoring approaches fail to simultaneously provide flexible deployment, quantitative measurement, and robustness under severe weather conditions. This paper makes three primary contributions. First, we propose a novel line-structure center adsorption algorithm that converts a single operator touch-point into a sub-pixel-precision conductor prompt, achieving prompt accuracy above 95% with one round of interactive correction. Second, we introduce—for the first time—SAM2’s streaming memory architecture for continuous zero-shot pixel-level tracking of galloping conductors under complex outdoor backgrounds including snow, ice, and poor illumination, achieving a segmentation IoU of 93.8% and zero identity switches over 500 consecutive frames, outperforming XMem (87.4%) and DeAOT (88.9%). Third, we develop a two-stage spatial correction framework combining vanishing-point-based inverse perspective mapping (IPM) with equidistant linear transformation (ELT), which eliminates perspective distortion inherent in non-orthogonal field imaging and enables quantitative measurement of galloping amplitude (error < 0.5 m), frequency (error < 0.1 Hz), and inter-phase spacing (ranging error < 1 m). The complete pipeline is implemented on a portable, tripod-mounted device (≤15 kg) integrating a monocular camera, laser rangefinder, and high-precision PTZ gimbal. Field validation at three 110/500 kV sites in Jiangsu Province under extreme winter conditions ($-4$ °C, Level 5 wind, continuous snowfall) confirms engineering-grade accuracy and practical robustness, providing a viable technical pathway for real-time non-contact galloping monitoring and disaster early warning. Full article

Hurricanes cause severe impacts on lives, livelihoods, and essential systems. Hurricane Melissa impacted Jamaica as a Category 5 cyclone, resulting in estimated losses of approximately 41% of national GDP (US$8.8 billion) and eliciting widespread damage to housing, healthcare, agriculture, and urban infrastructure. Agriculture sustained heavy losses, with 41,000 hectares of damaged farmland and the loss of more than 1 million livestock animals. These impacts resulted in exposed animal closures with biological hazards. Using systems thinking, the PESTHEEL framework, and a One Health lens, we argue for viewing Hurricane Melissa as series of cascading inter-related One Health threats of waterborne and vector-borne diseases, zoonoses, antimicrobial resistance, degraded indoor and outdoor air quality, chemical pollution, and shifting migration and border dynamics. These each unfold at different timings. A structured synthesis for Jamaica and other Caribbean Small Island Developing States is provided by integrating systems thinking, One Health, and the PESTHEEL framework. Immediate and lagged risk pathways are identified, and practical risk reduction actions are proposed to support anticipatory, multisectoral recovery: enhanced syndromic, laboratory, wastewater, vector, and rodent surveillance; resilient WASH and shelter systems; non-insecticidal and integrated vector management; biosecure aid and border protocols; environmental toxicology monitoring; and climate–health intelligence. Full article

This study presents a systematic review of Artificial Intelligence (AI) in vehicular bridge engineering, covering design, monitoring, and lifecycle decision support. The objective is to identify, classify, and critically analyze the main AI methods applied across the bridge lifecycle, including Machine Learning (ML), Deep Learning (DL), Artificial Neural Networks (ANNs), and Optimization Algorithms (OAs). The review follows the PRISMA 2020 framework to ensure transparency and reproducibility, considering publications from 2018 to 2026. The results show that AI applications span the entire bridge lifecycle; however, current research is predominantly concentrated in Structural Health Monitoring (SHM), damage detection, inspection, and predictive maintenance, while design-oriented applications—such as optimization, surrogate modeling, and structural analysis—remain comparatively less developed. Importantly, SHM data serve as a key input for data-driven modeling, enabling design optimization, reliability assessment, and lifecycle decision support. Classical ML methods remain effective for structured datasets, whereas DL models, particularly convolutional and recurrent neural networks, dominate image-based and time-series applications. In addition, hybrid physics-informed AI approaches are emerging to improve model reliability and interpretability. The review also identifies key challenges, including data quality limitations, lack of standardized methodologies, limited integration with engineering design codes, and barriers related to trust, expertise, and regulatory frameworks. Overall, the findings highlight a shift toward integrated digital frameworks, including digital twins and multimodal data fusion, to support more reliable monitoring and lifecycle decision-making. This study provides a comprehensive synthesis of current developments and outlines future research directions toward more resilient and intelligent bridge infrastructure systems. Full article

Automated pavement distress detection (PDD) is critical for the structural health monitoring (SHM) of transportation infrastructure, yet existing methods struggle with real-time multi-target detection under resource constraints. In this paper, YOLOv8-PDD was constructed based on YOLOv8 by introducing the large separable kernel attention (LSKA) mechanism module into the Spatial Pyramid Pooling—Fast (SPPF) module, replacing Complete-IoU (CIoU) loss with Distance-IoU (DIOU) loss as the loss function, and adopting Soft-Non-Maximum Suppression (NMS) to replace the original NMS algorithm. The proposed YOLOv8-PDD achieved 78.3% mean average precision with intersection over union above 0.5 (mAP@0.5 +8.1%) with a minimal complexity increase of +0.2 GFLOPs compared to the baseline YOLOv8n model. While incurring a negligible increase in latency (+0.09 ms), YOLOv8-PDD significantly outperforms YOLOv8n in detection accuracy (mAP@0.5 +8.1%), offering a superior accuracy–efficiency trade-off for real-time applications. YOLOv8-PDD performed well in detecting all categories, with AP values above 75% except for transverse crack and strip patch. Significant improvements in pothole detection AP@0.5 (+22.1%) and strip patch detection AP@0.5 (+17.7%) indicate superior small target and complex background adaptability. Our model achieved a detection efficiency of 68 frames per second (FPS) on consumer-grade CPUs (OpenVINO-optimized), outperforming 10 models (e.g., YOLOv5n and RTDETR-l) in accuracy–speed balance. Full article

Monitoring data from deep foundation pits exhibit significant nonlinear, nonstationary, and spatiotemporal coupling characteristics. Traditional methods struggle to simultaneously characterize their spatial correlations and temporal evolution patterns. To address these issues, on the basis of measured data from a typical deep foundation pit project in Beijing, this paper proposes a spatiotemporal feature-fused CNN-LSTM-Transformer prediction model for support pile deformation. By constructing a spatiotemporal matrix of monitoring data, the model achieves the synergistic fusion of spatial feature extraction, temporal dependency modeling, and global correlation perception. The results of the comparative analysis indicate that the proposed model demonstrates stable predictive performance across different pile locations and operating conditions. Its root mean square error (RMSE) and mean absolute error (MAE) are reduced by approximately 20% compared with those of the CNN and CNN-LSTM models. Particularly in areas with severe deep deformation and during high-fluctuation stages, the model effectively mitigates prediction lag and error accumulation, demonstrating a superior response capability to local abrupt changes. The findings suggest that this method can provide a reliable data-driven approach for the dynamic prediction of support structure deformation during deep foundation pit construction. Full article