Search Results (2,356)

Suspended ceiling systems are among the most seismically vulnerable non-structural components in buildings, posing significant life-safety risks and economic losses, yet understanding their full-field kinematic behavior under seismic loading remains a major experimental challenge. Conventional contact sensors offer limited spatial coverage and can alter the dynamic properties of lightweight panels due to mass loading. In contrast, non-contact optical alternatives are rarely feasible in shake-table environments due to restricted viewing angles, extensive areal coverage requirements, and the risk of equipment damage from falling panels. This study proposes an end-to-end three-dimensional displacement measurement framework for large-scale shake-table testing of suspended ceiling systems, employing consumer-grade cameras with purpose-built tools that cover the complete experimental workflow, including motion-based video trimming, semi-automated calibration, a robust multi-stage image-tracking pipeline that maintains trajectory continuity under extreme inter-frame displacements, and a ceiling system motion visualization and analysis tool. The framework was validated through a full-scale shake-table experiment continuously tracking 324 spatial nodes across 81 ceiling panels, achieving an RMSE below 3 mm in all spatial directions and exact peak-frequency agreement in 9 out of 10 test cases. A parallel processing architecture reduced total processing time from over 27 h to under 10 min without GPU acceleration, and six-degree-of-freedom rigid-body analysis resolved the complete panel failure sequence from constrained oscillation through multi-axis rotation to gravitational free fall, a level of kinematic detail unattainable with conventional instrumentation. This framework establishes a practical, scalable foundation for full-field seismic performance assessment of non-structural systems where conventional instrumentation is physically or logistically infeasible. Full article

Following the PRISMA-ScR framework for scoping reviews, we systematically searched five databases (Scopus, IEEE Xplore, ScienceDirect, SpringerLink, Web of Science) using a Boolean query combining real-time processing, 3D reconstruction, and deformation modelling terms. From 86 records identified, 56 peer-reviewed publications (2009–2025) were retained after two-stage screening and organized into a unified taxonomy covering sensing modalities (RGB-D, LiDAR, tactile), reconstruction pipelines (volumetric fusion, NRSfM, neural radiance fields), and deformation models (FEM, PBD, mass-spring, GNN-based surrogates, differentiable simulators). Of the 56 included works, 60% were published between 2022 and 2025, confirming the field’s rapid growth. Neural and implicit representations account for 20% of contributions, FEM-based methods for 16%, and hybrid or application-specific pipelines for 21%. Four systemic gaps emerge: the absence of a unified physics-aware benchmark; unresolved speed–accuracy trade-offs (PBD achieves >30 FPS on desktop GPUs for 10³–10⁴ vertex meshes but lacks mapping to physical material constants (Young’s modulus, Poisson’s ratio), limiting material fidelity; full-order FEM ensures physically consistent stress–strain behavior but runs at only 1–10 FPS without order reduction; reduced-order FEM recovers interactive rates for low-frequency deformation modes); fragile handling of occlusions and multi-object contact; and limited end-to-end integration of sensing and simulation. The findings support the presentation of a research roadmap centered on model order reduction, differentiable physics, multimodal sensing fusion, and standardized evaluation protocols, with implications for robust digital twins of deformable environments. Full article

This study proposes a practical framework for estimating instantaneous stay-cable tension in cable-stayed bridges based on the first-order frequency moment (FFM). The proposed framework combines cepstrum-guided modal decomposition, FFM-based instantaneous frequency estimation, windowed cepstrum-based consistency assessment, and energy-weighted multi-modal averaging to estimate instantaneous cable tension from measured vibration responses. Unlike conventional time–frequency analysis methods that rely on local peak extraction in the time–frequency domain, the proposed approach directly estimates instantaneous frequency from the local time–frequency energy distribution, thereby improving tracking robustness while maintaining computational efficiency under operational conditions. Numerical validation demonstrates reliable instantaneous frequency tracking under noisy and non-stationary vibration conditions while maintaining low computational cost. Field validation using acceleration- and displacement-based measurements from an in-service bridge further confirms the capability of the proposed framework to capture vehicle-induced transient tension variations. The results indicate that the framework provides reliable and physically consistent cable tension information under real operational conditions. These characteristics, together with computational efficiency and compatibility with existing monitoring systems, indicate strong potential for near-real-time structural health monitoring applications. Full article

Public open spaces (POS) in old communities are important settings for daily neighborhood life, yet many renovated POS remain underused after physical upgrading. Existing evaluations often rely on subjective perceptions, providing limited evidence on how facilities are associated with vitality. This study analyzes the associations between facility provision and POS vitality in 63 renovated POS across 11 old communities in Jiulongpo District, Chongqing, China. POS vitality is operationalized through two behavioral dimensions, use frequency and stay duration, derived from video detection and tracking using YOLOv8 and ByteTrack. Facility provision was then classified by facility type and examined in relation to the vitality indicators through descriptive analysis and Generalized Estimating Equations models. Descriptive evidence indicates substantial heterogeneity in both facility provision and POS vitality. Resting amenities and landscape elements are more commonly provided, whereas children’s facilities show the lowest provision and greater spatial selectivity. Higher use frequency and longer stay duration are concentrated in some POS. The Generalized Estimating Equations analysis further indicates that facilities are not associated with vitality in a uniform way. Children’s facilities show the strongest positive associations with both use frequency and stay duration despite their limited provision, supporting their key role in POS vitality. Landscape elements and lighting facilities are more closely associated with stay duration, highlighting the role of environmental support in sustaining longer use. In contrast, the negative associations for fitness facilities, together with the non-significant results for resting and sanitation amenities, suggest that not all facility provision translates into stronger vitality. Taken together, renovation performance should be judged not by the quantity of upgraded facilities alone, but by whether facilities support the behavioral dimensions of vitality that a POS is expected to achieve. Full article

Accurate tropical cyclone (TC) track prediction remains challenging, as numerical models suffer from high computational cost, substantial storage requirements, and physical parameterization uncertainties, while data-driven large AI models depend heavily on training data volume and high-resolution inputs, resulting in prohibitive computational overhead. To address these issues, this paper proposes TCN-GAN-DM, a three-stage deep learning framework based on the China Meteorological Administration (CMA) Tropical Cyclone Best Track Dataset. Specifically, a dual-stream temporal convolutional network (TCN) first extracts temporal features from track and meteorological sequences, respectively. A generative adversarial network (GAN) then takes these features and produces multiple physically plausible candidate tracks via noise injection. Finally, a conditional diffusion model (DM) refines the predicted positions through progressive denoising. Experimental results for TCs in 2024 show that under the fair deterministic comparison using a single fixed candidate, the model achieves a 6 h track error of 49.10 km, which is comparable to CMA-GFS (49.75 km) and HWRF (44.34 km), and substantially lower than the large AI model FuXi (120.44 km). When evaluating the oracle metric (best-of-K, K = 6) as an upper bound of coverage, the model achieves the smallest errors among all models at 6 h (24.04 km) and 12 h (55.81 km). In addition, the proposed model has advantages over CMA-GFS, HWRF, and FuXi in terms of computational resource consumption and hardware deployment cost. However, its mean track error increases more rapidly beyond 12 h, and at lead times of 18 h and 24 h the model is outperformed by HWRF, FuXi, and CMA-GFS, indicating that its current strength lies primarily in short-term prediction. Consequently, the practical utility of TCN-GAN-DM is currently demonstrated for 6–12 h TC track prediction, offering a new solution for disaster prevention and mitigation that balances accuracy and deployment cost at these specific time scales. Full article

Coastal typhoon deformation plays a critical role in determining typhoon tracks, intensity changes, precipitation and related flooding, storm surges, and typhoon waves, and thus is highly associated with coastal disaster patterns. This study proposes a three-level framework for typhoon wind field modeling through the integration of geometric characterization with physical-informed reconstruction. At its core, an elliptical fitting method is developed based on second-order moments to quantify the structural asymmetries. This geometric fitting method is incorporated into the reconstruction method of Holland–Miyazaki, creating a physically consistent model capable of simulating typhoon deformation processes during landfall. Validation through high-resolution Weather Research and Forecasting (WRF) simulations of Typhoon Chan-hom (2015) demonstrates the framework’s effectiveness, capturing elliptical eyewall deformation with aspect ratios exceeding 1.5, primarily driven by coastal topography and surface friction interactions. The method is further validated through Typhoon Mitag (2019), with mean wind component errors below 1 m/s, the average correlation coefficients surpassing 0.9, and wind direction mean absolute errors largely below 10°. This research provides a practical framework for quantifying and characterizing the wind field deformation during typhoon landfall in coastal regions, thereby supporting ther operational forecasting and disaster reduction in vulnerable coastal regions. Full article

Typhoon-induced landslides in coastal mountainous regions are controlled by the coupled effects of rainfall, wind, topography, and storm-track geometry. However, conventional static susceptibility models have limited ability to represent event-scale forcing under extreme weather conditions. This study develops a physics-guided dynamic landslide susceptibility framework and retrospectively applies it to the 2017 Tropical Cyclone Cempaka event in Pacitan Regency, Indonesia, where 743 landslides were identified. The framework integrates static terrain factors, antecedent wetness, event-scale rainfall accumulation and intensity, maximum wind speed, and a typhoon geometric exposure index derived from IBTrACS best-track information that represents track proximity, topographic shielding, rainfall-favored quadrant effects, and storm-motion effects. Under spatial block cross-validation, model performance improved progressively from the static baseline to the full-factor model, with the receiver operating characteristic area under the curve (ROC-AUC) increasing from 0.648 to 0.751, the precision–recall area under the curve (PR-AUC) reaching 0.826, and the F1-score reaching 0.744. The full-factor model also reduced missed landslide cases from 328 to 205 and concentrated predicted high-susceptibility zones along the typhoon exposure corridor. Additional parameter-sensitivity analyses further indicate that the event-based E_geo setting produced positive performance increments under the event-consistent quadrant convention. These results indicate that physically meaningful typhoon-exposure information can improve the spatial discrimination and interpretability of event-scale landslide susceptibility assessment. Full article

To achieve precise trajectory tracking for surface vessels subject to unknown dynamics, strict physical limitations, and external disturbances, this paper proposes an Adaptive Fixed-Time Control Framework that ensures a deterministic response under full constraints. First, navigation safety is guaranteed by employing a Barrier Lyapunov Function (BLF) to strictly confine vessel position states, enabling constrained position tracking without requiring prior knowledge of the desired trajectory. Second, addressing the input constraint aspect of the “full constraints” problem, a fixed-time auxiliary system is introduced to compensate for nonlinearities induced by actuator saturation, thereby maintaining control feasibility. Central to this framework is the realization of a deterministic response; by incorporating fixed-time convergence theory, the controller guarantees that velocity tracking errors converge within a predefined time bound independent of initial conditions. Furthermore, an RBF neural network combined with adaptive techniques is utilized to estimate unknown dynamics and external disturbance bounds online, enhancing robustness and safety in realistic marine environments. Full article

Centrifugal compressors are critical assets in the oil and gas, petrochemical, and power generation industries, where unplanned downtime results in severe economic and safety consequences. Despite the application of digital twin technology for predictive maintenance, existing approaches struggle to combine accurate degradation modeling with formal assurance evidence that regulators and operators demand before trusting machine learning-augmented systems. This paper proposes a hybrid digital twin framework whose architecture is structured around a formal safety case template, addressing both the accuracy and the trustworthiness challenges simultaneously. The methodology couples a first-principles thermodynamic model with a neural-network residual learner, and the complete system is organized through a design-stage safety case constructed in Goal Structuring Notation. The design stage identifies the requirements for operational deployment. Validation through a simulation study on a one-year synthetic operational dataset shows that the hybrid model reduces root-mean-square prediction error by over 50% for both pressure ratio and polytropic efficiency compared to the physics-only baseline. The anomaly detection module, presented here as a proof of concept, achieves 92% recall in identifying injected faults, and a composite health index tracks the progression of fouling, erosion, and seal wear over the simulated service life. This study is purely theoretical, with no experimental measurements conducted. It demonstrates the structural viability and coherence of the proposed framework within a controlled environment, providing a solid theoretical and computational foundation for future physical validation efforts. These findings provide preliminary evidence that embedding a structured safety argument into the design of a hybrid digital twin is technically feasible and beneficial for building the confidence needed to deploy such systems in safety-critical industrial environments. Full article

Accurate plant-level photovoltaic (PV) power estimation is important for performance monitoring, model benchmarking, and grid-integration studies. In bifacial horizontal single-axis tracking (HSAT) systems, this task is complicated by the coupled effects of front-side irradiance, rear-side irradiance, tracker position, and module temperature. This study proposes an algorithmic framework for same-time-step AC power estimation in a bifacial HSAT PV plant using field measurements of irradiance, tracker angle, module temperature, and inverter active power. The framework is not intended as an operational forecasting model because future irradiance and weather conditions are not predicted; instead, it evaluates how compact physics-based structure, interpretable nonlinear learning, and ensemble learning estimate measured AC power under nominal operating conditions. An empirical rear-to-front irradiance relationship was derived using solar-elevation bins and incorporated into a compact physics-based benchmark. This benchmark was compared with an additive Explainable Boosting Machine (EBM) and a Random Forest (RF) on a common test subset of 3916 observations. The physics-based model achieved an RMSE of 19.6 kW, an R² of 0.72, and an NRMSE of 0.38. The EBM improved these values to 17.09 kW, 0.786, and 0.334, respectively, while the RF achieved 15.96 kW, 0.814, and 0.312. Chronological validation showed weaker and more variable performance than randomized validation, indicating that temporal generalization remains challenging. Overall, the results support the use of interpretable PV-domain-guided learning as a transparent intermediate approach between compact physics-based modeling and more flexible ensemble regression. Full article

This study targets biochar utilization in seasonally frozen Northeast China and addresses the insufficient research on aging characteristics and mechanisms of low-temperature wheat straw biochar under long-term freeze–thaw stress. A 60-day simulated freeze–thaw test with 12 h −20 °C freezing and 12 h 0 °C thawing per daily cycle was carried out on 300 °C wheat straw biochar (B300). We tracked dynamic shifts in pH and water absorption during aging, and comprehensively characterized particle size, micromorphology, pore structure, elemental composition and surface functional groups for fresh (CK-B300) and fully aged (FC-B300) biochar. Freeze–thaw cycling caused drastic aging: the average particle size dropped by 33.09%, specific surface area increased by 13.86%, while total pore volume and average pore size fell by 31.47% and 54.9%, respectively. Freeze–thaw oxidation raised the O/C ratio and enriched -OH, C=O functional groups; biochar pH declined by 12.94% alongside improved water absorption. This study confirms that biochar aging is jointly controlled by ice-crystal physical fragmentation and water-temperature oxidation, providing basic data and theoretical support for evaluating and applying biochar in cold freeze–thaw zones. Full article

In the context of global climate warming, sea surface temperature (SST) rise and sea level (SL) rise are projected to amplify typhoon-related marine dynamic disaster risks. These are idealized sensitivity experiments designed to isolate the individual effects of SST warming and SL rise, not full climate projections. This study investigates Super Typhoon Doksuri (2023) using the WRF-SWAN-ROMS coupled model, with sensitivity experiments designed for SST (+0.8 °C, +2.0 °C, +3.5 °C) and SL rise (+0.4 m, +0.6 m, +0.8 m) scenarios referenced to IPCC AR6 projections. Results indicate that SST rise enhances typhoon intensity by approximately 16% at +3.5 °C, elevates mean wave height by 25.0%, and increases extreme significant wave height by 24.0%, with the extreme wave height sensitivity approximately 2.75 times that of the mean. Storm surge exhibits a nonlinear response, with the extreme surge sensitivity approximately 13.2 times that of the mean. SL rise has relatively minor effects on open sea areas but affects coastal regions notably, expanding the inundation area by approximately 47% under the 0.8 m scenario. The Taiwan Strait channeling effect amplifies wave heights and surges on the right side of the track. Comparative analysis suggests that SST indirectly amplifies disasters by enhancing typhoon intensity, while SL rise directly constrains nearshore dynamics through static water level elevation. These findings offer process-based insights into the contrasting physical mechanisms through which SST rise and SL rise affect coastal hazards in semi-enclosed regions and may inform future ensemble-based climate impact assessments. Full article

This paper investigates the output synchronization of fractional-order cyber-physical systems (FOCPSs) under communication constraints. To address limited bandwidth and high transmission costs, an event-triggered encoding-decoding sampled-data iterative learning control (ET-EDSDILC) protocol is proposed. The control law integrates a quantized sampling framework with an encoding–decoding mechanism to reconstruct control signals and address communication constraints. Furthermore, an event-triggered mechanism based on error energy attenuation (EEA) is developed to adjust communication frequency by monitoring error trends, thereby reducing unnecessary data transmissions. By applying fractional-order calculus and the contraction mapping principle, sufficient conditions for output synchronization are derived. Numerical simulations show that the proposed ET-EDSDILC framework reduces communication overhead and data redundancy while maintaining tracking performance, offering a solution for FOCPSs under communication constraints. Full article

Wavelength stability of distributed feedback (DFB) semiconductor lasers in dense wavelength division multiplexing (DWDM) systems hinges on sub-millikelvin temperature regulation, a task complicated by the nonlinear, multi-node dynamics of the thermoelectric cooler (TEC) and the purely reactive nature of conventional proportional–integral–derivative (PID) control. We present a physics-informed neural network (PINN) built around a residual correction architecture for hybrid feedforward–feedback TEC temperature control. Rather than penalizing physics-residual violations in the loss function, the architecture wires a simplified one-node thermal model directly into the network graph as a frozen baseline. A trainable branch then learns only the residual mismatch. Temporal lag features are appended to the input so that the network can reconstruct unmeasured internal thermal states from the cold-side temperature history, which proves essential for overcoming the partial-observability bottleneck inherent in multi-node TEC packages. Ablation experiments on a high-fidelity three-node TEC simulator show that all model variants (PINN, physics-feature-augmented NN, and pure NN) exceed R² = 0.993 when trained on the full dataset, yet the PINN’s advantage becomes pronounced under data scarcity. At a 3% training budget, it reaches R² = 0.966 versus 0.930 for the pure NN, implying an approximately 5.4× reduction in the data needed to reach a given accuracy target. In closed-loop validation, the PINN+PID hybrid settles 60% faster than standalone PID. Tracking RMSE drops by 69%, and peak disturbance deviation falls by 74%, across step, multi-setpoint, and current-perturbation scenarios. All results reported here are obtained in simulations. Experimental validation on physical DFB-TEC hardware is left to future work. Full article

Telemedicine is increasingly relevant in undergraduate medical education; however, most educational studies emphasize short-term interventions, learner satisfaction, or tele-Objective Structured Clinical Examination performance rather than evidence derived from sustained platform implementation. This multi-cohort longitudinal implementation study evaluated a multiplatform asynchronous telemedicine system integrated into simulation-based gynecology training across three consecutive academic periods at a medical simulation center in Ecuador. Platform-generated teleconsultation records were analyzed at the record level, with repeated records nested within student identifiers when students submitted more than one case. Because the expected number of submissions differed across cohorts as part of planned curricular refinements, cohort-level differences were interpreted descriptively as implementation and process indicators rather than as comparative evidence of learner performance. A total of 205 teleconsultation records from 95 student users were analyzed. Documentation quality was high for current illness documentation (98.5%), physical examination documentation (87.3%), and physiologically plausible vital signs (74.1%). Specialist responses were linked to 196/205 records (95.6%), with complete structured feedback among linked responses. Faculty expert review and learner-reported perceptions provided complementary educational evidence, including perceived usefulness of specialist feedback for gynecology learning. These findings support the feasibility of asynchronous telemedicine-supported simulation workflows and the value of platform-generated data for educational process evaluation, documentation monitoring, and feedback tracking, while not demonstrating individual competence improvement. Full article