Search Results (16,266)

Under global climate change, flood processes exhibit significant non-stationarity due to multiple driving factors, rendering traditional frequency analysis methods based on stationarity assumptions inadequate for accurate risk assessment. This study, focusing on the Kuitun River Basin and utilizing observed data from the Jiangjunmiao Hydrological Station (1959–2014), develops a joint design approach that addresses both non-stationarity and multivariate dependence. The approach integrates the Generalized Additive Model for Location, Scale, and Shape (GAMLSS) with copula functions and employs a parametric bootstrap to quantify the impacts of marginal parameter estimation and sample size uncertainty on design floods. The results indicate that flooding in the Kuitun River is influenced by precipitation, temperature, and snowmelt, with summer precipitation having the greatest impact. Marginal parameter uncertainty is significantly amplified at high return periods, and the confidence intervals of design values expand as the return period increases. In the joint framework, the OR criterion is more sensitive to parameter perturbations, with the 100-year flood peak and flood volume design values approximately 24.2% and 19.8% higher than those of the AND criterion, respectively. Increasing the sample size significantly reduces uncertainty; when the sample size increases from 56 to 500, the HDR area and confidence interval width decrease by approximately 60–70%, and the stability of joint flood design estimates improves significantly. The research findings can provide a scientific basis and technical support for flood analysis and risk management in the Kuitun River Basin under changing environmental conditions. Full article

Precise temperature control is crucial for maintaining product quality and optimizing energy efficiency in multi-zone continuous crystallizers. However, such industrial processes typically exhibit complex nonlinear dynamics and strong coupling effects. More critically, physical constraints often prevent sensor installation, rendering temperatures in key regions unobservable and challenging traditional closed-loop control strategies. To address partial observability and model uncertainty, this paper proposes a Model-Based Reinforcement Learning (MBRL) framework utilizing solely offline historical data. The core innovation lies in developing a Recursive State Space Model (RSSM) that serves not only as a high-fidelity digital twin but, more critically, is deployed as a real-time “virtual sensor” to infer unobservable system states. This virtual sensing capability provides precise state estimates for downstream policy optimization. Additionally, a multi-objective reward function is designed to balance tracking error, stability, and control cost. Experimental results demonstrate that the proposed virtual sensor exhibits exceptional long-term stability, maintaining high fidelity and effectively suppressing error accumulation during long-term multi-step autoregressive predictions. Consequently, the trained agent outperforms traditional Proportional-Integral-Derivative (PID) and Model Predictive Control (MPC) controllers, achieving over 67% improvement in temperature tracking accuracy while reducing control action costs by more than 93%, indicating smoother system operation and enhanced energy efficiency. Full article

Respiratory syncytial virus (RSV) is a leading cause of respiratory infections in infants and older adults, with epidemiological patterns shaped by viral evolution and diversity. To investigate the molecular epidemiology of RSV before and after the COVID-19 pandemic, we conducted genomic surveillance and phylodynamic analyses of RSV-A and RSV-B circulating in Maryland from 2018 to 2024. Whole-genome sequencing of RSV-positive samples (n = 451) was performed, and genomes were analyzed with phylogenetic and Bayesian methods to estimate evolutionary rates, population dynamics, selection pressures, and genetic diversity. RSV-A predominated in most seasons, while RSV-B showed episodic surges in 2018 and 2023. All RSV-A genomes belonged to the ON1 genotype, and RSV-B belonged to BA9, with sequential clade dominances including A.D.1, A.D.5.2, A.D.1.6, and B.D.E.1 across different epidemic seasons in Maryland. Bayesian analyses estimated evolutionary rates of 7.07 × 10⁻⁴ substitutions/site/year for RSV-A and 1.02 × 10⁻³ substitutions/site/year for RSV-B and temporal fluctuations in effective population size linked to pandemic-related disruptions. RSV-A displayed greater overall entropy, yet RSV-B evolved slightly faster. Genetic variability was concentrated in the G glycoprotein, with positively selected sites at codon 273 (RSV-A) and codon 217 (RSV-B). These findings demonstrate temporal fluctuations in RSV-A and RSV-B predominance, clade replacement, and ongoing viral adaptation throughout the COVID-19 era, underscoring the importance of integrated genomic and phylodynamic studies. Full article

The proliferation of Unmanned Aerial Vehicles (UAVs) poses a significant challenge for ISAR imaging. Conventional multi-target imaging methods, such as sequential CLEAN-based techniques, are often hindered by error propagation and sensitivity to noise, leading to degraded performance or even imaging failure, especially at low SNR. To address these issues, this paper proposes a novel robust imaging framework. The framework is built upon two key innovations: a partitioned block-wise compensation mechanism integrated with PSO for simultaneous and precise motion parameters estimation of multiple targets, which avoids local optima and error accumulation; and the application of Sequential Variational Mode Decomposition (SVMD) to adaptively separate and reconstruct signals, thereby suppressing inter-target aliasing and noise interference overlooked in prior studies. Simulations and measured-data experiments confirm that the proposed method maintains clear focusing and superior image quality even at low SNR, outperforming existing techniques in terms of image entropy, contrast, and resolution. This paper provides a robust and effective solution for high-resolution radar surveillance in complex multi-target scenarios. Full article

The rapid expansion of wind energy has increased the operational complexity of wind turbines, where component degradation, environmental variability, and maintenance decisions are tightly coupled. Artificial intelligence (AI) has been widely applied to support fault detection and operation and maintenance (O&M), yet many existing studies remain fragmented and insufficiently address practical challenges such as heterogeneous data, sparse fault labels, and cross-site generalization. This review provides an engineering-oriented synthesis of AI-based methods for wind turbine fault detection and O&M, focusing on drivetrain diagnostics as a representative application. The literature is organized along an end-to-end O&M workflow, including SCADA-based condition monitoring, component-level fault diagnosis, health assessment and remaining useful life estimation, multi-modal blade inspection, and DT (Digital Twin) integration. Traditional ML (machine learning), ensemble methods, deep learning, physics-informed learning, and transfer learning are reviewed with respect to their data requirements, operational assumptions, and deployment constraints. Beyond algorithmic performance, this review discusses data governance, alarm design, model updating, and interpretability, and summarizes public datasets and emerging data resources. The aim is to bridge methodological advances and practical O&M requirements, supporting reliable and deployable AI applications in wind energy systems. Full article

Accurate spatial–temporal crime prediction is a critical component of proactive public safety governance, yet it remains challenging due to complex dependency structures and severe data sparsity in real-world crime datasets. Most existing methods either focus on local spatial–temporal correlations or attempt to model global dependencies at fine-grained region levels, which limits their robustness under highly sparse and imbalanced crime distributions. In this paper, we propose GL-MoPA, a global–local modulated prototype attention framework for city-scale crime prediction. GL-MoPA integrates three key components. First, a local dependency modeling module is designed to capture fine-grained spatial and short-term temporal patterns. Second, a prototype-aware global attention mechanism aggregates region-level representations into semantically meaningful prototypes to efficiently model long-range dependencies. Third, a two-stage occurrence-aware prediction strategy decouples crime occurrence estimation from intensity regression to explicitly address data sparsity. We evaluate GL-MoPA on a real-world crime dataset from New York City covering four major crime categories. The experimental results show that GL-MoPA achieves state-of-the-art performance, consistently outperforming both classical statistical models and recent deep learning baselines. In particular, a robustness analysis shows substantial error reductions in sparse regions, while ablation studies reveal the complementary roles of individual model components. These results indicate that GL-MoPA provides an effective and robust solution for spatial–temporal crime forecasting under sparse-data scenarios. Full article

Accurate picking of microseismic P-wave arrival times is essential for the localization and monitoring of mining-induced seismic events. Conventional Short-Term Average/Long-Term Average (STA/LTA) detectors, while computationally efficient, are highly susceptible to noise interference. Conversely, deep learning approaches exhibit superior noise robustness but often involve substantial computational redundancy and compromised real-time performance. To address these limitations, we propose a novel two-stage picking framework that integrates STA/LTA with a lightweight U-Net, enabling rapid preliminary detection followed by fine-grained refinement. In the first stage, STA/LTA rapidly scans continuous waveforms to identify candidate windows potentially containing P-wave arrivals. In the second stage, a lightweight U-Net performs sample-level regression within each candidate window to refine arrival-time estimates with high precision. This coarse-to-fine paradigm effectively balances computational efficiency and picking accuracy. Experimental validation on 500 Hz microseismic data acquired from a coal mine in Gansu Province demonstrates that the proposed method achieves a hit rate of 63.21% within a tolerance window of ±0.01 s. This represents performance improvements of 25.42% and 40.47% over convolutional neural network (CNN) and STA/LTA methods, respectively, while reducing the mean absolute error to 0.0130 s. Furthermore, the model exhibits consistent performance on independent test sets, confirming its generalization capability and noise robustness. By combining the computational efficiency of STA/LTA with the representational power of deep learning, the proposed approach demonstrates significant potential for real-time industrial deployment. Full article

UAVs are widely used for all-weather, round-the-clock security inspections in urban and industrial areas. However, pure visible-light systems fail at night or in adverse weather conditions, while pure infrared methods are limited by thermal noise, low spatial resolutions, and high false alarm rates. Multispectral images render the task of object detection highly reliable and robust by providing complementary target feature information. This study suggests a frequency-based cross-attention transformer (FCAT) for multispectral object detection as a solution to this issue. This approach collects cross-modal complementary characteristics, effectively learns and integrates global contextual information via the cross-attention mechanism, and greatly increases multispectral object detection accuracy. At the same time, spatial-domain features are mapped to the frequency domain via the Fourier transform, and the scaled dot product attention is estimated via element-wise product operations, which break through the limitation of traditional spatial-domain matrix multiplication and effectively reduce the computational cost of the model. Additionally, this study independently builds a multi-scene multi-time climate visible–infrared dataset (OPVM-VIRD), which contains 20,025 target instances, to address the issue of the lack of all-weather cross-spectral data in object detection tasks from the perspective of UAVs. Experimental findings from the OPVM-VIRD, M3FD, and FLIR datasets demonstrate that our proposed approach outperforms prevailing state-of-the-art multispectral object detection algorithms on public benchmarks, while the FCAT model achieves an mAP50 score of 94.7% on our custom-built dataset—10.8% higher than ICAF. At the same time, the number of FCAT parameters is 85.26 M, which is significantly lower than that of mainstream models, such as ICAF. Therefore, the FCAT is a change detection strategy with strong model generalization abilities, and it has important application value in the all-day and all-weather security patrol of cities and industrial parks carried out by UAVs. Full article

Object pose estimation is fundamental for robotic manipulation, autonomous driving, and augmented reality, yet recovering the full 9-DoF state (rotation, translation, and anisotropic 3D scale) from RGB-D observations remains challenging for previously unseen objects. Existing methods either rely on instance-specific CAD models, predefined category boundaries, or suffer from scale ambiguity under sparse observations. We propose ProM-Pose, a unified cross-modal temporal perception framework for zero-shot 9-DoF object pose estimation. By integrating language-conditioned generative 3D shape priors as canonical geometric references, an asymmetric cross-modal attention mechanism for spatially aware fusion, and a decoupled pose decoding strategy with temporal refinement, ProM-Pose constructs metrically consistent and semantically grounded representations without relying on category-specific pose priors or instance-level CAD supervision. Extensive experiments on CAMERA25 and REAL275 benchmarks demonstrate that ProM-Pose achieves competitive or superior performance compared to category-level methods, with mAP of $75.0\%$ at $5^{°},2\mathrm{cm}$ and $90.5\%$ at ${10}^{°},5\mathrm{cm}$ on CAMERA25, and $42.2\%$ at $5^{°},2\mathrm{cm}$ and $76.0\%$ at ${10}^{°},5\mathrm{cm}$ on REAL275 under zero-shot cross-domain evaluation. Qualitative results on real-world logistics scenarios further validate temporal stability and robustness under occlusion and lighting variations. ProM-Pose effectively bridges semantic grounding and metric geometric reasoning within a unified formulation, enabling stable and scale-aware 9-DoF pose estimation for previously unseen objects under open-vocabulary conditions. Full article

Motor temperature prediction is critical for ensuring the reliability and safe operation of high-power-density electric drives. Since direct measurement of internal temperatures, especially rotor and magnet temperatures, is often impractical, virtual sensing has become an important research direction. This review provides a structured clarification of motor temperature prediction technologies. First, the physical foundations of motor thermal behavior are revisited, emphasizing multi-source loss generation, electro-thermal coupling mechanisms, and the dominant influence of time-varying boundary conditions. Second, existing estimation methodologies are systematically categorized into physics-based thermal models, observer- and identification-based approaches, and data-driven machine learning frameworks. Their mathematical principles, information bottlenecks, computational trade-offs, and deployment constraints are comparatively analyzed. Particular attention is given to hybrid and physics-informed methods, where reduced-order thermal networks, parameter adaptation, and learning-based residual correction are integrated to enhance robustness. Future developments should focus on uncertainty-aware estimation, lifecycle-adaptive modeling, and reliable temperature field inference under sparse sensing conditions. Full article

Forestry, nursery, and planting tasks involve repetitive trunk flexion, squatting, and kneeling, as well as manual handling, increasing musculoskeletal load, and the need for mobility-related safety measures. Passive exoskeletons could mitigate postural exposure and reduce the overall body workload. We conducted a preliminary study (n = 14) to test the feasibility of a protocol and estimated model- and task-specific trends during standardized simulated nursery activities in a laboratory setting. Participants simulated planting and seeding tasks (loads of 0.5–2 kg) and material handling and preparation tasks (loads of 5–15 kg) without an exoskeleton (No-EXO) and with three passive models (EXO 1–EXO 3). EXO 3 was excluded from the planting tasks for feasibility reasons. Whole-body kinematics were recorded using an IMU-based motion capture system and converted into time-based ergonomic exposure outcomes (OWAS and RULA). Physiological load was monitored via heart-rate (HR) measurements. Compared to the No-EXO condition, exoskeleton use shifted posture exposure towards lower-risk categories. The largest improvements were observed with EXO 2 and EXO 3 during material handling (OWAS: −18%/−20%; RULA action-level reduction: −25%/−39%) and with EXO 2 during planting/seeding (OWAS: −15%; RULA: −26%). HR_max did not increase across tasks or conditions and HR tended not to rise with higher workload when exoskeletons were used. Overall, the results suggest positive ergonomic and workload trends related to the model and tasks. Field validation on uneven terrain with full personal protective equipment and harness integration is needed to confirm usability and support and to define implementation requirements (fit, compatibility with PPE, and safe-use conditions). Full article

Understanding the motion parameters of floating ice is very important for characterizing the ice water dynamics of rivers during freezing periods. Due to the low spatiotemporal resolution of satellite images, limited observation range of unmanned aerial vehicles, and deformation of shore-based camera images, it is difficult to simultaneously quantify the translational and rotational motion characteristics of floating ice and long-distance transportation. This study used the unmanned aerial vehicle GPS joint observation method to observe and obtain various motion parameters such as local translation, rotation, and long-distance transportation in the curved section of the upper reaches of the Yellow River and the straight section of the middle reaches of the Yellow River during the winter of 2024–2025 under conditions of ice density of 50–90%. The velocity field obtained by the drone shows an average ice velocity of 1.27 m/s at the bend and 1.18 m/s in the straight section, with lateral velocity gradients of −0.245 to 0.050 s⁻¹ and −0.141 to 0.222 s⁻¹, respectively. The angular velocity of a single floating ice block is 0.008–0.016 rad/s at bends and 0.010–0.036 rad/s in straight sections. The angular velocity is positively correlated with the local shear strength, and the rotation direction is consistent with the sign of the velocity gradient. GPS tracking provides long-distance transportation trajectories, and the average difference between the speeds obtained by GPS and drones is 0.10 m/s, confirming the reliability of speed estimation based on drones. These results indicate that integrated unmanned aerial vehicle GPS observation can quantitatively characterize local floating ice movement and long-distance floating ice transport behavior, providing on-site parameters for river ice water dynamics research and hazard assessment, and has the potential to be applied to rivers in other cold regions. Full article

Sustainability monitoring relies on environmental indicator systems that integrate heterogeneous multivariate measurements across space and time; however, collinearity, non-Gaussian variability, and influential observations frequently destabilize classical multiblock methods and may bias indicator-based assessment and decision support. This study proposes a robust extension of the STATICO (STATIS–CO-inertia) framework to model common structures among paired environmental indicator blocks under realistic data contamination. The approach preserves the original triadic algebraic formulation while incorporating robust covariance estimation and adaptive weighting to reduce the influence of outliers and structurally unstable blocks. Robustification is implemented at the interstructure stage through a reformulated Escoufier’s RV coefficient and in the construction of the compromise space via robust distances. The RV coefficient, a multivariate generalization of the squared Pearson correlation computed between cross-product matrices, is used to quantify structural similarity between paired data blocks and to evaluate the stability of the compromise structure. Performance is evaluated using simulated datasets calibrated to represent Ecuadorian coastal monitoring conditions. The results show that Robust STATICO increases compromise dominance and stability, redistributes inter-block similarities more coherently, and improves discriminative representation in the factorial space, yielding more interpretable and environmentally plausible structures. Overall, the proposed method provides a reliable analytical tool for sustainability-oriented environmental monitoring by supporting stable identification of persistent multivariate patterns and robust comparison of indicator structures in complex systems. Full article

This paper proposes an advanced sensorless control strategy for Permanent Magnet Synchronous Motors (PMSMs) aimed at enhancing dynamic performance, robustness, and reliability while eliminating the need for mechanical sensors. The core contribution lies in a novel hybrid speed regulation framework that combines a terminal sliding mode control scheme with a high-order super-twisting algorithm (HOT-STA-SMC), ensuring finite-time convergence, effective chattering suppression, and strong disturbance rejection under varying operating conditions. For the inner current loop, an Exponential Reaching Law Sliding Mode Controller (ERL-SMC) is implemented to guarantee fast current response and precise current tracking, even in the presence of parameter uncertainties. Furthermore, the conventional Model Reference Adaptive System (MRAS) observer is embedded within the proposed control architecture, resulting in more accurate speed estimation and enhanced stability during load fluctuations. The complete control system is rigorously modeled and tested in MATLAB R2024b/Simulink, capturing the full interaction between machine dynamics, control loops, and observer mechanisms. The simulation results verify that the proposed design achieves superior torque smoothness, minimal current ripples, and fast transient response compared to conventional sensorless methods. By integrating high-order sliding modes with advanced adaptive observation, this work offers a robust and cost-effective solution for high-performance PMSM drives, suitable for demanding applications such as electric vehicles, renewable energy conversion, and industrial motion control. Full article

Fiscal and tax new media has emerged as a new channel for government-enterprise engagement, linking policy communication with firms’ sustainability-oriented decisions. This study hand-collects the launch status of official microblog accounts for finance and taxation departments in China’s prefecture-level cities. This paper combines these data with firm-level observations on China’s green enterprises from 2008 to 2022, and clearly defines the sample of green enterprises. Defining the sustainable spirit among green entrepreneurs from the perspective of entrepreneurship and innovation. This is to estimate how government communication and policy signaling shape firms’ sustainability-oriented behavior. Treating the introduction of official fiscal and tax new media as a quasi-natural experiment, we apply a staggered difference-in-differences design to identify its effect on green entrepreneurs’ sustainable spirit. The study finds that launching official fiscal and tax new media significantly stimulates the sustainable spirit of green entrepreneurs. Mechanism tests suggest that the effect operates through improvements in information infrastructure and governance capacity, including higher internet penetration, reduced fiscal and tax irregularities, and stronger digital governance. Particularly in regions with weaker government–business relations, more integrated administrative systems, lower fiscal pressure, and higher government subsidies, the promoting effect is more significant. Overall, the findings offer policy implications for strengthening the effectiveness of public digital communication and for fostering green entrepreneurs’ sustainable spirit. Full article