Search Results (343)

Rigid-body chain following on piecewise analytic paths is a fundamental subroutine in motion planning and multibody simulation. The problem is nontrivial when only the leader trajectory of the first node is available: enforcing fixed inter-node distances reduces to circle–curve intersection, which is generally multi-valued and becomes particularly challenging near non-smooth junctions. We present a Dichotomy Geometric Path Search (DGPS) framework that converts each constraint into a one-dimensional root-finding task and resolves the branch selection through no-backtracking ordering: at every time step, the admissible solution for the current node is the nearest feasible root in the past relative to its immediately preceding node. DGPS combines backward bracketing with bisection, achieving robust convergence. Compared with the inverse Jacobian method, which maps end-effector velocities to joint velocities via explicit inversion, the proposed approach avoids Jacobian inversion and globally coupled nonlinear solves. We further characterize the local structure of the zero set and establish monotonicity/uniqueness conditions that justify stable root selection across piecewise junctions. Extensive tests on representative piecewise trajectories (line–arc–line, polylines with corners, piecewise sinusoids, and time reparameterization) show that DGPS enforces distance constraints to near machine precision, produces interpretable speed/acceleration transients around non-smooth events, and exhibits computational costs consistent with iteration difficulty. The results support DGPS as a general, efficient solver requiring only the prescribed leader trajectory. Full article

This study investigates the vertical variation and temporal characteristics and indicates the sources of black carbon (BC) and brown carbon (BrC) within particulate matter fraction PM₁ during light (November–December 2024) and heavy (January–February 2025) haze episodes in Bangkok, Thailand, a topic where data are still limited data regarding Southeast Asian megacities. Continuous measurements were conducted at 30 and 110 m above ground level, together with particle size distribution measurement, micrometeorological observations, and backward air mass trajectory analysis. During the haze periods, the highest particle number concentrations occurred in the 0.3–0.4 µm size range, indicating dominant contributions from combustion-related emissions and secondary aerosol formation. Mean PM₁ mass concentrations during the heavy haze episodes were more than 2.5 times higher than those during light haze. BC concentrations increased substantially during heavy haze, while the BC fraction of PM₁ remained relatively constant (~10%). In contrast, the BrC fraction reached nearly 20%, reflecting an increasing influence of biomass burning emissions associated with regional transport. Combined analyses of BC/BrC relationships, wind-direction dependence, and air mass trajectories demonstrate mixed contributions from local fossil fuel combustion and long-range transport of biomass burning aerosols during severe haze events. Full article

This study addresses the problem of optimal resource allocation in nonlinear multi-module dynamic systems arising in complex computational and techno-economic processes, where numerical stability and strict enforcement of structural constraints are critical. The objective is to develop a computationally efficient optimal control algorithm capable of handling bilateral control constraints and external balance conditions without resorting to large-scale nonlinear programming or boundary-value shooting. The proposed method is based on a modified Lagrangian formulation, in which bilateral Karush–Kuhn–Tucker (KKT) conditions are analytically embedded into the optimality system. The resulting computational scheme consists of a coupled system of matrix and vector differential equations solved through a non-iterative backward–forward integration procedure. Numerical experiments conducted on a nonlinear model with Cobb–Douglas-type operators demonstrate the stable convergence of the trajectories toward a stationary regime, strict satisfaction of bilateral constraints, and consistent enforcement of balance relations throughout the planning horizon. Empirical scalability analysis indicates approximately cubic computational complexity with respect to the state dimension, while sensitivity tests confirm the numerical robustness across different integration tolerances and ODE solvers. These results demonstrate that the proposed structure-preserving framework provides a computationally stable and practically implementable approach to constrained optimal control in nonlinear multi-module systems. Full article

In the Yingjiang area of western Yunnan, precipitation is high throughout the year, making it one of the regions with the highest annual precipitation in mainland China. Extreme rainfall in this region often triggers severe flooding, yet the key mechanism of water vapor transport underlying abnormally heavy precipitation remains unclear. This study used automatic weather station observations of precipitation, the fifth-generation atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts, and Global Data Assimilation System (GDAS) data to analyze, for the first time, large-scale water vapor transport, precipitation mechanisms, and the primary water vapor sources and their contributions in this region. The results show the following: In the Yingjiang area, the water vapor sources at all height levels in summer are dominated by the southwest monsoon water vapor transport pathways, such as the Bay of Bengal and the Arabian Sea, with their total contributions to specific humidity and water vapor flux exceeding 70%. This indicates that low-latitude sea areas such as the Bay of Bengal and the Arabian Sea serve as key moisture source regions for Yingjiang in the global water vapor cycle. Water vapor transport over the windward slope causes strong low-level convergence and high-level divergence phenomena, and the suction effect leads to strong upward motion near the 850 hPa level. The pseudo-equivalent potential temperature isolines tilt along the mountain slope, maintaining an unstable stratification characterized by warm, humid lower layers and cold, dry upper layers, providing favorable thermal conditions for precipitation. In addition, in the summer of 2020, abnormally high southwest seasonal wind and air transport, combined with strong low-level convergence and high-level divergence of the vertical circulation structure, were key factors causing the abnormally high precipitation. This study provides an important reference for the prediction of extreme precipitation and the early warning of rainstorm disasters in the southwest monsoon region in the context of global climate change. Full article

To investigate the heat-generation mechanisms of journal bearings under different whirl motion and to clarify the corresponding temperature distribution characteristics, a computational fluid dynamics-based method was developed. The model incorporates temperature-dependent lubricant viscosity and employs an unsteady dynamic-mesh updating approach based on structured grids, enabling the automatic iterative tracking of the journal center during whirl motion. A thermal-effect analysis model that accounts for journal whirl trajectories was thereby established. The whirl orbit shape is characterized using elliptical eccentricity, and the effects of whirl direction, elliptical eccentricity, and whirl frequency on the circumferential temperature and pressure distributions of the journal are examined. Results show that under forward whirl, increasing whirl frequency and elliptical eccentricity initially enhances and then weakens local hydrodynamic pressure and viscous shear dissipation in the oil-film convergent region, producing pronounced first-order circumferential temperature nonuniformity and a high risk of thermal bending at intermediate frequencies. Under backward whirl, hydrodynamic effects are reduced and heat generation shifts from localized concentration to global shear dissipation, forming a relatively uniform second-order circumferential temperature field. Increasing elliptical eccentricity causes the whirl orbit to become more linear, improving load-carrying capacity and heat-transfer performance and thereby mitigating thermally induced vibration and oil-film whirl instability. Full article

Estimating the remaining useful life (RUL) of engineering systems is crucial for maintenance planning and the reliability of complex mechanical units. Accurate RUL predictions support timely interventions and help to prevent unexpected failures. This study proposes a statistically robust framework that models degradation signals up to the end of life using a hidden Markov model (HMM) with a simple-failure structure and an absorbing terminal state. The proposed method estimates state-dependent linear emission parameters and transition probabilities using a ridge-regularized expectation–maximization (EM) algorithm. The ridge penalty stabilizes slope estimates under limited data, while a robust Huber-based scale estimator reduces sensitivity to outliers in the sensor-derived health indicator. RUL is computed as a weighted expected time to absorption, combining transient-state survival characteristics with smoothed posterior-state probabilities obtained via the forward–backward algorithm. This yields a low-variance state-aware estimator that preserves the probabilistic structure of the HMM. Simulation studies show that the proposed ridge-regularized EM significantly reduces parameter variance and improves predictive accuracy compared with the baseline weighted least squares EM (WLS-EM). A real-data case analysis demonstrates further improvements in RUL estimation accuracy and smoother, more reliable prediction trajectories. Overall, the framework provides a robust and interpretable approach for practical prognostics applications. Full article

In complex urban traffic scenes, reliable pedestrian trajectory prediction is essential for Automated and Connected Electric Vehicles (ACEVs) and active safety systems. Despite recent progress, many existing approaches still suffer from limited long-term prediction accuracy, redundant temporal features, and high computational cost, which restricts their deployment on vehicles with constrained onboard resources. To address these issues, this paper presents a lightweight trajectory prediction framework named BiTraP-DGF. The model adopts parallel Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) temporal encoders to extract motion information at different time scales, allowing both short-term motion changes and longer-term movement tendencies to be captured from observed trajectories. A conditional variational autoencoder (CVAE) with a bidirectional GRU decoder is further employed to model multimodal uncertainty, where forward prediction is combined with backward goal estimation to guide trajectory generation. In addition, a gated sparse attention mechanism is introduced to suppress irrelevant temporal responses and focus on informative time segments, thereby reducing unnecessary computation. Experimental results on the JAAD dataset show that BiTraP-DGF consistently outperforms the BiTraP-NP baseline. For a prediction horizon of 1.5 s, CADE is reduced by 20.9% and CFDE by 22.8%. These results indicate that the proposed framework achieves a practical balance between prediction accuracy and computational efficiency for autonomous driving applications. Full article

Drone localization is essential for various purposes such as navigation, autonomous flight, and object tracking. However, this task is challenging when satellite signals are unavailable. This paper addresses database-free vision-only localization of flying drones using optimal window template matching and velocity fusion. Assuming the ground is flat, multiple optimal windows are derived from a piecewise linear segment (regression) model of the image-to-real world conversion function. The optimal window is used as a fixed region template to estimate the instantaneous velocity of the drone. The multiple velocities obtained from multiple optimal windows are integrated by a hybrid fusion rule: a weighted average for lateral (sideways) velocities, and a winner-take-all decision for longitudinal velocities. In the experiments, a drone performed a total of six medium-range (800 m to 2 km round trip) and high-speed (up to 14 m/s) maneuvering flights in rural and urban areas. The flight maneuvers include forward-backward, zigzags, and banked turns. Performance was evaluated by root mean squared error (RMSE) and drift error of the GNSS-derived ground-truth trajectories and rigid-body rotated vision-only trajectories. Four fusion rules (simple average, weighted average, winner-take-all, hybrid fusion) were evaluated, and the hybrid fusion rule performed the best. The proposed video stream-based method has been shown to achieve flight errors ranging from a few meters to tens of meters, which corresponds to a few percent of the flight length. Full article

To investigate the seasonal characteristics, sources, and regional transport patterns of precipitation components in the high-elevation mountainous regions, field sampling was conducted at Mt. Heng (Hunan, South China) from June 2021 to May 2022. In total, 114 precipitation samples were collected and subjected to chemical analysis, including pH, major inorganic ions, and heavy metals. During the study period, the precipitation at Mt. Heng was generally weakly acidic. The concentrations of metals and acidic anions (NO₃⁻ and SO₄²⁻) were higher in the winter and lower in the summer, whereas the concentration of the primary neutralizing cation, NH₄⁺, peaked during the summer. An association was observed between precipitation pH and metal concentrations, whereby acidic precipitation samples exhibited marginally elevated metal concentrations overall. An additional analysis of winter precipitation chemistry at Mt. Heng revealed an increasing trend of ions from 2015 to 2018, followed by a decrease from 2019 to 2021. This trend coincided with the concentrations of NO₂ and SO₂ in the surrounding cities, reflecting the results of clean air actions. The results of the source analysis revealed five major sources: secondary sources (41.5%), coal combustion (24.7%), a mixed source of biomass burning and aged sea salt (11.6%), dust (10.8%), and industrial emissions (11.4%). Backward trajectory cluster analysis revealed that air masses originating from the northern regions were generally more polluted than those from the southern regions. This study provides fundamental data and scientific support for regional atmospheric pollution control and ecological protection in South China. Full article

This study proposes a novel framework for allocating CO₂ emission rights within the Yangtze River Delta (YRD) urban agglomeration, tackling the inter-city CO₂ transmission dynamics frequently neglected in conventional allocation models. Current emission allocation methods fail to capture the spatial spillover effects of CO₂ emissions driven by atmospheric transport, resulting in potential inequities. Leveraging the WRF model to simulate carbon emissions across 27 cities, we develop a two-stage network Data Envelopment Analysis (DEA) model that integrates both emission generation and governance capacities. Our findings highlight significant inter-city CO₂ transmission, with the wind direction and speed playing a pivotal role in emissions spread. In contrast to traditional models, our approach considers the regional interdependence of emissions, enhancing both fairness and efficiency in the allocation process. The results indicate that cities with stronger governance systems, including green technology investments and effective air quality management, are rewarded with higher carbon allowances. Moreover, our model demonstrates that policies prioritizing environmental governance over raw emission levels can foster long-term sustainability. This work provides a comprehensive methodology for achieving a balanced allocation of emission rights that integrates economic growth, environmental management, and equity considerations within complex urban agglomerations. Full article

Background: Artificial intelligence (AI) is increasingly used in intensive care units (ICUs) to enable personalized care, real-time analytics, and decision support. Nutritional therapy—a major determinant of ICU outcomes—often remains delayed or non-individualized. Objective: This study aimed to review current and emerging AI applications in ICU nutrition, highlighting clinical potential, implementation barriers, and ethical considerations. Methods: A narrative review of English-language literature (January 2018–November 2025) searched in PubMed/MEDLINE, Scopus, and Web of Science, complemented by a pragmatic Google Scholar sweep and backward/forward citation tracking, was conducted. We focused on machine learning (ML), deep learning (DL), natural language processing (NLP), and reinforcement learning (RL) applications for energy/protein estimation, feeding tolerance prediction, complication prevention, and adaptive decision support in critical-care nutrition. Results: AI models can estimate energy/protein needs, optimize EN/PN initiation and composition, predict gastrointestinal (GI) intolerance and metabolic complications, and adapt therapy in real time. Reinforcement learning (RL) and multi-omics integration enable precision nutrition by leveraging longitudinal physiology and biomarker trajectories. Key barriers are data quality/standardization, interoperability, model interpretability, staff training, and governance (privacy, fairness, accountability). Conclusions: With high-quality data, robust oversight, and clinician education, AI can complement human expertise to deliver safer, more targeted ICU nutrition. Implementation should prioritize transparency, equity, and workflow integration. Full article

Accurately forecasting the operating temperature of lithium-ion batteries (LIBs) is essential for preventing thermal runaway, extending service life, and ensuring the safe operation of electric vehicles and stationary energy-storage systems. This work introduces a unified, physics-informed, and data-driven temperature-prediction framework that integrates mathematically governed preprocessing, electrothermal decomposition, and sequential deep learning architectures. The methodology systematically applies the governing relations to convert raw temperature measurements into trend, seasonal, and residual components, thereby isolating long-term thermal accumulation, reversible entropy-driven oscillations, and irreversible resistive heating. These physically interpretable signatures serve as structured inputs to machine learning and deep learning models trained on temporally segmented temperature sequences. Among all evaluated predictors, the Bidirectional Long Short-Term Memory (BiLSTM) network achieved the highest prediction fidelity, yielding an RMSE of 0.018 °C, a 35.7% improvement over the conventional Long Short-Term Memory (LSTM) (RMSE = 0.028 °C) due to its ability to simultaneously encode forward and backward temporal dependencies inherent in cyclic electrochemical operation. While CatBoost exhibited the strongest performance among classical regressors (RMSE = 0.022 °C), outperforming Random Forest, Gradient Boosting, Support Vector Regression, XGBoost, and LightGBM, it remained inferior to BiLSTM because it lacks the capacity to represent bidirectional electrothermal dynamics. This performance hierarchy confirms that LIB thermal evolution is not dictated solely by historical load sequences; it also depends on forthcoming cycling patterns and entropic interactions, which unidirectional and memoryless models cannot capture. The resulting hybrid physics-data-driven framework provides a reliable surrogate for real-time LIB thermal estimation and can be directly embedded within BMS to enable proactive intervention strategies such as predictive cooling activation, current derating, and early detection of hazardous thermal conditions. By coupling physics-based decomposition with deep sequential learning, this study establishes a validated foundation for next-generation LIB thermal-management platforms and identifies a clear trajectory for future work extending the methodology to module- and pack-level systems suitable for industrial deployment. Full article

Southwestern China serves as a critical region for carbon sources and sinks, influenced by both natural ecosystems and anthropogenic activities. The Shangri-La atmospheric background station (28.01° N, 99.73° E), the only regional station in southwestern China, provides essential data for understanding CO₂ dynamics. This study analyzes hourly CO₂ mole fractions from 2019 to 2022. Background signals were extracted using the Robust Extraction of Baseline Signal (REBS) algorithm, and air-mass trajectories were analyzed using HYSPLIT model and Potential Source Contribution Function (PSCF) and Concentration Weighted Trajectory (CWT) methods. The REBS-derived background CO₂ concentration increased from ~409 ppm in 2019 to ~417 ppm in 2022, yielding a growth rate of 1.9 ± 0.1 ppm yr⁻¹, slightly lower than the 2010–2014 rate reported previously and consistent with the recent global slowdown associated with ENSO-driven carbon–climate variability. A coherent seasonal cycle, with spring maxima and late-summer minima, reflects the combined influence of biospheric uptake and monsoonal inflow. Comparison with the global marine boundary layer and Waliguan records shows similar phase and amplitude, confirming the representativeness of Shangri-La as a regional background site, albeit with a one-month phase lag to Waliguan station due to regional climatic and phenological differences. Trajectory and wind analyses identify southern Indo-Myanmar and Sichuan–Yunnan regions as major transport corridors influencing high-CO₂ events. Overall, the results highlight that regional transport rather than local emissions dominates CO₂ variability at Shangri-La. The derived background and transport signals thus provide an updated and internally consistent characterization of carbon-cycle variability over the southeastern Tibetan Plateau, offering critical observational support for future regional carbon budget assessments. Full article

Heavy snowfall events in the Ural region have drawn significant attention due to their substantial frequency, the region’s relatively high population density and its developed network of roads and power lines. This study summarizes the main characteristics of the hazardous heavy snowfall (HHS) events (≥20 mm 12 h⁻¹) that have occurred in the Ural region between 1981 and 2025, as well as in related synoptic-scale environments, for the first time. The dataset consists of 116 HHS reports, with 12-hourly snowfall intensities ranging from 20 mm to 47.6 mm. The main characteristics of these events (snowfall amount, spatial distribution, inter-annual and seasonal variability and trends, associated weather phenomena, and related damage) are examined based on the data from weather stations, the ERA5 reanalysis, scientific literature, and media reports. While there is no statistically significant trend in HHS events, the frequency of the most damaging late spring and early autumn snowfalls has decreased. Using 72 h backward trajectories according to the NOAA HYSPLIT model and the ERA5 reanalysis, we classified the HHS events into five types according to air mass origin, and performed a composite analysis for each type. The main finding is that 46% of HHS reports are related to cyclones forming over the Caspian and Aral seas, resulting in a higher frequency of HHS events to the east of the Ural Mountains compared to the western part of the region. Full article

This study utilizes backward trajectory cluster analysis, the Potential Source Contribution Function (PSCF), Concentration Weighted Trajectory (CWT), and a random forest model to investigate the pollution characteristics of PM_2.5 and PM₁₀ in the “Urumqi-Changji Hui Autonomous Prefecture-Shihezi-Wujiaqu (U-C-S)” urban agglomeration. Findings indicate that on an annual basis, higher PM_2.5 concentrations are observed in the central part of the “U-C-S” urban agglomeration, southern Wujiaqu, and the Shihezi area, whereas PM₁₀ concentrations are lower in the high-altitude regions of the Tianshan and Bogda Mountains. Seasonally, both PM_2.5 and PM₁₀ concentrations significantly increase during winter, with summer exhibiting the best air quality. On a monthly scale, Urumqi’s central urban area shows a marked rise in PM_2.5 concentrations during winter, attributed to coal heating and stable weather conditions. Weekly patterns reveal higher pollution levels on weekdays compared to weekends. Daily data show that PM_2.5 concentrations are notably higher in winter compared to other periods, while elevated PM₁₀ levels in spring are primarily due to dust storms. Cluster analysis indicates that seasonal airflow paths significantly influence particulate matter concentrations. PSCF and CWT analyses demonstrate that the most severe PM_2.5 pollution in winter is concentrated in the northern part of the Bayingolin Mongol Autonomous Prefecture, southern Yining City, and across all areas of Urumqi. The random forest model provides robust predictions of particulate matter concentrations, aiding in the understanding and mitigation of future pollution trends. This study offers valuable insights for atmospheric particulate matter pollution research in the Xinjiang region and serves as a reference for similar urban agglomerations. Full article