Search Results (2,071)

To reduce the conservatism of local trajectory planning in dynamic road scenarios caused by redundant projection of predicted trajectories, this paper proposes a hierarchical hybrid trajectory-planning framework with a time-to-arrival (TTA)-driven dynamic risk-filtering mechanism. In the Frenet coordinate system, road boundaries, ego states, and static and dynamic obstacles are represented uniformly to construct an S–L fused risk field and an S–T spatiotemporal interaction graph, enabling the filtering of temporally irrelevant conflict regions based on TTA relationships. At the path-planning layer, risk-guided adaptive sampling is integrated with dynamic programming and quadratic programming to improve search efficiency and trajectory quality. At the speed-planning layer, spatiotemporal coordination is achieved through non-uniform discretization, safe-corridor extraction, and speed-profile optimization. Simulation results show that the proposed method generates safe, smooth, continuous, and executable local trajectories in scenarios involving static-obstacle avoidance, adjacent-vehicle cut-ins, non-motorized road-user crossings, and mixed multi-obstacle interactions, while reducing unnecessary deceleration and detours. Ablation results further indicate that adaptive sampling reduces the number of DP search nodes by approximately 50% and the average planning time by about 30%, while maintaining a nearly unchanged minimum safety distance. These findings demonstrate that the proposed framework effectively suppresses redundant conflict regions and improves planning efficiency, solution feasibility, and motion continuity without compromising safety. Full article

Reliable short-term prediction of bus travel time on signalized urban arterials is essential for improving service reliability and may provide a useful forecasting basis for prediction-informed transit signal priority (TSP) and arterial coordination applications. However, bus operations on urban arterials are highly variable due to stop dwell times, signal delays, and interactions with mixed traffic, leading to nonlinear and nonstationary travel time patterns with strong spatiotemporal dependence. This study proposes a hybrid KNN–ConvLSTM framework for short-term arterial bus travel time prediction using real-world field data. A K-nearest neighbors (KNNs) module is first employed to retrieve historical operation sequences that are most similar to the current corridor state, thereby reducing interference from mismatched traffic regimes and improving robustness. Smart-card (IC card) transaction data are incorporated as demand-related features to represent passenger activity and its impact on dwell time and travel time variability. The selected sequences are then organized into a corridor-ordered spatiotemporal representation and further refined by lightweight temporal enhancement operations, including relevance gating, multi-scale aggregation, adaptive feature fusion, and residual enhancement, before being fed into the convolutional long short-term memory (ConvLSTM) predictor. The proposed approach is evaluated using weekday service-hour data extracted from 30 days of real-world bus operation records collected from a typical urban arterial corridor in Changchun, China, and is compared with several benchmark models, including ARIMA, KNN, LSTM, CNN, ConvLSTM, Transformer, and DCRNN. The results indicate that the proposed KNN–ConvLSTM framework achieves an MAE of 40.1 s, an RMSE of 55.8 s, a SMAPE of 10.7%, and an R² of 0.878, outperforming all benchmark models. Specifically, compared with the Transformer baseline, the proposed framework reduces MAE by 1.5%, RMSE by 5.1%, and SMAPE by 7.0%, while increasing R² by 0.014. Compared with the DCRNN baseline, it reduces MAE by 10.7%, RMSE by 1.9%, and SMAPE by 2.7%, while increasing R² by 0.008. These findings demonstrate that similarity-aware retrieval combined with spatiotemporal deep learning can substantially enhance short-term bus travel time prediction on signalized urban arterials. More accurate short-term forecasts may support prediction-informed transit signal priority and arterial coordination by providing more reliable downstream arrival-time estimates. However, the generalizability of the reported results is still constrained by the relatively short 30-day observation period and the single-corridor case setting, and the operational and environmental effects of downstream applications remain to be validated through dedicated closed-loop control evaluation in future work. Full article

This study estimates China’s methane (CH₄) emissions from 43 specific emission sources in 2020 and projects future trends through 2050 under two scenarios: Current Legislation (CLE) and Maximum Technically Feasible Reduction (MFR). The analysis utilises the Greenhouse gas and Air pollution Interactions and Synergies (GAINS) model methane framework, incorporating updated province-level activity data to capture the pronounced regional heterogeneity inherent in emission profiles and mitigation capacities. The results reveal a national CH₄ budget of 1114 MtCO₂e in 2020, with the energy sector (59%) and agriculture (28%) emerging as the primary contributors. A substantial technical mitigation potential is identified; by 2050, emissions could be curtailed by up to 48% relative to the CLE scenario, representing a 46% reduction from 2020 levels. The energy and waste sectors emerge as the primary contributors to this potential. Specifically, coal mining CH₄ abatement constitutes 58% of the energy sector’s total reduction potential, while enhanced solid waste management accounts for 97% of the mitigation within the waste sector. Key measures include ventilation air methane (VAM) oxidation and pre-mining degasification, as well as anaerobic digestion and recovery and utilization for energy use. Owing to regional disparities in hydrothermal conditions (representing the combined influence of temperature and moisture), demographic status, economic development, the most effective mitigation strategies vary across provinces. For example, pre-mining degasification and VAM oxidation are most impactful in major coal-producing regions such as Shanxi, Inner Mongolia, and Shaanxi. In contrast, anaerobic digestion, recovery and utilization, and waste incineration play a dominant role in more economically developed and densely populated provinces such as Jiangsu, Shandong and Zhejiang. By delineating region-specific technological priorities, this study quantifies the maximum technical mitigation potential for China and offers guidance for other nations facing similar mitigation challenges. Full article

Scientifically assessing the spatiotemporal evolution of regional carbon storage is of great significance for achieving the “dual carbon” goals and optimizing territorial spatial patterns. This study integrated the PLUS and InVEST models to systematically reconstruct the spatiotemporal pattern of carbon storage in Hebei Province from 2000 to 2020, simulate its evolution trajectory under different scenarios in 2030, and identify its driving mechanisms using the GeoDetector model. The main findings are as follows: (1) From 2000 to 2020, cropland was the dominant land use type in Hebei Province, and carbon storage exhibited a spatial pattern of “high in the northwest, low in the southeast.” Carbon storage increased from 16.23 × 10⁸ t to 16.31 × 10⁸ t, with a significantly slowed growth rate after 2010. (2) Multi-scenario simulations for 2030 indicate that under the natural development and economic priority scenarios, construction land expands significantly while cropland and grassland continue to decrease. In contrast, carbon storage shows an increasing trend under the ecological protection and cropland protection scenarios. (3) Driving factor analysis reveals that the spatial differentiation of carbon storage is primarily controlled by natural factors such as slope, elevation, and NDVI, while the explanatory power of anthropogenic factors, particularly population density, has significantly increased. The interaction between NDVI and slope exhibits a synergistic enhancement effect. This study elucidates the coupling mechanisms between land use change and carbon storage under different policy orientations, providing a scientific basis for territorial spatial optimization and the formulation of differentiated carbon neutrality pathways in Hebei Province. Full article

Typhoon-induced wind loads pose severe threats to transmission systems. However, existing resilience assessment approaches typically rely on sparse meteorological station data and assume spatially uniform wind speed distributions along transmission corridors, which fail to capture the span-level spatial difference of wind fields. To address this limitation, this paper proposes a distributed optical fiber sensing (DOFS)-driven span-level resilience assessment and hardening optimization framework for transmission networks. First, a phase-sensitive optical time domain reflectometry (Φ-OTDR)-based distributed optical fiber sensing system is employed, utilizing optical fibers embedded in existing OPGW cables as sensing media. By capturing vibration responses of the fiber induced by wind–structure interaction, real-time spatiotemporal wind speed sequences at the individual span level are reconstructed through signal processing and inversion algorithms, providing high-spatial-resolution environmental input data for resilience evaluation. Second, a span-level failure probability quantification method is established using a load–strength interference model. On this basis, a resilience evaluation framework—“span-level asset damage cost—line-level critical corridor identification—system-level load shedding assessment”—is constructed, enabling cross-scale resilience quantification from component damage to system-level performance degradation. Third, a span-level gradient hardening optimization model is developed. By adopting a scenario pre-calculation and iterative updating strategy, coordinated solving of reinforcement decisions and failure scenarios is achieved, thereby maximizing resilience enhancement benefits. The proposed framework is validated using DOFS-measured wind speed data collected from a 500 kV transmission line along the Fujian coast during three real typhoon events—Typhoon Shantuo, Typhoon Trami, and Typhoon Koinu—supporting the reliability of the acquired span-level wind speed information. Case studies conducted on a modified IEEE RTS-24 system demonstrate that the proposed span-level hardening strategy can substantially reduce reinforcement cost compared with the conventional line-level hardening strategy. In the reported benchmark case, it achieves zero load-shedding penalty with a markedly lower hardening cost, and under the same budget constraint, it further yields lower expected load shedding and lower expected asset damage. Full article

In this study, global spatial autocorrelation, local spatial autocorrelation, Spearman correlation analysis, gray correlation analysis, entropy weight method, and the gravity model were used to analyze the spatiotemporal variation and environment-urban-economy-associated factors of air quality of 31 provinces in China during 2015~2022. From 2015 to 2022, the Air Quality Index (AQI) exhibited a downward trend in 30 out of 31 Chinese provinces, with the exception of Shaanxi Province. Concurrently, the annual average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, and CO declined across the study period. High-high clusters and low-high outliers were observed in northern China, whereas low-low clusters and high-low outliers were distributed in southern China. Twelve provinces (38.7%) showed positive correlation (0.095~0.95), 18 provinces (58.1%) showed negative correlation (−0.76~0.095), and only Anhui showed no correlation between AQI and O₃. The comprehensive AQI quality presented a dual-core model in Sichuan (in the southwest) and Henan (in the central part) of China, while the comprehensive AQI improvement rate presented a single-core model in Jiangsu in the east of China. The gravity models incorporating AQI and GDP revealed that both air quality and economic performance improved over the study period. The spatial pattern of pollution evolved from a multi-core structure to a non-core structure, whereas the pattern of economic growth transitioned from a non-core structure to a dual-core structure, with the Beijing-Tianjin-Hebei region and the Yangtze River Delta emerging as the primary urban agglomerations. Full article

Analyzing the dominant drivers of the Food-Water-Land-Ecosystem (FWLE) nexus in the future is important for improving sustainable development in dryland ecosystems. However, the future trajectories of food–water–land–ecosystem interactions in typical drought-prone regions remain poorly understood. To address this gap, this study coupled the Gray Multi-Objective Programming with Patch-generating Land Use Simulation (GMOP-PLUS) model and applied spatial analysis methods (including longitudinal and zonal statistical analysis, trade-off synergy analysis, and redundancy analysis) to examine the spatiotemporal differentiation patterns of the FWLE nexus in Xinjiang under different development scenarios. Over the past two decades, water yield in Xinjiang’s agricultural landscapes has declined by 57.4%, primarily due to land-use and land-cover changes. Under the 2030 sustainable development scenario, a custom optimization developed via the GMOP model that balances economic and ecological objectives, crop production and habitat quality are projected to increase by 47.9% and 55.1%, respectively. Moreover, redundancy analysis results indicate that the driving contribution of precipitation on the FWLE nexus is expected to reach 76.9% by 2030. These findings provide a clear delineation of priority spatial units for improvement within Xinjiang agro-ecosystem and offer a strategic pathway for balancing ecological conservation and economic development. Full article

High-geothermal tunnels are subjected to complex thermo–hydro–mechanical (THM) coupling effects, where the interaction of temperature, seepage, and stress significantly influences the stability of surrounding rock. To address the limitations of conventional models assuming uniform initial temperature, a THM-coupled numerical model incorporating an in situ temperature gradient is established based on the Sangzhuling Tunnel. The concept of the thermal influence zone is quantitatively defined by an equivalent-radius method, and its spatiotemporal evolution is systematically investigated. In addition, the distinct roles of temperature and pore water pressure in controlling deformation and plastic-zone evolution are comparatively clarified. The results show that the thermal influence zone expands nonlinearly with increasing initial rock temperature and gradually stabilizes over time. Temperature and pore water pressure both promote the development of the plastic zone, which predominantly propagates along directions approximately 45° to the horizontal. Under the geological and boundary conditions considered in this study, temperature plays a dominant role by inducing thermal stress and degrading mechanical properties, leading to significant expansion of the plastic zone and increased vault deformation. In contrast, pore water pressure mainly reduces effective stress, thereby influencing deformation distribution, especially at the tunnel invert. Overall, THM coupling significantly amplifies surrounding rock failure compared with single-field conditions. The findings provide quantitative insights into the evolution of the thermal influence zone and its coupled control on deformation and plasticity, offering a theoretical basis for support design and stability control in high-geothermal tunnels. Full article

Trajectory planning in dynamic multi-vehicle interaction environments faces three critical challenges, including the difficulty of quantifying spatial risk distributions, the complexity of characterizing behavioral uncertainty arising from the multimodal maneuvers of surrounding vehicles, and the challenge of simultaneously optimizing multiple competing objectives such as safety, efficiency, comfort, and energy consumption. To address these challenges, this paper proposes an Improved Driving Risk Field-based Multi-objective Trajectory Optimization (IDRF-MTO) method. First, a joint spatiotemporal social attention mechanism achieves unified modeling of spatial interactions, temporal dependencies, and spatiotemporal coupling, combined with a lateral–longitudinal intent strategy for multimodal trajectory prediction. Second, an improved dynamic risk field model is constructed comprising three components: a vehicle risk field that incorporates spatial orientation and motion direction factors for anisotropic risk representation, along with a collision tendency factor that converts objective risk into effective risk; a predicted trajectory risk field that achieves anticipatory quantification of future risk from surrounding vehicles through confidence-weighted fusion; and a driving environment risk field that encapsulates road geometry, static obstacles, and environmental conditions. Finally, a multi-objective cost function embedding risk field gradients is formulated, and multi-objective coordinated optimization is realized through a three-dimensional spatiotemporal situation graph with adaptive safety sampling. Simulation results demonstrate that the proposed method enhances safety while simultaneously improving comfort and efficiency and reducing energy consumption, exhibiting excellent planning performance in complex dynamic environments. Full article

Electroencephalogram (EEG) signals serve as the core information carrier for brain–computer interfaces (BCIs); however, their highly non-stationary nature, extremely low signal-to-noise ratio, and significant inter-individual variability pose considerable challenges for signal decoding. Existing deep learning methods struggle to strike a balance between multi-scale, fine-grained feature extraction and efficient long-range temporal modeling. To overcome this limitation, this study proposes a novel deep cascaded architecture, MSResBiMamba, which deeply integrates multi-scale spatiotemporal feature learning with cutting-edge long-sequence modeling techniques. The model first utilizes an enhanced multi-scale spatiotemporal convolutional network (MS-CNN) combined with a SE-channel attention mechanism to adaptively extract local multi-band features and dynamically suppress redundant artefacts. Subsequently, it innovatively introduces an enhanced bidirectional Mamba (Bi-Mamba) module to efficiently capture non-causal long-range temporal dependencies with linear computational complexity, whilst cascading multi-head self-attention mechanisms to establish global higher-order feature interactions. Extensive experiments on the BCI Competition IV-2a dataset demonstrate that MSResBiMamba achieves outstanding classification performance in multi-class motor imagery tasks, significantly outperforming traditional methods and existing state-of-the-art neural networks. Ablation studies and t-SNE visualisations further confirm the model’s robustness in feature decoupling and cross-subject applications, providing a high-precision, high-efficiency decoding solution for BCI systems. Full article

Urban resilience plays a critical role in sustainable regional development. This is particularly so for ecologically vulnerable urban agglomerations undergoing rapid urbanization. This study examines the spatiotemporal development and driving mechanisms of urban resilience in the Chengdu–Chongqing Urban Agglomeration (CCUA) via the perspective of ecological risk. Using panel data from 16 prefecture-level cities during 2010–2023, this study constructs ecological risk and urban resilience indices were constructed based on the entropy weight–TOPSIS method. The coupling coordination degree model was applied to analyze the interactive dynamics between the two subsystems, and a two-way fixed effects panel model was employed to identify the impact of ecological risk on urban resilience and its moderating mechanisms. The results show that urban resilience experienced a foundational stabilization phase followed by gradual improvement, while ecological risk underwent a three-stage transformation characterized by accumulation, stabilization, and decline. The coupling degree between ecological risk and urban resilience remained moderately high, indicating structural tension within the regional system. Econometric analysis indicates that ecological risk significantly suppresses urban resilience. Infrastructure development has a positive direct effect on resilience. However, it negatively moderates the marginal impact of ecological risk, indicating a nonlinear and conditional risk–resilience relationship. Full article

The synergistic control of multiple pollutants is critically challenged by complex nonlinear interactions, strong spatiotemporal heterogeneity, and the difficulty of tracing causal drivers. Deep learning offers high predictive power but suffers from the “black-box” problem, limiting its acceptance in environmental decision-making. Explainable Deep Learning (XDL) integrates physical mechanisms with interpretable algorithms, achieving both prediction accuracy and explanatory transparency. This review systematically evaluates the effectiveness and limitations of XDL in analyzing multi-pollutant interactions, with a comparative focus on atmospheric and aquatic environments. Key techniques, including SHAP, attention mechanisms, and physics-informed neural networks, are examined for their roles in synergistic monitoring, source apportionment, and regulatory optimization. The main findings reveal that: (1) XDL, particularly the “tree model + SHAP” paradigm, has become a dominant tool for quantifying driving factors, yet most attributions remain correlational rather than causal; (2) physics-informed fusion (soft vs. hard constraints) improves physical consistency but faces unresolved conflicts between data and physical laws, with current models lacking a conflict detection mechanism; (3) cross-media comparison shows a unified technical logic of “physical mechanism guidance + post hoc feature attribution”, but atmospheric applications lead in embedding advection–diffusion constraints, while aquatic research excels in spatial topology modeling via graph neural networks; (4) critical bottlenecks include the lack of causal inference, uncertainty-unaware interpretations, and data scarcity. Future directions demand a shift from correlation-only to causal-aware attribution, from blind fusion to conflict-detecting systems, and from no evaluation standards to domain-specific validation benchmarks. XDL is poised to transform multi-pollutant governance from experience-driven to intelligence-driven approaches, provided that verifiable interpretability and physical consistency become core design principles. Full article

This study investigates the spatiotemporal dynamics of zooplankton communities in the tropical low-latitude Northwestern Pacific Ocean based on field surveys conducted in August 2021 and November 2022. Redundancy analysis identified nitrate, silicate, temperature, and salinity as the primary factors influencing community structure. The distribution of dominant zooplankton groups exhibited close correlations with key environmental gradients, showing distinct habitat preferences corresponding to different hydrographic conditions. Zooplankton abundance in August 2021 was significantly higher than that in November 2022, which is presumably attributed to eddy-induced nutrient upwelling and enhanced primary productivity. Comparisons with adjacent marine regions reveal general consistency in overall zooplankton abundance and community species composition, while the observed seasonal discrepancies are closely associated with local unique hydrographic characteristics. These results highlight the role of nutrient–temperature–salinity interactions in structuring zooplankton communities and underscore their sensitivity to environmental variability. The findings provide a scientific basis for understanding pelagic ecosystem dynamics in oligotrophic waters and for developing management strategies under changing climate and oceanographic conditions. Full article

Task allocation for heterogeneous unmanned aerial vehicle (UAV) swarms requires complex spatiotemporal coordination. While traditional algorithms struggle to interpret abstract semantic intents, general large language models (LLMs) often suffer from physical hallucinations and superficial tactical reasoning. To address these limitations, we propose a generative task allocation paradigm augmented by a heterogeneous toolchain, shifting the approach from rigid numerical optimization toward tool-grounded semantic planning. To implement this and overcome domain data scarcity, we design a decoupled dual-model architecture. This architecture is optimized through an execution-manifold-anchored orthogonal evolution training method. By utilizing simulated self-play within a stable execution environment, this approach prevents gradient conflicts and autonomously generates abundant training data. Furthermore, to resolve the credit assignment problem in long-horizon scenarios, we develop a Recursive Causal Probe (RCP) algorithm. By tracing failures backward through the simulation, RCP synthesizes counterfactual preference data, effectively translating tactical mistakes into precise corrections for the planning model. Extensive simulations demonstrate that our method achieves an 82.34% mission success rate in complex scenarios, requiring significantly fewer interactive corrections than general LLMs, fully verifying its physical feasibility and practical robustness. Full article

Biomass, composed of natural polymers such as cellulose, hemicellulose, and lignin, can be converted into circular chemical feedstocks through thermochemical conversion processes like pyrolysis. Char conversion is the rate-limiting step in the thermochemical conversion process, and thus, char reactivity is essential for determining the overall efficiency of pellet-based thermochemical processes. Pyrolysis experiments were conducted on rice straw pellets of different sizes (i.e., 8, 10, and 12 mm) in a vertical quartz tube reactor at 700 °C, and then the chemical structure of chars sampled at different stages and locations within a 10 mm pellet was analyzed using Raman spectroscopy and Fourier transform infrared spectroscopy (FTIR). The results indicate that increasing the pellet size facilitates the growth of polycyclic aromatic structures, as evidenced by the observed variations in the abundance of typical aromatic compounds in bio-oil. This also promotes volatile–char interactions, leading to greater deposition of large aromatic structures on the char surface, thereby enhancing char aromatization. Analogous to the spatial scale effect of pellet size on char structure, the evolution of the char structure within a single pellet exhibits distinct spatial heterogeneity during the initial devolatilization and subsequent char aromatization stages due to the location-dependent coupling of heat/mass transfer limitations and aromatization reactions during pyrolysis. Furthermore, the spatiotemporal evolution of the char structure leads to differences in the specific reactivity: during the devolatilization stage at 75 s, the center exhibits the highest reactivity, whereas the outer surface becomes the most reactive in the subsequent char aromatization stage at 300 s. Full article