Search Results (251)

The post-COVID-19 tourism recovery exposes a structural divergence between economic resilience and environmental sustainability. Traditional tourism planning metrics consistently fail to diagnose how macroeconomic growth dynamics decouple from environmental pressures, leaving policymakers without empirical tools to identify structural vulnerabilities or prevent carbon-intensive recoupling during post-crisis transitions. This study integrates macroeconomic, environmental, and digital data across a global panel to map actionable pathways for sustainable tourism transitions. Employing a multi-stage methodology, the analysis first utilizes K-Means clustering (n = 80) to isolate the structural fixed effects of baseline destination archetypes driving a K-shaped recovery. Second, using a synchronized environmental panel (n = 41), a Decoupling Index evaluates eco-efficiency elasticity to test the alignment between tourism value recovery and aviation-induced CO₂ emissions. Third, regression analysis of an elite digital cohort (n = 18) measures dynamic exogenous catalysts, revealing that digital attractiveness, proxied by the global digital nomad market share, is a significantly stronger accelerator of recovery (β = 55.59, p = 0.019) than traditional physical air connectivity (β = −46.48, p = 0.036). Synthesizing these insights, a 2 × 2 Strategic Decision Matrix (n = 41) classifies destinations into Sustainable Leaders, Mass-Market Traps, Value Pivoters, and Vulnerable Laggards. The empirical results demonstrate that pre-pandemic structures do not deterministically dictate recovery (p > 0.05, Partial η² ≤ 0.077), yet rapid financial recovery often masks deep atmospheric vulnerabilities, with specific absolute decoupling leaders achieving exceptional value expansion alongside strict carbon contraction (e.g., Saudi Arabia, DE = −0.35; El Salvador, DE = −0.26). This framework provides a data-driven roadmap for policymakers to utilize “soft” digital infrastructure to transition from carbon-intensive, volume-dependent models toward value-optimized, low-emission ecosystems. Full article

Distributed-drive all-wheel steering (AWS) six-axle vehicles possess distinct advantages in power performance, maneuverability, and environmental adaptability. However, when navigating tight curves under sudden low-friction road conditions, their inherent long wheelbase and strong inter-axle coupling typically lead to compromised spatial maneuverability, trajectory decoupling between the vehicle nose and tail, and lateral dynamic instability. To resolve these critical issues, this paper proposes a geometry–dynamics coupled lateral control scheme with adaptive speed planning for six-axle vehicles under confined spatial and low-friction conditions by seamlessly fusing a dual-point preview mechanism with multi-mode steering mappings. First, a three-degree-of-freedom nonlinear vehicle dynamic model incorporating longitudinal, lateral, and yaw motions is constructed, alongside the formulation of extended Ackermann kinematic steering manifolds for three distinct modes: rear-axle steering, center steering, and crab steering. To rectify the kinematic under-constrained deficiency inherent in conventional single-point preview path-tracking architectures, a joint front-and-rear dual-point preview constraint mechanism is established. This framework permits the quantitative derivation of a spatial geometric reconstruction method for the instantaneous center of rotation (ICR), which algebraically maps the ideal ICR trajectory requirements onto the physical constraints of the selected steering modes. Consequently, complete geometric constraints on both the front and rear trajectories are achieved, enabling active compression of the vehicle’s turning radius. Furthermore, to handle sudden low-friction disturbances, road adhesion limits and vehicle lateral stability boundaries are explicitly incorporated to design a multi-scale adaptive preview distance dynamic scaling mechanism driven by dynamic safety margin corrections. By adaptively scaling the spatial constraint at the geometric layer, this mechanism proactively mitigates nonlinear tire sideslip force saturation via feedforward action, thereby preventing tracking divergence and catastrophic sideslip instability under physical adhesion limits. Co-simulations based on the high-fidelity TruckSim-Simulink platform demonstrate that, in standard curves, the proposed dual-point preview manifold fusion strategy reduces the minimum turning radius by 9.6–10.1% and shortens the cornering transit time by 7.5% compared with the traditional single-point preview mechanism. By actively constraining the front and rear trajectories, the trajectory decoupling between the vehicle nose and tail is effectively resolved. Under narrow-lane scenarios, the maximum lateral error is restricted within 0.78 m, representing a 37.6% reduction relative to the single-point preview, while the maximum steering angle of the front axle is compressed by approximately 18%, thereby significantly improving spatial passability and preventing intermediate body interference. Most notably, under low-friction surface disturbances, the dynamic-margin-corrected adaptive preview adjustment mechanism exhibits remarkable robustness, constraining the maximum lateral tracking error to within 0.68 m. The proposed geometry–dynamics coupled lateral control strategy successfully elevates the tight-curve maneuverability of heavy transport vehicles while concurrently reinforcing their lateral dynamic stability under limit combined spatial and adhesion constraints. Full article

Coordinating multiple unmanned aerial vehicle (UAV) systems under strict energy and temporal constraints remains a complex scheduling problem. Existing reinforcement learning methods typically rely on fixed-time-step modeling, which struggles to accommodate flight actions of varying durations and often leads to temporal mismatches between task planning and physical execution. To address this limitation, we propose an Adaptive Hierarchical Semi-Markov Decision Process (AH-SMDP) framework. This architecture decouples task allocation from execution by modeling variable-length actions via an SMDP. An event-driven synchronization mechanism is introduced to align the swarm’s decision-making rhythm with actual task completion times. Additionally, a state-aware reward formulation and a dynamic action space pruning strategy are designed to help UAVs balance energy efficiency with deadline compliance. Simulation results in multi-constraint environments demonstrate that the AH-SMDP framework effectively improves scheduling performance compared to standard MAPPO and PPO algorithms. Under the evaluated experimental settings, the proposed method yields improvements of approximately 30% in average task completion rate, 40% in energy reduction, and 60% in convergence stability. Ablation studies further suggest that this integrated framework offers a viable and effective approach for multi-UAV scheduling. Full article

To address the prevalent issues of slow convergence, low exploration efficiency, and large value estimation bias in traditional Deep Q-Networks for intelligent vehicle path planning, this paper proposes an improved Dueling Double Deep Q-Network (D3QN) path-planning method guided by the Dynamic Window Approach (DWA) heuristic. The Dueling Double DQN architecture decouples state value and action advantage representations, while the dual estimator of Double DQN mitigates Q-value overestimation. A Prioritized Experience Replay (PER) mechanism samples transitions non-uniformly based on Temporal Difference error with importance sampling correction, improving the reuse of critical samples and training stability. DWA evaluation criteria are transformed into dense heuristic reward signals, enabling the agent to receive continuous multi-dimensional guidance during exploration without executing online trajectory optimization. The environment augments the sparse navigation objective with a Chebyshev goal-progress term motivated by potential-based reward shaping theory together with auxiliary DWA-style channels. The policy-invariance property of potential-based shaping is referenced only for the goal term added to the sparse task reward rather than for the full composite training return. A continuous Ackermann steering kinematic model with a pure-pursuit path-tracking controller is adopted for deployment to ensure executable trajectories under non-holonomic constraints. The proposed method (DWA-D3QN) is systematically evaluated against sparse-reward D3QN, PBRS-guided D3QN, DQN, DDQN, Dueling DQN, APF-DQN, PPO, SAC, TD3, A*, and classical DWA in a grid map environment with static and dynamic obstacles. Results are reported with statistical significance over multiple random seeds. Under complex difficulty, DWA-D3QN achieves a success rate of 94.1 ± 3.4% with a collision rate of 5.9 ± 3.4% over 15 seeds, representing improvements of 64.1 and 8.4 percentage points over the sparse-reward and PBRS-guided D3QN baselines, respectively. Ablation experiments reveal the differentiated contributions of clearance, heading, and velocity shaping terms: clearance awareness provides the strongest single contribution, heading alignment reinforces directional guidance, and velocity regularization refines trajectory quality under the joint constraints of the former two. The full composite reward achieves the lowest variance among all evaluated DRL methods, confirming enhanced training stability. Comparisons with PPO, SAC, and TD3 confirm the statistically significant advantages of the proposed framework (PPO: $p=0.0010$, SAC: $p=0.0007$, TD3: $p=0.0024$). ROS/Gazebo validation with an Ackermann-steered vehicle achieves a success rate of 96.0% with a collision rate of 4.0% over 50 trials, further confirming the applicability of the learned policy in continuous-state environments with realistic vehicle kinematics. Full article

Urban mobility planning in smart cities requires sophisticated simulation tools, yet their complexity often creates a technical barrier for non-expert stakeholders. This paper presents a novel architecture that integrates generative artificial intelligence with digital twin technology to create an accessible and decision-support prototype. The framework employs a conversational AI agent based on Gemini 2.5 Flash Lite to interpret natural language intentions and translate them into validated simulation parameters. A critical safety layer, built using Pydantic, ensures that the agent’s stochastic outputs adhere to strict technical schemas and predefined logical bounds before execution. The underlying digital twin, developed with SimPy, NetworkX, and OSMnx, features a multi-source data integration strategy that includes demographic density (INE), tourism activity (ISTAC), and high-resolution traffic statistics (TomTom) to calibrate vehicle behavior. The architecture was technically demonstrated through a Technology Readiness Level (TRL) 4 proof-of-concept in Las Palmas de Gran Canaria, simulating multimodal scenarios including buses, the future MetroGuagua (BRT), and pedestrian flows. Results demonstrate a 96% success rate in intent recognition and configuration mapping, with end-to-end execution times under 20 min for a 19 h simulated day. This study demonstrates that LLM-driven orchestration, coupled with automated data pipelines and a decoupled microservice architecture, can lower technical barriers to urban simulation, which could support broader participation in future smart city deployments. Full article

Reliable selective picking of long-staple cotton remains challenging because dense dwarf canopies restrict robot operating space and increase boll occlusion, resulting in reduced target visibility and potential fiber damage during picking. To address these challenges, a mobile dual-arm robotic picking system integrating hierarchical depth perception, cotton-boll recognition, optimized motion planning, and three-finger flexible end-effectors was developed for autonomous picking in Xinjiang long-staple cotton fields. The proposed YOLOv7-DCN-SENet model reached 95.75% precision, 92.65% recall, and 97.19% mAP@0.5 on the test set, while the onboard computing platform operated at 101 FPS under the experimental configuration. Indoor and field experiments were conducted on directly visible upper-canopy open cotton bolls. The dual-arm robot achieved parallel picking success rates of 74.6% and 57.6%, with average picking cycles of 28.2 s and 34.9 s, respectively. Field performance was mainly limited by strong-light overexposure, depth-information loss, occlusion-induced localization errors, arm interference within narrow canopy spaces, and incomplete fiber separation during boll detachment. These results demonstrate the feasibility of autonomous dual-arm selective picking for long-staple cotton under dense planting conditions and provide a basis for further improvements in robotic cotton-picking systems. Full article

Low-carbon development (LCD) and intensive urban land use (IULU) are critical objectives for sustainable urban development. Existing studies have usually evaluated LCD or IULU separately, whereas the dynamic relationship between carbon-transition capacity and land-use intensification under rapid urbanization remains insufficiently clarified. This gap limits the ability of policymakers to design spatially differentiated and synergistic actions for achieving the Sustainable Development Goals (SDGs). This study investigates the relationship between LCD and IULU and its transformation within the sustainable development framework, using the Middle Reaches of the Yangtze River Urban Agglomeration (MRYRUA) in central China as a case study. Results indicate a strong positive correlation between LCD and IULU. Crucially, their coupling exhibited a distinct U-shape trajectory from 2005 to 2020; it decreased from 0.89 in 2005 to 0.73 in 2013 and then recovered to 0.84 in 2020, suggesting a relative weakening of the interaction followed by recoupling rather than complete decoupling. The identified U-shaped trajectory holds vital implications for other developing nations, suggesting that integrating low-carbon goals into spatial planning and land policies from the early stages of urbanization can pave the way for a faster transition to a green, intensive, and high-quality development model. Moreover, although both LCD and IULU exhibited positive trends, a widening gap was observed between provincial capitals and non-provincial cities. We, therefore, recommend integrating multi-stakeholder collaboration and implementing differentiated strategies to enhance the synergistic effects of LCD and IULU for cities at different phases of the LCD–IULU transition. Full article

Accurate monitoring of hydrological dynamics in complex perennial landscapes is a cornerstone for tropical agricultural sustainability. Traditional energy balance models based on orbital optical data often face methodological bottlenecks due to cloud cover and the “greening myth,” where optical indices fail to capture immediate water stress due to the non-linear decoupling between stomatal closure and pigment loss. This study developed a cloud-integrated multisensor framework to estimate actual evapotranspiration (ET_a) at a refined 100 m resolution in mountain coffee systems, utilizing active microwave proxies from Sentinel-1. We fused polarimetric metrics—Degree of Polarization (DoP) and Shannon Entropy (SE)—with land surface temperature and soil moisture data. Multiple Linear Regression (MLR) was compared against non-linear algorithms (Random Forest and SVR) to prioritize model parsimony and physical interpretability. The results show that MLR emerged as the most parsimonious and suitable model within this localized dataset scope (R² = 0.872; RMSE = 2.916 mm/8-day), outperforming complex “black-box” architectures. Soil moisture emerged as the dominant environmental driver of ET_a variability, while SAR-based metrics served as sensitive mechanical proxies for canopy geometric heterogeneity and macro-structural variations. Cross-correlation analysis revealed a 16-day lag, empirically indicating that biophysical water shifts temporally precede geometric canopy alterations. Operationally, this framework ensures temporal continuity under persistent cloud cover and provides high-fidelity spatial detailing for precision water management. This approach offers an auditable and scalable tool for watershed planning and climate resilience in tropical agriculture. Full article

Joint detection of strawberry growth stages and defective fruit is needed for harvest planning and quality screening, but field images make this task difficult because stage-related visual differences are subtle, flowers and early fruits are often small and densely distributed, and occlusion weakens localization reliability. This study develops Multi-Scale Refined Detection and Classification YOLO (MRDC-YOLO), a lightweight detector based on the YOLO11s framework, for this fine-grained detection scenario. The backbone, neck, and detection head are redesigned with three modules: a Multi-Scale Adaptive Edge Enhancement Module (MAEM), a Reparameterized Progressive Feature Aggregation (RPFA) module, and a Decoupled Cross-Scan Head (DCSH). MAEM strengthens boundary and texture responses for visually similar categories, RPFA reduces redundant multi-scale fusion while maintaining features for dense small targets, and DCSH introduces task-aware classification and regression branches with cross-scan-inspired spatial modeling for occlusion-sensitive localization. Experiments on a five-class strawberry dataset containing 5114 images show that MRDC-YOLO achieves 95.63% mAP@0.5 and 82.39% mAP@0.5:0.95. Over YOLO11s, the model yields a 2.06-percentage-point gain in precision and 1.34- and 1.53-percentage-point gains in mAP@0.5 and mAP@0.5:0.95, together with 10.7% fewer parameters and 8.9% lower GFLOPs. These results suggest that MRDC-YOLO improves fine-grained category discrimination and localization while retaining a smaller model size than the YOLO11s baseline. Full article

To address the demand for high-precision, high-efficiency, and standardized hydrographic information in intelligent shipping, this study systematically investigates key technologies for high-resolution hydrographic data parsing and intelligent information services. Focusing on the East China Sea, a space–air–ground integrated monitoring data access system is established. A hybrid data assimilation method combining four-dimensional variational (4D-Var) and ensemble Kalman filter is adopted to realize quality control, deep fusion, and optimal state estimation of multi-source heterogeneous hydrographic observations. A hybrid tidal harmonic response model is further developed to improve the refined forecasting accuracy of tide levels and ocean currents. A hierarchically decoupled system architecture is designed, and modules for data production, sharing, exchange, and visualization are developed in compliance with the international S-100 standard. By integrating hybrid spatiotemporal indexing, multi-level caching, and intelligent query optimization, the system achieves low-latency and high-concurrency service capabilities. Experimental results show that, compared with conventional models, the proposed framework reduces tidal forecast RMSE by approximately 15.8% under extreme weather, raises the continuity index of current vectors to 0.93, and cuts the S-100 product generation latency to less than 30 s. This research establishes a full-chain technical system from data parsing and product generation to intelligent services, providing a reliable technical support platform for ship intelligent navigation, dynamic route planning, and maritime safety assurance. Full article

Full-duplex acoustic telemetry is important for real-time bidirectional measurement and control in intelligent coiled-tubing operations, but its reliability in deep horizontal wells is limited by long-range dispersion, asymmetric flow-induced noise, and severe near-end self-interference. This study proposes an orthogonal frequency-band planning and synergistic interference suppression method for full-duplex acoustic communication in coiled tubing. A dispersion model and an asymmetric attenuation model were first established for a fluid-filled coiled-tubing cylindrical-shell waveguide to characterize the physical transmission constraints. A multiphysics multi-objective cost function was then formulated by considering dispersion flatness, channel attenuation, asymmetric noise adaptability, and spectral isolation, and an improved simulated annealing algorithm was used to optimize the uplink and downlink frequency bands. In addition, a three-stage suppression architecture integrating mechanical decoupling, physical-layer frequency isolation, and CEEMDAN–wavelet denoising was developed to reduce self-interference and residual nonstationary noise. Full-scale experiments on a 457.2 m coiled-tubing surface circulation system showed that the proposed method improved the output signal-to-interference-plus-noise ratio from −15 dB to 18.5 dB and maintained a bit error rate below 1.2 × 10⁻⁴ at 400 L/min. These results indicate that the proposed approach can enhance the robustness of full-duplex acoustic telemetry under strong flow-induced noise. Full article

Reliable delineation of brain tumor boundaries in multimodal magnetic resonance imaging (MRI) remains challenging despite substantial advances in deep learning–based segmentation. Although modern encoder–decoder architectures achieve strong volumetric overlap, precise geometric alignment of tumor contours remains inconsistent, particularly for small lesions and heterogeneous clinical cases. In neuro-oncology, even minor boundary deviations may influence surgical planning, radiotherapy targeting, and longitudinal treatment assessment. These limitations suggest that segmentation performance is not determined solely by network depth or loss design, but also by how multimodal information is structured prior to learning. We introduce CMYD-SurfaceNet, a scale-aware cascaded framework that restructures multimodal MRI inputs at the representation level to enhance boundary-sensitive segmentation. Rather than treating modalities as independently concatenated channels, selected sequences are first organized into a task-guided pseudo-RGB projection. This intermediate representation is subsequently transformed into the CMYK color space to disentangle shared luminance structure from modality-specific contrast dominance. To further encode geometric priors, a gradient-derived boundary density channel is incorporated to explicitly emphasize spatial discontinuities corresponding to tumor margins. The resulting CMYD representation is integrated within a two-stage nnU-Net cascade, where global tumor localization is followed by high-resolution region-of-interest refinement with auxiliary contour supervision. This scale-aware design improves sensitivity to small tumor components while stabilizing contour delineation. Extensive evaluation on the BraTS benchmark demonstrates consistent improvements in boundary-sensitive metrics. Compared with baseline nnU-Net, the proposed framework reduces HD95 from 3.6 mm to 2.4 mm and increases Surface Dice at 1 mm tolerance from 0.82 to 0.89, while maintaining competitive Dice performance. These findings suggest that representation-level structural decoupling, when combined with scale-aware refinement, may provide clinically relevant boundary-aware multimodal MRI segmentation support without increasing architectural complexity. Full article

Vision-and-Language Navigation in continuous environments (VLN-CE) requires embodied agents to ground natural language instructions into reliable long-horizon motion decisions under partial observability. Despite their strong semantic understanding and reasoning abilities, Multimodal Large Language Model (LVLM) struggle when directly applied to VLN, as they lack explicit spatial grounding, embodied memory, and awareness of geometric and reachability constraints, leading to perceptual misalignment and cascading decision errors in complex scenes. To address these limitations, we propose STAMP, a Spatial-Temporal Anchored Motion Planning framework for zero-shot VLN-CE, which systematically bridges the gap between pretrained world knowledge and embodied navigation. STAMP adopts a hierarchical design that decouples high-level semantic reasoning from low-level motion execution, enabling a frozen LVLM to operate over a structured, navigation-oriented abstraction. Its core novelty lies in a multimodal spatial-temporal anchoring mechanism that explicitly encodes instruction-relevant landmarks, action semantics, depth-aware geometry, and historical navigation context, together with an explicit Chain-of-Navigation reasoning process that constrains decision-making to navigation-critical cues. Furthermore, STAMP incrementally constructs an online, backtracking-enabled topological map, supporting robust planning under uncertainty. Extensive experiments demonstrate the effectiveness of the proposed STAMP framework, achieving performance comparable to state-of-the-art zero-shot methods on VLN-CE benchmarks and in real-world settings. Full article

Modern distribution networks face dual challenges: extremely asymmetric spatial power flows caused by the high-penetration integration of distributed renewables under normal operating conditions and asymmetric system faults triggered by extreme weather such as blizzards under extreme conditions. To address these imbalances, this paper integrates distributed energy storage (DES) and soft open points (SOPs) as flexible resources to propose a two-stage joint optimal planning method that balances operational economy and resilience enhancement. First, by incorporating the spatiotemporal evolution trajectory and distance attenuation effects of blizzards, a multi-dimensional scenario sets characterizing asymmetric faults and normal source-load fluctuations are constructed. Second, a joint optimal planning model minimizing the total lifecycle cost is established. The progressive hedging algorithm is then adopted to decouple cross-scenario variables for efficient parallel solving. Verified on both the IEEE 33-node and large-scale 123-node systems, the coordinated planning strategy effectively avoids redundant investment in a single type of device. By establishing a symmetrical balance of flexible resources, the proposed method significantly reduces network losses and renewable curtailment during normal operation, while minimizing the amount of system load shedding under extreme asymmetric faults. Full article

Timely and accurate assessment of rice productivity, encompassing grain yield, aboveground biomass (AGB), and harvest index (HI), is essential for harvest planning, supply chain coordination, and food security. This study evaluates the feasibility of predicting all three productivity components using satellite and weather time series data while examining trade-offs between forecast accuracy and operational lead time. Five machine learning models (CatBoost, Gaussian Process Regression (GPR), Random Forest, Ridge regression, and TabPFN) were compared across six in-season prediction windows (December to May) using Sentinel-2 vegetation indices (Normalized Difference Vegetation Index (NDVI), Chlorophyll Index Red Edge 2 (CIRE2), Land Surface Water Index (LSWI)), weather variables (minimum and maximum temperature and radiation), and agronomic records from 256 commercial and experimental rice fields in southern New South Wales, Australia, over four growing seasons (2022–2025) using leave-one-year-out cross-validation. Rolling in-season forecasts were evaluated across December–May; March was selected for further analysis as a practical window that balances accuracy and timeliness for decision-making, with minimal additional error reduction in later months closer to harvest. TabPFN had the lowest RMSE for yield prediction (RMSE = 1.85 t ha⁻¹, $r=0.72$), Ridge had the lowest RMSE for AGB (RMSE = 3.05 t ha⁻¹, $r=0.77$), while tree-based models yielded the lowest RMSE for derived HI (RMSE ≈ 0.07). HI prediction showed weak regional relationships, with direct prediction yielding $\left|r\right|\approx 0.24$ and derived HI (predicted yield divided by predicted AGB) showing $r\approx 0$. Although strong correlations ($r>0.9$) between HI and vegetation indices were observed within individual site-seasons, consistent with other studies, these relationships were highly variable across site-seasons, reflecting the difficulty of inferring HI from canopy reflectance when biotic and/or abiotic stresses decouple AGB accumulation from grain filling. Both direct and derived HI approaches yielded comparable errors, indicating that satellite and weather data lack information content for regional-scale HI prediction. These findings support satellite-based yield and AGB forecasting for operational use. Full article