Search Results (1,167)

Against the backdrop of the dual-carbon strategy promoting the green and low-carbon transformation of the shipping industry, pollutant emissions generated during vessel berthing operations have become a critical challenge in port environmental governance. To address the combined effects of the priority berthing policy for new energy vessels and time-of-use electricity pricing, a joint optimization model for berth and shore power allocation is developed with the objectives of minimizing the total economic cost of vessels and the environmental tax cost associated with pollutant emissions. An improved Adaptive Large Neighborhood Search algorithm (ALNS-II) is further designed to solve the model. Numerical experiments based on actual port data verify the effectiveness of the proposed model and the superiority of the algorithm. The results indicate that, under time-of-use electricity pricing, the priority berthing policy for new energy vessels can shorten their waiting time at anchorage and encourage fuel-powered vessels to shift toward electrification. When the peak-to-valley electricity price ratio increases from 4.1:1 to 7.5:1, the environmental tax cost of pollutant emissions decreases slightly, whereas the total economic cost of vessels rises by 4.17%, suggesting that the peak-to-valley electricity price ratio should not be set excessively high. In addition, increasing the proportion of new energy vessels to 70% is more conducive to improving the overall economic and environmental performance of ports. The findings provide a theoretical basis and decision support for the optimal allocation of port resources under the coordination of multiple policies. Full article

With the rapid development of applications such as smart cities and the industrial internet, the computation-intensive tasks generated by massive sensing devices pose significant challenges to traditional cloud computing paradigms. Unmanned aerial vehicle (UAV)-assisted edge computing systems, leveraging their high mobility and wide-area coverage capabilities, offer an innovative architecture for low-latency and highly reliable edge services. However, the practical deployment of such systems faces a highly complex multi-objective optimization problem featured by the tight coupling of task offloading decisions, UAV trajectory planning, and edge server resource allocation. Conventional optimization methods are difficult to adapt to the dynamic and high-dimensional characteristics of this problem, leading to suboptimal system performance. To address this critical challenge, this paper constructs an intelligent collaborative optimization framework for UAV-assisted edge computing systems and formulates the system quality of service (QoS) optimization problem as a mixed-integer non-convex programming problem with the dual objectives of minimizing task processing latency and reducing overall system energy consumption. A multi-agent joint task association and trajectory optimization (MA-JTATO) algorithm based on hybrid reinforcement learning is proposed to solve this intractable problem, which innovatively decouples the original coupled optimization problem into three interrelated subproblems and realizes their collaborative and efficient solution. Specifically, the Advantage Actor-Critic (A2C) algorithm is adopted to realize dynamic and optimal task association between UAVs and edge servers for discrete decision-making requirements; the multi-agent deep deterministic policy gradient (MADDPG) method is employed to achieve cooperative and energy-efficient trajectory planning for multiple UAVs to meet the needs of continuous control in dynamic environments; and convex optimization theory is applied to obtain a closed-form optimal solution for the efficient allocation of computational resources on edge servers. Simulation results demonstrate that the proposed MA-JTATO algorithm significantly outperforms traditional baseline algorithms in enhancing overall QoS, effectively validating the framework’s superior performance and robustness in dynamic and complex scenarios. Full article

Post-disaster emergency perishable material distribution is an essential part of emergency relief, which is of great significance to reducing disaster losses and casualties and improving rescue efficiency. However, in actual rescue, the demand information of disaster sites is often complex to determine, and the demand for emergency perishable materials needs to be clarified. Therefore, the single-cycle distribution makes it difficult to meet the demand for emergency perishable materials at disaster sites. To effectively improve the efficiency of emergency relief, this paper constructs a distribution optimization model with a multi-cycle vehicle path and the dual objectives of minimizing the distribution delay penalty and corruption cost and minimizing the unsatisfied demand. Initially, the fuzzy demand is addressed through the application of triangular fuzzy numbers and the most probable value weighting method. Following this, a two-stage optimization algorithm is devised by integrating the K-means++ algorithm with an enhanced Differential Evolutionary Whale Optimization Algorithm (DE-WOA). This algorithm operates by first clustering the affected points and subsequently solving the multi-objective model, thereby providing a robust methodology and strategic recommendations for the distribution of perishable materials across diverse scenarios. Our study reveals that the multi-objective model developed in this paper exhibits remarkable operability and practicality in the distribution of post-disaster emergency perishable materials, as evidenced by the verification via numerical examples. Through a comparison with the single-stage whale optimization algorithm, it is evident that the enhanced two-stage differential evolutionary whale optimization algorithm not only demonstrates a substantially faster convergence rate and a superior solution quality but also proves to be more suitably adapted to the proposed model. Significantly, the overall optimization performance has been augmented by 33%, thereby providing further substantiation of the efficacy of the designed improved algorithm. Full article

Underwater object detection plays a crucial role in the domain of marine engineering. Due to blur, uneven illumination and noise in underwater images, generic object detectors often fail to accurately detect underwater targets. Existing underwater object detection methods generally neglect the enhancement and refinement of multi-scale features, limiting further improvements in detection accuracy. In response to these challenges, we propose the Feature Enhancement and Alignment Pyramid Network (FEAPN), a novel underwater object detection framework. FEAPN consists of two key innovations. First, the Adaptive Feature Refinement Module (AFRM) is developed to adaptively enhance contextual features from complex backgrounds. Second, the Dual-path Feature Alignment Module (DFAM) is designed to align multi-scale features, utilizing cross-layer information to optimize feature representation. Extensive experiments demonstrate that FEAPN achieves state-of-the-art performance. Specifically, FEAPN achieves a 2.4% mAP improvement over the baseline and outperforms the current leading underwater detector by 1.2% mAP. Furthermore, the effectiveness of each component is validated through ablation studies. Full article

With the integration of renewable energy into traction power supply systems at a high proportion and penetration level, the intermittency and randomness of renewable energy output significantly intensify the fluctuation characteristics of traction loads, posing severe challenges to the stable operation and precise dispatch of the system. To effectively address the dynamic tracking and anti-disturbance issues arising from the dual uncertainties of source and load, this paper proposes a dual-timescale two-layer optimization dispatch strategy based on Model Predictive Control (MPC). In the upper-layer optimization, with the objective of optimal system economic operation, a multi-step rolling optimization method is adopted to formulate a long-timescale baseline dispatch plan, fully considering the temporal correlation of photovoltaic and wind power outputs and the periodic characteristics of traction loads. In the lower-layer optimization, aimed at smoothing power fluctuations and correcting prediction deviations, the technical advantages of supercapacitors—high power density and fast response—are utilized to perform real-time tracking and dynamic compensation of the upper-layer baseline plan. This effectively reduces the impact of prediction errors on control accuracy, achieves smooth control of tie-line power, and enhances overall system stability. Case study results based on an actual railway traction power supply system demonstrate that the proposed method can fully leverage the coordinated and complementary characteristics of the hybrid energy storage system, effectively suppress power fluctuations from renewable energy output and traction loads, and achieve economic operation objectives while ensuring system disturbance rejection performance, thereby validating the effectiveness and practicality of the strategy. Full article

This paper focuses on the dynamics modeling and control methods for an underactuated Dual-Unmanned Aerial Helicopter Slung Load System (DUH-SLS), which consists of two Unmanned Aerial Helicopters (UAHs) connected to the suspended load via two sling cables. The DUH-SLS is a multi-body coupled system with internal ideal constraint forces and has seven motion degrees of freedom (DOFs) in the longitudinal plane. In this paper, a set of independent and complete generalized coordinates is selected to describe the system’s motion. The dynamics model of DUH-SLS is established using Lagrange analytical mechanics. This approach, which avoids system internal forces, greatly improves modeling efficiency. Finally, the correctness of this dynamics model is validated using a virtual prototype of the DUH-SLS developed in the multi-body dynamics simulation software ADAMS. The DUH-SLS is a complex nonlinear controlled object, and the iterative Linear Quadratic Regulator (iLQR) method is introduced to design an integrated optimal controller to achieve trajectory tracking and swing suppression for the DUH-SLS. This method transforms the quadratic optimal control problem of nonlinear systems into a series of linear quadratic optimal control (LQR) problems through iterative optimization in function space, thus obtaining an optimal solution. The iLQR optimal controller requires offline iterative computation, but the optimal control obtained has a state feedback closed-loop form, which ensures robustness during online control. Numerical simulation results demonstrate that the proposed iLQR optimal controller exhibits excellent control performance in complex multi-task scenarios. Particularly in trajectory tracking tasks, the maximum average position tracking error of the iLQR controller is only 0.14 m, compared to 3.57 m and 3.11 m for the LQR and LMC (Lyapunov Method Controller) controllers, respectively. Furthermore, the controller demonstrates strong robustness against internal parameter perturbations and external complex wind disturbances, fully validating the effectiveness and superiority of the proposed approach. Full article

This study develops a multi-objective optimization framework for sustainable container truck dispatching in port logistics, addressing the limited joint consideration of environmental compliance, worker-sensitive assignment, and operational efficiency in traditional dispatching practice. The problem is formulated as a constrained assignment-and-scheduling model under time-window, compliance, capacity, and service requirements. To balance optimality and real-time responsiveness, a dual-path solution strategy is proposed, combining a mixed-integer linear programming (MILP) model for small-scale instances with a Priority-Based Constructive Heuristic with Conflict Resolution (PBCH-CR) for medium-to-large-scale scenarios. Computational experiments on scenario-based synthetic instances calibrated to empirical port-operation distributions show that PBCH-CR maintains 100% environmental compliance for assigned orders, improves familiarity-oriented matching relative to the FCFS baseline, and sustains strong emergency-response performance within sub-minute computation times. Sensitivity analysis further shows that improving urgency-oriented performance entails a reduction in freight-revenue-oriented performance. Overall, the framework provides a practical approach to balancing environmental compliance, operational efficiency, and worker-sensitive dispatching, with relevance to Sustainable Development Goals 11 and 13 and to SDG 8-related objectives. Full article

Wind–solar coupled hydrogen production DC microgrids have significant potential for improving renewable energy utilization and reducing the cost of hydrogen production. However, the randomness of wind–solar power causes frequent electrolyzer start–stop operations, accelerating lifetime degradation, while a single energy storage system cannot simultaneously suppress power fluctuations and regulate energy. Therefore, this study proposes a two-stage day-ahead energy scheduling optimization framework. A DBSCAN–K-means hybrid clustering method generates representative wind–solar power scenarios. A supercapacitor-based strategy mitigates high-frequency power fluctuations using empirical mode decomposition. Furthermore, a dual-scenario-driven electrolyzer scheduling strategy adapted to different wind–solar output conditions is developed, where power allocation is determined by battery state-of-charge and electrolyzer operating states, enabling stepwise power compensation and dynamic operating-state optimization. Case studies comparing wind–solar-only supply, a conventional strategy, and the proposed strategy demonstrate that the proposed strategy balances hydrogen production and economic objectives, and reduces annual electrolyzer start–stop cycles by 73%, thereby prolonging electrolyzer lifetime. Furthermore, the proposed framework enhances renewable energy utilization, reduces curtailment, and lowers lifecycle costs, thereby contributing to the development of sustainable hydrogen production systems. Full article

This study develops and evaluates three complementary predictive modeling frameworks for gearbox oil temperature forecasting: Stochastic Differential Equation (SDE) modeling with iterative Markov correction, multi-objective genetic algorithm-enhanced grey modeling (MOGA-GM(1,N)), and multi-output Gaussian Process Regression (MO-GPR). The study used supervisory control and data acquisition (SCADA) data from a 1.5 MW wind turbine gearbox, comprising 14 temperature measurements spanning 789 operational hours. The SDE framework partitions temperature evolution into deterministic aging effects and stochastic environmental perturbations, achieving a fitting accuracy of 2.5% and testing accuracy of 8.0% after thirty iterative corrections. The MOGA-GM(1,N) approach optimizes weight coefficients through the dual objective of minimizing the posterior difference ratio and maximizing small error probability, attaining first-class accuracy classification ($C=0.06$; $P=0.99$) while identifying mechanical loads and rotational speeds as dominant thermal drivers. MO-GPR demonstrates competitive performance with uncertainty quantification capabilities, achieving RMSE values of 2.51–7.48 depending on training SCADA data proportions. Comparative analysis shows that the iteratively refined SDE methodachieves the best prediction accuracy in this case study for continuous thermal trajectory forecasting, while MOGA-GM(1,N) excels at wear source diagnostics and operational factor analysis. The proposed framework addresses persistent challenges in wind turbine condition monitoring, including extreme nonlinearity, discontinuous data, and unpredictable thermal spikes. The results suggest potential for implementation in preventive maintenance systems, enabling timely intervention before critical thermal thresholds that precipitate component failure. Full article

Background/Objectives: Amorphous solid dispersions (ASDs) produced via hot-melt extrusion (HME) and fused deposition modeling (FDM) 3D printing represent a promising strategy for improving the performance of poorly water-soluble drugs. However, the integrated HME-FDM workflow is inherently energy-intensive, and sustainability considerations are rarely incorporated into formulation and process optimization. The present study aimed to develop and optimize indomethacin (IND) ASDs using a systematic Design of Experiment (DoE) framework that integrates electrical energy consumption as a quantitative response alongside pharmaceutical performance attributes. Methods: Polymer–plasticizer miscibility was screened using hot-stage microscopy, followed by filament preparation via HME. A factorial DoE was applied to optimize drug loading and extrusion temperature considering electrical energy consumption, extrusion yield, encapsulation efficiency, and residual crystallinity. Solid-state characterization was performed using DSC and XRD. The optimized filament was subsequently subjected to geometry screening and a second DoE to optimize platform temperature, nozzle temperature, and printing speed with respect to printing time, electrical energy consumption, and drug assay. Results: Complete drug amorphization was achieved within a defined thermal window, with residual crystallinity governed by kinetic dissolution constraints at lower extrusion temperatures. Electrical energy demand during both HME and FDM was strongly influenced by thermal setpoints and process duration. Multi-response overlay analysis identified sustainability-oriented operating windows for both stages. Experimental validation confirmed close agreement between predicted and observed responses, demonstrating simultaneous reduction in electrical demand and maintenance of dose accuracy and solid-state stability. Conclusions: This study demonstrates that electrical energy consumption can be systematically embedded as a quantitative design variable in pharmaceutical process optimization. The proposed dual-stage DoE strategy establishes a rational framework for developing 3D-printed ASD dosage forms that balance molecular performance and environmental efficiency. Full article

Human-to-robot (H2R) handovers are critical in human–robot interaction but are challenged by complex environments that impact robot perception. Traditional RGB-based perception methods exhibit severe performance degradation under harsh lighting (e.g., glare and darkness). Furthermore, H2R handovers occur in unstructured environments populated with fine-grained visual details, such as multi-angle hand configurations and novel object geometries, where conventional semantic segmentation and grasp generation approaches struggle to generalize. To overcome lighting disturbances, we present an H2R handover system with a dual-path perception pipeline. The system fuses perception data from a stereo RGB-D camera (eye-in-hand) and a time-of-flight (ToF) camera (fixed scene) under normal lighting, and switches to the ToF camera for reliable perception under glare and darkness. In parallel, to address the complex spatial and geometric features, we augment the Point Transformer v3 (PTv3) architecture by integrating a T-Net module and a self-attention mechanism to fuse the relative positional angle features between human and robot, enabling efficient real-time 3D semantic segmentation of both the object and the human hand. For grasp generation, we extend GraspNet with a grasp selection module optimized for H2R scenarios. We validate our approach through extensive experiments: (1) a semantic segmentation dataset with 7500 annotated point clouds covering 15 objects and 5 relative angles and tested on 750 point clouds from 15 unseen objects, where our method achieves 84.4% mIoU, outperforming Swin3D-L by 3.26 percentage points with 3.2× faster inference; (2) 250 real-world handover trials comparing our method with the baseline across 5 objects, 5 hand postures, and 5 angles, showing an improvement of 18.4 percentage points in success rate; (3) 450 trials under controlled adverse lighting (darkness and glare), where our dual-path perception method achieves 82.7% overall success, surpassing single-camera baselines by up to 39.4 percentage points; and (4) a comparative experiment against a state-of-the-art multimodal H2R handover method under identical adverse lighting, where our system achieves 75.0% success (15/20) versus the baseline’s 15.0% (3/20), further confirming the lighting robustness of our design. These results demonstrate the system’s robustness and generalization in challenging H2R handover scenarios. Full article

Distributed integrated energy systems embedding biomass combined heat and power (BCHP) have the potential to enhance energy supply reliability in rural areas and to support the low-carbon transformation. However, the sources and transmission paths of car-bon emissions remain difficult to quantify due to the multi-energy coupling and diverse conversion processes. To address these issues, this study develops a carbon flow tracking and scheduling strategy for BCHP-integrated distributed energy systems. First, a bio-chemical reaction process model for BCHP is established to enable a life cycle-based carbon emission accounting. Second, the flexible heat-to-power ratio characteristics of BCHP are considered to more accurately reflect multi-energy coupling under varying operating conditions. Third, a dual-objective optimal scheduling model is constructed by combining node carbon potential with operating costs, enabling the system to simultaneously minimize operating costs and carbon emissions. A case study of an integrated energy system in Anping County, Hebei Province, demonstrates that the proposed method reduces total carbon emissions by over 9.8%, increases renewable energy utilization by 15.2%, and lowers operating costs by 7.5%. The results reveal the carbon flow characteristics and emission reduction potential of rural distributed integrated energy systems embedding BCHP, providing methodological support and empirical evidence for refined low-carbon governance. Full article

As highway traffic demand continues to rise, research on balancing CO + HC emissions and traffic efficiency through variable speed limit (VSL) systems has become a critical topic. However, existing research has primarily focused on homogeneous road segments and connected autonomous driving scenarios, resulting in a gap in alignment with the operational requirements of actual road segments. To this end, this study focuses on heterogeneous highway sections as the core scenario. Based on the modified Greenshields model and the non-dominated sorting genetic algorithm (NSGA-II), it proposes a zoned VSL strategy optimized for dual objectives of traffic efficiency and CO + HC emissions. The case study results from the Qin-Nan section of the G75 Lanhai Expressway demonstrate that this strategy, through zonal differentiated speed limit setting, effectively enhances traffic flow stability and continuity. It achieves a synergistic increase in both traffic flow and vehicle speed while simultaneously curbing the progression of congestion during high-traffic scenarios. Additionally, this strategy achieves a cumulative reduction in CO + HC emissions of approximately 9.5% while maintaining traffic efficiency. It offers new insights for optimizing speed limit schemes on expressways under environmental considerations, demonstrating significant practical engineering value. Full article

Against the backdrop of China’s “Dual Carbon” goals, the impact of environmental regulation on heavily polluting firms has emerged as a critical point of academic and policy discourse. This study treats the implementation of China’s New Environmental Protection Law (NEPL) as a quasi-natural experiment and employs a difference-in-differences (DID) framework to estimate its effect on corporate performance. Our empirical results demonstrate that the NEPL significantly enhances the financial performance of heavily polluting firms, suggesting that stringent regulation does not necessarily come at the expense of economic efficiency. Mechanism analysis indicates that the law facilitates performance gains by optimizing managerial efficiency and mitigating information asymmetry resulting from heightened capital market scrutiny. Furthermore, heterogeneity analysis reveals that this positive impact is more pronounced among large-scale enterprises, non-state-owned enterprises (non-SOEs), and firms with robust green innovation capabilities. These findings provide important implications for policymakers, underscoring the role of stringent environmental regulation in driving corporate performance while simultaneously advancing environmental policy objectives. Full article

Background/Objectives: LINAC-based single-isocenter (SIT) stereotactic radiosurgery (SRS) enables efficient treatment of multiple brain metastases but may compromise target conformity and increase low-dose brain exposure, particularly for spatially distributed lesions. Dual-isocenter techniques (DITs) may mitigate these limitations, while Gamma Knife (GK) remains the reference standard for high-selectivity radiosurgery. This study compares SIT- and DIT LINAC-based SRS with GK, focusing on target conformity and low-dose brain exposure under equivalent, zero-margin targeting assumptions. Methods: Twenty-eight patients with multiple brain metastases (197 lesions) were included in this retrospective planning study. For each patient, three plans were generated: a GK plan and LINAC-based SIT and DIT plans using automated dynamic conformal arc optimization (Elements Multiple Brain Metastases). All plans were generated using a zero-millimeter GTV-to-PTV margin strategy. For DIT, lesions were automatically clustered and assigned to two isocenters. Target coverage required ≥99% of each GTV to receive the prescription dose. Plan quality was evaluated using the Paddick Conformity Index (PCI) on a per-lesion basis and low-dose brain volumes (V12, V10, V5, V4, and V3 Gy) on a per-patient basis. Paired non-parametric tests and multivariable models were used to assess technique-related differences and associations with total target volume and lesion count. Results: GK achieved the highest median PCI (0.83), followed closely by DIT (0.77), while SIT plans demonstrated significantly lower conformity (0.73). Compared with GK, the median PCI difference was −0.05 for DIT and −0.08 for SIT. Conformity for DIT remained stable across lesion volumes and lesion counts, whereas GK conformity increased modestly with lesion size. Low-dose brain exposure differed significantly between techniques at all dose levels (p < 0.001). GK consistently yielded the lowest Vx volumes, SIT the highest, and DIT intermediate values. Relative to GK, SIT plans showed progressively larger increases in low-dose exposure at lower dose levels (mean ΔV3 ≈ +149 cc), while DIT reduced this low-dose spread (mean ΔV3 ≈ +117 cc). Total target volume was the dominant predictor of low-dose brain exposure across all techniques, with a smaller additional contribution from lesion count. Conclusions: DIT LINAC-based SRS significantly improves target conformity and reduces low-dose brain exposure compared with SIT delivery, achieving dosimetric performance that closely approximates Gamma Knife under equivalent zero-margin targeting assumptions. While Gamma Knife remains the reference standard for low-dose sparing, dual-isocenter planning represents a clinically robust and scalable alternative that effectively balances plan quality and treatment efficiency in patients with multiple brain metastases. Full article