Search Results (44,402)

With the advancement in electronic attack technologies, intelligent jamming poses a significant challenge to the reliable transmission of wireless communications. Traditional anti-jamming methods often fail to adapt to dynamic nonlinear jamming environments. This paper addresses nonlinear swept-frequency jamming by modeling anti-jamming communication as a sequential decision-making problem and proposes an intelligent anti-jamming method based on proximal policy optimization (PPO) to optimize dynamic channel selection. Firstly, the channel selection problem is formalized as a Markov decision process (MDP), where a state space integrating jamming patterns and communication status is designed, the channel set is defined as the action space, and a multi-objective reward function trades off jamming avoidance against switching overhead. A dual-network architecture comprising a policy network and a value network is constructed, and the PPO algorithm is employed for policy updates, where a clipping mechanism is used to enhance training stability. The system optimizes the anti-jamming strategy online through a closed-loop process of “sensing–decision–learning–communication”. Simulation results demonstrate that compared to conventional methods, the proposed method significantly improves key performance indicators such as packet success rate and throughput. It can rapidly track changes in jamming, exhibiting excellent real-time performance and environmental robustness, and thus provides an effective solution for reliable communication in dynamic jamming environments. Full article

In the task planning of heterogeneous unmanned aerial vehicle formations, problems such as dynamic topological instability and sparse Pareto front exist, which affect the robustness of the planning. To address this, this paper proposes a cooperative task planning method based on multi-objective dolphin echolocation optimization driving. Firstly, a differentiated dynamic model of heterogeneous unmanned aerial vehicles covering different configurations such as rotors and fixed wings is constructed, and a dynamic communication topology model is established based on time-varying graph theory to quantify transmission delay and link stability. Then, a multi-objective optimization model is designed with task completion, energy balance, and time cost as the core, Bayesian networks are introduced to construct a dynamic threat field, and risk assessment and real-time response are achieved in complex environments. Based on this, a multi-objective dolphin echo optimization algorithm is adopted to solve the model, and its echo beam focusing search and adaptive weight allocation mechanism are utilized to effectively improve the convergence and distribution of the Pareto solution set. Finally, a “decision execution” hierarchical collaborative control architecture is constructed, utilizing the decision layer to output a global planning scheme and the execution layer to achieve rolling optimization and precise tracking of instructions through distributed model predictive control. The simulation test results show that this method can maintain high task completion, energy balance, and communication stability in different formation sizes and complex environments significantly better than traditional algorithms. When the formation size is between 20 and 60 sorties, the hypervolume (HV) index of this method is superior to that of the comparison method. In cases of sudden obstacles and complex electromagnetic interference scenarios, the average energy consumption of a single unmanned aerial vehicle after applying this method is maintained at 150–250 Wh, and the transmission delay is stable at 50–200 ms. The experimental results verify that this method has good planning robustness and collaborative real-time performance. Full article

Coordinating robot swarms requires jointly solving the interdependent Multi-Robot Task Allocation (MRTA) and Multi-Agent Path Finding (MAPF) problems under strict time and communication constraints, yet most existing methods rely on centralized planning or expose agents’ exact positions. In this study, a fully decentralized algorithm is proposed in which each agent estimates the positions and intended plans of others from broadcast bid values rather than shared coordinates, anticipating conflicts at intersections before moving and dynamically altering its movement or task assignment when it predicts it cannot reach its task in time. The method combines the Priority Inheritance with Backtracking (PIBT) algorithm for collision-free navigation with a novel Interval Type-3 Fuzzy Logic (IT3FL) mechanism for conflict resolution and congestion-aware rerouting. The approach was evaluated across seven benchmark environments against the centralized methods Enhanced Conflict-Based Search (ECBS) and ECBS with Task Allocation (ECBS-TA) and the Consensus-Based Auction Algorithm (CBAA). It reduced path cost by up to 7.10% relative to ECBS in open environments, while centralized methods remained superior in complex corridor-based maps. In the most demanding constrained scenario, it reduced solution cost by up to 47.03% and improved task completion by 35% over CBAA, demonstrating a robust, scalable decentralized alternative. Full article

Dimensional and transdiagnostic models have become central to contemporary efforts to move psychiatric nosology beyond DSM/ICD categories. This shift reflects persistent limitations of categorical syndromes as final biological targets, including within-diagnosis heterogeneity, cross-diagnostic comorbidity, developmental instability, and incomplete alignment with underlying mechanisms. This article examines a central unresolved problem in this transition: when, if ever, a descriptive or predictive psychiatric dimension can be interpreted as a candidate disease axis. We conducted a conceptual synthesis of major dimensional and transdiagnostic frameworks, including Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP), the general psychopathology factor, cross-disorder genomic models, clinical staging approaches, and data-driven subtyping. The analysis separates three levels of inference that are often conflated in psychiatric research: descriptive structure, predictive utility, and disease-level biological validity. The synthesis identifies a recurrent inferential error in which reproducible factors, clusters, or classifiers are prematurely treated as evidence of disease architecture. Such constructs may describe real covariance patterns or improve prognostic prediction without establishing biological validity. We propose an eight-domain hierarchical framework for promotion to candidate disease-axis status, organized into four core gatekeepers—replication across cohorts, ascertainment, and methods, developmental coherence, incremental prognostic value beyond diagnosis and nonspecific severity, and discriminability from nonspecific severity—and four supporting/disciplining domains: cross-level convergence, mechanistic constraint, clinical leverage, and explicit falsifiability/boundary conditions. On this basis, middle-level transdiagnostic spectra and selected cross-disorder genomic liabilities appear more defensible as candidate disease axes than highly global or weakly specified constructs. Psychiatry was justified in turning toward dimensional models, but dimensionality alone does not confer biological validity. The key task is not to choose between categories and dimensions, but to define the evidential thresholds under which dimensional constructs warrant ontological promotion. Full article

This paper addresses the bidirectional multi-chamber lock scheduling problem by formulating a multi-objective mixed-integer linear programming (MILP) model that simultaneously minimizes average ship waiting time and maximizes chamber utilization. A tailored adaptive large neighborhood search (ALNS) algorithm is developed specifically based on the principle of the destruction and reconstruction of solutions. The algorithm efficacy is validated using the real-word data from Huai’an Lock of the Subei canal. The scheduling rules and parameters are defined from practical operation records. Simulation results demonstrate that the ALNS-based optimization significantly improves lock performance with average chamber utilization increasing by 12.98% and waiting time decreasing by 44.40%. Sensitivity analyses on objective weights further confirm the robustness of the proposed method. Benchmark comparisons with a greedy heuristic, genetic algorithm (GA), and particle swarm optimization (PSO) highlight the effectiveness and computational efficiency of ALNS. This study further explores a threshold-based directional control strategy, showing that relaxing strict alternating-direction rules under asymmetric traffic demand can improve efficiency. The findings provide practical insights for lock scheduling, offering decision support for lock authorities in designing adaptive scheduling and directional control policies. Full article

In embodied-intelligence Industrial Internet of Things (IIoT), multi-AGV intelligent warehousing requires continuous processing of latency-sensitive tasks, such as environmental perception, inventory monitoring, and anomaly detection. Due to limited onboard computing capability and energy capacity, purely local execution can hardly satisfy real-time requirements, whereas fully cloud-based processing may incur excessive transmission delay and backhaul overhead. To address this issue, this paper investigates the joint optimization of AGV service-point migration and task offloading under a cloud-edge-end collaborative architecture. Considering the impact of service-point selection on wireless access, MEC resources, movement delay, and energy consumption, as well as the effect of offloading decisions on transmission, computation, and AGV-side energy cost, a dual-time-scale optimization model is formulated to minimize the long-term accumulated system delay while satisfying task latency and AGV energy constraints. To solve the resulting mixed discrete problem, a DPSO-MAPPO algorithm is proposed, where DPSO searches service-point plans satisfying movement and conflict constraints at the slow time scale, and MAPPO learns coordinated multi-AGV offloading policies at the fast time scale. The delay and energy feedback further enables coordination between the two types of decisions. Simulation results show that the proposed algorithm converges stably, reduces system delay by 13.55% compared with benchmark algorithms, and improves total energy consumption and energy-violation control. Full article

Trackless rubber-tired vehicles are the important auxiliary transportation equipment in coal mines. The main difficulty of their unmanned driving is that the underground environment information is complex but the onboard computing resources for perception and measurement are limited. To solve this conflict, this paper establishes a lightweight map dimension reduction framework to assist in path planning. Firstly, motivated by the idea of image convolution, the framework using the simplicity kernel is proposed for the high-resolution grid maps, which can reduce planning time while retaining the useful map information. Secondly, the reverse automata based on the greedy strategy are designed to get suitable machine-selected key points, which can solve the problem that some self-selected key points become impassable because of the dimension reduction. Moreover, a Bezier smoothing method based on slope interpolation is presented to avoid the collision between the smooth path and obstacle grid caused by the small number of path points planned on the reduced-dimension map. Finally, comparison experiments and downhole map experiment are carried out and discussed. The results show that using the proposed method to assist path planning can reduce time by 99.77% and reduce the number of redundant path points by 79.60%, and using the improved smoothing method from the framework can avoid collision risks caused by fewer path points. Full article

Ovarian cancer is a gynecological cancer, which, if metastasized and not detected early, can cause death among women. Therefore, accurate prediction of drug responses to ovarian cancer is needed. A gynecological pathologist inspects abnormality in tissues and provides a report for patients; however, this diagnostic process (1) is difficult to undertake; (2) requires experience; and (3) is time-consuming. Moreover, existing tools are imperfect. Hence, we present a computational pipeline to improve predictions of drug response pertaining to ovarian cancer. First, we downloaded digital pathology images pertaining to ovarian responses to bevacizumab from the Cancer Imaging Archive Repository. We employed a histogram of oriented gradients for images, constructed feature vectors, and used Fisher’s linear discriminant analysis to alter data representations through dimensionality reduction. This reduced-dimensionality data was used for regression analysis, employing support vector regression coupled with various kernels and calculating the area under the ROC curve (AUC). Experimental results were validated using transformer-based models (ViT and Swin) and other deep learning (DL) models (VGG16, ResNet50, InceptionV3, MobileNetV2, and EfficientNetB6). Our approach using a radial kernel (named SVRD + R) improved AUC performance by 17% compared to the best-performing transformer-based model (ViT). Likewise, AUC performance improved by 14.9% when compared against the best DL-based model (MobileNetV2). These results demonstrate feasibility, showing that induced models via the presented AI-based pipeline can lead to superior performance when investigating prediction problems pertaining to gynecologic cancer studies. Full article

In the context of the “dual-carbon” target, the large-scale integration of renewable energy sources leads to an increased risk of transient voltage instability at the high voltage direct current (HVDC) transmission receiving end. The HVDC transmission system possesses fast and accurate power regulation capability. After a fault occurs near the inverter station, reducing the DC current enables the reactive power from the compensation devices to be released and injected into the receiving-end power grid, thereby providing emergency voltage support for the receiving-end grid. To reduce control costs, an optimization model constrained by transient voltage violation is established, and the DC current modulation is acquired via an online solution. To maintain system stability and meet the requirements of online applications, it is crucial to rapidly solve the optimization model based on the grid operating mode and contingency information to update the emergency control strategy table in the special protection system (SPS). Conventional global orthogonal collocation (GOC) and adaptive orthogonal collocation (AOC)-based solution methods transform the optimization model in the continuous time domain into a nonlinear programming (NLP) problem for solution, which addresses the low efficiency of traditional rolling optimization. However, the GOC- and AOC-based solution methods improve the discretization accuracy of the model by pursuing global uniform densification of collocation points, making it difficult to balance solution accuracy and solution efficiency. To this end, this paper proposes an efficient interval partition dynamic adaptive orthogonal collocation (IP-DAOC)-based solution method. Firstly, the overall optimization time window is interval-partitioned into multiple initial intervals, and an interval-partitioned transient voltage stability emergency control optimization model is established. Furthermore, the interval length and the number of collocation points are dynamically adjusted according to the curvature of interpolation polynomials at collocation points in different intervals. Finally, after interval adjustment, the dynamic equations discretized in adjacent intervals are made continuous by reconstructing the differential matrix. This solution method reduces the total number of collocation points, thereby decreasing the scale of the NLP problem and narrowing the search space, significantly improving solution efficiency while ensuring solution accuracy. To verify the effectiveness of the proposed solution method, simulations are carried out on a modified IEEE 14-bus system. The results are compared with those of the traditional GOC- and AOC-based solution methods, which further demonstrate the superiority of the proposed solution method. Full article

Polymer coatings are integral to nearly every modern cardiovascular and endovascular device, including drug-eluting stents (DESs) and drug-coated balloons (DCBs), bioabsorbable vascular scaffolds (BVSs), occluders, grafts, and catheter and guidewire hydrophilic surfaces. Persistent complications, including late stent thrombosis, delayed endothelialization, hypersensitivity, and restenosis, show that coatings actively shape biological responses rather than acting as inert drug carriers. Their surface chemistry, drug release kinetics, and degradation behavior are upstream determinants of blood– and tissue–material responses that govern healing and failure. This review frames coating selection as a structure–property–biological response problem. It surveys the major classes of synthetic polymer coatings and the defining surface and bulk properties. This review also examines how composition and architecture control drug release, and traces the interfacial cascade of protein adsorption, coagulation and complement activation, platelet and leukocyte responses, and neutrophil extracellular trap (NET) formation. These mechanisms are linked to contemporary design strategies that improve hemocompatibility, limit thrombosis, promote endothelial recovery, and tune degradation, and to the standardization and translation gaps that remain. The central message is that polymer coatings are not biologically equivalent. Their surface chemistries and degradation profiles determine the thrombo-inflammatory outcomes. Therefore, coating design should be guided by intended biological response, not drug release alone. Full article

The increasing penetration of distributed generation (DG) in distribution systems poses significant operational challenges, including increased power losses, voltage profile deviations, and variations in short-circuit currents. These issues may compromise network safety, reliability, and the selectivity of protection schemes under different operating scenarios. This paper proposes a hybrid optimization methodology for the optimal placement and sizing of DG, aiming to minimize active power losses while ensuring voltage regulation and keeping short-circuit currents within permissible limits. An integrated approach is proposed that combines a mesh-to-radial network reconfiguration strategy with a modified Ant Lion Optimization algorithm, known as ALO-DG, enabling the simultaneous optimization of network topology and the allocation of distributed generators at candidate buses. The problem is formulated taking into account power balance constraints, voltage limits, distribution network capacity limits, and short-circuit current limits. The proposed methodology achieved substantial reductions in active power losses in the IEEE 33-bus and 69-bus test systems, reaching 84.42% and 91.56%, respectively. These improvements were accompanied by enhanced voltage profiles while preserving the radial operating structure of the distribution networks. Furthermore, the proposed hybrid methodology serves as a tool for the planning and operation of distribution systems with high DG penetration, particularly in scenarios where grid security and protection coordination are critical considerations. Full article

Directly coupled electrodynamic vibrators are widely used in vibration testing due to their ability to generate large propulsive forces. However, increasing the propulsive force typically requires higher driving currents, which leads to significant electrical heat generation and thermal management challenges in the armature coil. To address this issue, this study proposes a multi-objective parameter optimization framework for the design of armature coils in high-propulsive-force electrodynamic vibration tables. Two optimization objectives are formulated based on electromagnetic and thermal considerations: minimization of electrical heat generation in the armature coil; and improvement in cooling capability, characterized by the ratio between the cooling water channel area and the conductive cross-sectional area. The key geometric parameters of the coil, including winding configuration and cross-sectional dimensions, are treated as design variables. The resulting multi-objective optimization problem is solved using a multi-objective particle swarm optimization (MOPSO) algorithm to obtain a set of Pareto-optimal solutions that balance the two competing thermal objectives. The present work focuses on the pre-design-stage optimization of the armature coil after the rated propulsive force and geometric envelope of the vibrator have been specified. A representative high-propulsive-force electrodynamic vibrator is analyzed as a case study. Finite element thermal simulations show that the selected Pareto-optimal design reduces the peak armature-coil temperature by approximately 9.7–36.6% compared with the other investigated coil configurations under the same propulsive force condition. The proposed method provides an efficient approach for the thermally constrained parameter design of high-power electrodynamic vibrator armature coils. Full article

Low-carbon operation and reasonable carbon responsibility allocation are essential for improving source-load coordinated emission reduction in park integrated energy systems (PIESs). Existing allocation methods usually trace carbon emissions or calculate marginal contributions, but they still have difficulty distinguishing heterogeneous park users with different load rigidity, demand response (DR) capability, payment capability and real carbon-reduction potential. To address this problem, this paper proposes a carbon responsibility allocation method for PIESs considering user heterogeneity and develops a carbon-cost-feedback-based bi-level low-carbon scheduling model. First, park users are classified into high-energy-consuming industrial users, commercial and public service users, and energy infrastructure users according to quantitative criteria related to energy consumption scale, load continuity, adjustable load proportion and distributed-resource interaction capability. A heterogeneity indicator system is then established, including DR elasticity, electricity utilization efficiency, payment capability, DR potential and actual carbon-reduction potential. Second, an improved Shapley value allocation model is constructed by combining coalition marginal contribution with entropy-weighted heterogeneity correction. The allocation results are converted into user-side carbon responsibility cost signals and embedded into a bi-level optimal scheduling model, where the upper level minimizes the system operating cost and the lower level minimizes users’ integrated energy-use cost. Case studies show that, compared with the conventional economic scheduling scenario, the proposed model reduces the total system cost from CNY 5.0782 million to CNY 4.3258 million and decreases carbon emissions from 14,994.39 t to 10,874.62 t, corresponding to reductions of 14.82% and 27.47%, respectively. The results indicate that the proposed method can coordinate fairness-oriented carbon responsibility allocation with incentive-oriented low-carbon scheduling, supporting both SDG 11 and SDG 12. Full article

Combined Vertical–Horizontal Well Patterns (CVHWPs) have been increasingly applied in mature and complex reservoirs, such as the C5 Block. Their application is attractive because they provide extensive reservoir coverage and high development efficiency. However, close well spacing and the three-dimensional configuration of vertical and horizontal wells can induce strong stress-shadow interference. This interference makes fracture propagation difficult to control and may reduce stimulation effectiveness. To address this problem, a multi-well, multi-fracture induced-stress model for CVHWP stimulation was developed in this study. The model was validated using laboratory three-stage fracturing experiments, including two horizontal-well stages and one vertical-well stage, together with field observations. Across three stages, the calculated stress intensity factors at breakdown are closely matched, validating the induced-stress model. When the vertical well was fractured first, the horizontal principal-stress difference at the adjacent horizontal stage increased by 2.01 MPa, which was unfavorable for branched fracture development. In contrast, when the horizontal stage was fractured first, the stress difference decreased by 3.25 MPa at the subsequent horizontal stage and by 3.89 MPa at the vertical-well stage. This sequence is preferable because fractures generated from the vertical well impose a stronger stress perturbation on adjacent horizontal-well fractures than fractures generated from the horizontal well impose on the subsequent vertical-well fracture. Under the tested CVHWP conditions, the horizontal-well fractures tended to form nearly symmetric bi-wing planar fractures, whereas branched fractures were more likely to develop in the vertical well. Therefore, for CVHWP reservoirs with close vertical–horizontal well spacing and significant stress interference, fracturing the horizontal well before the vertical well is recommended to control fracture propagation and promote multiple-fracture formation. Field application of this sequence showed notable production improvement, indicating that the proposed method can provide practical guidance for unconventional well-pattern fracturing design. Full article

AI-enabled infrastructure systems increasingly govern access to emergency services, disaster relief, and utility restoration, yet they routinely produce inequitable outcomes even when allocation algorithms apply procedurally neutral rules. The standard explanation locates the cause inside the algorithm. This paper argues instead that inequity arises from the interaction between the algorithm and the physical environment in which it operates: network topology, resource locations, and demand distribution jointly constrain what any policy can achieve, and when those constraints are sufficiently binding, ethical infeasibility is structural rather than algorithmic. We introduce a constraint-based formulation that embeds ethical requirements into the feasible region, and a hierarchical Irreducible Infeasible Subsystem (IIS) procedure that attributes infeasibility to rule design, algorithmic choice, or physical infrastructure. We further establish the Structural Infeasibility Theorem, deriving closed-form bounds on inter-group disparity across all feasible policies. The framework was applied to zone-decomposable infrastructure allocation problems generally, with a metropolitan ambulance-dispatch system serving as a concrete instantiation. The study delivers four findings. First, the minimum-service violation may not be caused by the allocation algorithm itself; rather, it may arise from the physical layout of the infrastructure. Second, the observed efficiency–equity trade-off may not be an unavoidable feature of equitable allocation, but may instead reflect the difficulty of achieving equity within an underbuilt system. Third, before new infrastructure is added, improvements in equity may represent harm redistribution rather than harm reduction. Fourth, the IIS certificate can be translated into a concrete capital-investment requirement, showing what physical change may be needed to restore ethical feasibility. Full article