Search Results (1,123)

With the rapid advancement of artificial intelligence technology, multi-agent systems are being widely applied in fields such as autonomous driving and robotic collaboration. However, existing methods often suffer from the disconnection between intention recognition and strategy optimization, leading to inefficiencies in group collaboration. This paper proposes a multi-agent cooperative group game model based on Intention-Strategy Optimization (ISO-MAGCG). The model establishes a two-layer optimization framework encompassing intention and strategy, enabling dynamic adaptation through the co-evolution of upper-layer intention recognition and lower-layer strategy optimization. A Group Attention-based Intention Recognition Network (GAIN) is designed to efficiently capture complex interactions among agents. Furthermore, an Adaptive Group Evolution Algorithm (AGEA) is proposed to ensure the stability of large-scale cooperative endeavors. Experiments conducted in navigation, resource collection, and defense collaboration scenarios validate the effectiveness of the proposed method. Compared with mainstream algorithms such as QMIX, MADDPG, and MAPPO, ISO-MAGCG demonstrates significant superiority in metrics including task success rate and cooperative efficiency, achieving an average improvement of 8.4% in task success rate, a 12% enhancement in cooperative efficiency, and an intention recognition accuracy of 94.3%. The results indicate notable performance advantages and favorable scalability. Full article

This study involves the construction of a dynamic evolutionary game model involving three key participants, including the Group Purchasing Organization (GPO), medical institutions, and pharmaceutical suppliers, while comprehensively considering critical factors such as benefit compensation, bad debt risk, and fiscal costs. The model characterizes the strategy evolution of each participant under bounded rationality and imitation learning mechanisms. Based on the replicator dynamics equations, the evolutionary trajectories and equilibrium conditions of the three parties’ strategies are systematically derived. The Jacobian matrix is then used to analyze the local stability of eight boundary equilibria and potential internal mixed equilibria. Furthermore, to capture the optimal adjustment process of the compensation mechanism, the GPO’s compensation level is introduced into an optimal control framework. A controlled evolutionary system is formulated, and the dynamic optimal relationship between compensation intensity and system state is described using the Hamilton–Jacobi–Bellman (HJB) equation. Through analytical linearization and numerical simulations, the optimal feedback compensation law and its closed-loop evolutionary trajectory are obtained, allowing for a comparative analysis between the “fixed compensation” and “optimal compensation” scenarios. The results reveal that an appropriately designed dynamic compensation mechanism can significantly enhance system cooperation stability and overall social welfare. This provides a quantitative theoretical foundation and methodological tool for the refined design and dynamic regulation of pharmaceutical group purchasing policies. Full article

To address the difficulty of balancing trajectory-tracking accuracy and yaw stability for distributed-drive four-axle heavy trucks under high-speed and large-curvature cornering conditions, this paper proposes a hierarchical cooperative control strategy. The upper layer employs Sliding Mode Control (SMC) to achieve precise trajectory tracking, while the lower layer integrates a sliding-mode-based Direct Yaw Moment Control (DYC) and a differential braking allocation strategy to enhance vehicle stability. TruckSim–Simulink co-simulation results demonstrate that, under large-curvature scenarios such as S-shaped paths, sharp lane changes, and single-lane transitions, the proposed strategy outperforms the conventional SMC method. Specifically, the maximum lateral deviation is reduced by 19.23–23.02%, the peak heading angle error decreases from 5.3° to 3.5°, the maximum yaw rate drops from 12.6°/s to 4.6°/s (a 63.49% reduction), and the peak sideslip angle at the vehicle’s center of mass converges from 4.6° to 3.8° (a 17.39% decrease). The results indicate that the proposed strategy achieves coordinated optimization of trajectory tracking and yaw stability under high-speed, large-curvature cornering conditions, providing both theoretical support and engineering value for high-dynamic control of distributed-drive heavy trucks. Full article

The setting and scope of cooperation between geoparks and their partners significantly affect their professional development, overall sustainability of operation, the attractiveness of the area for visitors and economic stability. The article deals with the concept of cooperative management in the case study of the Banská Bystrica Geopark in the Slovak Republic, which presents a practical and realistic framework for the development of long-term partnerships, their mutual harmonisation and planning of common activities in accordance with the needs of a specific area. The application of these principles is described in the form of a case study, which represents a model educational product called “The Copper Yarn of the Spania Valley”. The resulting model confirms the importance sustainable approach, the importance of cooperation in the development of the product, which was created as a result of targeted cooperation between the Banská Bystrica Geopark and several regional partners, including the local government, professional institutions and stake-holders. The article points out the need to embed solid and clearly defined cooperation approaches into strategic documents at the national and regional levels, points out the need for state support and a clearly defined position of geoparks in the tourism system. The results could significantly contribute to their stability, sustainability and the effective functioning of partnerships in the territory of geoparks. Full article

With the rapid development of the low-altitude economy, UAV logistics delivery systems have garnered widespread attention due to their flexibility and efficiency. The cooperative delivery mode involving a UAV with a suspended payload and a ground vehicle represents a typical networked distribution scenario, whose performance is constrained by the tight coupling of sensing, communication, and control. In practical applications, sensor measurement noise and sudden disturbances propagate through the closed-loop system, severely degrading velocity synchronization and swing angle stability. To address this challenge, this paper focuses on a quadrotor UAV slung-load system and proposes a three-layer nested networked closed-loop control architecture for simultaneous velocity tracking of a moving ground target and swing angle stabilization. First, by establishing the system’s dynamic model, the mapping relationship between cable tension and the payload swing angle (based on sensor feedback) is revealed. Then, by setting the payload velocity as the outermost control objective and constructing a coupled error to drive a virtual swing angle actuator, the direct impact of noise in the raw sensor data is effectively mitigated. Subsequently, the desired acceleration of the UAV is derived through inverse computation, achieving synchronous optimization of velocity tracking and swing angle suppression. Theoretical analysis using Lyapunov methods demonstrates the stability of the closed-loop system in the presence of bounded delays. Simulation results show that the proposed method effectively suppresses payload swing, controls velocity synchronization error, and exhibits strong robustness against sensor noise and sudden disturbance. This study provides a control solution that improves the precision and robustness of sensor-enabled networked control systems in complex dynamic scenarios Full article

Based on an enhanced artificial potential field approach, this paper presents a control method for obstacle avoidance in vehicle platoons within Intelligent Vehicle-Infrastructure Cooperative Systems (I-VICS). To enhance safety during maneuvers, an inter-vehicle obstacle avoidance potential field model is established. By integrating virtual forces and a consistency control strategy into the control law, the proposed method effectively handles obstacle avoidance for vehicles operating at large inter-vehicle distances (80–110 m). Experimental validation using real-world trajectory data shows a 34% improvement in trajectory smoothness, as quantified by a proposed Vehicle Trajectory Stability (VTS) metric, leading to significantly safer avoidance maneuvers. A coordinated multi-vehicle obstacle avoidance strategy is further devised using a rotating potential field method, enabling collaborative and safe overall motion planning. Moreover, a path tracking strategy based on virtual force design is introduced to enhance platoon stability and reliability. Future work will focus on collision avoidance for vehicle platoons with varying inter-vehicle distances and will extend the consistency control and cooperative avoidance strategies to longer vehicle platoon to further improve overall traffic safety. Full article

This paper presents a control framework for multi-unmanned aerial vehicle systems that achieves safe and cooperative navigation in complex environments through a unified collision avoidance and trajectory guidance strategy. The principal innovation lies in the incorporation of velocity information into the design of a switching function, enabling more accurate assessment of collision risk and effectively reducing system conservativeness. Building upon this, an adaptive trajectory guidance mechanism is developed using collision avoidance information to ensure safe motion coordination among the vehicles. In addition, a closed-form solution for the dynamic system is derived, and its safety and stability are rigorously established through Lyapunov-based analysis. The effectiveness of the proposed framework is validated through simulation studies conducted on the MATLAB/Simulink platform (version R2020b), confirming reliable cooperative navigation in densely cluttered environments and guaranteeing dynamic safety. Full article

In this paper, an event-triggered optimal control strategy is proposed for three-phase grid-connected voltage source inverters (VSIs) based on the voltage-modulated direct power control (VM-DPC) principle. The optimization control problem of VSIs is addressed in the framework of nonzero sum (NZS) games to ensure mutual cooperation between active power and reactive power. To achieve optimal performance, the power components are driven to track their desired references while minimizing the individual performance index function. Accurate tracking of active and reactive powers not only stabilizes the grid but also guarantees compliant renewable integration. An adaptive dynamic programming (ADP) approach is adopted, where the critic neural network (NN) approximates the value function and provides optimal control policies. Moreover, an event-triggered mechanism with a dead-zone operation is incorporated to reduce redundant updates, thereby saving computational and communication resources. The stability of the closed-loop system and a strictly positive minimum inter-event interval are guaranteed. Simulation results verify that the proposed method achieves accurate power tracking, improved dynamic performance, and efficient implementation. Full article

Photo-induced bond linkage isomerization (BLI) in metal–nitrosyl compounds provides a molecular mechanism for controlling light-induced changes in refractive index and phase modulation. In this study, the ground and metastable states of a series of Ru–NO complexes and their Au₂₀, Ag₂₀, and mixed Au₁₀Ag₁₀ nanocluster hybrids were investigated by DFT and TDDFT calculations. The photochemical rearrangement between the linear, side-on, and O-bound forms of Ru–NO was examined together with their electronic transitions, oscillator strengths, and characteristic vibrational shifts. From these data, parameters describing radiative efficiency, non-radiative coupling, and metastable-state stability were derived to identify compounds with favorable properties for holography and photonic applications. Particular attention was given to the [(Salen)Ru(NO)(HS)@Au₂₀] complex, which shows a strong red-to-NIR response and balanced stability among its linkage isomers. Frequency-dependent polarizabilities α(ω) were calculated for its ground and metastable states and compared with those of the classical holographic material [Fe(CN)₅NO]²⁻ (nitroprusside). The refractive-index changes derived from α(ω) reveal that the Au₂₀–salen hybrid produces a much larger and more strongly wavelength-dependent Δn(λ) than nitroprusside. At 635 nm, the modulation reaches approximately 0.06 for the hybrid, compared with 0.02 for nitroprusside. This enhancement reflects the cooperative effect of the Ru–NO chromophore and the Au₂₀ nanocluster, which amplifies both polarizability and optical dispersion. The results demonstrate that coupling molecular photo-linkage isomerism with nanoplasmonic environments can significantly improve the performance of molecular systems for holography and optical-phase applications. Full article

Supramolecular proteins have emerged as promising templates for guiding metal ion mineralization into well-defined nanomaterials because of their structural versatility and chemical diversity. However, the precise control of metal ion nucleation on the different reactive sites of protein templates remains challenging. In this study, a genetically engineered hollow tobacco mosaic virus protein fiber (TMVF) with excellent structural stability was employed to achieve selective mineralization of noble metal nanostructures either on its external surface or within its internal channel. Moreover, the Pt/Pd bimetallic nanowire (NW) was also successfully prepared by co-depositing Pt and Pd on the TMVF. The bimetallic NWs demonstrated a peroxidase-like activity, which enabled their application for cholesterol detection by cooperating with cholesterol oxidase. Full article

Bacterial biofilms are critical contributors to chronic infections and antimicrobial resistance. Among the diverse extracellular matrix components, extracellular DNA (eDNA) and amyloid proteins have recently emerged as pivotal structural and functional molecules. Both individually contribute to biofilm stability and antibiotic tolerance, yet their cooperative roles remain underappreciated. This review aims to summarize current knowledge on the origins and functions of eDNA and amyloid proteins in biofilms, to highlight their molecular interactions, and to discuss how their synergistic effects promote biofilm-mediated resistance to antimicrobial agents. A comprehensive literature search was conducted using PubMed, Scopus, and Web of Science databases up to September 2025. Keywords included “biofilm”, “extracellular DNA”, “amyloid proteins”, “matrix”, and “antimicrobial resistance”. Relevant original research and review articles were systematically screened and critically analyzed to integrate emerging evidence on eDNA–amyloid interactions in bacterial biofilms. Current studies demonstrate that eDNA originates primarily from autolysis, active secretion, and host-derived DNA, while amyloid proteins are produced by multiple bacterial species, including Escherichia coli (curli), Pseudomonas aeruginosa (Fap), Bacillus subtilis (TasA), and Staphylococcus aureus (phenol-soluble modulins). Both molecules independently strengthen biofilm integrity and provide protective functions against antimicrobial agents. Importantly, recent evidence shows that eDNA can act as a nucleation template for amyloid fibrillation, while amyloid fibers stabilize and protect eDNA from degradation, creating a dense extracellular network. This synergistic eDNA–amyloid assembly enhances biofilm robustness, impedes antibiotic penetration, sequesters antimicrobial peptides, protects persister cells, and facilitates horizontal gene transfer of resistance determinants. The interplay between eDNA and amyloid proteins represents a central but underexplored mechanism driving biofilm-mediated antimicrobial resistance. Understanding this cooperative network not only deepens our mechanistic insights into bacterial pathogenesis but also highlights novel therapeutic targets. Strategies that disrupt eDNA–amyloid interactions may offer promising avenues for combating persistent biofilm-associated infections. Full article

Integrated hydro-wind-solar-storage (HWSS) bases are pivotal for advancing new power systems under the low carbon goals. However, the independent decision-making of diverse generation investors, coupled with limited transmission capacity, often leads to a dilemma in which individually rational decisions lead to collectively suboptimal outcomes, undermining overall benefits. To address this challenge, this study proposes a novel cooperative game-based method that seamlessly integrates grid congestion into capacity allocation and benefit distribution. First, a bi-level optimization model is developed, where a congestion penalty is explicitly embedded into the cooperative game’s characteristic function to quantify the maximum benefits under different coalition structures. Second, an improved Shapley value model is introduced, incorporating a comprehensive correction factor that synthesizes investment risk, congestion mitigation contribution, and capacity scale to overcome the fairness limitations of the classical method. Third, a case study of a high-renewable-energy base in Qinghai is conducted. The results demonstrate that the proposed cooperative model increases total system revenue by 20.1%, while dramatically reducing congestion costs and wind/solar curtailment rates by 86.2% and 79.3%, respectively. Furthermore, the improved Shapley value ensures a fairer distribution, appropriately increasing the profit shares for hydropower (from 28.5% to 32.1%) and energy storage, thereby enhancing coalition stability. This research provides a theoretical foundation and practical decision-making tool for the collaborative planning of HWSS bases with multiple investors. Full article

Supply chains play a crucial role in achieving the Sustainable Development Goals (SDGs) through the improvement of ESG performance. From the perspective of synergy between national auditing and corporate governance, this study integrates the SDGs into the supply chain ESG strategy and evaluates the spillover effects of national auditing on supply chain ESG performance, drawing on the quasi-natural experiment of China’s National Audit Office (NAO) auditing state-owned enterprises (SOEs). The findings illustrate that national auditing has a significant positive spillover effect on the ESG performance of supply chains. These findings remain robust after addressing potential endogeneity via placebo tests, PSM-DID, and Heckman two-step method. Heterogeneity analysis highlights that supply chains with strong cooperation stability, high concentration, and presence in the same industry have more pronounced ESG spillover effects. Mechanism analysis further demonstrates that national auditing enhances the ESG performance of supply chains by exerting imitative, mandatory, and normative pressures. Moreover, audit regulatory agencies should establish a mechanism for sharing audit results to exert mandatory institutional pressure, thereby ensuring this mechanism enables audits to fully fulfill their role in improving supply chain ESG performance. Full article

The aim of this study was to investigate the molecular interactions of heparin, diclofenac, and their supramolecular complexes with cyclooxygenase enzymes (COX-1 and COX-2) using computational docking techniques. Diclofenac is a widely used nonsteroidal anti-inflammatory drug (NSAID) that inhibits COX isoforms, whereas heparin is a polyanionic glycosaminoglycan with established anticoagulant and emerging anti-inflammatory properties. Supramolecular association between these agents may modulate their physicochemical behavior and target engagement. Molecular modeling, dual-drug docking, and molecular dynamics (MD) simulations were employed to characterize the interactions of heparin, diclofenac, and pre-formed heparin–diclofenac complexes with COX-1 and COX-2. Geometry optimization and lipophilicity (logP) estimates were obtained using HyperChem, while protein–ligand docking was performed in HEX using crystallographic COX structures from the Protein Data Bank. Docking poses were analyzed in Chimera, and selected complexes were refined through short MD simulations. Pre-formed heparin–diclofenac assemblies exhibited markedly enhanced docking scores toward both COX isoforms compared with single ligands. Binding orientation strongly influenced affinity: for COX-1, the heparin–diclofenac configuration yielded the most favorable interaction, whereas for COX-2 the diclofenac–heparin configuration was preferred. Both assemblies adopted binding modes distinct from free diclofenac, suggesting cooperative electrostatic and hydrophobic contacts at the enzyme surface. Supramolecular complexation also altered calculated logP values relative to the individual compounds. MD simulations supported the relative stability of the top-ranked complex–COX assemblies. These findings indicate that heparin–diclofenac assemblies may enhance and reorganize predicted COX interactions in a configuration-dependent manner and illustrate the utility of dual-drug docking for modeling potential synergistic effects. Such insights may inform the design of localized or topical formulations, potentially incorporating non-anticoagulant heparin derivatives, to achieve effective COX inhibition with reduced systemic exposure. However, the results rely on simplified heparin fragments, legacy docking tools, and short MD simulations, and should therefore be interpreted qualitatively. Experimental studies will be essential to confirm whether such supramolecular assemblies form under physiological conditions and whether they influence COX inhibition in vivo. Full article

This study presents the design and implementation of a real-time flight-path tracking control system for a quadrotor unmanned aerial vehicle (UAV) capable of accurately following a mobile ground target under dynamic and uncertain environmental conditions. The proposed framework integrates classical fixed-gain PID regulation executed on Pixhawk with its built-in adaptive mechanisms, namely autotuning, hover-throttle learning, and dynamic harmonic notch filtering, to enhance robustness under communication latency and disturbances. No machine learning PID tuning is used on Pixhawk; adaptive features are estimator based rather than ML based. The proposed system addresses critical challenges in trajectory tracking, including real-time delay compensation between the UAV and rover, external perturbations, and the requirement to maintain stable six-degree-of-freedom (DOF) control of altitude, yaw, pitch, and roll. A dynamic mathematical model, formulated using ordinary differential equations with embedded delay elements, is developed to emulate real-world flight behavior and validate control performance. Experimental evaluation demonstrates robust path-tracking accuracy, attitude stability, and responsiveness across diverse terrains and weather conditions, achieving a mean positional error below one meter and effective resilience against an 8.2 ms communication delay. Overall, this work establishes a scalable, computationally efficient, and high-precision control framework for UAV guidance and cooperative ground-target tracking, with potential applications in autonomous navigation, search-and-rescue operations, infrastructure inspection, and intelligent surveillance. The term “delay-aware” in this work refers to the explicit modeling of the measured 8.2 ms end-to-end delay and the use of Pixhawk’s estimator-based adaptive mechanisms, without any machine learning-based PID tuning. Full article