Search Results (1,270)

Driven by energy transition strategies, distributed resources are being extensively integrated into the distribution network (DN). However, sufficient coordination among these resources remains challenging due to their diverse ownership structures. To address this, a hybrid game-theoretic economic scheduling method for the distribution network based on grid–storage–load interaction is proposed. A two-layer game framework, “distribution network–shared energy storage–microgrid alliance (MGA)”, is established to enable coordinated utilization of flexible resources across the grid, storage, and load sides. The upper-layer distribution network determines time-of-use electricity prices to guide the energy strategies of storage and microgrid alliance. The lower-layer agents engage in a two-stage interaction: Stage 1, multiple microgrids (MGs) form an alliance to lease shared energy storage to smooth net-load profiles. The shared energy storage operator (SESO) then utilizes its surplus capacity to assist the distribution network in peak shaving, thereby maximizing its own revenue. Stage 2, the alliance facilitates mutual power support and implements demand response (DR), reducing its energy costs and assisting the system in peak shaving and valley filling. Case analysis demonstrates that, compared to baseline without coordination, the proposed method reduces the distribution network’s electricity procurement cost by 11.28% and lowers the system’s net load peak-to-valley difference rate by 56.53%. Full article

An inorganic–organic hybrid nano-adsorbent was prepared by chemical immobilisation of an organic Schiff base Cu (II) ion receptor, DHB ((E)-N-(1-(2-hydroxy-6-methyl-4-oxo-4H-pyran-3-yl) ethylidene) benzohydrazide), a selective dehydroacetic acid-based chemosensor, onto a mesoporous silica support. In order to prepare the sorbent, the silylating agent was anchored onto the silica. During this procedure, 3-Chloropropyl trimethoxy silane (CPTS) was attached to the surface, increasing hydrophobicity. By immobilising DHB onto the CPTS platform, the silica surface was activated, and as a result the coordination chemistry of the Schiff base generated a hybrid adsorbent with the capability to rapidly sequestrate Cu (II) ions from wastewater, as an answer to combat growing Waste Electrical and Electronic Equipment (WEEE) contamination in water supplies, in the wake of a prolonged consumerism mentality and boom in cryptocurrency mining. The produced hybrid materials were characterised by FTIR, proximate and ultimate analysis, nitrogen physisorption, PXRD, SEM, and TEM. The parameters influencing the removal efficiency of the sorbent, including pH, initial metal ion concentration, contact time, and adsorbent dosage, were optimised to achieve enhanced removal efficiency. Under optimal conditions (pH 7.0, adsorbent dosage 3 mg, contact time of 70 min, and 25 °C), Cu (II) ions were quantitatively sequestered from the sample solution; 93.1% of Cu (II) was removed under these conditions. The adsorption was found to follow pseudo-second-order kinetics, and Langmuir model fitting affirmed the monolayer adsorption. Full article

Dual Carbon Goals are driving transformation in China’s power system, where increased renewable energy penetration is accompanied by heightened fluctuations on the generation and load sides. Energy storage and microgrid coordination have emerged as key solutions. However, existing research faces the challenge of balancing microgrid operations, energy storage services, and the alignment of user demand with stakeholder interests. This paper establishes a tripartite collaborative optimization framework to balance multi-stakeholder interests and enhance system efficiency, assuming fixed energy storage capacity. Centering on a principal-agent game between microgrid operators and consumer aggregators, energy storage service providers are integrated into this dynamic. Microgrid operators set 24-h electricity and heat pricing while adhering to tariff constraints, prompting consumer aggregators to adjust energy consumption and storage strategies accordingly. The KKT conditional method is employed to solve the model, deriving optimal user energy consumption strategies at the lower level while solving marginal pricing equilibrium relationships at the upper level, balancing accuracy with information privacy. The creative contribution of this article lies in the first construction of a tripartite collaborative optimization architecture in which energy storage service providers are embedded in a game of ownership and subordination. It proposes a dynamic coupling mechanism between pricing power, energy consumption decision-making, and energy storage configuration under fixed energy storage capacity constraints, achieving a balance of interests among multiple parties. By building a case study using MATLAB (R2022b), we compare operation costs, benefits, and absorption rates across different scenarios to validate the framework’s effectiveness and provide a reference for engineering applications. Full article

This paper reviews Artificial Intelligence techniques for distributed energy management, focusing on integrating machine learning, reinforcement learning, and multi-agent systems within IoT-Edge-Cloud architectures. As energy infrastructures become increasingly decentralized and heterogeneous, AI must operate under strict latency, privacy, and resource constraints while remaining transparent and auditable. The study examines predictive models ranging from statistical time series approaches to machine learning regressors and deep neural architectures, assessing their suitability for embedded deployment and federated learning. Optimization methods—including heuristic strategies, metaheuristics, model predictive control, and reinforcement learning—are analyzed in terms of computational feasibility and real-time responsiveness. Explainability is treated as a fundamental requirement, supported by model-agnostic techniques that enable trust, regulatory compliance, and interpretable coordination in multi-agent environments. The review synthesizes advances in MARL for decentralized control, communication protocols enabling interoperability, and hardware-aware design for low-power edge devices. Benchmarking guidelines and key performance indicators are introduced to evaluate accuracy, latency, robustness, and transparency across distributed deployments. Key challenges remain in stabilizing explanations for RL policies, balancing model complexity with latency budgets, and ensuring scalable, privacy-preserving learning under non-stationary conditions. The paper concludes by outlining a conceptual framework for explainable, distributed energy intelligence and identifying research opportunities to build resilient, transparent smart energy ecosystems. Full article

In the context of large-scale renewable energy integration driven by China’s dual-carbon goals, and under distribution network scenarios with continuously increasing shares of wind and photovoltaic generation, this paper proposes a source–grid–load–storage coordinated planning method embedded with a multi-agent game mechanism. First, the interest transmission pathways among distributed generation operators (DGOs), distribution network operators (DNOs), energy storage operators (ESOs), and electricity users are mapped, based on which a profit model is established for each stakeholder. Building on this, a coordinated planning framework for active distribution networks (DN) is developed under the assumption of bounded rationality. Through an evolutionary-game process among DGOs, DNOs, and ESOs, and in combination with user-side demand response, the model jointly determines the optimal network reinforcement scheme as well as the optimal allocation of distributed generation (DG) and energy storage system (ESS) resources. Case studies are then conducted to verify the feasibility and effectiveness of the proposed method. The results demonstrate that the approach enables coordinated planning of DN, DG, and ESS, effectively guides users to participate in demand response, and improves both planning economy and renewable energy accommodation. Moreover, by explicitly capturing the trade-offs among multiple stakeholders through evolutionary-game interactions, the planning outcomes align better with real-world operational characteristics. Full article

Efficient route planning technology is the core support for ensuring the successful execution of unmanned aerial vehicle (UAV) flight missions. In this paper, the coordination issue of global route planning and local real-time obstacle avoidance in complex mountainous environments is studied. To deal with this issue, a hierarchical route planning framework is designed, including global route planning and AI-based local route re-planning using deep reinforcement learning, exhibiting both flexible versatility and practical coordination and deployment efficiency. Throughout the entire flight, the local route re-planning task triggered by dynamic threats can be executed in real time. Meanwhile, a multi-model DQN (MMDQN) agent with a Monte Carlo traversal iterative learning (MCTIL) strategy is designed for local route re-planning. Compared to existing methods, this agent can be directly used to generate local obstacle avoidance routes in various scenarios at any time during the flight, which simplifies the complicated structure and training process of conventional deep reinforcement learning (DRL) agents in dynamic, complex environments. Using the framework structure and MMDQN agent for local route re-planning ensures the safety and efficiency of the mission, as well as local obstacle avoidance during global flights. These performances are verified through simulations based on actual terrain data. Full article

Microneedle (MN) technologies have emerged as a groundbreaking platform for transdermal and intradermal drug delivery, offering a minimally invasive alternative to oral and parenteral routes. Unlike passive transdermal systems, MNs allow the permeation of hydrophilic macromolecules, such as peptides, proteins, and vaccines, by penetrating the stratum corneum barrier without causing pain or tissue damage, unlike hypodermic needles. Recent advances in materials science, microfabrication, and biomedical engineering have enabled the development of various MN types, including solid, coated, dissolving, hollow, hydrogel-forming, and hybrid designs. Each type has unique mechanisms, fabrication techniques, and pharmacokinetic profiles, providing customized solutions for a range of therapeutic applications. The integration of 3D printing technologies and stimulus-responsive polymers into MN systems has enabled patches that combine drug delivery with real-time physiological sensing. Over the years, MN applications have grown beyond vaccines to include the delivery of insulin, anticancer agents, contraceptives, and various cosmeceutical ingredients, highlighting the versatility of this platform. Despite this progress, broader clinical and commercial adoption is still limited by issues such as scalable and reliable manufacturing, patient acceptance, and meeting regulatory expectations. Overcoming these barriers will require coordinated efforts across engineering, clinical research, and regulatory science. This review thoroughly summarizes MN technologies, beginning with their classification and drug-delivery mechanisms, and then explores innovations, therapeutic uses, and translational challenges. It concludes with a critical analysis of clinical case studies and a future outlook for global healthcare. By comparing technological progress with regulatory and commercial hurdles, this article highlights the opportunities and limitations of MN systems as a next-generation drug-delivery platform. Full article

Swarm coverage by unmanned underwater vehicles (UUVs) is essential for inspection, environmental monitoring, and search operations, but remains challenging in three-dimensional domains under limited sensing and communication. Pheromone-based stigmergic coordination provides a low-bandwidth alternative to explicit communication, yet conventional single-field models are susceptible to depth-dependent sensing inconsistencies and multi-source signal interference. This paper introduces a dual-trail stigmergic coordination framework in which a virtual pheromone field encodes short-term motion cues while an auxiliary coverage trail records the accumulated exploration effort. UUV motion is guided by the combined gradients of these two fields, enabling more consistent behavior across depth layers and mitigating ambiguities caused by overlapping pheromone sources. At the macroscopic level, swarm evolution is modeled by a coupled system of partial differential equations (PDEs) describing vehicle density, pheromone concentration, and coverage trail. A Lyapunov functional is constructed to derive sufficient conditions under which perturbations around the uniform coverage equilibrium decay exponentially. Numerical simulations in three-dimensional underwater domains demonstrate that the proposed framework reduces coverage holes, limits redundant overlap, and improves robustness with respect to a single-pheromone baseline and a potential-field-based controller. These results indicate that dual-field stigmergic control is a promising and scalable approach for UUV coverage in constrained underwater environments. Full article

This paper considers how a system designer can program a team of autonomous agents to coordinate with one another such that each agent selects (or covers) an individual resource with the goal that all agents collectively cover the maximum number of resources. Specifically, we study how agents can formulate strategies without information about other agents’ actions so that system-level performance remains robust in the presence of communication failures. First, we use an algorithmic approach to study the scenario in which all agents lose the ability to communicate with one another, have a symmetric set of resources to choose from, and select actions independently according to a probability distribution over the resources. We show that the distribution that maximizes the expected system-level objective under this approach can be computed by solving a convex optimization problem, and we introduce a novel polynomial-time heuristic based on subset selection. Further, both of the methods are guaranteed to be within $1-1/e$ of the system’s optimal in expectation. Second, we use a learning-based approach to study how a system designer can employ neural networks to approximate optimal agent strategies in the presence of communication failures. The neural network, trained on system-level optimal outcomes obtained through brute-force enumeration, generates utility functions that enable agents to make decisions in a distributed manner. Empirical results indicate the neural network often outperforms greedy and randomized baseline algorithms. Collectively, these findings provide a broad study of optimal agent behavior and its impact on system-level performance when the information available to agents is extremely limited. Full article

The global countryside constitutes a complex social–ecological system undergoing profound transformation. Understanding how such systems navigate transitions and achieve resilient, sustainable outcomes requires examining the interactions and adaptive behaviors of multiple actors. This study investigates the restructuring of rural China through a complex adaptive systems lens, focusing on the county of Lin’an in Zhejiang Province. We employ a middle-range theory and process-tracing approach to analyze the co-evolutionary pathways shaped by the interactions among three key agents: local governments, enterprises, and village communities. Our findings reveal distinct yet interdependent behavioral logics—local governments and enterprises primarily exhibit instrumental rationality, driven by political performance and profit maximization, respectively, while villages demonstrate value-rational behavior anchored in communal well-being and territorial identity. Crucially, this study identifies the emergence of a vital integrative mechanism, the “village operator” model, underpinned by the collective economy. This institutional innovation facilitates the synergistic linkage of interests and the integration of endogenous and exogenous resources, thereby mitigating conflicts and alienation. We argue that this multi-agent collaboration drives a synergistic restructuring of spatial, economic, and social subsystems. The case demonstrates that sustainable rural revitalization hinges not on the dominance of a single logic, but on the emergence of adaptive governance structures that effectively coordinate diverse actor logics. This process fosters systemic resilience, enabling the rural system to adapt to external pressures and internal changes. The Lin’an experience offers a transferable framework for understanding how coordinated multi-agent interactions can guide complex social–ecological systems toward sustainable transitions. Full article

Berberine (BBR), a protoberberine alkaloid with a long history of medicinal use, has consistently demonstrated benefits in glucose–lipid metabolism and inflammatory balance across both preclinical and human studies. These diverse effects are not mediated by a single molecular target but by BBR’s capacity to restore network coordination among metabolic, immune, and microbial systems. At the core of this regulation is an AMP-activated Protein Kinase (AMPK)-centered mechanistic hub, integrating signals from insulin and nutrient sensing, Sirtuin 1/3 (SIRT1/3)-mediated mitochondrial adaptation, and inflammatory pathways such as nuclear Factor Kappa-light-chain-enhancer of Activated B cells (NF-κB) and NOD-, LRR- and Pyrin Domain-containing Protein 3 (NLRP3). This hub is dynamically regulated by system-level inputs from the gut, mitochondria, and epigenome, which in turn strengthen intestinal barrier function, reshape microbial and bile-acid metabolites, improve redox balance, and potentially reverse the epigenetic imprint of metabolic stress. These interactions propagate through multi-organ axes, linking the gut, liver, adipose, and vascular systems, thus aligning local metabolic adjustments with systemic homeostasis. Within this framework, BBR functions as a negentropic modulator, reducing metabolic entropy by fostering a coordinated balance among these interconnected systems, thereby restoring physiological order. Combination strategies, such as pairing BBR with metformin, Sodium-Glucose Cotransporter 2 (SGLT2) inhibitors, and agents targeting the microbiome or inflammation, have shown enhanced efficacy and substantial translational potential. Berberine ursodeoxycholate (HTD1801), an ionic-salt derivative of BBR currently in Phase III trials and directly compared with dapagliflozin, exemplifies the therapeutic promise of such approaches. Within the hub–axis paradigm, BBR emerges as a systems-level modulator that recouples energy, immune, and microbial circuits to drive multi-organ remodeling. Full article

With the advancement of digitization in the era of Industry 4.0 (I4.0), highly automated, semi-autonomous, and fully autonomous systems are emerging. Within this context, multi-agent systems (MAS) offer a promising approach for automating tasks and processes based on autonomous agents that work together in an overall system to increase the degree of system autonomy stepwise in a modular and flexible way. A critical research challenge is determining how these agents can collaboratively engage with both other agents and human operators to facilitate the gradual transition from automated to fully autonomous industrial systems. To close transparency and connectivity gaps, this study contributes with a framework for the collaboration of agents and humans in increasingly autonomous MAS based on a Digital Twin (DT). The framework specifies a standards-based data model for MAS representation and proposes to introduce a DT infrastructure as a service layer for system coordination, supervision, and interaction. To demonstrate the feasibility and assess the quality of the framework, it is implemented and evaluated in a case study in a real-world industrial scenario. Although additional long-term evaluations across different contexts are needed, the assessment of functional completeness and selected quality attributes show that the proposed framework provides a solid technical foundation that facilitates a transparent and seamless collaboration between agents and humans within increasingly autonomous industrial MAS. Full article

Chemotherapy resistance in colorectal cancer (CRC) represents a critical clinical challenge leading to treatment failure and poor patient prognosis. Artemisinin is a natural product isolated from Artemisia annua, and its clinically relevant derivatives include dihydroartemisinin (DHA) and artesunate. Beyond their established antimalarial efficacy, both artemisinin and its derivatives—collectively referred to as artemisinin-derived compounds (ADs)—have been increasingly recognized for their unique potential to reverse multidrug resistance in cancer. Unlike previous reviews focusing on isolated mechanisms, this review systematically constructs a multidimensional, synergistic attack network centered on ADs to elucidate their integrated actions against chemotherapy-resistant CRC. Mechanistically, ADs suppress cancer stem cell (CSC)-associated resistance phenotypes while concurrently reshaping the tumor immune microenvironment, highlighting a functional coupling between stemness inhibition and immune remodeling. In parallel, this review presents apoptosis reactivation and ferroptosis induction as complementary, dual-track cell death strategies that collectively circumvent apoptosis resistance. Moreover, ADs exert “one-strike–multiple-effects” through coordinated regulation of pro-survival signaling networks and immune-related pathways, including the induction of immunogenic cell death (ICD) and the modulation of immunosuppressive macrophage subsets. Beyond mechanistic insights, this review integrates emerging translational considerations, including clinical pharmacokinetics, safety and tolerability, formulation and delivery strategies, and rational combination therapy paradigms in CRC. Collectively, these findings position ADs as multi-dimensional modulators rather than a single-agent cytotoxic, providing a coherent mechanistic and translational rationale for their further development in chemotherapy-resistant CRC. Full article

Microservices as an emerging architectural approach have been widely applied in the development of online applications. However, in large-scale service systems, frequent data communications, complex invocation dependencies, and strict latency requirements pose significant challenges to efficient microservice orchestration. In addition, microservices need to frequently access the database to achieve data persistence, creating a mutual dependency between the two, and this symmetry further increases the complexity of service orchestration and coordinated deployment. In this context, the strong coupling of service deployment, database layout, and request routing makes effective local optimization difficult. However, existing research often overlooks the impact of databases, fails to achieve joint optimization among databases, microservice deployments, and routing, or lacks fine-grained orchestration strategies for multi-instance models. To address the above limitations, this paper proposes a joint optimization framework based on the Database-as-a-Service (DaaS) paradigm. It performs fine-grained multi-instance queue modeling based on queuing theory to account for delays in data interaction, request queuing, and processing. Furthermore this paper proposes a proximal policy optimization algorithm based on multi-stage joint decision-making to address the orchestration problem of microservices and database instances. In this algorithm, the action space is symmetrical between microservices and database deployment, enabling the agent to leverage this characteristic and improve representation learning efficiency through shared feature extraction layers. The algorithm incorporates a two-layer agent policy stability control to accelerate convergence and a three-level experience replay mechanism to achieve efficient training on high-dimensional decision spaces. Experimental results demonstrate that the proposed algorithm effectively reduces service request latency under diverse workloads and network conditions, while maintaining global resource load balancing. Full article

In this study, an adaptive event-driven coordination paradigm is proposed for achieving consensus in nonlinear multi-agent systems (MASs) over directed networks. First, a newly dynamic event-triggered mechanism with single-point historical information is introduced to minimize unnecessary network communication. And a more general form of an event triggering mechanism with moving window historical information is designed for further saving network resources. Considering that the use of historical information over a long period of time may cause deviations, an event-triggered mechanism that can adjust the maximum memory length is proposed in this work to minimize unnecessary network communication. Secondly, the unknown nonlinearities in the MAS model are addressed using the universal approximation capability of neural networks. Then, a methodology for distributed adaptive control under event-triggered mechanisms is introduced leveraging the memory-based command-filtered backstepping methodology, and the proposed scheme resolves the complexity explosion problem. Finally, a case study is conducted to validate the feasibility of the proposed method. Full article