Search Results (3,986)

This study examines how coordination, power, and interdependence shape regulatory governance in the decentralized edible bird’s nest (EBN) sector in Pulang Pisau, Indonesia. While decentralization is often associated with improved responsiveness and local adaptability, it frequently produces fragmented regulatory systems in which authority is distributed without effective coordination. Using an actor-centered qualitative design combined with the MACTOR method, this study analyzes influence–dependence relations, objective alignment, and coordination bottlenecks across key actors. The findings show that regulatory performance is shaped less by formal mandates than by relational positioning within the governance system. Actors controlling technical verification and documentary gateways occupy high-influence positions, while licensing authorities remain operationally dependent. Although most actors share common objectives—such as hygiene, quality assurance, and traceability—these are pursued through fragmented procedures, resulting in coordination failures and regulatory inequality. Producers bear the greatest compliance burdens despite having limited influence over regulatory processes. The study introduces the concept of relational administrative capacity to explain how decentralized governance outcomes depend on the alignment of authority, expertise, and procedural sequencing across interdependent actors. The findings suggest that improving regulatory performance requires strengthening coordination architectures rather than adding new rules. Full article

This paper presents a distributed V2G-enabled multiport DC charging system with a hierarchical charging management strategy. Unlike conventional architectures based on centralized power converter cabinets, the proposed system distributes bidirectional power converters within individual multiport dispensers, each equipped with a local charging power management device. This architecture improves system scalability, fault tolerance, and operational flexibility while enabling vehicle-level charging and V2G services. A hierarchical control framework is introduced, consisting of high-level optimal charging scheduling, mid-level power coordination among distributed dispensers, and low-level converter control. Key elements include modular power units that can be dynamically configured and expanded, providing a cost-effective and adaptable solution for growing EV markets. Experimental results obtained from a 45 kW modular DC charging prototype demonstrate an efficiency improvement of up to 2% at rated power compared to a non-modular charger. In contrast, the optimized charging strategy achieves an overall charging cost reduction of approximately 11% and a peak load demand reduction of up to 31%. Furthermore, stable bidirectional power flow, effective power sharing, and total harmonic distortion within regulatory limits are experimentally validated during both charging and V2G operation. The prototype is implemented to validate the proposed charging system in the laboratory environment. Full article

Integrated sensing and communications (ISAC) requires tight coordination between spatial signal design and multiple-access strategies to balance communication throughput and sensing accuracy under shared spectral and hardware constraints. However, existing ISAC frameworks with rate-splitting multiple access (RSMA) typically rely on fixed antenna arrays and decoupled optimization, which fundamentally limit their ability to adapt to fast channel variations and dynamic sensing requirements. This paper introduces a fluid antenna-enabled RSMA-assisted ISAC architecture, in which movable antenna ports are exploited as a new spatial degree of freedom to enhance adaptability in both communication and sensing operations. Fluid antenna systems (FAS) are deployed at both the base station and user terminals, allowing dynamic port selection that reshapes the effective channel and sensing beampattern in real time. We formulate a joint sum-rate maximization problem subject to explicit sensing-quality constraints, capturing the coupled impact of antenna port selection, RSMA rate allocation, and multi-beam transmit design. The proposed framework maximizes the communication sum-rate while ensuring that the sensing functionality satisfies a predefined sensing quality constraint. This constraint-based ISAC formulation guarantees that sufficient sensing power is directed toward the target while optimizing communication performance. The resulting optimization involves strongly coupled discrete and continuous decision variables, rendering conventional optimization methods ineffective. To address this challenge, a hierarchical deep reinforcement learning (HDRL) framework is developed, where an upper-layer deep Q-network (DQN) determines discrete antenna port selection and a lower-layer twin delayed deep deterministic policy gradient (TD3) algorithm optimizes continuous beamforming and rate-splitting parameters. Numerical results demonstrate that the proposed approach significantly improves system performance, achieving higher communication sum-rate while satisfying sensing requirements under dynamic propagation conditions. Full article

The rapid growth of renewable energy and the inherent volatility of wind power grid integration have imposed stringent requirements on power system security and economic operation. To address this challenge, energy storage systems (ESSs) are widely adopted as flexible regulation tools; however, their high capital costs make the shared energy storage model a more efficient and viable solution. This paper proposes an optimal configuration model for wind farms participating in shared energy storage (SES) based on cooperative game theory. First, integrating wind power output forecasting data and market electricity price information, a wind-storage combined optimization model accounting for wind power uncertainty is first established. Subsequently, a core pricing strategy integrating the core allocation rule with the Vickrey–Clarke–Groves (VCG) auction mechanism is proposed to realize the fair allocation of energy storage resources and effective revenue incentives. Finally, comparative experiments between the proposed core pricing mechanism and the fixed pricing mechanism verify its superiority in terms of social welfare, budget balance, and allocation fairness. The results demonstrate that the proposed mechanism not only enhances the overall social benefits of the wind-storage system but also effectively ensures the incentive compatibility of all participants and the stability of the alliance, providing feasible theoretical and methodological support for the economic dispatch of wind-farm-shared energy storage. Full article

With the acceleration of the global energy transition, the photovoltaic industry has become a significant force in the promotion of green development, and photovoltaic raw materials play a crucial role in this process. In this paper, 177 countries during the period of 2001 to 2024 were taken as the research subjects, with a focus on polysilicon and silicon wafers as components of upstream photovoltaic raw materials. Through a combination of the evolutionary analysis of nodes, the overall structure, and the three-dimensional structure with an exponential random graph model, the evolution and dynamic mechanisms of the global photovoltaic raw material trade network are explored. The study reveals the following: (1) The global PV raw material trade volume tended to increase from 2001 to 2024. (2) The global photovoltaic raw material trade network showed a tendency towards the “enhanced dominance of core countries and denser trade connections,” with the trade volume between core countries continuously expanding and the network density, average clustering coefficient, and connection efficiency increasing annually, which is a reflection of the globalization and regional cooperation of the global photovoltaic industry. (3) From the weighted out-degree and in-degree ranking evolution of the global photovoltaic raw materials trade network, it can be seen that China consolidated its core position, while Southeast Asian countries tended to transfer their processing and manufacturing links. The status of the United States and traditional industrial powers gradually declined, which is a reflection of the restructuring of the global industrial chain along with regional geopolitical agglomeration effects. (4) Internal attributes such as the national economic level, population size, and urbanization rate, as well as external network effects such as common language and geographical proximity, significantly influence the formation path of the photovoltaic raw material trade network. Moreover, the network exhibits distinct heterogeneous complementarity mechanisms and path dependence characteristics, with a structural evolution that tends toward stability and cooperative relationships showing significant time inertia. Overall, the global trade volume of photovoltaic raw materials continues to grow, and the core positions of major countries such as China, the United States, and Germany remain prominent but show a transitional trend towards Southeast Asian countries. The strengthening of the level of coordination and cooperation among global photovoltaic raw material producers to ensure supply chain stability, promote resource sharing and technological progress, and achieve the sustainable development of green energy policies is necessary. Full article

To address the problem of renewable energy curtailment and the need for operational economic optimization in integrated energy systems with high penetration of wind and solar power, a coordinated optimization method integrating thermoelectric decoupling, ammonia-blended combustion technology, and energy storage capacity leasing is proposed. First, a chaotic-improved Latin Hypercube Sampling (C-LHS) method, combined with an improved K-means clustering algorithm, is employed to generate representative wind–solar–load scenarios. This approach improves the efficiency of uncertainty scenario generation while reducing computational burden and maintaining solution accuracy. Secondly, by coordinating the operation of thermal energy storage and electric boilers, the “heat-led power generation” constraint is relaxed, and, in combination with ammonia-blended combustion in combined heat and power (CHP) units, the system’s flexibility and renewable energy accommodation capability are enhanced. Finally, with the objective of minimizing total operating cost, a day-ahead scheduling model incorporating electrical energy storage (EES) leasing optimization is established. For EES, under a shared energy storage market mechanism, the golden section search (GSS) algorithm is employed to optimize the day-ahead leasing capacity. The simulation results demonstrate that the proposed method improves renewable energy accommodation while maintaining economic performance, and effectively reduces the overall operating cost of the system. These findings confirm the effectiveness of the proposed strategy in enhancing both system flexibility and economic performance. Full article

Korea’s rising shares of variable renewable energy (VRE) and inflexible baseload increases the need for fast-responding and cost-effective flexibility. Most studies on power-to-heat (P2H) emphasize district-heating (DH) economics or load shifting, leaving the system-level impacts of its reserve provision capability unclear. We develop a mixed-integer linear programming model for reserve-constrained unit commitment (RCUC) that co-optimizes the power and DH systems. In addition, the model incorporates a P2H system capable of providing multiple reserve services. Reserve requirements are divided into static and dynamic terms, with the dynamic term represented as a piecewise-linear approximation of short-term VRE variability derived from weather-based generation profiles and evaluated at the scheduled VRE output. Using a 2030 winter week for Korea, we compare five cases: no EB; EB as load only; and EB contributing only to the secondary/regulation reserve requirement, only to the primary reserve requirement, or both. Under the KRW 1000/kWh curtailment-penalty case, EB as load reduces system operating cost compared to the baseline, and enabling reserve provision yields additional cost savings, with the largest benefit observed when primary reserve is provided. EB operation also shifts dispatch from coal and gas toward nuclear, VRE, and pumped storage, while reducing renewable curtailment. Overall, enabling P2H to contribute to reserve procurement, particularly in the primary reserve, delivers substantially greater value than representing P2H solely as a controllable load for energy shifting. Full article

In flexible DC transmission and AC-DC interconnection systems, the Self-Adaption Station and Line Commutation Converter (SLCC) integrates static var compensation with conventional thyristor conversion functionality. This enables dynamic reactive power support at the valve side while improving commutation conditions, thereby enhancing the voltage support capability and operational robustness of DC systems. Under high renewable energy penetration, power fluctuations and sudden ramping challenges principal–slave controlled SLCC DC interconnection systems with a trade-off between principal-side DC voltage regulation and capacity margin constraints: Disturbance-induced active power demands may exceed available margins, causing DC voltage deviations and increasing protection trip risks. Leveraging the active/reactive decoupling characteristics of the SLCC topology, this paper proposes a principal–slave coordinated control strategy that accounts for principal station capacity margins. Methodologically, capacity margins are explicitly embedded into the principal station control mode. By reconstructing key variables in the DC voltage outer loop and introducing a closed-loop suppression mechanism with “over-capacity power” as feedback, the principal station maintains continuous voltage regulation while avoiding entry into over-capacity operation zones. On the slave side, a power support mechanism is designed to coordinate regulation among generation, storage, and load under power balance and equipment capacity constraints. This coordination process is formulated as a multi-objective optimization problem balancing disturbance economic losses with generation/storage utilization, solved using NSGA-II. Simulation results demonstrate that this strategy suppresses the risk of principle station overcapacity, enhances power sharing coordination during disturbance conditions, and improves DC voltage dynamic performance. Full article

Achieving sustainability requires that energy transition generates measurable environmental benefits beyond the power sector, yet it remains unclear whether renewable electricity expansion is associated with lower waste sector methane intensity, a major source of short-lived climate forcing. Using a global country–year panel and two-way fixed effects, we examine whether this relationship, and its sustainability implications, varies with development stage, institutional quality, and waste system characteristics. We find no robust inverted-U Environmental Kuznets Curve once country and year fixed effects are included. Instead, higher renewable electricity shares are consistently associated with lower waste sector methane intensity, and this association strengthens with income. A 10-percentage-point increase in renewable share corresponds to about 2.7%, 4.2%, and 6.0% lower intensity at the 25th, 50th, and 75th income percentiles. The negative association is stronger in countries with higher governance quality, while waste management capacity and organic waste composition reveal additional heterogeneity in the observed association. Overall, electricity decarbonization alone is not a uniform instrument for reducing diffuse biological emissions; sustainable methane mitigation likely requires coordinated governance linking renewable transition with waste system modernization. Full article

In large-scale offshore wind power transmission systems, the offshore converter valves are typically based on the half-bridge Modular Multilevel Converter (MMC) topology. This design leads to considerable weight and high costs, presenting a critical bottleneck for the development of offshore wind power transmission. This paper proposes a hybrid topology consisting of paralleled MMCs connected in series with a Diode Rectifier Unit (DRU) to achieve lightweight offshore converter valves. The parallel configuration enhances the steady-state current-carrying capacity of the valve group to match the DRU valve group, and power balance among the paralleled MMCs is realized through an additional DC current-sharing control loop. A calculation method for the main circuit parameters of this lightweight topology is presented, along with a complete parameter calculation process. A design example based on actual engineering capacity is provided. PSCAD simulation results verify that the electrical quantities during steady-state operation of the hybrid topology are consistent with the designed parameters, confirming the correctness of the proposed parameter calculation method. Full article

This paper presents a dual-core RISC-V architecture designed for energy-efficient AI acceleration at the edge, featuring dynamic MAC unit sharing, frequency scaling (DFS), and FIFO-based resource arbitration. The system comprises two RISC-V cores that compete for shared computational resources—a single Multiply–Accumulate (MAC) unit and a shared external memory subsystem—governed by a channel-based arbitration mechanism with CPU-priority semantics, while each core maintains private instruction and data caches. The architecture implements a tightly coupled Neural Processing Unit (NPU) with CONV, GEMM, and POOL operations that execute opportunistically in the background when the MAC unit is available. Dynamic frequency scaling (DFS) with three levels (100/200/400 MHz) is applied to the shared MAC unit, allowing the dynamic acceleration of CNN workloads. The arbitration mechanism uses SystemC sc_fifo channels with CPU-priority polling, ensuring that CPU execution is minimally impacted by background AI processing while the NPU makes progress during idle MAC slots. The NPU supports 3 × 3 convolutions, matrix multiplication (GEMM) with 10 × 10 tiles, and pooling operations. The implementation is cycle-accurate in SystemC, targeting FPGA deployment. Experimental evaluation demonstrates that the dual-core architecture achieves 1.87× speedup with 93.5% efficiency for parallel workloads, while DFS enables 70% power reduction at low frequency. The system successfully executes simultaneous CPU and AI workloads, with CPU-priority arbitration ensuring no CPU starvation under contention. The proposed design offers a practical solution for embedded AI applications requiring both general-purpose computation and neural network acceleration, validated through comprehensive SystemC simulation on modern FPGA platforms. Full article

The high penetration of distributed energy resources (DERs) poses numerous challenges to community energy management, including intense source-load stochasticity, synchronized load surges triggered by multi-agent gaming, and potential privacy breaches. To tackle these issues, this paper proposes a coordinated energy trading framework driven by an intermediate market-rate pricing mechanism. Within this framework, a novel Multi-Agent Transformer Proximal Policy Optimization (MATPPO) algorithm is developed, adopting an LSTM–Transformer hybrid architecture and the centralized training with decentralized execution (CTDE) paradigm. During centralized training, an LSTM network extracts temporal evolution features from source-load data to handle environmental uncertainty, while a Transformer-based self-attention mechanism reconstructs the dynamic agent topology to capture spatial correlations. In the decentralized execution phase, prosumers make independent decisions using only local observations. This eliminates the need to upload internal device states, significantly enhancing the privacy of sensitive local information during the online execution phase. Additionally, a parameter-sharing mechanism enables agents to share policy networks, significantly enhancing algorithmic scalability. Simulation results demonstrate that MATPPO effectively mitigates power peaks and reduces the transformer capacity pressure at the main grid interface. Furthermore, it significantly lowers total community electricity costs while maintaining high computational efficiency in large-scale scenarios. Full article

Federated learning (FL) enables collaborative model training across distributed devices and robotic systems while preserving data privacy, making it well-suited for swarm robotics and edge-device-powered intelligence. However, FL remains vulnerable to adversarial behaviors such as data and model poisoning, particularly in real-world deployments where detection methods must operate under strict computational and communication constraints. This paper presents a practical, real-world federated learning framework that enhances robustness to adversarial agents in a ROS2-based edge-device swarm environment. The proposed system integrates the Federated Averaging (FedAvg) algorithm with a lightweight average cosine similarity-based filtering method to detect and suppress harmful model updates during aggregation. Unlike prior work that primarily evaluates poisoning defenses in simulated environments, this framework is implemented and evaluated on physical hardware, consisting of a laptop-based aggregator and multiple Raspberry Pi worker nodes. A convolutional neural network (CNN) based on the MobileNetV3-Small architecture is trained on the MNIST dataset, with one worker executing a sign-flipping model poisoning attack. Experimental results show that FedAvg alone fails to maintain meaningful model accuracy under adversarial conditions, resulting in near-random classification performance with a final global model accuracy of 11% and a loss of 2.3. In contrast, the integration of cosine similarity filtering demonstrates effective detection of sign-flipping model poisoning in the evaluated ROS2 swarm experiment, allowing the global model to maintain model accuracy of around 90% and loss around 0.37, which is close to baseline accuracy of 93% of the FedAvg algorithm only under no attack with a very minimal increase in loss, despite the presence of an attacker. The proposed method also maintains a false positive rate (FPR) of around 0.01 and a false negative rate (FNR) of around 0.10 of the global model in the presence of an attacker, which is a minimal difference from the baseline FedAvg-only results of around 0.008 for FPR and 0.07 for FNR. Additionally, the proposed method of FedAvg + cosine similarity filtering maintains computational statistics similar to baseline FedAvg with no attacker. Baseline results show an average runtime of about 34 min, while our proposed method shows an average runtime of about 35 min. Also, the average size of the global model being shared among workers remains consistent at around 7.15 megabytes, showing little to no increase in message payload sizes between baseline results and our proposed method. These results demonstrate that computationally lightweight cosine similarity-based detection methods can be effectively deployed in real-world, resource-constrained robotic swarm environments, providing a practical path toward improving robustness in real-world federated learning deployments beyond simulation-based evaluation. Full article

Transdisciplinary Research (TDR) leverages shared concepts to foster mutual learning among diverse stakeholders, relying on “boundary objects” to shape collective identities and visions. However, the existing literature often overlooks the critical roles of subjectivity and conflict in this process. This paper introduces an analytical framework to examine the construction of these objects, using the “Oasis Project” in the Ecuadorian Andes as a central case study. A research-action project on pesticide-free territories in Ecuador unearthed a question during its implementation on how to achieve collective action when key actors are in conflict with each other. Using TDR to find boundary objects where different viewpoints can find shared meaning, it was determined that there is not enough conceptual clarity in the literature around how conflict can actually help achieve coordination. Using a variety of qualitative methods, such as interviews, participatory observation, and analysis of WhatsApp group message texts, this study shows how the novel concepts of boundary entanglements and conflicts can help other researchers and practitioners facilitate impactful TDR. This study emphasizes three transformative lessons for sustainability science: first, boundary objects are inherently dynamic, evolving through continuous social negotiation rather than static definition; second, their successful consolidation requires deep integration into local knowledge systems, cultural norms, and governance structures; and third, and perhaps most critically, conflict and operational breakdowns are not indicators of failure; rather, they serve as vital diagnostic tools that unveil hidden power relations and epistemic boundaries, providing essential moments for critical reflection and the recalibration of collaborative sustainability strategies. Full article

This article examines how algorithmic mediation reshapes social trust and solidarity in the post-secular age. Historically grounded in shared moral horizons shaped by religion, tradition, and communal practices, trust has increasingly been displaced by technocratic governance, market rationality, and algorithmic systems that mediate work, cognition, communication, and political life. Through a critical analysis of contemporary developments—including algorithmic labour management, neurotechnology, large language models, digital public spheres, technological sovereignty, and global AI governance—the article argues that algorithmic mediation intensifies the fragility of trust by instrumentalizing human agency, fragmenting public reason, and concentrating power within opaque technological infrastructures. Against technological determinism and purely procedural approaches to ethics, the article advances a normative framework rooted in solidarity and the common good. Drawing on post-secular perspectives, a retrieval of natural law normativity, and the resources of Catholic Social Teaching, it contends that trust cannot be sustained through efficiency, prediction, or regulation alone. Instead, social trust depends upon relational goods—dignity, responsibility, participation, and truth—that resist reduction to data-driven optimization. Reclaiming solidarity therefore requires re-embedding AI within moral horizons capable of guiding technological development toward integral human flourishing. In this sense, the governance of AI emerges not merely as a technical challenge but as a decisive moral and political task for post-secular societies. Full article