Search Results (983)

High-penetration renewable energy significantly increases uncertainty, dynamic network coupling, and the need for secure and coordinated smart-grid dispatch. To address the limitations of conventional forecasting-based and static graph-based methods, this paper proposes a unified dispatch framework that integrates topology-informed dynamic graph learning, privacy-aware multi-agent symbiotic reinforcement learning, and structural causal intervention analysis. The dispatch problem is formulated as a constrained partially observable stochastic game, in which multiple agents coordinate generation adjustment, reserve allocation, and congestion-aware corrective actions under engineering constraints. A physics-informed dynamic graph convolutional module captures both fixed physical topology and stress-dependent operational couplings, while a KL-regularized multi-agent reinforcement learning scheme improves cooperative task allocation under renewable fluctuations. Federated optimization with Rényi differential privacy is introduced to protect sensitive local operational information during training. In addition, a structural causal module provides intervention-based interpretation of how wind variation, load escalation, and line stress affect dispatch cost, congestion risk, and renewable curtailment. Experiments on a public-trace-driven benchmark based on a modified IEEE 30-bus system show that the proposed method achieves the best overall performance among the compared baselines, reducing dispatch-cost RMSE to 3.82, locational-price MAE to 2.95, renewable curtailment to 4.8%, and the constraint-violation rate to 0.30%. Overall, the framework shows favorable performance on the test benchmark, provides post hoc intervention-based interpretation of dispatch outcomes, and is evaluated under a reproducible benchmark construction and assessment protocol. Full article

Precision medicine is facing a critical transition driven by the growing complexity of biological data and the insufficient ability of current models to translate such data into clinically meaningful information. Linear, single-gene approaches are no longer adequate to explain the multifactorial nature of most modern diseases, whose phenotypes emerge from combinations of genetic, molecular, and environmental factors. Network-based precision medicine addresses this by providing a systemic framework capable of integrating heterogeneous omics data, interactomes, and clinical information to identify disease modules and novel therapeutic opportunities. The distinct novelty of this review is its focus on the potential of “network language” as the primary driver for realizing precision medicine through professional collaboration. We argue that networks are not merely tools that achieve precision “per se”; rather, their transformative power lies in their ability to serve as a shared and interpretable interface grounded in network theory. By offering this common conceptual ground, the paradigm bridges the deep cultural and methodological gaps between clinicians and data analysts, enabling effective cooperation between figures with fundamentally different, and often divergent, backgrounds. Practical tools—such as biological network analysis and Molecular Tumor Boards—demonstrate how computational modeling and clinical expertise can be successfully combined to generate actionable insights. Ultimately, network-based precision medicine represents a decisive step toward reconstructing the patient’s complexity and promoting a genuinely personalized clinical approach in which quantitative analysis and medical reasoning act synergistically through multidisciplinary integration. Full article

Background: Pediatric surgical care in Germany is delivered within a highly decentralized system, and training structures vary considerably between institutions. Adequate operative exposure is essential for competency-based training. The specialty requires a high number of operative procedures during training, yet concerns have been raised that residents may not achieve the required case numbers within the standard training period. The German Model Training Regulations (Musterweiterbildungsordnung, MWBO) define 22 procedural categories with specific case number targets for pediatric surgery. However, the extent to which current training structures allow for the fulfillment of these requirements remains unclear. This study examines the distribution of procedures and residents across different hospital types and estimates whether the available procedural volume may be sufficient under simplified allocation assumptions. Methods: We conducted a nationwide analysis of pediatric surgical training capacity in Germany using procedural data from hospital quality reports published by the Federal Joint Committee (Gemeinsamer Bundesausschuss, G-BA) between 2012 and 2023. A total of 3440 OPS codes were assigned to 22 training categories, and case volumes were analyzed across different hospital types. The estimated training capacity was calculated assuming even distribution of cases among residents, and that all eligible procedures are performed with full resident access. Results: Data from an average of 82.3 pediatric surgical departments per year were analyzed, including 29.7% university hospitals, 58.7% non-university departments, and 11.7% other institutions. Most departments reported fewer than five residents. Between 2012 and 2023, the mean number of residents increased slightly across all hospital types, while inpatient numbers declined. Consequently, inpatient exposure decreased from 469.8 to 354.0 cases per resident per year. Patient exposure differed significantly by institutional category (p < 0.001), with higher exposure in non-university departments compared with university hospitals. Across all hospital types, the mean number of fulfilled procedural training categories declined over time. No institution met the target numbers for all categories without cooperation with other units. Thoracic surgery procedures were least frequently covered, whereas appendectomies and inguinal hernia repairs were most consistently fulfilled. Distinct patterns of subspecialization emerged, with trauma procedures less frequently reported at university hospitals and thoracic procedures less frequently reported at non-university departments. Although the overall national procedural volume appears sufficient for most training requirements, low-volume and highly specialized procedures were concentrated at selected centers, limiting their accessibility for trainees. Conclusions: Even though there are sufficient pediatric surgical procedures in Germany, they are unevenly distributed between hospitals. Under a simplified allocation model, many pediatric surgical departments in Germany currently lack sufficient procedural volume to meet training requirements in the defined training timeframe for all trainees. Structural reforms—including mandatory national documentation, minor MWBO adjustments and the creation of training networks—are necessary to ensure comprehensive and equitable pediatric surgical education. Without these changes, extended training durations and reduced trainee satisfaction may contribute to workforce shortages and limit the future quality of pediatric surgical care in Germany. Full article

Cooperation plays a central role in the sustainability of agricultural value chains. This study analyzes cacao and dairy producer organizations in the Department of Caquetá, Colombia, with three objectives: to characterize their current organizational status, to understand legal representatives’ perceptions of the role of cooperation in strengthening their organizations, and to examine the structure of cooperation networks across different stages of the value chains. A qualitative approach was adopted, based on structured interviews, participatory network-mapping workshops, and secondary data analysis. The results show that cacao organizations exhibit higher levels of institutional consolidation than dairy organizations. Representatives’ perceptions indicate that in cacao, cooperation is primarily oriented toward administrative and strategic strengthening, whereas in dairy it is concentrated on production, budgeting, and marketing. Network analysis reveals a predominance of linking-type cooperation, characterized by vertical relationships with external actors, which enhances access to resources but also generates dependency. Overall, network structure and the prevailing types of cooperation influence organizational autonomy and collective performance in Amazonian contexts. Full article

The shift to renewable energy sources is widely seen as a promising way to reduce carbon emissions and mitigate the impacts of climate change. The abundance of renewable energy resources in Africa has enormous potential to reduce greenhouse gas emissions and promote climate resilience. This study conducted a bibliometric analysis of research trends in the adoption of renewable energy systems for climate change mitigation in Africa from 1993 to the first quarter of 2025. The results showed a steady growth in publications during the 2000s, with a growing annual rate of approximately 12.7%, reaching a peak in 2024, indicating increasing research interest in Africa. The thematic analysis highlights key but underdeveloped and emerging themes, including climate change mitigation, renewable energy sources, greenhouse gas assessment, climate change, energy policy, economic growth, carbon emissions, energy consumption, rural electrification, and energy transformation for further investigation. These findings also revealed regional disparities, highlighting the need to strengthen institutional capacity, develop clear long-term policies, and develop innovative financing mechanisms to expedite the deployment of renewable energy. Additionally, results from network analysis and emerging keyword detection revealed that enhanced regional and international cooperation, grid modernization, and technological innovation, such as energy storage and digital solutions, are vital in the developmental efforts to enhance optimized resource utilization and ensure energy access and security. The study thus provides insights into existing research gaps and future research directions, which will benefit policymakers, academics, and related stakeholders in their efforts to utilize Africa’s renewable energy potential to mitigate climate change, enable sustainable development, and achieve energy security throughout the continent. Full article

Since 1978, Chinese–foreign cooperation in running schools (CFCRS) has evolved from fragmented pilot initiatives into a policy-coordinated system of higher education internationalization. This study employs an exploratory sequential mixed-methods design to examine how national policy shifts reshaped the structure of CFCRS collaboration networks between 1978 and 2025. Integrating longitudinal policy analysis with Social Network Analysis (SNA), the research identifies five policy-driven stages: exploratory opening, legal institutionalization, regulated development, quality enhancement, and strategic repositioning. Network analysis shows that increasing density, expanding degree centrality of leading institutions, and greater diversification of international partners reflect growing integration into global transnational higher education networks. At the same time, persistent structural concentration in key institutional hubs and regulated entry into partnerships indicate strong path dependence shaped by state-steered governance. The network also exhibits a disciplinary shift toward engineering and STEM collaborations aligned with national innovation strategies, alongside gradual spatial diffusion from coastal regions toward central and western provinces. Conceptually, the findings demonstrate that state-coordinated internationalization can generate dense and diversified collaboration networks without fully liberalizing governance structures. The CFCRS case thus illustrates a model of hybrid governance, where centralized policy coordination coexists with expanding network-based international partnerships. Full article

Following China’s completion of the removal of provincial boundary toll stations and expressway network integration reform, a large number of electronic toll collection (ETC) gantries were deployed along expressway mainlines nationwide, transforming these facilities from dedicated toll terminals into pervasive traffic-sensing infrastructure covering the entire road network. However, the data value and technological potential embedded in this major infrastructure transformation have not yet been systematically reviewed. This paper adopts a narrative review methodology, incorporating 71 publications identified through multi-database systematic searches. The review is organized along the functional upgrade path of ETC gantries, covering the progression from toll terminals to traffic sensing nodes, multi-source fusion hubs, and finally vehicle–road–cloud cooperative control nodes, and synthesizes research progress in expressway traffic sensing, multi-source data fusion, safety operations, and emerging applications. The review reveals that ETC data have enabled a diverse methodological repertoire spanning travel time estimation, traffic flow prediction, origin–destination (OD) matrix inference, toll plaza safety analysis, dynamic pricing strategies, and environmental impact assessment. Nevertheless, a single ETC data source suffers from inherent limitations: spatial–temporal resolution constrained by gantry spacing and real-time capability limited by transmission latency. This fundamental contradiction constitutes the core driving force behind multi-source data fusion and vehicle–road–cloud integration technologies. The paper further argues that establishing a closed-loop pipeline integrating sensing, fusion, decision, and control and anchored on ETC gantry nodes represents the key direction for realizing intelligent expressway transformation. Full article

Water exhibits unique interfacial properties that arise from the collective organization of its hydrogen-bond network. Establishing clear links between molecular-scale interactions and macroscopic observables remains a central challenge in understanding the behavior of liquid water. In this work, we combine experimental measurements of the contact angle of sessile water drops with quantum-chemical modeling of small water clusters (H₂O)_n (n = 2–6) to explore multiscale effects of hydrogen-bond cooperativity. The cluster calculations reveal a nonlinear, saturating evolution of hydrogen-bond geometries with increasing cluster size, reflecting the onset of cooperative many-body effects. Experimentally, the evolution of the apparent contact angle during evaporation is quantified using both conventional geometry and a non-invasive geometrical-optical method based on analysis of the dark refractive ring, which provides independent validation against conventional goniometric measurements. The evaporation dynamics are further interpreted within the diffusion-limited framework of the Popov model, indicating that the temporal evolution of the apparent contact angle is primarily consistent with geometry-controlled mass loss under diffusion-limited conditions, rather than requiring variations in intrinsic surface energy. By combining macroscopic contact-angle measurements with molecular-level cluster analysis, this study offers a qualitative multiscale perspective in which minimal cooperative hydrogen-bond motifs provide molecular context for interpreting interfacial behavior, without implying direct quantitative prediction of macroscopic interfacial observables. Full article

Global supply chains face increasing vulnerability to disruptions from geopolitical tensions, natural disasters, and demand shocks. The global trade network for railway vans, critical for transcontinental freight transport, remains understudied despite its foundational role in global logistics. This study addresses the gap in understanding how the railway vans trade network structure evolves and responds to different types of shocks, moving beyond static analyses to capture dynamic vulnerabilities. Using UN Comtrade data (2013–2024), multi-level network analysis examined structural evolution at macroscopic, mesoscopic, and microscopic scales. Three risk propagation models simulated supply disruption, demand shock, and cooperation disruption scenarios to assess systemic vulnerabilities. The network transformed from a polycentric to core-periphery structure, with China dominating exports (67 partners in 2024) and Germany leading European integration. Supply disruptions from Romania and Czechia affected up to 114 countries under low risk absorption capacity (α = 0.1), while demand shocks from the USA impacted 53 countries. The disruption of strategic trade links, such as China–Australia, triggered severe systemic risks. The systemic criticality of risk sources varies by shock type, requiring context-specific resilience strategies. The findings guide policymakers in identifying critical vulnerabilities and designing targeted interventions for enhancing supply chain resilience in infrastructure sectors. 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

Vehicular Ad Hoc Networks (VANETs) have become the foundation for the implementation of intelligent transportation systems and new vistas for road safety and traffic efficiency. However, these networks are still susceptible to Sybil attacks, a form of attack that requires malicious entities to create a series of fake identities in order to have an out-of-proportion influence. The present paper puts forth a new Sybil attack detection framework that combines Verifiable Delay Functions (VDFs) in synergistic cooperation with a hierarchical fog-cloud computing structure. Our method does not rely on any additional properties of VDFs but uses them to prove uniqueness computationally, deploying purposefully placed fog nodes for effective localized detection. We mathematically formulate a multi-layered detection algorithm that processes interactions between vehicles on two fog (and cloud) layers to produce suspicion scores using spatiotemporal consistency and VDF challenge-response patterns. Security analysis proves the system’s ability to resist a range of Sybil attack variants with performance evaluation outperforming at detection above 97.8% and false positives below 2.3%. The incorporation of machine learning techniques also extends detection capabilities, and our hybrid VDF-ML method proves better adaptation to the changing attack patterns. Details of implementation and detailed simulations in various traffic situations prove the feasibility and efficiency of our proposed solution to set a new level playing ground for secure VANET communications. Full article

Recently, distributed DC microgrids have gained prominence due to their modular design, scalability, and seamless integration with renewable energy sources. However, ensuring robust operation of distributed secondary control schemes remains challenging, particularly in the presence of unavoidable communication disruptions and parametric uncertainties encountered in practice. Most existing control strategies either assume ideal communication networks or address fault tolerance and communication constraints separately, which limits their applicability in realistic networked environments. This paper proposes an event-triggered fault-tolerant distributed secondary control framework for DC microgrids operating under communication faults. An embedded averaged model is incorporated to support fault-tolerant decision-making and to guide event-triggered communication updates. In addition, an auxiliary recovery mechanism is introduced, enabling neighboring converters to cooperatively compensate for information loss during communication interruptions without centralized supervision. Lyapunov-based stability analysis establishes boundedness and practical convergence of the closed-loop system under event-triggered updates and bounded disturbances while explicitly excluding Zeno behavior. The simulation results under communication fault scenarios demonstrate that the proposed approach achieves accurate DC bus voltage regulation with steady-state deviations below 1% while restoring proportional power sharing with an averaged error within 5%. The embedded model error remains bounded throughout the fault interval, and fault-tolerant control actions are triggered sparsely with well-separated inter-event times on the order of tens of milliseconds, thereby significantly reducing the communication burden. These results confirm the effectiveness and robustness of the proposed framework for the resilient operation of distributed DC microgrids under practical communication constraints. Full article

Underwater acoustic sensor networks (UASNs) have emerged as a pivotal technology for ocean exploration, tactical surveillance, and environmental monitoring. However, the underwater acoustic channel poses severe challenges, including high propagation delay, limited bandwidth, and rapid time-varying multipath fading, which significantly degrade communication reliability. Cooperative communication, which exploits spatial diversity via relay nodes, offers a promising solution to these impairments. In this paper, we investigate the joint optimization of relay selection and power allocation in UASNs to maximize the long-term system energy efficiency and throughput. This problem is inherently complex due to the hybrid action space, which couples the discrete selection of relay nodes with the continuous allocation of transmission power, and the absence of real-time, perfect channel state information (CSI). To address these challenges, we propose a novel deep hybrid reinforcement learning (DHRL) framework utilizing a parameterized deep Q-Network (P-DQN) architecture. Unlike traditional approaches that discretize power levels or relax discrete constraints, our approach seamlessly integrates a deterministic policy network for continuous power control and a value-based network for discrete relay evaluation. Furthermore, we incorporate a prioritized experience replay (PER) mechanism to improve sample efficiency by focusing on rare but significant channel transition events. We provide a comprehensive theoretical analysis of the algorithm’s complexity and convergence properties. Extensive simulation results demonstrate that the proposed DHRL algorithm outperforms state-of-the-art combinatorial bandit algorithms and conventional deep reinforcement learning baselines in terms of system energy efficiency, and also exhibits superior robustness against channel estimation errors. Full article

The rapid deployment of surveillance cameras in urban, industrial, and domestic environments has intensified the need for intelligent systems capable of analyzing video streams beyond the limitations of single-camera setups. Unlike traditional single-camera approaches, multi-camera systems expand spatial coverage, reduce blind spots, and enable consistent tracking of people and objects across non-overlapping views, thereby improving robustness against occlusions and viewpoint changes. This article presents a comprehensive review of multi-camera vision systems published between 2020 and 2025, covering application domains including public security and biometrics, intelligent transportation, smart cities and IoT, healthcare monitoring, precision agriculture, industry and robotics, pan–tilt–zoom (PTZ) camera networks, and emerging areas such as retail and forensic analysis. The review synthesizes predominant technical approaches, including deep-learning-based detection, multi-target multi-camera tracking (MTMCT), re-identification (Re-ID), spatiotemporal fusion, and edge computing architectures. Persistent challenges are identified, particularly in inter-camera data association, scalability, computational efficiency, privacy preservation, and dataset availability. Emerging trends such as distributed edge AI, cooperative camera networks, and active perception are discussed to outline future research directions toward scalable, privacy-aware, and intelligent multi-camera infrastructures. Full article

Background/Objectives: Comparative sociogenomics combines multiple scientific fields to investigate the genetic basis of social behavior across species. Our aim was to uncover the genetic roots of human sociability with possible implications for autism, a neurodevelopmental disorder characterized by social and communication deficits. Methods: We conducted molecular network analysis on 659 sociability-related genes from different animal species, including humans. Results: We identified a network of 240 genes strongly associated with autism (p < 10⁻¹⁵), with 194 inferred. These genes were grouped into 23 functional communities related to cell–cell junctions and communication, inflammatory and synaptic signaling, neurotransmitter receptors and semaphorin signaling among the more enriched meta-pathways. Some network genes were clustered in nine chromosomal bands (FDR < 0.25), indicating genes’ functional cooperation, shared evolutionary history, and coordinated regulation, and few genes are physically in linkage with ASD genes (within 0.5 cM) or controlled by human-accelerated regions. Conclusions: The most compelling inferred autism risk genes are MED12, FZD9, and DMD since they are differentially expressed in autistic brains, physically linked to key autism genes, controlled by human-accelerated regions, or mapped to chromosomal regions enriched in network genes. If validated, they could represent novel biomarkers, advancing the understanding of autism’s genetic makeup. Full article