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        <title>Frontiers in Physics | Social Physics section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/physics/sections/social-physics</link>
        <description>RSS Feed for Social Physics section in the Frontiers in Physics journal | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-05-03T16:02:47.562+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1815539</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1815539</link>
        <title><![CDATA[Structural measurement and resilience of China’s copyright trade dependency network under international regulation: a directed weighted network analysis]]></title>
        <pubdate>2026-04-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>XiaoXuan Zhang</author><author>Wenhong Qi</author><author>Han Liang</author>
        <description><![CDATA[In the context of globalization, copyright trade has become a pivotal arena for national cultural diplomacy and the competition of soft power. Although China has faced a persistent trade deficit over the past decade, there is an urgent need to investigate its deep-seated dependencies and risks from the perspective of network structures. This study constructs an ego-centric network model centered on China to analyze the intensity and distribution characteristics of its connections with various trade partners. The results indicate that network connections are highly concentrated among a few core hubs, exhibiting significant structural imbalance. China, within this network, finds itself in a predicament of extensive connections but insufficient control. The discussion suggests that it is imperative to optimize the network structure by implementing precise diversification strategies, building inter-community bridges, and shaping node advantages in specific subfields. These measures aim to enhance the overall resilience and structural power of China’s copyright trade system.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1743945</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1743945</link>
        <title><![CDATA[PriRS: an AI-driven framework for privacy and reliability in cyber–physical–social systems data sharing]]></title>
        <pubdate>2026-04-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xu Yao</author><author>Kun Zhang</author><author>Yingwei Liang</author><author>Chenghui Liu</author><author>Taipeng Zhu</author><author>Fangfang Zhou</author>
        <description><![CDATA[Cyber–physical–social systems (CPSS) impose stringent requirements for data sharing security and regulatory compliance. However, existing solutions fail to bridge the gap between rigid smart contracts and flexible social regulations. The core research question is: how can we enforce complex, human-readable regulatory policies within rigid blockchain transactions without creating scalability bottlenecks? To address this, we propose PriRS, an AI-driven privacy and reliability framework. First, we utilize a large language model (LLM)-based compliance oracle within a trusted execution environment (TEE). This agent intelligently analyzes regulations to ensure strict compliance before data authorization. Second, we introduce a “majority voting group data sharing” mechanism. By combining Shamir’s secret sharing with conditional proxy re-encryption, we move heavy coordination off-chain. This ensures fairness and significantly improves throughput. Experimental results on the Sepolia testnet demonstrate that PriRS reduces on-chain gas consumption by 92.3% compared to state-of-the-art schemes. The AI-driven oracle achieves 96.0% accuracy and 98.0% precision on policy violation detection, while maintaining 100% deterministic consistency across repeated runs in the TEE. Consequently, PriRS provides a highly efficient, secure, and legally compliant foundation for decentralized CPSS data markets.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1739822</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1739822</link>
        <title><![CDATA[Research on collaborative game of scientific and technological achievements productization based on “administrative committee + enterprise” mode from the perspective of CPSS]]></title>
        <pubdate>2026-04-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Meng Qiu</author><author>Jifa Wang</author><author>Haitao Ji</author><author>Taojia Zhang</author>
        <description><![CDATA[The productization of scientific and technological achievements in high-tech industrial parks is a key link in promoting the deep integration of the innovation chain and the industrial chain. However, this process involves complex interactions among multiple actors, with intertwined risks and unclear governance mechanisms. Based on the CPSS perspective and grounded in evolutionary game theory, this study constructs a game model among high-tech industrial parks, park technology enterprises, and academic research institutions. It focuses on analyzing the impact of the “administrative committee + enterprise” model on CPSS risk management and multi-stakeholder governance, and further examines its influence mechanism on the game equilibrium of the productization of scientific and technological achievements in high-tech industrial parks. The findings indicate that the productization of scientific and technological achievements in high-tech industrial parks is a process of coordinated interaction among three parties. The park promotes the organic linkage of the physical, information, and social components in CPSS through support measures such as technology maturation investment, market engagement organization, policy implementation, and digital management, thereby achieving systematic governance among multiple actors and accelerating the productization process. As the core of transformation, park technology enterprises drive the transition from technology to products through strengthened R&D and model iteration. Academic research institutions, as the source of technology, address implementation challenges through achievement openness and collaborative transformation. In addition, different support measures show significant differences in their impact mechanisms on CPSS risk management and game equilibrium. Technology maturation investment and digital management exhibit a “threshold-driven” effect, promoting the evolution of the game from a non-cooperative equilibrium to a stable cooperative equilibrium through a dual “technology—management” trust mechanism. Market engagement and policy implementation present an “inverted U-shaped” effect through institutional incentives and coordination mechanisms, where optimal cooperation strategies exist only at moderate levels. Government special subsidies display a “switch-type” threshold effect, where crossing a critical value can rapidly activate multi-actor collaboration.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1778336</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1778336</link>
        <title><![CDATA[A novel approach for fair incentive social physical data based on blockchain-federated learning]]></title>
        <pubdate>2026-04-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Heba G. Mohamed</author><author>Hung Tran-Huy</author><author>Trang Hoang-Thu</author><author>Iyas Qaddara</author><author>Bong Jun Choi</author><author>Asma Hassan Alshehri</author><author>Ijaz Ahad</author><author>Hui Liu</author>
        <description><![CDATA[IntroductionA key research focus in FL is the incentive mechanism. To ensure that all data owners actively contribute their data for model training, it is necessary to establish a fair incentive system that encourages them to share useful data. A well-functioning incentive system enables all participants to continuously and effectively train models, which in turn enhances the accuracy of the ultimately trained federated model.MethodsThis paper proposes a new algorithm for optimizing the incentive mechanism. Initially, clients who possess high-quality data can participate in the training due to their reputation value. The client entrusted local data training to the high-performance fog node by auctioning local training tasks to it. The aim of this action was to improve the efficiency of local training and tackle the problem of differing performance levels among clients. Finally, the global gradient aggregation algorithm removes malicious clients from the local gradient.Results and DiscussionResults from the simulation demonstrate that the suggested algorithm outperforms current algorithms.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1834371</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1834371</link>
        <title><![CDATA[Correction: Investor herding behavior in social media sentiment]]></title>
        <pubdate>2026-04-01T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Jinjoo Yoon</author><author>Gabjin Oh</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1752770</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1752770</link>
        <title><![CDATA[The R&D game of technological achievements in industrial parks under the “administrative committee + enterprise” model: a CPSS perspective]]></title>
        <pubdate>2026-03-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Meng Qiu</author><author>Haitao Ji</author><author>Miao Wang</author><author>Jifa Wang</author>
        <description><![CDATA[Under the background of digitalization and collaborative innovation, high-tech industrial parks have gradually evolved into complex innovation systems characterized by multi-agent interaction. From the perspective of social physics, this thesis constructs a tripartite evolutionary game model involving high-tech industrial parks under the “administrative committee + enterprise” mode, park technology enterprises, and academic research institutions within the CPSS (Cyber-Physical-Social System) framework, and analyzes the influence mechanisms of key parameters in the information layer, physical layer, and social layer on system stability through replicator dynamic equations and numerical simulation. The results indicate that cooperative R&D of scientific and technological achievements is not a linear process of input accumulation, but a nonlinear evolutionary system driven by cross-layer coupling relationships. The simulation results show that the level of value-added services, as the support intensity of the information layer, exhibits a significant threshold effect. Moderate information support can reduce information asymmetry and promote system convergence toward a collaborative equilibrium, whereas excessively low or excessively high levels may lead to system instability. Investment shareholding changes the stability interval of the system by influencing resource allocation and benefit distribution structures. Appropriate participation contributes to the formation of a risk-sharing mechanism, while excessive shareholding weakens enterprises’ incentives for R&D. The subsidy ratio reflects governance intensity in the social layer. Moderate intervention can promote the formation of collaboration, whereas excessive intervention may cause strategic distortion and system disturbance. Strategic returns constitute a key variable driving the system from a non-cooperative state to a stable collaborative equilibrium. When system-level collaborative benefits exceed a critical threshold, a cross-layer positive feedback mechanism emerges.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1705699</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1705699</link>
        <title><![CDATA[Evolutionary game analysis of multiple stakeholders in e-commerce intellectual property based on social co-governance theory]]></title>
        <pubdate>2026-03-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ji Li</author><author>Han-Yu Lu</author>
        <description><![CDATA[IntroductionEstablishing a social co-governance system for intellectual property (IP) protection in e-commerce is crucial for strengthening the IP protection framework, fostering a conducive business environment, and promoting the high-quality development of China’s e-commerce sector. MethodsGrounded in social co-governance theory, this study constructs a multi-stakeholder framework for e-commerce IP protection, encompassing the government, e-commerce platforms, operators, and consumers. An evolutionary game model is developed to capture stakeholders' strategic interactions, and numerical simulations are performed to examine the dynamic evolution of their behavioral strategies.ResultsThe results indicate that strategic decisions are significantly influenced by action costs and benefits, the intensity of rewards and penalties, and social reputation. The simulation analysis further shows that well-designed reward-punishment mechanisms can effectively enhance stakeholders’ incentives for IP protection.DiscussionThese mechanisms facilitate the emergence of a stable social co-governance equilibrium and enhance the overall effectiveness of IP protection in e-commerce environments. The findings provide theoretical insights and policy implications for improving collaborative governance mechanisms in digital marketplace regulation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1795522</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1795522</link>
        <title><![CDATA[A physics-inspired elastic deformation model for quantifying urban network resilience: resistance and recovery in the Yangtze River Delta]]></title>
        <pubdate>2026-03-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tingting Xu</author><author>Dongdong Bai</author>
        <description><![CDATA[Urban agglomerations are increasingly vulnerable to external shocks, yet existing resilience assessments often rely on static network models that treat cities as homogeneous nodes, neglecting the dynamic processes of resistance and post-shock recovery. To address this limitation, this study proposes a novel resilience-weighted complex network model inspired by the physical principles of elastic deformation. By integrating the entropy method with a modified gravity model, we quantified the resistance and recovery capabilities of 41 cities within the Yangtze River Delta (YRD) urban agglomeration. Unlike traditional approaches, our model simulates the heterogeneous responses of individual cities to external disturbances. The results demonstrate that hub cities, such as Shanghai and Nanjing, exhibit superior resistance and recovery characteristics, playing a critical role in stabilizing the regional network. Simulation experiments reveal a counter-intuitive phenomenon: while intentional attacks targeting hub nodes cause a sharper initial decline in network efficiency compared to random attacks, the superior recovery speed of these key nodes enables the network to restore connectivity more rapidly. These findings challenge the assumption of permanent node failure in conventional simulations and provide a robust, physics-based framework for optimizing regional risk management and urban planning strategies.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1740603</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1740603</link>
        <title><![CDATA[Dynamic evolution game of regional innovation ecosystem with multiple actors under digitalization]]></title>
        <pubdate>2026-03-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zitong He</author><author>Xiaolin Ma</author><author>Yuhan Hu</author><author>Hongyu Liu</author><author>Xiaodong Dong</author>
        <description><![CDATA[Regional innovation ecosystems play a crucial role in advancing national innovation capacity. However, the question of how to foster sustained collaborative innovation among diverse actors within these ecosystems under digitalization remains underexplored. This study aims to investigate the dynamic mechanisms and key factors influencing synergistic innovation behavior among multiple stakeholders in digitally enabled regional innovation ecosystems. Drawing on evolutionary game theory, we develop a tripartite game model involving core enterprises, complementary parties, and the government. A simulation analysis is conducted using the Zhongguancun Science and Technology Park as a case context to examine the evolutionary trajectories of cooperation strategies. The results indicate that: (1) increasing the intensity of digital investment by innovation agents significantly enhances the stability and sustainability of the regional innovation ecosystem; (2) core enterprises can stimulate cooperative innovation by providing incentives to complementary parties in the digital context; (3) innovation actors exhibit substantial positive spillover effects, facilitating the circulation and integration of digital resources and data elements; and (4) government subsidies and penalties positively influence system stability and accelerate the convergence of evolutionary dynamics. By integrating a digital perspective into the analysis of regional innovation ecosystems, this study contributes to theoretical discussions on innovation cooperation and provides practical insights for local governments seeking to improve synergistic mechanisms within digital innovation ecosystems. The findings also offer strategic references for promoting the sustainable development of regional innovation systems under digitalization.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1774969</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1774969</link>
        <title><![CDATA[Modeling the coupled propagation of emotion, information, and disease considering emotional atmosphere in multi-layer networks]]></title>
        <pubdate>2026-03-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Liang’an Huo</author><author>Yuanzhen Dai</author>
        <description><![CDATA[The interaction between information diffusion and emotional contagion exerts substantial influences on disease transmission. In this coupled process, both individual and collective emotional atmospheres play crucial roles by shaping the efficiency of information diffusion and, through their impact on protective behaviors, determining the overall dynamics of disease transmission. This paper proposes a three-layer network propagation model driven by emotional atmosphere to characterize the coupled mechanism of emotional contagion, information diffusion, and disease transmission. The model incorporates an emotional atmosphere function based on the Losada ratio to quantify the threshold effect of collective emotional climates on individual emotional state transitions. Using a microscopic Markov chain approach, we derive the system’s dynamic equations and the epidemic outbreak threshold. The accuracy of the model is verified through Monte Carlo simulations, and the regulatory effects of key parameters on the propagation process are examined. The findings indicate that positive emotions facilitate information diffusion and strengthen individual protective behaviors, thereby effectively suppressing disease transmission. Conversely, negative emotions inhibit information diffusion and weaken protective willingness and compliance, thus accelerating disease transmission. Furthermore, the synergistic interaction between information dissemination and positive emotions exerts a stronger inhibitory effect on disease transmission, significantly reducing the final scale of the epidemic. This research provides a theoretical basis for incorporating emotional management strategies into public health policies while expanding the research framework of multi-layer network propagation dynamics.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1704185</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1704185</link>
        <title><![CDATA[A novel framework for outlier detection in financial markets: a complex network approach with visibility graphs]]></title>
        <pubdate>2026-03-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hao Li</author><author>Linan Chen</author><author>Gaixian Chai</author><author>Luyi Zhang</author>
        <description><![CDATA[IntroductionThe increasing complexity and non-linearity of financial markets make traditional linear models inadequate for systemic risk assessments. This study aims to develop a new framework for identifying outlier events and critical transitions in financial markets.MethodsWe propose a framework that combines a complex network approach with visibility graph algorithms. First, a comprehensive financial stress index (FSI) for China is constructed by integrating sub-indices from the bond, stock, money, and foreign exchange markets, along with time-varying cross-market correlations. The FSI and sub-index time series are converted into complex networks via the visibility graph algorithm. A Rayleigh-entropy-based overlapping influence algorithm is introduced to detect critical risk nodes, addressing node interdependencies and network loops overlooked by traditional percolation theories. The framework is validated using daily data from January 2015 to June 2025, with cross-market stress transmission examined through a vector autoregression model.ResultsThe approach effectively identifies major financial stress periods and cross-market stress transmission paths. A stock market shock raises the bond market stress index by 0.08 within five trading days. A reserve requirement ratio cut by the People’s Bank of China reduces the average money market stress index by 0.12 within 30 days. Stress originates and spreads across sub-markets with measurable time lags, and the bond market credit spread is the dominant driver of stochastic fluctuations in the FSI.DiscussionThe proposed network-based method, paired with model-driven transmission analysis, serves as a robust dynamic tool for monitoring and early warning of financial systemic risk. It provides data-supported insights for both academic researchers and policymakers.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2025.1697310</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2025.1697310</link>
        <title><![CDATA[Identification of time points for LNG throughput: a complex network approach]]></title>
        <pubdate>2026-03-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Bohao Liu</author><author>Yongqiang Sun</author><author>Yue Feng</author><author>Shiwen Sun</author>
        <description><![CDATA[Critical nodes play a decisive role in shaping the intrinsic structure of complex networks. They help uncover the interconnections within real-world systems. Recognizing these key nodes is a central topic in complex network research. Liquefied Natural Gas (LNG), as an emerging clean energy source, has become more important in China’s energy supply system. This is especially true under the low-carbon and environmental protection agenda, owing to its clean and efficient properties. The Shenzhen Dapeng Bay LNG hub port, the largest in China and featuring the highest density of receiving terminals, ensures energy provision for the Pearl River Delta as well as Hong Kong and Macao. In the context of rising LNG demand and the substantial disruptions to ship berthing and departure caused by the COVID-19 pandemic, systemic risks in energy supply are progressively spreading. This study transforms LNG throughput data at the Shenzhen Dapeng Bay hub port into a complex network model using a visibility graph approach, focusing on its structural characteristics and key temporal points to analyze LNG throughput dynamics over time. Furthermore, the visual network node contraction algorithm combined with the Entropy Weight - TOPSIS (Entropy Weight - Technique for Order Preference by Similarity to Ideal Solution) method is employed to determine the key nodes in the LNG throughput visualization map, thereby determining pivotal time points along the LNG throughput timeline. By examining the dynamic features of China’s energy market and projecting future supply and demand, this research offers valuable decision-making insights for policymakers, investors, and energy enterprises.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1762844</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1762844</link>
        <title><![CDATA[Commentary: The impact and role mechanism of digital finance on the economic development of resource-based cities--an empirical study from China]]></title>
        <pubdate>2026-03-06T00:00:00Z</pubdate>
        <category>General Commentary</category>
        <author>Liang Yang</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1753750</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1753750</link>
        <title><![CDATA[Networked evolutionary game analysis of low-carbon technology diffusion]]></title>
        <pubdate>2026-03-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaoxiao Sun</author><author>Jianguo Du</author><author>Xiaowen Zhu</author>
        <description><![CDATA[Developing and deploying low carbon technology is essential for alleviating energy poverty and mitigating environmental pressures, with enterprises serving as critical actors in this transition. To explore the determinants of low carbon technology adoption and diffusion among enterprises, this study constructs a complex network evolutionary game model that integrates behavioral mechanisms and incentive structures. The model examines how behavioral factors, including herd behavior and organizational inertia, together with incentive factors such as policy, economic, social, and technological drivers, shape the diffusion dynamics. The results reveal three key findings: (1) Direct policy incentives and demand-side drivers, such as subsidies and consumer green preferences, exert a stronger and more immediate influence on low-carbon technology adoption than indirect regulatory measures, particularly in the early stages of diffusion. (2) Behavioral factors exhibit asymmetric effects: herd behavior can impede early adoption when participation is low, whereas moderate organizational inertia stabilizes long-term adoption once diffusion takes hold. (3) Policy incentives, market demand, and social supervision interact in a nonlinear and partially substitutable manner, indicating that coordinated policy mixes can significantly accelerate diffusion across enterprise networks. The results suggest that policy and demand-side drivers play a dominant role in accelerating LCT diffusion, while behavioral and social factors primarily influence the timing and stability of adoption. Overall, the study provides an analytical perspective on the role of LCT diffusion in supporting enterprise-level green transformation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1786937</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1786937</link>
        <title><![CDATA[Dynamic social network anomalous behavior detection based on spatiotemporal multi-view graph attention fusion network]]></title>
        <pubdate>2026-02-27T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jimin Wang</author>
        <description><![CDATA[The development of online social networks is accompanied by intricate abnormal interaction phenomena severely impairing the ecosystem’s credibility. Current anomaly detection approaches find it challenging to balance accuracy and robustness when tackling dynamic structural changes, heterogeneous relationships, and lack of labeled data. To address these challenges, this paper proposes ST-MVAN, a Spatio-Temporal Multi-View Attention Network for unsupervised anomaly detection. The proposed framework integrates three core components: (1) in the spatial dimension, we construct heterogeneous relational subgraphs and design an improved Graph Convolutional Network (GCN) that incorporates edge attributes as additive bias and leverages sparse attention to filter structural noise; (2) for feature fusion, an Efficient Channel Attention (ECA) mechanism is introduced to adaptively assign importance weights to multi-view features; and (3) in the temporal dimension, a bidirectional GRU captures dynamic evolutionary dependencies. Finally, a joint Encoder-Decoder framework calculates anomaly scores based on reconstruction errors. Furthermore, we perform experiments on the Digg and Yelp datasets to validate that our method achieves an AUC improvement of up to 12.26% compared to baseline methods. These results demonstrate that ST-MVAN can effectively mitigate structural noise and enhance the security of dynamic social network environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2025.1617607</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2025.1617607</link>
        <title><![CDATA[Multi-agent task allocation method based on the cost-effectiveness maximization multi-round auction algorithm]]></title>
        <pubdate>2026-02-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yu Zhou</author><author>Qing Lan</author><author>Xuan Yang</author><author>Luda Wang</author><author>Guowei Li</author><author>Shuguang Li</author><author>Ting Lyu</author>
        <description><![CDATA[Multi-agent task allocation plays a crucial role in achieving efficient collaboration in heterogeneous multi-agent systems, especially in complex and dynamic environments. However, existing auction-based task allocation approaches often focus primarily on economic optimization or bid-oriented allocation while insufficiently considering the compatibility between agent capabilities and task attribute requirements, along with the overall cost-effectiveness from the task owner’s perspective. To address these limitations, in this paper, we propose a task allocation framework, which integrates task fitness modeling with cost-effectiveness maximization, and further develop a distributed multi-round auction mechanism. In particular, a task fitness model is constructed to quantitatively evaluate the suitability of agents for different tasks by combining multiple capability dimensions, where the importance of different task attributes is determined using the analytic hierarchy process (AHP). Based on this, a cost-effectiveness metric is defined by jointly considering agent bids and task fitness, and a multi-round auction algorithm, with dynamic bidding and an improved payment rule, is designed to maximize the overall task cost-effectiveness while ensuring incentive compatibility and individual rationality. Extensive simulation results demonstrate that the proposed approach significantly improves task cost-effectiveness and maintains high task execution suitability compared with conventional first-price, second-price, and existing multi-round auction mechanisms.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1747280</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1747280</link>
        <title><![CDATA[How digital–real economy integration shapes Little Giant recognitions across Chinese cities]]></title>
        <pubdate>2026-02-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yantong Guo</author><author>Ooi Kok Loang</author>
        <description><![CDATA[IntroductionDigital transformation is often expected to enhance regional innovation, yet a high level of digitalization does not necessarily translate into real-economy upgrading. This study examines whether digital–real economy integration (DREI) increases Little Giant SME recognitions and whether such effects spill over across cities.MethodsUsing a balanced panel of 276 Chinese cities from 2019 to 2023, DREI is constructed with a coupling–coordination index based on entropy-weighted digital and real-economy subindices. Two-way fixed-effects models and a spatial Durbin model are applied to distinguish local impacts from indirect spillovers. Channel regressions using credit depth and R&D intensity assess the plausible mechanisms, and regime dependence is evaluated with a fiscal-capacity threshold specification. Measurement robustness is assessed using alternative DREI reconstructions.ResultThe results consistently show that higher DREI is associated with more Little Giant recognitions, with indirect spillovers accounting for a large share of the total effect. Credit deepening emerges as the most immediate channel, while short-run innovation mediation is weaker in this short panel. Threshold evidence indicates larger marginal gains in fiscally constrained cities, which is consistent with diminishing returns where fiscal capacity is higher.DiscussionPolicy implications point to diffusion-ready integration and a local implementation plus regional coordination approach, with greater marginal attention to fiscally constrained areas; moreover, the evidence suggests that spillover effects account for a large share of the total impact, indicating that coordination across cities is essential for translating DREI into broader recognition gains.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1650701</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1650701</link>
        <title><![CDATA[A fault diagnosis method for business management system based on convolutional neural network]]></title>
        <pubdate>2026-02-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Meini Li</author><author>Zihao Wang</author>
        <description><![CDATA[The complexity and dynamism of business management system pose higher requirements for the accuracy and timeliness of fault diagnosis. This paper proposes a compensation distance evaluation technique-kernel principal component analysis-convolutional neural network-bidirectional long short-term memory network (CDETKPCA-CNN-BiLSTM) that integrates attention mechanism to address the limitations of traditional diagnostic methods in nonlinear and high-dimensional data scenarios. The bidirectional long short-term memory (BiLSTM) layer and attention mechanism layer further improve the accuracy and reliability of fault diagnosis. Feature extraction is performed in business management system data from both time and frequency domains, effectively utilizing temporal information to form an initial feature set. To address the issue of data redundancy in business management system, a compensation distance evaluation technique and kernel principal component analysis (CDETKPCA) feature fusion method is proposed. Through CDET, the initial feature set is screened and weighted to guide KPCA feature fusion processing, generating a fused feature set for subsequent fault diagnosis research. The experimental results show that CDETKPCA-CNN-BiLSTM can extract effective information more efficiently and significantly improve analysis accuracy. And this provides a new technical method for fault diagnosis in business management system.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1788972</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1788972</link>
        <title><![CDATA[Correction: The influence of digital transformation on the supply chain finance level]]></title>
        <pubdate>2026-02-09T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Zongtuan Liu</author><author>Jiajie Zhang</author><author>Lei Cao</author><author>Ying Xu</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2025.1733200</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2025.1733200</link>
        <title><![CDATA[Simulation of high-frequency trading risks and regulatory strategies in China’s financial market based on multi-layer complex networks]]></title>
        <pubdate>2026-02-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiao Jian</author><author>Zhilin Yin</author><author>Hao Li</author>
        <description><![CDATA[This study addresses the dual structural characteristics of China’s financial market—namely, “retail-investor dominance (80% of trading volume) versus foreign capital’s technological monopoly (0.3% of institutions controlling 43.6% of order flow).” By constructing a multi-layer complex network agent-based model (ABM) that integrates regulatory, core institutional, market-maker, and retail investor layers, it systematically simulates risk transmission mechanisms and regulatory strategies in high-frequency trading (HFT) environments. The findings reveal that HFT exacerbates market unfairness through technological latency advantages. When communication latency differentials exceed 50 milliseconds, retail order interception rates increase nonlinearly to 82%. Moreover, as the strategy homogenization coefficient ρ surpasses the critical threshold of 0.65, the market undergoes a percolation phase transition, with systemic risk probability jumping from 0.2 to over 0.7, which may trigger liquidity crises such as “flash crashes.” Traditional regulatory approaches, hindered by response delays averaging 2.1 h, struggle to cope with the real-time nature of HFT and the challenges posed by algorithmic black boxes. Based on the simulation results, policy recommendations centered on “anti-technological-monopoly,” “real-time algorithmic resonance monitoring,” and “regulatory intelligence” are proposed to develop a modernized and computationally executable regulatory framework tailored to China’s market structure, thereby enhancing both market stability and fairness.]]></description>
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