<?xml version="1.0" encoding="utf-8"?>
    <rss version="2.0">
      <channel xmlns:content="http://purl.org/rss/1.0/modules/content/">
        <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>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-08-15T12:55:55.253+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1930892</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1930892</link>
        <title><![CDATA[Construction and fitting of an infectious disease propagation network based on age stratification and family hyperedges]]></title>
        <pubdate>2026-08-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhijia Liu</author><author>Xiujuan Ma</author><author>Fuxiang Ma</author><author>Jun Yin</author><author>Xin Yang</author>
        <description><![CDATA[BackgroundAge-dependent social activity and household contact patterns can substantially influence infectious disease transmission. This study aims to develop a network framework that jointly represents age stratification and household contact structures.MethodsThe population was divided into youth, middle-aged, and elderly groups, and family hyperedges were used to represent household membership and generate cross-age household contacts. An Age Family Hyperedge Multilayer Network (AFHMN) was constructed using differentiated intra-layer topologies. Eight simulation scenarios were examined under the SIR and SIRS frameworks, with BA and ER networks used as benchmarks. The models were further evaluated using four empirical infectious disease datasets and the RMSE, MAE, MAPE, R2, and DTW metrics.ResultsThe mixed topology, in which the youth and middle-aged layers use BA networks and the elderly layer uses an ER network, showed the closest agreement with the social characteristics of the three age groups. AFHMN achieved lower RMSE and MAE and higher R² values than the benchmark networks across the four empirical datasets and showed more stable overall agreement with infection peaks and temporal transmission patterns.ConclusionAFHMN provides a practical multilayer modeling framework for investigating how age-dependent social structures and household contacts jointly regulate infectious disease transmission.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1849102</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1849102</link>
        <title><![CDATA[Effects of strategy-relationship coevolution on the emergence of cooperation in multilayer networks]]></title>
        <pubdate>2026-08-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mengmeng Liu</author>
        <description><![CDATA[Since social interactions are inherently embedded in multiple relational contexts, single-network models often fall short in explaining the evolution of cooperation. This study develop a two-layer coevolutionary model where behavioral strategies in the upper interaction layer are coupled with the lower signed emotional layer, representing friendly or hostile ties. The framework of this study allows behavioral strategies, emotional attitudes, and the network structure to coevolve dynamically. It found that the dynamics of the emotional layer influence the evolutionary outcomes. Counterintuitively, a relatively low cross-layer coupling strength proves more favorable for sustaining cooperation. It also showed that stochasticity is crucial for breaking the monostability of the defection-dominated state. It provides the necessary conditions for the system to enter another stable cooperative state or mixed strategy, effectively leading to the existence of system bistability. In the end, although the evolution of emotions changes the distribution of the final probability of cooperation, it is more like a regulator of cooperation frequency and cannot significantly improve the overall level of cooperation. This highlights how the constantly evolving relationship environment affects the trajectory of social cooperation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1864135</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1864135</link>
        <title><![CDATA[Modeling multi-system coupled risk propagation and AI-driven adaptive emergency resource allocation for large-scale sporting events]]></title>
        <pubdate>2026-08-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xin Hui</author><author>Qian Zheng</author><author>Yuan Yao</author><author>Yuxin Yan</author>
        <description><![CDATA[Large-scale sporting events are complex socio-technical systems in which local disturbances may propagate across crowd, transportation, facility, security, and medical subsystems. Traditional static risk assessment methods are limited in describing such cross-system cascading processes and their implications for emergency resource allocation. This study develops an exploratory simulation framework that combines weighted complex-network representation, directional subsystem coupling, resilience evaluation, and Deep Q-Network (DQN)-based adaptive resource scheduling. The event environment is represented as a synthetic, domain-informed 24-node network across five functional subsystems; therefore, the numerical results should be interpreted as mechanism-level simulation evidence rather than venue-specific forecasts. Under the baseline scenario, coupling effects increase the peak system-wide risk by approximately 21%, and the overall risk exceeds the warning threshold within 18 time steps if no intervention is implemented. Compared with the no-intervention baseline, the DQN-based adaptive scheduling strategy improves the composite resilience index by 39.5% and reduces the peak risk to approximately 48% of the baseline level. These findings suggest that cross-system coupling and adaptive resource allocation are important considerations for simulation-based safety management of large-scale sporting events, while future work should calibrate the framework using venue-specific operational data.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1930552</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1930552</link>
        <title><![CDATA[Correction: Considering weights in real social networks: a review]]></title>
        <pubdate>2026-08-07T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>M. Bellingeri</author><author>D. Bevacqua</author><author>F. Sartori</author><author>M. Turchetto</author><author>F. Scotognella</author><author>R. Alfieri</author><author>N. K. K. Nguyen</author><author>T. T. Le</author><author>Q. Nguyen</author><author>D. Cassi</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1875639</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1875639</link>
        <title><![CDATA[Structural evolution and product-level heterogeneity in global digital product trade networks]]></title>
        <pubdate>2026-07-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yin-Ting Zhang</author><author>Mo-Lei Chen</author><author>Hao Wang</author>
        <description><![CDATA[Digital technologies have increasingly reshaped the organization of international trade, yet limited attention has been paid so far to the structural characteristics of global digital trade networks. This study constructs an international digital product trade network based on bilateral trade flows in six categories of digital products from 2007 to 2023 and examines its evolution using network analysis. The results show that the global digital product trade network has undergone a process of structural deepening, characterized by a relatively stable number of participating economies but substantial growth in trade links, network density, and trade value. The network also exhibits a persistent but multidimensional core structure, with the United States maintaining the strongest embedded position, while China dominates in trade scale and intermediary roles. At the product level, significant structural heterogeneity is observed. Electronic components and equipment form the most commercially intensive network, whereas other digital manufacturing products are more structurally connected and diversified. By contrast, computer manufacturing displays weaker cohesion and higher concentration, indicating a more hierarchical and potentially vulnerable structure. These findings highlight the importance of network structure, product heterogeneity, and diversification for understanding the resilience and governance of digital trade.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1839225</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1839225</link>
        <title><![CDATA[Machine learning analysis of cross-industry innovation efficiency: evidence from Chinese listed companies (2006–2023)]]></title>
        <pubdate>2026-07-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tan Yang</author><author>Haiqing Hu</author><author>Pufeng Wu</author><author>Huanqing Liu</author>
        <description><![CDATA[BackgroundHow listed firms convert R&D spending into patent outputs is central to innovation management and applied econometrics. We treat InnoEff1 as a reduced-form innovation-conversion indicator—not a DEA/SFA frontier-efficiency score.MethodsUsing 40,706 Chinese listed firm-years (2006–2023) across 20 industry segments, we prioritize a restricted-variable Gradient Boosting specification that excludes contemporaneous patent stocks overlapping the outcome numerator. Validation combines five-fold GroupKFold blocking by firm, a strict 2019–2023 time hold-out, DEA/SFA-style benchmarks, and industry-balanced subsample checks.ResultsUnder GroupKFold, the restricted model attains R2 ≈ 0.414 (time hold-out ≈0.144), versus ≈0.989 (hold-out ≈0.978) when patent overlaps are retained—quantifying mechanical fit inflation. Tabulated cross-industry mean InnoEff1 spans 0.088 (Table 1); Kruskal–Wallis rejects equal distributions (H ≈ 2133, P < 10−10), and 28 of 45 Table-1 pairwise contrasts remain significant after Holm–Bonferroni adjustment. Lagged R&D intensity and financial covariates dominate SHAP attributions in the restricted model.ConclusionThe contribution lies in validated machine-learning practice—leakage control, interpretability, and transparent benchmarking—not in near-unity R2 diagnostics. Predictive patterns are associative; they do not justify causal claims that broad-based policies dominate sector-specific innovation support.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1809470</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1809470</link>
        <title><![CDATA[Regional analysis of study abroad search indices in China based on visibility graph theory: temporal patterns and regional coordination]]></title>
        <pubdate>2026-07-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Li Wang</author><author>Jun-Chao Ma</author>
        <description><![CDATA[IntroductionUnderstanding the temporal dynamics and regional variation of study-abroad search attention is important for interpreting educational mobility intentions in a highly digitized information environment.MethodsThis study applies visibility graph theory to Douyin/Juliang Suanshu study-abroad search indices across 31 mainland Chinese provincial-level units from 4 June 2022 to 31 May 2025. Provincial series are aggregated into seven major regions, with summation used as the primary aggregation method and PCA used for aggregation sensitivity analysis. Regional natural visibility graphs are benchmarked against 100 size- and density-matched random graphs, alternative degree distributions are fitted, and regional network complexity is evaluated using the entropy weight method (EWM) with bootstrap uncertainty.ResultsThe results show that all regional visibility graphs have substantially higher clustering than random benchmarks and small-world coefficients above 42, while the degree distributions are better interpreted as heavy-tailed than as uniquely confirmed power laws. Regional time series are strongly synchronized, with a mean off-diagonal zero-lag correlation of 0.987 and no systematic lead-lag pattern within a 30-day window. EWM ranks Central China highest in the daily analysis, followed by East China and South China, but bootstrap intervals overlap and the ordering is sensitive to weekly aggregation. Weekly visibility-graph community detection identifies 5–7 temporal communities per region and recurring transition dates around February 2023, September 2023, March 2024, and late 2024.DiscussionThese findings clarify the temporal organization of study-abroad search attention and provide a network-based framework for analyzing regional educational search behavior.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1837668</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1837668</link>
        <title><![CDATA[Public investment search behavior as an external attention signal: visibility-graph evidence from Douyin data in Shandong Province]]></title>
        <pubdate>2026-06-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mingwei Cui</author><author>Shiwen Sun</author><author>Hao Wang</author>
        <description><![CDATA[IntroductionPublic search behavior provides a high-frequency external attention signal for understanding changes in market expectations in the digital economy. During periods of macroeconomic adjustment and investment uncertainty, search attention may capture shifts in public concern, information demand, and expectation formation.MethodsUsing Douyin search data on the theme of “investment” in Shandong Province from 4 June 2022 to 1 August 2025, this study constructs a Public Investment Search Network based on the Visibility Graph algorithm. The analysis examines temporal fluctuation, phase-based evolution, network topology, community differentiation, topological indicators, degree distribution, and robustness under alternative network constructions.ResultsThe results show clear phase-based aggregation and divergence in public investment attention. The network exhibits a heavy-tailed degree distribution and small-world-like characteristics. Attention evolves through a cyclical process of concentration, dispersion, and rebalancing under the combined influence of policy stimuli, market fluctuations, and information diffusion. Changes in clustering coefficient, modularity, volatility, and Shannon entropy further reveal the self-organizing features of public investment search behavior.DiscussionThe findings suggest that the public investment search network provides a structural representation of collective attention and offers supplementary information for monitoring market signals and changes in public expectations. The study describes the structure of public investment-related attention rather than directly testing firm-level investment responses. Future research may combine search-network indicators with firm-level investment, innovation, or financial data to further examine how external attention signals are incorporated into corporate decisions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1864040</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1864040</link>
        <title><![CDATA[Analysis of IIoT data using visibility graphs and complex network methods]]></title>
        <pubdate>2026-06-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jiwei Xu</author><author>Kaigong Wang</author><author>Jiaqi Wang</author><author>Jiayu Wu</author><author>Xinyan Lv</author><author>Qun Song</author>
        <description><![CDATA[With the increasing volume of time series data generated in the Industrial Internet of Things (IIoT), characterizing the intrinsic structural relationships within such data has become a key challenge in complex system analysis. The visibility graph (VG) method maps time series into complex networks, enabling the representation of dynamic features in a topological form and providing an effective tool for nonlinear time series analysis. Compared with traditional approaches that primarily focus on statistical features, VG can reveal latent structural information and correlation patterns within the sequence. Based on this, the VG method is applied in this study to the analysis of power consumption time series from electric vehicle charging stations. The time series are transformed into complex networks, and their structural characteristics are systematically investigated from a complex network perspective. By constructing visibility graph networks of the power consumption sequences, statistical analyses of node degree, degree distribution, and overall topological structure are performed to characterize differences in system structure under different operating states and scenarios. The results show that the constructed VG networks exhibit pronounced structural heterogeneity. The node degree distributions display clear heavy-tailed behavior and approximately follow a power-law scaling within a certain range. Meanwhile, a small number of highly connected nodes play a dominant role in the overall network structure, while most nodes remain weakly connected. Further analysis reveals significant structural differences across operating conditions, reflecting the complex dynamic evolution of charging behavior under different scenarios. On this basis, classification models are built by combining statistical features with VG-based structural features for validation. The results demonstrate that incorporating VG features can improve classification performance to a certain extent, indicating that the VG method provides additional structural information for time series analysis. Overall, the proposed approach offers a useful framework for structural modeling and anomaly detection of IIoT time series data.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1865724</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1865724</link>
        <title><![CDATA[Editorial: Security, governance, and challenges of the new generation of cyber-physical-social systems, Volume II]]></title>
        <pubdate>2026-06-26T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Yuanyuan Huang</author><author>Jianping Gou</author><author>Xin Lu</author><author>Amin Ul Haq</author><author>Qifei Wang</author><author>Jiazhong Lu</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1828292</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1828292</link>
        <title><![CDATA[Chaotic characteristics and adaptive regulation of institutional investors’ behavior under cost heterogeneity]]></title>
        <pubdate>2026-06-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Qianbo Lai</author><author>Jifa Wang</author><author>Meng Qiu</author><author>Miao Wang</author>
        <description><![CDATA[Based on the “minimum complexity” modeling principle, this study constructs a tripartite Cournot game model for institutional investors’ behavioral interaction under cost heterogeneity and analyzes it systematically from static equilibrium and dynamic evolution perspectives. First, a nonlinear dynamic system for the tripartite game is established to rigorously derive the theoretical stability conditions of the system’s equilibrium points. Second, numerical simulation is used to depict the system’s stability region and chaotic characteristics and explore the influence mechanism of key parameters on investor behavior. Finally, an adaptive control strategy is designed to regulate the system’s chaotic state effectively. The results show that the system’s stability is jointly affected by the nonlinear synergy of institutional investors’ adjustment rates and cost heterogeneity with significant parameter threshold effects; cost-heterogeneous investors exhibit distinct behavioral differentiation, with low-cost ones having more strategic adjustment space and high-cost ones’ decisions highly depending on low-cost counterparts; within the model framework, parameter regulation can drive the system from a chaotic state toward a stable state. Extensive robustness checks support the validity of the results under the model assumptions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1755888</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1755888</link>
        <title><![CDATA[Threshold analysis of multi-group SIR models with heterogeneous mixing]]></title>
        <pubdate>2026-06-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Michele Bellingeri</author><author>Ayse Humeyra Bilge</author><author>Ayse Peker-Dobie</author><author>Sevgi Harman</author>
        <description><![CDATA[We analyze the spread of a social interaction agent of finite duration of interest, such as a petition, behavioral trend, or opinion, through a heterogeneous population using a multi-group SIR model. By integrating the model equations, we obtain explicit final-size relations and identify threshold conditions that determine whether propagation can be sustained. For one-, two-, and three-group systems, we show how within-group reinforcement and cross-group influence shape the geometry of the no-propagation boundary. The results provide a clear geometric characterization of heterogeneous diffusion and offer practical guidance for designing interventions that either promote or inhibit spread.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1700712</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1700712</link>
        <title><![CDATA[Artificial Leviathan: exploring social evolution of LLM agents through the lens of hobbesian social contract theory]]></title>
        <pubdate>2026-06-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Gordon Dai</author><author>Weijia Zhang</author><author>Jinhan Li</author><author>Siqi Yang</author><author>Chidera Onochie lbe</author><author>Srihas Rao</author><author>Arthur Caetano</author><author>Misha Sra</author>
        <description><![CDATA[IntroductionThe emergence of Large Language Models (LLMs) and advancements in Artificial Intelligence (AI) offer an opportunity for computational social science research at scale. Building upon prior explorations of LLM agent design, our work introduces a simulated agent society where complex social relationships dynamically form and evolve over time.MethodsAgents are given psychological drives and placed in a sandbox survival environment. We conduct an evaluation of the agent society through the lens of Thomas Hobbes’s seminal Social Contract Theory (SCT), analyzing whether agents seek to escape a brutish “state of nature” by surrendering rights to an absolute sovereign in exchange for order and security.ResultsIn our experiments, agents initially engage in unrestrained conflict, mirroring Hobbes’s depiction of the state of nature. However, as the simulation progresses, social contracts emerge, leading to the authorization of an absolute sovereign and the establishment of a peaceful commonwealth founded on mutual cooperation.DiscussionThe congruence between our LLM agent society’s evolutionary trajectory and Hobbes’s theoretical account indicates the capability of LLM’s to model intricate social dynamics that replicate forces which potentially shape human societies. By enabling insights into group behavior and emergent societal phenomena, LLM-driven multi-agent simulations hold potential for advancing our understanding of social structures, group dynamics, and complex human systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1901025</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1901025</link>
        <title><![CDATA[Editorial: Exploring human interactions through sociophysics: dynamics of opinion formation]]></title>
        <pubdate>2026-06-16T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Michele Bellingeri</author><author>Francisco Welington Lima</author><author>Alireza Abbasi</author><author>Roy Lindelauf</author><author>Valerio Restocchi</author><author>Xiu-Xiu Zhan</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1820346</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1820346</link>
        <title><![CDATA[Riemannian Geometry Attention Heterogeneous Graph Network for complex networks: uncertainty modeling of signal propagation and cross-entity risk prediction in social and ESG governance networks]]></title>
        <pubdate>2026-06-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jixian Zhang</author>
        <description><![CDATA[With the development of green finance and the complexity of the social network ecosystem, ESG risk governance and social network risk prevention and control have become core issues in the intelligent management of complex networks. Both belong to typical complex heterogeneous networks and share common topological characteristics such as multi-agent interaction and uncertainty of signal propagation. However, their inherent heterogeneity and non-linear geometric structure make it difficult for traditional models to collaboratively capture the semantic correlation and geometric features of networks, which seriously restricts the accuracy of risk analysis. To this end, this paper proposes the Riemannian Geometry-Aware Heterogeneous Graph Network (RGA-HGN), which deeply integrates Riemannian geometry and heterogeneous graph attention mechanisms to realize the unified modeling of signal propagation uncertainty in social networks and ESG governance networks, as well as the accurate prediction of cross-agent risks. The model unifies multi-type node features through type-aware Euclidean embedding, retains the inherent geometric structure of the network by virtue of Riemannian manifold projection, and quantifies network risk correlation and captures the uncertainty of signal propagation using Riemannian geometry attention. Based on multi-source public data, a heterogeneous network of 30 Dow Jones enterprises is constructed, and the model is compared with 9 benchmark models on three core tasks. The results show that RGA-HGN significantly outperforms all baseline models. This study fills the research gap of geometric deep learning in the fusion analysis of social and ESG heterogeneous networks, and provides a universal and generalizable framework for complex network risk analysis.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1848971</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1848971</link>
        <title><![CDATA[Information spreading in mixed groups in aircraft cabins with face-to-face interaction]]></title>
        <pubdate>2026-05-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yu Bai</author><author>Xiangying Gao</author><author>Pengfei Chen</author>
        <description><![CDATA[The spread of information through face-to-face contacts among distinct passenger groups in the confined, dynamic environment of commercial aircraft cabins is critical for service quality, yet our understanding remains largely empirical and qualitative, leaving the quantitative roles of heterogeneous interaction patterns mechanistically unresolved. To address this gap, we adopt the classical SIR epidemic model to describe information spreading within mixed passenger groups, and using representative flight scenarios, we construct a dynamic network model grounded in a discrete-time Markov chain, which allows us to explicitly separate movement patterns from information propagation patterns. Through simulations, we examine how transmission intensity, interaction probability, and the parameters of the power-law contact distributions affect the ultimate information coverage and spreading speed. Results reveal that both individual-level contact heterogeneity and cross-group transmission intensity jointly determine the final coverage and spreading speed, with transmission intensity between different passenger groups exerting a particularly pronounced influence on the overall spread. Conversely, elevated transmission probabilities within the cabin crew exhibit a moderating effect on the progression of information spreading. These findings underscore the critical role of group-level transmission dynamics and offer quantitative insights for designing more effective communication strategies in aviation services.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2026.1824500</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2026.1824500</link>
        <title><![CDATA[Research on early warning of global supply chain risks for China’s nickel ore imports: an interpretable deep learning approach]]></title>
        <pubdate>2026-05-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Weiming Gao</author><author>Yalin Lei</author><author>Li Li</author><author>Yuanchen Sun</author><author>Mingda Li</author><author>Sanmang Wu</author>
        <description><![CDATA[China’s nickel ore import supply chain can be regarded as a complex system exposed to coupled disturbances from geopolitical uncertainty, transport disruptions, market concentration, and demand growth. To capture the nonlinear evolution of these interacting risks, this study develops an interpretable computational modeling framework for risk assessment, early warning, and trade-inventory optimization. A four-dimensional indicator system covering availability, acceptability, accessibility, and controllability is first constructed, and an entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is used to derive the composite risk index. Based on this index, a multi-model early warning framework is established and compared across representative machine-learning and deep-learning methods. The results show that the multilayer perceptron (MLP) achieves the best overall predictive performance among the benchmark models, indicating strong capability in capturing nonlinear and shock-driven risk fluctuations. SHAP analysis further reveals that inventory variation, transportation risk, and import concentration are the most influential drivers, followed by new energy vehicle demand growth and geopolitical risk. An MLP-based inversion model is then used to optimize trade-inventory coordination, reducing the risk index to 31.1 under the optimal scenario. The study provides an interpretable computational modeling framework for understanding, anticipating, and governing nickel ore import supply chain risk under complex global uncertainty.]]></description>
      </item><item>
        <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>
      </channel>
    </rss>