Accurate modeling of commuting flows is important for urban governance, traffic planning, and resource allocation. However, the combined influence of individual intentions, geographic constraints, and social dynamics leads to considerable heterogeneity in commuting patterns, making it difficult to develop generation models that generalize across cities. To address this issue, we propose SEDAN, a Structure-Enhanced Diffusion model conditioned on Attributed Nodes for generalizable OD matrix generation. SEDAN models a city as an attributed graph. Each region is treated as a node with demographic and point-of-interest features, and commuting flows are modeled as weighted edges. Adjacency and distance matrices are incorporated to characterize spatial structure. Based on this representation, we design a fusion mechanism within SEDAN to jointly model semantic information and spatial information. Regional semantic attributes are used to model latent travel demand through graph-transformer-based node interactions, while spatial structure is injected into the generation process as explicit constraints. The adjacency matrix guides attention weights to strengthen interactions between neighboring regions. Meanwhile, the distance matrix serves as a diffusion condition to capture spatial proximity and travel impedance. The fusion of urban semantics and spatial constraints enables SEDAN to generate OD matrices that are both behaviorally plausible and geographically coherent. Experiments on real-world OD datasets from U.S. cities show that SEDAN achieves a 7.38% improvement in RMSE over the state-of-the-art baseline, WEDAN. It also remains robust across heterogeneous urban scenarios and varying structural patterns. Our work provides an effective and generalizable solution for commuting OD matrix generation. The code is available at https://anonymous.4open.science/r/SEDAN.

Predicting individual panic emotional arousal timing before manifestation is essential for proactive emergency intervention. Existing methods incorporate cognitive elements but none explicitly model the emotional arousal process, making them ill-suited for emotional arousal timing prediction. We argue that grounding prediction in appraisal emotion theory is necessary because it explicitly models this process, but three problems must be solved. (1) Appraisal theory posits that emotion arises from simultaneous evaluation across multiple threat dimensions, yet no prior work fuses these inputs into risk perception. (2) Existing cognitive models lack an Emotion node, decoupling threat appraisal from emotional arousal and forcing emotions to be inferred indirectly from behaviors. (3) Given their generalizable cognitive reasoning, current approaches adopt LLMs as the primary decision-maker, yet overlook the fragility and hallucination-proneness of their outputs. To address these issues, we introduce PanicCognitivePath (PCP), a framework that addresses all three. A Psychological Safety Distance (PSD) model, grounded in psychological distance theory, maps four-domain signals into a unified risk metric as the entry condition for subsequent cognitive reasoning. An explicit Emotion node grounded in appraisal emotion theory is introduced into BDI, forming a Belief-Desire-Emotion-Intention (BDEI) pathway. Agents whose risk metric exceeds the PSD threshold enter this pathway, coupling threat appraisal directly to emotional arousal. The BDEI pathway governs all state transitions while the LLM is confined to parameter estimation for the Belief-to-Desire transition, confining hallucinations to a single step and preventing error propagation. Experiments on Hurricane Sandy show PCP improves arousal timing accuracy by 10.68% over baselines, reduces peak count error to 7.07%.

In the context of rapid advancements in information technology, rumors emerge frequently and propagate with ever-increasing speed. Investigating the underlying mechanisms of rumor dissemination is therefore of paramount importance for effective rumor containment and mitigation. However, existing studies often neglect the intrinsic interplay between individual cognitive heterogeneity and the temporal dynamics of rumor propagation. To address this gap, this paper proposes a temporal Ignorant-Spreader-Removed-Quitter (T-ISRQ) rumor spreading model to capture realistic behavioral diversity. By incorporating individual-level attributes—activity potential, attractiveness, and cognitive level, we derive both the system dynamical equations and the critical threshold for the T-ISRQ model. Experiments under realistic statistical conditions yield significant findings. A positive correlation between activity potential and cognitive level increases the critical threshold for rumor propagation and restricts its large-scale diffusion. In contrast, a negative correlation between these two attributes lowers the critical threshold and facilitates widespread rumor outbreaks. From the perspective of temporally heterogeneous networks, this study proposes a novel theoretical framework for understanding the underlying mechanisms of rumor propagation.

Consumer-grade drones equipped with low-cost sensors have emerged as a cornerstone of Autonomous Intelligent Systems (AISs) for environmental monitoring and hazardous substance detection in urban environments. However, existing studies predominantly focus on single-source search problems, neglecting the complexities of real-world scenarios where the number and location of hazardous sources are unknown. To address this gap, we propose the Dynamic Likelihood-Weighted Cooperative Infotaxis (DLW-CI) approach to enhance search efficiency and accuracy by integrating the Infotaxis strategy with optimized source term estimation and a dedicatedly-designed cooperative mechanism. Specifically, we introduce a multiple particle filter-based source term estimation method, in which each filter independently estimates the parameters of a potential unknown source, enabling scalable multi-source detection. Additionally, we develop a dynamic likelihood-weighted cooperative mechanism to prevent redundant estimation for the same source, thereby improving energy efficiency while expanding search coverage. Experimental results demonstrate that DLW-CI significantly outperforms baseline methods in terms of success rate, estimation accuracy, and root mean square error, particularly in scenarios with relatively few sources. The approach is further validated in a computational fluid dynamics (CFD)-generated diffusion scenario, confirming its robustness under realistic conditions. Our findings highlight the potential of DLW-CI to enhance environmental safety monitoring in smart city infrastructure, offering a scalable solution for multi-source detection using consumer drone networks.

Accurately predicting public panic sentiment on social media is crucial for proactive governance and crisis management. Current efforts on this problem face three main challenges: lack of finely annotated data hinders emotion prediction studies, unmodeled risk perception causes prediction inaccuracies, and insufficient interpretability of panic formation mechanisms limits mechanistic insight. We address these issues by proposing a Psychology-driven generative Agent framework (PsychoAgent) for explainable panic prediction based on emotion arousal theory. Specifically, we first construct a fine-grained panic emotion dataset (namely COPE) via human-AI (Large Language Models, LLMs) collaboration, combining scalable LLM-based labeling with human annotators to ensure accuracy for panic emotion and to mitigate biases from linguistic variations. Then, we construct PsychoAgent integrating cross-domain heterogeneous data grounded in psychological mechanisms to model risk perception and cognitive differences in emotion generation. To enhance interpretability, we design an LLM-based role-playing agent that simulates individual psychological chains through dedicatedly designed prompts. Experimental results on our annotated dataset show that PsychoAgent improves panic emotion prediction performance by 13% to 21% compared to baseline models. Furthermore, the explainability and generalization of our approach is validated. Crucially, this represents a paradigm shift from opaque “data-driven fitting” to transparent “role-based simulation with mechanistic interpretation” for panic emotion prediction during emergencies. Our implementation is publicly available at: https://github.com/supersonic0919/PsychoAgent.

Consumer-grade drones equipped with low-cost sensors have emerged as a cornerstone of Autonomous Intelligent Systems (AISs) for environmental monitoring and hazardous substance detection in urban environments. However, existing research primarily addresses single-source search problems, overlooking the complexities of real-world urban scenarios where both the location and quantity of hazardous sources remain unknown. To address this issue, we propose the Dynamic Likelihood-Weighted Cooperative Infotaxis (DLW-CI) approach for consumer drone networks. Our approach enhances multi-drone collaboration in AISs by combining infotaxis (a cognitive search strategy) with optimized source term estimation and an innovative cooperative mechanism. Specifically, we introduce a novel source term estimation method that utilizes multiple parallel particle filters, with each filter dedicated to estimating the parameters of a potentially unknown source within the search scene. Furthermore, we develop a cooperative mechanism based on dynamic likelihood weights to prevent multiple drones from simultaneously estimating and searching for the same source, thus optimizing the energy efficiency and search coverage of the consumer AIS. Experimental results demonstrate that the DLW-CI approach significantly outperforms baseline methods regarding success rate, accuracy, and root mean square error, particularly in scenarios with relatively few sources, regardless of the presence of obstacles. Also, the effectiveness of the proposed approach is verified in a diffusion scenario generated by the computational fluid dynamics (CFD) model. Research findings indicate that our approach could improve source estimation accuracy and search efficiency by consumer drone-based AISs, making a valuable contribution to environmental safety monitoring applications within smart city infrastructure.

针对当前混合式教学方法容易忽视创新实践能力培养的问题,该文结合CDIO工程教育模式,提出人工智能赋能的线上线下混合式教学方法.该文基于线下授课为教学主线,线上虚拟实验为创新实践环节的教学模式,结合人工智能系统与学习分析等技术为教学中的创新实践环节赋能,进一步丰富课程资源、提升学习体验、提高教学质量.该文以寻源算法学习为例,深入开展混合式教学方法在培养学生创新实践能力、促进学习参与度与成就感、发展量化分析评价方法中的应用与实践,并为相关课程提供可资借鉴的混合式教学实施方案.

Since the outbreak of the coronavirus disease 2019 (COVID-19), the issue of how to maintain economic development while containing the epidemic has become a significant concern for decision-makers. Though lockdown measures are verified to be very effective in containing the epidemic, its economic costs and other influences have not been fully explored. As a result, decision-makers in many countries are still hesitant to include the lockdown measure in an intervention strategy in response to COVID-19. To address this issue, we propose a universal computational experiment approach for policy evaluation and adjustment based on the Artificial societies, Computational experiments, Parallel execution (ACP) concept. First, we innovatively construct a model via observable CO2 emissions, which is able to estimate the economic costs affected by nonpharmaceutical interventions. Furthermore, based on the population movement data, a risk source model is proposed to estimate the local transmission risk for any prefectures outside the epicenter. Finally, we integrate the data models in a high-resolution agent-based artificial society and carry out large-scale computational experiments supported by the Tianhe supercomputer. Policy adjustments and evaluations are carried out in four cities: Wenzhou, Guangzhou, Beijing, and Wuhan. Our research findings show important implications for policy-making: 1) the local transmission of a city can be almost contained if lockdowns are adopted immediately when the risk index is larger than 1.645, 1.960, or 2.576 at the 90%, 95%, or 99% confidence interval, respectively; 2) if lockdowns are required, in-advance lockdown measures facilitate mitigation efficacy and reduce economic loss; and 3) lockdowns lasting for 7–14 days in a prefecture would be effective in controlling the spread of the epidemic. The duration of the measure should be prolonged with the increment of the initial transmission risk.

The Corona Virus Disease 2019 (COVID-19) pandemic is still imposing a devastating impact on public health, the economy, and society. Predicting the development of epidemics and exploring the effects of various mitigation strategies have been a research focus in recent years. However, the spread simulation of COVID-19 in the dynamic social system is relatively unexplored. To address this issue, considering the outbreak of COVID-19 at Nanjing Lukou Airport in 2021, we constructed an artificial society of Nanjing Lukou Airport based on the Artificial societies, Computational experiments, and Parallel execution (ACP) approach. Specifically, the artificial society includes an environmental model, population model, contact networks model, disease spread model, and intervention strategy model. To reveal the dynamic variation of individuals in the airport, we first modeled the movement of passengers and designed an algorithm to generate the moving traces. Then, the mobile contact networks were constructed and aggregated with the static networks of staff and passengers. Finally, the complex dynamical network of contacts between individuals was generated. Based on the artificial society, we conducted large-scale computational experiments to study the spread characteristics of COVID-19 in an airport and to investigate the effects of different intervention strategies. Learned from the reproduction of the outbreak, it is found that the increase in cumulative incidence exhibits a linear growth mode, different from that (an exponential growth mode) in a static network. In terms of mitigation measures, promoting unmanned security checks and boarding in an airport is recommended, as to reduce contact behaviors between individuals and staff.

Cooperative spatial exploration in initially unknown surroundings is a common embodied task in various applications and requires satisfactory coordination among the agents. Unlike many other research questions, there is a lack of simulation platforms for the cooperative exploration problem to perform and statistically evaluate different methods before they are deployed in practical scenarios. To this end, this paper designs a simulation framework to run different models, which features efficient event scheduling and data sharing. On top of such a framework, we propose and implement two different cooperative exploration strategies, i.e., the synchronous and asynchronous ones. While the coordination in the former approach is conducted after gathering the perceptive information from all agents in each round, the latter enables an ad-hoc coordination. Accordingly, they exploit different principles for assigning target points for the agents. Extensive experiments on different types of environments and settings not only validate the scheduling efficiency of our simulation engine, but also demonstrate the respective advantages of the two strategies on different metrics.

The growing complexity of real-world systems necessitates interdisciplinary solutions to confront myriad challenges in modeling, analysis, management, and control. To meet these demands, the parallel systems method rooted in the artificial systems, computational experiments, and parallel execution (ACP) approach has been developed. The method cultivates a cycle termed parallel intelligence, which iteratively creates data, acquires knowledge, and refines the actual system. Over the past two decades, the parallel systems method has continuously woven advanced knowledge and technologies from various disciplines, offering versatile interdisciplinary solutions for complex systems across diverse fields. This review explores the origins and fundamental concepts of the parallel systems method, showcasing its accomplishments as a diverse array of parallel technologies and applications while also prognosticating potential challenges. We posit that this method will considerably augment sustainable development while enhancing interdisciplinary communication and cooperation.

PurposeThe purpose of this paper is to achieve effective governance of online rumors through the proposed rumor propagation model and immunization strategy.Design/methodology/approachThe paper leverages the agent-based modeling (ABM) method to model individuals from two aspects, behavior and attitude. Based on the analysis and research of online data, we propose a rumor propagation model, namely the Untouched view transmit removed-Susceptible hesitate agree disagree (Unite-Shad), and devise an immunization strategy, namely the Gravity Immunization Strategy (GIS). A graph-based framework, namely Pregel, is used to carry out the rumor propagation simulation experiments. Through the experiments, the rationality of the Unite-Shad and the effectiveness of the GIS are verified.FindingsThe study discovers that the inconsistency between human behaviors and attitudes in rumor propagation can be explained by the Unite-shad model. Besides, the GIS, which shows better performance in small-world networks than in scale-free networks, can effectively suppress rumor propagation in the early stage.Research limitations/implicationsThis paper provides an effective immunization strategy for rumor governance. Specifically, the Unite-Shad model reveals the mechanism of rumor propagation, and the GIS provides an effective governance method for selecting immune nodes.Originality/valueThe inconsistency of human behaviors and attitudes in real scenes is modeled in the Unite-Shad model. Combined with the model, the definition of diffusion domain is proposed and a novel immunization strategy, namely GIS, is designed, which is significant for the social governance of rumor propagation.

Strategy evaluation and optimization in response to troubling urban issues has become a challenging issue due to increasing social uncertainty, unreliable predictions, and poor decision-making. To address this problem, we propose a universal computational experiment framework with a fine-grained artificial society that is integrated with data-based models. The purpose of the framework is to evaluate the consequences of various combinations of strategies geared towards reaching a Pareto optimum with regards to efficacy versus costs. As an example, by modeling coronavirus 2019 mitigation, we show that Pareto frontier nations could achieve better economic growth and more effective epidemic control through the analysis of real-world data. Our work suggests that a nation's intervention strategy could be optimized based on the measures adopted by Pareto frontier nations through large-scale computational experiments. Our solution has been validated for epidemic control, and it can be generalized to other urban issues as well.

It has become a promising research topic that using the Artificial Society (AS) solves social governance problems such as emergency management, environmental protection, and urban planning. The unceasing evolution of the AS requires high demands for the accuracy of the basic models in AS. With the development of the IoT and data science, researchers can refine the models in AS by contrasting the big data harvested from real society. This paper aims to improve the accuracy of the population mobility model in AS based on the analysis of the real-world dataset about urban population mobility and the comparison between the real-world data and the data generated from AS. To this end, firstly, the datasets are processed and analyzed by Spark. To improve the efficiency in this phase, we design the RDD structure for population mobility data and propose a novel query method, named partition traversal method. Then, the population mobility patterns are analyzed and compared, which are extracted from the datasets of real-world and AS, respectively. Based on the diversity between the two patterns, the optimization scheme of the population mobility model is finally proposed by adjusting the workplace selection. This study is an important reference for the modeling and optimization of population mobility in AS.

With the development of network models, the importance of community structure had caught much attention. How can the community size and the community distance affect the network structure remains unexplored. Therefore, in this paper, the MoncSid-N and the MoncSid-E are proposed in response to the issue The community size and distance preferences are introduced in these two models. The networks generated by the MoncSid-N show a better similarity to the real-world networks. The network metrics, including average degree, distribution of node degree, and distribution of community size, are used to analyze the performance of the MoncSid-N. Meanwhile, the MoncSid-E solves the problems of the evolution of large-scale networks. A parallel implementation by Pregel of the MoncSid-E is proposed. It is shown that the network with millions of nodes can be generated by the MoncSid-E efficiently. Based on the plenty of simulations and the comparison of real-world networks, the performances of the MoncSid-N and the MoncSid-E are testified.

The negative consequences, such as healthy and environmental issues, brought by rapid urbanization and interactive human activities result in increasing social uncertainties, unreliable predictions, and poor management decisions. For instance, the Coronavirus Disease (COVID-19) occurred in 2019 has been plaguing many countries. Aiming at controlling the spread of COVID-19, countries around the world have adopted various mitigation and suppression strategies. However, how to comprehensively eva luate different mitigation strategies remains unexplored. To this end, based on the Artificial societies, Computational experiments, Parallel execution (ACP) approach, we proposed a system model, which clarifies the process to collect the necessary data and conduct large-scale computational experiments to evaluate the effectiveness of different mitigation strategies. Specifically, we established an artificial society of Wuhan city through geo-environment modeling, population modeling, contact behavior modeling, disease spread modeling and mitigation strategy modeling. Moreover, we established an evaluation model in terms of the control effects and economic costs of the mitigation strategy. With respect to the control effects, it is directly reflected by indicators such as the cumulative number of diseases and deaths, while the relationship between mitigation strategies and economic costs is built based on the CO2 emission. Finally, large-scale simulation experiments are conducted to evaluate the mitigation strategies of six countries. The results reveal that the more strict mitigation strategies achieve better control effects and less economic costs.

Recently, spatial interaction analysis of online social networks has become a big concern. Early studies of geographical characteristics analysis and community detection in online social networks have shown that nodes within the same community might gather together geographically. However, the method of community detection is based on the idea that there are more links within the community than that connect nodes in different communities, and there is no analysis to explain the phenomenon. The statistical models for network analysis usually investigate the characteristics of a network based on the probability theory. This paper analyzes a series of statistical models and selects the MDND model to classify links and nodes in social networks. The model can achieve the same performance as the community detection algorithm when analyzing the structure in the online social network. The construction assumption of the model explains the reasons for the geographically aggregating of nodes in the same community to a degree. The research provides new ideas and methods for nodes classification and geographic characteristics analysis of online social networks and mobile communication networks and makes up for the shortcomings of community detection methods that do not explain the principle of network generation. A natural progression of this work is to geographically analyze the characteristics of social networks and provide assistance for advertising delivery and Internet management.

As online social networks play a more and more important role in public opinion, the large-scale simulation of social networks has been focused on by many scientists from sociology, communication, informatics, and so on. It is a good way to study real information diffusion in a symmetrical simulation world by agent-based modeling and simulation (ABMS), which is considered an effective solution by scholars from computational sociology. However, on the one hand, classical ABMS tools such as NetLogo cannot support the simulation of more than thousands of agents. On the other hand, big data platforms such as Hadoop and Spark used to study big datasets do not provide optimization for the simulation of large-scale social networks. A two-tier partition algorithm for the optimization of large-scale simulation of social networks is proposed in this paper. First, the simulation kernel of ABMS for information diffusion is implemented based on the Spark platform. Both the data structure and the scheduling mechanism are implemented by Resilient Distributed Data (RDD) to simulate the millions of agents. Second, a two-tier partition algorithm is implemented by community detection and graph cut. Community detection is used to find the partition of high interactions in the social network. A graph cut is used to achieve the goal of load balance. Finally, with the support of the dataset recorded from Twitter, a series of experiments are used to testify the performance of the two-tier partition algorithm in both the communication cost and load balance.

It is theoretical and practical significance for epidemic prevention and control to evaluate epidemic control measures using information technology so as to guide the implementation of epidemic control measures.Based on ACP approach and statistical data,a platform of computation experiment for artificial city was constructed,from which artificial models were established for three typical places,namely community,school and factory,to support the evaluation of specific epidemic control measures.The control measures of COVID-19 were evaluated through the computational experiments.The results showed that the control measures of quarantine and self-protection could only delay the peak of the number of infected people in the epidemic,but had no obvious effect on reducing the total number of infected people.Only the isolation measure with huge economic cost could significantly inhibit the development of the epidemic.The effect of the isolation measures showed that effectively blocking the “person-to-person”transmission chain was the key to the epidemic control.Therefore,it was recommended to adopt comprehensive means involving inspection and quarantine,big data,artificial intelligence and so on to accurately screen the patients and potential infected people.