Terrorists often take the chemical clusters as the attacking target because of the adverse impacts of a chemical accident on society and the environment. In addition to some fixed countermeasures, previous studies have verified the feasibility of a patrol in addressing adversarial attacks. However, the previous patrolling practices fail to tackle the terrorist attacking problems in a large-scale area cost-effectively. To further tackle the protection issue with cost-beneficial solutions in a large-scale scenario, i.e., in a chemical cluster, we propose an area-partition-involved collaborative patrolling (APCP) game. We first leverage the proposed greedy deployment algorithm to determine the initial deployment of defenders (patrollers), including the quantity and position of patrol vehicles. Then, the large-scale area is partitioned into multiple smaller areas by using the collaborative idea of static partitioning. In the meantime, corresponding patrolling graphs are constructed based on graphic modeling methods. Finally, the APCP game is built between patrol vehicles (namely defender) and potential terrorists (namely attacker), in which patrol vehicles aim at detecting attack behaviors of terrorists by intelligently scheduling the patrolling routes. After formalizing the problem into a sequential game, we compute the Stackelberg equilibrium through the MultiLPs algorithm. Through case studies of three practical chemical cluster scenarios, the results explicitly show the superiority of our proposed APCP game by saving up to 25.48 % patrolling costs in a one-shot game compared to the results before partition. As for the collaborative patrolling problem in a large-scale area, the methods and models proposed in this paper can facilitate the management department of chemical clusters with intelligently scheduled patrolling routes, which can effectively reduce the cost of patrollers, and better protect the chemical cluster.

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Estimating gas source terms is essential and significant for managing a gas emission accident. Optimization method, as a kind of estimation methods, is helpful to figure out the source terms by solving the inverse problem. Significantly, the performance of optimization method on source term estimation is affected by the accuracy of forward dispersion model. To enhance the estimation accuracy, previous works have demonstrated the feasibility of using Back Propagation Neural Network (BPNN) trained by actual experimental datasets as a forward dispersion model. However, the overall accuracy of source estimation is still limited by backward estimation methods. Most related studies used a single optimization algorithm to estimate source terms, which usually fails to realize the requirements of both high calculation accuracy and satisfying computational efficiency. Therefore, a hybrid strategy was proposed in this study to combine optimization algorithms with different characteristics, including particle swarm optimization, genetic algorithm and simulated annealing algorithm, to not only achieve high accuracy in global searching, but also converge to a stable result efficiently. Finally, extensive experiments are conducted to testify our proposed hybrid optimization algorithms. The Skill scores of hybrid optimization algorithms decrease obviously compared to those of single optimization algorithm. Hence, the proposed hybrid strategy is potentially useful for guiding the combination of optimization algorithms for gas source terms estimation, which further contributes to deal with a gas emission accident with satisfying calculation accuracy and computational efficiency.

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Many widely used numerical models of rock fracture based on mesoscale laboratory test characterisation of effective ‘intact’ strength parameters neglect microstructure effects. They therefore cannot explain grain boundary and pore effects on crack propagation and consequently are inadequate for models of rock destruction that exploit point and indentation stresses. Understanding deep drilling processes involving drill-bit buttons and/or water-jetting where rock loading is concentrated in domains with fewer mineral grains will therefore require models with microstructure. To investigate microscale failure mechanisms of granular rocks in diverse scenarios, we target a porous sandstone and introduce a novel workflow consisting of a computerized tomography (CT) based microstructure construction approach and a complementary mechanical numerical approach. The construction approach extracts the realistic rock microstructure and transforms the large voxel number CT-scan data into significantly fewer triangular elements. The finite-discrete element method (FDEM) with grain-based model (GBM) is adopted to solve the mechanics. The microscale failure mechanism of sandstone during the Brazilian test was thoroughly analysed using the numerical results together with the post failure CT-scan test data. The build-up of compressive and tensile stress chains, micro-crack nucleation, local relaxation, chain switching and final crack-path development exploiting pores was illustrated, revealing the micro-to-macro failure mechanism in time and space. Fracture paths in the specimens during Brazilian tensile test were dominated by the pores and the inter-grain boundaries. The tensile strength of the inter-grain joints was estimated to be at least 3.67 times the mesoscale specimen's intact tensile strength, while the pores account for 72.76% of the fracture path. The influence of the cementation distribution and microscale discontinuities was investigated with numerical cases.@en

Chemical clusters can be attractive targets for terrorism, due to the extreme importance of them as well as due to the existence of dangerous materials. Patrolling is scheduled for better securing chemical clusters. However, the current patrolling strategies fail on competing with intelligent attackers and therefore can be non-optimal. The so-called chemical cluster patrolling (CCP) game is proposed in this paper. The CCP game employs game theory as a methodology, aiming at randomly but strategically scheduling security patrols in chemical clusters. The patroller and the attacker are modelled as the two rational players in the CCP game. The patroller's strategy is defined as probabilistically traveling within the cluster or patrolling some plants while the attacker's strategy is formulated as a combination of an attack target, the start time of the attack, and the attack scenario to be used. The Stackelberg equilibrium and a robust solution which takes into consideration of the patroller's distribution-free uncertainties on the attacker's parameters are defined for predicting the outcome of the CCP game. Results of the case study indicate that the patrolling strategy suggested by the CCP game outperforms both the fixed patrolling route strategy and the purely randomized patrolling strategy.

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The accurate prediction of hazardous gas dispersion process is essential to air quality monitoring and the emergency management of contaminant gas leakage incidents in a chemical cluster. Conventional Gaussian-based dispersion models can seldom give accurate predictions due to inaccurate input parameters and the computational errors. In order to improve the prediction accuracy of a dispersion model, a data-driven air dispersion modeling method based on data assimilation is proposed by applying particle filter to Gaussian-based dispersion model. The core of the method is continually updating dispersion coefficients by assimilating observed data into the model during the calculation process. Another contribution of this paper is that error propagation detection rules are proposed to evaluate their effects since the measured and computational errors are inevitable. So environmental protection authorities can be informed to what extent the model output is of high confidence. To test the feasibility of our method, a numerical experiment utilizing the SF₆ concentration data sampled from an Indianapolis field study is conducted. Results of accuracy analysis and error inspection imply that Gaussian dispersion models based on particle filtering and error propagation detection have better performance than traditional dispersion models in practice though sacrificing some computational efficiency.

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Traces of user activities recorded in online social networks open new possibilities to systematically understand the information diffusion process on social networks. From the online social network WeChat, we collected a large number of information cascade trees, each of which tells the spreading trajectory of a message/information such as which user creates the information and which users view or forward the information shared by which neighbors. In this work, we propose two heterogeneous non-linear models, one for the topologies of the information cascade trees and the other for the stochastic process of information diffusion on a social network. Both models are validated by the WeChat data in reproducing and explaining key features of cascade trees.
Specifically, we apply the Random Recursive Tree (RRT) to model the growth of cascade trees. The RRT model could capture key features, i.e. the average path length and degree variance of a cascade tree in relation to the number of nodes (size) of the tree. Its single identified parameter quantifies the relative depth or broadness of the cascade trees and indicates that information propagates via a star-like broadcasting or viral-like hop by hop spreading. The RRT model explains the appearance of hubs, thus a possibly smaller average path length as the cascade size increases, as observed in WeChat. We further propose the stochastic Susceptible View Forward Removed (SVFR) model to depict the dynamic user behavior including creating, viewing, forwarding and ignoring a message on a given social network. Beside the average path length and degree variance of the cascade trees in relation to their sizes, the SVFR model could further explain the power-law cascade size distribution in WeChat and unravel that a user with a large number of friends may actually have a smaller probability to read a message (s)he receives due to limited attention.@en

Game theory has been employed in academia to study the improvement of security in chemical plants. Being able to model intelligent interactions between adaptive adversaries and defenders is the main advantage of game theory, while the main criticisms of the usage of game theory is that it is mathematically complicated and that it over-simplifies reality. The ANSI/API standard 780 on Security Risk Assessment for the petroleum and petrochemical industries (abbreviated as the “API SRA methodology”), conversely, provides a systematic approach for obtaining qualitative or semi-quantitative data, and is criticized on its failure at modelling strategic (and intelligent) adversaries. Integration of game theory and the API SRA methodology for improving chemical plant protection is therefore an interesting domain of study. In this paper, the API SRA methodology bridges the gap between “chemical security reality” and “chemical security theory (that is, game theoretic models)”, by providing quantitative inputs for game theoretic models and by reflecting on game theoretic results with respect to industrial practice.

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The chemical industry is very important for the world economy and this industrial sector represents a substantial income source for developing countries. However, existing regulations on controlling atmospheric pollutants, and the enforcement of these regulations, often are insufficient in such countries. As a result, the deterioration of surrounding ecosystems and a quality decrease of the atmospheric environment can be observed. Previous works in this domain fail to generate executable and pragmatic solutions for inspection agencies due to practical challenges. In addressing these challenges, we introduce a so-called Chemical Plant Environment Protection Game (CPEP) to generate reasonable schedules of high-accuracy air quality monitoring stations (i.e., daily management plans) for inspection agencies. First, so-called Stackelberg Security Games (SSGs) in conjunction with source estimation methods are applied into this research. Second, high-accuracy air quality monitoring stations as well as gas sensor modules are modeled in the CPEP game. Third, simplified data analysis on the regularly discharging of chemical plants is utilized to construct the CPEP game. Finally, an illustrative case study is used to investigate the effectiveness of the CPEP game, and a realistic case study is conducted to illustrate how the models and algorithms being proposed in this paper, work in daily practice. Results show that playing a CPEP game can reduce operational costs of high-accuracy air quality monitoring stations. Moreover, evidence suggests that playing the game leads to more compliance from the chemical plants towards the inspection agencies. Therefore, the CPEP game is able to assist the environmental protection authorities in daily management work and reduce the potential risks of gaseous pollutants dispersion incidents.

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Spatial contacts among human beings are considered as one of the influential factors during the transmission of contagious diseases, such as influenza and tuberculosis. Therefore, representing and understanding spatial contacts plays an important role in epidemic modeling research. However, most current research only considers regular spatial contacts such as contacts at home/school/office, or they assume static social networks for modeling social contacts and omit travel contacts in their epidemic models. This paper describes a way to model relatively complete spatial contacts in the context of a large-scale artificial city, which combines different data sources to construct an agent-based model of the city Beijing. In this model, agents have regular contacts when executing their daily activity patterns which is similar to other large-scale agent-based epidemic models. Besides, a microscopic public transportation component is included in the artificial city to model public travel contacts. Moreover, social contacts also emerge in this model due to the dynamic generation of social networks. To systematically examine the effect of the relatively complete spatial contacts have for epidemic prediction in the artificial city, a pandemic influenza disease progression model was implemented in this artificial city. The simulation results validated the model. In addition, the way to model spatial contacts in this paper shows potential not only for improving comprehension of disease spread dynamics, but also for use in other social systems, such as public transportation systems and city level evacuation planning. @en