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The papers in this special section focus on modeling and simulation in disaster and emergency management. The COVID-19 pandemic of 2020 has wrought damage globally to a degree that is totally unprecedented in human history.Within three months that the first known death from the illness caused by the so-called novel Coronavirus was reported by state media in China on the 11th of January 2020, close to 1.8 million people worldwide had tested positive, with more than 100,000 deaths reported. By the time the pandemic will have been declared to be under control, millions more are expected to have been infected and tens (possibly hundreds) of thousands more will have perished as a result of the disease. Not only are the numbers of COVID-19 infected people and deaths staggering. Economies throughout the world have suffered tremendously, with many companies forced to shut down, or at the very least significantly scale down, their production of goods and services as a result of the need for social distancing and self-isolation required to prevent the unmitigated transmission of the disease. All this has led to hundreds of millions worldwide losing their employment or other sources of livelihood. Governments of all nations, large and small, developed and underdeveloped, have needed to pump into their economy and onto their struggling citizens whatever financial resources they could manage to generate. [ABSTRACT FROM AUTHOR]

In this paper, our objective is to propose a stability concept within the graph model for conflict resolution (GMCR) that does not require knowledge about preferences of other decision makers (DMs) in the conflict and is flexible to analyze the conflict with variable horizon. For that we propose the use of the maximin decision rule within the GMCR. More specifically, we consider a GMCR with two DMs and introduce the concept of Maximinh stability with horizon h for a given DM. The Maximinh stability concept was inspired by the notion of limited-move stability with horizon h present in the GMCR literature and it is adequate for modeling cautious DMs in conflicts where they have no knowledge about the other DM’s preferences. We establish what is the relationship among the Maximinhstabilities for different horizons and the relationship among Maximinh stability and other GMCR stability concepts. Finally, we present an application to illustrate the proposed stability concept, namely, a neuroscience technological selection conflict in China. [ABSTRACT FROM AUTHOR]

Due to its brief form and growing popularity, microblogging is becoming people’s favorite choice for seeking information and expressing opinions. Messages received by a user mainly depend on whom the user follows. Thus, recommending users with similar interests may improve the experience quality for information receiving. Since messages posted by microblogging users reflect their interests, and the keywords in the messages indicate their main focus to a large extent, we can discover users’ preferences by analyzing the user-generated contents. Moreover, users’ interests are not static, on the contrary, they change as time goes by. Based on such intuitions, in this paper, we propose a temporal-topic model to analyze users’ possible behaviors and predict their potential friends in microblogging. The model learns users’ latent preferences by extracting keywords on aggregated messages over a period of time via a topic model, and then the impact of time is considered to deal with interest drifts. The experimental results of friend recommendations on Sina Weibo, one of the most popular microblogging sites in China, have demonstrated the effectiveness of our model. [ABSTRACT FROM AUTHOR]

High-frequency harmonic resonance has occurred frequently in the traction power-supply system (TPSS) when high-speed trains that are equipped with pulsewidth-modulation converters were serviced in the China High-Speed Railway (HSR). This phenomenon can result in serious voltage distortion, overheating of electrical components, and electromagnetic interference (EMI) of communication circuits. In order to address this issue, a complete mathematical model of the 2\,\times\,27.5-kV ac railway system, by means of the nodal admittance matrix, is presented to attain equal modeling of TPSS and modern train for harmonic studies in this paper. The impact of the train on resonance behavior of TPSS by employing resonance-mode assessment and modal sensitivity analysis are investigated. Finally, numerical simulation results using the presented model are compared to measurements results of the China HSR to verify the accuracy of the TPSS model and Norton model of the train, and proved that the method of resonance frequency shifts described is doable. The comparison results illustrate that the proposed model and harmonic resonance assessment can be useful for determining and mitigating the harmonic resonance problems in TPSS as well as the power system. [ABSTRACT FROM AUTHOR]

In this paper, we aim to analyze the impact of dc transmission on self-organized criticality (SOC) of the East China power system (ECPS), which is a large-scale, ac–dc interconnected and classical receiving-end power system. To this end, a detailed ac–dc cascading failure analysis model is established to study the SOC of this system. In the proposed model, some important issues of dc transmission and several kinds of stochastic factors are considered. The corresponding algorithm realized on the platform of the PSS/E software is discussed as well. Simulation based on the 2010 East China ac–dc power system is performed and the following results are obtained: 1) the dc issues such as the operation modes and the control parameters have obvious effects on the SOC of the ac–dc system and 2) the risk of cascading failure and blackout can be decreased by deliberately choosing the dc operation modes and control parameters. [ABSTRACT FROM PUBLISHER]

The stability of deep soft rock tunnels under seepage conditions is of particular concern. Aiming at thoroughly discussing seepage actions and their consequences on the support schemes of such structures, the host rocks of the Weilai Tunnel situated in the Guangxi province of China are used as the research subject. Emphasis is placed on adequately examining the seepage conditions, stresses, displacements and plastic zone radii along the surrounding rocks of such tunnels, taking into consideration the Mogi–Coulomb strength criterion and the elastic-plastic theory. Explicitly, this article proposes analytical solutions for stresses, displacements and plastic radii around deep tunnels in soft rocks under seepage conditions by considering the aforesaid criterion and nonlinear elastoplastic approaches. Subsequently, based on the strain-softening model, the coupled actions of seepage and softening on the rocks surrounding the tunnel are studied. In order to investigate the effects of relevant influencing factors on tunnel stability, parametric studies are thoroughly examined. According to the results, it is revealed that the support scheme of deep soft rock tunnels must be of the highest resistance possible to better decrease the plastic zone and the tangential stress along the host rocks. Moreover, throughout the surrounding rocks, the dissemination of pore water pressure is strongly affected by the uneven permeability coefficient under anisotropic seepage states. The combined effects of softening and seepage are very dangerous for the surrounding rocks of deep-buried tunnels. It is also shown that the seepage pressure substantially affects the plastic radii and tunnel displacements. Under high seepage pressure, the surface displacements of the tunnel are excessive, easily exceeding 400 mm. To better guarantee the reasonable longevity of such tunnels, the long-term monitoring of their support structures with reliable remote sensors is strongly recommended. [ABSTRACT FROM AUTHOR]

The Shangdu power plant has four 600-MW turbine-generators connected to the North-China Grid through two 500-kV transmissions, which are compensated with 45% fixed series capacitors. Extensive studies conducted on the system model indicate that the system suffers from multimodal subsynchronous resonance (SSR). To solve the problem, a countermeasure is developed by combining the supplementary excitation damping control (SEDC) and the torsional stress relay (TSR). In this paper, the characteristics of the SSR problem are investigated. Then the developed SEDC is presented. To validate the effectiveness of the proposed SEDC as well as the results of model studies, field tests were conducted under various operating conditions. The tests fully exposed the realistic threat of SSR in the system. Meanwhile, it is demonstrated that the developed SEDC can improve torsional damping significantly, and thus solve the multimodal SSR problem effectively. This is the first time in China that practical SEDCs have been developed and their ability to mitigate multimodal SSR has been verified in a real series-compensated system. [ABSTRACT FROM AUTHOR]

In modern transmission systems, duplicated protections are widely employed to enhance reliability, and global positioning system (GPS) substation clocks are set up to deliver a higher degree of synchronization. Thus, redundant alarms tagged with synchronized timestamps would be received during a fault. Both redundancy and timestamps of alarms consist of rich and useful information. Given this background, an effort is made to develop a novel analytic model for power system fault diagnosis so as to well utilize redundancy and temporal information of alarms. An alarm preprocessing procedure is proposed to preserve such properties without impairing diagnosis accuracy and efficiency. A new fault hypothesis is established to systematically take account of malfunctions of protective devices, missing and distorted alarm messages, and timestamp errors in the proposed model. An objective function is developed for fault diagnosis, and could accommodate various protection schemes and bus configurations. Based on the presented fault diagnosis method, a software package is developed. Several fault scenarios occurred in an actual power system in China are served for demonstrating the correctness and efficiency of the developed software package. [ABSTRACT FROM PUBLISHER]

Planetary rovers may traverse terrains with complex geometries and variable physical properties, but their mobility behaviors are complicated and difficult to simulate precisely. This article focuses on high-fidelity dynamic modeling and simulation for a type of rovers that incorporate single-input-multi-output joints to enhance terrain adaptability, which has been used on China's Tianwen-1 Mars rover. A novel multibody dynamic model and its solutions are derived first with consideration of single-input-multi-output joints. Then, a unified terramechanics model is proposed, considering variable terrain surfaces and covering rover's motion states of skidding, slipping, and steering, solved the problem of simulation instability caused by model switching between soft and hard terrains. As the contact areas of wheels with various terrains and resultant sinkage are dominant factors to ensure fidelity but difficult to determine, a new terrain modeling method for calculating contact area and wheel sinkage is developed using digital elevation map with physical properties. A simulation system is developed, integrating all the above models, and verified with physical experiments and commercial software. The relative simulation errors that have been achieved are less than 5.9% for bogie angles, 6.1% for drawbar pull, and 3.4% for slip ratios, demonstrating high fidelity simulation results. [ABSTRACT FROM AUTHOR]

Purpose: This article aims to provide an integrated model and methodology for discovering valuable innovation knowledge and creative users in online innovative communities. Design/methodology/approach: The super-network integration approach is applied in constructing the user's innovation knowledge super-network (UIKSN) model based on knowledge fragments discovered from user-generated content (UGC) data using text mining methods. The social network analysis (SNA) methodology is then used to analyze the KSN model. An empirical research is conducted with China's Xiaomi online community to illustrate the UIKSN model and the SNA methodology. Findings: Compared with current methods, the proposed KSN model and SNA methodology are demonstrated to be more effective and valid in discovering valuable innovation knowledge including valuable innovations, innovation trends, creative users and their knowledge background, etc. Research limitations/implications: The results of KSN modeling and analysis are inevitably influenced by the performance of text mining methods. Practical implications: More and more companies start to adopt online community user's innovations in their new product developments. However, it is difficult to discover users’ opinions and innovations from the UGC due to its tremendous volume. Therefore, the KSN model and SNA methodology presented in this article are helpful for companies to manage online communities and to utilize users’ innovations effectively and efficiently. Originality/value: The article provides two main contributions in the study of online communities: 1) a well-justified model (users' innovation KSN, UIKSN) to explore online users’ contributions; 2) a new and more effective methodology to discover and analyze valuable innovations and innovative users. [ABSTRACT FROM AUTHOR]

As a new energy vehicle, fuel cell vehicles (FCVs) have the advantages of pollution-free and zero-emission, which have attracted wide attention from all over the world. By predicting the ownership of FCVs, decision-makers can grasp the future development potential of FCVs, so that the national government can adopt corresponding policies and measures. To this end, based on analyzing the development trend of FCVs, this article proposes an FCVs ownership prediction method based on the generalized Bass model. From the perspective of technology, application, and market conditions of FCVs, this article first analyzes the development trend of FCVs in the future, which provides the basis for the establishment of the ownership prediction method and the estimation of model parameters. Then, considering the influence of levelized cost of driving (LCD) reduction and hydrogen refueling stations (HRSs) constructions, a prediction method of FCVs ownership based on the generalized Bass model is proposed, and the model parameters are estimated by Grey Verhulst model, electric vehicles data, and liquefied natural gas vehicles data. Through the proposed method, the ownership of FCVs in China from 2021 to 2070 is predicted and the results show that the ownership of FCVs in China will reach about 1.43 million in 2030 and about 10.41 million in 2040. Finally, through sensitivity analysis, this article discusses the influence of external factors such as policy, LCD, and HRSs on the market diffusion of FCVs, and puts forward corresponding suggestions. [ABSTRACT FROM AUTHOR]

Deep learning is considered to be a promising method for total transfer capability (TTC) evaluation due to its high speed and accuracy for nonlinear and complicated problems. However, the operating scenarios of power systems are complex and ever changing. It is difficult to train a single deep model using an overly complex dataset containing operating scenarios with high variability. In addition, making the deep model transparent to validate for operators is another challenge to its application in the power system. To enable the practical application of deep models in TTC evaluation, the operating sample space is clustered into simpler spaces via a two-stage clustering method, and the idea of neighborhood model for the deep TTC evaluation network (DTEN) is proposed. Then, the local post hoc interpretability for the DTEN based on first-order control variable sensitivity is presented. During the interpretability analysis, the quasi-steady state is introduced to assess the coupling effects among different inputs. Case studies on the IEEE 39-bus system and a real-world regional system in China validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Recently, frequency coupling oscillation events, such as harmonic oscillations, subsynchronous and supersynchronous oscillations, driven by multiple converters with different switching frequencies have brought new challenges of ac/dc grid modeling, despite the traditional one of improving the accuracy and efficiency. There is an urgent requirement of practical engineering projects to produce a modeling method that can produce instantaneous and harmonic phasor waveforms simultaneously, dedicated to harmonic interaction analysis of large-scale ac/dc grids. To realize the above-mentioned targets, the harmonic phasor domain (HPD) modeling of power electronic based dc grids is proposed. The HPD modeling is then combined and coordinated with electromagnetic transient (EMT) based ac-grid models based on the proposed HPD transmission line model (HPD-TLM). The overall HPD cosimulation is realized by the EMT-based ac-grid models, HPD-based dc-grid models, and HPD-TLM, which is governed by the decoupled and coordinated time sequences. The performance (efficiency and accuracy of instantaneous and harmonic phasor waveforms) for the HPD cosimulations is validated on practical large-scale voltage source converter modular multilevel converter based ac/dc grids in China. [ABSTRACT FROM AUTHOR]

Model-based incoherent polarimetric decomposition is a frequently used technique to analyze multilook data of polarimetric synthetic aperture radars (POLSARs). The purpose of this study is to analyze and compare different model-based incoherent polarimetric decomposition algorithms from the polarimetric information change aspect. For the input of a model-based incoherent polarimetric decomposition algorithm, polarimetric entropy was used to represent the polarimetric information of a coherency matrix. For the output of a model-based incoherent polarimetric decomposition algorithm, there are usually several decomposed components. To quantitatively represent their total polarimetric information, a new concept, generalized polarimetric entropy, was proposed which generalized the concept of polarimetric entropy based on the information entropy additivity of information theory. Generalized polarimetric entropy consists of two parts named as polarimetric power entropy and polarimetric residual entropy, respectively. Polarimetric power entropy describes the distribution status of the Span values of all decomposed components. Polarimetric residual entropy represents the residual randomness of all decomposed components. With the three new concepts, eight model-based incoherent polarimetric decomposition algorithms were compared and analyzed. Two real POLSAR images, respectively, derived by the E-SAR airborne system of Germany and the GF-3 satellite of China were used for the experiments. Experimental results had illustrated several useful conclusions. [ABSTRACT FROM AUTHOR]

As online video service continues to grow in popularity, video content providers compete hard for more eyeball engagement. Some users visit multiple video sites to enjoy videos of their interest while some visit exclusively one site. However, due to the isolation of data, mining and exploiting user behaviors in multiple video websites remain unexplored so far. In this work, we try to model user preferences in six popular video websites with user viewing records obtained from a large ISP in China. The empirical study shows that users exhibit both consistent cross-site interests as well as site-specific interests. To represent this dichotomous pattern of user preferences, we propose a generative model of Multi-site Probabilistic Factorization (MPF) to capture both the cross-site as well as site-specific preferences. Besides, we discuss the design principle of our model by analyzing the sources of the observed site-specific user preferences, namely, site peculiarity and data sparsity. Through conducting extensive recommendation validation, we show that our MPF model achieves the best results compared to several other state-of-the-art factorization models with significant improvements of F-measure by 12.96, 8.24 and 6.88 percent, respectively. Our findings provide insights on the value of integrating user data from multiple sites, which stimulates collaboration between video service providers. [ABSTRACT FROM AUTHOR]

Due to the maturity of wide area measurement technology, phasor measurement unit (PMU)-based dynamic equivalent modeling method (PDM) has been wildly used in large-scale power system modeling. However, traditional “event-based” PDM equivalent model may face serious robustness issues, such as although derived equivalent models show the good ability on reproduction of the system behaviors under recorded events where they derived from, they may not work well for other possible disturbances. In this article, to improve the robustness of equivalent model, a novel method on dynamic equivalent modeling of power system is proposed. Based on the result of coherency identification, generators inside the equivalent system can be aggregated into different coherent subgroups and represented by multimachine equivalent model. Key parameter selection, which can shrink the scale of identified parameters, is designed to avoid multiple solutions of the equivalent model. Moreover, a multiobjective parameter identification algorithm is developed to fully consider the PMU measurements in different system events. In addition, hybrid dynamic simulation is performed so that each equivalent submodel can be identified separately. Finally, the effectiveness of the proposed method is verified in an actual power grids in China. [ABSTRACT FROM AUTHOR]