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With the proliferation of online social networks, the identification or classification of real-life relationship between users has been very useful for many applications such as financial fraud detection. In real life, usually people with different relationships would present gifts with special meanings to each other on different dates. In many Asian cultures, especially in Chinese culture, the red packet is a traditional form of monetary gift. With the rapid development of the Internet, people gradually began to give electronic red packets instead of paper ones as the means of money gifting on social network platforms. As motivated, in this paper we advocate a novel approach that exploits users’ red packet interactions for users relationship identification on WeChat, one of the largest social platforms in China. Specifically, we analyze the WeChat red packets network, identify the real-life relationship types between users through mining the semantic information of the amount and sending time of each red packet. In order to better capture the red packet gifting behaviors between users for relationship identification, on one hand, we construct an Amount-Date Graph and apply the graph embedding method to learn embeddings of the amount and sending date of each red packet. On the other hand, we propose a novel sequential model, Cross & Attention Sequence Model (CASM), which explicitly learns the interactions between the latent semantic information of each red packet’s amount and sending date in the red packets sequence between two users. To validate our approach, we conduct comprehensive experiments on a real-world WeChat Users Red Packets dataset that involves 8 kinds of real-life relationships. The experiments show that our proposed approach performs significantly better than baselines and achieves 81.70 percent prediction accuracy. [ABSTRACT FROM AUTHOR]

In the coronavirus epidemic, many Chinese hospitals have established buffer zones to prevent the spread and transmission of the virus. The buffer zone is a monitored and separate area where the patients who need hospitalizations after the quick treatments in the emergency department can temporarily wait for the Covid-19 test and receive some healthcare services to stabilize their conditions. Because the beds in the buffer zones are limited, the managers face the patient admission control problem for the buffer zone. This management and control problem is challenging since the patient arrivals are uncertain, and the patients’ conditions are different. In this paper, we build the infinite- and finite-horizon Markov decision process (MDP) models for this problem. We use the uniformization method to discretize the patient flow. We propose various iteration algorithms to solve the MDP models and obtain the optimal and threshold policies. Numerical experiments validate the advantages of the policies obtained by the algorithms in this paper over the current policies of hospitals. Note to Practitioners—The ongoing COVID-19 pandemic has been causing enormous damage to people’s health, jobs, and well-being. COVID-19 has affected almost all countries globally and has changed the operation mode of the healthcare system, especially the hospitals. The hospitals are the frontlines of healthcare service and the battle with the COVID-19 pandemic. This article is motivated by our collaborations with hospitals in Shanghai, China. In China, many hospitals establish buffer zones: a monitored area where the patients who need hospitalizations after the quick treatments in the emergency department can temporarily wait for the Covid-19 test and receive some healthcare services to stabilize their conditions. Because the zone’s capacity is limited, the managers must make dynamic patient admission control decisions according to multiple factors, such as patients’ health conditions and the usage of beds in the zone. We propose two MDP models to solve this complex problem. Several iteration algorithms are designed to solve the MDP models and obtain the optimal and threshold policies. Based on hospitals’ real-life data, we show the methods presented in this paper can help hospital managers make more reasonable decisions. Although we focus on the hospital’s buffer zone in China, the methodology and approach for this problem can be extended to other practical hospital management scenarios in the coronavirus pandemic. For example, For example, some hospitals have admission control problems for coronavirus patients due to hospital capacity limitations. The hospital has to decide if a patient is accepted as an inpatient or suggested to home quarantine. In such a case, the admission control problem can also be solved by the methodologies in the paper. [ABSTRACT FROM AUTHOR]

The radio block center (RBC) is one of the most essential ground systems in a high-speed train control system both in Europe and in China. The RBC handover procedure is an important function of RBC, which affects the transport efficiency, reliability and safety of railways. Analysis of crucial states in the RBC handover procedure is helpful to determine whether there are potential risks in the procedure, and to locate the fault in time when a fault occurs. In this paper, we study a property, called C-detectability, of the RBC handover. This property has been defined in discrete event systems and requires that the crucial states can be determined uniquely by observing the system output. Taking the RBC handover procedure in the Chinese train control system level 3 (CTCS-3) as an example, we first model the RBC handover procedure using Petri nets, which are a graphical and mathematical modeling tool to formalize the behavior of discrete event systems. Then, based on the notion of basis reachability graph, an efficient approach is used to check C-detectability of the Petri net modeling the handover procedure. Note to Practitioners—The railway system is a safety-critical system. System safety is essential to the railway system. It is necessary to provide formal methods to model the system and analyze its properties. The motivation of this work is to present a general modeling framework of railway systems for the crucial states estimation. The crucial states estimation of the railway system has not been discussed yet in the literature despite its importance due to its relationship with the safety of the railway. In particular, estimating the crucial states in the railway system helps to determine whether there are potential risks in the system. In this paper, we investigate a new state estimation property (C-detectability), which is related to fault diagnosis, marking estimation and fault identification. Future research will consider the use of timed Petri nets to increase the modeling power and to allow the performance analysis of the railway system. [ABSTRACT FROM AUTHOR]

The online retail market shows a sharp increase in traffic during holiday sales. The ability to distinguish customers who will likely purchase is critical for provisioning traffic and for providing cost-effective promotions. This paper uniquely studies the browsing and purchasing behaviors of online shoppers during a yearly sale event in China, the world’s largest online marketplace. Based on 31 million action logs gathered from wide residential areas, we characterize the steps leading to purchases and determine their precursors. We investigate the effect of time (e.g., date, time of date), environment (e.g., platform, viewed category), and action (e.g., session time, clicks, sequence) on purchases. Action cues from shopping behaviors can be used for early detection. While most shoppers start with strong intentions to purchase, yet the moment of ordering comes rather impulsively within 30 seconds to several minutes of browsing. The predictive accuracy reaches as a high AUC of 0.924. The findings in this paper provide an understanding of traffic during mega sale events that can help online shops plan and provide a better user experience for upcoming shopping festivals. [ABSTRACT FROM AUTHOR]

Conventionally, the 2-parameter Weibull model, Arrhenius-Weibull model, has been used vastly for transformer aging-dependent unavailability modeling. However, this model only uses the lifetime feature to describe the transformer's degradation process and to calibrate the Weibull parameters, which harms the accuracy of aging-dependent unavailability forecasting. In response, this paper develops a 3-calibratable-parameter Weibull model for evaluating the transformer aging-dependent unavailability. In the proposed model, both the individualized aging threshold and lifetime are taken into the calibration of the Weibull parameters to accurately characterize the heterogeneity in transformer populations. First, a degree of polymerization (DP) analysis and Monte Carlo Simulation (MCS) based approach is proposed for estimating the transformers’ uncertain aging thresholds and lifetimes. Then, the Maximum Likelihood Estimate and Particle Swarm Optimization are jointly adopted to model the relationship among the calibratable Weibull parameters, aging threshold, and lifetime. Finally, the analytical formula of aging-dependent unavailability is derived from the established 3-calibratable-parameter Weibull model using an integral-discretization method. A real utility application example in China's Chongqing power system has been presented to validate and demonstrate the practicality and usefulness of this method. [ABSTRACT FROM AUTHOR]

Drones, or general UAVs, equipped with cameras have been fast deployed with a wide range of applications, including agriculture, aerial photography, and surveillance. Consequently, automatic understanding of visual data collected from drones becomes highly demanding, bringing computer vision and drones more and more closely. To promote and track the developments of object detection and tracking algorithms, we have organized three challenge workshops in conjunction with ECCV 2018, ICCV 2019 and ECCV 2020, attracting more than 100 teams around the world. We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i.e., (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi-object tracking. In this paper, we first present a thorough review of object detection and tracking datasets and benchmarks, and discuss the challenges of collecting large-scale drone-based object detection and tracking datasets with fully manual annotations. After that, we describe our VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South. Being the largest such dataset ever published, VisDrone enables extensive evaluation and investigation of visual analysis algorithms for the drone platform. We provide a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and conclude the challenge as well as propose future directions. We expect the benchmark largely boost the research and development in video analysis on drone platforms. All the datasets and experimental results can be downloaded from https://github.com/VisDrone/VisDrone-Dataset. [ABSTRACT FROM AUTHOR]

High renewable energy penetration increases the electricity seasonal imbalance in the long-term timescale. Power system planning needs to consider the optimal configuration of various flexibility resources and electricity balance in different timescales. The coupling of multiple timescales largely increases the computation complexity of the power system planning problem. Thus, this paper presents an efficient source-grid-storage co-planning model which incorporates a year-round hourly operation simulation. To improve the computation efficiency of the planning model, from the temporal scale, a self-adaptive compact panorama time series (CPTS) model is applied, which greatly reduces the number of variables related to short-term decisions. From the spatial scale, a network-constrained relaxed clustered unit commitment (NC-RCUC) model is introduced, which significantly reduces the number of variables related to unit commitment decisions. Case studies on the modified Garver’s 6-node system and HRP-38 system prove the validation and efficiency of the proposed model (“HRP” stands for high renewable penetration). The studies on the China power grid in 2035 demonstrate the future planning results of generation, transmission and storage in China power systems based on the proposed model. [ABSTRACT FROM AUTHOR]

Urban power grids are facing many operational and expansion challenges to meet further demand growth and increased reliability requirements. Advanced transmission technologies have been considered by the electric utilities to effectively increase the utilization of existing infrastructure and operational flexibility. The focus of this paper is on VSC-HVDC technologies for urban power grid enhancement and modernization. First, a potential technical scheme is proposed for converting an existing AC circuit to DC operation, which could boost the power transfer capability of the critical transmission corridor and increase network operational flexibility. Second, this paper proposes three operation modes for the VSC-HVDC interties in urban power grids corresponding to normal, emergency and island operating conditions, respectively. An integrated, adaptive emergency control strategy (AEC) is proposed that can enable adaptive power flow responses of the VSC-HVDC intertie under varying system operating conditions and critical contingencies. The flexibility and effectiveness of the proposed operational principles of urban VSC-HVDC intertie and the corresponding control strategies are verified in PSCAD/EMTDC using a realistic urban power grid in China. [ABSTRACT FROM AUTHOR]

China is currently the world's largest producer and consumer of iron and steel. This industry is one of the most energy- and emission-intensive industries in China. It is unsuspicious that energy conservation and emission reduction in this industry are crucial for China to fulfill its obligations of reducing greenhouse gas emission and mitigating global climate change. An appropriate allocation of CO2 emissions permit is the most important precondition for establishing an efficient and effective emissions trading system that had recently been launched in China's pilot regions. This paper proposes a new emissions permit allocation method based on a Nash equilibrium data envelopment analysis model. This method is more appropriate in emissions permit allocation for China's iron and steel firms in an imperfectly competitive market where the product price is endogenous. A noncooperative game is formulated and the mixed complementarity problem is utilized to identify a Nash equilibrium in the allocation of emissions permit (AEP). The most up-to-date AEP using a unique data set of China's major iron and steel enterprises in 2014 is provided in this paper. The results show that the AEP improves the desirable output generation by satisfying the reduction targets of the undesirable outputs. [ABSTRACT FROM AUTHOR]

Voltage sag insurance (VSI) is an effective measure to reduce the voltage sag loss and risk for enterprises with insufficient mitigation confidence or economic ability. However, due to the lack of understanding of voltage sag risk (VSR), it is difficult for power utilities to identify the enterprises with VSI demands, and it is also unable to realize differentiated premium according to different enterprises' situations. To solve the above problems, this paper proposes an improved voltage sag premium model based on VSR. Firstly, the VSR factors are analyzed, such as the hazard, the vulnerability, the exposure. The process immune time is introduced to describe the vulnerability. Based on the traditional consideration of only the ability of users to resist voltage sag, the ability of users to recover from voltage sag is further considered. Then, the risk evaluation model of voltage sag is established. The evaluation results can help power utilities to judge whether enterprises have the demand of VSI. Finally, the improved premium model of VSI based on VSR is constructed to ensure the differentiation and rationalization of the premium. The correctness and rationality of this method are proved by ten manufacturing enterprises in China by analyzing the VSR and comparing the premium before and after the improvement. [ABSTRACT FROM AUTHOR]

The High Energy Photo Source (HEPS) is being built in China and the magnets are in batch production. The octupole magnets with skew quadrupole component used in the storage ring have been designed and manufactured. The octupole has an aperture of 30 mm, a field gradient of 735000 T/m3 and a length of 0.26 m. In this paper, detailed magnetic design of HEPS octupole magnets is presented. Different design schemes of the skew quadrupole coils are compared, and the temperature effect on magnetic center and the integral field in the first batch of octupole magnets are also given. In addition, the influence of the combined power-on mode on the magnetic field is studied. [ABSTRACT FROM AUTHOR]

Traditional steganography methods are usually designed on a lossless channel; thus, messages are often not extracted correctly from an image transmitted over a lossy channel that includes attacks such as scaling. To address this issue, in recent years, the field of robust steganography has emerged. In this paper, the process of image scaling by interpolation is first observed and serves as the basis for proposing the idea of inverse interpolation. Subsequently, the idea of constructing an inverse interpolation equation set is proposed to solve the problem of intersectional blocks during the inverse interpolation process. Then, the scaling factor’s valid range of inverse interpolation is analyzed. Next, the inverse interpolation is successfully applied in robust image steganography. A method that combines antiscaling and antidetection is proposed. Afterward, actual tests on the top 9 mobile phone brands with 28 models and 2 social communication apps that are currently popular in China are done. The scaling factor’s valid range of the proposed method is verified to match the actual lossy channel. The experimental results show that the proposed method achieves a reliable extraction of embedded messages for common interpolation scaling attacks while maintaining high statistical detection resistance. [ABSTRACT FROM AUTHOR]

This paper proposes a load transfer optimization (LTO) model involving hot-spot temperature (HST) and top-oil temperature (TOT) limits of transformers. This work aims to address the overload problem and transformer HST and TOT excursions via LTO. First, the equivalent thermal model for the HST and TOT of transformers under external cooling conditions are discussed, and then the corresponding linearized HST and TOT constraints with varied thermal resistances are derived using the discretization method. Moreover, the proposed LTO model is formulated on the reconfigurable topology of high voltage distribution networks (HVDNs) in China. This proposed LTO model can achieve the optimal network reconfiguration scheme subject to temperature limits and system operation constraints, with the least switching operation cost and the maximum margins of reactive power exchange between HVDNs and transmission systems. Finally, the effectiveness of the proposed LTO model is validated by the modified IEEE-14 node system and the practical HVDNs in China. [ABSTRACT FROM AUTHOR]

The hybrid LCC/VSC cascaded HVDC (HC-HVDC) scheme, in which the LCC is adopted as rectifier, and one LCC in series with multiple paralleled VSC based MMCs is adopted as inverter, combines the advantages of both LCC and VSC-MMC technologies. Presently, a HC-HVDC project is planned to deliver the hydro power from Baihetan to Jiangsu in China. One of the major challenges is to suppress the DC overcurrent issue of VSC-MMCs especially under commutation failure condition of LCC at inverter side. Based on Baihetan-Jiangsu project under construction, the model of HC-HVDC system is developed in PSCAD/EMTDC. In the system, the rectifier employs a dual 12-pulse LCC, while the inverter adopts a hybrid scheme, i.e., one LCC converter in series with three paralleled VSC-MMC converters. This paper addresses the main cause and feature of the DC overcurrent, then proposes an overcurrent suppression control method for HC-HVDC system based on fuzzy clustering and identification approach. And the system transient performances with the proposed control approach are evaluated under AC fault conditions. The results show that the proposed approach can not only suppress the DC overcurrent following the commutation failure to a certain extent, but also improve the post-fault recovery performance of the overall system. [ABSTRACT FROM AUTHOR]

Machine learning approaches have exhibited high potential in power system transient stability assessment (TSA), yet their initial preparation stages of stability knowledge base generation (SKBG) based on time-domain simulations often undergo high computational costs. In fact, how to ease the heavy computational burden of SKBG without sacrificing the reliability is still a significant challenge. To address this problem, this paper develops a semi-supervised ensemble learning (SSEL) framework for reliable SKBG acceleration, without additional need for computing hardware upgrading. In particular, it performs detailed simulations for a minority of cases and fast simulations for the majority ones to reduce the total computation time. Considering the absence of stability status information of those fast simulated cases, an SSEL scheme is systematically designed to reliably label their stability status. Before implementing SSEL, two concise feature descriptors are first introduced to efficiently extract transient features from multiplex system trajectories. Then, all the cases are characterized in a unified feature space, whereby a series of semi-supervised support vector machines are trained in randomly formed subspaces. Afterward, these single machines are systematically combined to derive an enhanced SSEL model, which is able to make reliable and robust labeling decisions. Further, a backtrace strategy is carefully devised for SSEL, so as to maintain the high reliability of SKBG. Test results on the IEEE 39-bus system and the realistic GD Power Grid in South China illustrate the excellent performances of the proposed framework on SKBG acceleration. [ABSTRACT FROM AUTHOR]

Construction of the linearized state-space model is the key step of eigenvalue analysis for SSO study of power systems with large-scale renewable power generation (RPG) integrated. This paper proposes a novel method to construct the state matrix of the AC/DC hybrid power system with RPG within the discrete-time domain. The continuous-time state-space model of each AC/DC component as well as RPG units in the power system, is discretized together with their corresponding control. The discretized equation of each component is then represented by an equivalent circuit consisting of parallel historical current and conductance. According to the power system configuration, the discrete-time equivalent circuit of the whole AC/DC hybrid system is established, and the nodal analysis method is applied for the construction of the discrete-time state-space model of the power system. In this way, the state matrix can be acquired much more conveniently than that of continuous-time state-space method, which needs topology analysis to find independent state variables of the network from the proper tree. The proposed method has been applied in the eigenvalue analysis for the SSO study of a case system adapted from the AC/DC hybrid transmission system in northwest China. Eigenvalue results are in high accordance with time-domain simulation results in PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

Power system planning is essential for ensuring development of a safe and stable power system. Articles on power system planning have been published in recent decades. In this paper, bibliometric analysis is applied to power system planning to identify its basic characteristics, and to summarize the research hotspots and trends. A total of 4562 publications were obtained from the Web of Science during 1971–2020. The 4562 publications cover 8136 keywords, 1526 journals, 107 countries/territories, and 2519 institutions. IEEE Transactions on Power Systems is the key journal in power system planning; USA, China, and England are the countries that dominate publication production. Research hotspots and trends were analyzed using author-keyword co-occurrence analysis and keyword burst detection. Four clusters were identified and topics from Cluster I (central nodes as power system stability and transient stability) and Cluster IV (central nodes as power system simulation and economics) are important for power system planning research. However, their relative popularities have declined over the past two decades. This indicates that Cluster II (central nodes as renewable energy, wind power, and energy storage) and Cluster III (central nodes as distributed generation and smart grids) will be the focus of future research. [ABSTRACT FROM AUTHOR]

This paper investigates a new multi-objective order assignment and scheduling problem for personal protective equipment (PPE) production and distribution during the outbreak of epidemics like COVID-19. The objective is to simultaneously minimize the total cost and maximize the PPE supply timeliness. For the problem, we first develop a bi-objective mixed-integer linear program (MILP). Then an $\epsilon $ -constraint combined with logic-based Benders decomposition method is proposed based on some explored properties. We then extend the proposed model to handle dynamics and randomness. In particular, we design a predictive reactive rescheduling approach to address random order arrivals and manufacturer disruptions. Computational experiments on a real case from China and 100 randomly generated instances are conducted. Results show that the proposed algorithm significantly outperforms an adapted $\epsilon $ -constraint method combined with the proposed MILP and the widely used non-dominated sorting genetic (NSGA-II) in obtaining high-quality Pareto solutions. Note to Practitioners—The unprecedented outbreak of COVID-19 and its rapid spread caught numerous national and local governments unprepared. Healthcare systems faced a vital scarcity of PPEs. The urgency of producing and delivering PPEs increases as the number of infected cases rapidly increases. A key challenge in response to the epidemic is effectively and efficiently matching the demands and needs. Performing practical and efficient order assignment and scheduling for PPE production during the COVID-19 outbreak is critical to curbing the COVID-19 pandemic. This work first proposes a bi-objective mixed-integer linear program for optimal order assignment and scheduling for PPE production. The aim is to achieve an economical and timely PPE production and supply. A novel method that combines the $\epsilon $ -constraint framework and the logic-based Benders decomposition is proposed to yield high-quality Pareto solutions for practical-sized problems. Computational results indicate that the proposed approaches are practical and feasible, which can help decision-makers to perform acceptable order assignment and scheduling decisions. [ABSTRACT FROM AUTHOR]

This paper investigates the ownership and location strategies as contingency factors, which affect the relationship between SMEs’ open innovation activities and innovation performance in the context of emerging economies. Drawing from the corporate strategy and open innovation literature, we argued that SMEs with state and foreign ownership can more effectively capture economic rents from open innovation in a weak intellectual-property-rights regime. Meanwhile, the subnational environments in which SMEs locate their businesses is also crucial for them to capture value from open innovation. Using the data of 1050 SMEs in China, we found that state ownership positively and significantly moderates the relationship between openness and innovative performance, while foreign ownership partially supports the same effect. By disentangling the pro-business environment and the market development, we found that for SMEs that are located in a region with a stronger pro-business environment and a higher level of market development, open innovation can bring more value than it does for those located in regions with lower levels of those factors. Overall, this paper also contributes to our understanding of the complementary relationships between ownership and location strategies and open innovation. [ABSTRACT FROM AUTHOR]

Calculation of geomagnetically induced current (GIC) flowing through power system during the geomagnetic storm has attracted more attention recently. However, for high voltage power systems with transmission lines over hundreds or even thousands of kilometers, the earth model and geomagnetical field generally vary significantly. So, it is essential to take them into consideration using limited earth survey sites and geomagnetic observatories. To address this problem, a Kriging method is introduced in this paper to make earth model and geomagnetical field interpolations. It has the characteristic of spatial autocorrelation by considering not only the distances between predicted points and training points but also the distances between training points themselves. Finally, a case study of the Central China 1000 kV ultra-high voltage (UHV) grid is carried out to illustrate the applicability and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Converting wind energy into ammonia (WtA) has been recognized as a promising pathway to produce “green” ammonia compared with traditional coal-based technologies. As the key part of WtA, Power-to-Ammonia (PtA) has great potential to facilitate the usage of wind generation. This paper proposes a co-planning approach for regional wind resources-based ammonia industry and the electric network (EN). To this end, PtA is first modeled as a flexible power load of power systems with spatial and temporal constraints on hydrogen supply chains (HSC). Then a novel co-planning model of WtA and EN is established to optimize the WtA configuration and the EN expansion. An alternating direction method of multipliers (ADMM) based algorithm is introduced to effectively solve this model. Real data of Inner Mongolia Province in China is adopted to verify the effectiveness and significance of the proposed approach. It is shown that the siting and operation flexibility of PtA with HSC can reduce the expansion burden of EN. The co-planning of WtA and EN can significantly enhance wind power utilization and reduce total investment costs. Furthermore, feasibility analysis on WtA in comparison with coal-to-ammonia (CtA) and ultra-high voltage transmission (UHV) provides helpful guidelines for the realization of WtA. [ABSTRACT FROM AUTHOR]

While many machine learning approaches have been widely applied to power system online dynamic stability assessment, how to sufficiently learn spatial-temporal correlations from system transients without losing the interpretability is still a challenging issue. In this paper, a novel networked time series shapelet learning approach is proposed to learn spatial-temporal correlations for transient stability assessment (TSA) in an interpretable manner. Specifically, a network impedance-based adjacency matrix is first introduced to characterize spatial networked correlations. Based on graph structural regularization, this matrix is effectively incorporated into the subsequent learning procedure as spatial constraints. Taking time series trajectories acquired from multiple buses as the inputs, networked shapelet learning is heuristically performed to learn critical sequential features, i.e., networked shapelets, for TSA model derivation. With the learning procedure strategically guided by inherent spatial-temporal correlations of the system, the obtained data-driven TSA model is able to perform highly reliable and interpretable online TSA. Numerical test results on the IEEE 39-bus test system and the realistic GD Power Grid in China verify the superior performances of the proposed approach. [ABSTRACT FROM AUTHOR]

Public–private partnership (PPP) is effective in delivering infrastructure projects. In China, PPP has been widely adopted in project finance ever since the government has permitted the private sector to get involved in the investment and operation of urban infrastructure. However, there seem to be many spurious PPP projects emerging in the market, which may bring about losses to the stakeholders and lead PPP away from orderly and standardized development. This paper attempts to address this issue by applying the fuzzy analytic hierarchy process to investigate two infrastructure PPP projects, and analyze the characteristics of genuine and spurious PPP projects in China. Recommendations for mass implementation of PPP and ways of identifying spurious PPP projects are summarized to guide the regulation of infrastructure PPP projects. The paper also provides a reference for project selection and policy-making in the future. [ABSTRACT FROM AUTHOR]

Quality inspection, a widely adopted practice in supply chains, measures whether delivered products conform to prespecified quality requirements. Due to potential economic benefits, suppliers may deliberately manipulate products to falsify test results. However, unqualified products could cause severe problems in supply chains or even tragedies, such as China’s tainted milk scandal. In this paper, we propose a belief-desire-intention modeling approach to predict suppliers’ behavior and provide inspection suggestions to buyers to overcome the problem. Assuming access to the production process, our approach can represent suppliers’ knowledge of production and deception to mimic their reasoning processes and predict their deception intentions. This flexible approach can also adapt to environmental changes and deliver effective results. In this paper, we build a prototype system for supply chain quality inspections based on the proposed method. We conduct laboratory experiments to collect data for computational assessments of the performance of the prototype. It is shown that our proposed approach is more accurate than classic machine learning methods in detecting suppliers’ deceptions. [ABSTRACT FROM AUTHOR]

Nominal grid data of geostationary remote sensing satellites are fundamental for generating the subsequent products. It can be obtained by normalized projection models, mainly based on the imaging mode. However, there are only definitions and primary descriptive equations for the normalized geostationary projection (NGP) model in the existing literature, while the corresponding imaging mode and the physical interpretation are missing, thus hindering the understanding of the produced nominal grid dataset as well as the subsequent products based on the grid. This paper first derived the imaging mode for NGP based on the limited literature. In addition, another new imaging mode was introduced and analyzed based on NGP. The corresponding projection model [nonstandard normalized geostationary projection (NNGP)] was proposed, which is entirely consistent with the situation of America’s Geostationary Operational Environmental Satellite-R Series (GOES-R) and Chinese Fengyun-4A (FY-4A). Furthermore, this paper proposed a novel nominal projection model for frame imaging, which is consistent with the imaging mode of China’s Gaofen-4. Finally, extensive experiments were designed to comparatively analyze the three nominal grids and demonstrate a detailed difference. By providing a theoretical basis for nominal grid selection, this research is highly significant for the efficient near-real-time production and further applications of geostationary images, as well as the conversion between different datasets resulting from different nominal grid data. In addition, our models are sufficiently tested during the on-orbit running of FY-4A, the first satellite of China’s second-generation three-axis stabilized geostationary meteorological satellite series. The algorithms provide the technical support for the high-precision image navigation and registration and play a significant role in robustly producing the meteorological data with similar quality to those from GOES-R. [ABSTRACT FROM AUTHOR]

Though intercell scheduling problems have been studied in the literature, extant algorithms can hardly come into play in practice. This is because of two reasons: 1) transportation among cells, which is important for practical intercell scheduling, has not been adequately considered and 2) the problem size is large in practice, which may lead to intolerable computation efficiency. The motivation of this paper is to automatically design intercell scheduling heuristics that are suitable for practical application. A genetic programming algorithm with a pretraining strategy (GP-PS) is proposed. Production within cells and transportation among cells are simultaneously considered, and a cooperative coevolutionary framework is designed. To evolve better heuristics, a PS is developed. A speedup strategy is designed to accelerate the evolutionary process. Comparative experiments are conducted with other GP-based algorithms, speedup strategies, and with some state-of-the-art intercell scheduling algorithms. GP-PS is also put into use in a large manufacturing enterprise of China. Computational experiments and application results both verify the effectiveness of GP-PS. Note to Practitioners—Although intercell scheduling problems have been studied in the literature, it is still common to rely on manually generated schedules in industrial environments. This paper does not attempt to seek solutions directly, but try to automatically design good heuristic rules to address the problem, so that the computation efficiency that is required by real-time decision and the optimization performance that is important for a complex problem, can be satisfied at the same time. [ABSTRACT FROM AUTHOR]

Operational indices optimization is crucial for the global optimization in beneficiation processes. This paper presents a multitasking multiobjective evolutionary method to solve operational indices optimization, which involves a formulated multiobjective multifactorial operational indices optimization (MO-MFO) problem and the proposed multiobjective MFO algorithm for solving the established MO-MFO problem. The MO-MFO problem includes multiple level of accurate models of operational indices optimization, which are generated on the basis of a data set collected from production. Among the formulated models, the most accurate one is considered to be the original functions of the solved problem, while the remained models are the helper tasks to accelerate the optimization of the most accurate model. For the MFO algorithm, the assistant models are alternatively in multitasking environment with the accurate model to transfer their knowledge to the accurate model during optimization in order to enhance the convergence of the accurate model. Meanwhile, the recently proposed two-stage assortative mating strategy for a multiobjective MFO algorithm is applied to transfer knowledge among multitasking tasks. The proposed multitasking framework for operational indices optimization has conducted on 10 different production conditions of beneficiation. Simulation results demonstrate its effectiveness in addressing the operational indices optimization of beneficiation problem. Note to Practitioners—Operational indices optimization is a typical approach to achieve global production optimization by efficiently coordinating all the indices to improve the production indices. In this paper, a multiobjective multitasking framework is developed to address the operational indices optimization, which includes a multitasking multiobjective operational indices optimization problem formulation and a multitasking multiobjective evolutionary optimization to solve the above-formulated optimization problem. The proposed approach can achieve a solution set for the decision-making. The simulation results on a real beneficiation process in China with 10 operational conditions show that the proposed approach is able to obtain a superior solution set, which is associated with a higher grade and yield of the product. [ABSTRACT FROM AUTHOR]

Traffic radar-based speed meter (TRSM) is an important measuring instrument for measuring the speed of target vehicle based on the radar Doppler effect, and has been legally used in road vehicle speeding enforcement for more than 30 years in China. TRSM with target position determination (TRSM-TPD) is a new generation of TRSM with the advantages of speeding enforcement by lane, high accuracy of speed measurement and target position determination, high efficiency, low cost, etc. This paper proposes a new verification method for TRSM-TPD in road vehicle speeding enforcement. The proposed verification method includes the simulated speed calibration method for initial verification in the laboratory, and the online speed and position calibration method for subsequent verification in the field. The range of simulated radial speed is up to (1∼400) km/h and the maximum permissible error is within ±0.03 km/h, which can satisfy the full-range and high-accuracy speed calibration requirements for TRSM-TPD. The uncertainty of the reference facility in actual traffic is 0.12% within the speed range of (60∼250) km/h, where coverage factor k = 3. The simulated and online calibration results validate the effectiveness of the proposed verification method and the feasibility of the realized target speed simulator and the established reference facility. [ABSTRACT FROM AUTHOR]

Impedance modeling of a permanent magnet synchronous generator (PMSG)-based wind energy conversion system (WECS) usually ignores the machine-side dynamics, instead, the machine-side system is simplified as a constant power source, while merely keeping the detailed grid-side converter model. This simplification is mainly based upon the intuition that the dc-bus capacitor is large enough so that the machine- and grid-side dynamics can be decoupled. However, the simplification probably does not hold in practice since there is always a tendency to minimize the capacitor size to achieve a smaller package for installation. Therefore, the machine-side system should not be ignored, otherwise maybe leading to error for stability analysis. To address this issue, this paper developed a complete ac-side impedance model of the PMSG-based WECS. Then, impact factors about the coupling characteristics between machine- and grid-side systems of the PMSG-based WECS are discussed. Finally, a case study of sub/super synchronous oscillation is carried out on a practical offshore wind farm integrated with a modular multilevel converter based high-voltage dc (MMC-HVDC) transmission system in China. The results show that ignoring the machine-side system would lead to an optimistic stability judgement, which confirms the necessity of the complete model of PMSG-based WECS. Furthermore, the system can be stabilized by either changing the couplings between machine- and grid-side systems of PMSG-based WECS or implementing active damping in the wind farm side MMC. [ABSTRACT FROM AUTHOR]

The uncertain nature of renewable energy resources (RERs) and fast demand response lead to recurring voltage violations in the power systems, which causes frequent transformer tap shifting and capacitor switching. Therefore, this paper resorts to the static var compensators (SVCs) to manage the bus voltages based on the multi-agent deep reinforcement learning (MA-DRL) algorithm. The proposed scheme includes several system agents and SVC agents to collaboratively adjust the injected reactive power to restrict the bus voltages within the normal range. All the agents are trained centrally and executed separately, which requires minimum communication cost. The IEEE 14-bus system, IEEE 300-bus system, and China 157-node urban power grid are used to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The multi-terminal hybrid LCC/MMC HVDC has been applied to overcome the simultaneous commutation failure problem caused by the multi-infeed LCC-HVDCs in the East China. The high-speed and selectivity line protection method is required for the multi-terminal hybrid HVDC system. In this paper, the reflection and refraction of the fault traveling waves at different kinds of line terminals are characterized by transfer functions. Then the fault voltage as well as its first peak time at different line terminals when different internal and external faults occur are derived respectively. The conclusion proves that the first peak time of line-mode fault component voltage (FPTV) for internal faults is smaller than that for external faults, which is independent of fault impedance and fault type as well. Therefore, a FPTV-based non-unit ultra-high-speed dc line protection method is proposed. The algorithm is implemented in the developed protection prototype and validated in the Kun-Liu-Long three-terminal hybrid HVDC RTDS test system. The test results show that the proposed method can fast and correctly identify line faults in the multi-terminal hybrid HVDC system, even with 500 Ω fault impedance. Comparing with the traveling wave protection method of ABB, the proposed method improves the sensitivity when high-impedance faults occur. [ABSTRACT FROM AUTHOR]

Drivers have always been confronted with real-time parking difficulties when driving on urban roads, especially in crowded downtown or beauty spots. On the other hand, privacy leakage risks on users’ private parking preferences and the sensitive data of parking lots have triggered increasing worries. Some literatures endeavor to improve parking service qualities through multi-consideration parking decision optimization on edge sides or cloud computing based on outsourced data storage. And some other literatures propose a number of privacy-preserving methods, such as cryptography and authentication, but these privacy strategies are at the expense of other qualities of parking services, especially the real-time performance. In this paper, we propose a fuzzy skyline parking recommendation scheme for real-time parking recommendation based on roadside traffic facilities. Linguistic parking information instead of raw parking-related data is used in fuzzy skyline fusion. We evaluated our solution with real-world data sets collected from parking facilities in Wulin downtown, Hangzhou city, China. The evaluation results show that our approaches achieve an average accuracy of parking recommendation over 91%, low communication cost, and quick response time with privacy protection. [ABSTRACT FROM AUTHOR]

The rotational inertia and primary frequency response capabilities of power systems are declining as synchronous generators (SGs) are replaced by inverter-based variable renewable energy (VRE) resources. Therefore, when a high VRE proportion is present, it becomes essential to include frequency security constraints in power system planning. Under this circumstance, the decline in SGs would deteriorate the system frequency response performance; thus, inverter-based resources are supposed to provide frequency response support. This paper presents a frequency-constrained stochastic planning method for high VRE share systems. Frequency response support from wind farms is considered in the planning process for the first time, and the unique characteristics of wind power support are addressed. Specifically, the uncertainty of and variations in wind power support capabilities are modeled from the field-measured data, and the optimal combination of various support schemes can be determined by introducing binary variables with novel linear constraints into the planning process. Moreover, the nonlinear frequency constraints are transformed and embedded in the planning model by the proposed computationally efficient adaptive piecewise linearization method. The planning is formulated as a stochastic optimization model to consider the VRE power uncertainty. Finally, case studies on the IEEE 30-bus system validate the proposed method, with results indicating time savings of 22%. Further, compared with the case that only SGs provide frequency support, the results based on the East China system demonstrate that considering wind power support can improve the VRE share from 49.5% to 79.7%. [ABSTRACT FROM AUTHOR]

Time delays are inevitable in the power systems with wide-area control signals, which may compromise the control pe-rformance and thus jeopardize the stability of the power systems. To analyze the impacts of system parameters on critical oscillation modes of a time-delayed power system (TDPS), a new method for eigenvalue trajectory tracking of TDPS is proposed in this paper based on the continuation theory of invariant subspaces. When small changes are imposed on system parameters, such as parameters of wide-area damping controllers (WADCs) and time delays, the invariant subspaces are continuously predicted and corrected, followed by the eigenvalues and eigenvectors. In the process, the predictor and corrector are computed by solving linear equations sparsely and in blocks, which is highly scalable and computationally efficient for large-scale power system applications. The 16-generator 68-bus test system and two real-world power grids in China are used to test and demonstrate the proposed method. The results show that the proposed method can accurately and efficiently track eigenvalue trajectories for TDPS, especially for large-scale power grids. In addition, the sensitivities of eigenvalues and eigenvectors with respect to WADCs’ parameters and time delays are obtained as by-products of the approach. [ABSTRACT FROM AUTHOR]

In practical power systems, it is still very challenging to figure out a cost-effective undervoltage load shedding (UVLS) scheme that can reliably and adaptively react to the short-term voltage stability (SVS) problem. Faced with this challenge, this paper develops an intelligent data-driven predictive UVLS scheme for online SVS enhancement. Inspired by valuable ideas in model predictive control and supplementary excitation control in power systems, a novel deep feedback learning machine (DFLM) is designed to precisely predict future voltage violations after UVLS execution. With the help of the DFLM, the UVLS scheme is aware of potential effects of various candidate UVLS actions. Owing to this desirable nature, it can adaptively respond to diverse SVS conditions and optimize UVLS decisions in a non-iterative way. Further, two well-designed strategies, i.e., stepwise constraint relaxation and incremental DFLM adaptation, are introduced to enhance the scheme's reliability and adaptability during online application. Numerical test results on the Nordic test system and the realistic North GZ Power Grid in China showcase the excellent performances of the proposed scheme. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. We are pleased to announce that the June 2014 issue of the IEEE Transactions on Dielectrics and Electrical Insulation (TDEI) will be dedicated to recognize and celebrate the 60th Anniversary of Electrical Insulation and Dielectric Research and Development in China. [ABSTRACT FROM PUBLISHER]

This paper explores how to promote business model innovation through intraindustry search and extraindustry search with the assistance of fit dynamic capabilities (internal coordination capability and stakeholder engagement capability). Based on evolutional learning and business model literatures, six hypotheses are proposed and examined with data from 204 firms in China. The results show that intraindustry search has an inverted U-shaped effect on business model innovation, whereas extraindustry search has a positive effect. Internal coordination capability strengthens the effect of intraindustry search but weakens that of extraindustry search. Stakeholder engagement capability strengthens the effect of extraindustry search but weakens that of intraindustry search. Our findings enrich the business model literature by identifying different roles of intraindustry search and extraindustry search as the antecedents of business model innovation and their fitness with different types of dynamic capabilities. [ABSTRACT FROM AUTHOR]

With the increasing development of AC/DC hybrid power systems, local synchronous generators in receiving-end grids are being replaced by high-voltage direct current (HVDC) power. The risk of short-term voltage instability exists because of the decline in dynamic reactive power resources from generators, the presence of high-proportioned induction motors and the demand for vast reactive power of HVDC under AC faults. In this situation, unit commitment (UC) should take short-term voltage stability into account. Hence, crucial generators can be retained to provide sufficient dynamic reactive power. This paper proposes a UC model incorporating short-term voltage stability constraints (SVSCUC). Furthermore, a decomposition-based solution approach is presented, which involves an alternate iterative solving of UC and differential-algebraic equations (DAEs). Time-domain simulation (TDS) is utilized to quantify the stability with an indicator. Linear stable cuts are formulated through trajectory sensitivity analysis and added into the UC. The UC is decomposed into a master problem based on mixed-integer linear programming (MILP) and subproblems. The SVSCUC is illustrated using benchmark systems and a real system in eastern China. The results show the effectiveness and efficiency of the proposed methods. Violations of short-term voltage stability for credible contingencies are eliminated under the commitment of SVSCUC. [ABSTRACT FROM AUTHOR]

The irregular forbidden zone (FZ) is a common phenomenon with giant hydropower plants developed in the past two decades in China. Irregular shapes of FZs significantly challenge hydro unit commitment (HUC). This paper proposes a novel MILP based framework for HUC considering irregular FZ related constraints including the FZ constraint, effects of linearization errors in both the net head and the output, and the FZ crossing constraint. In the framework, the FZ constraint is handled by the optimal convex partitioning algorithm and the common structure-based formulation method. Inspired by the planar translating robot placement problem, linearization errors are considered by the Minkowski sum method. To handle the FZ crossing constraint, we then propose a graph theory-based approximate formulation method. The framework is integrated into a HUC model with an objective of peak shaving. The model is then tested with a batch of real-world instances of a cascade hydropower system formed by ten giant units with highly irregular FZs. The results show our framework can effectively consider the irregular FZ related constraints. The major advantage of our framework is its ability to handle the irregular shapes of FZs without any tedious and error-prone manual processing. [ABSTRACT FROM AUTHOR]

As part of the ongoing China Fusion Engineering Test Reactor (CFETR) studies, this work focused on the study of the electromagnetic (EM) loads distribution on inboard (IB) and outboard (OB) blanket vertical segments. In this paper, a major central disruption scenario with a 36-ms linear current quench was performed in the ANSYS finite element software. EM forces and moments, both on the single module and the complete blanket segment, were calculated for the updated helium-cooled ceramic breeder (HCCB) blanket. Furthermore, the effect of EM loads with respect to poloidal segmentations on the back plate (BP) was studied. Results show that the EM forces and moments in the IB blanket barely changed, but in the OB blanket, great changes occurred. With poloidal segmentations increasing, EM forces loads on the BP will be reduced. This paper may provide important data to support subsequent optimization of the BP of CFETR and could also be a technical reference for other fusion organizations. [ABSTRACT FROM AUTHOR]

Land surface temperature and emissivity (LST&E) are key variables for a wide variety of surface–atmosphere studies, and it is very important to validate the accuracy of different LST&E retrieval algorithms. In this paper, the accuracy of the Advanced Spaceborne Thermal Emission and Reflection (ASTER) temperature emissivity separation (TES) algorithm with the water vapor scaling (WVS) method and the ASTER standard LSE&E products (without WVS) was validated using 12 ASTER scenes from May 2012 to September 2012 with concurrent ground LST and emissivity measurements collected in an arid area in northwest China during the Heihe Watershed Allied Telemetry Experimental Research experiment. Both the National Center for Environmental Prediction (NCEP) and MOD07 atmospheric profile products were employed to perform the atmospheric correction. The results showed that the WVSTES LSTs retrieved from the two profiles both demonstrate good accuracies, with average biases of 0.34 and 0.24 K and average root-mean-square errors (RMSEs) of 1.52 and 1.46 K for the NCEP and MOD07 profiles, respectively. When the WVS was not applied, we obtained an average bias of 1.26 K and an average RMSE of 2.05 K for the AST08 product. The emissivities from the WVSTES algorithm with the two profiles both showed good agreements with the ground CE312 measurements, with mean differences of the five ASTER bands that were less than 0.01. AST05 showed anomalous emissivity spectra and underestimated the emissivity values of graybody surfaces when compared with the ground data, with mean differences of greater than 0.015, which were more obvious for cases with high water vapor. This paper demonstrated that the WVS method is critical for retrieving ASTER TES LST with accuracies within 1.5 K and emissivity within 0.015 for a wide range of atmospheric conditions and land surface types. [ABSTRACT FROM AUTHOR]

The advances of high-frequency vacuum electron devices (VEDs) in China are reviewed in this paper, including traveling-wave tubes (TWTs) based on novel slow-wave structures (SWSs), high harmonic TWT amplifier, gyrotrons, Smith–Purcell (SP) radiation sources, surface plasmon polaritons (SPPs) sources, and terahertz (THz) radiation sources in ion-focused beam-plasma system. As one of the most widely used VEDs, TWTs based on folded type SWSs, planar SWSs, and grating type SWSs are studied theoretically and experimentally from Ka-band to R-band. Using folded type SWSs, two TWT amplifiers are developed. As high-power millimeter wave and THz wave sources, gyrotron TWTs, gyrotron oscillators, and gyroklystrons are presented. Moreover, this paper shows SP radiation sources, SPPs sources, and THz radiation sources in ion-focused beam-plasma system open good prospects for developing high-frequency VEDs with new mechanisms, which undoubtedly brings about innovative changes for the development of VEDs. [ABSTRACT FROM AUTHOR]

As a natural result of the electrified integration and electrical energy revolution, the electromagnetic launch (EML) technology will be inevitably used for future launch missions. This paper has summarized research focuses in this field according to the published papers on EML in China during the past decade and mainly introduced the current research and progress in several key technologies, including launcher, integrated launch package, high-power high-density supply, and overall control/maintenance. Some common pressing problems are pointed out to provide direction for the application of EML technology in engineering and many other fields, such as launcher life, miniaturization of power supply, and improvement of energy availability. [ABSTRACT FROM AUTHOR]

The insulation paper loss of life (LOL) of a distribution transformer (DT) is largely determined by its winding hot-spot temperature. It is mainly estimated via the transformer load, ambient temperature, and related physical parameters. Fortunately, advanced metering infrastructure (AMI) can provide load profiles of DTs, allowing a cost-effective LOL estimation solution. However, the AMI dataset contains measurement errors that significantly reduce LOL estimation accuracy. A forecasting method is needed to recover the erroneous AMI data. This is a challenging problem, as the load profiles of DTs are nonstationary and volatile. We propose an ensemble of stacked long short-term memory (ES-LSTM) networks to simultaneously capture load consumption patterns of DTs and their correlations. The ES-LSTM consists of two independent LSTM networks and a feed-forward neural network (FFNN) stacked on top of them. A two-stage training procedure is applied. In the first stage, two LSTM networks are trained to capture the daily or weekly load patterns. In the second stage, the FFNN is trained to optimally combine the last hidden outputs of the two LSTM networks. Field verification is conducted using real AMI data obtained from an urban distribution utility in China. The test results confirmed the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Stationless bike-sharing systems such as Mobike are currently becoming extremely popular in China as well as some other big cities in the world. Compared to traditional bicycle-sharing systems, stationless bike-sharing systems do not need bike stations. Users can rent and return bikes at arbitrary locations through an App installed on their smart phones. Such a convenient and flexible bike-sharing mode greatly solves the last mile issue of the commuters, and better meets their real bike usage demand. However, it also poses new challenges for operators to manage the system. The first primary challenge is how to accurately estimate the real bike usage demand in different areas of a city and in different time intervals, which is crucial for the system planning and operation. This paper for the first time proposes a data driven approach for bike usage demand inference in stationless bike-sharing systems. The idea is that we first estimate the demands in some regions and time intervals from a small number of observed bike check-out/in data directly, and then use them as seeds to infer the region-level bike usage demands of an entire city. Specifically, we formulate this problem as a matrix completion task by modeling the bike usage demand as a matrix whose two dimensions are time intervals of a day and regions of a city, respectively. With the observation that POI distribution of a region is an important indicator to bike demand, we propose to utilize inductive matrix factorization by considering POIs as side information. As the bike usage data are highly correlated in both spatial and temporal dimensions, we also incorporate the spatial-temporal correlations as well as the balanced bike usage constraint into a joint optimization framework. We evaluate the proposed model on a large Mobike trip dataset collected from Beijing, and the experimental results show its superior performance by comparison with various baseline methods. [ABSTRACT FROM AUTHOR]

Faults on the alternating current (AC) transmission lines could cause commutation failures of nearby line-commutated converter-based high-voltage direct-current (LCC-HVDC) systems, which in turn, may challenge the fault detection of AC systems. To address this issue, this paper proposes a fast fault detection method based on instantaneous active power and fault variation component to alleviate the impacts of commutation failures and reactive power compensation while mitigating the dead zone problem. Upon that, an integral-based differential protection algorithm is presented to enhance reliability. Simulations are carried out on a real hybrid AC/DC system in North China with different fault locations and resistances to verify the effectiveness of the proposed method. Finally, we further validate the proposed method by leveraging on-site recorded fault data. [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]

The popularity of online contents in social media frequently experiences ebb and flow, and thus its evolution often involves different stages, such as burst and valley. Exploring the patterns of popularity evolution, especially how burst forms and decays, and even further, predicting the trends of popularity evolution is both an important research topic and beneficial to support decision making for many applications, such as emergency management, business intelligence, and public security. Previous work on popularity prediction has focused on predicting the popularity volume of online contents, and at most, popularity burst and ignored the exploration of popularity evolution and the prediction of its stages. To fill this gap, in this paper, we propose our method for the popularity stage prediction problem both at the microscopic level and macroscopic level. At the microscopic level, we first extract multiple dynamic factors and infer future evolution stage by considering the contributions of different dynamic factors. At the macroscopic level, we extract the overall evolution patterns of popularity stages and adopt a pattern matching-based method to predict future popularity stages. We evaluate the proposed approach using tweets in SinaWeibo, the most popular Twitter-like social media platform in China. The experimental results show the effectiveness of our proposed approach in predicting popularity evolution stages. [ABSTRACT FROM AUTHOR]