Economic development has contributed to the rapid expansion of China's steel industry during the past two decades, which has resulted in numerous problems including increased energy consumption and excessive environmental pollution. This study examines changes in crude steel production, steel scrap consumption, energy consumption, CO₂ emissions and steel stocks per capita from 2000 to 2014. Scenario analysis based on QGT equation is provided to accurately assess China's steel demand. Under three different scenarios, the peak of steel production and the variation trend of energy consumption, CO₂ emissions, steel stocks per capita and steel scrap are analyzed from 2010 to 2030. Based on Chinese situation, the most reasonable variation trend of China's steel production is proposed, which will increase from 626.7 Mt in 2010 to approximately 914 Mt in 2020, then gradually decrease to about 870 Mt in 2030. Steel stocks per capita will increase from 3.8 t/cap in 2010 to 8.09 t/cap in 2020 (the inferior limit of completing industrialization), then reach 11.46 t/cap in 2030 and stabilize. The peaks of energy consumption and CO₂ emissions in steel industry are expected to reach 505.37 Mtce and 1444.1 Mt in 2020, respectively. The scrap ratio is expected to reach 0.36 by 2030, when steel scrap resources will be relatively sufficient. This paper can provide corresponding theoretical basis for the government to make decision-making of macro-control.

This paper examines the relationship between stock market(KSE-100), money market(M2 and 180 days T-bill rate), and foreign exchange market(ER:PKR/USD) in Pakistan by using monthly data covering the period from 2000:M1 to 2015:M12. The study investigates long-run equilibrium relationship between these three financial markets by employing Johansen and Juselius^[1] cointegration tests. Long-run and short-run causality relationship between stock market and other macroeconomic variables is also established by employing vector error correction model(VECM) and pairwise granger causality tests. The results of multivariate cointegration test(trace test) indicate a one cointegrating vector, and the significant normalized cointegrating coefficients are evident of long run equilibrium relationship between all the selected variables. Negative and significant ECT(?1) for all variables during full sample period witness the presence of long-run causality connection among variables, while during the military regime and democratic regime, significant difference of long-run causal connections are identified across the regimes. Moreover, the results of granger causality test also indicate that there are significant variations in the causality relationship among variables across the regimes. Therefore, it is essential for forecasting, planning and policy making to consider the importance of political governance system while analyzing the historical cointegration among financial market and make the necessary adjustments accordingly.

In most exiting portfolio selection models, security returns are assumed to have random or fuzzy distributions. However, uncertainties exist in actual financial markets. Markets are associated not only with inherent risk, but also with background risk that results from the differences among individual investors. This paper investigated the compliance of stock yields to the fuzzy-natured high-order moments of random numbers in order to develop a high-moment trapezoidal fuzzy random portfolio risk model based on variance, skewness, and kurtosis. Data obtained from the Shanghai Stock Exchange and Shenzhen Stock Exchange was used to assess the influence on the proposed model of both background risk and the maximum level of satisfaction of the portfolio. The empirical results demonstrated that the differences between the maximum and minimum variance, skewness, and kurtosis values of the portfolio were positively correlated with the variance of the background risk.

An insurance-package is a combination being tie-in at least two different categories of insurances with different underwriting-yield-rate. In this paper, the optimal insurance-package and investment problem is investigated by maximizing the insureros exponential utility of terminal wealth to find the optimal combination-share and investment strategy. Using the methods of stochastic analysis and stochastic optimal control, the Hamilton-Jacobi-BellmanèHJBé equations are established, the optimal strategy and the value function are obtained in closed form. By comparing with classical results, it shows that the insurance-package can enhance the utility of terminal wealth, meanwhile, reduce the insureros claim risk.

The E-supply chain is formed gradually along with the development of network, which is getting more attention among enterprises with unique advantages. Three E-supply chain operation modes are constructed in this paper, then the optimal pricing and advertising strategies under those modes are studied and compared, which are demonstrated with numerical examples. The results of comparison and analysis show that: Selling price, network platform service level, advertising investment and the profits of manufacturer, network platform and E-supply chain all increase with advertising effectiveness of stimulating demand growth. Under centralized decision-making mode, service level is highest, advertising investment is largest and the profit of E-supply chain is highest as well. When manufacturer leads decentralized decision-making mode, not only network service level, advertising investment and the profit of manufacturer can gain better results, but also profit of network platform can be higher while the advertisement effect of increasing demand is big enough. Additionally, it is confirmed that centralized decision-making is better than decentralized decision-making for system operation. Besides, decentralized decision-making mode led by manufacturer is superior to it led by network platform on the condition that advertisement effect is obvious.

This paper considers a worst-case investment optimization problem with delay for a fund manager who is in a crash-threatened financial market. Driven by existing of capital inflow/outflow related to history performance, we investigate the optimal investment strategies under the worst-case scenario and the stochastic control framework with delay. The financial market is assumed to be either in a normal state (crash-free) or in a crash state. In the normal state the prices of risky assets behave as geometric Brownian motion, and in the crash state the prices of risky assets suddenly drop by a certain relative amount, which induces to a dropping of the total wealth relative to that of crash-free state. We obtain the ordinary differential equations satisfied by the optimal investment strategies and the optimal value functions under the power and exponential utilities, respectively. Finally, a numerical simulation is provided to illustrate the sensitivity of the optimal strategies with respective to the model parameters.

We consider an M/M/2 queueing system with two-heterogeneous servers and multiple vacations. Customers arrive according to a Poisson process. However, customers become impatient when the system is on vacation. We obtain explicit expressions for the time dependent probabilities, mean and variance of the system size at time t by employing probability generating functions, continued fractions and properties of the modified Bessel functions. Finally, two special cases are provided.

In this paper we reanalyze Said’s (2011) work by retaining all his assumptions except that we use the first-price auction to sell differentiated goods to buyers in dynamic markets instead of the second-price auction. We conclude that except for the expression of the equilibrium bidding strategy, all the results for the first-price auction are exactly the same as the corresponding ones for the second-price auction established by Said (2011). This implies that the well-known "revenue equivalence theorem"holds true for Said’s (2011) dynamic model setting.

This paper studies the optimal consumption-investment strategy with Heston's stochastic volatility(SV) model under hyperbolic absolute risk aversion(HARA) utility criterion. The financial market is composed of a risk-less asset and a risky asset, whose price process is supposed to be driven by Heston's SV model. The risky preference of the individual is assumed to satisfy HARA utility, which recovers power utility, exponential utility and logarithm utility as special cases. HARA utility is of general framework in the utility theory and is seldom studied in the existing literatures. Legendre transform-dual technique along with stochastic dynamic programming principle is presented to deal with our problem and the closed-form solution to the optimal consumption-investment strategy is successfully obtained. Finally, some special cases are derived in detail.

The aim of this paper is to measure the effect of sensational seeking, visual merchandising and collectivism on impulsive buying behavior. Valid sample size for this study was 300 comprising of all age groups. Mall intercept method which is a kind of convenience sampling was used for collecting the data. The data was collected by preselected enumerators. Scale used for this study had established reliabilities. After ascertaining the normality of data a typical multiple steps, procedure was adopted for this study. The conceptual framework tested through Structural equation modeling and was found to be relevant in understanding the impact of predictor variables on impulsive buying behavior. A strong and positive relationship was found between sensational seeking, and no relationships were found between, collectivism and impulsive buying, and visual merchandising and impulsive buying. One of the contributions of this study is that it has explored the relationships of collectivism, and sensational seeking with impulsive buying which have not been explored that extensively.

On the social network platform, complaints about the public policy formulation and implementation issues arise largely. Through the information aggregation, frequent interaction, word-ofmouth and emotional resonance on online social network, these information will lead to the outbreak of the network complaints. It brings difficulties and challenges in public management. China is under a period of social transformation, and there are various problems and contradictions. Emergency can easily arouse group complaints on online network, and it will lead to network public opinion through inducing social emotional resonance, which is harmful to social security and stability. This paper has built the evaluation index system from four dimensions with complaint text's quality, transmission timeliness, user interaction degree and emotional resonance excited by emergency. Then, we establish the dynamic influence measure model of online netizens complaint theme based on an entropy weight model. The measure model is proved to be reasonable and effective by the empirical research of Sina Weibo data. The model can effectively solve the measure problem of group complaints influence when the emergencies arouse social emotional resonance. It has important theoretical significance and practical value for public policy decision-maker on listening to online group complaints, understanding public opinion, and making public policy.

We employ the static and dynamic copula models to investigate whether technical indicators provide information on volatility in the next trading day, where the volatility is measured by daily realized volatility. Our empirical results, based on long samples of 8 well-known stock indexes, suggest that a significant and asymmetric tail dependence between the technical indicators based on moving average and the next day volatility. The level of dependence change over time in a persistent manner. And the dependence structure presents some distinct differences between emerging market indexes and developed market indexes. These results indicate that the technical indicators can provide information on the next day volatility at extremes, and are less informative at normal market.

The aim of this paper is first to establish a general prediction framework for turning (period) term structures in COVID-19 epidemic related to the implementation of emergency risk management in the practice, which allows us to conduct the reliable estimation for the peak period based on the new concept of "${\mathbf{Turning~~ Period}}"$ (instead of the traditional one with the focus on "Turning Point") for infectious disease spreading such as the COVID-19 epidemic appeared early in year 2020. By a fact that emergency risk management is necessarily to implement emergency plans quickly, the identification of the Turning Period is a key element to emergency planning as it needs to provide a time line for effective actions and solutions to combat a pandemic by reducing as much unexpected risk as soon as possible. As applications, the paper also discusses how this "Turning Term (Period) Structure" is used to predict the peak phase for COVID-19 epidemic in Wuhan from January/2020 to early March/2020. Our study shows that the predication framework established in this paper is capable to provide the trajectory of COVID-19 cases dynamics for a few weeks starting from Feb.10/2020 to early March/2020, from which we successfully predicted that the turning period of COVID-19 epidemic in Wuhan would arrive within one week after Feb.14/2020, as verified by the true observation in the practice. The method established in this paper for the prediction of "${\mathbf{ Turning~~ Term ~~(Period) ~~Structures}}"$ by applying COVID-19 epidemic in China happened early 2020 seems timely and accurate, providing adequate time for the government, hospitals, essential industry sectors and services to meet peak demands and to prepare aftermath planning, and associated criteria for the Turning Term Structure of COVID-19 epidemic is expected to be a useful and powerful tool to implement the so-called "dynamic zero-COVID-19 policy" ongoing basis in the practice.

Domino effect is a fairly common phenomenon in process industry accidents, which makes many process industry accidents serious and the consequent losses enhanced. Domino effect of the major accidents in chemical cluster is emphasized. Many researchers have studied domino effect in chemical clusters from different perspectives. In the review, we summarize the research from three aspects:The statistical analysis of domino accidents in chemical process industry, the evaluation of domino accidents and the prevention of domino accidents in chemical clusters by game theory. From the analysis, we can find the characteristic of domino accidents such as the time and the location, the origin and causes of domino accidents. The methods of assessing domino effects such as quantitative risk assessment (QRA), Bayesian networks (BN) and Monte Carlo simulation (MCS) are analyzed.The prevention of domino accidents in chemical clusters using game theory is seldom, and there is still much space for improvement in enterprises' efforts to prevent risk of domino accidents.

This paper studies consensus of a class of heterogeneous multi-agent systems composed of first-order and second-order agents with intermittent communication. For leaderless multi-agent systems, we propose a distributed consensus algorithm based on the intermittent information of neighboring agents. Some sufficient conditions are obtained to guarantee the consensus of heterogeneous multi-agent systems in terms of bilinear matrix inequalities (BMIs). Meanwhile, the relationship between communication duration and each control period is sought out. Moreover, the designed algorithm is extended to leader-following multi-agent systems without velocity measurements. Finally, the effectiveness of the main results is illustrated by numerical simulations.

This paper proposes a group decision making method based on entropy of neutrosophic linguistic sets and generalized single valued neutrosophic linguistic operators. This method is applied to solve the multiple attribute group decision making problems under single valued neutrosophic liguistic environment, in which the attribute weights are completely unknown. First, the attribute weights are obtained by using the entropy of neutrosophic linguistic sets. Then three generalized single valued neutrosophic linguistic operators are introduced, including the generalized single valued neutrosophic linguistic weighted averaging (GSVNLWA) operator, the generalized single valued neutrosophic linguistic ordered weighted averaging (GSVNLOWA) operator and the generalized single valued neutrosophic linguistic hybrid averaging (GSVNLHA) operator, and the GSVNLWA and GSVNLHA operators are used to aggregate information. Furthermore, similarity measure based on single valued neutrosophic linguistic numbers is defined and used to sort the alternatives and obtain the best alternative. Finally, an illustrative example is given to demonstrate the feasibility and effectiveness of the developed method.

This paper makes a theoretical and empirical study on the impact of economic growth and financial development on the environment in China. Through the establishment of econometric models, some conclusions have been found as follows: Firstly, there's Environmental Kuznets Curve in China in the long and short term; Secondly, China's financial interrelations ratio and financial efficiency can alleviate environmental pollution, and in the long term financial interrelations ratio has a stronger effect, instead, in the short term financial efficiency has a stronger effect; Moreover, in the long term financial interrelations ratio and financial efficiency have a positive moderating effect that can weaken the impact of economic growth on the environment, whereas financial interrelations ratio's moderating effect is stronger; Finally, this article makes conclusion and inspiration for the improvement of China's environmental quality.

In order to overcome the problem of low convergence precision and easily relapsing into local extremum in fruit fly optimization algorithm (FOA), this paper adds the idea of differential evolution to fruit fly optimization algorithm so as to optimizing and a algorithm of fruit fly optimization based on differential evolution is proposed (FOADE). Adding the operating of mutation, crossover and selection of differential evolution to FOA after each iteration, which can jump out local extremum and continue to optimize. Compared to FOA, the experimental results show that FOADE has the advantages of better global searching ability, faster convergence and more precise convergence.

This paper chooses the monthly real growth rate of industrial added values, which have been released by the China National Bureau of Statistics, as the benchmark indicator. By using the large quantity of collected data, the actual value of indicators is obtained through deflating them by price index. Based on this result, 26 indicators from various areas of the economy are regarded as China's macro economic prosperity indicators via methods such as K-L approach, time difference correlation analysis as well as grading system, which correspond well with the fluctuation of benchmark indicator. Furthermore, this paper analyzes and forecasts the economic growth rate cycle of China by composite index and early warning signal system.

The rise of Big Data brings the financial innovation opportunities as well as challenges. This paper reviews different fields of big-data-based financial innovations as well as the scientific discoveries and theoretical breakthroughs of risk analysis with respect to these financial innovations. Based on the current research status, several key problems are put forward and their relative solutions are discussed. The three mean aspects are listed as the pricing and risk measuring for data-driven financial innovation products or services; the changes that data-driven financial innovation would bring to finance industry, which involve operation, resources allocation and ecosystem; and questions and solutions of systemic risk management based on Big Data analytics. Finally, predictions towards the hotspots frontier and developing trends for further data-driven financial innovation are proposed.

A novel reliability-based approach has been developed for multi-state engine systems. Firstly, the output power of the engine is discretized and modeled as a discrete-state continuoustime Markov random process. Secondly, the multi-state Markov model is established. According to the observed data, the transition intensity is determined. Thirdly, the proposed method is extended to compute the forced outage rate and the expected engine capacity deficiency based on time response. The proposed method can therefore be used for forecasting and monitoring the reliability of the multi-state engine utilizing time-domain response data. It is illustrated that the proposed method is practicable, feasible and gives reasonable prediction which conforms to the engineering practice.

This paper extends the DSSW model to accommodate rational arbitrageurs, optimistic investors and pessimistic investors. We model the price impact by using daily data and create a new methodology to calculate the optimistic and the pessimistic. The new sentiment indicator has high correlation with the other traditional ones, and as a proxy variable of individual share or financial market on daily, it could distinguish the optimistic and the pessimistic. In the empirical research, we develop a time-series model and a cross-section model respectively to explore the explanatory power of highly frequent investor sentiment to idiosyncratic volatility and capital asset mispricing. The results show that the new sentiment indicator can explain 21.31% of idiosyncratic volatility to individual share on average, and it has a great explanation of 36% to capital asset mispricing.

For an integrated supply chain with an online direct channel and a traditional retail channel competing with each other, solutions are be identified as to the two channels' ordering policies, product pricing strategies, and optimal product output, when product costs in the two channels are disrupted in different ways. Findings are as follows: 1) For the integrated supply chain, when unanticipated events lead to product cost increase, market size will shrink, and the system profit is harmed. In contrast, when unanticipated events lead to reduced product cost, market size will expand, and system profit increases; 2) Production strategies applicable to normal situations have certain robustness, and should be maintained when product cost disruption caused by unanticipated events is relatively small; 3) When product cost disruption caused by unanticipated events is relatively large, product sales price should be first adjusted, and aligned with the way that product cost is disrupted. Meanwhile, order quantity and product output should also be properly adjusted. That is, order quantity and output need to be reduced when product cost increases; order quantity and output need to be increased when product cost is reduced. In the end, this paper employs numerical examples to testify the findings. Research conclusions help to further enrich and extend the theoretic basis of “supply chain disruption management”, and are helpful for researchers' further study.

In this paper, the consensus problem for heterogeneous multi-agent systems composed of first-order and second-order agents is investigated with directed network topologies. Based on a system transformation method, this consensus problem is turned into a consensus problem for homogeneous multi-agent systems. With certain assumption on the control parameters, firstly, necessary and sufficient condition for consensus is proposed with fixed topology. Secondly, sufficient condition is proposed for heterogeneous multi-agent systems to achieve consensus with switching topologies. Finally, simulation examples are presented to verify the effectiveness of the theoretical results.

Combat modeling is an important area of military operations. System of system counterworkis an important mode of information-based war which is a mode of “network centered” instead of “platform centered” and “capability oriented” instead of “function oriented”. Under the conditions ofinformationization, the combat model must therefore address these challenges by properly representingthe networked efficient based on mutual relations among combat entities. The implementation processof combat system of systems capability is analyzed which is the result of complex interactions betweenthe entities in four domains through a sequence of action processes. The combat network model ofcombat system of systems is described which reflects the fundamental structure of combat system ofsystems. The entity with three types of functions and five types of relations in the combat network isanalyzed. The capability loop is defined and the evaluation index of combat network of combat systemof systems is proposed based on the capability loop analysis. Finally, an example is used to illustratethe methodology.

This paper studies a single server M/G/1 stochastic clearing queue operating in a 3-phase environment, where the time length of the rst and third phase are assumed to follow exponential distributions, and the time length of the second phase is a constant value. At the completion of phase 1, the system moves to phase 2, and after a xed time length, the system turns to phase 3. At the end of phase 3, all present customers in the system are forced to leave the system, then the system moves to phase 1 and restarts a new service cycle. Using the supplementary variable technique, we obtain the distribution for the stationary queue at an arbitrary epoch. We also derive the sojourn time distribution and the length of the server's working time in a cycle.

In this paper, some new operational laws for intuitionistic linguistic numbers are defined via Archimedean t-norm and s-norm. The prominent feature of these operations is that these operations are closed. Some main properties of these operations, like commutativity, associativity and distribution law, are investigated. Based on these operational laws, intuitionistic linguistic weighted arithmetic averaging operator is given to aggregate intuitionistic linguistic information. Furthermore, in order to reduce uncertain information of intuitionistic linguistic number, hesitancy degree is divided into degrees of membership and non-membership in proportions, and new expected function and score function are built and used to rank intuitionistic linguistic numbers. Finally, an approach is proposed to solve multiattribute decision making problems in which attribute weights are real numbers and attribute values are intuitionistic linguistic numbers, and a real example is provided to show the effectiveness and applicability of the new method.

Monte Carlo simulation is an efficient method to estimate quantile. However, it becomes a serious problem when a huge sample size is required but the memory is insufficient. In this paper, we apply the stream quantile algorithm to Monte Carlo simulation in order to estimate quantile with limited memory. A rigorous theoretical analysis on the properties of the ε_n-approximate quantile is proposed in this paper. We prove that if ε_n=o(n^-1/2), then the ε_n-approximate α-quantile computed by any deterministic stream quantile algorithm is a consistent and asymptotically normal estimator of the true quantile q_α. We suggest setting ε_n=1/(n^1/2 log₁₀n) in practice. Two deterministic stream quantile algorithms, including of GK algorithm and ZW algorithm, are employed to illustrate the performance of the ε_n-approximate quantile. The numerical example shows that the deterministic stream quantile algorithm can provide desired estimator of the true quantile with less memory.

Consider a closed-loop supply chain including a manufacturer, a retailer and two third party recyclers as the background. A coordination and optimization model is built by using the downside-risk function, Cournot model and government subsidy excitation function. The effect of risk characteristics, government subsidy and Cournot competition on supply chain is analyzed, and the impact of revenueand-expense sharing contract is studied in the channel, which shows that the contract cannot coordinate and optimize the closed-loop supply chain. Also, risk sharing contract and expense sharing contract are designed, which can offer the desired downside protection to the retailer, provide more profits to the agents, and accomplish channel coordination and optimization. Moreover, an application example is given for testing the effectiveness and feasibility of the contract, and the bound and rule of the contract parameters are given. Finally, by the analysis of numerical simulation and sensitivity of the model based on the contract, the validity and practicability of the model are verified, and the relationship between government subsidy, risk characteristics, competitive characteristics with the supply chain is obtained. This study provides decision support and decision-making reference for the development of remanufacturing industry.

The paper focuses on modeling longitudinal customer behavior and develops a dynamic programming (DP) to show how customer transaction database may be used to guide marketing decisions such as pricing and the design of customer reward programs. Dynamic programming is not as a tool to marketing decisions making in this research but rather as a description of consumer behavior. The results show that the method provides a means for evaluating the effectiveness of marketing strategy, for example, customer reward programs. Moreover, the findings from the model estimation indicate that reward program can actually increase the customer's purchase level and stimulate the repeat purchase behavior.

The generalized autoregressive conditional heteroskedasticity (GARCH) type models are used to investigate the volatility of Bangladesh stock market. The findings of the study demonstrate that the index volatility characteristics changes over time. The article shows that the data are divided into three sub-periods: pre crisis, crisis, and post crisis. Accordingly, the results of the findings indicate changes in the GARCH-type models parameter, risk premium and persistence of volatility in different periods. A significant “low-yield associated with high-risk” phenomenon is detected in the crisis period and the “leverage effect” occurs in each periods. The investors are irrational which is based on assumption of risk and return characteristics of assets. Consequently, the market is not as mature as developed market. It is found in the article that the threshold generalized autoregressive conditional heteroskedasticity (TGARCH) model is more accurate for the model accuracy. Additionally, statistic error measurements indicate that GARCH model is more efficient than others and it has also more forecasting ability.

Cox's proportional hazard models with time-varying coefficients have much flexibility for modeling the dynamic of covariate effects. Although many variable selection procedures have been developed for Coxs proportional hazard model, the study of such models with time-varying coefficients appears to be limited. The variable selection methods involving nonconvex penalty function, such as the minimax concave penalty (MCP), introduces numerical challenge, but they still have attractive theoretical properties and were indicated that they are worth to be alternatives of other competitive methods. We propose a group MCP method that uses B-spline basis to expand coefficients and maximizes the log partial likelihood with nonconvex penalties on regression coefficients in groups. A fast, iterative group shooting algorithm is carried out for model selection and estimation. Under some appropriate conditions, the simulated example shows that our method performs competitively with the group lasso method. By comparison, the group MCP method and group lasso select the same amount of important covariates, but group MCP method tends to outperform the group lasso method in selection of unimportant covariates.

Based on the system dynamics (SD) model, this paper puts forward a quantitative method to evaluate the earthquake emergency plan in China. Firstly, we analyze the disaster system structure and the content of plan. Using the analysis results, we establish a system dynamics (SD) model and then carry out its simulation. According to the simulation results, the rescue effect using the plan of 2012 is better than that of 2006. From the policy level, in order to reduce loss, government should maintain communication smoothly, improve the ability of self and mutual help, strengthen the management of public opinion, and pay more attention to secondary disasters.

Truck and trailer routing problem (TTRP) is one of the most frequently encountered problem in city distribution, particularly in populated and intensive downtown. This paper addresses this problem and designs a novel backtracking search algorithm (BSA) based meta-heuristics to solve it. The initial population is created by T-sweep heuristic and then based on the framework of backtracking search algorithm, four types of route improvement strategies are used as building blocks to improve the solutions of BSA in the process of mutation and crossover. The computational experiments and results indicate that the proposed BSA algorithm can provide an effective approach to generate high-quality solutions within the satisfactory computational time.

Grey systems theory, probability theory, fuzzy systems
theory and rough set theory are known as four uncertain systems
scientific methodologies. After thirty years of development, grey
systems theory has formed its own disciplinary framework, whose
content includes grey philosophy, grey mathematics, processing and
technology systems of grey information, as well as grey systems
engineering. However, as a developing area of scientific
activities, a lot of theoretical contents are still imperfect and
awaiting further studies. The purpose of this paper is to acquaint
the readers with the development history, the main content and the
major challenges of grey system theory. It is our hope that this
paper could play the role of attracting the attention of the
international academic community to the theory
so that the development of this theory could be further promoted.

Online media have brought tremendous changes to civic life, public opinions, and government administration. Compared with traditional media, online media not only allow individuals to browse news and express their views more freely, but also accelerate the transmission of opinions and expand influence. As public opinions may arouse societal unrest, it is worth detecting the primary topics and uncovering the evolution trends of public opinions for societal administration. Various algorithms are developed to deal with the huge volume of unstructured online media data. In this study, dynamic topic model is employed to explore topic content evolution and prevalence evolution using the original posts published from 2013 to 2017 on the Tianya Zatan Board of Tianya Club, which is one of the most popular BBS in China. Based on semantic similarities, topics are grouped into three themes: Family life, societal affairs, and government administration. The evolution of topic prevalence and content are affected by emergent incidents. Topics on family life become popular, while themes "societal affairs" and "government administration" with bigger standard deviations are more likely to be influenced by emergent hot events. Content evolution represented by monthly pairwise distance matrix is very easy to find change points of topic content.

A novel decision-making method based on evidential reasoning is proposed for solving the two-sided matching problem with uncertain information under multiple states in this paper. Firstly, the discernment frame of evidence is constructed for two-sided matching. Secondly, the preference ordinal values given by two-sided decision-makers are transformed into rank belief degrees. On this basis, and with two-sided satisfaction as the goal, two-sided rank belief degrees are taken as pieces of evidence, and satisfaction degrees of two-sided matching are obtained through evidence fusion. Then, a decision-making model based on satisfaction degrees is constructed in order to obtain the matching solution. Finally, an illustrative example demonstrates the application of the proposed approach.

This paper constructs the relationship between consumption and economic growth by a structure equation model and uses the provincial panel data of 29 provinces (municipalities, autonomous regions) from 1992 to 2010 in China, using maximum likelihood estimation method to analyze empirically the relationship between the consumption and economic growth in China. The result shows that the path coefficients between consumptions and economic growth are all positive, that suggests the consumption has significant positive effects on the economic growth. Also in this paper, it gives a new try to use a structural equation model to research the relationship between consumption and economic growth.

In recent years, it has been a hot pot to explore the effectiveness of basic medical insurance. However, due to different sample characteristics, time series length, models and so on used by authors, there exists big difference among relevant papers. In this paper, we try to apply meta regression analysis (MRA, a prevalent statistic literature review method) to explore its influence on family expenditure, and 90 sample observations were extracted from 49 important literatures. We find: 1) All the index construction, sample characteristic, model selection, control factors will influence the conclusions of medical insurance effectiveness; 2) The implement of basic medical insurance will increase household consumption other than the new rural cooperative systems; 3) The implement of basic health insurance-has not really reduce family's medical expense. Thus, we induce that adverse selection exists in China's medical insurance.

With the vigorous development of equipment manufacturing industry in China, higher requirements to the equipment supportability are put forward. How to evaluate the supportability of equipments (especially the aviation equipment-aircraft) objectively and correctly is the problem to be solved in the development of aviation equipments construction, demonstration and battle application. Aimed at the needs of the supportability analysis of complex equipment systems-aircraft, a model of aircraft support concept evaluation based on DEA (data envelopment analysis) and PCA (principal component analysis) is proposed. The model is used to evaluate a certain aircraft support concept. The process and the results of evaluation show that proposed model is feasible and effective. The model is suitable for advanced aircraft support concept evaluation. The feasibility and effectiveness of the proposed model is verified by the analysis of the evaluation results. This method is applicable to the evaluation of aircraft support concepts.