The paper first discussed the influences of the historical information anchoring on the optimal discretionary monetary policy and monetary policy rule. Then, combined with the method of numerical simulation, it compared the social welfare of two kinds of optimal monetary policy and non-monetary policy in two cases—the historical inflation information is exogenously given or endogenously determined by the policy decision. The results showed that, on the average, the existence of the historical information anchoring will improve the effectiveness of the optimal discretionary monetary policy on the condition of low historical inflation, but weakens the effectiveness of the monetary policy rule. In view of one-period policy effectiveness, the monetary policy is effective even under the condition of flexible price adjustment. Regardless of the anchoring degree, the monetary policy rule is more effective than the optimal discretionary monetary policy. But in view of multi-period policy effectiveness, if the inter-temporal information anchoring exists, the monetary policy will give birth to sustained negative effect after improving social welfare in the first period. It is necessary to avoid the expectation trap of the policy self-fulfillment when the central bank wants to implement a monetary policy.
It is usually very difficult to appraisal real estate's value because the price's fluctuation and market difference. This paper researches the effect and applicability for the nine assessment equity test models that are very popular in real estate appraisal. Ratio, vertical and horizontal inequities are tested by spatial econometrics based on market difference and high-density communities. It is found that spatial econometrics methods could reduced effectively inequities, especially for vertical inequity. Relative to horizontal inequity test results, both two spatial econometrics methods are better in vertical inequity test. Concretely speaking, SAR (spatial autoregressive model) models are better than SEM (spatial error model) models. The regressive effects of vertical inequity are verified for PF, Cheng, IAAO, Bell, Sunderman and Chapp test models. Moreover, AD model are more effective than Goolsby model for assessment equity test in high-density area. This paper provides micro evidence to improve the accuracy in real estate assessment.
This paper attempts to discuss the relationship between multimarket contact, market entry and firm performance as a starting point to provide suggestions for firms how to adopt rational and effective competitive strategy in the dynamic competitive environment. The proposed hypotheses were verified by regression analysis and robustness test. The results show that: firstly, multimarket contact has an inverted U-shape influence on market entry; secondly, market entry has a shadowing effect on the relationship between multimarket contact and firm performance. That is, the positive direct effect of multimarket contact on firm performance is greater than the negative indirect effect multimarket contact on firm performance through market entry; and then the political background of CEO has a negative moderating influence on the relationship between multimarket contact and market entry. Therefore, based on the above analysis results, this paper argues that the firm takes the initiative to increase the degree of multimarket contact with competitors can be regarded as an important non-market strategy.
Since China has been promoting supply-side structural reform determinedly, it is particularly important for us to explore the relationship between employment changes and supply-side reform. This paper, from the supply side, decomposes the determinants of employment into technological development factor, economic structure factor, imported inputs changes and economic growth factor, using structural decomposition techniques and non-competitive input-occupancy-output tables which capture processing trade. The results suggest that the decline in economic growth will depress employment growth and imported inputs had positive effects on employment growth. The technological development improved labor productivity, but also increased the risk of unemployment. Meanwhile, technological development has accelerated the adjustment of economic structure. In 2002-2007, the adjustment of economic structure had positive impacts on the employment of secondary industries, while these positive impacts transferred to the tertiary industries in 2007-2012. It implies that the demand for employment in China has gradually transferred to service industries with economic transition.
This paper has built the evolutionary game model of incidence rate considering safety incidents in the view of the existing problems of risk supervision on port hazardous chemicals, export three levels of security risks including low, middle and high on the coordinate axis of the incidence rates of security incidents and concluded the laws of evolution of systems, then, carry out the dynamic simulation analysis. The research shows the rate of cost to benefit of port transportation enterprises and government supervision department is the threshold to distinguish the levels of security risks, while faced the lower security risk, both port transportation enterprises and government supervision department will ignore the lower security risk and treat it negatively, while faced the middle security risk, both port transportation enterprises and government supervision department will choose opposite strategy, while faced the higher security risk, port transportation enterprises will abide by the law to transportation consciously and government supervision department will choose loose supervision, There is phenomenon of incentive game in the supervision of port hazardous chemicals, in the long runs, it is needed to strengthen the punishment for ineffective supervision of government supervision department rather than only to increase the punishment for illegal transportation of port transport enterprises.
Recreational carrying capacity (RCC) is one of the most important indictors used in measuring the use limit of the forest park resource. However, the consensus has not been reached with respect to its connotation and measuring metrics in the academic circles. The vast majority of the RCC studies in existing literature are focused on the social carrying capacity and physical-engineering aspects only, which are commonly presented by the maximum number of tourists allowed to entry the park site during a specific time period (yearly or daily). Nevertheless, to some degree, this measurement partially deviates the underlying RCC concept because of the fact that the RCC composes of multitude elements, including but not limited to the factors such as natural, social, economic and cultural. In this paper, we try to establish a new RCC theoretical framework based on recreation utility maximization theory, product characteristic theory and the park environmental attributes. In the process, the choice experiment (CE) and orthogonal design methods are used for questionnaire design following by utilizing the conditional Logit model for parameter estimates. A total of more than 700 park visitors to the Shenyang National Forest park of Liaoning province in China were interviewed for data collection. The survey questionnaire was focused on those park attribute questions such as vegetation, coverage, water quality, number of rubbishes along the park trail path, admission fees, and park management such as crowding, etc. The results indicate that the sequential order of tourist's preferences over those recognized environmental attributes are as follows: vegetation coverage, density of visitors, visibility through the water, number of rubbish and admission fees. Of which the threshold for the carrying capacity of vegetation coverage is 78%; the amount of garbage, 3/20 m; the density of tourists, 14/200 m2; and visibility through the water 1.45 m, respectively. Based on these research findings, related policy and management implications are addressed accordingly.
As the business environment changes rapidly nowadays, business model should be kept transforming over time. Only the company that strives to innovate can maintain its competitive advantage. This paper proposes the analytical framework of the dynamic transformation of business model by integrating the iceberg theory of business model and the Wei-Zhu six factor model into the business process change model. The proposed analytical framework provides instructions for company to change their business model. Based on this analytical framework, this study analyzed the evolution of business model of Wanda Group and drew some managerial implications from the results.
In traditional cooperative game, the worth of a coalition depends only on the coalition. In the environment of externalities, the worth of a coalition not only depends on the coalition itself, but also is affected by other players' coalitions. This paper studies cooperative games with externalities, and analyzes the behaviors of players in these games. We use partition function to describe the worth of coalitions and put forward the definition of stable coalition structures. Then, we illustrate the existence of stable coalition structures. We get the sufficient condition that the coalition structure is stable by analyzing the properties of partition function. Finally, we design an evolutionary algorithm to find these stable coalition structures. We obtain the following conclusions: for a given coalition structure, if the set of all coalition structures can be divided into two separate parts based on this coalition structure, and each part satisfies some properties, this coalition structure is stable.
Engineering general contracting creates the conditions for project optimization. For the union general contracting project, optimization profit distribution relates to the general contracting advantage whether can be fully achieved. Based on the principle of revenue-sharing, the negotiation mechanism is introduced into the project optimization profit distribution of design-construction union engineering general contract. Considering fairness preference of the designer and the contractor, build the negotiation model of project optimization profit distribution. Afterwards, this paper set three experiment scenarios: only the designer or the owner has fairness preference, and both two sides have fairness preference. Through negotiation simulated experiment, we analyzed the effects of the fairness preference of both two sides on the negotiation cycle, the coefficient of profit distribution, the optimization revenue and the net income of both sides. Result shows that: negotiation can effectively solve the problem of the profit distribution of union general contracting project optimization. In addition, the appropriate behavior of fairness preference by the two sides is beneficial to improve their own benefit. However, the designer and the contractor focus too much on fairness preference will increase the negotiation cycle and it is not conducive to the achievement of the optimization.
The one-to-many two-sided matching problem between applicants and positions with tenants is studied. At first, we describe the problem of two-sided matching between applicants and positions with tenants. Then, we give the definitions of applicants and positions with tenants matching, individual rationality, stable matching and fair matching alternatives. Furthermore, basis on the constrain of σ-steady, we design the improved-equitable selection (I-ES) with tenants algorithm. Finally, a numerical example is given to illustrate the feasibility and effectiveness of the proposed method.
The concept of dual hesitant fuzzy linguistic set is based on dual hesitant fuzzy set and linguistic variables. Motivated by this idea, this paper proposes the concept of interval-valued dual hesitant uncertain linguistic set used to solve more complex decision making. Firstly, this paper studies the concept, operations, score function, accuracy function, Hamming distance, and ranking method of interval-valued dual hesitant uncertain linguistic variables. Then, the interval-valued dual hesitant uncertain linguistic generalized Banzhaf Choquet integral operator is proposed. Meantime, some properties of this operator are investigated. To determine the optimal fuzzy measure on criteria set, the model based on maximization deviation method and Banzhaf function is established. Furthermore, an approach to muti-criteria decision making problem with incomplete weight information and interactive condition under interval-valued dual hesitant uncertain linguistic environment is developed. Finally, an example is shown to verify the effectiveness of the given method.
A multiple-attribute decision making method based on prospect stochastic dominance criterion is proposed to solve the problem of stochastic multiple attribute decision making with multiple reference points on each attribute. Firstly, the profit-loss matrix about reference point is transformed from the decision matrix based on prospect theory. Secondly, the prospect stochastic dominance relation is determined according to the prospect stochastic dominance criterion which is defined by the radio of the enclosed areas from the two cumulative distribution functions. Then, considering the attribute correlation, the fuzzy measure is used to obtain the comprehensive prospect stochastic dominance matrix. Finally, PROMETHEE Ⅱ method is used to rank the schemes. The feasibility and validity of the proposed method are verified by a case study.
This paper evaluates the non-market value of construction waste recycling based on contingent value method, through investigating Mianyang residents' cognition and willingness to pay for the construction waste. The result shows that the non-market value of the construction waste recycling in Mianyang is 829 million yuan, and accounts for 2.19% of total output value of construction industry of Mianyang in 2015. The annual willingness to pay of urban and rural residents in 2015 is 584.88 yuan per household and 208.08 yuan per household, and accounts for 0.91% and 0.55% of annual household income respectively. This indicates that the non-market value of construction waste recycling has high non-market value, but the amount of residents' average willingness to pay accounts for a lower-proportion in their annual household income, which means that the residents' willingness to pay is still low. The key factors affecting the willingness to pay for urban and rural residents are cognitive level of construction waste and transparent management of construction waste recycling funds. Therefore, strengthening publicity and education on the construction waste recycling and transparent management of the funds for construction waste recycling are effective means to improve the residents' willingness to pay for the construction waste.
This paper develops a joint economic design approach to preventive maintenance and variable sampling size exponentially weighted moving average (VSS EWMA) chart for monitoring process variance. Incipient fault resulting in shift of process quality variance is considered and it contributes to five renewal scenarios of the production process. On this basis, a mathematical model is given to minimize the expected cost per unit time. In the model, the cycle cost and the cycle time of each scenario are established based on each sampling interval, instead of on in-control process and out-control process adopted by previous models. It can avoid the complex calculation of the average runs length during out-of-control period and other issues. The results of a case show that the hourly cost of the model is lower than that of the previous method used in the enterprise where preventive maintenance period schedule and control chart design are planed independently. Finally, orthogonal-array experimental design and multiple regressions are employed to demonstrate the sensitivity of the time and cost parameters.
Utilizing the attribute that automotive ro-ro storage yard can compress time-space distance of vehicle transportation resolves the problem of market emergency demand in the vehicle supply chain system. In this paper, the storage yard is regarded as an important node, which combines the planned order and additional emergency order. An incorporative distribution problem of the planned and additional emergency orders is considered for the gathered distribution of vehicles in the yard. A 0-1 integer programming model is presented, so as to make all types of vehicles belonging to these two orders realize centralized distribution. Which provides conditions for centrally efficient loading. One parking spaces incorporative distribution algorithm is designed to obtain a satisfied initial vehicles distribution. Moreover, combined with attraction degree search, the suboptimal solution can be solved quickly. Numerical experiments show, the proposed method have better performance compared with the branch-and-bound method and the way of orderly distribution. Experiments of practical application make further certification that the heuristic algorithm can solve large-scale examples efficiently.
In view of the disadvantages of the existing traffic guidance system to determine the position and the issue of variable message signs (VMS) unilaterally, a systematic method is proposed to determine the VMS inducing strategy at a certain level of demand in the road network. The method establishes a bi-level programming model considering both of the restriction of the system construction cost and the path decision mentality of the traveler. The upper model considering the traffic management expectations for the overall benefit of the network and the constraint on the construction cost of VMS, the lower model uses stochastic dynamic traffic assignment model that accords with traveler's path choice psychology. It is shown by application that the total network travel cost is a combination of network traffic demand, probability of path transformation, location of VMS settings, release cycle and content of traffic guidance information in different constraint of the construction cost of the system, so we need to consider both the above factors when explore VMS guidance strategy that ensure that the overall travel costs of the network is the lowest. The research results will offer the theoretical support for the decision making of the traffic management department.
This paper established a NL based travel mode choice model including P& R as an access mode, analyzed the influence of the parking ticket price on P& R travel mode share, and established the relation model between the parking ticket price and the number of people using P& R facilities. According to the condition of supply and demand, two types of P& R parking pricing optimization strategies with utilization rate maximization and revenue maximization as optimization goals were proposed respectively from the perspective of the operation improvement of P& R parking lot, and the suggestions about the expansion scale and operation strategies in the future to the ''demand exceeds supply" type of P& R parking lot were also suggested. In the case study of TIANTONGYUANBEI P& R parking lot, the pricing optimization strategy was fully analyzed. The results of the study can put forward the proposal from the perspective of the pricing to improve the problem that most of the operated P& R facilities are facing the situation of supply exceeds demand or demand exceeds supply.
Imbalanced data exists widely in all domains of our daily life, such as disease diagnosis, mineral resource detection, etc. For the classification of imbalanced data, while ensemble classifiers gave a promising solution for classifying such skewed data, existing ensemble classifiers assume all kinds of imbalanced data share the same characteristics, and a universal solution was carefully designed. However, imbalanced data can be unequable based on its imbalanced ratio, the number of features of the number of examples available for training, so it's difficult to get good results in all of the data set. In this paper, we propose an adaptive multiple classifier system based on differential evolution algorithm (DE-AMCS), system can choose optimal integration of learning model to complete the classification task. 10 datasets from KEEL are selected to verify the efficiency of DE-AMCS, and 5 state-of-the-art imbalanced data classification algorithms are also tested for comparison. Experimental results show that the DE-AMCS is competitive or outperforms the state-of-the-art by using various evaluation metrics as indicators. Finally, DE-AMCS is applied to 5 wells of Jianghan Oil Field. For each well, the precision reaches 100%.
In order to solve dynamic multi-objective optimization problems, a new dynamic multi-objective gravitational searching algorithm which is based on decomposition technique is proposed in this paper. Firstly an environmental monitoring strategy based on the changes of each objective function optimal solution is adapted to monitor the environment. If the environment doesn't change, we will use the static multi-objective gravitational searching algorithm to solve the problem. If the environment changes, we will use a hybrid prediction model response to changes in the environment. The hybrid prediction model is based on the similarity of the optimal solution of the adjacent sub population and the optimal solution of the same weight vector corresponding to the sub population. Finally, compared with the advanced static multi-objective algorithm and the forecasting method are compared on four test problems. Experimental results suggest that the proposed algorithm has obvious effectiveness and advantages which shows great capability of evolving convergent, diverse and uniformly distributed Pareto fronts.
It is known that the traditional prewhitening heteroscedasticity-autocorrelation consistent (HAC) methods have finite sample bias, leading to a spurious regression between stationary processes. This paper proposes a modified prewhitening HAC method by the first-order and higher-order median-unbiased estimation, and adjusts the standard error of t-statistics in OLS estimation through the modified prewhitening HAC methods, and reveals their applicability in spurious regression probability between stationary processes. The result shows that: first, the modified prewhitening HAC methods can reduce finite sample bias in long-run variance estimation; second, the modified HAC methods have a robustness property to persistence of data process, GARCH heteroscedasticity and non-normal innovations.
Accurate PM2.5 concentration forecasting is crucial for protecting public health and improving air quality. However, the randomness, non-linearity and non-stationarity of PM2.5 concentration series increase the difficulty in PM2.5 concentration forecasting. In order to improve the accuracy of PM2.5 concentration forecasting, this paper proposes a novel hybrid model based on two-layer decomposition technique integrated fast ensemble empirical mode decomposition (FEEMD), variational mode decomposition (VMD) and extreme learning machine (ELM) model optimized by differential evolution (DE) algorithm. To testify the validity of the proposed model, the PM2.5 concentration series of Beijing and Shijiazhuang are taken as the test cases to conduct empirical study. Based on the experiment results, the following two conclusions can be obtained: 1) compared with single decomposition technique, the proposed two-layer decomposition technique can efficiently decrease the characteristics of non-linearity and non-stationarity of PM2.5 concentration series; 2) the proposed FEEMD-VMD-DE-ELM model can precisely forecast the PM2.5 concentration.
Men's health has been seriously damaged due to prostate cancer in recent years. Fuzzy neural network can be used to diagnose prostate cancer, and fuzzy rules can be extracted from the diagnosis model. In order to solve the problem with low interpretable rules extracted by fuzzy neural network, a structure adaptive fuzzy neural network (SAFNN) method is proposed. By modifying the loss function, this method can control the combination of similar membership functions, adjust the structure of fuzzy neural networks adaptively and reduce the number of fuzzy rules in the process of model training. Moreover, this method can extract interpretable rules and guarantee the diagnosis accuracy. To simplify the calculation process and improve training efficiency, particle swarm optimization (PSO) algorithm is adopted to train the structure and parameters of the model. We also conduct experiment studies with the inspection data of prostate diseases provided by National Clinical Medicine Information Center. The experiment result verifies the efficiency of the proposed method in prostate cancer diagnosis and interpretable rules extraction.
Aiming at defects of slow learning speed, trapped in local solution and inaccurate operating result of traditional BP neural network in the practical application of drought prediction, a drought prediction model based on parallel ensemble learning algorithm of good point set glowworm swarm optimization algorithm (GPSGSO) and back propagation neural network (BPNN) is proposed. Firstly, a new kind of improved glowworm swarm algorithm based on good point set theory and inertia weight function is constructed, and the validity of the algorithm is analyzed theoretically. Secondly, GPSGSO algorithm and BPNN are combined to construct parallel ensemble learning algorithm. GPSGSO is used to optimize the weight and threshold of BPNN, and the ensemble strategy is carried out for the best weights and thresholds. Finally, the parallel ensemble learning algorithm is applied to the prediction of agricultural drought disaster, which can accurately determine the drought level. The effectiveness of the GPSGSO algorithm in terms of convergence speed, accuracy and stability is verified by 8 Benchmark functions. At the same time, agricultural meteorological data of Northern Anhui Province is used to simulate validate experiment, the experimental results show that the algorithm has obvious advantages over the traditional BPNN, GSO-BPNN and GA-BPNN algorithm in terms of convergence speed, operation accuracy and stability. Therefore, the drought prediction model based on GPSGSO-BPNN parallel learning algorithm can effectively improve the accuracy of agricultural drought prediction.
According to the problems of less sample data, the influence factors are complex and changeable, and the low prediction accuracy of inventory requirements in drones air-materials demands, giving a systematical analysis of drones air-materials demands ways under the existing classical small sample, there is an unique advantage to use least squares regression methods to small sample data and variable muticorreiation. A drones air-materials demands predictions method based on partial least squares model is proposed. Selecting the parameters of flight hours, flight lift landing numbers, proficiency degree of operator, unusual temperature and humidity in the environment, fault rate of air-materials, skill level of serviceman and so on, the principle of partial least square method and model modeling step are analyzed, and the model of drones air-materials demands predictions is built, the influence factors of the material are studied. The experiment results indicate that the precision of prediction model is improved, the absolute relative errors of mean were 4.87%, in the predict results, it indicating this ways can apply to drones air-materials demands prediction, and can meet practical need.