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Abstract: Fish disease is becoming the major factor damaging the sustainable development of aquaculture and the quality security of aquatic product in China. Many researchers provide assistances and advice in fish disease diagnosis and appropriate treatment measures. However, the risk of misdiagnosis, incorrectly treatment or over-treatment also exists due to carrying out after disease outbreaks. So it is necessary to develop an early warning system. Taking into account that fish disease outbreak is mainly caused by suboptimal water quality in north of China, the best way is to develop decision aids tool that can assess and early warning the risk of fish disease based on water quality management. This paper introduces a knowledge-based early warning system for fish disease via water quality management. The architecture, the process for knowledge engineering, main components and their functions, especially a series of water quality management mathematic models are described. At present, the system is in pilot in the city of Tianjin in China. The stage achievements in developing the intellectual system of fish disease early warning are summarized. [Copyright &y& Elsevier]

This paper proposes an effective approach to deal with undesirable outputs and simultaneously reduces the dimensionality of data set. First, we change the undesirable outputs to be desirable ones by reversing, then we do principal component analysis (PCA) on the ratios of a single desirable output to a single input. In order to reduce the dimensionality of data set, the required principal components have been selected from the generated ones according to the given choice principle. Then a linear monotone increasing data transformation is made to the chosen principal components to avoid being negative. Finally, the transformed principal components are treated as outputs into data envelopment analysis (DEA) models with a natural assurance region (AR). The proposed approach is then applied to real-world data set that characterizes the ecology performance of 17 Chinese cities in Anhui province. [Copyright &y& Elsevier]

Churn prediction is becoming a major focus of banks in China who wish to retain customers by satisfying their needs under resource constraints. In churn prediction, an important yet challenging problem is the imbalance in the data distribution. In this paper, we propose a novel learning method, called improved balanced random forests (IBRF), and demonstrate its application to churn prediction. We investigate the effectiveness of the standard random forests approach in predicting customer churn, while also integrating sampling techniques and cost-sensitive learning into the approach to achieve a better performance than most existing algorithms. The nature of IBRF is that the best features are iteratively learned by altering the class distribution and by putting higher penalties on misclassification of the minority class. We apply the method to a real bank customer churn data set. It is found to improve prediction accuracy significantly compared with other algorithms, such as artificial neural networks, decision trees, and class-weighted core support vector machines (CWC-SVM). Moreover, IBRF also produces better prediction results than other random forests algorithms such as balanced random forests and weighted random forests. [Copyright &y& Elsevier]

Data envelopment analysis (DEA) requires input and output data to be precisely known. This is not always the case in real applications. This paper proposes two new fuzzy DEA models constructed from the perspective of fuzzy arithmetic to deal with fuzziness in input and output data in DEA. The new fuzzy DEA models are formulated as linear programming models and can be solved to determine fuzzy efficiencies of a group of decision-making units (DMUs). An analytical fuzzy ranking approach is developed to compare and rank the fuzzy efficiencies of the DMUs. The proposed fuzzy DEA models and ranking approach are applied to evaluate the performances of eight manufacturing enterprises in China. [Copyright &y& Elsevier]

Abstract: This paper proposed a self-learning, self-adapting algorithm (ANN-GA-Cascades) for extracting fuzzy rules, which is based on fusion of soft computing. We could use it to attain the fuzzy rules of oiliness in oil exploration: firstly, supervised learning of training sample is performed by using neural networks, with the inputs being the simplest well-logging attribute set which is relevant to the oiliness attributes, and the outputs being the corresponding oiliness partition C k (dry layer, water layer, inferiority layer and oil layer). When the neural network attained precision or the maximum iteration steps, the kth output node of neural network will be the corresponding partition of decision character, with the output function being ψ k = f(x i ,(WG1) ij ,(WG2) jk ), in which (WG1) ij are the connection weights between input layer and hidden layer, (WG2) jk are the connection weights between hidden layer and output layer. Then, the genetic algorithm (GA) was used to randomly assemble the input character and ψ k as the fitness function. In this way, the optimal chromosome will be the fuzzy rule of partition C k . Finally, the empirical study application of this algorithm on oil well oilsk81 and oilsk83 of Jianghan oilfield in China has proved to be satisfactory. [Copyright &y& Elsevier]

Abstract: How to effectively predict financial distress is an important problem in corporate financial management. Though much attention has been paid to financial distress prediction methods based on single classifier, its limitation of uncertainty and benefit of multiple classifier combination for financial distress prediction has also been neglected. This paper puts forward a financial distress prediction method based on weighted majority voting combination of multiple classifiers. The framework of multiple classifier combination system, model of weighted majority voting combination, basic classifiers’ voting weight model and basic classifiers’ selection principles are discussed in detail. Empirical experiment with Chinese listed companies’ real world data indicates that this method can greatly improve the average prediction accuracy and stability, and it is more suitable for financial distress prediction than single classifiers. [Copyright &y& Elsevier]

• Novel grey forecasting model called the neural ordinary differential grey model (NODGM) is proposed. • NODGM can be learned through a training procedure. • NODGM has a wider application range and can learn the characteristics of different data samples. • The predicting sequence is obtained by using the Runge–Kutta method to solve the model. Due to the efficiency of grey models in predicting the time series of small samples, grey system theory has been well studied since it was first proposed and has now become an important method for small sample prediction. Inspired by the neural ordinary differential equations (NODE), this paper proposes a novel grey forecasting model called the neural ordinary differential grey model (NODGM). The NODGM model includes a novel whitening equation that allows the prediction model to be learned through a training procedure. Therefore, compared with other models whose structures and terms need to be artificially predefined based on the laws of real samples, the NODGM model has a wider application range and can learn the characteristics of different data samples. Then, to obtain a model with better prediction performance, we apply NODE to train the model. Finally, the predicting sequence is obtained by using the Runge–Kutta method to solve the model. In the experiments, we apply NODGM model to two energy samples and then contrast the experimental results with the results of some classical grey models to validate the effectiveness of NODGM model. The comparison results from predicting China's annual crude oil consumption and forecasting oilfield production in northern China show that the prediction accuracies achieved by NODGM model are 28% and 8% higher, respectively, than those achieved by the state-of-the-art grey forecasting models. [ABSTRACT FROM AUTHOR]

• It proposes a hybrid multi-period network structure with feedback. • It provides an unbiased evaluation under the cooperation hypothesis. • A binary heuristic algorithm is proposed to obtain the optimal efficiency. • It is empirically supported by using the 5-year ecological data of China. Decision-making unit (DMU) internal structure simulation is the basis for network Data Envelopment Analysis (DEA) to open "black box" and evaluate system efficiency with complex internal structure. Based upon summarizing and analyzing the existing model assumptions in network DEA, this paper proposes a hybrid multi-period DEA model with feedback to open the internal structure of the DMU system, as well as to provide horizontal comparison of the efficiency change of a same DMU at different time periods. In the model construction, the global production frontier is used for multi-period evaluation, Chebyshev distance is used to construct an unbiased two-stage model. Under the cooperation hypothesis, it is considered that the two stages are equally important, which solves the defect that the current two-stage method is not unique in its optimal solution and has two-stage contribution bias. A binary heuristic algorithm is proposed to reduce the time complexity of model solving while maintaining relatively high accuracy. The correctness and feasibility of the algorithm are demonstrated through the investigation of the relevant properties. Finally, the 5-year ecological data of China is used for illustrative application, providing suggestions for future environmental governance. Several comparative experiments are conducted to demonstrate the advantages of our proposed model. [ABSTRACT FROM AUTHOR]

• A network closest target model for a two-stage system is built. • A closest target frontier projection is obtained. • Efficient stages are used for the closest target for an inefficient DMU. • A case of 16 leading commercial banks in China is analyzed. Traditional data envelopment analysis (DEA) models mainly set the furthest targets as the frontier projection for inefficient decision-making units (DMUs). To achieve the efficient status with less effort, the closest target models are introduced which projected the least input/output improvement for inefficient DMUs. However, these works typically consider each DMU as a "black box" in the closest target setting, and thus these models cannot be directly extended to a system with network structure because there may be no efficient DMU for reference. This paper fills the gap by developing a closest target model for a two-stage system. Instead of constructing the efficient frontier only by the system efficient DMUs in the "black box" system, all the extreme efficient stages of the DMUs are considered to form the closest target for an inefficient DMU. Using our network closest target (NCT) model, a case of the 16 leading commercial banks in China is analyzed. The results show that these commercial banks performed steadily in both efficiency and input/output required improvement during the study period. Moreover, compared with the network furthest target (NFT) model, NCT requires each DMU less improvement on inputs/outputs, and usually obtain the higher efficiency. That is, NCT is more feasible, economical and optimistic. Lastly, we discuss the proportion of each dominating peer referred by the inefficient banks and the importance of these peers is ranked accordingly. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we suggest that flexible hiring and joint research and development are two ways to acquire outside knowledge resources, which facilitate inter-organizational knowledge sharing and enable firms to overcome the constraints of internalized knowledge search in their innovation processes. We conducted a survey of 182 firms in Xi’an, China, which indicates that the majority of the surveyed firms have realized the importance of acquiring outside knowledge resources, and they are adopting flexible hiring policy and joint R&D strategy to make good use of outside knowledge resources. We then explore whether the two ways facilitate cross-organizational knowledge acquisition and inter-organization knowledge sharing, and hence contribute to effective innovations. We find that flexible hiring and joint R&D are effective ways to employ outside knowledge resources. Moreover, the effectiveness of innovations increases with both ways. These findings show that organizations can acquire outside knowledge resources to improve their innovations through flexible hiring and joint R&D. [Copyright &y& Elsevier]

Abstract: The traditional methods of estimating economic contribution rate of education (ECRE) are based on hard computing such as statistical methods, which ignore both the long-term effect of education and the lagged effect of education on economy growth. This paper proposes the fusion method of neural networks, fuzzy systems and genetic algorithms made in the realm of soft computing to estimate the ECRE. Firstly, a target system (a country or a region) is categorized softly according to the level of Science and Technology (S&T) progress. Secondly, potential human capital stock and actual human capital stock in the same cluster are calculated and set up the internal correlation between them (fuzzy mapping). Thirdly, we conceptualize actual human capital as one production factor, joined with the other two production factors, land and fixed asset, to set up the fuzzy mapping to economic growth. Finally, we obtain the ECRE through two marginal rates, namely marginal economic growth to actual human capital stock, and marginal actual human capital to potential human capital. This method greatly reduces the bias in the ECRE that results from the indirect and lagged effects of education. It therefore identifies the effect of education on economic growth more explicitly. Based on the level of S&T progress, 31 provinces in China could be classified into three clusters. The first cluster (developed S&T) has an ECRE of 11.60%, and contains two provinces; the second cluster (developing S&T) has an ECRE of 8.82%, and contains 11 provinces; the third cluster (underdeveloped S&T) has an ECRE of 1.49% and contains 18 provinces. [Copyright &y& Elsevier]

Abstract: This paper explores the quantitative relationship between knowledge sharing and performance, with contextual factors in consideration. First, we argue that both knowledge sharing and its contextual factors should be associated with performance. Then, we analyze the multi-dimensional characteristics of knowledge sharing and propose six measures for it. Next, we model the relationship between knowledge sharing and performance, integrating various contingent factors with the model framework, some of which have significant influences on the relationship between knowledge sharing and performance. After that, we propose four alternative models and corresponding propositions for knowledge sharing-contingent variables relationship, and present a quantitative formulation of the relationship between knowledge sharing and performance. Finally, we conduct a survey of 249 organizations in Xi’an, China, and show the empirical results. Our propositions about the knowledge sharing-performance relationship and contingent factors are supported by the survey. The empirical results suggest that managers have to pay attention to some contingent factors while they commit to knowledge sharing. This study adds to the understanding of the effects of knowledge sharing on performance, and gives implications to the practice of knowledge sharing. [Copyright &y& Elsevier]

Abstract: When a pig shows disease symptoms, it is important to make an accurate diagnosis to support control strategies. Diagnosing diseases in pigs requires considerable expertise. Only a few experts have the ability to do this, and each expert has his own specific domain. To make it more generally accessible and reduce the waiting time, an expert system named Pig-Vet has been developed by China Agricultural University. Based on investigations, this paper analyzes users'' needs and describes the architecture, main components, and their functions. The system has over 300 rules and 202 images and graphics for different types of diseases and symptoms. It can diagnose 54 types of common diseases of pigs. At present the system is in pilot in north of China. The stage achievements in developing the intellectual expert system of pig disease diagnosis are summarized. [Copyright &y& Elsevier]

The integration of customer relationship management (CRM) and supplier relationship management (SRM) to facilitate supply chain management in the areas of supplier selection using a help desk approach has become a promising solution for manufacturers to identify appropriate suppliers and trading partners to form a supply network on which they depend for products, services, and distribution. In this paper, an intelligent customer–supplier relationship management system (ISRMS) using the case based reasoning (CBR) technique to select potential suppliers is discussed. By using ISRMS in Honeywell Consumer Product (Hong Kong) Limited, it is found that the outsource cycle time from the searching of potential suppliers to the allocation of order is greatly reduced. [Copyright &y& Elsevier]

When solving group decision-making (GDM) problems, the proper values of consensus thresholds are vital for consensus checking and improving. Decision makers are usually not confident to select a proper threshold. In the linguistic setting, this paper presents a new process to support the determination of consensus thresholds. A new concept, namely random consensus index, is presented to serve as a reference object so that the risk preference of a decision maker could be induced by comparing the quality of the information in hands with the averaging quality of randomly generated information. In this sense, admissible consensus thresholds could be determined automatically, and decision makers are not necessary to focus on the detail of consensus measures. Furthermore, the process is applied to the GDM problems with probabilistic linguistic preference relations, and a new GDM approach is presented. Different from the existing contributions, the consensus is measured by the probability of a set of preference relations being with acceptable consensus, and then the consensus is improved by revising the involved probability distributions. All the involved parameters are intuitive and interpretable. A case study, regarding the departure audit in China, demonstrates that the proposed GDM approach is effective even if the original preferences of experts are with low consistency and consensus. • A process of determining consensus thresholds of LPRs is presented. • Decision maker's risk attitude is induced by comparing with random information. • A novel GDM approach based on probabilistic LPRs is presented. • Consensus of probabilistic LPRs is measured and explained by the probabilities. • Consensus is improved by revising the associated probabilities of terms. [ABSTRACT FROM AUTHOR]

• An AIC rule based variable selection approach for eco-efficiency evaluation is proposed. • This approach investigates any possible variable set generated from all potential variables. • A real data set on 30 China provinces' industrial systems is used to demonstrate the proposed approach. • The most influential variables in eco-efficiency evaluation can be determined with robustness. Eco-efficiency evaluation has received increasing public attention and plays an important role in the business community. In many practical applications, the decision-makers are interested in which eco-variables take a significant effect on eco-efficiency evaluation and how to select proper variables in situations where there are a large number of alternative variables. This paper approaches these problems based upon the Akaike information criteria (AIC) rule. The proposed approach can investigate all possible variable sets and identify the most influential variables. A real data set about 30 industrial systems in China has been used to illustrate the proposed approach. We find the most influential undesirable output in determining provincial industrial systems' eco-efficiency of China is Sulphur dioxide emission. This result is robust under different eco-efficiency measurements. It is of great significance for decision-makers to achieve eco-efficiency improvement. [ABSTRACT FROM AUTHOR]

The aim of this paper is the generation of a time-series based statistical data-driven procedure in order to track an outbreak. At first are used univariate time series models in order to predict the evolution of the reported cases. Moreover, are considered combinations of the models in order to provide more accurate and robust results. Additionally, statistical probability distributions are considered in order to generate future scenarios. Final step is the build and use of an epidemiological model (tSIR) and the calculation of an epidemiological ratio (R 0) for estimating the termination of the outbreak. The time series models include Exponential Smoothing and ARIMA approaches from the classical models, also Feed-Forward Artificial Neural Networks and Multivariate Adaptive Regression Splines from the machine learning toolbox. Combinations include simple mean, Newbolt-Granger and Bates-Granger approaches. Finally, the tSIR model and the R 0 ratio are used for estimating the spread and the reversion of the pandemic. The suggested procedure is used to track the COVID-19 epidemic in Greece. This epidemic has appeared in China in December 2019 and has been widespread since then to all over the world. Greece is the center of this empirical study as is considered an early successful paradigm of resistance against the virus. [ABSTRACT FROM AUTHOR]

• Customer with time window assignment is considered and modeled after collaboration. • A multi-objective optimization model is established to study the CMDVRPTWA. • A hybrid algorithm includes K -means, CW and E-NSGA-II is devised to solve the model. • A practical study is conducted to show the applicability of proposed mechanism. In logistics operation, delivery times are often uncertain for customers, and accommodating this uncertainty poses operation challenges as well as extra cost for logistics service providers. The delivery time uncertainty is particularly an issue if there are multiple service providers in a logistics network. To address this issue, we formulate and solve a collaborative multi-depot vehicle routing problem with time window assignment (CMDVRPTWA) to effectively reduce the impact of changing time windows on operating costs. This paper establishes a bi-objective programming model that optimize the total operating cost and the total number of delivery vehicles. A hybrid heuristic algorithm consisting of K -means clustering, Clarke–Wright (CW) saving algorithm and an Extended Non-dominated Sorting Genetic Algorithm-II (E-NSGA-II) is presented to efficiently solve CMDVRPTWA. The clustering and CW saving algorithm are employed to increase the likelihood of finding the optimal vehicle routes by identifying a feasible initial solution. The E-NSGA-II procedure combines partial-mapped crossover (PMC), relocation, 2-opt* exchange and swap mutation operations to find the optimal solution with pre-defined iteration and termination rules. Profit allocation schemes are then analyzed using the Game Quadratic Programming (GQP) method, and the optimal sequences of joining coalitions are obtained based on the principle that coalition participants' benefits should be non-decreasing when a new participant joins the coalition. We conduct three empirical studies on a small-scale example, on several benchmark datasets and on a large-scale logistics network in Chongqing city, China. Further comparative analysis indicates that E-NSGA-II outperforms most other algorithms in solving CMDVRPTWA. This novel approach identifies profit allocation strategies that ensure the stability and reliability of the collaborative coalitions in the context of flexible customer service time windows, and can be utilized to improve the efficiency of urban logistics and intelligent transportation networks. [ABSTRACT FROM AUTHOR]