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With rapid industrial developments, air pollution has become a hot issue globally. Accurate prediction of PM2.5 (a category of particulate pollutant with a diameter of less than 2. 5 μ m) has been a critical topic, as it can provide valuable information for government decision-making and policy control in environmental management affairs. In this paper, we propose a deep learning model based on graph neural networks (GNNs) to predict the next 48hr PM2.5 concentration in Taiwan. In this model, monitoring stations are regarded as nodes and edges are the distances between monitoring stations. Hence, the distribution of the stations can be perceived as a graph. GNNs are promising in processing non-grid structure data that can be represented as a graph. By incorporating the GNN and gated recurrent units (GRUs), this model can effectively capture the long-term spatial–temporal features in air quality time-series data. In addition, we also investigated the problem of predicting PM2.5 concentrations in the areas without monitoring stations or at sites far away from the stations. This problem has not captured researchers' attention whose methods are based on GNN. The problem is, however, quite challenging as these areas do not have historical air quality data, leading to low prediction quality. Finally, we performed experiments to verify the effectiveness of the proposed model based on actual data sources obtained in Taiwan. The results show that the proposed model exhibits satisfactory prediction performance compared to existing models. [ABSTRACT FROM AUTHOR]

In recent years, pre-trained transformer encoders have achieved successful milestones in natural language understanding (NLU). However, they still suffer from the representations with insufficient semantics, the sequences with finite lengths, and the need for a considerable number of parameters. These issues lead to difficulties not only in practical deployment under limited computational resources but also in downstream applications. To address such issues, this paper presents ELICE, a method of embedding language through an informative contrastive-encoder. Through the strategies of unordered composition and dropout-based contrastive learning, ELICE can enhance long documents embedding under the desirable model size and producing generic textual representations by condensing multiple unstructured features, such as titles, metadata, and segments of content. Furthermore, the contrastive learning by the NT-Xent objective is experimentally proven to ameliorate the document-level embedding space for a more isotropic one. ELICE was evaluated on four tasks of the CLUE benchmark (Mandarin NLU tasks). With the basic version, the ELICE-base has 12 million parameters and achieves 99% of the performance of the BERT-base (108 million) on average. Furthermore, on the IMDb sentiment classification (English NLU task), ELICE-base (13 million) achieves 95% of the accuracy of XLNet (110 million). Additionally, we deployed ELICE in a practical system for real-time ad-hoc document retrieval regarding legal compliance in the financial industry, and the fine-tuned ELICE outperformed the pre-trained baseline and the whitening representations. These results demonstrate the effectiveness, the robustness, and the flexibility of ELICE in the field of language understanding. • Achieve desirable model size by unordered composition and contrastive learning. • Adopt normalized temperature-scaled cross-entropy loss in pre-training/fine-tuning. • Redistribute non-smooth anisotropic embedding space about generic textual semantics. • Realize the application of legal compliance in Taiwan's finance industry. [ABSTRACT FROM AUTHOR]

• We extend the single feature selection model to feature combination selection model. • The accuracy on our selection model based on FDAF-score are obviously optimized. • Our method combined with separate 7 classification models are tested on 10 data sets. Default discrimination of credit card refers to the phenomenon in which banks discriminate against certain groups of customers based on their credit status. The challenges faced by banks and other financial institutions in evaluating borrowers and making lending decisions are inseparable from the discrimination of customers' credit status. Feature selection is a critical step in default discrimination, and the selection of features directly affects the results of default discrimination. Therefore, the identification of a group of optimal features to maximize the ability of default identification is the core problem tackled in this research. The innovation of this paper is that it extends the single feature selection model based on F-score and Fisher discriminant analysis (FDAF-score) to the feature combination selection. Taking the maximum FDAF-score of the combination of the whole features as the standard, an optimal feature combination with the maximum discrimination ability is deduced. It avoids the disadvantage that single feature selection cannot ensure that the combination of selected features still has the maximum default discrimination ability. In this paper, we find that the improved feature combination selection model based on FDAF-score was better than the single feature selection FDAF-score model. The results were confirmed in four credit data sets (binary classification) of Australia, Germany, Taiwan and Japan, and six comparative data sets (five of which are multi-classification data sets). When the feature combination selection method is combined with seven common classification models, except for image and German credit data sets, the classification accuracy of most models is higher than the FDAF-score single feature selection method. [ABSTRACT FROM AUTHOR]

Abstract: Chronic disease has gradually become a major fatality cause in Taiwan. Being afflicted with such illnesses elevate vulnerability to other complications as well. Therefore, this paper adopts a preventative perspective and ascertains the impacts of important physiological indicators and clinical test values for various chronic illnesses. The paper investigates five chronic diseases: hypertension, diabetes, cardiovascular disease, disease of the liver, and renal disease. Utilizing chronic diseases risk factors to establish early-warning criteria may reduce the complication occurrence rate. K-nearest neighbor, linear discriminant analysis, and sequential forward selection are utilized, which is divided into two parts. The first part classifies and screens both healthy persons and those affiliated with the abovementioned chronic illnesses for characteristic value determination. The second part determines the critical value of the important risk factors of each chronic illness and builds early-warning criteria to recognition the chronic illnesses. This paper uses data from a medical center in Taiwan to verify the proposed methodology. The results reveal that classifying materials and screening important factors are both positively efficient with a corrected rate of 80%. Additionally, through the important factors of early-warning criteria, not only can help patients understand the risks of suffering diseases, but also effectively offer diagnosis reference criteria for medical personnel. [Copyright &y& Elsevier]

Abstract: The international trade of Korea and Taiwan has been heavily dependent upon international seatransportation owing to geo-political aspects. Therefore, the two countries have exerted to develop and promote ocean-going shipping industry in order to support their economies. Recent financial crisis in together with the economic slowdown has reduced seaborne trade cargoes, which resulted in remarkably deteriorated revenues of the container shipping sector. Major container shipping companies of both countries such as Evergreen, Yang Ming, Hyundai, and Hanjin under our study are no exception. This paper intends to achieve two-fold aims. The first applies entropy to find the relative weights of financial ratios of the four companies each year. In so doing, we can find the weights variance for the period of 1999–2009 based on the financial performance of the above companies. The second is to rank the companies in the period by grey relation analysis. On the basis of findings in this paper, we suggest business policy implications to mitigate impacts of the financial tsunami in the context of world shipping area. [Copyright &y& Elsevier]

Abstract: Elementary and junior high school buildings in Taiwan are designed to serve not only as places of education but also as temporary shelters in the aftermath of major earthquakes. Effective evaluation of the seismic resistance of school buildings is a critical issue that deserves further investigation. The National Center for Research on Earthquake Engineering (in Taiwan) currently employs performance-target ground acceleration (AP ) as the index to evaluate school structure compliance with seismic resistance requirements. However, computational processes are complicated, time consuming, and require the input of many experts. To address this problem, this paper developed an evolutionary support vector machine inference system (ESIS) that integrated two AI techniques, namely, the support vector machine (SVM) and fast messy genetic algorithm (fmGA). Based on training results, the developed system can predict the AP of a school building in a significantly shorter time base, thus increasing evaluation efficiency significantly. The validity of ESIS was tested using the 10-Fold Cross-Validation method. Another aim of this paper is to retain and apply expert knowledge and relevant experience to the solution of similar problems in the future. [Copyright &y& Elsevier]

Abstract: The rapid development of information technology and the emergence of the Internet have created a borderless business environment and intensified market competition. Riding on the globalization trend, high-tech companies have been gradually leveraging information technology in order to shorten their manufacturing processes, enhance productivity with lower costs and prompt delivery to meet the customers’ needs. To achieve these targets and maintain competitive advantages, companies have been introducing enterprise resource planning (ERP) and knowledge management (KM). This paper finds, via literature review, that most scholars focus only on the deployment of ERP systems and improvement of flows. Few have introduced the KM concept into ERP systems. This paper collated the literature relevant to ERP and KM and integrates the findings to introduce the ERP KM concept. The most important thing is to establish a detailed introduction plan and a prior understanding of the critical success factors (CSFs) for ERP KM introduction. This paper summarizes the CSFs for ERP KM introduction via literature review and examines the influence of these CSFs on management performance. A questionnaire survey is conducted to collect the relevant data and SPSS 10.0 (statistics software) is run for statistical and multiple regression analyses. Among these CSFs, support from senior managers, corporate vision, reengineering of corporate flows and project management, selection of appropriate consulting firms and software suppliers, the identification of suitable employees to take part in ERP introduction and the proper training and education programs have positive influences on management performance. In the multiple regression analysis, all of the individual constructs are positively and significantly correlated. The explanatory power of individual variables is high. It is hoped that the research finding can serve as a reference for ERP KM introduction to corporations. [Copyright &y& Elsevier]

Abstract: Enterprises evaluate customers’ preferences of high-tech products to appropriately adjust product lines when the technologies are ready for commercialization. Conjoint analysis (CA) is usually applied to measure customers’ preferences while the product line design model (PLDM) is used to simulate customers’ purchase decisions based on these preferences. The optimal product line extension scheme (PLES) can then be found. For customers unfamiliar with a new technology, it is difficult to precisely appraise preferences for new products. This paper proposes fuzzy PLDM that combines fuzzy theory with CA to take preference uncertainty into consideration. To precisely apply fuzzy preferences, fuzzy rating is used to simulate customers’ purchase decisions. This paper focuses on the notebook industry in Taiwan. Optimal PLES are obtained under Fuzzy and Crisp scenarios. The results show that customers with high preference uncertainty have different purchase decisions under above scenarios, which leads to a great inconsistency between the optimal PLES obtained from Fuzzy and Crisp scenarios. The Fuzzy scenario takes preference uncertainty into consideration and uses stricter standards to judge whether customers buy products with new technology. [ABSTRACT FROM AUTHOR]

Abstract: In international trade offset practices (in Taiwan known as industrial cooperation program, ICP) have received increased attention over the past 20 years. In the coming 10 years, the Taiwanese government may expend roughly US$16 billion for purchasing Patriot-III missiles, P-3 long-range anti-submarine planes, and diesel-engine submarines from the United States through foreign military sale, and can achieve US$8 billion ICP credit the largest in Taiwanese history. Offsets or ICP can be regarded as fuzzy multiple criteria decision-making (MCDM) problems, therefore, the fuzziness and uncertainty of subjective perception should be considered. This paper provides an alternative approach, the non-additive fuzzy integral, to deal with the fuzzy MCDM problems especially when there is dependence among considered criteria. The main purpose of this paper is to discuss with Taiwan’s ICP Optimal Offset Transaction Policy and propose a framework of drawing on ICP credit in future. This paper considers the four aspects of policy, ability, economy, and environment, to establish a set of fuzzy AHP multiple criteria decision mode to identify the evaluative criteria variables and project item’s order for ICP project. This decision mode was identified as a workable method. [ABSTRACT FROM AUTHOR]

Abstract: Global enterprises require extended logistics operations that integrate channel intermediaries into the network. The method for deriving integrated models for logistics hubs that improve the efficiency of manufacturers’ global operations is the focus of this paper. Under government sponsorship, six industrial logistics hubs were implemented by leading manufacturing sectors across Taiwan over a five year period from 2004 until 2008. Each industry sector had unique industry characteristics, business strategies, and logistics models. Therefore, this paper describes how generalized and quick to implement integrated logistics hubs are developed by studying the successful reference models and systems used by six independent industrial sectors. The research results provide a field tested method for deriving integrated logistics hub models in different manufacturing economies with notes that provide sufficient methodological detail for repeating the construction of logistics hubs in other manufacturing economies. [ABSTRACT FROM AUTHOR]

Abstract: In the recent years, industrial clusters have received considerable attention from economists and industrial analysts, because they are seen as the main reason for economic growth and success of certain economic region. This study systematically reviews past researches of industrial cluster. The purpose of this paper is to contribute to the understanding of this issue regarding the driving forces for the growth of industrial cluster and find out the priority among these cluster policies. Taiwan HsinChu Science Park is a prime example for this paper, and its connection with the innovative participators. We begin with an examination of the literature on cluster about its driving forces and policies upon which we propose a conceptual framework. In doing so, we explore the cluster-based industrial system. Then this research adopts the Fuzzy Analytic Hierarchy Process as the analytical tool. The Fuzzy Analytic Hierarchy Process method is used to determine the weightings for evaluation dimension among decision makers. From our research results, the Factor Conditions is the most important driving force for advancing the industrial cluster performance. Moreover, the promotion of international linkages policy and broader framework policies rank the first two priorities for cluster policy. Overall, this paper concludes with some simulations of cluster policy alternatives confronting the industry and the Taiwanese government. [Copyright &y& Elsevier]

Abstract: Taking into consideration technology acceptance factors, website service quality and specific holdup cost factors, this research explores how shopping websites to establish their competitive advantages through these dimensions. We begin with an examination of the literature on shopping websites about its competitive advantages upon which we propose a conceptual framework. Then this research adopts the fuzzy TOPSIS as the analytical tool that determines the weights of each criterion. Fuzzy theory provides a proper tool to encounter with uncertainties and complex environment. The purpose of this paper is to use the fuzzy TOPSIS method based on fuzzy sets in solving MCDM problems. From our research results, the security and trust are the most important factors for improving the competitive advantage of shopping website. Moreover, the PCHome and Yahoo Taiwan rank the first two positions for shopping websites. This paper draws on the research results for implications of managerial practice, and then suggests some empirical tactics in order to enhance management performance for the website shopping industry. [Copyright &y& Elsevier]

For the recent years, there are more and more surveillance cameras set on lanes, train/bus stations, hospitals, schools, banks, supermarkets, shopping malls, etc., to improve the safety of people around them. In Taiwan, the so-called “E-Patrol” system is used to support policemen for providing patrol services and several criminal cases have been solved with the use of surveillance cameras of “E-Patrol” system. It is well known that inappropriate settings of surveillance cameras will result in some dead angles and oversetting of surveillance cameras will waste limited resources. The setting of surveillance cameras is an important issue and a complicated NP problem. In this paper, we consider the design problem of multiple-type surveillance cameras on various lanes in which two kinds of variables have to be decided simultaneously, namely, discrete variables for types of surveillance cameras and continuous variables for locations of surveillance cameras. The main purposes of this paper are (i) to propose an immune based two-phase approach for solving the optimal design of multiple-type surveillance camera problem, and (ii) to show the excellent performance of the proposed two-phase approach. [Copyright &y& Elsevier]

Abstract: Conventional time series models have been applied to handle many forecasting problems, such as financial, economic and weather forecasting. In stock markets, correct stock predictions will bring a huge profit for stock investors. However, conventional time series models produce forecasts based on some strict statistical assumptions about data distributions, and, therefore, they are not very proper to forecast financial datasets. This paper proposes a new forecasting model using adaptive learning techniques to predict TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock index) with multi-stock indexes (NASDAQ stock index and Dow Jones stock index). In verification, this paper employs seven year period of TAIEX stock index, from 1997 to 2003, as experimental datasets, and the root mean square error (RMSE) as evaluation criterion. The performance comparison results show that the proposed model outperforms the listing methods in forecasting Taiwan stock market. Besides, from statistical test results, it is showed that the volatility of Dow Jones and the NASDAQ affect TAIEX significantly. [Copyright &y& Elsevier]

Since the banking industry was liberalized in 1991, the financial holding company has become more and more competitive in Taiwan. Thus, it is an important for financial holding companies to be operated efficiently. This paper focuses on a study, which used data envelopment analysis (DEA) technique to compare the performance of 14 financial holding companies in Taiwan. We provide the conceptual model for simultaneous analysis of operating and human resource performance in this paper. The analysis models of DEA include CCR, BCC, Bilateral, Slack-Based Measure, and the FDH model. We analyze overall efficiency, pure technique efficiency and scale efficiency of the financial holding companies in Taiwan. We also use bilateral model to measure and compare efficiency between the bank-based holding companies and non bank-based holding companies. And we investigate the most productive scale size for the financial holding companies. Finally, we provide some management suggestions for the financial holding companies. [Copyright &y& Elsevier]

Abstract: One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock market data to give individuals or institutions useful information about the market behavior for investment decisions. The enormous amount of valuable data generated by the stock market has attracted researchers to explore this problem domain using different methodologies. This paper investigates stock market investment issues on Taiwan stock market using a two-stage data mining approach. The first stage Apriori algorithm is a methodology of association rules, which is implemented to mine knowledge and illustrate knowledge patterns and rules in order to propose stock category association and possible stock category investment collections. Then the K-means algorithm is a methodology of cluster analysis implemented to explore the stock cluster in order to mine stock category clusters for investment information. By doing so, this paper proposes several possible Taiwan stock market portfolio alternatives under different circumstances. [Copyright &y& Elsevier]

Abstract: This paper aims to explore the true managerial efficiencies of the branches of a case bank in Taiwan. With 123 branches of the case bank comprising the sample, the study finds that, after the adjustment of environmental factors and statistical noise, managerial efficiency values from a three-stage data envelopment analysis (DEA) varies significantly from the traditional DEA model. This finding suggests that environmental variables have significant effect on branch efficiency. Moreover, scale inefficiency is the major cause of operating inefficiency in the case bank, and most branches are operating at the stage of increasing return to scale. With regards the branches’ business scope, those that operate loan and wealth management services have better managerial efficiency than those that focus on wealth management only. In terms of deposit amount, branches with a higher deposit amount generate better managerial efficiency. Finally, the results for regional location show no significant effect on branches’ managerial efficiency in Taiwan. [Copyright &y& Elsevier]

Abstract: This paper proposes a strategy for using students’ complementary competencies in cooperative learning to increase their English learning performance. The concept of complementary learning is based on the idea that teaching is learning. The foundation of the complementary learning concept is composed of three stages proposed to derive the optimal learning clusters—input stage, genetic algorithm (GA) stage, and output stage. In tests and verification of the feasibility of using optimal complementary learning clusters in increasing students’ English learning outcome, comparisons between the experimental group (the optimal complementary learning clusters) and the control group showed that students in the experimental group had higher performances in listening, speaking, and reading competencies than those in the control group. Finally, according to the respective importance weights of different English competencies in different learning objectives, the fuzzy linguistic terms were applied to derive optimal complementary learning clusters to maximize students’ learning outcome. [Copyright &y& Elsevier]

Abstract: In recent years, many academy researchers have proposed several forecasting models based on technical analysis to predict models such as . After reviewing the literature, two major drawbacks are found in past models: (1) the forecasting models based on artificial intelligence algorithms (AI), such as neural networks (NN) and genetic algorithms (GAs), produce complex and unintelligible rules; and (2) statistic forecasting models, such as time series, require some basic assumptions for variables and build forecasting models based on mathematic equations, which are not easily understandable by stock investors. In order to refine these drawbacks of past models, this paper has proposed a model, based on adaptive-network-based fuzzy inference system which uses multi-technical indicators, to predict stock price trends. Three refined processes have proposed in the hybrid model for forecasting: (1) select essential technical indicators from popular indicators by a correlation matrix; (2) use the subtractive clustering method to partition technical indicator value into linguistic values based on an data discretization method; (3) employ a fuzzy inference system (FIS) to extract rules of linguistic terms from the dataset of the technical indicators, and optimize the FIS parameters based on an adaptive network to produce forecasts. A six-year period of the TAIEX is employed as experimental database to evaluate the proposed model with a performance indicator, root mean squared error (RMSE). The experimental results have shown that the proposed model is superior to two listing models (Chen’s and Yu’s models). [Copyright &y& Elsevier]

Abstract: Stock price variation predictions are at the core of many research issues, and neural networks (NNs) are widely applied and were proven to be more efficient than time series forecasting for stock price forecasting. However, this type of research always determines the parameter settings of the NNs rationally through a trial-and-error methodology. This paper integrates design of experiment (DOE), Taguchi method, and back-propagation NN (BPNN) to construct a robust engine to further optimize the prediction accuracy under a robust DOE-based predictor. Adopting data from Taiwan Stock Exchange (TWSE), the technical analytical indexes and β value of the listed stocks of TWSE were computed. The research results indicated that the proposed approach can effectively improve the forecasting rate of stock price variations. [Copyright &y& Elsevier]

Abstract: This paper sampled approximately 9.3 million entries of data, concerning payments from 300,000 credit card customers over the past two years of Bank A in Taiwan. By applying data mining techniques to decipher customers’ behavior and perform risk analysis, the clustering algorithms divides card users into 9 groups of different levels of contributions and risk profiles, according to their consumption patterns. We generalize a set of clustering rules to identify high risk customer groups in advance. Therefore, the proposed suggestions could tell who was a bad risk and either deny their application or, for those who were already cardholders, start shrinking their available credit and increasing minimum payments to squeeze out as much cash as possible before they defaulted. On the other hand, banks are advised to adjust credit limits in a timely manner for the customer groups whose risks are low and contributions are high, in addition to the provision of value added services, in order to enhance earnings. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a new method based on genetic algorithms to solve the optimal default point of the KMV model. In our empirical study, we compare the GA-KMV model with the QR-KMV and KMV models. The results indicate that the percentage of correctness of the GA-KMV model is higher than those for the other two models. This is to say, the GA-KMV model has a better goodness of fit. We also obtain the optimal default point for a Taiwan listed company. This can help us to predict the default point and improve the bank’s risk management performance. [Copyright &y& Elsevier]

Abstract: Since 2000, the Industrial Development Bureau (IDB) of the Government in Taiwan has been implementing a policy of Electronic Manufacturing Promotion (e-MP) to assist firms to implement Business to Business Information System (B2BIS). In general, the actual performance of e-MP policy has just been based on the data given in a final report produced by the funded firm. Consequently there is no clear evidence as to the efficacy of the e-MP policy. Therefore, this paper mainly covers two parts (i) A Fuzzy analytic hierarchical process (Fuzzy AHP) was utilized to explore the weights of each policy item according to project leaders’ response of 17 funded cases to see whether the current e-MP policy really benefits firms and (ii) financial data of 30 cases was collected from the stock exchange in Taiwan to understand the actual performance and Fuzzy AHP was utilized to find the funded cases’ initial expectation to see whether the adoption of B2BIS by those cases funded by the Government actually met initial expectation. The results of this study indicate that the e-MP policy need to be adjust and costs decreased and work efficiencies rose are most important benefit in most of the funded cases after introduction of B2BIS and this meet their initial expectation. Finally, implications and suggestions are given for the e-MP policy to facilitate adjustments by the IDB. [Copyright &y& Elsevier]

Abstract: Under limited resources in corporation education training, to enhance human resources quality, making education training program planning more efficient is a significant issue in training future talents. In accordance with Taiwan TrainQuali System (TTQS), the basic training structure is ton specify P (Plan) and D (Design). Ensuing results will be easier and successful. From TTQS database of Bureau of Employment and Vocational Training, corporations in Taoyuan, Hsinchu and Miaoli winning Gold Medals (Group B) have gaps outside control line in P and D. Enhancement is needed in the gap. The paper aims at a certain company winning Gold Medals in Taoyuan, Hsinchu and Miaoli to locate hidden or unobvious information with data mining, which will help future education training course planning and design. The researchers use two-stage clustering (SOM and K-means) under data mining theory to collect personnel training data of Automobile Corporation A in Taiwan and China with data mining and analysis. The results under the two algorithms will serve as reference for future education training courses. In the end, in combination of back-propagation neural network to develop education training prediction model, the research offers reference for writing knowledge management system to enhance effects of personnel participation in training at corporations. [ABSTRACT FROM AUTHOR]

Abstract: Financial holding companies in Taiwan play an important role in the process of economic development. Facing the financial globalization and market liberalization, competition between financial holding companies is growing. In data envelopment analysis (DEA) studies, the efficiency measurement can have a two-stage structure. A common approach to the two-stage problem is to apply a standard DEA model separately in each stage; such approaches treat the stages in a two-stage process as operating independently of one another. Different from previous studies, this paper takes the series relationship of the two individual stages into account in measuring the profitability and marketability efficiencies of the Taiwan financial holding companies. It is found that the overall efficiencies of all financial holding companies are inefficient and the low efficiency score of the whole process is mainly due to the low efficiency score of the marketability process. Decomposing the overall efficiency into the component efficiencies helps a company identify the stage that causes inefficiency. [ABSTRACT FROM AUTHOR]

Abstract: This paper establishes a simulation model for the supply chain of the hospital logistic system (SCHLS) based on the dynamic Taguchi method. The model derives optimal factor level combinations in the SCHLS setting when establishing adjustable contracting capacity between SCHLS and the pharmaceutical company. To attempt the goal, this study adopts the SCHLS case in the North Alliance of the Department of Health of Taiwan. The data collection uses a simulation based on Taguchi’s L 18 orthogonal table for SCHLS. This work also proposes an optimal approach, including the neural network (NN) and genetic algorithm (GA), to obtain an optimal robust design in achieving optimal SCHLS multi-performance. The research results show that the predicted response of multi-performance in the optimal approach is better than that in Taguchi. [ABSTRACT FROM AUTHOR]

Abstract: The automation of the cargo clearance process has been a high-priority item on the agenda of the customs services of the world. Conventional clearance of import and export goods uses paper-based manual procedures, added as much as 10% to the product cost and led to delays and time penalties, lost business opportunities and reduced competitiveness. The World Bank report said “countries that cannot adapt quickly to the new e-trade paradigm will fall behind in export–import competitiveness.” As an export-oriented country, Ministry of Finance of Taiwan had been committed to reforming and speeding up the cargo customs clearance to sustain the trade competitiveness. The purpose of this research is to explore the digital capital measures of automated cargo clearance business website. The content analysis and fuzzy analytic hierarchy process were used while collecting and analyzing the data. The research subject is TradeVan, a semi-government controlled web-based service provider. This study has identified four dimensions to measure the digital capital of business website that is Internet relational capital, Internet customer capital, Internet innovative capital, and Internet service capital. The research findings may be referenced for the purpose of the design and development of successful website applications to catch the revolutionary opportunities and benefits of digital economy. [ABSTRACT FROM AUTHOR]

Abstract: Data Envelopment Analysis (DEA) is one of the best-known efficiency evaluation methods due to its advantages in selection of weights. Many research papers have extensively discussed the issue of weight restrictions, rather than those implied in the model itself. However, this often leads to a failure to represent the relations of certain weights, as well as underestimation of the efficiency of Decision Making Units (DMUs). When analyzing the medical sectors of Taiwan with the developed models and CCR, it is found that efficiency underestimation by efficient DMUs is more serious than that of inefficient DMUs. In addition, underestimation occurs when weights are concentrated in the same output, however, every output of referenced DMU is the same times of corresponding output of targeted DMU. [ABSTRACT FROM AUTHOR]

Abstract: Science Park provides a unique environment for accelerating technological innovation. The purpose of this paper is to analyze efficiency and productivity growth of six industries in Taiwan Hsin Chu Industrial Science Park for the period 2000–2006. From the results, we know that industrial industrialists should not only enhance their managerial skills but also increase and improve innovative performance. Government also plays an important and critical role that enables business and consumers to adapt to the demands and opportunities of the new economy. The results of these analyses illustrate some policy implications for Taiwan and other countries facing the similar problems. [ABSTRACT FROM AUTHOR]

Abstract: The multimodal trip planning system (MTPS) described in this paper can efficiently identify reasonable journeys. Our methodologies could be implemented in future transit trip information systems. An effective MTPS would be of value to transit agencies or administrators due to the large number of different yet related characteristics of the transit network. In Taiwan, no successful MTPS exists owing to the fact that there are many intercity transit options, including airlines, ships, intercity buses, rail, high-speed rail, local buses, and mass rapid transit. The purpose of this study is to develop an MTPS that will ensure that transit stops are accurately sequenced and integrated into appropriate route patterns, including schedule and route data, as well as intermodal transfer locations and multi-agency transit center walking directions. The MTPS will provide landmark-to-landmark journey planning for transit users. We combine a data structure and multimodal trip planning algorithm in the MTPS that can swiftly propose reasonable travel plans. Finally, we develop a transit trip planning system for Taiwan. [Copyright &y& Elsevier]

Abstract: In this paper, we improve the slack-based measure (SBM) of efficiency in context-dependent data envelopment analysis (DEA) and apply in measuring the performance of 34 selected Taiwan‘s International Tourist Hotels. Empirical results indicate that (1) the market differentiates five performance levels forming the benchmark structure for 34 hotels; (2) the hotels with higher attractiveness can be served as learning targets for the hotels in the lagging levels so as to establish the best path for performance improvements; (3) the hotels in the leading levels can use lower progress to analyze potential competitors in the lagging levels. The results of this study can provide hotel’s managers with insights of competitive advantage and help them with strategic decision making. [Copyright &y& Elsevier]

Abstract: This paper presents an approach which combines the orthogonal array experiment technique and an ant direction hybrid differential evolution algorithm (ADHDEOA) in determining the tilt angle for photovoltaic (PV) modules. In this study, an orthogonal array (OA) is first conducted to obtain the initial solution. Next, an ant direction hybrid differential evolution (ADHDE) is applied to search for a near optimum solution. The purpose of this research is to maximize the output power of the module. The sun’s position at any time and location was predicted by the mathematical procedure of Julian dating; then, the solar irradiation was obtained at each site under a clear sky. The searching for global optimal solution is applied to a practical experimental system. The results show that the annual optimal angle for the Taipei area is 23.18°; for Taichung, 22.32°; for Tainan, 21.62°; for Kaosiung, 20.83°; for Hengchung, 20.27°; for Hualian, 22.18°; and for Taitung, 20.89° in seven Taiwanese areas, and the actual best annual tilt angles are close to the computer simulation results. [Copyright &y& Elsevier]

Abstract: The university performance assessment has become one of the most important debates for the universities in Taiwan. This study aims at developing an intellectual capital (IC) evaluation model to facilitate the understanding of their contribution to the university performances. A conceptual framework that incorporates IC with the university assessment scheme in Taiwan is proposed to construct the IC components as the central intangible resources of universities that are concentrated on its link to university performance ultimately. Analytic hierarchy process (AHP) is applied to formulate and prioritize the IC measurement indicators for constructing the IC evaluation model as decision guidelines under which the development and productive use of investments in intangible assets can be made. In this paper, a fuzzy approach is integrated with AHP method to make up the vagueness about the degree of importance of decision-makers on judgment. An illustrative example is provided of the proposed model for developing a visualized form of Shu-Te University distinction tree. [Copyright &y& Elsevier]

Abstract: Outsourcing manufacturing is a trend in today’s global business operations. Vendor selection is an essential factor affecting outsourcing operations performance and has been determined as a multiple criteria decision making (MCDM) problem. The purpose of this paper is to propose a novel hybrid MCDM technique to cope with the complex and interactive vendor evaluation and selection problem which can determine the structural relationships and the interrelationships amongst all the evaluation’s dimensions and support the Analytic Network Process (ANP) method to arrange appropriate weightings to each dimension and criterion in the evaluation model by summarizing the opinions of the experts. Finally, the overall performance ranking of all alternatives can be obtained to assist the decision making. Taiwan semiconductor industry is the largest provider in worldwide market. It is vertically disintegrated and outsourcing is a main stream practice. Wafer-testing is a critical manufacturing step in the semiconductor supply chain that distinguishes whether the IC can meet the specifications. Consequently, it’s crucial to establish a thorough model for selecting the wafer-testing vendors in order to achieve the success of the outsourcing operation. A case study of a Taiwan semiconductor company in selecting its wafer-testing outsourcing vendor by using the proposed MCDM technique is demonstrated to enhance the decision making quality. The results and proposed solution can be referred to by not only the semiconductor companies, but also other industries so that they can improve the vendor selection process in order to achieve a higher performance and satisfaction level. [Copyright &y& Elsevier]

Abstract: The banking industry plays a key role in the process of Taiwan’s economic development, and the structure of the financial system in Taiwan has turned from a controlled system into a liberalized one. In this paper, we employ the Malmquist productivity index approach, which is calculated from efficiency scores based on DEA linear programming technique, to measure the technical efficiency and productivity change of the 25 commercial banks in Taiwan over the post Asian crisis period 1997–2001. We find that the technical efficiencies of 15 banks have been improving while 10 banks have been declining over the period. It is also found that the banking industry has a decrease in technical efficiency but owns upward shifts of technology since year 1998. Based on technical efficiency and the efficiency change of the banks, we classify the 25 banks in Taiwan into four categories to help realize the competitiveness and technical progress of the banks. Some of the commercial banks need to search for financial innovation activities and carry on production differentiation to be competitive in the market. [Copyright &y& Elsevier]

Abstract: In this paper, we proposed a modified turbulent particle swarm optimization (named MTPSO) method for the temperature prediction and the Taiwan Futures Exchange (TAIFEX) forecasting, based on the two-factor fuzzy time series and particle swarm optimization. The MTPSO model can be dealt with two main factors easily and accurately, which are the lengths of intervals and the content of forecast rules. The experimental results of the temperature prediction and the TAIFEX forecasting show that the proposed model is better than any existing models and it can get better quality solutions based on the high-order fuzzy time series, respectively. [Copyright &y& Elsevier]

Abstract: The TAIFEX (Taiwan Stock Index Futures) forecasting problem has attracted some researchers’ attention in the past decades. Several forecast methods for the TAIFEX forecasting based either on the statistic theorems have been proposed, but their results are not satisfied. Fuzzy time series is used to doing forecasting but the forecasted accuracy still needs to be improved. In this paper we present a new hybrid forecast method to solve the TAIFEX forecasting problem based on fuzzy time series and particle swarm optimization. The experimental results show that the new proposed forecast model is better than any existing fuzzy forecast models and is more precise than four famous statistic forecast models. [Copyright &y& Elsevier]

Abstract: In this paper, we elaborate a neural network model to predict innovation performance with fuzzy rules, as well as implement an adaptive neuro-fuzzy inference systems (ANFIS) to measure the innovation performance through technical information resource and innovation objective. Building on the findings from fuzzy neural network approach, using Sugeno ANFIS, we also compared the artificial neural network with statistical techniques. We found strong support for ANFIS method has better results than the neural network and statistical techniques with regards to forecast performance. Finally, on the basis of our analysis, our results hold an important lesson for decision makers who may clearly picture the rules and adjust the resource allocation to meet their innovation objectives. [Copyright &y& Elsevier]

Abstract: In this paper, we propose and implement a data modeling system to measure the similarity of stock market behavior in different time periods. To implement this system, we also propose a data distribution approximation model, Polygon descriptor, and a shape difference measurement, called Deforming distance method. The Polygon descriptor can model a data distribution by characterizing the data dependency relationship among the data variables, and the Deforming distance is designed to be translation, scale, and rotation invariant. The Polygon descriptor and Deforming distance method are combined to measure the similarity between data sets based on the shape of data distribution. Stock price and EPS (earn per share) data during different time periods were selected to verify the modeling and measuring power of the proposed model and method. The Polygon descriptor is used to model the collected Stock price and EPS data for their dependency relationship. Then, difference between Polygon descriptors can be measured by using the Deforming distance measurement method for their Deforming distance values. Based on the Deforming difference values, the similarity of stock market behaviors in two time periods can be assessed, and the similar stock market behavior of different time periods can be identified. To demonstrate the capabilities of the proposed method and system, real-world data of Taiwan stock market during the period from 1986 to 2006 were collected and used. Experimental results show that the Polygon descriptor successfully captures the data dependencies between stock price and EPS, and the Deforming difference based similarity measurement estimates the changes of market behavior better than some commonly used methods. A web-based prototype system is available at http://www.csie.cntu.edu.tw/~pslai/TWStockSIdx/ for public trial, evaluation, comments and suggestions. [Copyright &y& Elsevier]

Abstract: It is important to increase their value by managing intellectual capital (IC), since fabless firms face an intensely competitive environment. The aim of this paper is develops a two-stage production process including IC capability and IC efficiency to characterize the IC performance of the fabless firms using a non-parametric frontier method – data envelopment analysis (DEA). The IC performance rating should be considered as a key element for achieving greater innovation and competitive advantages. The results show that IC efficiency is better than IC capability for these fabless firms; 15.8% of fabless firms perform well in both models and these firms can be treated as benchmarks for others; most firms operate at decreasing returns to scale, indicating that firms are facing a highly competitive environment; further mergers and acquisitions among firms should be considered so as to achieve economies of scale; the critical input/output measures will also help firms improve their performance and identify the key factors that impact a firm’s performance. [Copyright &y& Elsevier]

Abstract: This paper proposes a web-based interactive speaking improvement system for EFL learners. Using fuzzy matching, the system automatically compares the learner’s and the system’s pronunciation and immediately provides corrective interaction whereby the student can improve speaking via continuous imitation and practice at the computer. The system, having been tested on Taiwanese students who needed to achieve a speaking proficiency equivalent to CEF Level A2, is found to be highly effective for the intended purpose. [Copyright &y& Elsevier]

Abstract: In physics, a spectrum is, the series of colored bands diffracted and arranged in the order of their respective wave lengths by the passage of white light through a prism or other diffracting medium. Outside of physics, a spectrum is a condition that is not limited to a specific set of values but can vary infinitely within a continuum. In commerce, an effective visualization tool, especially for stakeholders or managers, is a brand spectrum diagram highlighting where the company’s brands and products are situated compared to other competitors. This paper investigates the research issues on product and brand spectrum in the beverage product market of Taiwan, which proposes using the Apriori algorithm of association rules, and clustering analysis based on an ontology-based data mining approach, for mining customer and product knowledge from the database. Knowledge extracted from data-mining results is illustrated as knowledge patterns, rules, and maps in order to propose suggestions and solutions to beverage firms for possible product development, promotion, and marketing. [Copyright &y& Elsevier]

Abstract: In this paper we construct the consumer loan default predicting model through conducting the empirical analysis on the customers of unsecured consumer loan from a certain financial institution in Taiwan, and adopt the borrower’s demographic variables and money attitude as the real-timeaneous discriminant information. Furthermore, we construct respectively through four predicting methods, such as DA, LR, NN and DEA–DA, to compare the suitability of these four mentioned methods. The results show that DEA–DA and NN are possessed better predicting capability and they are the optimal predicting model that this study longing for. In addition, this study showed that the default loan predicting model will be possessed higher level of predicting capability after added money attitude. [Copyright &y& Elsevier]

This paper considers how to conduct the efficiency measurement and ranking problem of the tutorial system of some higher education institutions (HEIs) in Taiwan more convincingly and persuasively. Measuring the efficiencies of tutors and rewarding the outstanding tutors are conducted once each academic year and have become an important part of the administration practices. The current performance evaluation methods consider only the output factors inhered in vagueness, while the input are neglected. In order to better reflect the actual situations that different tutors consume various amounts of input to produce various amounts of outputs, an IDEA (imprecise data envelopment analysis) model is proposed to measure the efficiencies of tutors under different possibility levels by incorporating the current methods to serve as output factors and additionally considering the input factor. The proposed evaluation approach, which contains efficiency measurement, ranking guide and ties broken rule, can be applied easily to obtain objective and fair results of the addressed problem. A numerical example is used to illustrate the implementation of the proposed approach. [Copyright &y& Elsevier]

The electronic learning (e-learning) has gradually become more and more important in today’s school in Taiwan. Many colleges and universities offer distance e-learning courses or programs for students. An effective teaching or learning through a distance web e-learning system depends on many factors (or criteria). The analytic hierarchy process (AHP) model is suitable for dealing with the multi-criteria problems. This paper utilizes the consistent fuzzy preference relations (CFPR) in AHP model to evaluate these factors. The CFPR is computational simplicity and guarantees the consistence of decision matrices. Rating the criteria is important. An empirical example using CFPR in AHP model to find the weights is presented. The weight can point out which factor is important, especially when the time, manpower, and financial support are limited. The rating results can be directly used to evaluate the distance e-learning effectiveness and can provide teachers and decision-makers in schools important information for improving e-learning practice in the future. [Copyright &y& Elsevier]

New product development (NPD) is indeed the cornerstone for companies to maintain and enhance the competitive edge. However, developing new products is a complex and risky decision-making process. It involves a search of the environment for opportunities, the generation of project options, and the evaluation by different experts of multiple attributes, both qualitative and quantitative. To perceive and to measure effectively the capability of NPD are real challenging tasks for business managers. This paper presents a 2-tuple fuzzy linguistic computing approach to deal with heterogeneous information and information loss problems during the processes of subjective evaluation integration. The proposed method which is based on the group decision-making scenario to assist business managers to measure the performance of NPD manipulates the heterogeneous integration processes and avoids the information loss effectively. Finally, its feasibility is demonstrated by the result of NPD performance evaluation for a high-technology company in Taiwan. [Copyright &y& Elsevier]

Abstract: In order to maintain technical competitiveness, companies have an ongoing need to identify and foster core competencies for technical professionals in the area of research and development (R&D). Exploring core competencies is, however, a qualitative problem. The rough set theory (RST), which has recently become well known as a data-mining technique, is a good tool for data reduction in qualitative analysis. Thus, the rough set approach is suitable for dealing with the problem of exploring competencies. This study extends practical applications of RST in mining the critical competencies for R&D technical professionals employed by semiconductor manufacturers in Taiwan. This paper demonstrates a successful reduction of the numerous essential competencies into a more compact set, and arrived at six core competencies which can be regarded as the as indispensable attributes for the design of competency development programs for R&D technical professionals. [Copyright &y& Elsevier]

Abstract: Medical data usually contain high degree of uncertainty that leads to problems such as diagnosis disparity. With the continually rising expenditures of health care, controlling the level of the expenditures by improving the diagnosis process is an emergent task and poses a greater challenge. Rough sets have been shown to be a useful technique to deal with data with uncertainty. This paper applies rough sets to identify the set of significant symptoms causing diseases and to induce decision rules using the data of a Taiwan’s otolaryngology clinic. The data are limited to rhinology and throat logy and contain 657 records with each record including 12 condition attributes and one decision attribute. The clinic’s physician agrees with the set of significant symptoms identified and favors the reduct containing one critical symptom. To generate decision rules, we use LEM2 and Explore algorithms and discuss these rules in terms of numbers of rules and implications of certain rules. Our results show that LEM2 generates fewer numbers of certain rules and LEM2’s proportions of approximate rules are higher. We also observe that LEM2’s certain rules form a more sensible pattern that Explore lacks. Discovery of the pattern is considered to be potentially helpful in improving the medical diagnosis. [Copyright &y& Elsevier]

Abstract: This paper presents an integration prediction method including grey model (GM), Fourier series, and Markov state transition, known as Markov–Fourier grey model (MFGM), to predict the turning time of Taiwan weighted stock index (TAIEX) for increasing the forecasting accuracy. There are two parts of forecast. The first one is to build an optimal grey model from a series of data, the other uses the Fourier series to refine the residuals produced by the mentioned model. Finally, the Markov state scheme is used for predicting the possibility of location results to promote the intermediate results generated by the Fourier grey model (FGM). It is evident that the proposed approach gets the better result performance than that of the other methods. [Copyright &y& Elsevier]

Abstract: Fuzzy time series model has been successfully employed in predicting stock prices and foreign exchange rates. In this paper, we propose a new fuzzy time series model termed as distance-based fuzzy time series (DBFTS) to predict the exchange rate. Unlike the existing fuzzy time series models which require exact match of the fuzzy logic relationships (FLRs), the distance-based fuzzy time series model uses the distance between two FLRs in selecting prediction rules. To predict the exchange rate, a two factors distance-based fuzzy time series model is constructed. The first factor of the model is the exchange rate itself and the second factor comprises many candidate variables affecting the fluctuation of exchange rates. Using the exchange rate data released by the Central Bank of Taiwan, we conducted several experiments on exchange rate forecasting. The experiment results showed that the distance-based fuzzy time series outperformed the random walk model and the artificial neural network model in terms of mean square error. [Copyright &y& Elsevier]