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Abstract This study investigated policy-driven implementations of internationalization in higher education institutions in Taiwan by evaluating its effects as perceived by students and faculty. For organizing the internationalization indicators, we designed a fuzzy questionnaire in which the review indicators were classified into context (C), input (I), process (P), and outcome (O) dimensions according to the context, input, process, and outcome (CIPO) model. This study used statistical models to interpret valid questionnaire returns from 612 students and faculty members from various universities. The results indicated wider gaps between the importance and feasibility of implementing internationalization in the I dimension and partially in the C dimension. In addition, the perceptions of students and faculty suggested a wide chasm between expected and actual results of the CIPO dimensions. Typically, the students favored implementing internationalization regarding the C, I, P, and O indicators more than did the faculty. The diversity of internationalization, based on implementations in public and private sector universities, is demonstrated in the C and I dimensions, with gender-based perceptions and distinctions between participants in universities and technological universities being exhibited in some indicators. This study provides a method for detecting key stakeholders' perceptions of implementing internationalization policies in higher education institutions. [ABSTRACT FROM AUTHOR]

The measurement of volatility is of fundamental importance in finance. Standard market practice adopted for volatility estimation from option prices leads to a considerable loss of information and the introduction of an element of arbitrariness in the volatility index computation. We propose to adopt fuzzy regression methods in order to include all the available information from option prices, and to obtain an informative volatility index. In fact, the obtained fuzzy volatility indices not only offer a most possible value, but also a lower and an upper bound for the interval of possible values, providing investors with an additional source of information. We also propose a defuzzification procedure to select a representative value within this interval. Moreover, we investigate the occurrence of truncation and discretization errors in volatility index computation by adopting an interpolation-extrapolation method. We also test the forecasting power of each volatility index on future realized volatility. [ABSTRACT FROM AUTHOR]

The premise of dealing with uncertainty in this article is to figure out the impact of uncertainties on project performance. This article proposes a framework for quantitative analysis that combines a fuzzy mathematics approach with system dynamics theory. Simulations generated using a model based on the framework show that uncertainties affect project performance by changing project parameters, and the mechanisms involved depend on the sources of the uncertainties. Furthermore, while technical and financial uncertainties mainly influence project costs, uncertainties related to markets, regulations, and organizational structures affect both project duration and cost. Finally, strategies for coping with uncertainties in a resource allocation perspective are presented. [ABSTRACT FROM AUTHOR]

Abstract: The efficacies of continuous positive airway pressure (CPAP) are well documented in decreasing the apnoea–hypopnoea index in patients with obstructive sleep apnoea. To guarantee these efficacies, CPAP manufacturers must thoroughly test these devices to ensure the flow of oxygenated air to the patient at various temperatures during a prescribed time frame. The calculation of the percent oxygen in a “bimixture” of gas can be done by measuring the travel time of a sound wave through the gas, and the travel time is proportional to the density of the air. We utilized existing multisensor tubes that were developed to collect and measure oxygen flow, diffusion, speed, temperature, and time metrics. Then these metrics were analysed using a Markov fuzzy, statistical, artificial neural network, nearest‐neighbour predictive approach to determine the interactions between these variables. An improved Kalman filter method was employed to reduce noise, increase viscosity, and obtain correct data from the CPAP system. [ABSTRACT FROM AUTHOR]

Objectives: This study aims to analyze various psychological propensities with fuzzy questionnaires in physical education. Motivations: Fuzzy questionnaires with fuzzy statistical analysis can better reflect people's psychological thinking. Method: Fuzzy descriptive statistics, fuzzy correlation coefficients and fuzzy regression models. Finding: Comparing among three scales, fuzzy Kaiser-Meyer-Olkin (KMO), Cronbach's a and traditional one: fuzzy Mastery-Avoidance had a positive correlation with self-esteem. Fuzzy positive self-esteem significantly impacted predictive Mastery-Approach (AF_MA) and Mastery-Avoidance (AF_M_a); while traditional positive self-esteem significantly impacted predictive Performance-Avoidance (A_P_a) and Mastery-Avoidance (AF_M_a). Further, fuzzy and traditional negative selfesteem significantly impacted predictive AF_MA, simultaneously fuzzy negative self-esteem significantly impacted predictive AF_MA. Only fuzzy negative self-esteem significantly impacted predictive AF_MA. Innovations: Traditional psychological examination technology has been used for many years, while a new approach with fuzzy statistical analysis on artificial intelligence technology will promote the quality of sampling survey and marketing information. Value: This study offered new examination skill in analyzing psychological propensity in physical education. [ABSTRACT FROM AUTHOR]

It is recognized that handling uncertainty is essential to obtain more reliable results in modeling and computer simulation. This paper aims to discuss the logistic equation subject to fuzzy uncertainties in the initial conditions and parameters. Here we consider the population density at a specific time as a fuzzy variable in which the possibility distribution function depends on the possibility distribution functions of the environmental carrying capacity, the initial population density and the intrinsic growth rate. We provide closed-form expressions for the expected value of the fuzzy variables population density and for the time of maximum growth. We also perform some numerical simulations to illustrate our main results. [ABSTRACT FROM AUTHOR]

In this paper, the classical statistical test based on intuitionistic fuzzy hypotheses in relation to the underlying population parametric is extended. In this approach, the type-I, type-II, power of test, andp-value are extended for intuitionistic fuzzy hypotheses. Throughout the paper, some applied examples are provided for both parametric and non parametric cases to clarify the discussions. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive mutation particle swarm optimization (AMPSO) to realize multi-objective optimization design method through scale-based product platform theory model. The Pareto-optimal solution was obtained via AMPSO, then the fuzzy statistics algorithm is presented to extract the optimal solution of multi-objective optimization problem. The Multi-objective Optimization Method was carried out in two stages. In the first stage, each product is optimized independently via AMPSO and the product platform constant parameter and its value is obtained according to the change ratio of design variables; In the second stage, the scaling variables of each product are solved via AMPSO based on the optimization objectives improving the performance in constraint of restrictions and the best compromise solution is extracted based on fuzzy statistics algorithm. [ABSTRACT FROM AUTHOR]

The food retailers have to make their supply chains more customer-driven to sustain in modern competitive environment. It is essential for them to assimilate consumer’s perception to improve their market share. The firms usually utilise customer’s opinion in the form of structured data collected from various means such as conducting market survey, customer interviews and market research to explore the interrelationships among factors influencing consumer purchasing behaviour and associated supply chain. However, there is abundance of unstructured consumer’s opinion available on social media (Twitter). Usually, retailers struggle to employ unstructured data in above decision-making process. In this paper, firstly, by the help of literature and social media Big Data, factors influencing consumer’s beef purchasing decisions are identified. Thereafter, interrelationships between these factors are established using big data supplemented with ISM and Fuzzy MICMAC analysis. Factors are divided as per their dependence and driving power. The proposed frameworks enable to enforce decree on the intricacy of the factors. Finally, recommendations are prescribed. The proposed approach will assist retailers to design consumer centric supply chain. [ABSTRACT FROM PUBLISHER]

Purpose Ultrasound ( US) imaging has been widely used in breast tumor diagnosis and treatment intervention. Automatic delineation of the tumor is a crucial first step, especially for the computer-aided diagnosis ( CAD) and US-guided breast procedure. However, the intrinsic properties of US images such as low contrast and blurry boundaries pose challenges to the automatic segmentation of the breast tumor. Therefore, the purpose of this study is to propose a segmentation algorithm that can contour the breast tumor in US images. Methods To utilize the neighbor information of each pixel, a Hausdorff distance based fuzzy c-means ( FCM) method was adopted. The size of the neighbor region was adaptively updated by comparing the mutual information between them. The objective function of the clustering process was updated by a combination of Euclid distance and the adaptively calculated Hausdorff distance. Segmentation results were evaluated by comparing with three experts' manual segmentations. The results were also compared with a kernel-induced distance based FCM with spatial constraints, the method without adaptive region selection, and conventional FCM. Results Results from segmenting 30 patient images showed the adaptive method had a value of sensitivity, specificity, Jaccard similarity, and Dice coefficient of 93.60 [ABSTRACT FROM AUTHOR]

In this paper, we consider the following functional equation af(x + y) + bf(x - y) + cf(y - x) = (a + b)f(x) + cf(-x) + (a + c)f(y) + bf(-y) for a fixed real numbers a; b; c with a = b + c and a ≠ 0. We study the fuzzy version of the generalized Hyers-Ulam stability for it in the sense of Mirmostafaee and Moslehian. [ABSTRACT FROM AUTHOR]

Life time data analysis is regarded as one of the significant out-shoots of statistics. Classical statistical techniques reckon life time observations as precise numbers and solely cover variation among the observations. In fact, there are two types of uncertainty in data: variation among the observations and the fuzziness. To this effect, the analysis techniques, which do not consider fuzziness and are only based on precise life time observations, use incomplete information; hence lead to pseudo results. This study aimed at generalizing parameters estimation, survival functions, and hazard rates for fuzzy life time data. [ABSTRACT FROM AUTHOR]

Three organic fertilizers of manure sheep, sewage sludge and municipal waste were mixed with soil at the rates of 0%, 1.25% and 2.5% (w/w), respectively. In a greenhouse experiment, corn seed were grown on pots of 3 kg treated contaminated soils and irrigated. Sixty days after sowing, the aerial plant parts were harvested and analyzed for Cadmium (Cd) and Lead (Pb) contents. Since all of data in this research are fuzzy, we need an extended version of analysis of variance (ANOVA) to investigate on these fuzzy observations. In this paper, as a method to compare several populations, the fuzzy analysis of variance (FANOVA) has been used where the collected data considered fuzzy rather than crisp numbers and therefore all calculations are based on FANOVA method. Although, ANOVA based on vague data can lead to a fuzzy decision, but measuring the vagueness of this fuzzy decision is one of advantages of proposed method from the applied point of view. [ABSTRACT FROM AUTHOR]

The aim of this paper is to review the literature that has addressed direct and inverse problems in option pricing in a fuzzy setting. In a direct problem, the stochastic process for the underlying asset is assumed and the option prices are derived by no-arbitrage or equilibrium conditions. In an inverse problem, the option prices are taken as given and used to infer the underlying asset process. Models are divided into discrete-time and continuous-time ones. Special attention is paid to real options, a particular class of nonfinancial options that are used to evaluate real investments. Directions for future research are outlined. In particular in inverse problems, there is still room for promising research, both in discrete time and in continuous time. Moreover, given that many proposed methods remain difficult to use in practice, there is mainly the need to apply the fuzzy models obtained on real market data and to compare the results with nonfuzzy techniques in order to assess the usefulness and the improvements in the modeling of imprecise data with fuzzy sets and fuzzy random variables. [ABSTRACT FROM AUTHOR]

In this study, a developed fuzzy-stochastic method with Hurwicz criterion (FSH) is proposed for planning land utilization in a wetland ecosystem under uncertainty. FSH can not only deal with natural stochastic uncertainties represented as random variables (such as water flow), but also handle subjective impressions expressed as fuzzy sets (such as economic data), even it can reflect the compromise of decision makers’ risk preferences in decision-making processes based on Hurwicz criterion. The proposed method can be applied to a practical land utilization management. Rational land utilization plan (including withdrawing reclamation and wetland recovering (WRWR)) is advocated to relive such contradictions artificially, where WRWR can improve ecological function, leading more intangible ecological benefits to human activities. Meanwhile, trading-oriented mechanism is introduced to increase the economic productivity of land plan. Based on various conjunctive goals of socio-economic development and eco-environmental sustainability, results of ecological effects of wetland ecosystem, land utilization patterns, pollution-mitigation schemes, trading mechanisms and system benefits under varied scenarios are obtained and analyzed. The results indicate that land-indicator-based optimization with trading-oriented mechanism can improve the efficiency of land utilization plan in the mass to satisfy regional water resource/environmental load. Meanwhile, tradeoffs between economic benefit and system-failure risk under optimistic/pessimistic option would support generating a robust plan associated with risk control for land utilization in wetland ecosystem under uncertainties. These detections can avail local policymakers to adjust existing land utilization plan with more efficient and sustainable manners, with aim to harmonize economic development and environmental protection in wetland ecosystem. [ABSTRACT FROM AUTHOR]

Process equipment failures (PEFs) are recognized as one of the leading causes of process accidents. Failure modes and effect analysis (FMEA) as a risk assessment technique, has widely been used in a variety of process industries. The conventional form of FMEA uses three parameters of severity (S), occurrence (O), and detection (D) as risk factors to calculate a risk priority number (R.P.N) and rank the failure modes based on this number. But several shortcomings associated with the FMEA have limited its applicability. This study aims at the development of an extension of FMEA that could efficiently handle the vagueness and uncertainty exists in the experts’ judgments in process of failure modes ranking in conventional FMEA. In this paper we used the concept of the Z number to capture the inherent uncertainty exists in the experts’ judgments. In addition, we used Shannon entropy concept to deploy objective weights to adjust subjective weights assigned by experts. Furthermore, the fuzzy VIKOR technique applied to rank and prioritize the failure modes based on the minimum individual regret and the maxi group utility. A numerical example is presented to illustrate an application of the proposed method in a geothermal power plant (GPP). Results are also compared with the conventional FMEA. A sensitivity analysis was conducted to validate the obtained results. Findings indicate that the application of the proposed approach (subjective-objective ranking) in fuzzy environment can improve the applicability of the conventional FMEA method. [ABSTRACT FROM AUTHOR]

Contrast Stretching is an important part in medical image processing applications. Contrast is the difference between two adjacent pixels. Fuzzy statistical values are analyzed and better results are produced in the spatial domain of the input image. The histogram mapping produces the resultant image with less impulsive noise and smooth nature. The probabilities of gray values are generated and the fuzzy set is determined from the position of the input image pixel. The result indicates the good performance of the proposed fuzzy based stretching. The inverse transform of the real values are mapped with the input image to generate the fuzzy statistics. This approach gives a flexible image enhancement for medical images in the presence of noises.

Life time data are mainly used in medicine, public health, and engineering sciences. The classical statistics related to these fields are based on precise measurements. However, according to modern measurement science precise measurement of continuous phenomena is not possible. In addition the observed quantity is more or less precise. In order to obtain realistic results, in addition to classical and Bayesian inference, the most up to date model for this are fuzzy number approaches which are more suitable and realistic. In this study, I consider the classical and Bayesian inference criteria for the Pareto distribution to obtain more realistic results for fuzzy observations of life time. In addition to stochastic variation the proposed generalized estimators cover fuzziness of the life times as well. [ABSTRACT FROM AUTHOR]

Despite the growing number of studies focusing on urban vulnerability to climate change, adaptive capacity, which is a key component of the IPCC definition of vulnerability, is rarely assessed quantitatively. We examine the capacity of adaptation in the Concepción Metropolitan Area, Chile. A flexible methodology based on spatial fuzzy modelling was developed to standardise and aggregate, through a stepwise approach, seventeen indicators derived from widely available census statistical data into an adaptive capacity index. The results indicate that all the municipalities in the CMA increased their level of adaptive capacity between 1992 and 2002. However, the relative differences between municipalities did not change significantly over the studied timeframe. Fuzzy overlay allowed us to standardise and to effectively aggregate indicators with differing ranges and granularities of attribute values into an overall index. It also provided a conceptually sound and reproducible means of exploring the interplay of many indicators that individually influence adaptive capacity. Furthermore, it captured the complex, aggregated and continued nature of the adaptive capacity, favouring to deal with gaps of data and knowledge associated with the concept of adaptive capacity. The resulting maps can help identify municipalities where adaptive capacity is weak and identify which components of adaptive capacity need strengthening. Identification of these capacity conditions can stimulate dialogue amongst policymakers and stakeholders regarding how to manage urban areas and how to prioritise resources for urban development in ways that can also improve adaptive capacity and thus reduce vulnerability to climate change. [ABSTRACT FROM AUTHOR]

Copyright of Technical Transactions / Czasopismo Techniczne is the property of Sciendo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Transportation facility and automotive service enterprise location is an interesting and important issue. In practice, such factors as customer demand, allocations, even locations of customers and facilities are usually changing, thus making facility location problematic with uncertainty. To account for it, some researchers have addressed stochastic/fuzzy models for locating an automotive service enterprise. However, probabilistic/fuzzy models are not suitable to describe all kinds of uncertainty, but only randomness or fuzziness. In fact, the uncertain environment of locating an automotive service enterprise is a mixed one with both randomness and fuzziness. To handle this issue in a practical manner, this work proposes fuzzy random tradeoff issues for it. Moreover, some regional constraints can greatly influence its location. By taking the vehicle inspection station as a typical example, this work presents new fuzzy random cost-profit tradeoff models of its location problem with regional constraints. A hybrid algorithm integrating fuzzy random simulation and genetic algorithms is adopted to solve the proposed models. Additionally, some risk factors have a great impact on decision making when faced with a location problem. This work thus conducts a risk performance analysis for locating an automotive service enterprise. Some numerical examples are given to illustrate the proposed models. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

In this paper, we want to analyze the performance of a Histogram Equalization technique with various proposed Histogram Equalization techniques and want to show that the technique that is proposed in this paper is comparatively better in terms of various parameters and the name of the technique is Local Window Histogram Equalization using Gaussian Filter (LWHE-G) in image enhancement which is based on Local Histogram Equalization and a Gaussian filter that removes noise and improves the quality of the image. Our main aim is to reduce the amount of computational resources required by previous traditional methods and to enhance the image using local information and to show that image is enhanced with greater detail and lower noise compared to the several existing methods that were previously used. [ABSTRACT FROM AUTHOR]

This study investigates the application of fuzzy regression to the viscometric analysis of polymer–polymer miscibility by using experimental viscosity data for polystyrene (PS), poly(styrene-co-acrylonitrile) (PSAN) and PS/PSAN blends in CHCI 3 and DMF at different temperatures. The first stage includes the calculations of the fuzzy thermodynamic interaction parameter based on the results from the fuzzy regression models. In the second stage, the possibility and necessity measure-dependent results of the immiscibility (or miscibility) are presented and compared with the traditional miscibility estimations. This study shows that the fuzzification of traditional regression as well as the decision making on miscibility may have a good potential to be used for viscometric studies. [ABSTRACT FROM AUTHOR]

Copyright of Eastern-European Journal of Enterprise Technologies is the property of PC TECHNOLOGY CENTER and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The case of nonresponse in multivariate stratified sampling survey was first introduced by Hansen and Hurwitz in 1946 considering the sampling variances and costs to be deterministic. However, in real life situations sampling variance and cost are often random (stochastic) and have probability distributions. In this article, we have formulated the multivariate stratified sampling in the presence of nonresponse with random sampling variances and costs as a multiobjective stochastic programming problem. Here, the sampling variance and costs are considered random and converted into a deterministic NLPP by using chance constraint and modified E-model. A solution procedure using three different approaches are adopted viz. goal programming, fuzzy programming, and D1distance method to obtain the compromise allocation for the formulated problem. An empirical study has also been provided to illustrate the computational details. [ABSTRACT FROM PUBLISHER]

Similarity measures computed by kernels are well studied and a vast literature is available. In this work, we use distance-based kernels to define a new similarity measure for fuzzy sets. In this sense, a distance-based kernel on fuzzy sets implements a similarity measure for fuzzy sets with a geometric interpretation in functional spaces. When the kernel is positive definite, the similarity measure between fuzzy sets is an inner product of two functions on a Reproducing kernel Hilbert space. This new view of similarity measures for fuzzy sets given by kernels leverages several applications in areas as machine learning, image processing, and fuzzy data analysis. Moreover, it extends the application of kernel methods to the case of fuzzy data. We show an application of our method in a kernel hypothesis testing on fuzzy data. [ABSTRACT FROM AUTHOR]

Statistical institutes are focusing on variety of data sources from traditional surveys to big-data. Many of these data and concepts can be expressed as crisp values. But many other data cannot be expressed by precise values. In order to collect, store and manage the fuzziness in data we have adapted the fuzzy meta model as an extension of traditional relational database. Furthermore, experts' knowledge often contains vagueness and subjectivity. If we store this knowledge in a fuzzy database we can build knowledge management systems capable to cope with fuzziness. Statistical institutes cooperate in the data exchange. We have briefly discussed a simple way of extending the SDMX standard to cope with the fuzzy data in a way that does not influence exchanging precise values. Our research was focused on examining promising ways for managing fuzziness of real world because statistical institutes have been starting to analyze variety of promising data sources where not all data are always precise. [ABSTRACT FROM AUTHOR]

The ability of fuzzy logic systems to make use of human interpretable linguistic terms makes them very attractive for many applications. These systems make use of membership functions to represent the linguistic variables. The choice of type of the membership function to use and the associated parameters affect the performance of the system. The current types of membership functions like triangular, trapezoidal, Gaussian functions etc., make use of parameters that are possibilistic in nature thus requiring expert knowledge or other sophisticated methods in order to choose the parameters. In this paper, we propose a newtype of membership function constructed based on fuzzy estimators. This membership function depends entirely on well-known statistical parameters like the mean, standard deviation and confidence intervals and thus the parameters are easier to choose. Another advantage of this membership function is that it is suitable for modeling systems that exhibit both randomness and fuzziness. Furthermore, in contrast to the parameters of other membership functions, in the case where the parameters are tuned and optimized for a particular application, the final parameters of the proposed membership function can have useful statistical interpretations and provide a better understanding of the system. [ABSTRACT FROM AUTHOR]

Anomalies are subsequences that exhibit departures from normal state of operation. In this paper, to solve the problems of unknown data distribution, control limit determination, multiple parameters, training data and fuzziness of 'anomaly', a self-adaptive and unsupervised model is developed for finding anomalies in data streams. A salient feature is a synergistic combination of both statistical and fuzzy set-based techniques. Anomaly detection problem is viewed as a certain statistical hypothesis testing which is realized in an unsupervised mode. At the same time, 'anomaly' is a much more complex concept and as such can be described with fuzzy set theory. Fuzzy sets bring a facet of robustness to the overall scheme and play an important role in the successive step of hypothesis testing. Because of the fuzzification, parameters determination is self-adaptive and no parameter needs to be specified by the user, what's more, there is no need to consider the data distribution in statistical hypothesis testing in this paper. The approach is validated with a number of experiments, which help to quantify the performance of constructed algorithm. [ABSTRACT FROM AUTHOR]

Eigenvalues and eigenvectors are one of the important topics over bipolar fuzzy linear algebra. In order to develop the bipolar fuzzy linear space we introduce in this article, the similarity relations, eigenvalues and eigenvectors of bipolar fuzzy matrices (BFMs). Idempotent, diagonally dominant and spectral radius of BFMs are considered here. Also, some properties and results of eigenvalues and eigenvectors for BFMs are investigated. [ABSTRACT FROM AUTHOR]

Control charts are the most popular process monitoring techniques designed to determine whether a process is in a state of statistical control or not. When a process change occurs, the control chart exhibits an out-of-control signal. In most cases, the signal is followed by a substantial amount of delay. To address this drawback, supplementary techniques have been considered to be employed along with the control charts to identify the exact time of the process change. This paper presents a hybrid method for estimating the change point on x ¯ chart when neither the change type nor its magnitude are known. For this purpose, two sets of features with the most discriminatory power between x ¯ chart patterns are selected. Then the feature vectors are extracted from the control chart patterns (CCPs) and served as an input to a classification scheme which is comprised of several support vector machine (SVM) classifiers. After parameters tuning, the classifiers are built and used for classifying the CCPs and identifying the change type. Once the change type is determined, the fuzzy statistical clustering (FSC) and the maximum likelihood (ML) estimators are employed to identify the process change point. The performance of selected features, the classification scheme, and the change point estimators are evaluated by conducting several simulation studies. Empirical results show that in identifying the change types, the proposed classification scheme is more accurate than two recent CCP classification methods. The results also confirm that the proposed hybrid method offers an accurate estimate of the process change point, as compared to the most recent methods developed for the change point estimation. [ABSTRACT FROM AUTHOR]

Fuzzy modeling and fuzzy statistics provide useful tools for handling empirical situations affected by vagueness and imprecision in the data. Several fuzzy statistical models and methods (e.g., fuzzy regression, fuzzy principal component analysis, fuzzy clustering) have been developed over the years. Generally the standard LR-fuzzy data representation has been used in these methods. However, several empirical contexts, such as human ratings and decision making, may show more complex fuzzy structures which cannot be successfully modeled by the LR representation. In all these cases another type of fuzzy data representation, the so-called LHIR representation, should be preferred instead. In particular, this novel representation allows to handle with fuzzy data which are characterized by non-convex membership functions. In this paper, we address the problem of summarizing large datasets characterized by two-mode non-convex fuzzy data. We introduce a novel dimension reduction technique (NCFCA) based on the framework of Component Analysis and Least squares programming. Finally, to better highlight some important characteristics of the proposed model, we apply NCFCA to three empirical datasets concerning behavioral and socio-economic issues. [ABSTRACT FROM AUTHOR]

Fuzzy statistics provides useful techniques for handling real situations which are affected by vagueness and imprecision. Several fuzzy statistical techniques (e.g., fuzzy regression, fuzzy principal component analysis, fuzzy clustering) have been developed over the years. Among these, fuzzy regression can be considered an important tool for modeling the relation between a dependent variable and a set of independent variables in order to evaluate how the independent variables explain the empirical data which are modeled through the regression system. In general, the standard fuzzy least squares method has been used in these situations. However, several applicative contexts, such as for example, analysis with small samples and short and fat matrices, violation of distributional assumptions, matrices affected by multicollinearity (ill-posed problems), may show more complex situations which cannot successfully be solved by the fuzzy least squares. In all these cases, different estimation methods should instead be preferred. In this paper we address the problem of estimating fuzzy regression models characterized by ill-posed features. We introduce a novel fuzzy regression framework based on the Generalized Maximum Entropy (GME) estimation method. Finally, in order to better highlight some characteristics of the proposed method, we perform two Monte Carlo experiments and we analyze a real case study. [ABSTRACT FROM AUTHOR]

This research investigated the internationalization policy and involved evaluating the effects of policy-driven reform on universities in the context of neoliberal theory. In this study, 293 professors from various universities were invited to express their opinions on the selected indicators reflected the implementation of internationalization in institutional level. Fuzzy mean and defuzzification were applied to transform the fuzzy interval data. The concept of fuzzy distance was used to interpret the gap between importance and feasibility of implementation of internationalization in specific indicators. According to the data analysis, the policy-driven reform led to changes in academic culture. The result reveals most respondents in this research assessed internationalization-related indicators positively and assigned them a high value. However, there is a gap between the importance and feasibility of the internationalization in terms of specific indicators which reviewed at the university level. In particular, this study found that implementing the internationalization policy has substantially increased the gap related to the input dimension. The finding will provide suggestions for policy makers and university authorities to improve the current situation. [ABSTRACT FROM AUTHOR]