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In this paper on the basis of the f*‐Divergence, a comparison criterion between fuzzy Information Systems is presented. This criterion is called “f*‐Divergence Criterion”.

Several coefficients, called divergences, have been suggested in the statistical literature to reflect the fact that some probability distributions are “closer together” than others and consequently that it may be easier to distinguish between the distributions of one pair than between those of another. When comparing three biological populations, it is often interesting to measure how two of them “move apart” from the third. Deals with the statistical analysis of this problem by means of bivariate divergence statistics. Provides a unified study, depicting the behaviour and relative merits of traditional divergences, by using the (h,ø), divergence family of statistics introduced by Menéndez et al.

Goodness‐of‐fit test based on K_ϕ‐divergence between observed and theoretical frequencies are considered. The asymptotic chi‐square null distribution and three alternative approximations to the exact distribution function of this family are compared in small samples. Numerical results are presented for the symmetric null hypothesis for different multinomial sample sizes with various cell numbers. Exact power under specific alternatives to the symmetric null hypothesis are calculated and a comparison with the family of power divergence statistics is made.

To provide a new family of test statistics to solve the Behrens‐Fisher problem and to compare it with the classic test statistics through a different simulation studies.

A general procedure for testing composite hypothesis to k samples of different size problems on the basis of the Renyi's divergence is used to develop a new parametric family of test statistics that contains as a particular case the classical likelihood ratio test. The scope of the paper is to find out if some member of the new family of test statistics it is preferable to the classical ones.

Some members of the new parametric family of test statistics behave remarkably well in comparison to the classic ones, as the different computational studies reveal.

This paper offers a new way to solve the Behrens‐Fisher problem that it is preferable in some cases to the known procedures.

The decision rule which minimizes the probability of error, in the discrimination problem, is the Bayes decision rule which assigns x to the class with the highest a posteriori probability. This rule leads to a partial probability of error which is given by Pe(x) = 1−max p(C2lx) for each x e X. Prior to observing X, the probability of error associated with X is defined as Pe = EX [Pe(x)]. Tanaka, Okuda and Asai formulated the discrimination problem with fuzzy classes and fuzzy information using the probability of fuzzy events and derived a bound for the average error probability, when the decision in the classifier is made according to the fuzzified Bayes method. The aim is to obtain bounds for the average error probability in terms of (αβ)‐information energy, when the decision in the classifier is made according to the fuzzified Bayes method.

Consideration is given to the problem of optimally choosing of a fixed number of experiments in sequential form from a class of available experiments. The applications of ø entropy measure in the sequential design of experiments is studied by defining ø terminal entropy. Finally, the process is established when a sufficient experiment exists. An illustrative example, which demonstrates the usefulness of the results obtained, is included.

Read (1984) presented an asymptotic expansion for the distribution function of the power divergence statistics whose speed of convergence is dependent on the parameter of the family. Generalizes that result by considering the family of (h, φ)‐divergence measures. Considers two other closer approximations to the exact distribution. Compares these three approximations for the Renyi’s statistic in small samples.

Almond oil is a gourmet product with functional food characteristics owing to its high almond oil content and high nutritional quality. One of the primary constraints on its production is the lack of information regarding oil extraction from an industrial perspective, including by-products generation.

The performance, quality and composition characteristics were analyzed, both from the physical-chemical and organoleptic point of view, of the almond oils obtained through two pressure systems: screw press (SP) and hydraulic press (HP). To ensure the success of almond oil production at a commercial scale, in this work, an economic study of the costs of the process was carried out as a complementary part of optimizing the production of virgin almond oil.

Physicochemical analysis showed little difference, just in total sterols (HP 2069, SP 2153) and some quality indexes (K232: HP 1.63, SP 2.13; peroxide index: HP 1.74, SP 0.95), in contrast to sensory analysis. Consumer judges valued roasted almond oil extracted using a HP the best. The production cost of the oil extracted with the SP was €23.05/l. With the HP it was €25.13/l, owing to the lower oil yield in the extraction. The most expensive treatment was for the HP with toasted almonds (€27.76/l), owing to the greater need for processing.

Production costs derived from the method used have received little attention. This paper presents data that allow for the transference between academic and industrial ambit and their economic viability.

The analysis of data collected from user interactions with educational and information technology has attracted much attention as a promising approach to advancing our understanding of the learning process. This promise motivated the emergence of the field of learning analytics and supported the education sector in moving toward data-informed strategic decision making. Yet, progress to date in embedding such data-informed processes has been limited. The purpose of this paper is to address a commonly posed question asked by educators, managers, administrators and researchers seeking to implement learning analytics – how do we start institutional adoption of learning analytics?

A narrative review is performed to synthesize the existing literature on learning analytics adoption in higher education. The synthesis is based on the established models for the adoption of business analytics and finding two projects performed in Australia and Europe to develop and evaluate approaches to adoption of learning analytics in higher education.

The paper first defines learning analytics and touches on lessons learned from some well-known case studies. The paper then reviews the current state of institutional adoption of learning analytics by examining evidence produced in several studies conducted worldwide. The paper next outlines an approach to learning analytics adoption that could aid system-wide institutional transformation. The approach also highlights critical challenges that require close attention in order for learning analytics to make a long-term impact on research and practice of learning and teaching.

The paper proposed approach that can be used by senior leaders, practitioners and researchers interested in adoption of learning analytics in higher education. The proposed approach highlights the importance of the socio-technical nature of learning analytics and complexities pertinent to innovation adoption in higher education institutions.