Decision making in the manufacturing environment using the technique of precise order preference

Wrong decisions in manufacturing systems can jeopardize the continuity of production and reduce productivity and efficiency. The ref ore, it is ess ential to mak e the rig ht dec isions in solving the problems encountered in manufacturing environments. In the literature, there are many methods developed to be used in solving decision-making problems. The results of different methods used in solving the same problem are different from each other. Thus, the rankings obtained by the different methods to solve the same decision-making problem in the manufacturing environment are different. Different rankings obtained for the same problem cause inconsistencies and it is not easy to determine which sort of order is better. In this study, the use ofthe technique ofprecise order preference (TPOP) is proposed to solve the decision-making problems in manufacturing systems. Three case studies a re p resented t o illustrate the use o f the TPOP method to solve decision-making problems in manufacturing systems. The c ase studies show that the TPOP method can be used easily to solve decision-making problems in manufacturing systems. Furthermore, the consistencies of the multi-criteria decision-making methods used in this study are analyzed using Spearman's correlation coefficient values. TPOP method has the highest Spearman's correlation value for three case studies

A Survey on Physical Education and Sport Department Students’ Opinion about Cooperative Learning Method

The aim of the study is to find out the opinion of the students who had taken two different courses as theoretical and applied courses about this learning strategy with the help of cooperative learning method and to match the opinion of students who are studying theoretical and applied courses with each other. Totally, 55 students, 25 of which are the ones who studied applied “Basic Gymnasium” in the curriculum of Physical Education and Sports Academy of Education Faculty in the academic year of 2009-2010, and, 30 of which are the ones who studied theoretical “Physical Education and Fundamentals of Sports” at the same department in the academic year of 2010-2011, constitute the sample group of cooperative learning method that was applied. At the end of courses, student opinion scale that was developed by the researchers in order to detect the interests of students about the related method was applied to the students. In order to find out the opinion of the students about the method, their answers to the questions were evaluated statistically and percentage distributions were given comparatively as a result of the applications that have been done. According to the data that was gathered, the application of cooperative learning method in physical and sports department was confronted positively. The study has revealed that the students to whom cooperative learning method was applied in applied courses had more positive thoughts than the students to whom cooperative learning method was applied in theoretical courses. Keywords: Cooperative Learning, Theoretical Courses, Applied Courses, Students’ Opinion

Evaluation of service quality using SERVQUAL scale and machine learning algorithms: a case study in health care

Purpose This study aims to propose a service quality evaluation model for health care services. Design/methodology/approach In this study, a service quality evaluation model is proposed based on the service quality measurement (SERVQUAL) scale and machine learning algorithm. Primarily, items that affect the quality of service are determined based on the SERVQUAL scale. Subsequently, a service quality assessment model is generated to manage the resources that are allocated to improve the activities efficiently. Following this phase, a sample of classification model is conducted. Machine learning algorithms are used to establish the classification model. Findings The proposed evaluation model addresses the following questions: What are the potential impact levels of service quality dimensions on the quality of service practically? What should be prioritization among the service quality dimensions and Which dimensions of service quality should be improved primarily? A real-life case study in a public hospital is carried out to reveal how the proposed model works. The results that have been obtained from the case study show that the proposed model can be conducted easily in practice. It is also found that there is a remarkably high-service gap in the public hospital, in which the case study has been conducted, regarding the general physical conditions and food services. Originality/value The primary contribution of this study is threefold. The proposed evaluation model determines the impact levels of service quality dimensions on the service quality in practice. The proposed evaluation model prioritizes service quality dimensions in terms of their significance. The proposed evaluation model finds out the answer to the question of which service quality dimensions should be improved primarily

Developing a scoring function for NMR structure-based assignments using machine learning

Determining the assignment of signals received from the ex- periments (peaks) to speci_c nuclei of the target molecule in Nuclear Magnetic Resonance (NMR1) spectroscopy is an important challenge. Nuclear Vector Replacement (NVR) ([2, 3]) is a framework for structure- based assignments which combines multiple types of NMR data such as chemical shifts, residual dipolar couplings, and NOEs. NVR-BIP [1] is a tool which utilizes a scoring function with a binary integer programming (BIP) model to perform the assignments. In this paper, support vector machines (SVM) and boosting are employed to combine the terms in NVR-BIP's scoring function by viewing the assignment as a classi_ca- tion problem. The assignment accuracies obtained using this approach show that boosting improves the assignment accuracy of NVR-BIP on our data set when RDCs are not available and outperforms SVMs. With RDCs, boosting and SVMs o_er mixed results

Age, growth and reproductive properties of an invasive species Carassius gibelio (Bloch, 1782) (Cyprinidae) in the Ikizcetepeler Dam Lake (Balikesir), Turkey

Background and Purpose: The Prussian carp Carassius gibelio is well known as a hazardous fish species for native fish communities. Ikizcetepeler Dam Lake inhabits some fish species as Cyprinus carpio, Leuciscus cephalus, and Barbus species. The dam lake has major economic importance to the area for both fisheries and drinking water. The reason to select C. gibelio was due to the dominant species in stagnant and slow running waters and possible harmful interactions with native species. Materials and methods: A total of 480 specimens of C. gibelio were monthly collected by gill nets during a 1-year period from Ikizcetepeler Dam Lake. Age was determined from scales. Length-weight relationships, von Bertalanffy equation were used to estimated for growth. Sex were determined by macroscopic observation of gonads. Spawning period of this species was determined according to gonado-somatic index (%). Results: Females and males reached a maximum age groups of VI. Total length was 23.6–31.3 cm in females and 23.0–31.4 cm in males. The male and female ratio was 1:3.52 (M:F) in the favor of females. The Von Bertalanffy growth equations (in length) were found as Lt= 34.89.(1-e–0.11(t+7.66)), Lt= 32.09 (1-e–0.23(t+5.83)) for females and males, respectively. The estimated b values were given as 2.886 and 2.981 for females and males, respectively (b<3). The condition of the fish increased during early summer. Spawning period of this species occured between April and July. Conclusions: With a comparison of the relevant literature, the studied Prussian carp population was characterized by a shorter life span and more rapid growth during the first years of life. These can be considered as typical features of invasive species

New Generalization of Eulerian Polynomials and their Applications

Abstract: In the present paper, we introduce Eulerian polynomials with parameters a and b and give the definition of them. By using the definition of generating function for our polynomials, we derive some new identities in Analytic Numbers Theory. Also, we give relations between Eulerian polynomials with parameters a and b, Bernstein polynomials, Poly-logarithm functions, Bernoulli and Euler numbers. Moreover, we see that our polynomials at a = −1 are related to Euler-Zeta function at negative inetegers. Finally, we get Witt&apos;s formula for new generalization of Eulerian polynomials which we express in this paper

Developing a Scoring Function for NMR Structure-based Assignments using Machine Learning

Abstract. Determining the assignment of signals received from the experiments (peaks) to specific nuclei of the target molecule in Nuclear Magnetic Resonance (NMR 1 ) spectroscopy is an important challenge. Nuclear Vector Replacement (NVR) ([2, 3]) is a framework for structurebased assignments which combines multiple types of NMR data such as chemical shifts, residual dipolar couplings, and NOEs. NVR-BIP [1] is a tool which utilizes a scoring function with a binary integer programming (BIP) model to perform the assignments. In this paper, support vector machines (SVM) and boosting are employed to combine the terms in NVR-BIP&apos;s scoring function by viewing the assignment as a classification problem. The assignment accuracies obtained using this approach show that boosting improves the assignment accuracy of NVR-BIP on our data set when RDCs are not available and outperforms SVMs. With RDCs, boosting and SVMs offer mixed results