An investigation on artificial reproduction of Mugil cephalus

Cultured nine years old breeder M. cephalus specimens were subjected to eight artificial breeding treatments from December till February 2003. In treatments 1-5, breeders received two injections of CPH and LIIRFI—A2 coupled with Domperidone or a mixture of CPH and HCG in an interval of 24 hours. Female breeders in treatments 6-8 received a gradual daily injection of 500 1U HCG per kilogram of body weight for 5 days. Male breeders in treatments 6-8 were given 5-10mg of MT-a-17 in addition to HCG and then subjected to two injections similar to that of treatments 1-5. Results showed that male breeders in stages +2 and +3 of all treatments that had received HCG produced more milt than those injected with MT-a-17, such that each male was used 2-6 times for milting purposes. Of 27 female breeders, 22 spawned 1-2.6 million eggs among which eight females' eggs were fertilized 10-95%. Hatching rate was between 0.008 to 88.9% and a maximum of 2 million larvae were produced. The best time for artificial breeding is December when mean egg width is ,600 microns. Statistical analysis of egg width, hatching rate and larvae production showed a significant difference between treatments 1-5 and 6-8 (p<0.05). This proved the supremacy of multiple injections including HCG for artificial breeding of Mugil cephalus

A New Method for Defuzzification and Ranking of Fuzzy Numbers Based on the Statistical Beta Distribution

Granular computing is an emerging computing theory and paradigm that deals with the processing of information granules, which are defined as a number of information entities grouped together due to their similarity, physical adjacency, or indistinguishability. In most aspects of human reasoning, these granules have an uncertain formation, so the concept of granularity of fuzzy information could be of special interest for the applications where fuzzy sets must be converted to crisp sets to avoid uncertainty. This paper proposes a novel method of defuzzification based on the mean value of statistical Beta distribution and an algorithm for ranking fuzzy numbers based on the crisp number ranking system on R. The proposed method is quite easy to use, but the main reason for following this approach is the equality of left spread, right spread, and mode of Beta distribution with their corresponding values in fuzzy numbers within (0,1) interval, in addition to the fact that the resulting method can satisfy all reasonable properties of fuzzy quantity ordering defined by Wang et al. The algorithm is illustrated through several numerical examples and it is then compared with some of the other methods provided by literature

Effective indicators in human capital productivity of urban management organization

Human capital is considered as a strategic resource among the main resources of any organization and an important factor in producing and presenting services to the society. Improving the productivity of human capital is the main cause of the improvement of productivity in organizations. Management of human capital development, focuses on the quantitative models and technics to test the productivity of the entire system. The central goal is to determine common and distinguishable indicators of human capital productivity in two levels of staff (individual) and organization (management). According to the previous researches and available managerial theories, 29 variables were selected for two main indicators in two levels of staff and organization. Due to the complexity of testing the productivity in the urban management organizations and the need to determine the variables, exploratory factor analysis test was randomly distributed and conducted among 350 people from the target society of human capital urban management (Tehran Municipality) during four phases with setting out a questionnaire consisted of 22 items in Likert scale which distributed randomly. Output of first phase supported 19 variables out of 29 first variables. In the next phase, matrix of variables (partial indicators), was formed due to the correlation coefficients and classified and defined according to two main research indicators by variance analysis in which weight of organization index and staff index were calculated 0.623 and 0.597, respectively. Therefore, programing for the improvement according to process cycle for both main indicators and other exploratory indicators was offered

A Full Ranking for Decision Making Units Using Ideal and Anti-Ideal Points in DEA

We propose a procedure for ranking decision making units in data envelopment analysis, based on ideal and anti-ideal points in the production possibility set. Moreover, a model has been introduced to compute the performance of a decision making unit for these two points through using common set of weights. One of the best privileges of this method is that we can make ranking for all decision making units by solving only three programs, and also solving these programs is not related to numbers of decision making units. One of the other advantages of this procedure is to rank all the extreme and nonextreme efficient decision making units. In other words, the suggested ranking method tends to seek a set of common weights for all units to make them fully ranked. Finally, it was applied for different sets holding real data, and then it can be compared with other procedures

General and multiplicative non-parametric corporate performance models with interval ratio data

Open Access articleThe increasing intensity of global competition has led organizations to utilize various types of performance measurement tools for improving the quality of their products and services. Data envelopment analysis (DEA) is a methodology for evaluating and measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. All the data in the conventional DEA with input and/or output ratios assumes the form of crisp numbers. However, the observed values of data in real-world problems are sometimes expressed as interval ratios. In this paper, we propose two new models: general and multiplicative non-parametric ratio models for DEA problems with interval data. The contributions of this paper are fourfold: (1) we consider input and output data expressed as interval ratios in DEA; (2) we address the gap in DEA literature for problems not suitable or difficult to model with crisp values; (3) we propose two new DEA models for evaluating the relative efficiencies of DMUs with interval ratios, and (4) we present a case study involving 20 banks with three interval ratios to demonstrate the applicability and efficacy of the proposed models where the traditional indicators are mostly financial ratios

An overall profit Malmquist productivity index with fuzzy and interval data

Although crisp data are fundamentally indispensable for determining the profit Malmquist productivity index (MPI), the observed values in real-world problems are often imprecise or vague. These imprecise or vague data can be suitably characterized with fuzzy and interval methods. In this paper, we reformulate the conventional profit MPI problem as an imprecise data envelopment analysis (DEA) problem, and propose two novel methods for measuring the overall profit MPI when the inputs, outputs, and price vectors are fuzzy or vary in intervals. We develop a fuzzy version of the conventional MPI model by using a ranking method, and solve the model with a commercial off-the-shelf DEA software package. In addition, we define an interval for the overall profit MPI of each decision-making unit (DMU) and divide the DMUs into six groups according to the intervals obtained for their overall profit efficiency and MPIs. We also present two numerical examples to demonstrate the applicability of the two proposed models and exhibit the efficacy of the procedures and algorithms