Ergonomic risk factors associated with muscuslokeletal disorders in computer workstation

Ergonomics Risk Factors (ERFs) at computer works are commonly related to Musculoskeletal Disorders (MSDs) such as repetitive movements, doing work in awkward postures and static postures while prolonged seating at works. The main objective of this study was to investigate the ergonomic risk factors associated with MSDs among employees in computer workstation. In this study, the data were obtained by structured interview using self-reported questionnaire and direct observation. The results show that there is significant association between neck and stress score with musculoskeletal symptoms and among office workers. As a conclusion, by assessing ERFs at workplace, the effectiveness of workplace interventions can be evaluated without waiting for changes in the prevalence of MSDs

Time series analysis by fuzzy linear regression

Fuzzy set theory constitutes the theoretical background for abstractly formalizing the vague phenomenon of complex systems. Vague data are defined herein as specialized fuzzy sets, i.e., fuzzy numbers, and a fuzzy linear regression model is described as a fuzzy function with such numbers as vague parameters. We applied a generic algorithm to identify the associated coefficients of the model, and provide both analytically and graphically, a linear approximation of the vague function, together with description of its potential application. We also provide an example of the fuzzy linear regression model being employed in a time series with economic indicators, namely the evolution of the unemployment, agricultural production, and construction between 2009 and 2011 in the Czech Republic. We selected this period since it represents the period when the financial and economic crisis started, and a certain degree of uncertainty existed in the evolution of economic indicators. Results take the form of fuzzy regression models in relation to variables of the time-specific series. For the period 2009-2011, analysis confirmed assumptions held by the authors on the seasonal behaviour of such variables and connections between them. In 2010, the system behaved in a fuzzier manner; hence, relationships between variables were vaguer than otherwise, brought about by factors such as difference in the elasticity of demand, state interventions, globalization, and transnational impacts.Web of Science321

LP Methods for Fuzzy Regression and a New Approach

Linear Programming (LP) methods are commonly used to construct fuzzy linear regression (FLR,) models. Probabilistic Fuzzy Linear Regression (PFLR) [9] and Unrestricted Fuzzy Linear Regression (UFLR) [3] are two of the mostly applied models that employ LP methods. In this study, a modified fuzzy linear regression model which use LP methods is proposed. PFLR, UFLR and proposed model compared in terms of mean squared error (MSE) and total fuzziness by using two simulated and one real data set

LP Methods for Fuzzy Regression and a New Approach

Linear Programming (LP) methods are commonly used to constructfuzzy linear regression (FLR) models. Probabilistic Fuzzy Linear Regression(PFLR) [9] and Unrestricted Fuzzy Linear Regression (UFLR) [3]are two of the mostly applied models that employ LP methods. In this study,a modified fuzzy linear regression model which use LP methods is proposed.PFLR, UFLR and proposed model compared in terms of mean squared error(MSE) and total fuzziness by using two simulated and one real data set.</p