فهرست مطالب mahdi darvishi

Physical fatigue is one of the major risk factors for work-related musculoskeletal disorders and has many life and financial costs. The impact of physical/biomechanical, psychosocial, environmental, and individual risk factors on muscle fatigue is undeniable. The aim of this study is to model the phenomenon of muscle fatigue (as output) in the hand in work environments based on these risk factors (as input) using soft computing methods.

In the first step, associated risk factors of fatigue for 156 subjects (in three job categories) were assessed using Copenhagen environmental, psychosocial, demographic, and Man-TRA tools. Then, the Roman-Liu equation and mean square amplitude of acceleration waves were used to measure fatigue with a dynamometer and a three-axis accelerometer, respectively. Finally, according to the nature of risk factors and the phenomenon of fatigue, six categories (24 methods) of supervised machine learning (SML) based on classification were selected. MatLab software (MatLab R2017b, The Mathworks Inc., MA, U.S.A.) was used to fit the models using SML.

The best-fitted models in the first and second half of the work shift were obtained using support vector machine methods. Physical risk factors had a significant impact on physical fatigue. After filtering low-priority risk factors, in the first half of the work shift, the most optimal model had an accuracy of 71.8%, precision of 72.5%, sensitivity of 76.9%, specificity of 70.8%, and discrimination power equal to 73%. In the second half of the work shift, the accuracy, precision, sensitivity, and specificity of the optimized model were 60.3%, 57.5%, 50%, and 46.9%, respectively, and the discrimination power was obtained at about 62%.

The fitted models for hand fatigue had acceptable performance in both sections of the shift but can still be optimized. Therefore, it is necessary for future studies to improve the quality of input and output data and include other dimensions affecting fatigue such as cognitive workload and type of work shift in future models.

Given the importance of the role of local management in urban and rural development and understanding the factors that influence it this research seeks to examine social participation and its importance, analyze its relationship with the performance of local governments (city and village councils, municipal-rural) using field data. This research is a mixed one and has been done in a quantitative way through survey research and in the qualitative part, focused interviews have been used.The population of this study is 18 years old and older people living in Damavand towns and villages according to Cochran's formula, the sample size is 382 data were collected through questionnaire and interview form in this research simple random sampling method is used.Interviews were also conducted with the use of purposeful sampling to theoretically saturate the experts and councilors.In conclusion, it can be concluded that there is a strong correlation between social participation and research dependent variable (performance of local administrations) with a coefficient of 0.511. That is, with the increase in social participation, local management performance improves as well.

The purpose of the present study is to prioritize safety risk in nuclear installations using network analysis method. Evaluation of safety risk indices in nuclear installations was done using network analysis method. Related indicators are: machinery and equipment collision during extraction and excavation, temperature changes at the heart of the facility, collision of machinery and equipment with underground facility during excavation, acid and chemical injection, radiographic exposure, and exposure to radionuclides. , UV contact during welding, material deposition during flushing, environmental dyeing during painting, throwing welding sparks. The findings of the study showed that existing multi-criteria decision-making methods cannot accurately prioritize the risks. While the ANP method used in this study is not only capable of separating hazards more accurately, it can also express their importance to each other based on their degree of risk. It was found that the existing methods cannot accurately prioritize the hazards. In other words, management has no idea of the limited resources available to eliminate or control the same risks. While the proposed method in this study is not only capable of separating the hazards more accurately but also can express their importance to each other based on their degree of risk.

Increasing technology and population in the two recent decades have led to destructing nature and changing its land cover. Remote sensing is one of those new technologies which provide accessibility for managing earth resources. the dynamic information of remote sensing can play an effective role for monitoring and analysis of land use change using digital change detection techniques. Method used for the rate of development of remote sensing, is on the basis geometric correction, spatial and spectral enhancement of available satellite images with regard to the maximum likelihood supervised classification and post classification method. At first we produced land use maps by using the satellite images of landsat. The results of this study are, in addition to the impacts of environmental sensitivity visualization development trend based on the visual display, calculate of the land use changes together in developed area and production of digital map changes, to support land management.