2Introduction: 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. Material and 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. Results: 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%. Conclusion: 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.

Decision-making as one of the principles of management is considered an important factor in prosperity of the organizations. This is so important that managers use efficient Tools to improve the quality of their decisions. Steel industry is one of the major industries in this country; consequently, it deserves special attention. In this paper, the main aim is to use scientific methods to manage crude steel consumption in the country. However, the literature shows that it is relatively difficult to yield accurate results in the prediction of consumption, especially in long-term horizon. Researchers believe that high level of complexity and uncertainty in financial markets is main reason of this matter. Therefore, in this paper, a hybrid of intelligent and Soft Computing models have been used as an effective way in order to model the complexities and uncertainties simultaneously in the data. In this way, the list of variables is recognized based on the literature and expert opinions. Then the linear and nonlinear relationships and also correlations between variables are evaluated and final explanatory variables specified. Finally, four models including hard classic, Soft classic, hard intelligent and Soft intelligent are designed to predict steel consumption in both short and long term horizons and their results are compared with each other. Empirical results indicate that using the hard intelligent model makes improvement 22. 68% and 41. 41% in comparison with hard classic model in short and long term horizons respectively in Root Mean Squared Error (RMSE). In addition, the Soft intelligent model makes improvement 43. 01% and 92. 72% in comparison with Soft classic model and hard classic model respectively in short term horizon and 34. 68% and 91. 53% in long term horizon. Results of the study indicate superiority of the Soft intelligent models over hard intelligent models and superiority of hard intelligent models over hard classic models. Results of the study indicate superiority of the Soft intelligent models and hard intelligent models over hard intelligent models and hard classic models respectively.

Scour depth around bridge abutment is a crucial parameter to design the protective spur dike. Costly and time consuming experiments make it difficult to evaluate the scour depth in the problems involving scour phenomena. However, Soft Computing and regression methods may be applied based on the experimental results. In this paper, a set of experiments is performed and a database including 127 records is collected to evaluate the relation between scour depth and five independent variables including abutment length, flow discharge, flow depth, spur dike length and Spur dike distance from abutment to upstream. This paper presents a new application of the multi-layer perceptron neural network (MLP), group method of data handling (GMDH), non-linear regression (NLR) and multiple linear regression (MLR) to predict the scour depth. A sensitivity analysis is also performed to evaluate the influence of each variable on the scour depth. Results indicate that the first three methods are efficient and accurate enough to be applied in practical applications with determination coefficient (R2) above 90%, while, the MLR has shown a poor performance in this paper. It is observed that MLP and GMDH outperform other methods based on the test data. However, explicit equation derived by NLR has a major advantage to be applied in the field applications without skilled operators and computer packages.

Potholes on roads are regarded as serious problems in the transportation domain, and ignoring them lead to an increase in accidents, traffic, vehicle fuel consumption, and waste of time and energy. As a result, pothole detection has attracted researchers’ attention, and different methods have been presented for it up to now. Data analysis methods such as machine learning and Soft Computing have been widely used for detection purposes. They rely on a dataset and propose a system that can detect a special event in similar datasets. Their effectiveness can be measured by evaluating their accuracy in detecting the event. Image processing involves a wide range of analytics that are used to extract specific information from images. The majority of image processing programs require massive Computational power. The major part of previous research is based on image processing. They utilize dedicated cameras which are embedded in vehicles to take images and analyze them through massive image processing programs. This scheme requires dedicated hardware that is not typically available on vehicles. In this paper, a new scheme is proposed, which uses accelerometer and GPS sensors. These types of sensors are available in today’s smartphones as well as modern vehicles. The data generated by these sensors is processed via Soft Computing to increase the accuracy of pothole detection. The proposed algorithm uses a combination of a fuzzy system and evolutionary algorithms. Fuzzy systems have been widely used to model the real-world problems that are described by uncertainty and ambiguity. Evolutionary algorithms (e. g., genetic algorithms) try to imitate evolutionary science in solving hard problems. Genetic algorithm and harmony search are used to adjust membership functions of the proposed fuzzy system. For evaluation, a case study has been conducted with regard to detect potholes on Ghaffari Street in Birjand. To this end, a real dataset has been collected and used for implementing the proposed method. Experimental results show the high accuracy of the proposed algorithm in comparison to other solutions. They reveal that the accuracy of the proposed genetic fuzzy algorithm is 98 percent and for the proposed harmony fuzzy algorithm is 99 percent.

An efficient methodology is proposed to optimal design of structures for earthquake loading.In this methodology to reduce the optimization overall time, a serial integration of wavelet transforms, neural networks and evolutionary algorithms are employed. In order to reduce the Computational work of the structural time history analysis, a discrete wavelet transform is used by means of which the number of points in the earthquake record is decreased. Also, an advanced meta model, called self-organizing generalized regression is employed to predict the time history responses. The optimization task is achieved by an evolutionary algorithm called virtual sub population method. A 6-storey space steel frame structure is designed for optimal weight for the El Centro earthquake induced loads. The numerical results demonstrate the efficiency and Computational advantages of the proposed methodology.

Forecasts of streamflows are required for many activities associated with the planning and operation of components in a water resource system. This paper demonstrates the application of two different intelligent approaches including adaptive neuro-fuzzy (ANFIS) based on grid partition and Gene Expression Programming (GEP) for the prediction of monthly streamflows. In the first part of the study, ANFIS and GEP models were used in one-month ahead streamflow forecasting and the results were evaluated. Monthly run-off data of 21 years from two stations, the Safakhaneh Station on the Sarough-Chay Stream and the Senteh Station on the Kherkherh-Chay Stream in the Zarrineh-rud Basin of Iran were used in the study. The effect of periodicity on the model’s forecasting performance was also investigated. By application of periodicity coefficient in GEP model, determination coefficient in the case of the best input combination for Safakhaneh and Senteh increased 0.19 and 0.25, respectively. In the second part of the study, the performance of the ANFIS and GEP techniques was tested for streamflow estimation using data from the nearby river. The results indicated that the GEP and ANFIS models could be employed successfully in forecasting streamflow. In this case, for the best input combination, root mean square error (RMSE) for ANFIS and GEP obtained equal to 4.88 and 4.89 respectively. However, GEP is superior to ANFIS in giving explicit expressions for the problem.

MicroRNAs (miRNAs) are short non-protein coding and single-stranded small RNA molecules with a critical role in the regulation of gene expression. These molecules are crucial regulatory elements in diverse biological processes such as apoptosis, development, and progression. miRNA genes have been associated with various human diseases, particularly cancer, and considered as a new biomarker. After the discovery of miRNAs, many researches have focused on identifying and characterizing miRNA genes in cancer. The various expression levels of miRNAs between cancer cells and normal cells are very crucial to diagnosis, prognosis, and treatment of many cancers. Many Computational and experimental Tools have been employed to characterize miRNAs. However, there exist some challenges in identifying miRNA using both Computational and experimental Tools due to miRNA features. The present review briefly introduced miRNA biology and certain Computational and experimental Tools for identifying and profiling miRNAs in cancer. Furthermore, we presented the advantages and challenges of these Tools.