Work-related musculoskeletal disorders are a global problem which evolves at different workplaces such as industries, administrative, and agriculture sectors. In various studies, such disorders were assessed through multiple methods. It is necessary to evaluate different tools to use them in diverse communities. The aim of this study was to assess the validity of the new ergonomic evaluating method of Novel Ergonomic Postural Assessment (NERPA) method in Iran. Methods: The employees (n=455) of operational units of four companies (drug producers, printing and publishing houses, dairy, and drinks producers) were assessed in 2014. It was a cross-sectional and descriptive-analytical study. One of the researchers developed a questionnaire that was applied to collect demographic data. The NERPA, Rapid Upper Limb Assessment (RULA), and Rapid Entire Body Assessment (REBA) methods were utilized to analyze posture risk factors. Spearman correlation and Kappa agreement were used to analyze the collected data through SPSS V22. Results: Findings indicated that printing company had the best and pharmaceutical industries had the worst state regarding RULA's results. The risk levels between NERPA and REBA were not statistically significant (P>0.05), however, that was significant with RULA's outcome. Also, the results of NERPA and other two methods were correlated significantly (P<0.05). Pain in the lumbar area was implied to be the most prevalent problem (35.1%). Discussion: Data of the present study suggest that NERPA method was a valid tool compared to RULA. The NERPA method could be used to evaluate standing tasks among industrial workers. However, the concurrent validity of NERPA method compared with results of REBA, as a widely used method, were not verified.

Precise and accurate estimates of groundwater level might be of great importance for attaining sustainable development goals and integrated water resources management. Compared with alternative numerical models, soft computing methods are promising tools for groundwater level simulation and prediction, which need more hydrogeological and aquifer characteristics. The central aim of this research is to explore the performance of such well-accepted data-driven models to simulate the groundwater level (GWL t ) with emphasis on major meteorological components, including; precipitation (P), temperature (T), evapotranspiration (ET) dataset on a monthly interval. Arti cial neural network (ANN), fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS), group method of data handling (GMDH), and least-square support vector machine (LSSVM) are used to predict one-, two-, and three-month ahead groundwater level in an uncon ned aquifer. The main meteorological components (T t , ET t , P t , P t−1 ) and GWL for one, two, and three lag-time (GWL t−1 , GWL t−2 , GWL t−3 ) were used as input for different scenarios to attain precise and accurate prediction. The results showed that all models could have the best simulation for one month ahead in scenario 5, comprising

Background Gestational diabetes is a growing problem worldwide, with risks for both the woman and the baby. Stress has been shown to be linked with diabetes, and therefore research is examining the effect of relaxation on blood pressure. Aim To assess the effect of relaxation on blood glucose and blood pressure in women with gestational diabetes mellitus. Methods This quasi-experimental study was performed with a sample of 80 participants. Fasting blood glucose and systolic and diastolic blood pressure were measured before and after the intervention, which was a 10-week programme of home mind-body and relaxation. Findings Both systolic blood pressure and fasting blood glucose in the control group were significantly higher (P<0.001). Diastolic blood pressure in both groups was not found to be significantly different (P=0.151). Conclusions Relaxation exercises reduce fasting blood glucose and systolic blood pressure in women with gestational diabetes mellitus.