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This study investigates the ability of different digital soil mapping (DSM) approaches to predict some of physical and chemical topsoil properties in the Shahrekord plain of Chaharmahal-Va-Bakhtiari province, Iran. According to a semi-detailed soil survey, 120 soil samples were collected from 0 to 30 cm depth with approximate distance of 750 m. Particle size distribution, coarse fragments (CFs), electrical conductivity (EC), pH, organic carbon (OC), and calcium carbonate equivalent (CCE) were determined. Four machine learning techniques, namely, artificial neural networks (ANNs), boosted regression tree (BRT), generalized linear model (GLM), and multiple linear regression (MLR), were used to identify the relationship between soil properties and auxiliary information (terrain attributes, remote sensing indices, geology map, existing soil map, and geomorphology map). Root-mean-square error (RMSE) and mean error (ME) were considered to determine the performance of the models. Among the studied models, GLM showed the highest performance to predict pH, EC, clay, silt, sand, and CCE, whereas the best model is not necessarily able to make accurate estimation. According to RMSE%, DSM has a good efficiency to predict soil properties with low and moderate variabilities. Terrain attributes were the main predictors among different studied auxiliary information. The accuracy of the estimations with more observations is recommended to give a better understanding about the performance of DSM approach over low-relief areas.

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The main objectives of this study were to compare binary logistic regression as an indirect approach and multinomial logistic regression as a direct approach to produce soil class maps in the Zarand region of southeast Iran. With indirect prediction, the occurrence of relevant diagnostic horizons was first mapped, and subsequently, various maps were combined for a pixel‐wise classification by combining the presence or absence of diagnostic horizons. In direct prediction, the dependent variable was the great group itself, so the probability distribution of the great soil groups was directly predicted. Among the predictors, the geomorphology map was identified as an important tool for digital soil mapping approaches as it helped to increase the accuracy. The results of prediction showed larger mean probability values for each great soil group in the areas actually covered by the great soil groups compared with other areas, indicating the reliability of the prediction. In most predictions, the global purity was slightly better than the actual purity for the models; however, both models provided poor predictions for Haplocambids and Calcigypsids. The results showed that soils with better prediction were those much influenced by topographical and geomorphological characteristics and soils with very poor accuracy of prediction were only slightly influenced by topographical and geomorphological characteristics. An advantage of the indirect method is that it gives insight into the causes of errors in prediction at the scale of diagnostic horizons, which helps in the selection of better covariates.

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Religious and spiritual practices are related to physical and mental health. Social support is an important source to aid coping, but this is not without its difficulties. This study was conducted to investigate the relationship between religious coping and self-care in a sample of Iranian cancer patients. In this cross-sectional study (October-December, 2015), 380 cancer patients were entered into the study using non random sampling (accessible sampling). Data were collected using socio-demographic, religious coping (R-COPE), and self-care questionnaires. Male patients (48.39 ± 13.39; 95% CI 46.41-50.38) were older than the females patients (45.33 ± 18.44; 95% CI 42.79-47.87). The findings indicated that there was a significant correlation between self-care and positive religious coping (r = .188, p = .009). Also there was a significant relationship between self-care and a history of smoking (p < .05). It seems that improving the level of positive religious affiliation can have beneficial effect on the self-care of cancer patients. Therefore, it is necessary to conduct these studies with greater scale and more different societies to achieve more reliable results about the effects of religious coping on self-care behaviors in cancer patients.