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This study has investigated the effect of customer relationship management systems (CRM) on the customer knowledge creation process to produce innovative products or deliver innovative services in Parsian Insurance Company in a unique Iranian culture with completely different features from Islamic and Middle-East countries, in terms of research factors. Data was collected using a questionnaire, in a research population of 600 managers of the company's headquarters, and representatives in Tehran province. The sample size was calculated as 243 using the Cochran formula; 280 questionnaires were distributed and 250 were returned. The structural equation modelling was used for statistical analysis of data. The research findings showed that CRM systems have affected the process of customer knowledge creation. The findings also confirmed that all components of customer knowledge creation process affect the innovative product/service production.

Soil contamination by heavy metals is very important for environmental scientists due to the greater mobilization of metals and the possibility of contamination of groundwater. One of the most effective tools for evaluating the risk is the combination of experimentation with computer modeling. Modeling techniques are important in assessing the potential risks associated with heavy metals in the environment. Determination of models that can precisely evaluate the heavy metals in soils is an important need of agricultural researches, which could eradicate the weaknesses in the measurement of heavy metals in soils. The purpose of the present study is to test and compare different models according to their suitability for describing the estimation of heavy metals. The models used in this study were multilayer perceptron neural network (MLP), M5 model tree (M5) and bagging approach (BM5P). The data from 164 sampling sites from Neyshabur and Mashhad plains were taken in this study. The inputs combination according to feature selection-based correlation was used to feed the models. To model soil heavy metals, soil attributes, namely sand, silt, clay (as texture fractions), organic carbon, pH and available phosphorus, were entered in some models. To evaluate the performance of various techniques used in this study, several statistical indexes, including the correlation coefficient, root-mean-square error, Nash–Sutcliffe coefficient, Willmott’s index (d) and mean absolute error, were assessed. Comparison of different models for Fe, Cu, Mn and Zn indicated that MLP is the most suitable method for estimations of Fe and Mn, whereas BM5P and M5P are the most suitable models for determinations of Cu and Zn, respectively. This study concluded that machine learning models can be successfully applied to the rapid prediction of soil heavy metals using soil variables.

Groundwater is a vital source for drinking and agricultural purposes in semiarid region of Neyshabur area (Iran). The present study assessed the groundwater quality and mapped the spatial variation of water samples in terms of suitability for drinking and irrigation purposes. A total 402 groundwater samples were collected from the field with global positioning system (GPS) from 2010 to 2013 and analyzed for pH, calcium (Ca2+), magnesium (Mg2+), sodium (Na+), potassium, bicarbonate, sulfate, chloride, sodium adsorption ratio (SAR), electrical conductivity (EC), total dissolved solids, and total hardness (TH). A GIS-based ordinary kriging method with best fit semivariogram models was used for preparation of thematic maps of groundwater quality parameters. The results were evaluated and compared with WHO (2011) recommended water quality standard. Results showed that 68.40% of SAR, 25% of Mg2+, 32.62% of Na+, and 1.74% of TH of the total groundwater samples are suitable for the irrigation purpose. Consequently, 55.57% of EC, 89.19% of TDS, 0.75% of pH, and 6.25% of K+of the total groundwater samples are suitable for the drinking purpose as per the WHO standard. The groundwater quality in the study area is very hard and slightly alkaline in nature. The spatial distribution map of groundwater quality showed 80% of the area suitable for drinking purpose; whereas, 90% of the area demarcated for irrigation purpose.

The accurate estimation of available soil phosphorus (ASP) is required for the effective management of environmental resources and improving agricultural productivity. Direct measurement of ASP at large scales is time-consuming and expensive. Therefore, many empirical models or pedotransfer functions (PTFs) have been developed to obtain ASP. This study introduces a new model that estimates ASP from easily measurable soil properties by using the multi-objective group method of data handling (mGMDH) approach. Organic carbon, clay content, calcium carbonate equivalent, pH and ASP were obtained from 100 soil samples, which were collected by the stratified random sampling strategy in Mashhad Plain, Khorasan-e-Razavi Province, Northeast Iran. The suggested model for the prediction of ASP generated satisfactory results in terms of the statistical performance criteria. The coefficient of determination (R2) and mean square error (MSE) values of the mGMDH model in training step were 0.606 and 4.9671 mg kg−1, respectively, whereas those were 0.504 and 5.6685 mg kg−1, in the testing step. It was concluded that only 50 % of variations of ASP could be described by selecting easy-to-measure soil variables and owing to the key role of phosphorus in soil fertility and precision agriculture, the other related factors such as terrain attributes and fertilizer management should also be considered as they influence the distribution of the soil phosphorus.

Salinization and alkalization of land resources are the major obstacles to their optimal usage in many arid and semi-arid regions of the world, including Iran, since potential evapotranspiration is more noteworthy than precipitation in these areas. The amount of water that enters the soil is low and this results in salt accumulation in soils, which makes the soil infertile. Moreover, existence of salts, for example, sodium, in soils causes dispersion of soil particles and soil degradation, and intensifies soil erosion too. Monitoring exchangeable sodium percentage (ESP) variability in soils is both time-consuming and costly. However, in order to estimate the amounts of amendments and land management, it is necessary to know ESP variation and values in sodic or saline and sodic soils. Thus, introducing a method, which utilizes easily obtained indices to estimate ESP indirectly is more optimized and economical. Input and output data, i.e., ECe(dS m−1), clay (%), pH and ESP (%) were collected and measured from 100 soil samples in light of a stratified random sampling from Mashhad Plain, Khorasan-e-Razavi Province, Northeast Iran. This study aims to propose some models to estimate ESP by easily obtained properties of soil. In this regard, the efficiency of artificial intelligence-based (AI) models (i.e., Artificial Neural Network, ANN, and Adaptive Neuro-Fuzzy Inference System, ANFIS) was investigated and compared. Accuracy results showed that owing to highest R2and the lowest mean square error (MSE), ANFIS model predictions were superior to the MLP model for indirect estimation of soil exchangeable sodium percentage.

A convenient one-pot approach to bridged heterobimetallic gold(I) palladium(II) bis(acyclic diaminocarbene) complexes is presented. The methodology exploits the graduated nucleophilicities of aliphatic and aromatic amines connected via an aromatic unit. This enables a straightforward access to bridged complexes from easily available isonitrile metal precursors.