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Abstract Background The labor supply of nurses, as one of the main healthcare workers, is an important issue in health human resources planning in all health systems. Finding the factors affecting it, could help policymakers to solve the shortage of nursing work supply. The present study aimed to investigating the quantity and factors affecting the nurses' labor supply in Iran. Method In this cross-sectional study, a sample of 598 nurses working in public hospitals of Shiraz (Iran) were selected via proportionate stratified random sampling method. The required data was collected using a structured questionnaire which asked working hours and other related factors. To analyze the data, descriptive statistics, univariate analysis and multivariate linear regression were performed using STATA 15. The multivariate labor supply model was estimated separately for married and single nurses. Results The average weekly working hours of nurses was 54.65 h in all medical centers and 50.28 h in the main hospital. The regression results showed that the labor supply of nurses with work experience (β = − 0.368, P = 0.014), satisfaction with work shift arrangement (β = − 2.473, P = 0.001), income between 60-89 million rial (β = − 14.046, P = 0.002), income between 90-119 million rial(β = − 12.073, P = 0.012), and working in the emergency department (β = − 5.043, P = 0.017) had negative and significant relationship; But there was a positive and significant relationship with satisfaction of the work environment (β = 1.86, P = 0.011), workload at work (β = 1.951, P = 0.023) and employment status (contractual employees) (β = 4.704, P = 0.004). Conclusion The labor supply function of nurses is affected by demographic, economic and non-economic factors. The most contributing factors were related to non-economic variables. It seems that the non-financial cost and benefits related to the job as well as internal factors have more important role on the nurses' labor supply.

Agriculture is considered one of the primary elements for socioeconomic stability in most parts of Sudan. Consequently, the irrigation water should be properly managed to achieve sustainable crop yield and soil fertility. This research aims to predict the irrigation indices of sodium adsorption ratio (SAR), sodium percentage (Na%), permeability index (PI), and potential salinity (PS) using innovative machine learning (ML) techniques, including K-nearest neighbor (KNN), random forest (RF), support vector regression (SVR), and Gaussian process regression (GPR). Thirty-seven groundwater samples are collected and analyzed for twelve physiochemical parameters (TDS, pH, EC, TH, Ca+2, Mg+2, Na+, HCO3−, Cl, SO4−2, and NO3−) to assess the hydrochemical characteristics of groundwater and its suitability for irrigation purposes. The primary investigation indicated that the samples are dominated by Ca-Mg-HCO3 and Na-HCO3 water types resulted from groundwater recharge and ion exchange reactions. The observed irrigation indices of SAR, Na%, PI, and PS showed average values of 7, 42.5%, 64.7%, and 0.5, respectively. The ML modeling is based on the ion’s concentration as input and the observed values of the indices as output. The data is divided into two sets for training (70%) and validation (30%), and the models are validated using a 10-fold cross-validation technique. The models are tested with three statistical criteria, including mean square error (MSE), root means square error (RMSE), and correlation coefficient (R2). The SVR algorithm showed the best performance in predicting the irrigation indices, with the lowest RMSE value of 1.45 for SAR. The RMSE values for the other indices, Na%, PI, and PS, were 6.70, 7.10, and 0.55, respectively. The models were applied to digital predictive data in the Nile River area of Khartoum state, and the uncertainty of the maps was estimated by running the models 10 times iteratively. The standard deviation maps were generated to assess the model’s sensitivity to the data, and the uncertainty of the model can be used to identify areas where a denser sampling is needed to improve the accuracy of the irrigation indices estimates.

In the present study, land suitability evaluation by parametric and AHP approaches was evaluated for two forest plantations including oak (Quercus robur) and pine (Pinus silvestris) in Toroq watershed, northeast of Iran. Over 416 grid points were studied by exact climate, soil and terrain requirements of tree species. The land suitability indices by parametric approach ranged from 23.49 to 72.02 and 28.27 to 69.67 for oak and pine, while by AHP approach the values varied between 34.48–87.11 and 37.98–78.20, respectively. The most limiting factors for developing plantation of two species in the study area were altitude, aspect, soil depth, and soil texture. The spatial distribution of land suitability demonstrated a similar pattern for the two species, where from the east toward the west of the plain the values of limitations increase and the suitability classes persistently decrease. The regression coefficient (R2) of land suitability indices by two models varied between 0.876 and 0.753 for oak and pine species, respectively.

Illustrating the spatial distribution and potential sources of soil properties, and heavy metals, viz., Fe, Zn, Mn, and Cu, are the vital prerequisites for decreasing their pollution. The 68 composite agricultural soil samples in triplicates were collected by employing grid method to evaluate the concentration of pH, sand, silt, clay, organic carbon, P, K, and heavy metals. Multivariate techniques (Pearson’s correlation, heatmap, principal component analysis, and nonmetric multidimensional scaling), geostatistcal techniques, and contamination indices were employed. The contents of Fe, Zn, Mn, and Cu were lower than the limits for Iran EPA guidelines and Earth’s crust. The results of contamination factor and potential ecological risk index (RI) showed that agricultural soils have less contamination and low ecological risks. The enrichment factor (EF), geoaccumulation index (Igeo) and modified ecological risk index (MRI) indicated that 99%, 86.7% and 52.4% agricultural soil samples showed very high enrichment and ecological risks of heavy metals. Both anthropogenic activities and natural factors were responsible for heavy metal contents. The results of geostatistcal analysis revealed that Zn is accumulated more in Central regions, whereas Cu and Mn accumulated more in South and Northeastern regions of the studied area for EF, Igeo, and modified potential ecological RI.

Cation exchange capacity (CEC) is a soil property that significantly determines nutrient availability and effectiveness of fertilizer applied in lands under different managements. CEC’s accurate and high-resolution spatial information is needed for the sustainability of agricultural management on farms in the Nagaland state (northeast India) which are fragmented and intertwined with the forest ecosystem. The current study applied the digital soil mapping (DSM) methodology, based on the CEC values determined in soil samples obtained from 305 points in the region, which is mountainous and difficult to access. Firstly, digital auxiliary data were obtained from three open-access sources, including indices generated from the time series Landsat 8 OLI satellite, topographic variables derived from a digital elevation model (DEM), and the WorldClim dataset. Furthermore, the CEC values and the auxiliary were used data to model Lasso regression (LR), stochastic gradient boosting (GBM), support vector regression (SVR), random forest (RF), and K-nearest neighbors (KNN) machine learning (ML) algorithms were systematically compared in the R-Core Environment Program. Model performance were evaluated with the square root mean error (RMSE), determination coefficient (R2), and mean absolute error (MAE) of 10-fold cross-validation (CV). The lowest RMSE was obtained by the RF algorithm with 4.12 cmolc kg−1, while the others were in the following order: SVR (4.27 cmolc kg−1) c kg−1) c kg−1) c kg−1). In particular, WorldClim-based climate covariates such as annual mean temperature (BIO-1), annual precipitation (BIO-12), elevation, and solar radiation were the most important variables in all algorithms. High uncertainty (SD) values have been found in areas with low soil sampling density and this finding is to be considered in future soil surveys.

Soil is a substantive environmental medium that is subjected to various physiochemical challenges derived by natural, as well as human activities. The present review attempts to summarize the pollution status of soil from the catchments areas of Beas and Ravi Rivers in Punjab, India, as reported by different workers. Principal component analysis (PCA) showed that anthropogenic activities and lithogenic factors are the major sources of metals. The average values of heavy metals of Beas River for heavy metals, Cr, Cu, Co, and Cd, were lower than the values suggested by Awasthi, European Union, and Ewers. The average values of C (0.29%), P (0.05 mg/g), and N (0.13 mg/g) were found for the Beas River, whereas for Ravi River the average values recorded were C (0.32%), P (0.01 mg/g), and N (0.16 mg/g). The results of contamination factor (CF) indicate that soil of Beas River is less contaminated by the metals. The results of ecological risk index indicate that metals showed low ecological risk in the soils of Beas River.

The increase in soil salinity due to human-induced processes poses a severe threat to agriculture on a regional and global scale. Soil salinization caused by natural and anthropogenic factors is a vital environmental hazard, specifically in semi-arid and arid regions of the world. The detection and monitoring of salinity are critical to the sustainability of soil management. The current study compared the performance of machine learning models to produce spatial maps of electrical conductivity (EC) (as a proxy for salinity) in an alluvial irrigation plain. The current study area is located in the Isparta province (100 km2), land cover is mainly irrigated, and the dominant soils are Inceptisols, Mollisols, and Vertisols. Digital soil mapping (DSM) methodology was used, referring to the increase in the digital representation of soil formation factors with today’s technological advances. Plant and soil-based indices produced from the Sentinel 2A satellite image, topographic indices derived from the digital elevation model (DEM), and CORINE land cover classes were used as predictors. The support vector regression (SVR) algorithm revealed the best relationships in the study area. Considering the estimates of different algorithms, according to the FAO salinity classification, a minimum of 12.36% and a maximum of 20.19% of the study area can be classified as slightly saline. The low spatial dependence between model residuals limited the success of hybrid methods. The land irrigated cover played a significant role in predicting the current level of EC.

Predicting soil chemical properties such as soil organic carbon (SOC) and available phosphorus (Ava-P) content is critical in areas where different land uses exist. The distribution of SOC and Ava-P is influenced by both natural and anthropogenic factors. This study aimed at (1) predicting SOC and Ava-P in a piedmont plain of Northeast Iran using the Random Forests (RF) and Cubist mathematical models and hybrid models (Regression Kriging), (2) comparing the models’ results, and (3) identifying the key variables that influence the spatial dynamics of soil SOC and Ava-P under different agricultural practices. The machine learning models were trained with 201 composite surface soil samples and 24 ancillary data, including climate (C), organism (O), topography- relief (R), parent material (P) and key soil features (S) according to the SCORPAN digital soil mapping framework, which can predictively represent soil formation factors spatially. Clay, one of the most critical soil properties with a well-known relationship to SOC, was the most important predictor of SOC, followed by open-access multispectral satellite images-based vegetation and soil indices. Ava-P had a similar set of effective variables. Hybrid approaches did not improve model accuracy significantly, but they did reduce map uncertainty. In the validation set, Ava-P was calculated using the RF algorithm with a normalized root mean square (NRMSE) of 96.8, while SOC was calculated using the Cubist algorithm with an NRMSE of 94.2. These values did not change when using the hybrid technique for Ava-P; however, they changed just by 1% for SOC. The management of SOC content and the supply of Ava-P in agricultural activities can be guided by SOC and Ava-P digital distribution maps. Produced digital maps in which the soil scientist plays an active role can be used to identify areas where concentrations are high and need to be protected, where uncertainty is high and sampling is required for further monitoring.

ABSTRACT The drawing of maps of soil quality at a large scale is increasingly being more useful to land planners and stakeholders. Nevertheless, it involves different methodological steps from the description of soil profiles in the field until the regional mapping of integrative soil quality index (IQI) values. The development of proper scoring functions is a paramount task for the calculation of these IQI values since every parameter needs to be standardized accordingly and weighting factors are usually estimated by multivariate techniques. The main goal of this study was to map soil quality in the Spanish region of Extremadura (commonly known by its rangelands called dehesas). To do that, i) we gathered information from 194 soil profiles described throughout the region, ii) we calculated the weighting factors of ten meaningful parameters used as indicators by using multivariate techniques (Principal Component Analysis, PCA; and Analytic Hierarchy Process, AHP), and iii) we developed standard scoring functions (SSFs) that represent the singularity of every variable (less is better, more is better). We established upper and lower limits for standardizing the values of each indicator properly. Regarding weighting factors, soil texture was highlighted by the PCA and nutrients by the AHP. Once IQI values were calculated, two regional maps of soil quality were drawn by using interpolation methods (ordinary kriging). The IQI maps showed remarkable spatial differences in soil quality presumably induced by land management. We conclude this methodology could be useful and we encourage other colleagues to test its effectiveness in places where soil data are available.

Abstract 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.

Considering the important role of soil fertility and nutrient management in the modern agriculture seems to be a key step in appropriate site-specific fertilizers management for crop production. The present study was conducted to prepare a soil fertility zonation map based on soil nutrient elements including total nitrogen, available potassium and phosphorus, magnesium, manganese and iron and soil chemical parameters comprising cation exchange capacity, organic carbon, salinity and pH by integrated Fuzzy and AHP approaches for potato production in Rokh plain, northeast of Iran. In this regard the most important soil chemical parameters and nutrient elements in 0-30 cm depth of the soil was analyzed and mapped. The S-shaped fuzzy membership function was subsequently defined for each factor to fuzzify soil fertility parameters. The soil fertility map was prepared by weighing factor layers by the AHP approach and summation of factor layers by IDW interpolation function in GIS. The values of the soil fertility index in the scale of 0 to 1 ranged from 0.104 to 0.574, classified the study area in very low (922.90 km2), low (566.10 km2) and moderate fertility (14.86 km2) classes which comprises 61.37%, 37.64% and 0.99% of the surface area, respectively. A regression between soil fertility values and potato yield in the study area revealed a high correlation (R2 = 0.91) between the observed results which validate the zonation of the fertility classes in the region.

The present study was conducted to explore the fractal behavior and establish fractal dimensions of soil physical and chemical properties (i.e., sand, silt, and clay contents, bulk density, degree of moisture saturation, pH, organic carbon content, total nitrogen, available phosphorus, and available potassium) to characterize their spatial patterns. Soil samples were collected from 0-30 (surface) and 30-60 cm (subsurface) depths from an agricultural field, Mashhad Plain, Northeast Iran. Descriptive statistics and fractal analysis were used to describe the extent and form of variability. Spatial patterns of the soil properties were estimated using GS+ 10.0 software. Soil properties showed low to high variations in both surface and subsurface layers across the field, where bulk density and pH being the most reliable soil physical and chemical properties in the study area. The variability was high (CV > 35%) for total N, available P, available K and organic carbon in both surface and subsurface soils and it could be attributed to management practices and micro-topographical variations as these are the dynamic properties of soil. The fractal dimension (D) values of soil physical properties ranged from 1.398 to 1.913 at the surface, and from 1.874 to 1.934 at the subsurface indicating both short and long range variations. The D values for the chemical properties ranged from 1.331 to 1.975, and 1.148 to 1.990 in the surface and subsurface layers, respectively. The results showed that fractal analysis could be employed to effectively describe the structure of soil heterogeneity in spatial scale for effective agricultural and environmental management of soil.

The contamination of potentially toxic elements (PTEs) in agricultural soils is a serious concern around the globe, and modelling approaches is imperative in order to determine the possible hazards linked with PTEs. These techniques accurately assess the PTEs in soil, which play a pivotal role in eliminating the weaknesses in determining PTEs in soils. This paper aims to predict the concentration of Cu, Co and Pb using neural networks (NNs) based on multilayer perceptron (MLP) and boosted regression trees (BT). Statistical performance estimation factors were rummage-sale to measure the performance of developed models. Comparison of the coefficient of correlation and root mean squared error suggest that MLP-established models perform better than BT-based models for predicting the concentration of Cu and Pb, whereas BT models perform better than MLP established models at predicting the concentration of Co.

Processing improves the visual quality of food; however, the ingredient and nutritional values may alter. In present study, the sugar samples (refined, raw, gur (Jaggery/jaggeree) and molasses) were collected from 20 different agro-climatic regions of Pakistan and analyzed for the chromium concentration, total phenolic and antioxidant activity, in order to evaluate the processing effect. The concentration of Cr was determined by digestion method using atomic absorption spectrophotometer, while antioxidant activity was determined by DPPH (1,1-diphenyl-2-picrylhydrazyl) and reducing power. It was observed that chromium content in Jaggery was 74% higher than raw sugar, while molasses showed 21.27% higher concentration versus jaggery. The total phenolic contents were found considerably higher in molasses (3751 μg GAE/g) followed by Jaggery (3285 μg GAE/g), raw sugar (27.75 μg GAE/g) and refined sugar (23.81 μg GAE/g). The DPPH scavenging activity and reducing power was also found dependent to sugar type. Form results, it can be concluded that processing significantly affected the chromium contents, total phenolics and antioxidant activity.

Artificial neural network (ANN) model was developed and tested for estimating soil phosphorus (P) in Kouhin watershed area (1000 ha), Qazvin province, Iran using terrain analysis. Based on the soil distribution correlation, vegetation growth pattern across the topographically heterogeneous landscape, the topographic and vegetation attributes were used in addition to pedologic information for the development of ANN model in area for estimating of soil phosphorus. Totally, 85 samples were collected and tested for phosphorus contents and corresponding attributes were estimated by the digital elevation model (DEM). In order to develop the pedo-transfer functions, data linearity was checked, correlated and 80% was used for modeling and ANN was tested using 20% of collected data. Results indicate that 68% of the variation in soil phosphorus could be explained by elevation and Band 1 data and significant correlation was observed between input variables and phosphorus contents. There was a significant correlation between soil P and terrain attributes which can be used to derive the pedo-transfer function for soil P estimation to manage nutrient deficiency. Results showed that P values can be calculated more accurately with the ANN-based pedo-transfer function with the input topographic variables along with the Band 1.

In recent years, the term Resistance economy is raised by the great Leader of Iran and then it is turned into the common concept of political economy issues. Various theorists presented their views by various approaches; some people investigated by economy literature, others by Islamic economy and evaluated the role of culture and politics in formation of resistive economy. Regarding Resistance economy, at first we should have a true understanding of national economy and global economy and the pressure of the global dominancy system on Iran. After defining the concept of resistance economy, the present study evaluated its importance and goals and then the pathology of Iran economy and barriers and requirements of resistance economy fulfillment and the solutions of eliminating the barriers based on resistive economy were considered. Finally, some solutions are presented to develop resistance economy.

The proper delineation of site-specific management zones is very important in the agricultural land management of potentially degraded areas. There is a necessity for the development of prospective tools in management plans to correctly understand the land degradation processes. In order to accomplish this, we present a pedo-geomorphological approach using soil texture, land elevation and flow vector aspects to distinguish different management zones and to discretize soil micronutrients. To achieve this goal, we conducted the study in the Neyshabur plain, Northeast Iran. For data collection, grid sampling (500 × 500 m) was used with 70 specific points. Soil samples were collected in triplicates from various sites as composite samples (0−30 cm) to analyse clay, Zn, Mn, Cu and Fe. Using the altitude information (obtained with GPS at each sampling point), flow vectors were also modelled for all selected points. Based on the values of altitude, flow vectors and clay, management zones were delimited using geographic information systems. The best data organization was obtained from the combination of clay + elevation + flow vector attributes, generating two different management zones. In this circumstance, the lowest fuzzy performance index (FPI) and modified partition entropy (MPE) values were generated. It can be observed that the management zone 1 (MZ1) is located in the areas with a lower elevation and higher content of clay. On the other hand, the MZ2 was characterized by areas with a higher elevation and lower clay content. This study concluded that the design of management zones, using pedo-geomorphological information could reduce the time and cost of sampling necessary to assess potentially degraded areas of land.

Abstract Background Metabolic syndrome is a prevalent and common health problem and numerous studies have revealed the role of diet and lifestyle change in prevention of metabolic syndrome. However, the novel dietary index, cardioprotective index (CPI) and its association with metabolic syndrome is not investigated yet. In the current study, we evaluated the association between metabolic syndrome and its components, CPI, Nesfatin-1 and Omentin-1in a cross-sectional study. Methods Three hundred forty eight overweight and obese individuals with metabolic syndrome were recruited. Subjects underwent anthropometric and laboratory assays including metabolic markers, Nesfatin-1 and Omentin-1 with commercial kits. Results Those at the first tertile of CPI had lower high density lipoprotein concentrations (HDL) and higher low density lipoprotein concentrations (LDL), triglyceride (TG), systolic and diastolic blood pressures (SBP, DBP) levels compared with those at the highest tertiles (P 0.05). Additionally, being at the third tertile of CPI was accompanied with significantly higher Nesfatin-1 and Omentin-1 levels compared with lowest tertiels (P