محبوبه صادقی

Soil is one of the important natural resources of any country, which plays an important role in preserving the environment and producing food. Increasing and decreasing the amount of total soil nitrogen due to various agricultural methods, the entry of industrial wastewater into water and other factors, leads to microbial contamination of soil, reduced vegetation and deficiency in agricultural products needed by humans. Mapping soil nutrient distribution helps mangers in decisions. Since laboratory analysis of these parameters is time consuming and costly across large scales, attempts have been made in recent years to study soil nitrogen based on remote sensing techniques. In this regard, the present study investigated the applicability of remote sensing predicting soil total nitrogen in the east of Lenjan city.

Nitrogen reference points were identified by analyzing 50 randomly-selected surface soil samples from 0-20 cm depth. Nitrogen of soil samples was measured by Kjeldahl method after drying soil at 25 ° C, passing through a 2 mm mesh sieve and transferring to the laboratory, to compare the final results obtained from field sampling and remote sensing technology. Landsat 8 OLI Satellite Image of 2019 (Path 164/Row 37) was obtained and geometric and radiometric correction were applied. Cloud cover for image provided was less than 10%. To reduce the effect of atmospheric diffusion on the quality of image, radiation and atmospheric correction were performed using the FLASH model. the Landsat-8 satellite image (rows 164 and 37) taken on 15 Sep. 2019 and along with three topographic indices of elevation, slope and topographic wetness index (TWI) were introduced to the multiple linear regression and random forest models. 

The digital elevation map of the area showed elevation values ​​between 1100 and 2050 meters. The slope of the study area was less than eight percent. Numerical values ​​of TWI index near water bodies were found to be 0.77. DVI and EVI index values ​​increased with increasing vegetation cover. NDVI index showed values ​​higher than 0.3 and NDWI index as a water index showed a maximum value of 0.77. The SAVI index showed high differences from areas without cover to sparse cover and areas with strong vegetation. SBI index and SI salinity indices showed very high variability in terms of soil parameters in barren lands. Nitrogen regression model was built using three indices RVI, DVI and TWI with p-values of 0.049 and 0.00, respectively. In the nitrogen random forest model, however, plant and soil indices played a more important role in model construction with an of r2 value of 0.44.

Total soil nitrogen in soil parameters showed correlation with density and sand and clay from soil texture and in topographic parameters with elevation and spectral indices with EVI RVI, SAVI, NDWI, NDVI and DVI at the level of 0.01 and with SI3 of salinity indices at the 0.05 level. In soil parameters, silt is correlated with sand and clay at the level of 0.05 and sand with clay as well as density with clay are correlated at the level of 0.01. The results of this study showed that the topographic condition of the region along with red and near infrared-based indices had a significant role in predicting soil total nitrogen. Results also showed a slight difference was observed between the two models in predicting soil nitrogen.

Nowadays, soil salinization is one of the world’s major threats that reduce soil productivity by intensifying the process of desertification and land degradation. Since laboratory analysis is mostly time consuming and costly, especially in large scales, attempts have been made to study soil salinity using remote sensing techniques in recent years. The present study assessed the potential of remote sensing in predicting soil surface salinity in the east of Lenjan                 City. Salinity reference points were identified based on analyzing 50 randomly selected surface soil samples. Satellite indices including DVI, NDVI, EVI, MSAVI, SAVI, RVI, NDWI, SI1, SI2, SI3 and SBI were derived from a Landsat-8 satellite image (path and row of 164 and 37) acquired on September 13, 2019. These indices along with three topographic indices of elevation, slope and topographic wetness index (TWI) were introduced to the Multiple Linear regression and Random Forest models. The linear regression model was built using band 6, RVI, NDVI, elevation and TWI with a p-value of 0.049. In the Random Forest model, band 7, slope, band 5 and elevation were among the most important parameters. The r2 value of this model was 0.21. The results of this study showed that topographic indices had also great importance in salinity prediction. Moreover, comparison of the results indicated that Random Forest had a higher accuracy than the regression model for determining salinity in the study area.

Hand washing is one of the most efficient and effective ways to prevent the transmission of microorganisms from one person or one place to the patients. However, despite the importance of hand washing by the medical personnel, the results of some studies indicate lack of proper hand washing by them. The present study aimed to determine the barriers to hand washing from the viewpoint of nurses working in general hospitals affiliated to Shiraz University of Medical Sciences in 2017. This was a cross-sectional and descriptive-analytical study conducted on 312 nurses working in general hospitals affiliated to Shiraz University of Medical Sciences. The required data were collected using a valid and reliable questionnaire consisting of two parts, including the studied nurses' demographic characteristics and 48 items related to the hand hygiene barriers in four dimensions of individual barriers (12 items), managerial barriers (19 items), equipment barriers (10 items), and environmental barriers (7 items). The collected data were analyzed using SPSS 21.0 through independent t-test and ANOVA. The p value of less than 0.05 was regarded as statistically significant. The results showed that equipment barriers and individual barriers had, respectively, the highest (2.85 ± 0.65) and lowest (2.62 ± 0.46) means. Also, there were significant relationships between the means of managerial and equipment barriers and the studied nurses' sex, between the means of individual and managerial barriers and the nurses' employment status, between the means of equipment barriers and the nurses’ marital status, and finally, between the means of managerial barriers and the nurses' working shifts (P-value<0.05). According to the results, in order to remove the barriers to the nurses' hand washing, the following suggestions can be made: running adequate and appropriate training courses and developing a proper culture in the hospital wards to increase hand washing, using appropriate monitoring and motivational systems; providing timely, adequate facilities and equipment and also selecting appropriate disinfectants for nurses' hand washing, etc.