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Radiation detection at high count rate suffers from pulse pile-up, where the counting data and energy information of the system are affected by the overlapping of the system output pulses. There exist various pile-up correction strategies to recover the true information of the pulses, among which pulse-tail extrapolation is a well-known method focused on in this study.Present work aims to use a mono-exponential model for extrapolating the pileup-distorted trailing edge of a pulse, to provide a reference line for calculating the true amplitude of its subsequent overlapping pulse. To this goal, the auto-regression on linear operations (ARLO) method is examined and compared with two integration-based methods (the Foss and the Matheson methods), as well as the non-linear least squares (NLS) method.Despite a higher sensitivity to noise, the ARLO method was able to provide a simple, non-iterative solution with a performance over 400 times faster than the NLS algorithm, according to the analysis of a high count rate set of experimental pulses from a NaI(Tl) detection system. Foss and Matheson methods also provided solutions reasonably faster than NLS (but not surpassing ARLO), performing exactly the same as each other with results very close to NLS, benefiting from their non-iterative nature.

Introduction In recent years, one of the most significant environmental crises has been the phenomenon of wind erosion and dust emission. Wind erosion is considered one of the effective factors in desertification and land degradation in dry regions. The process of wind erosion, due to the transport of soil nutrients along with fine particles, is recognized as one of the limiting factors for soil fertility in many parts of the world. Evaluating this type of erosion and estimating the soil loss requires the installation of various measuring stations. Establishing and equipping these stations and providing the necessary equipment require large costs and a long time. In addition to direct measurement methods in wind erosion, the use of modeling results, especially in combination with remote sensing techniques, to study and predict environmental effects, trends, and risk assessment has greatly contributed to research in the last two decades. Therefore, this study aims to monitor and investigate the warning zones of wind erosion in northwest Golestan Province using the wind erosion hazard index (WEHI) model. Materials and Methods To assess wind erosion in the western part of Golestan Province, the WEHI model was implemented. This model predicts the sensitivity of the landscape to wind erosion by considering a set of surface and climatic thresholds and using a geographic information system (GIS). In this model, wind erosion severity is determined in three classes: low, moderate, and severe, by multiplying wind speed by the percentage of bare soil and dividing it by the percentage of soil moisture percentage. To monitor wind erosion, the normalized difference moisture index (NDMI) was used to evaluate soil moisture, and the modified bare soil index (MBI) was used to assess bare soil. For validation, these indices were compared to field data and plots in each working unit. Additionally, three statistical parameters, Pearson correlation coefficient, coefficient of determination (R2), and root mean square error (RMSE), were employed to calculate the correlation between these indices and ground data. Furthermore, a Markov chain model was used to examine changes in wind erosion classes. Finally, after monitoring wind erosion and considering a threshold value for this model, the area of warning zones during the statistical period was investigated. Results and Discussion The results obtained from the WEHI model indicate severe wind erosion intensity in the working units of Atark floodplain deposits, saline lands, longitudinal dunes, Barchan dunes, bare lands, and margins area of wetlands. According to the WEHI model, the region was divided into three classes: low (54% frequency), moderate (21% frequency), and severe (25% frequency). The model classified the northern regions into severe and moderate classes, while the southern areas of the region fell into the low wind erosion class. High correlation coefficients between the WEHI model indices based on remote sensing and field data demonstrate the model's ability to monitor wind erosion over time and at different scales. Wind erosion monitoring results showed that the high wind erosion class increased from 59,940.88 ha to 71,698.3 ha, indicating an increase of 11,757.43 ha. Spatial analysis of wind erosion classes indicated that most changes occurred in central areas, with most areas around the Sangartappeh playa and central regions changed to severe wind erosion class, while western, eastern, and central areas changed to the low wind erosion class. Finally, monitoring the warning zones revealed an increase of 41,000 hectares in the areas under warning, in the western, northwestern, and central regions. Conclusion In this study, the performance of the WEHI model in assessing wind erosion risk in the western part of Golestan Province was confirmed. Although factors such as roughness, soil structure, and organic matter content are not directly considered in the model, they are indirectly incorporated in scoring the percentage of bare soil. To control wind erosion in these areas, planting salt-tolerant species and implementing soil fertility enhancement strategies, such as soil mulching in heavy-textured areas, are recommended. Finally, mechanical operations and the establishment of windbreak networks are suggested for controlling wind erosion in abandoned land units. This research can serve as a useful approach for planning and managing vulnerable areas to wind erosion in northwest Golestan Province.

Radiation detection systems working at high count rates suffer from the overlapping of their output electric pulses, known as pulse pile-up phenomenon, resulting in spectrum distortion and degradation of the energy resolution. Pulse tail extrapolation is a pile-up correction method which tries to restore the shifted baseline of a piled-up pulse by extrapolating the overlapped part of its preceding pulse. This needs a mathematical model which is almost always nonlinear, fitted usually by a nonlinear least squares (NLS) technique. NLS is an iterative, potentially time-consuming method.The main idea of the present study is to replace the NLS technique by an integration-based non-iterative method (NIM) for pulse tail extrapolation by an exponential model. The idea of linear extrapolation, as another non-iterative method, is also investigated. Analysis of experimental data of a NaI(Tl) radiation detector shows that the proposed non-iterative method is able to provide a corrected spectrum quite similar with the NLS method, with a dramatically reduced computation time and complexity of the algorithm. The linear extrapolation approach suffers from a poor energy resolution and throughput rate in comparison with NIM and NLS techniques, but provides the shortest computation time.

Soil contamination by heavy metals is a significant environmental problem worldwide. Human activities, including mining, are a source of pollution to natural resources. In this study, the phytoremediation potential of rangeland species including, Artemisia sieberi Besser, Zygophyllum atriplicoides Fisch. and C.A. Mey for the absorption of heavy metals of Ni, Co, As, Cr and V was investigated around the bauxite crusher of the Jajarm alumina mine in the direction of the prevailing wind. Soil sampling was performed at the distances of 450-700, 800-1200, 1400-2000 and 2500-5000 meters from the center of the bauxite crusher, and the control site. For this purpose, the seeds were planted in pots with a 3:1:1 ratio of sand, fertilizer and soil. Two-months later, they were transferred to pots with soils sampled from target distances. After completing of a vegetative period, the phytoremediation potential of the species was performed a factorial experiment in a completely randomized block design with three replications in greenhouse conditions. Results indicated that As, Ni, Cr, and Co absorption in above/underground of A. sieberi tissues were higher than Z. atriplicoides. But V absorption in above/underground of Z. atriplicoides tissues were higher than that for A. sieberi and between the vanadium accumulation in the aerial and underground organs of the target species and distances a significant difference was observed at the level of 1%. The amount of heavy metals transfer factor for A. sieberi and Z. atriplicoides was more and less than one in turn. Therefore, in terms of the transfer factor, A. sieberi has a potential for phyto-extraction and Z. atriplicoides has a potential for phyto-stabilization. The results also showed that A. sieberi and Z. atriplicoides were recognized as hyper-accumulating of nickel and cobalt metals. These species can therefore be recommended for the ecological restoration of soils polluted by heavy metals, especially nickel and cobalt in similar regions.

Introduction Arid and semi-arid areas often have sparse and scattered vegetation cover. In many arid regions of the world, open spaces between plants are occupied by particular living organisms called biological soil crusts (BSCs) or biocrusts. BSCs are the dense population of living organisms such as cyanobacteria, algae, fungi, lichens, and mosses in different proportions that live on the soil surface or within the upper few millimeters of soil. The aim of the present study was to investigate the effect of biological soil crusts on surface runoff quality in hillslopes around Ajigol Wetland in the Golestan Province of Iran. Materials and Methods The study area is located in the northern part of Golestan Province. Elevation in the study area ranges from 7 to 32 meters above sea level. The topography of the area is gentle and the land surface is composed of loess deposits. According to the climatic conditions, the research area is classified as arid and semi-arid regions. Due to a lack of rainfall, high evaporation, and uneven distribution of rainfall throughout the year, as well as high soil salinity, it has low-growing and weak rangeland plants. The vegetation in the area is composed mainly of annual grass species scattered heterogeneously. They often appear after rain events and have a short growth period finishing the life cycle in one season. In this research a field rainfall simulator was used. First, field visits were conducted to select places for positioning rainfall-runoff simulation plots in different types of biocrusts. To eliminate the effect of slope on runoff processes, locations were selected whose slope was around the dominant slope of the region (around 20 %). Rainfall-runoff simulations were carried out using a rainfall simulator over 1 x 2-m plots with and without biological soil crusts. The intensity of the simulated rainfalls was about 80 mm h-1 and the duration of each simulation was 30 min. The plots were positioned over five different types of surface cover including 1) dominant moss cover, 2) dominant lichen cover, 3) mixed (moss + lichen) cover, 4) dominant shrub (Artemisia spp.) cover, and 5) Bare land. Sampling and measurement of some runoff quality variables (sediment, electrical conductivity (EC), acidity (pH), color, and Total Dissolved Solids (TDS)) were conducted at 15-minute intervals during the simulation, plus one more sample from a mixture of runoff of the whole simulation. For some other water quality variables (organic carbon, nitrate, phosphorus, and potassium) measurements were made only at the end of the simulation from the total runoff mixture. The data were analyzed using graphical methods (plots) and statistical tests: analysis of variance (ANOVA), Kruskal-Wallis, and Tukey’s test. Results and Discussion The results showed that sediment concentrations were significantly (P ˂ 0.05) lower for biocrust-covered plots compared to the plots without biocrusts. Extreme differences were observed for the bare soil. EC, pH, color and TDS values also had significant differences between different covers. For organic carbon, phosphorus, nitrate, and potassium, no significant differences (P ˂ 0.05) between covers were detected by statistical tests though some notable differences were discernible on plots. The origin of runoff EC is mostly inorganic substances and it is caused by natural and human-induced pollution. There was a significant difference (P ˂ 0.05) between shrub-covered plots and plots with a combination of moss and lichen. EC for the shrub cover (Artemisia spp.) was found to be significantly higher than the mixed moss and lichen cover. The reason can be attributed to the increase in permeability and soil moisture in BSC dominated areas. Increased infiltration of water by biocrusts causes salts and ions to move deeper into the soil and this reduces the salinity of upper soil layer and surface runoff. With regard to runoff color, a significant difference (P˂ 0.05) was observed between bare soil and the other cover types. By producing polysaccharides and viscous materials, BSCs preserve and stabilize the soil surface materials and reduces the transport of metal ions (such as iron and manganese), decayed plant materials, organic matter, and animal waste as the main factors for the coloration of runoff. In contrast, more detachment and transfer of materials from bare soil have caused the runoff to become thicker and darker. The amount of sediment concentration from bare land was higher than shrubland and biocrust covers. For example, the average sediment concentration in the runoff from plots of bare land was about three times that of Artemisia plots. Another notable point was the large difference in sediment concentration between bare soil plots themselves. The reason for this was attributed to the presence of remaining roots of annual plants in the bare soil plots, which influence runoff and soil loss. Conclusions Overall, the results indicate the major effect of BSCs on runoff quality. So, taking proper measures to protect them and prevent their destruction is of great importance for soil and water conservation as well as water quality preservation in downstream wetlands. Therefore, it is necessary for government agencies to pay more attention to BSC-covered hillslopes around the Ajigol Wetland so no more damages are imposed on these fragile unique resources. As no comprehensive map of BSC covers the study area is present, it is recommended that such a map be prepared using satellite and drone imagery. Then, by combining the results of this study with the information obtained from mapping and generalizing it to the entire region, it is possible to make an overall estimate of the effect of BSCs on the water quality of downstream wetlands which is necessary for better-informed planning and decision-making. Exclosure and cover protection measures to prevent physical damages caused by human activities need to be implemented for the BSC-covered areas so they can continue their function as living mulch and protect soil from water and wind erosion.

In this research, the desertification process is evaluated using 3 climate indicators and 4 groundwater indices in the form of a modal model in a period of 20 years (2008-2017) in the west of Golestan province with the aim of providing a desertification warning system.Then, by determining the threshold limits for the influential indicators in the desertification process of the region, the desertification early warning system was presented based on four indicators. The results of the index change the trend showed that drought indices, total soluble materials, electrical conductivity, and groundwater depth had the greatest impact on desertification in the region, respectively. Desertification warning maps have shown an increase in drought indicators, total solubility, and electrical conductivity from west to east. Also, the groundwater depth index in the central areas has the highest warning range. According to the results obtained from monitoring desertification indicators, the electrical conductivity index during the years 1999, 2001, 2004 to 2006 and 2009, the Total dissolved solids index in the years 1998, 1999, 2001, 2004, 2006 and 2007, Groundwater depth index of 2001, 2002, 2006, 2008 and 2014, Finally, the drought index is in the warning range during the years 1999 to 2003, 2006 to 2008, 2011, 2014, 2015 and 2016. Irrigated lands, drylands and wetlands in the East region affected by desertification warning have been that on this basis is suggested for better management of the land, the threshold defined in this study as the basis for the work to be placed.

Dust event is one of the atmospheric events of world arid and semi-arid regions that had a notable increase in a recent year and negative effects in different parts. MODIS imagery provides an acceptable data source for accurate and timely monitoring of dust storms. However, there are useful dust indices based on MODIS imagery. In this study, it has been used an improved brightness temperature adjusted dust index by Compound the brightness temperatures of three thermal infrared MODIS bands including band 20, band 31 and band 32 to monitor six representative dust storms over the West of Middle East between 2000 and 2016. When the dust storm indices of MODIS including the brightness temperature difference index in bands 32 and 31 (BTD32-31) and the normalized difference dust index (NDDI) and BADI compared together, the BADI index more accurately estimated the spatial density of dust storms in our study area. The regression analysis has been showing significant correlations between the BADI index and MODIS Deep Blue Aerosol Optical Depth values. For the Five dust storms, the determination coefficients (R2) of the regression between the BADI index and MODIS Deep Blue AOD values were 0.44, 0.48, 0.67, 0.53 and 0.45 (P < 0.01), respectively. Considering that BTD 32-31 and NDDI are two widely used indices to detect dust storm, we compared the results obtained using the BTD 32-31, NDDI and BADI for detection of a dust-storm event that occurred on 17 Jul 2016 in order to illustrate the advantages of the BADI. The BADI index with the standard density index of MODIS Deep Blue Aerosol Optical Depth had the statistically significant relationship at P ≤ 0.01.

Desertification has turned into a serious hazard especially for developing countries such as Iran. Experimental models are the most suitable methods for assessment of the desertification intensity. The aim of this study was to evaluate the desertification hazard and risk of the management programs in the desert plains of Segzi in Isfahan province of Iran. According to the MEDALUS model, the results showed that the risk of desertification falls into three classes: very low (5.6 %), severe (21.13 %), and very severe (73.27)% . Climatic criteria (score 193), wind erosion (score 176), and management and policy (score 172) have the greatest impact on desertification severity in the region, respectively. The desertification risk of the region was also evaluated with regard to the desertification hazard map, the environmental elements at risk as well as their sensitivity .The risk assessment results were calculated and classified into five categories : very low, low, medium, high and very high, wherein high and very high classes cover 40% of the area. The highest class denotes to a very low desertification risk with 39% of the area. Based on the obtained results, ground specifications and opinions of experts, an appropriate management plan was proposed. Under critical condition and based on high risk values, plans and control measures such as proper management, the use of new irrigation methods, the use of salinity resistant crops and windbreaks , and also in some areas, preserving the status quo can be applied to prevent the risk of desertification .

Understanding of distribution of plants and analyzing their spatial pattern reveal the interactions of plants and environment. Nebka as an indicator phenomenon of plant-sediment interaction, plays an important role in protecting of vegetation and sand stabilization. The aim of the present study was to evaluate the interaction of Nebka and plant based on a field study, measuring the morphometric parameters, and precise point positioning. Therefore, 322 Nebkas and 119 plants were determined in the northern region of Aq-Qala, Golestan province. In order to investigate and determine the spatial pattern and interactions of Nebkas and plants, and impact of soil parameters on their distribution pattern, univariate and bivariate functions including g and mark correlation functions were used. The pair correlation function showed that spatial pattern of plant at distance of less than 23 m was irregular. This means there is no competition between plants at distance of less than 23 m. Furthermore spatial pattern of Nebka was irregular at distance of less than 23 m. Bivariate g function at distances less than 50 m indicated positive correlation between plant and Nebka. Therefore, all plants and Nebkas have a positive impact on formation and distribution of each other. Results of the MCF function with 95% probability showed that soil properties such as EC, pH, OM, SAR, and skewness of Nebka sediments influence the Nebka’s pattern in the study area.

Although root crops are widely cultivated in Iran, little is known about soil loss due to crop harvesting (SLCH). We assessed the annual exported soil, soil organic matter (SOM) and nutrients from 47 farms under root crops in southwestern Iran. Soil losses for garlic, potato, sugar beet, radish and beetroot were estimated as 6.27, 2.52, 2.26, 4.10 and 6.95, Mg ha−1, respectively, which on average was of the order of soil losses by water erosion in the watershed basins of Iran. Total N, P2O5and K2O losses were estimated as 36.61, 1.10 and 31.50 kg ha−1and their costs as 18.24, 0.74 and 19.93 US$ ha−1, respectively. For the whole country, total soil, N, P2O5, K2O and SOM losses for garlic, potato and sugar beet were estimated as 731.7 × 103, 836, 27, 476 and 14.5 × 103Mg, respectively (radish and beetroot were excluded due to no reliable data on their planted areas). Correlation analysis showed no significant relationship between soil properties and SLCH (except soil moisture content for radish and clay content for beetroot). The findings indicated that the exported soil, SOM and nutrients were at such levels that SLCH should be considered in soil erosion studies. [ABSTRACT FROM AUTHOR]

A combination of Finite Difference Time Domain (FDTD) and Monte Carlo (MC) methods is proposed for simulation and analysis of ZnO microscintillators grown in polycarbonate membrane. A planar 10 keV X-ray source irradiating the detector is simulated by MC method, which provides the amount of absorbed X-ray energy in the assembly. The transport of generated UV scintillation light and its propagation in the detector was studied by the FDTD method. Detector responses to different probable scintillation sites and under different energies of X-ray source from 10 to 25 keV are reported. Finally, the tapered geometry for the scintillators is proposed, which shows enhanced spatial resolution in comparison to cylindrical geometry for imaging applications. [ABSTRACT FROM AUTHOR]

Purpose In pharmacokinetic analysis of Dynamic Contrast Enhanced MRI data, a descriptive physiological model should be selected properly out of a set of candidate models. Classical techniques suggested for this purpose suffer from issues like computation time and general fitting problems. This article proposes an approach based on Artificial Neural Networks (ANNs) for solving these problems. Methods A set of three physiologically and mathematically nested models generated from the Tofts model were assumed: Model I, II and III. These models cover three possible tissue types from normal to malignant. Using 21 experimental arterial input functions and 12 levels of noise, a set of 27,216 time traces were generated. ANN was validated and optimized by the k-fold cross validation technique. An experimental dataset of 20 patients with glioblastoma was applied to ANN and the results were compared to outputs of F-test using Dice index. Results Optimum neuronal architecture ([6:7:1]) and number of training epochs (50) of the ANN were determined. ANN correctly classified more than 99% of the dataset. Confusion matrices for both ANN and F-test results showed the superior performance of the ANN classifier. The average Dice index (over 20 patients) indicated a 75% similarity between model selection maps of ANN and F-test. Conclusions ANN improves the model selection process by removing the need for time-consuming, problematic fitting algorithms; as well as the need for hypothesis testing. [ABSTRACT FROM AUTHOR]

Fuel cells are used as a power generator in distributed generation and aircrafts applications. This paper proposes a new high step-up DC-DC converter with zero-voltage switching (ZVS) of switches and zero current switching (ZCS) of diodes, to be used as fuel cell's power conditioner. To have a ripple free input current, a coupled inductor can be used at the input side of the converter. The voltage gain of the converter is calculated and required conditions for soft switching have been presented. The voltage stress of elements of the proposed converter is discussed, the efficiency of converter is derived and finally experimental results have been used to validate the properties of the converter. [ABSTRACT FROM AUTHOR]

Abstract It is of fundamental importance to model the relationship between geo-environmental factors and piping erosion because of the environmental degradation attributed to soil loss. Methods that identify areas prone to piping erosion at the regional scale are limited. The main objective of this research is to develop a novel modeling approach by using three machine learning algorithms—mixture discriminant analysis (MDA), flexible discriminant analysis (FDA), and support vector machine (SVM) in addition to an unmanned aerial vehicle (UAV) images to map susceptibility to piping erosion in the loess-covered hilly region of Golestan Province, Northeast Iran. In this research, we have used 22 geo-environmental indices/factors and 345 identified pipes as predictors and dependent variables. The piping susceptibility maps were assessed by the area under the ROC curve (AUC). Validation of the results showed that the AUC for the three mentioned algorithms varied from 90.32% to 92.45%. We concluded that the proposed approach could efficiently produce a piping susceptibility map. Graphical abstract Unlabelled Image Highlights • Piping susceptibility can be predicted with SVM, MDA, FDA, and LR models. • Piping erosion occurrence are mainly controlled by silt content based on ME model. • Bulk density had the most influence on the occurrence of piping based on LR model. • The SVM model shows the best result in piping prediction (SVM = 93.15%). [ABSTRACT FROM AUTHOR]

The freeze-thaw phenomenon is one of the factors, which has an important role in increasing the qualitative and quantitative loss of soil. However, the dynamics and mechanism of freeze-thaw processes remain unknown. Therefore, adoption of appropriate conservation measures in the highland and mountains region and reduction of the effects of a freeze-thaw cycle on soil loss through application of environmentally friendly and feasible techniques of application of soil microenterprises is critical. Bacteria and cyanobacteria play an important role in the ecosystem function by improving soil physical and chemical properties. Nonetheless, the effectiveness of such microorganism on controlling soil loss has not been studied yet. The present study was accordingly designed to inhibit soil loss through inoculation of soil microorganisms including bacteria and cyanobacteria at 0.25 m2-plots under the freeze-thaw process and simulation of rainfall of 70 mmh−1 nested at the slope of 20%. To this end, the most suitable bacteria and cyanobacteria effective in soil and water conservation and available in the soil organism's micro-bank were selected, purified, and propagated as a biological method to alleviate freeze-thaw cycle effects. The experimental treatments were prepared in the form of bacterial additions, cyanobacterial additions, and control at the plot scales. After 60 days under freeze-thaw cycles, the results of statistical analyses confirmed significant inhibition (P < 0.0001) of soil loss and its components through inoculation of bacteria and cyanobacteria. Bacteria and cyanobacteria inoculation treatments reduced mean soil loss by 3.03 and 7.07 times, respectively. They also declined mean sediment concentrations by 2.00 and 2.90 times, respectively. In addition, the bacterial and cyanobacterial inoculated treatments reduced soil loss peaks by 3.80 and 8.20 times and increased the times to peak of soil loss by 1.11 and 1.35 times, respectively. Unlabelled Image • Soil loss under a freezing and thawing cycle and soil microorganisms inoculation was studied. • Bacteria and cyanobacteria inhibited the effects of freezing and thawing cycle on soil loss. • Peak time and sediment concentration were better controlled by microorganisms inoculation. • Application of bacteria and cyanobacteria is suggested to be used for soil erosion control. [ABSTRACT FROM AUTHOR]