Evaluating Automatic Detector of Wetting Front for Monitoring Water Movement in Soil

In order to increase water productivity at farm level and smart irrigation, appropriate systems are essential so that can measure the moisture content in a proper and acceptable accuracy. In this regard, the wetting front device was made at the Soil and Water Research Institute and was evaluated in 3 textures: light, medium, and heavy at 3 levels of water salinity: no salinity (S0), 5(S1), and 10 (S2) dS/m. Then, to evaluate and measure the device in the mentioned conditions, it was tried to simultaneously measure the device numbers and soil sampling at certain depths at intervals of 24 h. After collecting data on the time of arrival of the moisture front to the soil depth and changes in soil moisture, statistical analysis was performed by soil sampling and soil detection device. The results showed that the device reacts in different textures and salinities. Moreover, the sensitivity of its sensors to sudden changes in soil moisture due to the arrival of the moisture front to a certain depth of soil has acceptable accuracy. Statistical results showed that the device has about 6 to 9% normal error in determining soil moisture in non-saline conditions, 28 to 41% in terms of using water with salinity of 5 dS/m, and 31-37% when a water with salinity of 10 dS/m was used. The model efficiency index also showed that the device is very useful in non-saline conditions with an average efficiency of 0.75 and is not recommended in saline conditions with negative efficiency index

Evaluating Reference Evapotranspiration Using Data Mining Methods and Comparing it with the Results of Water Requirement System in Qazvin Province

IntroductionThe accuracy of determining reference evapotranspiration (ET0) is an important factor in estimating agricultural and garden water requirements. The complexity of the evapotranspiration process and its dependence on meteorological data have made it difficult to accurately estimate this variable. Non-linearity, inherent uncertainty and the need for diverse climatic information in ET0 estimation have been the reasons that have made researchers interested in data mining methods such as artificial neural network (ANNs), random forest (RF) and support vector machine (SVM). Dos et al. (2020) evaluated the performance of machine learning methods to estimate daily ET0 with limited meteorological data. Their results showed that machine learning methods estimate ET0 with high accuracy, even in the absence of some variables. The use of artificial intelligence models in estimating ET0 with high accuracy has become popular in recent years, but the complexity of these models makes it difficult to apply them to regions with different climatic conditions) Feng and Tian, 2021.( Therefore, the aim of this study is to show that different data mining methods are suitable for daily ET0 estimation, which can reach a comprehensive and simple model with high accuracy by using minimal weather data.Materials and MethodsIn this research, the accuracy of data mining methods in estimating ET0 was evaluated in comparison with the plant water requirement system (FAO-Penman-Monteith standard method). For this purpose, data related to meteorological parameters such as sunshine hour, air temperature, wind speed, and relative humidity air were collected from ten synoptic stations and five climatology stations of Qazvin province in a period of 10 years (1389-1399). The ET0 extracted from the plant water requirement system was calculated based on the Penman-Moanteith method of FAO 56 and on a daily time scale, which is the actual value (measured) with the estimated values obtained by data mining methods (ANNs, RF and SVM) were evaluated. In order to validate the obtained results, the data of each station was divided into two sets of training (two-thirds of data) and testing (one-third of data). Finally, the generalizability of the mentioned methods in estimating ET0 was investigated based on NRMSE, R2, RMSE, MBE, EF and d Criteria.Results and DiscussionThe results showed that the ET0 values of the plant water requirement system have a good correlation with the estimated ET0 values of ANNs, RF, and SVM methods. In this research, the accuracy of the results of ANNs method was relatively higher than the other two methods. The results of statistical investigations and diagrams showed that ANNs, RF and SVM methods, considering all meteorological parameters (mean air temperature, average relative humidity, sunshine hours and wind speed) as input to the model, in Qazvin synoptic station with altitude 1279 meters and the climatology station of Rajaei power plant with a height of 1318 meters, estimated ET0 with higher accuracy in both training and testing steps.In the ANNs method, the values of NRMSE and R2 at Qazvin synoptic station in both training and testing steps are equal to 0.11 and 0.97, respectively, and at Rajaei Power Plant climatology station in both training and testing steps are equal to 0.10 and 0.97, respectively. In this research, the accuracy of estimating the value of ET0 in two ANNs and RF methods is close to each other and higher than the SVM method. On the other hand, the fitting speed of the ANNs method is very long compared to the RF method, and considering all aspects, it can be said that the RF method has a more suitable approach for estimating the ET0 value. The results of this research showed that the value of ET0 is not only based on air temperature, but may change under the influence of other factors such as air pollution, and is also strongly influenced by regional conditions such as topography and altitude.ConclusionThe results of this research, in addition to better investigation of ET0, help to know more influential factors in each region and can be used in regions with similar climatic conditions. For example, in the current study area, it was found that the role of average air temperature is greater than other climatic parameters and has a greater impact on ET0. Therefore, it can be said that increasing the average daily air temperature will increase ET0 and subsequently increase the water requirement of plants. As a result, by using these methods and paying attention to these points, it is possible to avoid water stress and possible reduction of the production

Application of HYDRUS (2D/3D) for Predicting the Influence of Subsurface Drainage on Soil Water Dynamics in a Rainfed-Canola Cropping System

The HYDRUS (2D/3D) model was applied to investigate the probable effects of different subsurface drainage systems on the soil water dynamics under a rainfed-canola cropping system in paddy fields. Field experiments were conducted during two rainfed-canola growing seasons on the subsurface-drained paddy fields of the Sari Agricultural Sciences and Natural Resources University, Mazandaran Province, northern Iran. A drainage pilot consisting of subsurface drainage systems with different drain depths and spacings was designed. Canola was cultivated as the second crop after the rice harvest. Measurements of the groundwater table depth and drain discharge were taken during the growing seasons. The performance of the HYDRUS-2D model during the calibration and validation phases was evaluated using the model efficiency (EF), root mean square error (RMSE), normalized root mean square error (NRMSE) and mean bias error (MBE) measures. Based on the criteria indices (MBE = 0.01–0.17 cm, RMSE = 0.05–1.02 and EF = 0.84–0.96 for drainage fluxes, and MBE = 0.01–0.63, RMSE = 0.34–5.54 and EF = 0.89–0.99 for groundwater table depths), the model was capable of predicting drainage fluxes as well as groundwater table depths. The simulation results demonstrated that HYDRUS (2D/3D) is a powerful tool for proposing optimal scenario to achieve sustainable shallow aquifers in subsurface-drained paddy fields during winter cropping. Copyright © 2017 John Wiley & Sons, Ltd

Three-dimensional super-resolution imaging: from cellular surface to interior

Single-molecule localization microscopy has greatly improved our understanding of biology by providing super-resolution images of biological processes and structures. However, it is still very challenging to apply this technique to thick tissues. A 3D imaging system based on single-molecule localization microscopy is presented to allow high-accuracy drift-free (< 0.7 nm lateral; 2.5 nm axial) imaging many microns deep into a cell. When imaging deep within a cell, distortions of the point-spread function result in an inaccurate and very compressed Z distribution. For the system to accurately represent the position of each molecule, a series of depth-dependent calibrations are required. The system and its allied methodology were developed to image the type-2 Ryanodine receptor (RYR2) in the cardiac myocyte at a depth of several microns. It enabled us to resolve the structure of the individual (30 nm square) receptors giving a result similar to that obtained with electron tomography. We also present an optical setup using an electrically tunable lens to actively stabilize a single-molecule localization microscope in three dimensions (RMS ~ 0.7 nm lateral; ~ 2.7 nm axial). The effectiveness of the ETL was demonstrated by imaging endosomal transferrin receptors near the apical surface of B-lymphocytes at a depth of 8 µm. This stabilization system enables a more accurate topological cluster analysis. We have used these super-resolution imaging approaches to examine overlap between the RYR2 and the L-type Calcium channel (Cav1.2) on the cellular surface and within the rat ventricular myocyte. We accurately imaged receptors down to a depth of 6 µm below the surface, and for the first time, using light microscopy, we were able to image individual receptors. The distribution of RyR2 and Cav1.2 parallel each other and vary greatly between the surface, just below the surface, and deep in the interior. We have also used two-color super-resolution microscopy to quantify receptor organization on the plasma membrane of follicular (FO) and marginal zone (MZ) B cells. We have found that B cell receptors (BCR) on the surface of MZ B cells were more dispersed and exhibited less clustering than those on FO B cells.Science, Faculty ofChemistry, Department ofGraduat

Austenite decomposition in an X80 linepipe steel

The final microstructure and resulting mechanical properties in the heat-affected zone (HAZ) of welded linepipes are predominantly determined by austenite decomposition during cooling after welding processes. Thus, a full understanding of continuous cooling transformation of austenite is a key step toward improving the overall performance of linepipes. The main objective of the current study is to investigate the influence of cooling rate, prior austenite grain size and niobium content of austenite on austenite decomposition kinetics and the resulting microstructures for an X80 linepipe steel. To consider the significant effect of the niobium solid solution level on the transformation of austenite, two thermal histories were developed. For the first case, Nb was dissolved in solid solution prior to austenite decomposition. In contrast, the second scenario involved the formation of Nb(C,N) precipitates prior to austenite decomposition, i.e. leaving a low level of Nb in solid solution. Austenite grain growth studies were conducted to obtain grain sizes similar to those observed in the HAZ of the girth-welded steel, i.e. 5-80μm. Furthermore, employing appropriate thermal cycles, continuous cooling transformation (CCT) tests were conducted to examine the effect of niobium condition, austenite grain size and cooling rate on austenite decomposition behavior of the steel. Cooling rates varied in the range of 3−100ºC/s and dilation measurements were utilized to capture the transformation kinetics of austenite upon cooling. The resulting microstructures, which usually consist of ferrite, bainite and martensite-austenite (MA) constituents, were examined using optical microscopy. They were revealed using appropriate etchants and the corresponding phase volume fractions were subsequently measured in accordance with ASTM standards. Hardness measurements were also conducted on CCT samples.Applied Science, Faculty ofMaterials Engineering, Department ofGraduat

A Comparison of Executive Functions and Academic Help-Seeking in Students with Attention Deficit Hyperactivity Disorder (ADHD) and Normal Students

Background and Aim:  Attention deficit hyperactivity disorder (ADHD) is a common behavioral problem in children worldwide and affects the lives of patients, parents, and society. The main feature of this disorder is a stable attention deficit hyperactivity disorder and impulsivity model in people who have more frequent and severe symptoms compared to people who are at the same level of growth. The present study aimed to determine the difference between executive functions and help-seeking in students with attention deficit hyperactivity disorder (ADHD) and normal students. Methods:  The present study was a descriptive and causal-comparative study and its statistical population consisted of all students (girls and boys) in the fourth to sixth grades of the elementary school in Yasuj in the academic year 2019-2020. Among the statistical population, 50 normal students were selected using the multistage cluster sampling method, and 50 students were selected with attention deficit hyperactivity disorder using the purposive method. Data were collected using the academic help-seeking scale by Ryan and Pintrich (1997), and the Behavior Rating Inventory of Executive Function (BRIEF) (2000). After collecting and extracting data, participants' scores were analyzed using analysis of variance (univariate and multivariate). Results:   The results of multivariate analysis of variance indicated that there was a significant difference between executive functions and academic help-seeking in students with attention deficit hyperactivity disorder and normal students. The results also indicated that there was a significant difference between the two groups of students with attention deficit hyperactivity disorder and normal students in terms of components of executive functions (behavior regulation, metacognition), and components of academic help-seeking (accepting help and avoiding help-seeking). Conclusion:  Children who suffer from attention deficit hyperactivity disorder have more executive dysfunctions than normal children and benefit from help-seeking behaviors to solve fewer problems. Therefore, it is necessary to consider psychological interventions and training to improve and solve the problems of these students