Generative models of shapes for 2D boundaries have applications in object detection and inference from 2D images.We investigate how to learn this generative model from a training set of shape functions.The quality of the correspondence establishment significantly affects the quality of the shape models.A state-of-the-art approach for establishing correspondence is to define a regularized empirical risk for generative models, and by minimizing this risk, the correspondence between shapes is determined.The choice of the regularization parameters of the risk has a significant effect on the quality of the correspondence.In this article, by estimating the effective dimension of the principal component analysis model and using the entropy estimation of eigenvalues algorithm, we consider the effect of error variance in determining the regularization parameter for correspondence establishment.Using Our proposed algorithm leads to the following improvements in the correspondence establishment for shape models of the objects that exist in JSRT chest radiography images: 0.5 mm specificity improvement, and training time reduction from 600 seconds to 300 seconds, compared to the minimum description length method.Moreover, the specificity of the correspondence established by our proposed method is better than that established by experts' manual landmarks in terms of specificity.

Introduction Knowledge of actual evapotranspiration is valuable for assessing water availability in policy and decision-making of water resources and agriculture. Despite all improvements, the measurement of actual evapotranspiration is accompanied by difficulty in some locations. In this regard, an accurate method for actual evapotranspiration estimation is linked to the reference evapotranspiration (ETo) determination as a significant component. The Food and Agriculture Organization of the United Nations (FAO) Penman-Monteith method is widely recognized for its high accuracy and making it a globally accepted standard. Despite the acceptability of the FAO Penman-Monteith method, the need for a large amount of reliable weather measurements, such as solar radiation and wind speed, has challenged the method. These data are often not available in developing countries, and the issue is related to the limited number of equipped meteorological stations or inaccuracies of measurement. Therefore, the need for an alternative ETo method seems necessary, and the efficient artificial intelligence techniques with a low number of input data can obtain accuracy equal to the FAO method. In this regard, the preprocessing step with a selection of important input data is more important. This study introduces a novel approach by systematically comparing multiple preprocessing methods for ETo estimation by integrating decision making techniques to improve data selection and model accuracy. The preprocessing methods belong to the correlation concept, regression analysis, and decision making approach, with different normalization methods. To increase the accuracy of decisions, more than one evaluation criteria were considered in the analysis.Materials and Methods The analysis of this study is focused on eleven stations (1992-2021). The station's spatial distribution consists of the North, West, North-West, East, and center of Iran. The preprocessing step in the modeling process has great importance in deriving the effective and precise factors as the input data. Several preprocessing methods were investigated in this study to identify the dominant input data for ETo estimation. They include the Pearson correlation coefficient, Kendall’s tau-b correlation coefficient, standardized Beta coefficient, stepwise regression, Shannon’s entropy, and simple additive weighting with fuzzy normalization. These methods were selected for their ability to assess important variables with data analysis from different aspects by correlation detection and data normalization, ensuring accurate ETo estimation. The Pearson correlation coefficient can distinguish the correlation between independent and dependent variables; higher values indicate higher dependency. The emphasis of stepwise regression is on the best and most impressive variables from a large set of variables. Decision making is not always between two options, and sometimes we have to make the right selection among several options. In this case, a multi-criteria decision is made, depending on the sensitivity of the problem, for which certain methods can help to reach the best option. Some methods are illustrated to solve MCDM problems, such as Shannon’s entropy. The process of entropy analysis is to assign the weights of the objective criterion. The assumption of entropy analysis is the importance of data with high-weight indicators relative to the data with low-weight indicators. The regression analysis aims to minimize the error between observed and forecasted values; this matter can be possible by SVR, which used as the model in this study. Results and Discussion The maximum Pearson correlation coefficient in the monthly scale is related to the solar radiation, maximum and minimum temperature in all stations. This matter was preserved by τ Kendall correlation coefficient. The derived meteorological data in the stepwise regression at the annual scale can be described as the relative humidity, wind speed, solar radiation, maximum temperature in Maku, wind speed, maximum temperature, solar radiation, sunshine hours in Yazd. Decision making analysis needs some criteria, and five criteria, RMSE, R, MAE, NSE, and GMER, were applied in Shannon’s entropy method. The selected are used to find the best solution from all data (Tmax, Tmin, RH, U, S, and R), and different combinations of data. The combination 3-7; the number of input data is equal to 3, and the data are wind speed, solar radiation, and sunshine hours, has the highest weight, pink in Maku. In the monthly scale and the combination with five input data, the RMSE of all stations related to Shannon’s entropy is higher than fuzzy normalization, except Mashhad with the same RMSE in the two methods, and Zanjan and Yazd with a low error of Shannon’s entropy. In two scales, the performance of fuzzy normalization is in a good state. In the annual scale, the Pearson correlation and stepwise regression have the same function. In the monthly scale, stepwise regression has poor performance. The selection of input data based on fuzzy normalization could decrease the error of the simulation. Conclusion The results indicated that the normalization process had better performance in the preprocessing method based on the MCDM approach relative to the other methods. The average of the criteria showed that the best method has no limitations regarding to the three types of different climates, wet, semiarid, and arid, and the fuzzy normalization had good performance. This method has no geographical limitation. Determining an efficient method for the preprocessing step has an acceptable response in all climates, which is one of the strengths and innovations of the research. One of the things that can strongly affect the preprocessing method based MCDM approach is the type of decision making method. In the decision making problem, the used method for normalization of the decision matrix has high importance in information extraction. In general, maximum temperature, relative humidity, wind speed, solar radiation, sunshine hours (annual), and minimum temperature (monthly) were introduced as the effective data. The reason for the better performance of certain data combination is related to the high dependency of these combinations with ETo variation.Generally, using the exact method as the preprocessing step in each climate based on the data capabilities of area and selection of the effective data can upgrade the efficiency of ETo estimation. It can led to the precise determination of water availability and strong policymaking in irrigation planning, agricultural studies.

Introduction: The infiltration process is one of the most important components of the hydrologic cycle. Quantifying the infiltration water into soil is of great importance in watershed management. Prediction of flooding, erosion and pollutant transport all depends on the rate of runoff which is directly affected by the rate of infiltration. Quantification of infiltration water into soil is also necessary to determine the availability of water for crop growth and to estimate the amount of additional water needed for irrigation. Thus, an accurate model is required to estimate infiltration of water into soil. The ability of physical and empirical models in simulation of soil processes is commonly measured through comparisons of simulated and observed values. For these reasons, a large variety of indices have been proposed and used over the years in comparison of infiltration water into soil models. Among the proposed indices, some are absolute criteria such as the widely used root mean square error (RMSE), while others are relative criteria (i.e. normalized) such as the Nash and Sutcliffe (1970) efficiency criterion (NSE). Selecting and using appropriate statistical criteria to evaluate and interpretation of the results forinfiltration water into soil models is essential because each of the used criteria focus on specific types of errors. Also, descriptions of various goodness of fit indices or indicators including their advantages and shortcomings, and rigorous discussions on the suitability of each index are very important. The objective of this study is to compare the goodness of different statistical criteria to evaluate infiltration of water into soil models.Comparison techniques were considered to define the best models: coefficient of determination (R2), root meansquare error (RMSE), efficiency criteria (NSEI) and modified forms (such as NSEjI, NSESQRTI, NSElnI and NSEiI). Comparatively little work has been carried out on the meaning and interpretation of efficiency criteria (NSEI) and its modified forms used to evaluate the models.Materials and Methods: The collection data of 145 point-data of measured infiltration of water into soil were used. The infiltration data were obtained by the Double Rings method in different soils of Iran having a wide range of soil characteristics. The study areas were located in Zanjan, Fars, Ardebil, Bushehr and Isfahan provinces. The soils of these regions are classified as Mollisols, Aridisols, In ceptisols and Entisols soil taxonomy orders. The land use of the study area consisted of wheat, barley, pasture and fallow land. The parameters of the models (i.e. Philip (18), Green and Ampt (3), SCS (23), Kostiakov (6), Horton (5), and Kostiakov and Lewis (11) models) were determined, using the least square optimization method. All models were fitted toexperimental infiltration data using an iterative nonlinear regression procedure, which finds the values of thefitting parameters that give the best fit between the model and the data. The fitting process was performed usingthe MatLab 7.7.0 (R2008b) Software Package. Then, the ability of infiltration of water into soil models with themean of coefficient of determination (R2), root mean square error (RMSE), efficiency criteria (NSEI) and modified forms (such as NSEjI, NSESQRTI, NSElnI and NSEiI) were determined and goodness of criteria was compared for the selection of the best model.Results and Discussion: The results showed the mean of RMSE for all soils cannot always be a suitableindex for the evaluation of infiltration of water into soil models. A more valid comparison with NSEI, NSEjI, NSESQRTI, NSElnI indices indicated that these indices also cannot apparently distinguish among the infiltration models for the estimation of cumulative infiltration. These indices are sensitive to the large amount of data. The NSEiI index with giving more weight to infiltration data in shorter times was selected as the most appropriateindex for comparing models. According to the NSEiI index, Kostiakov and Lewis, Kostiakov, SCS, Philip, Horton, and Green and Ampt models were the best models in approximately 72.42, 44.83, 26.9, 53.11, 11.73 and1.0 percent of soils, respectively.Conclusion: The results of this study indicated that the ability of modified forms of NSE indices inevaluation of infiltration of water into soil models depend on the influence of models from infiltration datavalues in different time series. This encourages us to be cautious on the application and interpretation ofstatistical criteria when evaluating the models.

Introduction: The rapid increase in population, growth of urbanization and industrialization in recent years, which is generally associated with an increase in demand and energy consumption, and as a result, an increase in pollutant emission sources, has exacerbated air pollution as one of the biggest current crises of urban societies and consequently health risks and related social inequalities in terms of time and space. On the other hand, meteorological parameters directly affect the amount of pollutants as well as the duration of their presence in the atmosphere, and the present research was conducted in order to investigate this effect and discover the relationships between criteria air pollutants and atmospheric elements.Material and Methods: In addition to investigating the status of meteorological elements (temperature, precipitation, wind speed, relative humidity, radiation, sunshine hours and cloudiness) and air pollutants (carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3) and particulate matters with aerodynamic diameters less than 10 microns and 2.5 microns (PM10 and PM2.5)) in Tabriz city during 2004-2021, the present study has explored the relationships between pollutants and meteorological parameters in monthly and seasonal time scales using Pearson's correlation test at the 95% confidence level and the effect of these elements on the concentration of pollutants using Multiple Linear Regression (MLR) and Generalized Additive Model (GAM) in R 4.3.1 statistical software.Results and Discussion: Based on the results of Pearson correlation analysis, NO2 and PM2.5, SO2 and PM2.5 pollutants and PM2.5 and PM10 pollutants have shown a significant positive correlation in pairs, so it seems that these pollutants have similar emission sources. Also, the results of this research demonstrate that the concentration of air pollutants in Tabriz was affected by weather conditions during the entire statistical period in the monthly and seasonal time scales. NO2 and PM2.5 pollutants had the most negative monthly correlation with the parameters of temperature, wind speed and sunshine hours and the most positive correlation with relative humidity; PM2.5 had the most positive correlation with pressure; CO and SO2 had the most negative correlation with radiation; O3 had a strong positive correlation with temperature, wind speed and sunny hours and the most negative correlation with pressure, relative humidity and cloudiness; and NO2 and PM10 pollutants had the most positive correlation with cloudiness. The results of fitting Multiple Linear Regression (MLR) and Generalized Additive Model (GAM) for each criteria in Tabriz city indicated the better performance of GAM in analyzing the relationships between all air pollutants and the set of independent variables except NO2.Conclusion: The results of this research indicate that the effect of atmospheric elements on the concentration of pollutants in Tabriz city is different depending on the type of pollutant and at different times, and it can be acknowledged that the effect of a specific meteorological parameter on air pollution is uncertain. However, wind speed, radiation, temperature and air pressure are the most important meteorological elements related to the concentration of pollutants in Tabriz city. Also, the results suggest that both MLR and GAM can describe the variability of the response variable by a set of predictor variables and explain the linear and non-linear relationships between them. However, considering the non-linear relationship between the concentration of atmospheric pollutants and meteorological elements, GAM is able to justify a higher percentage of changes in all criteria atmospheric pollutants except NO2.

In this article, an attempt has been made to estimate the amount of sound transmission loss in a flat oval channel by applying the approach of statistical energy analysis. Correct estimation of sound transmission loss in an air conditioning channel is of great importance due to the harmful effects of noise pollution in the environment on human health. Simulation with the statistical energy analysis method is a powerful approach to estimate sound and vibration in problems in which we deal with complex and multi-part systems; is considered. In this method, first, a system is divided into several subsystems, and then by writing a matrix equation that includes the energy exchanges between subsystems and energy loss coefficients; It is investigated from the perspective of vibration and sound estimation.On average, the model presented in this research is able to estimate the sound transmission loss in different dimensions of the air conditioning channels according to the experimental results in the accuracy range of ± 2.5 dB. Considering that it seems that the results obtained from modeling with this method are in good agreement with the experimental data; The results of this research can be used as an efficient approach to estimate noise in oval shaped channels stretched in different lengths.

In hydrological models, in order to better model the runoff process, it is necessary to calibrate the model using observational data. In the process of calibration of hydrological models, in addition to the quality of observation data and the optimization algorithm, the objective function also affects the efficiency of the model. In most studies, statistical criteria such as NSE and RMSE are used as objective functions in the calibration process of hydrological models. Given the structure of the model and the relationships used in each of the evaluation criteria, each of them has good performance in simulating a part of the hydrograph. One of the important parameters of each basin, which is a kind of basin reaction indicator for different discharge values, is the Flow Continuity Curve (FDC). In this study, the efficiency of objective functions based on flow continuity curve and statistical objective functions in optimizing the parameters of the HBV hydrological model in Ziyarat Watershed of Golestan Province was investigated and compared. After introducing input data to model using DDS algorithm, model was calibrated 100 times for each objective function. When model was calibrated, using optimized parameter sets model output for calibration and validation period was obtained. Results showed that criteria such as NSE and KGE have better performance in predicting high flows, criteria such as RMSE and AME predicted moderate flow discharge better and criteria based on FDC had better performance in predicting low flows. In prediction different parts of hydrograph FDC objective function has the best performance, RMSE and MAE were in sound order and NSE and KGE did not have suitable performance.

Characterization of physical and chemical soil criteria is a key step in understanding the source of spatial variability in the productivity across agricultural fields (21). Crop yield variability can be caused by many factors, including spatial variability of soil texture, crop management, soil physical and chemical properties and nutrient availability (45). Understanding the spatial variability of soil physical and chemical characteristics is essential for crop management, as it is directly contributing to variability in growth and yield of crop (38 & 14). Hence, understanding their spatial variability across agricultural fields is essential in optimizing the application of agricultural inputs and crop yield and it could help significantly in managing the spatial variability in the productivity of soil agroecosystems (30 & 14). Therefore, the objectives of this study were: (i) evaluate the effect of soil physical and chemical criteria on yield indices of wheat and (ii) to investigate the correlation between physical and chmical soil properties and wheat yield. Materials and Methods Samplings were performed based on random-systematic method from 30 fields in Khorasan-e Razavi province during 2017 and 2018. Studied characteristics were texture, organic matter (OM), organic carbon (OC), total nitrogen (TN), available P, available K, pH and C: N ratio of soil and seed yield, biological yield, straw yield, 1000-seed weight and harvest index (HI) of wheat. Multiple regression model was used to identify the relationship between soil variables (independent variables) and wheat yield indices (dependent variables). In addition, determining the most important factors of soil physical and chemical properties which have on wheat yield criteria was done by stepwise regression analysis. Results and discussion The results revealed showed that the mean values of seed yield, straw yield, biological yield, 1000-seed weight and HI of wheat were observed with 3588. 47 kg. ha-1, 7362. 80 kg. ha-1, 10951. 27 kg. ha-1, 35. 40 g and 48. 56%, respectively. The highest and the lowest standard errors were computed for biological yield (198. 40) and 1000-seed yield (0. 74), respectively. Also, The effect of soil textures was significant (p≤ 0. 05) on soil chemical criteria and wheat yield. The maximum OM, OC, TN, available P, available K and pH were observed for sandy clay with 1. 86%, 1. 09%, 0. 18%, 166. 20 ppm, 0. 05 ppm and 7. 37, respectively. The maximum seed yield and biological yield were related for clay soil (with 4313. 83 and 11924. 86 kg. ha-1, respectively). The highest correlation coefficients were computed for OM (r=0. 935**) and OC (r=0. 933**) with 1000-seed weight. The most important factors influencing wheat yield by using step by step regression were OM, available P, TN and available K, respectively. Conclusion Longterm sustainability of agroecosystems depends on soil quality and its fertility. Poor soil management practices can lead to degraded soil and environmental quality and reduction in crop yields. Results suggest that novel management approaches are needed to maintain the longterm sustainability of soil resources and crop yields without seriously degrading the environment that this will help in reducing the cost of fertilization and improving soil and environmental quality without altering crop yields