علیرضا سبزواری

The use of remote sensing, due to the provision of timely data and the high capability of image analysis, as well as the possibility of studying in a wide range with acceptable accuracy, is of great help to the planners and implementers of the agricultural sector. Remote sensing is one of the effective tools for monitoring, studying and determining the level of cultivation of agricultural and horticultural products, especially in large areas. Therefore, Annual Landsat images are valuable resources that enable crop monitoring in issues related to diagnosis, crop yield prediction, and crop-cultivation pattern studies.

Landsat 8 and OLI satellite images related to 2018 were used to estimate the overall level of crops in the area and to separate the agricultural area from other areas. These images were downloaded from the site http://earthexplorer.usgs.gov, which is related to row 37 and path 166. In order to collect additional information used in this research, Google Earth images and ground control points taken with a GPS device, Garmin model 64s, were used. Finally, the area under cultivation of the products was calculated. The statistics of the Ministry of Agricultural Jihad in the crop year 2016-2017 were used to evaluate the results. The classification steps are done to classify different classes.

For this purpose, in this research, to estimate the monitoring of main crops in the area and to classification agricultural crops, satellite images of Landsat 8 sensors with OLI sensor and NDVI index related to the time series of one-year images were used. The area of wheat and barley cultivated land in the region was estimated to be 10639 hectares, which shows an error of about 6.8% compared to the results of Jihad Keshavarzi statistics, which is 9956.56 hectares. After classifying the satellite images, using teaching samples that were not involved in the classification process, the accuracy of the classified image was evaluated. In this research, after calculating the cultivated area in GIS, the results were validated by comparing with the available statistics of Jihad-Agriculture and regional service centers and time control points by GPS, kappa coefficient and general accuracy coefficient. Supervised classification method with support vector machine algorithm and artificial neural network was used to separate farms. The crop classification maps were divided into 5 categories, wheat and barley, summer crops, rice crops, irrigated crops and non-agricultural lands. The lowest amount of commission error in both classification methods, 8.1 %, is related to summer cultivation, and the emission error in non-agricultural lands was 0.5 % in support vector machine method and 2.5% in artificial neural network method. Sentinel-2 images with a spatial resolution of 10 meters were used to prepare the classification map.

Conclusion :

After classification, the maps produced by satellite images were compared with reference data and ground facts, as well as with the help of satellite images available in Google Earth software. Then the error matrix was formed. Kappa coefficient and overall accuracy were obtained in the vector machine method as 0.74 and 68%, respectively, and in the neural network method as 0.70 and 76% respectively.

Thermogels, having unique sol-gel phase transition properties, have recently received much attention. These types of hydrogels show a phase transition when exposed to temperature changes. This feature is the key to the application of thermogels in medicine. The thermogelling behavior is the result of a combination of multiple heat response mechanisms. These mechanisms include the behavior of the lowest critical temperature of the solution, micellization and micellar aggregation of thermogel-forming polymers. Thermogels have wide applications in medical engineering and especially in tissue engineering. Injectability is one of the most important properties of thermogels for use in tissue engineering, which induces high compatibility, easy loading of cells and growth factors, and help in tissue regeneration to these hydrogels. In recent years, the use of thermogels in drug delivery and the study of thermogel drug delivery systems have become very common. Thermogels can deliver the drug to the desired location in a targeted manner and increase the life of the drug by protecting the drug in the body. Thermogels have also been considered as a suitable cell culture medium in various researches due to their three-dimensional porous structure. In addition, these materials have been used as self-cleaning membranes due to their ability to reduce fibrous encapsulation and facilitate analyte diffusion in biosensors. In this article, an attempt was made to review the research conducted in the field of functional mechanisms of thermogels and the progress achieved in this field. In the following, with a practical attitude, the research conducted in the field of using thermogels in medical engineering, such as use in drug delivery, tissue engineering, 3D culture of cells/stem cells, and membranes and sensors, was examined.

One of the decision-making methods using quantitative data is multi-criteria decision-making, which helps the manager make rational decisions by considering different conflicting criteria. Planning for the optimal use of water and soil resources, in addition to their conservation, involves increasing production, income growth for farmers, and rural economic prosperity. Given the limited resources, the optimal cropping pattern should be appropriate and effective for each region. The selection of an appropriate cropping pattern and, if necessary, adjusting the cultivation based on regional and national needs and the relative advantage of products in different regions is of great importance. So far, many studies have been conducted to optimize cropping patterns. The main reasons for the low productivity in agricultural production are the inappropriate allocation of resources and production factors. Although farmers are faced with various options for agricultural activities and crop selection, their production factors are limited. By formulating and implementing an optimal cropping pattern in a region, it is possible to familiarize farmers with the available potentials while considering the constraints of production resources, reducing risk, ensuring system stability, improving income in production, and creating the groundwork for the growth and prosperity of agricultural regions. This leads to agricultural development, increased profitability, and profitability. Decision-making is one of the most important and fundamental tasks of management, and the quality of decision-making determines the achievement of organizational goals. However, the main focus of research has been on improving the productivity of crops based on water and soil resources, with little attention given to the subject of agricultural mechanization and the criteria of regional operators. Considering the criteria of farmers in decision-making processes increases the acceptance of programs and better collaboration in implementing them. Additionally, most studies have only used one decision-making method, but each decision-support method provides a unique outcome and may differ from the results of other methods. Therefore, in this study, multiple decision-making methods were evaluated and compared to determine the best method to be used. Based on the given explanation, the objective of this research is to prioritize the factors influencing the determination of suitable cropping patterns for agricultural products using four different decision-making methods and introduce the best method.

The research area is sited in Silakhour Plain, in Lorestan Province, between the cities of Doroud and Borujerd, at geographical coordinates 38 degrees 36 minutes north and 48 degrees 31 minutes east, in coordinate zone 39. In this study, Analytic Hierarchy Process (AHP), TOPSIS, VIKOR, and Simple Weighted Average methods were used for decision-making. The parameters investigated in this study included technical-agricultural, economic, macro-governmental, soil and climate, social, and production support factors. These factors included sub-factors such as the presence of mechanized planting and harvesting equipment, farming unit larger than one hectare, water requirements of plants, distance from the place of consumption, farming unit smaller than one hectare, crop profitability, required capital, suitable market for the product, customs and farmer habits, farmer education, crop cultivation experience, guaranteed purchase of the product, government incentive policies, physical characteristics of the soil in the region, chemical characteristics of the soil in the region, average temperature during the growing season, elevation of the region, average rainfall in the region, insurability of the product, sustainability of crop production, availability of seeds compatible with regional conditions, and prevalent pests in the region. To validate all judgments made in the analytic hierarchy process method, the inconsistency ratio was calculated using Expert Choice11 software. Based on this, the inconsistency ratio was calculated to be less than 0.1 in all steps of this method. If the consistency ratio is 0.1 or less, it indicates consistency in the comparisons and confirms the validity of the judgments. To reach a general consensus on the ranking of parameters, the method of merging average ranks was used.

In this study, 22 indicators were identified, including the presence of mechanized planting and harvesting equipment (0.061), operational unit larger than one hectare (0.021), crop water requirement (0.014), distance from consumption site (0.008), operational unit smaller than one hectare (0.006), product profitability (0.125), required cash capital (0.116), suitable market for the product (0.020), agricultural customs and habits (0.028), education of the farmer (0.006), crop cultivation experience (0.130), guaranteed purchase of the product (0.3), government incentive policy (0.122), physical characteristics of the soil in the region (0.075), chemical characteristics of the soil in the region (0.022), average temperature during the growth season (0.022), elevation of the region (0.008), average precipitation in the region (0.006), insurability of the product (0.013), stability of crop production (0.007), availability of seeds compatible with the regional conditions (.0004), and common pests in the region (0.0002). Among the mentioned parameters, cash capital (0.236), water requirement for cultivation (0.233), product profitability (0.098), and operational unit larger than one hectare (0.039) are considered the most important factors. Certain purchase of the product (0.3), product profitability (0.0125), government incentive policy for products (0.122), and required cash capital for cultivation (0.116) were identified as the most important factors influencing the cropping pattern, respectively, using the hierarchical weighted method. product profitability and required cash capital are among the influential factors in the design of cropping patterns for agricultural products. The results showed that the ranking of agricultural products for inclusion in the regional cropping pattern differs in each decision-making method. Although grains and sugar beets have high rankings in all groups, it is necessary to validate and finalize these methods with integrated approaches to reach a general conclusion. In a multi-criteria decision-making problem, multiple decision-making methods may be used because decision-makers do not limit themselves to one decision-making method, and they may obtain different results using different methods. In fact, in such situations where the results of different methods of multi-criteria decision-making are not the same, the question is which option should be chosen. To reach a general conclusion, it is necessary to validate and finalize these methods with integrated validation and finalization approaches. Among the integration methods,

The selection of cropping patterns is one of the main factors in increasing agricultural productivity. Optimal allocation of land and determination of suitable crop production in each region to prevent unwarranted consumption of inputs and reduce the disaster of lack of food supply after scientific study of factors affecting cropping pattern and taking into account different indices. Therefore, the present study determines the factors affecting the cropping pattern and their prioritization. This research was conducted in Silakhor agricultural area in Dorud Lorestan. The research method used in this study was a library, skillful interviews, and inspection (collecting questionnaires from farmers). The variables affecting the cropping pattern were extracted using exploratory factor analysis and then Shannon entropy-based TOPSIS method was used to prioritize crop cultivation. Factor analysis results indicated that the variables affecting sustainable crop pattern selection in the region can be classified into six factors, mechanization-farming, soil and climate, macro-government management, production support, social and production margin. These factors explained 61.41% of the variance of the effective variables in selecting the cropping pattern. Also, in the four-criterion entropy method, Access to cash capital of cultivation needed (0.236), Crop water requirement (0.233), crop profit (0.098), and cultivation area more than 1 ha (0.039). They had the highest priority in choosing the pattern of cultivation. Finally, the results of TOPSIS Multi-Criteria Decision Making showed that autumn sugar beet (0.598), Wheat (0.589), Barley (0.558), Canola (0.556), autumn peas (0.515). ), Rice (0.499), Quinoa (0.471), and saffron (0.390), respectively, have the highest priority for being included in the region's cultivation pattern.

In recent years, the main objective of developing new hydrogel systems has been to convert biomass into environmentally-friendly hydrogels. Hybrid hydrogels are usually prepared by graft copolymerization of acrylic monomers onto natural polymers or biomass. In this study, sugarcane bagasse was used to prepare semi-synthetic hybrid hydrogels without delignification, which is a costly and timeconsuming process. Sugarcane bagasse as a source of polysaccharide was modified using polymer microgels based on acrylic monomers such as acrylic acid, acrylamide and 2-acrylamido-2-methyl propane sulfonic acid which were prepared through inverse emulsion polymerization. By this process, biomass as a low-value by-product was converted into a valuable semi-synthetic hydrogel. In the following, the effect of latex type¸ the aqueous-to-organic phase ratio in the polymer latex, time and temperature of modification reaction on the swelling capacity of the hybrid hydrogel were evaluated. The chemical reaction between sugarcane bagasse and acrylic latex was carried out during heating of the modified bagasse which led to obtain a semisynthetic hydrogel with 60% natural components and 40% synthetic components. Among the latexes with different structures, poly(AA-NaAA-AM-AMPS) was the most suitable polymer latex for the conversion of biomass into hydrogel. The bagasse modified with this latex had a water absorption capacity up to 112 g/g, while the water absorption capacity of primary sugarcane bagasse was only equal to 3.6 g/g. The prepared polymer hydrogels were characterized using Fourier transform infrared spectroscopy (FTIR), dynamic-mechanical thermal analysis (DMTA), thermal gravimetric analysis (TGA), scanning electron microscopy (SEM) and determination of the amount of swelling capacity.

Opioids are routinely omitted at the induction of general anesthesia for cesarean delivery because of concerns about neonatal respiratory depression. The subsequent unmodified maternal stress response to tracheal intubation reduces placental perfusion and a short-acting opioid may afford advantages at the induction, without subsequent neonatal depression. The aim of the present study was to assess sufentanil effects administered before the induction of anesthesia for cesarean delivery on the mother's hemodynamic and neonate's Apgar. In this double blind clinical trial study, cesarean delivery cases were divided into 2 groups (50 in each group). The case group received sufentanil 0.15? g/kg at the induction of anesthesia with thiopental 4 mg/kg and succinylcholine 2 mg/kg but the control group with the same induction method did not receive sufentanil. Systolic and diastolic blood pressure, heart rate of mothers and first and fifth minute Apgar scores of neonates were assessed. Systolic and diastolic blood pressure and heart rate before induction of anesthesia had not significant difference. But after induction of anesthesia blood pressure and heart rate in control group raised more than case group significantly (p=0.001). First and fifth minute Apgar scores of neonates had not significant difference. Heart rate average in laryngoscopy and intubation time was 116.96± 9.48 that had significant difference with case group average 99.96±12.78 (p=0.001). The results showed the significant effects of sufentanil at the induction of general anesthesia for cesarean delivery on stress controlling without significant effects on neonates Apgar. So sufentanil using is helpful in hypertensive pregnant cases or pre-eclampsia.