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The use of deficit irrigation technique has become inevitable due to the lack of water resources in many parts of the world. The goal of this study is to improve the performance of border irrigation under deficit strategy by determining the optimal cutoff time (Tco). For this purpose, field experiments and simulation modeling were carried out. The experimental borders were different in terms of inflow discharge, soil texture and length. 1024 combinations included different physical and management factors were analyzed by the WinSRFR software. By determining the optimal Tco for each combination, fifteen regression equations were extracted for three irrigation levels and five advance times (Ta) (times when water advanced to 30 to 70% of the border lengths). Two indexes including Y (combination of efficiency and uniformity indices) and Y′ (combination of efficiency, uniformity, and requirement efficiency) were used to evaluate border irrigation performance. Based on the validation results, the relationship between Tco and Ta at the 70% of the border length was introduced as a suitable option. The performance of the selected equation was evaluated using the field data. The results illustrated that the calculated values of Y and Y′ from the proposed method was in high agreement with theses from the common optimization method. Tco obtained from the proposed relationship improved the Y and Y′ indices by 9.4 and 6.6%, respectively, compared to the field conditions. The proposed relationship will guarantee application efficiency above 60%, uniformity and requirement efficiency above 80%.

Carpinus wood is produced throughout Eastern Asia, Iran, Europe and North America, and its fine characteristics have turned it into a common choice in industries such as furniture, construction, and paper manufacture. The main goal of this study is to analyze the physical properties (dry density, humid density, basic density, and shrinkage) of Carpinus species in three different regions in northern Iran. The samples for this study were prepared according to ISO 3129 standard. According to the results of this study, there was a significant difference in most of the physical properties, which is associated with their different densities.

Beech (Fagus orientalis Lipsky) forests in Iran are one of the most important sources of the hardwood species used for lumber, furniture, and interior object design due to its hardness, wear resistance, strength, and excellent bending capabilities. Furthermore, Iran is third most important country for walnut wood production after China and United States. Therefore, in this study, we compared specific mechanical properties between beech wood obtained from Sangdeh (Iran) and Georgia and four different kinds of walnut woods in Iran. Physical and mechanical tests were performed according to ISO 3129 (2012) and ASTM (D143-14) standards. The moisture content of all samples was 12% during mechanical tests. The mean dry density of Sangdeh and Georgian beech obtained was 0.61 and 0.65 g/cm3, respectively, while the mean dry density of Noor, Shahrekord, Mashhad, and Mako walnut woods measured 0.62, 0.59, 0.62, and 0.57 g/cm3, respectively. The results showed significant differences among the properties of the Sangdeh and Georgian species and the four different walnut tree woods. Overall, the obtained strengths of Georgian timber were higher than that of the Iranian beech, which was attributed to the higher density of Georgian timber. Furthermore, due to the higher density of the walnut species in the Noor and Mashhad regions, the measured mechanical strengths of these trees were higher than those of other walnut species. The obtained results provide relevant information to determinate the future applications of each wood source.

Fast Simultaneous Localization and Mapping (SLAM) algorithm is capable of real-time implementation due to logarithmic time complexity which results in decrease of computational cost. In this algorithm state vector of a robot merely includes planar location of the robot and its angle to the horizontal plane. It has fewer components comparing to state vector in extended Kalman filter method which consists of location of all environmental features. In existing methods for implementing this algorithm, robot movement is considered to be totally in planar movement; while if moving on a slope changes the pitch angle of the robot, it causes errors in the algorithm. Correcting these errors will lead to a precise 2D mapping and 3D localization. This paper details the modification added to conventional Fast-Slam algorithm to accommodate this requirement by using an IMU. Simulation and experimental results shows the effectiveness of such modification.