tree root, as a reinforcing agent, may improve the Stability of soil slopes by two mechanisms: a decrease in the developed shear stress via distribution of applied loads within a larger area, an increase in the mobilized shear resistance via its tensile strength. The latter, in addition to mechanical properties of root, is a function of diameter, number, orientation and deflection angle of the roots with respect to the potential slip surface. Coppin & Richard analytical model was used to asses to what extend willow tree roots can improve the Stability factor of a planar slip surface. The required data were provided by a series of in-situ tests in Taleghan region using a large scale field shear stress test apparatus. Tests were run in a natural deposit with and without willow root to measure cohesion and internal friction angle of soil under different root density. At the end of the tests, the diameter and angle at which the roots were deviated and cut were measured in the sheared zone. Experimental results were used in the model for different water levels, revealing that for a 45 degree planar slip surface and a root density of 5 to 26 percent the safety factor rises between 0.5 to 36 percent.

Many researchers have reported the synthesis of metal nanoparticles by plant extracts and their potential applications. Cherry tree leaf extract is one of the effective materials in the synthesis of AgNPs, which is confirmed by the results of FTIR analysis. Did AgNPs according to the design of the experiment carried out by the Mixture Design method by mixing three regenerating substances (cherry tree leaves 0-3 ml), basic salt substance (1 mM solution of AgNO3 5-9 ml) and stabilizing agent (AloeVera gel) (0-1 ml) was synthesized by green method. After the optimization, the results showed that the amounts of 2.51, 5.79 and 0.69 mL respectively for reducing agent, basic solution and stabilizing agent were the most suitable mode of synthesis of nanoparticles, and in this mode, the highest concentration of 16.88 ppm, the lowest average particle size of 36 nm, the most appropriate PDI of 0.43 and the highest zeta potential of 25 mV will be obtained. The properties of synthesized AgNPs were evaluated in optimal conditions and the results of UV-Vis and DLS analyzes confirmed the obtained results with slight differences. AgNPs showed good antioxidant properties of 28.45% and antibacterial properties against Gram-positive (Staphylococcus aureus) and Gram-negative (Escherichia coli) bacteria. Antifungal properties were also found to be 65.55%.

The aim of this study was to investigate the effect of using portulaca oleracea as a source of plant antioxidants for feeding fattening male lambs, on carcass traits, meat quality, and oxidative Stability. Twenty-one male lambs with an average weight of 24±1.5 kg and 150±15 days old were fed with one of the experimental rations included a diet without portulaca oleracea as control, a diet containing 7.5%, and a diet containing 15% portulaca oleracea, for 84 days. The muscle (Longissimus dorsi) samplewas used to determine pH, chemical composition, colorimetric properties, and oxidative Stability of meat. Meat lipid oxidation was determined after 1, 7, and 30 days of refrigerated storage using thiobarbituric acid (TBARS). The use of portulaca oleracea in diets improved meat color characteristics such as L, chroma, and Hue. The highest ash concentration and the lowest meat moisture were observed in the treatment of 15% portulaca oleracea (P<0.05). Meat pH was the lowest in the first hour after slaughter but did not differ at 24 h. Compared to the control, in both treatment groups feeding the lambs with portulaca significantly reduced the concentration of Malondialdehyde in the meat of the lambs and increased the meat oxidative Stability (P<0.05). The results of this experiment showed that feeding portulaca oleracea by improving properties such as color and increasing the meat oxidative Stability improves meat quality, which may have lead to an increase in its nutritional value in terms of human health.

Background: Selenium nanoparticles (SeNPs) are highly sought after in diverse industries for their distinct properties and advantages. SeNPs can be synthesized via several methods, including the use of microwave, bain-marie, autoclave, and heater. Objective:  The objective is to optimize the SeNP synthesis formulation, emphasizing Stability, concentration, particle size minimization, and uniformity using central composite design. Materials and methods: The method involves autoclave heating at 121 °C under 1.5 bar pressure for 15 minutes. Prunus persica tree leaf extract and Aloe Vera gel serve as a regenerating agent and stabilizer, respectively. Four responses including SeNPs concentration, average particle size, zeta potential, and dispersion index (PDI), were assessed according to the experimental design. The optimal synthesis point was determined and evaluated for SeNP imaging, antioxidant, and antifungal properties. Results: Results indicate that the optimal SeNPs formulation includes 5.73 mL of Prunus persica tree leaf extract, 13.45 mL of sodium selenite salt solution, and 0.80 mL of Aloe Vera gel. Conclusion: The optimal formulation of selenium nanoparticles (SeNPs) achieved in this study, using Prunus persica tree leaf extract as a reducing agent and Aloe Vera gel as a stabilizer, demonstrates superior properties including high Stability, a small average particle size, and a favorable zeta potential. These characteristics make the SeNPs well-suited for applications requiring enhanced antioxidant and antifungal activities. The findings underscore the importance of optimizing synthesis parameters to maximize the functional properties of SeNPs.

Satellite products are the only available data source with adequate spatial coverage, however, their data do not match the observed values ​​and have biases, although this discrepancy cannot be fixed precisely, however, a solution to reduce the bias is data recalibration. Currently, machine learning techniques are used to improve the accuracy of forecasting various types of weather phenomena, so regression solving such problems through methods based on machine learning and deep learning is very efficient. The daily precipitation of 19 rain gauge stations of the Ministry of Energy between 2010 and 2021 was extracted and compared to the average values ​​of their corresponding daily precipitation pixels in the ERA5 database. To measure the data, three algorithms D-tree, KNN, and MLP were used. The range of changes of correlation coefficient in MLP, D-tree, and KNN is equal to [0.87, 0.98], [0.75, 0.97], and [0.4, 0.87], respectively. In addition, the range of changes for RMSE in MLP varies from 0.7 to 2.4 mm per day, and these changes for D-tree and KNN are calculated between 0.8 to 2.2 and 1.2 to 2.5, respectively. In 75% of stations, RMSE in MLP, D-tree, and KNN algorithms is less than 1.5, 1.9, and 2.2 mm per day, respectively. The range of bias changes in MLP is [0.18, -0.6 mm per day] and this range of changes for D-tree and KNN is respectively [0.16, 0.5 mm per day] and [0.6, -0.8 mm per day] have been calculated. The bias of corrected data and observed values ​​in MLP, D-tree, and KNN algorithms for the middle of the stations is -0.09, -0.11, and -0.16 mm per day, respectively. The evaluation of the performance of three machine learning algorithms (MLP, D-tree, and KNN) in correcting the daily precipitation of the ERA5 database and the comparison of CC, RMSE, and bias statistical indices for the reproduced data compared to ground values ​​showed that in all three statistical indices, the MLP algorithm works better than the others and has good accuracy for correcting the daily precipitation.