mojtaba hassanzadeh

In order to compare the yield response of wheat, barley and triticale to nitrogen, a split plot experiment was conducted in Mashhad during the 2017-2018 crop year. Nitrogen factor with two levels of non-foliar application (N1) and application of urea fertilizer 46% in the flowering stage at a rate of 20 kg / ha (N2) were allotted to the main plots and cereal type factor in the subplots. The results showed that N2 treatment increased grain yield by 12.6%, which was mainly due to the increase in the contribution of current photosynthesis in grain yield formation from 43.6 to 51.2% and had little relationship with the efficiency of dry matter distribution to grain. Also, N2 treatment, despite a significant reduction in 1000-seed weight, increased the number of seeds per spike by 20.9% and grain weight per spike by 12.9%. Nitrogen foliar application increased the contribution of current photosynthesis in grain yield formation from 43.6% to 51.2% and decreased the rule of stem dry matter remobilization in yield formation from 72.4% to 53.4%. Despite having the same harvest index as wheat, triticale had 13.3% higher grain yield than wheat due to its higher biological yield. The amount of current photosynthesis in triticale was %46.8 more than wheat, but the relative contribution of stem reserves to yield formation was not significantly different. The main reason for the higher in triticale compared to wheat was the higher number of grains per spike, and the difference in grain weight was less important. The results indicate that grain weight is not affected by nitrogen uptake and transport processes and is mostly a function of photosynthesis. Based on these results, the source-sink balance in triticale resulted in higher yield of it compared to wheat and barley. It seems that nitrogen in the flowering stage can increase the yield of wheat and barley by preventing abortion of florets and increasing the size of the reservoir as well as improving the strength of the source by increasing the current photosynthesis.

Nowadays, Hybrid Electric Vehicles (HEVs) are introduced in order to reduce fuel consumption and emissions. The issue that is very important in HEVs, is how to split power between main components of powertrain. Best energy management can be obtained when all future conditions are available. With the advancement of the intelligent systems, access to the road conditions, traffic and other online information has been provided up to the limited prediction horizon. In this paper, a combination of predictive control and Dynamic Programming methods have been used for obtaining online sub-optimal trajectory. Change in the state of traffic in the path has great effect on reduction of fuel consumption. Therefore, According to the state of traffic, a fuzzy logic system is proposed for the online estimating of the vehicle speed. Unlike many energy management methods that use historical data, the proposed strategy leads to reducing the dependence of the controller on the drive cycle. The simulation is implemented on a Plug-in Hybrid Electric Vehicle with parallel structure. The proposed method is compared with Dynamic Programming and instantaneous optimization. Evaluation of results shows that the proposed method, while simplicity and avoiding complicated mathematical relationships, in addition to fuel consumption reduction compared with instantaneous optimization, can manage SOC, properly. The results of this method are close to the global optimal solution of Dynamic Programming.