Carotenoid accumulation during grain development in durum wheat (Triticum turgidum L. var. durum)

Yellow pigment (YP) concentration is an important quality trait in durum wheat (Triticum turgidum L. var durum) and is comprised primarily of carotenoids. The main objective of our study was to measure the accumulation of carotenoids during the grain fill period to improve our understanding of the physiological basis for differences among durum wheat cultivars. Thirteen cultivars and breeding genotypes with large variation in total YP concentration (15 µg g-1) were studied. Spikes were sampled from replicated field plots in 2007 and 2008 near Saskatoon and Swift Current, Saskatchewan, Canada, at 14, 21, 28 and 35 days after heading (DAH). The remainder of each plot was combined at grain maturity for YP and carotenoid analysis. Carotenoids were extracted with 1:1 methanol:dichloromethane (0.1% BHT) and quantified with HPLC. Trans (E)-lutein was the predominant carotenoid at maturity and was detected at 14 DAH in all genotypes. The rate and duration of E-lutein accumulation was variable among genotypes expressing high, intermediate and low YP. The accumulation of all carotenoids was lowest in genotypes expressing low YP, and suggests rate limitations early in the carotenoid biosynthetic pathway. E-zeaxanthin concentrations were highest in mature grain, but no significant differences were detected among genotypes. However, the ratio of E-zeaxanthin to E-lutein was inversely correlated with total YP, suggesting that the â,å branch of lycopene cyclization is favoured over the â,â branch in high-YP genotypes. These results provide insights to the regulation of the carotenoid biosynthetic pathway during grain fill stage in durum wheat and will facilitate breeding for higher carotenoid concentration

HEV fuel optimization using interval back propagation based dynamic programming

In this thesis, the primary powertrain components of a power split hybrid electric vehicle are modeled. In particular, the dynamic model of the energy storage element (i.e., traction battery) is exactly linearized through an input transformation method to take advantage of the proposed optimal control algorithm. A lipschitz continuous and nondecreasing cost function is formulated in order to minimize the net amount of consumed fuel. The globally optimal solution is obtained using a dynamic programming routine that produces the optimal input based on the current state of charge and the future power demand. It is shown that the global optimal control solution can be expressed in closed form for a time invariant and convex incremental cost function utilizing the interval back propagation approach. The global optimality of both time varying and invariant solutions are rigorously proved. The optimal closed form solution is further shown to be applicable to the time varying case provided that the time variations of the incremental cost function are sufficiently small. The real time implementation of this algorithm in Simulink is discussed and a 32.84 % improvement in fuel economy is observed compared to existing rule based methods.M.S

Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble

One of the primal challenges faced by utility companies is ensuring efficient supply with minimal greenhouse gas emissions. The advent of smart meters and smart grids provide an unprecedented advantage in realizing an optimised supply of thermal energies through proactive techniques such as load forecasting. In this paper, we propose a forecasting framework for heat demand based on neural networks where the time series are encoded as scalograms equipped with the capacity of embedding exogenous variables such as weather, and holiday/non-holiday. Subsequently, CNNs are utilized to predict the heat load multi-step ahead. Finally, the proposed framework is compared with other state-of-the-art methods, such as SARIMAX and LSTM. The quantitative results from retrospective experiments show that the proposed framework consistently outperforms the state-of-the-art baseline method with real-world data acquired from Denmark. A minimal mean error of 7.54% for MAPE and 417kW for RMSE is achieved with the proposed framework in comparison to all other methods.Comment: https://www.climatechange.ai/papers/neurips2022/4