Haplotype-resolved assembly is the optimal approach to characterize structural variants (SVs) in the human genome. Current long-read technologies, such as high-fidelity (HiFi) reads provide both low sequencing error and long-range information which enable phased, diploid assembly. Here we introduce embSV, a haplotype-phasing-assisted diploid assembly-based SV detection tool, which takes advantage of a word-embedding model inspired by natural language processing to faithfully partition HiFi reads for local haplotype-specific assembly. The genome-wide haplotype-resolved assembly then allows us to comprehensively detect SVs. To thoroughly test the performance and robustness of embSV and compare it with existing SV callers, we have run several benchmarking experiments. Tuning different parameters that can affect breakpoint shift and alternate sequence similarity between called SVs and the gold standard reveals that embSV outperforms existing tools and is the most robust tool to parameter change.

The number of vehicles is rapidly increasing. An effective control strategy for Hybrid Electric Vehicles (HEVs) is important. In this paper, we present a two-level control strategy that combines the Adaptive-Equivalent Consumption Minimization Strategy (A-ECMS) and the Adaptive Dynamic Programming (ADP). At the lower level, the A-ECMS is used to convert the consumed charge into Equivalent Fuel Consumption (EFC) for every sample moment, and a PI controller is used to adjust the values of the equivalent factor of the A-ECMS. At the upper level, the ADP is used to find the minimum of EFC corresponding to equivalent factor for every sample moment, and it maintains the State of Charge (SOC) of battery to charge and discharge smoothly in a high-efficiency field for the HEV. As by the ADP, we look into the future and then we can have a better estimate for the equivalent factor than the ordinary A-ECMS. In this way, we can save energy as well as calculate instantaneous parameters for the control strategy. Compared with a typical rule-based control strategy, the proposed control method saves EFC up to 10.3% and the stability of the SOC is increased by more than 60%, tested on benchmarks.

The choice of model orientation is a very important issue in Additive Manufacturing (AM). In this paper, the model orientation problem is formulated as a multi-objective optimization problem, aiming at minimizing the building time, the surface quality, and the supporting area. Then we convert the problem into a single-objective optimization in the linear-weighted way. After that, the Genetic Algorithm (GA) is used to solve the optimization problem and the process of GA is parallelized and implemented on GPU. Experimental results show that when dealing with complex models in AM, compared with CPU only implementation, the GPU based GA can speed up the process by about 50 times, which helps to significantly reduce the optimization time and ensure the quality of solutions. The GPU based parallel methods we proposed can help to reduce the execution time and improve the efficiency greatly, making the processes more efficient.