Evaluating system architectures for driving range estimation and charge planning for electric vehicles

Due to sparse charging infrastructure and short driving ranges, drivers of battery electric vehicles (BEVs) can experience range anxiety, which is the fear of stranding with an empty battery. To help eliminate range anxiety and make BEVs more attractive for customers, accurate range estimation methods need to be developed. In recent years, many publications have suggested machine learning algorithms as a fitting method to achieve accurate range estimations. However, these algorithms use a large amount of data and have high computational requirements. A traditional placement of the software within a vehicle\u27s electronic control unit could lead to high latencies and thus detrimental to user experience. But since modern vehicles are connected to a backend, where software modules can be implemented, high latencies can be prevented with intelligent distribution of the algorithm parts. On the other hand, communication between vehicle and backend can be slow or expensive. In this article, an intelligent deployment of a range estimation software based on ML is analyzed. We model hardware and software to enable performance evaluation in early stages of the development process. Based on simulations, different system architectures and module placements are then analyzed in terms of latency, network usage, energy usage, and cost. We show that a distributed system with cloud‐based module placement reduces the end‐to‐end latency significantly, when compared with a traditional vehicle‐based placement. Furthermore, we show that network usage is significantly reduced. This intelligent system enables the application of complex, but accurate range estimation with low latencies, resulting in an improved user experience, which enhances the practicality and acceptance of BEVs

Allelic genotyping reveals a hierarchy of genomic alterations in mantle cell lymphoma associated to cell proliferation.

Mantle cell lymphoma (MCL) is a distinct subentity of non-Hodgkin lymphoma, characterized by the chromosomal translocation t(11;14)(q13;q32) leading to an overexpression of cyclin D1 in virtually all cases. However, additional cytogenetic aberrations are apparent in the vast majority of MCL. Applying LOH analysis in 52 MCL patient samples, we confirmed frequent alterations in 9p21 (28.6%) and p53 (28.9%) but also detected allelic losses in 1p21, 9q21, 13q13-14, 13q31-32, 17p13.1, and 17p13.3 in 28–45% of cases and allelic gains in 3q27-28 and 19p13.3 in 14–22% of cases. In addition, losses in the 2p23 and 7q22-35 genomic regions not previously described to be altered in MCL were identified in up to 20% of cases. Applying multivariate analysis, a cluster of genomic aberrations including 1p21, 3q27, 7q22-36, 6p24, 9p21, 9q31, and 16p12 alterations was identified which was closely associated to cell proliferation as determined by Ki67 immunostaining. This proliferation-dependent network of oncogenic alterations complements the previously identified proliferation expression signature described by RNA expression profiling in MCL