Battery cell balance of electric vehicles under fast-DC charging

Electric vehicle (EV) range, recharge opportunities and time to recharge are major barriers to mainstream acceptance. Fast-DC charging has the potential to overcome these barriers. This research investigates the impact of fast-DC charging on battery cell balance, charge capacity and range for an EV travelling long distances on an 'electric-highway'. Two commercially available EVs were exposed to a series of discharge and fast-DC charge cycles to measure cell balance and charge capacity. The vehicles' battery management systems (BMS) were capable of successfully balancing individual cells and hence maintaining the batteries' charge capacity. Although fast-DC charge levels and discharge safety margins significantly reduced the vehicles' charge capacity and range as stated by the manufacturer, these values remained stable for the test period. In regards to cell balance and charge capacity, our research suggests that fast-DC charging technology is a feasible option for EVs to travel large distances in a day

Illuminating trap density trends in amorphous oxide semiconductors with ultrabroadband photoconduction

Under varying growth and device processing conditions, ultrabroadband photoconduction (UBPC) reveals strongly evolving trends in the defect density of states (DoS) for amorphous oxide semiconductor thin-film transistors (TFTs). Spanning the wide bandgap of amorphous InGaZnO$_x$ (a-IGZO), UBPC identifies seven oxygen-deep donor vacancy peaks that are independently confirmed by energetically matching to photoluminescence emission peaks. The sub-gap DoS from 15 different types of a-IGZO TFTs all yield similar DoS, except only back-channel etch TFTs can have a deep acceptor peak seen at 2.2 eV below the conduction band mobility edge. This deep acceptor is likely a zinc vacancy, evidenced by trap density which becomes 5-6x larger when TFT wet-etch methods are employed. Certain DoS peaks are strongly enhanced for TFTs with active channel processing damage caused by plasma exposure. While Ar implantation and He plasma processing damage are similar, Ar plasma yields more disorder showing a 2x larger valence-band Urbach energy and two orders of magnitude increase in the deep oxygen vacancy trap density. Changing the growth conditions of a-IGZO also impacts the DoS, with zinc-rich TFTs showing much poorer electrical performance compared to 1:1:1 molar ratio a-IGZO TFTs owing to the former having a ~10xlarger oxygen vacancy trap density. Finally, hydrogen is found to behave as a donor in amorphous indium tin gallium zinc oxide TFTs.Comment: 11 pages, 6 figure

Context-specific activations are a hallmark of the neural basis of individual differences in general executive function

Common executive functioning (cEF) is a domain-general factor that captures shared variance in performance across diverse executive function tasks. To investigate the neural mechanisms of individual differences in cEF (e.g., goal maintenance, biasing), we conducted the largest fMRI study of multiple executive tasks to date (N = 546). Group average activation during response inhibition (antisaccade task), working memory updating (keep track task), and mental set shifting (number–letter switch task) overlapped in classic cognitive control regions. However, there were no areas across tasks that were consistently correlated with individual differences in cEF ability. Although similar brain areas are recruited when completing different executive function tasks, activation levels of those areas are not consistently associated with better performance. This pattern is inconsistent with a simple model in which higher cEF is associated with greater or less activation of a set of control regions across different task contexts; however, it is potentially consistent with a model in which individual differences in cEF primarily depend on activation of domain-specific targets of executive function. Brain features that explain commonalities in executive function performance across tasks remain to be discovered

Taylor dispersion of gyrotactic swimming micro-organisms in a linear flow

The theory of generalized Taylor dispersion for suspensions of Brownian particles is developed to study the dispersion of gyrotactic swimming micro-organisms in a linear shear flow. Such creatures are bottom-heavy and experience a gravitational torque which acts to right them when they are tipped away from the vertical. They also suffer a net viscous torque in the presence of a local vorticity field. The orientation of the cells is intrinsically random but the balance of the two torques results in a bias toward a preferred swimming direction. The micro-organisms are sufficiently large that Brownian motion is negligible but their random swimming across streamlines results in a mean velocity together with diffusion. As an example, we consider the case of vertical shear flow and calculate the diffusion coefficients for a suspension of the alga <i>Chlamydomonas nivalis</i>. This rational derivation is compared with earlier approximations for the diffusivity

The purpose of peer review in the case of an open-access publication

First scientific journals were simply a way of informing colleagues about new research findings. In due course, they started filtering out unreasonable claims, and introduced a peer-review system. The purpose of peer reviewing changed with time. Since the middle of the past century, commercial publishers have owned a large number of scientific journals and as a result, the marketable value of a submitted manuscript has become an increasingly important factor in publishing decisions. Recently some publishers have developed business schemes which may stop this tendency. In the case of an open-access publication, the marketable value of a manuscript is not the primary consideration, since access to the research is not being sold. This innovation challenges scientists to re-consider the purpose of peer review. This editorial indicates some of the commonly used criteria for publication that consequently should receive less or little emphasis under the open-access model

Modeling and Validating Chronic Pharmacological Manipulation of Circadian Rhythms

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110096/1/psp4201334-sup-0010.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110096/2/psp4201334-sup-0009.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110096/3/psp4201334-sup-0011.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110096/4/psp4201334-sup-0008.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110096/5/psp4201334-sup-0005.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110096/6/psp4201334-sup-0012.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110096/7/psp4201334-sup-0006.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110096/8/psp4201334-sup-0013.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110096/9/psp4201334.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110096/10/psp4201334-sup-0007.pd

Collaborative Deep Learning for Recommender Systems

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recent advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art

Evolutionary Multi-Objective Design of SARS-CoV-2 Protease Inhibitor Candidates

Computational drug design based on artificial intelligence is an emerging research area. At the time of writing this paper, the world suffers from an outbreak of the coronavirus SARS-CoV-2. A promising way to stop the virus replication is via protease inhibition. We propose an evolutionary multi-objective algorithm (EMOA) to design potential protease inhibitors for SARS-CoV-2's main protease. Based on the SELFIES representation the EMOA maximizes the binding of candidate ligands to the protein using the docking tool QuickVina 2, while at the same time taking into account further objectives like drug-likeliness or the fulfillment of filter constraints. The experimental part analyzes the evolutionary process and discusses the inhibitor candidates.Comment: 15 pages, 7 figures, submitted to PPSN 202

Multiple publications: The main reason for the retraction of papers in computer science

This paper intends to review the reasons for the retraction over the last decade. The paper particularly aims at reviewing these reasons with reference to computer science field to assist authors in comprehending the style of writing. To do that, a total of thirty-six retracted papers found on the Web of Science within Jan 2007 through July 2017 are explored. Given the retraction notices which are based on ten common reasons, this paper classifies the two main categories, namely random and nonrandom retraction. Retraction due to the duplication of publications scored the highest proportion of all other reasons reviewed