Lithium-Ion Desolvation Induced by Nitrate Additives Reveals New Insights into High Performance Lithium Batteries

Electrolyte additives have been widely used to address critical issues in current metal (ion) battery technologies. While their functions as solid electrolyte interface forming agents are reasonably well-understood, their interactions in the liquid electrolyte environment remain rather elusive. This lack of knowledge represents a significant bottleneck that hinders the development of improved electrolyte systems. Here, the key role of additives in promoting cation (e.g., Li⁺) desolvation is unraveled. In particular, nitrate anions (NO₃⁻) are found to incorporate into the solvation shells, change the local environment of cations (e.g., Li⁺) as well as their coordination in the electrolytes. The combination of these effects leads to effective Li⁺ desolvation and enhanced battery performance. Remarkably, the inexpensive NaNO₃ can successfully substitute the widely used LiNO₃ offering superior long-term stability of Li⁺ (de-)intercalation at the graphite anode and suppressed polysulfide shuttle effect at the sulfur cathode, while enhancing the performance of lithium–sulfur full batteries (initial capacity of 1153 mAh g⁻¹ at 0.25C) with Coulombic efficiency of ≈100% over 300 cycles. This work provides important new insights into the unexplored effects of additives and paves the way to developing improved electrolytes for electrochemical energy storage applications.     

Stress in interactive applications: analysis of the valence-arousal space based on physiological signals and self-reported data

Measuring users’ emotional reaction to interactive multimedia and hypermedia is important. One particularly popular self-reported method for emotion assessment is the Valence-Arousal (VA) Scale: a 9?×?9 affective grid. This paper aims to identify specific stress region(s) in the VA space by combining self-reported ratings (pairs of VA) and physiological signals (skin conductance). To this end, 31 healthy volunteers participated in an experiment by performing five stressful interaction tasks while their skin conductance was monitored. The selected interaction tasks were most frequently listed as stressful by a separate group of 15 interviewees. After each task, participants expressed their perceived emotional experience using the VA rating space. Our findings show which regions in the VA rating space may reliably indicate self-reported stress that is in alignment with one’s measured skin conductance while using interactive applications. One additional important contribution of this work is the proposed approach for the empirical identification of affect regions in the VA space based on physiological signals.     

基建仓库

基建仓库项目位于莱瓦顿的Harlingervaart运河岸边,作为周边建筑的仓库,它具有独特的趣味性良好的形体感,因此被认为是立于运河沿岸广场上的一座雕塑。建筑的入口处挑出悬臂,并且顺势突出建筑南侧的体量     

Modeling heterogeneous bacterial populations exposed to antibiotics: The logistic‐dynamics case

In typical in vitro tests for clinical use or development of antibiotics, samples from a bacterial population are exposed to an antibiotic at various concentrations. The resulting data can then be used to build a mathematical model suitable for dosing regimen design or for further development. For bacterial populations that include resistant subpopulations—an issue that has reached alarming proportions—building such a model is challenging. In prior work, we developed a related modeling framework for such heterogeneous bacterial populations following linear dynamics when exposed to an antibiotic. We extend this framework to the case of logistic dynamics, common among strongly resistant bacterial strains. Explicit formulas are developed that can be easily used in parameter estimation and subsequent dosing regimen design under realistic pharmacokinetic conditions. A case study using experimental data from the effect of an antibiotic on a gram‐negative bacterial population exemplifies the usefulness of the proposed approach. © 2015 American Institute of Chemical Engineers AIChE J, 61: 2385–2393, 2015     

Fast and Exact Warping of Time Series Using Adaptive Segmental Approximations

Similarity search is a core module of many data analysis tasks, including search by example, classification, and clustering. For time series data, Dynamic Time Warping (DTW) has been proven a very effective similarity measure, since it minimizes the effects of shifting and distortion in time. However, the quadratic cost of DTW computation to the length of the matched sequences makes its direct application on databases of long time series very expensive. We propose a technique that decomposes the sequences into a number of segments and uses cheap approximations thereof to compute fast lower bounds for their warping distances. We present several, progressively tighter bounds, relying on the existence or not of warping constraints. Finally, we develop an index and a multi-step technique that uses the proposed bounds and performs two levels of filtering to efficiently process similarity queries. A thorough experimental study suggests that our method consistently outperforms state-of-the-art methods for DTW similarity search.     

Reverse nearest neighbors in large graphs

A reverse nearest neighbor (RNN) query returns the data objects that have a query point as their nearest neighbor (NN). Although such queries have been studied quite extensively in Euclidean spaces, there is no previous work in the context of large graphs. In this paper, we provide a fundamental lemma, which can be used to prune the search space while traversing the graph in search for RNN. Based on it, we develop two RNN methods; an eager algorithm that attempts to prune network nodes as soon as they are visited and a lazy technique that prunes the search space when a data point is discovered. We study retrieval of an arbitrary number k of reverse nearest neighbors, investigate the benefits of materialization, cover several query types, and deal with cases where the queries and the data objects reside on nodes or edges of the graph. The proposed techniques are evaluated in various practical scenarios involving spatial maps, computer networks, and the DBLP coauthorship graph.     

Reference structure tomography

Reference structure tomography (RST) uses multidimensional modulations to encode mappings between radiating objects and measurements. RST may be used to image source-density distributions, estimate source parameters, or classify sources. The RST paradigm permits scan-free multidimensional imaging, data-efficient and computation-efficient source analysis, and direct abstraction of physical features. We introduce the basic concepts of RST and illustrate the use of RST for multidimensional imaging based on a geometric radiation model.