Boosting Zn^(2+)kinetics via the multifunctional pre-desolvation interface for dendrite-free Zn anodes

Aqueous zinc ion batteries(AZIBs)are an advanced secondary battery technology to supplement lithiumion batteries.It has been widely concerned and developed recently based on the element abundance and safety advantages.However,AZIBs still suffer from serious problems such as dendrites Zn,hydrogen evolution corrosion,and surface passivation,which hinder the further commercial application of AZIBs.Herein,an in-situ ZnCr_(2)O_(4)(ZCO)interface endows AZIBs with dendrite-free and ultra-low polarization by realizing Zn^(2+)pre-desolvation,constraining H2O-induced corrosio n,and boosting Zn^(2+)transport/deposition kinetics.The ZCO@Zn anode harvests an ultrahigh cumulative capacity of~20000 mA h cm^(-2)(cycle time:over 4000 h)at a high current density of 10 mA cm^(-2),indicating excellent reversibility of Zn deposition,Such superior performance is among the best cyclability in AZIBs.Moreover,the multifunctional ZCO interface improves the Coulombic efficiency(CE)to 99.7%for more than 2600 cycles.The outstanding electrochemical performance is also verified by the long-term cycle stability of ZCO@Zn//α-MnO_(2) full cells.Notably,the as-proposed method is efficient and low-cost enough to enable mass production.This work provides new insights into the uniform Zn electrodeposition at the scale of interfacial Zn^(2+)predesolvation and kinetics improvement.

基于时空数据的地下空间基础设施智能监测系统

基于时空大数据的智能感知、机理认知和劣化预知,不仅促进了基础设施安全的发展,同时也是基础设施建设向智能化转变的基础理论和关键技术。地下空间利用的发展,形成了深、大、聚的三大特征和立体的城市布局。然而,与地上的建筑物和桥梁相比,发生在地下的疾病和退化更为隐蔽,难以识别,在建设和服务期间仍然存在许多挑战。针对这一问题,本文总结了现有的方法,并在现实世界的空间安全管理中评估了它们的长处和短处,并在统一的智能监控系统中,讨论关键科学问题和解决方案。

Mining Typical Treatment Duration Patterns for Rational Drug Use from Electronic Medical Records

Rational drug use requires that patients receive medications for an adequate period of time.The adequate duration time of medications not only improve the therapeutic effect of medicines,but also reduce the side effects and adverse reactions of medicines.This paper proposes a data-driven method to mine typical treatment duration patterns for rational drug use from electronic medical records (EMRs).Firstly,a quintuple is defined to describe drug use duration statistics (DUDS) for each drug and treatment record is further represented with DUDS vector (DUDSV).Next a similarity measure method is adopted to compute the similarity between treatment records.Meanwhile,a clustering algorithm is used to cluster all patient treatment records to extract typical treatment duration patterns including typical drug sets,effective drug use day sets,and the DUDSs of each typical drug.Then the extracted typical treatment duration patterns are evaluated and annotated based on patients' demographic information,disease severity scores,treatment outcome and diagnostic information.Finally,a real-world EMR dataset is performed to indicate that the approach we proposed can effectively mine typical treatment duration patterns from EMRs and recommend the appropriate treatment regimens for patients based on their admission information.

A Simulation Research Towards Better Leverage of Sales Ranking

As a kind of the most significantly popular information in markets,the sales ranking has great impacts on consumer choice.However,there are few discussions on how sales ranking should be provided to consumers in the literature.This paper aims to answer the following two questions:1)To what extent does the sales ranking influence consumer choices;2)When the sales ranking should be provided to consumers.To do so,this paper first constructs a sales ranking model and then provides detailed simulation experiments to demonstrate the model.The experimental results show that for markets where consumer preferences are dramatically different,such as music and movie markets,sales rankings do not have significant influences on consumer choices and should not be provided to consumers until a large number of early independent consumer choices have been accumulated.But for markets in which consumer preferences are similar,such as markets for official supplies,sales rankings have more influences on consumer choices and should be provided to consumers earlier.Furthermore,an evolution strategy is proposed to ascertain the most suitable sales rankings(characterised by suitable influence strength and suitable release time)for some specified online markets.The comparison results show that the optimized sales rankings not only can help consumers discover higher-quality products but also can improve overall sales.

Cluster Analysis in Data-Driven Management and Decisions

Clustering plays an important role in management and decision-making processes.This paper first discusses three types of cluster analysis methods-centroid-based,connectivity-based,and density-based.Then the challenges to traditional clustering in new business environments are highlighted,with algorithmic extensions and innovative efforts for coping with data that is dynamic,large-scale,representative,non-convex,and consensus in nature.In addition,three application cases are illustrated,where clustering is incorporated into the overall solution in the contexts of management support,business of sharing economy,and healthcare decision assistance.