Dendrite-free Zn deposition initiated by nanoscale inorganic-organic coating-modified 3D host for stable Zn-ion battery

A 3D nanostructured scaffold as the host for zinc enables effective inhibition of anodic dendrite growth.However,the increased electrode/electrolyte interface area provided by using 3D matrices exacerbates the passivation and localized corrosion of the Zn anode,ultimately bringing about the degradation of the electrochemical performance.Herein,a nanoscale coating of inorganic-organic hybrid(α-In_(2)Se_(3)-Nafion)onto a flexible carbon nanotubes(CNTs)framework(ISNF@CNTs)is designed as a Zn plating/stripping scaffold to ensure uniform Zn nucleation,thus achieving a dendrite-free and durable Zn anode.The intro-duced inorganic-organic interfacial layer is dense and sturdy,which hinders the direct exposure of deposited Zn to electrolytes and mitigates the side reactions.Meanwhile,the zincophilic nature of ISNF can largely reduce the nucleation energy barrier and promote the ion-diffusion transportation.Consequently,the ISNF@CNTs@Zn electrode exhibits a low-voltage hysteresis and a superior cycling life(over 1500 h),with dendrite-free Zn-plating behaviors in a typical symmetrical cell test.Additionally,the superior feature of ISNF@CNTs@Zn anode is further demonstrated by Zn-MnO_(2)cells in both coin and flexible quasi-solid-state configurations.This work puts forward an inspired remedy for advanced Zn-ion batteries.

KeyEE:Enhancing Low-Resource Generative Event Extraction with Auxiliary Keyword Sub-Prompt

Event Extraction(EE)is a key task in information extraction,which requires high-quality annotated data that are often costly to obtain.Traditional classification-based methods suffer from low-resource scenarios due to the lack of label semantics and fine-grained annotations.While recent approaches have endeavored to address EE through a more data-efficient generative process,they often overlook event keywords,which are vital for EE.To tackle these challenges,we introduce KeyEE,a multi-prompt learning strategy that improves low-resource event extraction by Event Keywords Extraction(EKE).We suggest employing an auxiliary EKE sub-prompt and concurrently training both EE and EKE with a shared pre-trained language model.With the auxiliary sub-prompt,KeyEE learns event keywords knowledge implicitly,thereby reducing the dependence on annotated data.Furthermore,we investigate and analyze various EKE sub-prompt strategies to encourage further research in this area.Our experiments on benchmark datasets ACE2005 and ERE show that KeyEE achieves significant improvement in low-resource settings and sets new state-of-the-art results.

Medical Knowledge Graph:Data Sources,Construction,Reasoning,and Applications

Medical knowledge graphs(MKGs)are the basis for intelligent health care,and they have been in use in a variety of intelligent medical applications.Thus,understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field.To this end,we offer an in-depth review of MKG in this work.Our research begins with the examination of four types of medical information sources,knowledge graph creation methodologies,and six major themes for MKG development.Furthermore,three popular models of reasoning from the viewpoint of knowledge reasoning are discussed.A reasoning implementation path(RIP)is proposed as a means of expressing the reasoning procedures for MKG.In addition,we explore intelligent medical applications based on RIP and MKG and classify them into nine major types.Finally,we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.

Fusion Model for Tentative Diagnosis Inference Based on Clinical Narratives

In general,physicians make a preliminary diagnosis based on patients’admission narratives and admission conditions,largely depending on their experiences and professional knowledge.An automatic and accurate tentative diagnosis based on clinical narratives would be of great importance to physicians,particularly in the shortage of medical resources.Despite its great value,little work has been conducted on this diagnosis method.Thus,in this study,we propose a fusion model that integrates the semantic and symptom features contained in the clinical text.The semantic features of the input text are initially captured by an attention-based Bidirectional Long Short-Term Memory(BiLSTM)network.The symptom concepts,recognized from the input text,are then vectorized by using the term frequency-inverse document frequency method based on the relations between symptoms and diseases.Finally,two fusion strategies are utilized to recommend the most potential candidate for the international classification of diseases code.Model training and evaluation are performed on a public clinical dataset.The results show that both fusion strategies achieved a promising performance,in which the best performance obtained a top-3 accuracy of 0.7412.