A dynamic database of solid-state electrolyte(DDSE)picturing all-solid-state batteries

All-solid-state batteries(ASSBs)are a class of safer and higher-energy-density materials compared to conventional devices,from which solid-state electrolytes(SSEs)are their essential components.To date,investigations to search for high ion-conducting solid-state electrolytes have attracted broad concern.However,obtaining SSEs with high ionic conductivity is challenging due to the complex structural information and the less-explored structure-performance relationship.To provide a solution to these challenges,developing a database containing typical SSEs from available experimental reports would be a new avenue to understand the structureperformance relationships and find out new design guidelines for reasonable SSEs.Herein,a dynamic experimental database containing>600 materials was developed in a wide range of temperatures(132.40–1261.60 K),including mono-and divalent cations(e.g.,Li^(+),Na^(+),K^(+),Ag^(+),Ca^(2+),Mg^(2+),and Zn^(2+))and various types of anions(e.g.,halide,hydride,sulfide,and oxide).Data-mining was conducted to explore the relationships among different variates(e.g.,transport ion,composition,activation energy,and conductivity).Overall,we expect that this database can provide essential guidelines for the design and development of high-performance SSEs in ASSB applications.This database is dynamically updated,which can be accessed via our open-source online system.

Discrimination of polysorbate 20 by high-performance liquid chromatography-charged aerosol detection and characterization for components by expanding compound database and library

Analyzing polysorbate 20(PS20)composition and the impact of each component on stability and safety is crucial due to formulation variations and individual tolerance.The similar structures and polarities of PS20 components make accurate separation,identification,and quantification challenging.In this work,a high-resolution quantitative method was developed using single-dimensional high-performance liquid chromatography(HPLC)with charged aerosol detection(CAD)to separate 18 key components with multiple esters.The separated components were characterized by ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UHPLC-Q-TOF-MS)with an identical gradient as the HPLC-CAD analysis.The polysorbate compound database and library were expanded over 7-time compared to the commercial database.The method investigated differences in PS20 samples from various origins and grades for different dosage forms to evaluate the composition-process relationship.UHPLC-Q-TOF-MS identified 1329 to 1511 compounds in 4 batches of PS20 from different sources.The method observed the impact of 4 degradation conditions on peak components,identifying stable components and their tendencies to change.HPLC-CAD and UHPLC-Q-TOF-MS results provided insights into fingerprint differences,distinguishing quasi products.

Enhancing AI System Privacy:An Automatic Tool for Achieving GDPR Compliance in NoSQL Databases

The EU’s Artificial Intelligence Act(AI Act)imposes requirements for the privacy compliance of AI systems.AI systems must comply with privacy laws such as the GDPR when providing services.These laws provide users with the right to issue a Data Subject Access Request(DSAR).Responding to such requests requires database administrators to identify information related to an individual accurately.However,manual compliance poses significant challenges and is error-prone.Database administrators need to write queries through time-consuming labor.The demand for large amounts of data by AI systems has driven the development of NoSQL databases.Due to the flexible schema of NoSQL databases,identifying personal information becomes even more challenging.This paper develops an automated tool to identify personal information that can help organizations respond to DSAR.Our tool employs a combination of various technologies,including schema extraction of NoSQL databases and relationship identification from query logs.We describe the algorithm used by our tool,detailing how it discovers and extracts implicit relationships from NoSQL databases and generates relationship graphs to help developers accurately identify personal data.We evaluate our tool on three datasets,covering different database designs,achieving an F1 score of 0.77 to 1.Experimental results demonstrate that our tool successfully identifies information relevant to the data subject.Our tool reduces manual effort and simplifies GDPR compliance,showing practical application value in enhancing the privacy performance of NOSQL databases and AI systems.

Classification of Cybersecurity Threats, Vulnerabilities and Countermeasures in Database Systems

Database systems have consistently been prime targets for cyber-attacks and threats due to the critical nature of the data they store.Despite the increasing reliance on database management systems,this field continues to face numerous cyber-attacks.Database management systems serve as the foundation of any information system or application.Any cyber-attack can result in significant damage to the database system and loss of sensitive data.Consequently,cyber risk classifications and assessments play a crucial role in risk management and establish an essential framework for identifying and responding to cyber threats.Risk assessment aids in understanding the impact of cyber threats and developing appropriate security controls to mitigate risks.The primary objective of this study is to conduct a comprehensive analysis of cyber risks in database management systems,including classifying threats,vulnerabilities,impacts,and countermeasures.This classification helps to identify suitable security controls to mitigate cyber risks for each type of threat.Additionally,this research aims to explore technical countermeasures to protect database systems from cyber threats.This study employs the content analysis method to collect,analyze,and classify data in terms of types of threats,vulnerabilities,and countermeasures.The results indicate that SQL injection attacks and Denial of Service(DoS)attacks were the most prevalent technical threats in database systems,each accounting for 9%of incidents.Vulnerable audit trails,intrusion attempts,and ransomware attacks were classified as the second level of technical threats in database systems,comprising 7%and 5%of incidents,respectively.Furthermore,the findings reveal that insider threats were the most common non-technical threats in database systems,accounting for 5%of incidents.Moreover,the results indicate that weak authentication,unpatched databases,weak audit trails,and multiple usage of an account were the most common technical vulnerabilities in database systems,each accounting for 9%of vulnerabilities.Additionally,software bugs,insecure coding practices,weak security controls,insecure networks,password misuse,weak encryption practices,and weak data masking were classified as the second level of security vulnerabilities in database systems,each accounting for 4%of vulnerabilities.The findings from this work can assist organizations in understanding the types of cyber threats and developing robust strategies against cyber-attacks.

Comprehensive analysis of advanced glycation end-products in commonly consumed foods:presenting a database for dietary AGEs and associated exposure assessment

Advanced glycation end-products(AGEs)are a group of heterogeneous compounds formed in heatprocessed foods and are proven to be detrimental to human health.Currently,there is no comprehensive database for AGEs in foods that covers the entire range of food categories,which limits the accurate risk assessment of dietary AGEs in human diseases.In this study,we first established an isotope dilution UHPLCQq Q-MS/MS-based method for simultaneous quantification of 10 major AGEs in foods.The contents of these AGEs were detected in 334 foods covering all main groups consumed in Western and Chinese populations.Nε-Carboxymethyllysine,methylglyoxal-derived hydroimidazolone isomers,and glyoxal-derived hydroimidazolone-1 are predominant AGEs found in most foodstuffs.Total amounts of AGEs were high in processed nuts,bakery products,and certain types of cereals and meats(>150 mg/kg),while low in dairy products,vegetables,fruits,and beverages(<40 mg/kg).Assessment of estimated daily intake implied that the contribution of food groups to daily AGE intake varied a lot under different eating patterns,and selection of high-AGE foods leads to up to a 2.7-fold higher intake of AGEs through daily meals.The presented AGE database allows accurate assessment of dietary exposure to these glycotoxins to explore their physiological impacts on human health.

Database Search Behaviors: Insight from a Survey of Information Retrieval Practices

This study examines the database search behaviors of individuals, focusing on gender differences and the impact of planning habits on information retrieval. Data were collected from a survey of 198 respondents, categorized by their discipline, schooling background, internet usage, and information retrieval preferences. Key findings indicate that females are more likely to plan their searches in advance and prefer structured methods of information retrieval, such as using library portals and leading university websites. Males, however, tend to use web search engines and self-archiving methods more frequently. This analysis provides valuable insights for educational institutions and libraries to optimize their resources and services based on user behavior patterns.

Databases of 2D material-substrate interfaces and 2D charged building blocks

Discovery of materials using“bottom-up”or“top-down”approach is of great interest in materials science.Layered materials consisting of two-dimensional(2D)building blocks provide a good platform to explore new materials in this respect.In van der Waals(vdW)layered materials,these building blocks are charge neutral and can be isolated from their bulk phase(top-down),but usually grow on substrate.In ionic layered materials,they are charged and usually cannot exist independently but can serve as motifs to construct new materials(bottom-up).In this paper,we introduce our recently constructed databases for 2D material-substrate interface(2DMSI),and 2D charged building blocks.For 2DMSI database,we systematically build a workflow to predict appropriate substrates and their geometries at substrates,and construct the 2DMSI database.For the 2D charged building block database,1208 entries from bulk material database are identified.Information of crystal structure,valence state,source,dimension and so on is provided for each entry with a json format.We also show its application in designing and searching for new functional layered materials.The 2DMSI database,building block database,and designed layered materials are available in Science Data Bank at https://doi.org/10.57760/sciencedb.j00113.00188.

Application of Thermodynamic Database to Corrosion of ZrO_(2) Containing Submerged Entry Nozzle in Steel Continuous Casting Process

The CALPHAD thermodynamic databases are very useful to analyze the complex chemical reactions happening in high temperature material process.The FactSage thermodynamic database can be used to calculate complex phase diagrams and equilibrium phases involving refractories in industrial process.In this study,the FactSage thermodynamic database relevant to ZrO_(2)-based refractories was reviewed and the application of the database to understanding the corrosion of continuous casting nozzle refractories in steelmaking was presented.

Impact of index admission cholecystectomy vs interval cholecystectomy on readmission rate in acute cholangitis: National Readmission Database survey

BACKGROUND Elective cholecystectomy(CCY)is recommended for patients with gallstone-related acute cholangitis(AC)following endoscopic decompression to prevent recurrent biliary events.However,the optimal timing and implications of CCY remain unclear.AIM To examine the impact of same-admission CCY compared to interval CCY on patients with gallstone-related AC using the National Readmission Database(NRD).METHODS We queried the NRD to identify all gallstone-related AC hospitalizations in adult patients with and without the same admission CCY between 2016 and 2020.Our primary outcome was all-cause 30-d readmission rates,and secondary outcomes included in-hospital mortality,length of stay(LOS),and hospitalization cost.RESULTS Among the 124964 gallstone-related AC hospitalizations,only 14.67%underwent the same admission CCY.The all-cause 30-d readmissions in the same admission CCY group were almost half that of the non-CCY group(5.56%vs 11.50%).Patients in the same admission CCY group had a longer mean LOS and higher hospitalization costs attrib-utable to surgery.Although the most common reason for readmission was sepsis in both groups,the second most common reason was AC in the interval CCY group.CONCLUSION Our study suggests that patients with gallstone-related AC who do not undergo the same admission CCY have twice the risk of readmission compared to those who undergo CCY during the same admission.These readmis-sions can potentially be prevented by performing same-admission CCY in appropriate patients,which may reduce subsequent hospitalization costs secondary to readmissions.

Large Language Model Based Semantic Parsing for Intelligent Database Query Engine

With the rapid development of artificial intelligence, large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. These models have great potential to enhance database query systems, enabling more intuitive and semantic query mechanisms. Our model leverages LLM’s deep learning architecture to interpret and process natural language queries and translate them into accurate database queries. The system integrates an LLM-powered semantic parser that translates user input into structured queries that can be understood by the database management system. First, the user query is pre-processed, the text is normalized, and the ambiguity is removed. This is followed by semantic parsing, where the LLM interprets the pre-processed text and identifies key entities and relationships. This is followed by query generation, which converts the parsed information into a structured query format and tailors it to the target database schema. Finally, there is query execution and feedback, where the resulting query is executed on the database and the results are returned to the user. The system also provides feedback mechanisms to improve and optimize future query interpretations. By using advanced LLMs for model implementation and fine-tuning on diverse datasets, the experimental results show that the proposed method significantly improves the accuracy and usability of database queries, making data retrieval easy for users without specialized knowledge.

Exploration of the Maturity Evaluation of College Students’Innovation and Entrepreneurship Projects Based on Computer Database Technology

The college innovation and entrepreneurship program is a powerful means to enhance students’innovation and entrepreneurship skills.Evaluating the maturity of innovation and entrepreneurship projects can stimulate students’enthusiasm and initiative to participate.Utilizing computer database technology for maturity evaluation can make the process more efficient,accurate,and convenient,aligning with the needs of the information age.Exploring strategies for applying computer database technology in the maturity evaluation of innovation and entrepreneurship projects offers valuable insights and directions for developing these projects,while also providing strong support for enhancing students’innovation and entrepreneurship abilities.

Computer Network and Database Security Technology Optimization

With the continuous development of computer network technology, its applications in daily life and work have become increasingly widespread, greatly improving efficiency. However, certain security risks remain. To ensure the security of computer networks and databases, it is essential to enhance the security of both through optimization of technology. This includes improving management practices, optimizing data processing methods, and establishing comprehensive laws and regulations. This paper analyzes the current security risks in computer networks and databases and proposes corresponding solutions, offering reference points for relevant personnel.

Divide and recombine approach for warranty database: estimating the reliability of an automobile component

The continuously updated database of failures and censored data of numerous products has become large, and on some covariates, information regarding the failure times is missing in the database. As the dataset is large and has missing information, the analysis tasks become complicated and a long time is required to execute the programming codes. In such situations, the divide and recombine (D&R) approach, which has a practical computational performance for big data analysis, can be applied. In this study, the D&R approach was applied to analyze the real field data of an automobile component with incomplete information on covariates using the Weibull regression model. Model parameters were estimated using the expectation maximization algorithm. The results of the data analysis and simulation demonstrated that the D&R approach is applicable for analyzing such datasets. Further, the percentiles and reliability functions of the distribution under different covariate conditions were estimated to evaluate the component performance of these covariates. The findings of this study have managerial implications regarding design decisions, safety, and reliability of automobile components.

我国体育科学研究国际化探究——基于SSCI、A&HCI的文献计量分析

采用文献计量学的方法,对我国发表于1975—2010年之间SSCI和A&HCI两大检索系统以"Sport Sciences"为主题的所有研究文献进行文献定量研究,从多个角度(文献数量、作者与机构、文献类型、语种、学科分布以及来源期刊等)做了详细的归类统计和分析。研究结论:SSCI和A&HCI收录我国体育科学研究文献数量还很有限,文献来源期刊的影响较低,文献国际影响力尚未形成规模;被收录的文献绝大多数为来源于香港高校;文献类型以"论文"、"会议摘要"为主;文献语种以英语为主;与我国学者合作撰写SSCI和A&HCI收录文献最多的国家为美国。在此基础上,对如何提高我国体育科学研究工作者在SSCI、A&HCI期刊上的发文量、促进我国体育科学研究与国际的接轨提出了若干条建议,以期为我国体育科学研究和科研管理提供可资参考的数据与资料。

SCI(E)、SSCI、A&HCI收录中国期刊的最新统计结果分析

目的对Web of Science(含SCIE、SSCI、A&HCI)收录的中国期刊进行统计并逐一确认,以摸清SCIE、SSCI、A&HCI收录中国期刊的确切数量。方法在Web of Science平台的地址字段输入"China"、"Taiwan"、"Hong Kong"及"Macao"等词汇进行检索;再使用"结果分析工具"(Analyze Re-sults)对检索结果进行分析,按"来源出版物"(Source Titles)进行排序,对刊载中国作者论文最多的前500种期刊逐一进行甄别,确认其主办单位是否是属于中国。结果 Web of Science数据库中的SCIE、SSCI以及A&HCI库共收录中国期刊207种,他们来自于全国22个省、直辖市、自治区以及特别行政区,其中大陆地区出版的期刊有148种,台湾地区出版的期刊有39种,香港地区出版的期刊20种。SCI收录33种,SCIE收录188种,SSCI收录16种,A&HCI收录9种。在207种期刊中,高校主办期刊约占1/4。结论中国入选Web of Science数据库SCIE、SSCI以及A&HCI三个引文数据库的期刊有200余种,但是高影响因子的期刊不多(未考虑学科差异),有95%的入选期刊2010年的影响因子在2.0以下。提高办刊质量,扩大入选期刊的国际影响力是我们未来努力的方向。

Database-oriented storage based on LMDB and linear octree for massive block model

Data organization requires high efficiency for large amount of data applied in the digital mine system. A new method of storing massive data of block model is proposed to meet the characteristics of the database, including ACID-compliant, concurrency support, data sharing, and efficient access. Each block model is organized by linear octree, stored in LMDB（lightning memory-mapped database）. Geological attribute can be queried at any point of 3D space by comparison algorithm of location code and conversion algorithm from address code of geometry space to location code of storage. The performance and robustness of querying geological attribute at 3D spatial region are enhanced greatly by the transformation from 3D to 2D and the method of 2D grid scanning to screen the inner and outer points. Experimental results showed that this method can access the massive data of block model, meeting the database characteristics. The method with LMDB is at least 3 times faster than that with etree, especially when it is used to read. In addition, the larger the amount of data is processed, the more efficient the method would be.

人文社会科学研究国际化科研水平计量学分析——以东华大学被SSCI、A&HCI(1975—2009)收录文献为案例

以东华大学1975—2009年被SSCI和A&HCI收录文献为案例,从国际化产出情况、学科分布、论文合作国家或地区分布、来源期刊、影响力以及上海地区近5年人文社会科学国际显示度等方面,揭示人文社会科学研究的历史和现状,以更好地利用国际科学数据资源促进我国人文社会科学研究的进一步发展,提升人文社会科学成果的国际显示度。

建国以来大陆各地区人文社会科学研究国际化学术产出定量分析--基于SSCI、A＆HCI(1956～2006)的研究

利用SSCI和A＆HCI（1956—2006），按大陆各省域检索搜集各地区人文社科国际发文数量，按地区发文量排序和统计整理地区的学科分布。对比国内核心期刊论文产出的地区分布，可以发现国际发文的地区不均衡更加明显。区分国际发文的高中低产区，并分别对各地区人文社科研究国际化发展的特点、层次状态与学科的国际化发展强、弱势状况及国际合作等进行阐述。

1997-2004年SSCI、A&HCI收录我国论文的统计与分析

本文对SSCI、AHCl 1997-2004共八年间收录的中国著者论文数量及其变化趋势、论文合作国家或地区分布、期刊分布、机构分布、语言分布、主题分布等情况进行了统计分析。发现我国大陆被SSCI、A&HCI收录的期刊与论文仍然甚少,语言及机构分布规律仍然未发生变化,但论文主题与国际合作态势却能够在一定程度上反映该时期我国社科研究的热点。

Establishment and Share of Tea Germplasm Database of Yunnan Province

[Objective] The paper was to establish tea germplasm database of Yunnan Province,and promote sharing of the tea germplasm resources.[Method] Eight hundred and thirty copies of tea germplasm resources of Yunnan Province were first systematically documented by using Access database software,the generic description of 631 tea resources and characteristic description of 300 tea resources were submitted for e-platform,then linked with the national e-platform for natural scientific and technological resources,and the tea germplasm database of Yunnan Province was established.[Result] Based on the conservation and utilization status of tea germplam resources,the sharing and utilization framework of tea germplam resources was presented.Many problems and suggestion about tea germplasm resources in the process of conservation,documentation concordance and sharing were pointed out.For example,conservation areas were separated and system was not completed;the main traits assessment and identification researching work had not completely accomplished and sharing was inefficient.[Conclusion] The paper laid foundation for standardized,digitized and information-based management of tea germplasm resources.