Designing and Implementing an Advanced Big Data Governance Platform

Contemporary mainstream big data governance platforms are built atop the big data ecosystem components,offering a one-stop development and analysis governance platform for the collection,transmission,storage,cleansing,transformation,querying and analysis,data development,publishing,and subscription,sharing and exchange,management,and services of massive data.These platforms serve various role members who have internal and external data needs.However,in the era of big data,the rapid update and iteration of big data technologies,the diversification of data businesses,and the exponential growth of data present more challenges and uncertainties to the construction of big data governance platforms.This paper discusses how to effectively build a data governance platform under the big data system from the perspectives of functional architecture,logical architecture,data architecture,and functional design.

Use of Data Mining to Support the Development of Knowledge Intensive CAD

In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ity can only be attained through the deployment of knowledge. To embed knowledge into a CAD system to form a knowledge intensive CAD (KIC) system is one of way to enhance the design compatibility of a manufacturing company. The most difficu lt phase to develop a KIC system is to capitalize a huge amount of legacy data t o form a knowledge database. In the past, such capitalization process could only be done solely manually or semi-automatic. In this paper, a five step model fo r automatic design knowledge capitalization through the use of data mining is pr oposed whilst details of how to select, verify and performance benchmarking an a ppropriate data mining algorithm for a specific design task will also be discuss ed. A case study concerning the design of a plastic toaster casing was used as an illustration for the proposed methodology and it was found that the avera ge absolute error of the predictions for the most appropriate algorithm is withi n 17%.

International Papers Contribution on Artificial Intelligence Promotes the Application and Development of Big Data in the Petroleum Industry

Artificial intelligence is a new technological science that researches and develops theories,methods,technologies and application systems for simulating,extending and expanding human intelligence.It simulates certain human thought processes and intelligent behaviors(such as learning,reasoning,thinking,planning,etc.),and produces a new type of intelligent machine that can respond in a similar way to human intelligence.In the past 30 years,it has achieved rapid development in various industries and related disciplines such as manufacturing,medical care,finance,and transportation.

Deep learning from three-dimensional lithium-ion battery multiphysics model part I:Data development

Fast growing demands for electric vehicles require better longevity,safety and reliability for next-generation high-energy battery technologies.A data-centered battery management system is thus desired to interpret complex battery data and make decisions for properly managing multi-physics battery dynamics.Nowadays,Battery informatics are emerging as promising solutions by leveraging advanced machine learning tools to deliver accurate prediction of battery performance,health and safety,but is hurdled by a scarcity of data.To mitigate this issue,this study presents one of the first studies for data development through both experimental studies and three-dimensional(3-D)multi-physics modeling to underpin a deep learning framework with indepth examination for battery performance and thermal risk prediction.Specifically,PartⅠfocused on the development of the battery model which was thoroughly validated and analyzed to guarantee the model ac-curacy by two steps:firstly,we validated the multi-physics model against two commercial Lithium-ion batteries,i.e.,Panasonic NCR18650B and 18650BD;Then,the coupling between thermal and electrochemical battery behaviors were analyzed deeply to demonstrate insights obtained from the model,such as voltage evolution and maximum local temperature(hot spot).The developed model proves to be capable of providing insightful and reliable data for the training of convolutional neural network and long short-term memory(CNN-LSTM)in partⅡ.

Integration of Soil Information System and Interactive Self-Organizing Data for Agricultural Developing Zones in Red Soil Region

Integration of soil information system (SIS) and interactive self-organizing data (ISODATA) was studied to establish proper agricultural developing zones in red soil region of southern China which are of crucial importance to farmers, researchers, and decision makers while utilizing and managing red soil resources. SIS created by using ARC/INPO was used to provide data acquisition, systematic model parameter assignment, and visual display of analytic results. Topography, temperature, soil component (e.g., organic matter and pH) and condition of agricultural production were selected as parameters of ISODATA model. Taking Longyou County, Zhejiang Province as the case study area, the effect of the integration and recommendations are discussed for future research.

A Tailor-made Data Quality Approach for Higher Educational Data

Purpose: This paper relates the definition of data quality procedures for knowledge organizations such as Higher Education Institutions. The main purpose is to present the flexible approach developed for monitoring the data quality of the European Tertiary Education Register(ETER) database, illustrating its functioning and highlighting the main challenges that still have to be faced in this domain.Design/methodology/approach: The proposed data quality methodology is based on two kinds of checks, one to assess the consistency of cross-sectional data and the other to evaluate the stability of multiannual data. This methodology has an operational and empirical orientation. This means that the proposed checks do not assume any theoretical distribution for the determination of the threshold parameters that identify potential outliers, inconsistencies, and errors in the data. Findings: We show that the proposed cross-sectional checks and multiannual checks are helpful to identify outliers, extreme observations and to detect ontological inconsistencies not described in the available meta-data. For this reason, they may be a useful complement to integrate the processing of the available information.Research limitations: The coverage of the study is limited to European Higher Education Institutions. The cross-sectional and multiannual checks are not yet completely integrated.Practical implications: The consideration of the quality of the available data and information is important to enhance data quality-aware empirical investigations, highlighting problems, and areas where to invest for improving the coverage and interoperability of data in future data collection initiatives.Originality/value: The data-driven quality checks proposed in this paper may be useful as a reference for building and monitoring the data quality of new databases or of existing databases available for other countries or systems characterized by high heterogeneity and complexity of the units of analysis without relying on pre-specified theoretical distributions.

Emerging Trends for Microbiome Analysis： From Single-Cell Functional Imaging to Microbiome Big Data

Method development has always been and will continue to be a core driving force of microbiome science, In this perspective, we argue that in the next decade, method development in microbiome analysis will be driven by three key changes in both ways of thinking and technological platforms： ① a shift from dissecting microbiota structure by sequencing to tracking microbiota state, function, and intercellular interaction via imaging; ② a shift from interrogating a consortium or population of cells to probing individual cells; and ③a shift from microbiome data analysis to microbiome data science. Some of the recent methoddevelopment efforts by Chinese microbiome scientists and their international collaborators that underlie these technological trends are highlighted here. It is our belief that the China Microbiome Initiative has the opportunity to deliver outstanding ＂Made-in-China＂ tools to the international research community, by building an ambitious, competitive, and collaborative program at the forefront of method development for microbiome science.

ADAPTABLE DESIGN IN PRODUCT DEVELOPMENT

A newly emerging design pattern, named as adaptable design （AD）, which aims at developing products that are adaptable from design to post-life cycle, is discussed. AD consists of four main phases： product modeling, design platform, specific design and product redesign. A new process-based design data model （PDDM） is presented which is organized according to the principles of convenient knowledge extraction, data representation, layout, sharing and reuse. Based on the PDDM, a universal design platform for product family development is established, which has characters of modularity, parameter-driven, variant design, etc. The framework of the platform is also proposed as a conceptual structure and overall logical organization for generating a family of products. AD methodology is successfully applied to develop a family of tunnel boring machine （TBM） for different engineering projects, with the efficiency of our developing team being greatly increased.

Effect of building energy efficiency standards on carbon emission efficiency in commercial buildings

The building sector plays a crucial role in the worldwide shift toward achieving net-zero emissions.Building energy efficiency standards(BEESs)are highly effective policies for reducing carbon emissions.Therefore,exploring the provincial variations in carbon emission efficiency(CEE)in the building sector and identifying the effect of BEESs on CEE is crucial.This study focuses on commercial buildings in China and applies a difference in differences model to evaluate the impact of BEESs on the CEE of commercial buildings.The slacks-based measure–data envelopment analysis model is employed to assess the CEE of commercial buildings in 30 Chinese provinces from 2000 to 2019.Furthermore,heterogeneous tests are used to explore how climate characteristics and economic conditions affect the efficiency of BEESs.The results indicate that BEESs positively influence the CEE of commercial buildings.Specifically,a 1%increase in the intensity of BEESs causes a 0.1484%increase in the CEE of commercial buildings.Moreover,the impact of BEESs is particularly pronounced in the southern and western provinces.This study provides valuable scientific evidence for governments to enhance BEESs implementation.

Reporting Children’s Development below the Test Floor: Looking Back and Forth to Describe Individual Strengths and Needs

The test floor effects seen in standardised tests lead to a standardised score of 1 or less with a flat profile that hides a child’s individual strengths and needs. The Griffiths III community of practitioners requested advice on the reporting of children’s development below the floor of the test, so that individual strengths and needs can be described. This paper reports the third phase of research following an earlier Scoping Review and a wider literature review. To confirm quality control, Phase 3 was conducted in a retrospective manner using the same methodology as the earlier phases but in a reverse direction. Peer reviewer comments and key elements from the Scoping Review and keywords from the publications were tabulated. Data analysis included a change of perspective to that of the child and their individual rights with respect to the literature themes already described in Phase 2. These confirmed that there is little specific guidance in the literature, but that computational advances for homogeneous populations and especially disaggregated data offer some solutions. A greater balance between broad biopsychosocial models and standardised models of assessment should be sought by practitioners together with the use of disaggregated data to highlight issues that pertain to individual subsets of results. This will ensure that the child’s right for their individual strengths and needs to be described together with a plan for management, may be met.

SD大鼠胚胎-胎仔发育毒性试验背景数据的建立

目的总结国家药物安全评价监测中心2013-2022年SD大鼠胚胎-胎仔发育毒性试验各指标的正常值范围,建立SD大鼠胚胎-胎仔发育毒性试验各指标的背景数据库,为药物胚胎-胎仔发育毒性评价提供参考。方法对本中心2013-2022年11项SD大鼠胚胎-胎仔发育毒性试验中对照组共计205只孕鼠和3037只胎鼠各项胚胎发育和胎仔生长发育指标进行统计分析,计算其平均数、标准差、变异系数和95%置信区间。指标包括孕鼠妊娠期体重和体重增长幅度、孕鼠摄食量、妊娠结局(妊娠率、平均黄体数、平均着床数、平均活胎数、活胎率、吸收胎率、死胎率)、胎仔生长发育情况(胎仔重、胎盘重、性别比)、胎仔外观异常率、内脏异常率和骨骼异常率。结果孕鼠妊娠期体重呈增长趋势,妊娠后期体重增长幅度明显增大。孕鼠摄食量随着妊娠时间的增加呈增长趋势。妊娠第20天进行剖腹产,妊娠率为93.2%,平均黄体数、着床数和活胎数分别为18.0±3.2,15.9±2.8和14.8±3.0,活胎率为93.4%,死胎率为6.6%;胎仔雄/雌性别比为0.94,平均体重为(3.6±0.3)g,胎盘平均重量为(0.6±0.3)g。胎仔外观异常发生率约为0.2%,内脏异常率约为0.8%。骨骼异常率约为1.2%,未骨化和骨化不全的发生率较高,主要发生于胸骨和舌骨等,胎仔的掌骨骨化数、跖骨骨化数和骶尾椎骨化数分别为7.0±0.7,8.0±0.1和7.4±0.5,第Ⅰ~Ⅳ胸骨骨化率较高,平均为98.6%~99.9%,第Ⅴ胸骨骨化率为(68.0±28.4)%,第Ⅵ胸骨骨化率为(82.8±23.9)%。结论初步建立了本GLP实验室SD大鼠胚胎-胎仔发育毒性试验中各指标背景数据库,为生殖毒性研究提供参考。

单细胞测序数据的生物信息学分析方法与应用

单细胞测序技术是一项新技术。文章从单细胞测序技术的基本原理和发展历程入手,突出其在生物医学领域的重要性,详细探讨生物信息学分析的关键步骤,包括数据预处理、细胞异质性分析、基因表达分析以及轨迹推断和空间基因组学等高级技术,阐述单细胞测序在癌症研究、发育生物学、免疫学和神经科学等领域的应用,揭示其在理解细胞异质性和生物过程动态中的核心作用。文章旨在为研究人员提供有关单细胞测序技术及其分析方法的全面概述,展望其在生物医学研究领域的广阔应用前景。

基于大数据的教学评价研究

文章基于教育大数据的视角,结合教育评价活动的转型,对教育大数据的采集、可视化分析与呈现技术等开展了研究。文章认为,教育大数据技术推动了教育评价中数据驱动决策的实现,为多方参与教育评价、实现发展性学生评估提供了良好的支持。平板电脑、数码笔、可穿戴设备等能够实时地将不同类型的学习数据数字化,实现了对学生学习全过程数据的采集,为教育领域中实现基于数据分析与理性证据的教育评估与决策提供了数据基础。各类可视化分析工具能够在稀疏的教育大数据中过滤、挖掘各类隐含的教育信息和规律,帮助我们理解学生个人知识体系的构建过程,探索学生个体的社会学习网络的演化规律,揭示教育事件在特定时空呈现的特征。

大数据理念下的发展性学习评价系统设计研究

大数据时代的到来为教育带来了一场新变革,数字化学习产生了数量庞大、形式多样的教育数据。传统的数据处理方式难以分析和处理这些非结构化、半结构化的教育大数据,迫切需要引入一种有效的技术方法。同时,发展性评价作为新型的评价方式,其"关注个体差异,注重多元评价"的理念,适应了数字化学习评价的时代要求。结合全数据采集、知识增值可视化、个性化评价等理念,文章构建了大数据支持的数字化学习发展性评价系统,并对各子系统的功能和内容进行了系统化设计,最后对该系统的工作流程进行了分析。

义务教育阶段学生数据分析观念的评价框架建构

"数据分析观念"作为《义务教育数学课程标准(2011年版)》提出的10个核心概念之一,是学生在义务教育阶段学习统计与概率的核心目标.评价学生的数据分析观念的发展水平,是教育工作者在义务教育阶段数学课程与教学中不能回避的任务.基于对数据分析观念内涵的分析和国内外的相关研究成果的借鉴,建构了一个包括3个维度(对数据的意识与感悟,数据分析方法的意识与感悟,现实现象随机性的意识与感悟),每个维度包括由低到高的3或4个水平的评价框架.

学生数据分析观念发展水平的研究反思

发展学生的数据分析观念作为课程设计和教学的核心,已经得到了广泛共识.数据分析观念的内涵,主要包括通过数据收集和分析提取信息、通过数据体会随机性、利用数据解决问题.前两者更加指向统计过程和统计方法自身,后者则更加指向运用统计解决问题.国内有关统计认知的研究刚刚展开,大多数的研究是针对学生理解某些统计概念而展开的,系统研究学生统计认知发展阶段的较少.国外的研究相对比较丰富,并且形成了一些统计思维的发展框架.

基于大数据分析的学习评价

针对目前高校课程评价手段单一,不能满足学生个性化学习的要求,本文提出了一种具有个性化和发展性特征的学习评价模型。该模型基于学生课堂教学、课外自学和部分日常生活构成的大数据,通过数据挖掘技术,给出课程学习过程中对每个学生的评价。学习评价关注学习的每个阶段,给出的建议符合学生个性化要求,并可帮助教师因材施教。教学实践结果显示,这种学习评价有助于改善学生的学习。

关联规则挖掘Apriori算法研究综述

关联规则挖掘是数据挖掘中的一项重要技术,Apriori算法是关联规则挖掘中的一种经典算法。在信息技术的发展过程中,随着海量数据的收集和存储,从数据库中挖掘出相关联规则的数据集变得极为重要。为此,对国内外有关Apriori算法的研究现状、算法的原理、优化算法的思想进行了探讨,同时分析了几种经典的优化算法,最后对Apriori算法未来的发展趋势进行了预测和展望。

0～3岁儿童发育迟缓监测研究

目的探索0～3岁儿童发育迟缓监测预防的有效性及数据直报的可行性。方法采用整群抽样的方法,在北京市五个区县22个社区对20907名0～3岁儿童进行为期1年的发育监测,采用问卷、信息报表方式收集相关数据,并与非项目社区监测结果进行比较分析。结果社区儿童发育迟缓筛查率62.18%,0岁组筛查率明显高于1岁组、2岁组,三个年龄组筛查率明显高于非项目社区筛查率。筛查阳性儿童268人,转诊253名儿童,转诊率为94.4%;诊断发育迟缓儿童97名,发育迟缓现患率为7.46‰;43.28%的评估儿童接受干预与康复训练。结论 0～3岁儿童发育迟缓监测方法可以提供监测的有效性,并在数据直报方面具有可行性和有效性。

教学大数据监测预警平台设计与应用

发展性评价是我国教学评价模式改革的重要方向,但发展性评价存在组织困难、数据难以收集、反馈困难等问题。大数据技术为发展性评价提供新的技术推动力量。通过教学发展性评价中的大数据需求分析,提出教学大数据监测预警的概念,并探讨其内涵及意义。设计教学大数据监测预警平台,构建其总体架构、功能结构、实体画像和预警模型,并对其应用机制、硬件架构、模型应用进行分析和实证。该技术方案对大数据在教学过程中的应用具有积极的借鉴作用。