Literature classification and its applications in condensed matter physics and materials science by natural language processing

The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.

A Review of the Status and Development Strategies of Computer Science and Technology Under the Background of Big Data

This article discusses the current status and development strategies of computer science and technology in the context of big data.Firstly,it explains the relationship between big data and computer science and technology,focusing on analyzing the current application status of computer science and technology in big data,including data storage,data processing,and data analysis.Then,it proposes development strategies for big data processing.Computer science and technology play a vital role in big data processing by providing strong technical support.

Analysis of Stress Backlogs during Case-File Processing in Forensic Science Laboratory

Case-file backlogs were identified as one of the cause factors affecting the competitiveness of the forensic science laboratory (FSL). Backlogs represent case-files?that remain unprocessed or unreported within a selected time interval (year, week or month) which leads to increased customer complaints, rework, cost of analysis, degradation of biological samples, etc. Case-file backlogging was quantified in three consecutive years (2014 to 2016), using the following parameters: case-files received and case-files processed, difference of which gives case-files backlogged. There was a need to define time interval for a case-file to be regarded as backlogged (that is, one week), results of which can translate into backlogged case-files per month or year. A data collection tool was established and used for three work stations (forensic chemistry, biology/DNA and toxicology laboratories). The tool includes starting and ending date for each?time interval, in which the numbers of case-files received and processed were entered followed by computing the backlogs. It was observed that, case-files reported?increased between 2014 and 2016 leading to a decrease in backlogged case-files.?The annual percentage of the case-files backlogged was highest for forensic?toxicology. The highest number of case-files backlogged was observed for forensic?chemistry, followed by forensic biology/DNA. The number of case-files?backlogged per analyst per year was highest in 2014 and dropped continuously?towards 2016, being comparably higher in forensic biology/DNA and chemistry.?Probability density functions (PDFs) and cumulative distribution functions (CDFs)?of backlogs data indicated that a large number of backlogs created in previous?weeks were eliminated. It was concluded that the effect of case-file backlogging on FSL competitiveness can be minimized by continued management effort in backlog elimination.

Sentence,Phrase,and Triple Annotations to Build a Knowledge Graph of Natural Language Processing Contributions—A Trial Dataset

Purpose:This work aims to normalize the NLPCONTRIBUTIONS scheme(henceforward,NLPCONTRIBUTIONGRAPH)to structure,directly from article sentences,the contributions information in Natural Language Processing(NLP)scholarly articles via a two-stage annotation methodology:1)pilot stage-to define the scheme(described in prior work);and 2)adjudication stage-to normalize the graphing model(the focus of this paper).Design/methodology/approach:We re-annotate,a second time,the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising:contribution-centered sentences,phrases,and triple statements.To this end,specifically,care was taken in the adjudication annotation stage to reduce annotation noise while formulating the guidelines for our proposed novel NLP contributions structuring and graphing scheme.Findings:The application of NLPCONTRIBUTIONGRAPH on the 50 articles resulted finally in a dataset of 900 contribution-focused sentences,4,702 contribution-information-centered phrases,and 2,980 surface-structured triples.The intra-annotation agreement between the first and second stages,in terms of F1-score,was 67.92%for sentences,41.82%for phrases,and 22.31%for triple statements indicating that with increased granularity of the information,the annotation decision variance is greater.Research limitations:NLPCONTRIBUTIONGRAPH has limited scope for structuring scholarly contributions compared with STEM(Science,Technology,Engineering,and Medicine)scholarly knowledge at large.Further,the annotation scheme in this work is designed by only an intra-annotator consensus-a single annotator first annotated the data to propose the initial scheme,following which,the same annotator reannotated the data to normalize the annotations in an adjudication stage.However,the expected goal of this work is to achieve a standardized retrospective model of capturing NLP contributions from scholarly articles.This would entail a larger initiative of enlisting multiple annotators to accommodate different worldviews into a“single”set of structures and relationships as the final scheme.Given that the initial scheme is first proposed and the complexity of the annotation task in the realistic timeframe,our intraannotation procedure is well-suited.Nevertheless,the model proposed in this work is presently limited since it does not incorporate multiple annotator worldviews.This is planned as future work to produce a robust model.Practical implications:We demonstrate NLPCONTRIBUTIONGRAPH data integrated into the Open Research Knowledge Graph(ORKG),a next-generation KG-based digital library with intelligent computations enabled over structured scholarly knowledge,as a viable aid to assist researchers in their day-to-day tasks.Originality/value:NLPCONTRIBUTIONGRAPH is a novel scheme to annotate research contributions from NLP articles and integrate them in a knowledge graph,which to the best of our knowledge does not exist in the community.Furthermore,our quantitative evaluations over the two-stage annotation tasks offer insights into task difficulty.

Evolution process of rock mass engineering system using systems science

The rock mass engineering system （RMES） basically consists ofrock mass engineering （RME）, water system and surroundingecological environments, etc. The RMES is characterized by nonlinearity,occurrence of chaos and self-organization （Tazaka, 1998;Tsuda, 1998; Kishida, 2000）. From construction to abandonmentof RME, the RMES will experience four stages, i.e. initial phase,development phase, declining phase and failure phase. In thiscircumstance, the RMES boundary conditions, structural safetyand surrounding environments are varied at each phase, so arethe evolution characteristics and disasters （Wang et al., 2014）.

ABSTRACTS from TRANS NFsoc（Chineseedition） Vol．4，No．4，Dec．1994 A－Geology and Mining；B－Mineral Processing and Metallurgy；C－Materials Science and Engineering

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A retrospective view on the history of natural sciences in XX-XXI

The presented paper is dedicated to a new ret-rospective view on the history of natural sci-ences in XX-XXI cc, partially including the sci-ence philosophy (mainly, the problems of the scientific realism, i.e. the correspondence of science to reality) and also a novel scheme for different classes of sciences with different ob-jects and paradigms. There are analyzed the chosen “great” and “grand” problems of phys-ics (including the comprehension of quantum mechanics, with a recently elaborated new chapter, connected with time as a quantum obs- ervable and time analysis of quantum processes) and also of natural sciences as a whole. The particular attention is paid to the interpretation questions and slightly to the aspects, inevitably connected with the world- views of the res- earchers (which do often constitute a part of the interpretation questions).

Significant Breakthrough in Industrial Test of the "Methanol to Olefins" Process Developed by Dalian Institute of Chemical Physics,Chinese Academy of Sciences

A process of ＂Methanol or Dimethylether to Olefins＂ developed by Dalian Institute of Chemical Physics （DICP）, designated as the DMTO process, has attained great success in industrial scaling up testing. DICP, by collaborating with the Xinxing Coal Chemical Co., Ltd. of Shaanxi Province and the Luoyang Petrochemical Engineering Co. of the SINOPEC Group, operated successfully a 50t（methanol）/d unit for the conversion of methanol to lower olefins, with a methanol conversion of close to 100%, and a selectivity to lower olefins（ethylene, propylene and butylenes） of higher than 90%. On 23rd August, the industrial test project has passed a state appraisal. The experts of the Appraisal Group, headed by Prof. YUAN Qingtang, academician of Chinese Academy of Engineering, drew the conclusions that the DMTO process, by utilizing a proprietary SAPO-34 catalyst system and a recycling fluidized bed reaction system for the production of lower olefins from methanol, is the first unit in the world having a capacity of producing nearly ten thousand tons lower olefins per year. The technological level of the industrial test is at a leading position internationally. This accomplishment will provide a sound base for the subsequent commercialization of the DMTO process.

Big Data Analytics Using Graph Signal Processing

The networks are fundamental to our modern world and they appear throughout science and society.Access to a massive amount of data presents a unique opportunity to the researcher’s community.As networks grow in size the complexity increases and our ability to analyze them using the current state of the art is at severe risk of failing to keep pace.Therefore,this paper initiates a discussion on graph signal processing for large-scale data analysis.We first provide a comprehensive overview of core ideas in Graph signal processing(GSP)and their connection to conventional digital signal processing(DSP).We then summarize recent developments in developing basic GSP tools,including methods for graph filtering or graph learning,graph signal,graph Fourier transform(GFT),spectrum,graph frequency,etc.Graph filtering is a basic task that allows for isolating the contribution of individual frequencies and therefore enables the removal of noise.We then consider a graph filter as a model that helps to extend the application of GSP methods to large datasets.To show the suitability and the effeteness,we first created a noisy graph signal and then applied it to the filter.After several rounds of simulation results.We see that the filtered signal appears to be smoother and is closer to the original noise-free distance-based signal.By using this example application,we thoroughly demonstrated that graph filtration is efficient for big data analytics.

DEBRA: On the Unsupervised Learning of Concept Hierarchies from (Literary) Text

With this work, we introduce a novel method for the unsupervised learning of conceptual hierarchies, or concept maps as they are sometimes called, which is aimed specifically for use with literary texts, as such distinguishing itself from the majority of research literature on the topic which is primarily focused on building ontologies from a vast array of different types of data sources, both structured and unstructured, to support various forms of AI, in particular, the Semantic Web as envisioned by Tim Berners-Lee. We first elaborate on mutually informing disciplines of philosophy and computer science, or more specifically the relationship between metaphysics, epistemology, ontology, computing and AI, followed by a technically in-depth discussion of DEBRA, our dependency tree based concept hierarchy constructor, which as its name alludes to, constructs a conceptual map in the form of a directed graph which illustrates the concepts, their respective relations, and the implied ontological structure of the concepts as encoded in the text, decoded with standard Python NLP libraries such as spaCy and NLTK. With this work we hope to both augment the Knowledge Representation literature with opportunities for intellectual advancement in AI with more intuitive, less analytical, and well-known forms of knowledge representation from the cognitive science community, as well as open up new areas of research between Computer Science and the Humanities with respect to the application of the latest in NLP tools and techniques upon literature of cultural significance, shedding light on existing methods of computation with respect to documents in semantic space that effectively allows for, at the very least, the comparison and evolution of texts through time, using vector space math.

“新医科”背景下医学图像处理教学软件的研究与开发

针对“新医科”背景下医学图像处理课程所面临的形势和教学现状,开发了与其教学过程高度契合的医学图像处理教学软件,该教学软件可实现医学图像的线性灰度变换、开窗显示、缩放、旋转、镜像、中值滤波、微分锐化、边缘检测、直方图获取和直方图均衡,并可在一定范围内调节线性灰度变换、开窗显示、缩放、旋转、中值滤波、微分锐化、边缘检测的参数,同时用不同算法实现了医学图像的缩放功能。该教学软件用于包头医学院医学图像处理课程理论与实验教学中,可提高学生学习的主动性和积极性,能强化学生对放射技师考点知识的理解,为后续课程的学习打下坚实基础,最终实现“新医科”背景下包头医学院医学影像技术专业“医工”“、医理”的深度交叉融合。

一种面向项目管理信息系统集成的复杂事件处理方法

大型项目管理信息系统中实时产生的海量事件,对深度挖掘系统的隐含特征具有重要意义。通过分析科技计划项目管理的业务逻辑和事件结构,提出项目管理事件模型。为描述事件之间的关系,定义项目管理事件操作符,实现事件的逻辑、算数和时间计算。基于复杂事件处理技术,提出一种面向项目管理信息系统的事件处理算法PMEP,执行事件处理规则,分析系统产生的海量事件,得到深层次的项目管理复杂事件。实验表明,PMEP算法可实现项目管理信息系统中海量事件的实时处理。

中小企业安全管理员工作异化形成机理研究

为探明中小企业安全管理员工作异化的原因及形成机理,进而提出针对性防范措施,深度访谈中小企业的25名安全管理员,采用“情境过程”框架,遵循扎根理论程序,建构了中小企业安全管理员工作异化形成机理模型,并分析了相关因素对中小企业安全管理员工作异化的影响。结果表明:当地应急管理部门、中小企业领导和员工在各自行为逻辑主导下,受内外部情境影响,分别选择形式化监管、模糊性问责,应付式管理、表面化支持,象征性参与和折扣性执行的行为策略。受这些行为策略的刺激,中小企业安全管理员产生自保、同情员工等心理,进而对应选择盲目追随、避责为先,一味顺从、不争权利,放宽要求、处罚宽松等行为策略,最终导致工作异化发生。

水利科技成果转化:现状问题、关键环节及发展建议

阐述了科技成果转化的意义,梳理了水利科技成果公告的发布情况及水利科技成果转化的主要途径,重点分析了新形势下水利科技成果转化现状、面临的主要问题、困难和挑战等。根据成果转化相关法律法规,梳理了科技成果转化的基本要求、主要流程和关键环节等,从提高产出成果质量、对接市场需求、加大扶持力度、加强成果宣传等方面提出促进水利科技成果转化的建议,为进一步提高水利科技创新能力、加速水利科技成果转化提供思路借鉴。

研究生专业技术课程科教融合的创新实践探索--以微弱信号检测与处理课程为例

研究生教育是我国高等教育体系的重要组成部分,肩负着培养具备创新能力的科技领域人才的神圣使命。在新工科背景下,如何实现课堂教学与科学研究融合、达到学术素养和工程能力共同提升的培养目标,已成为高校教育教学改革面临的新课题。该文选取北京航空航天大学仪器科学与技术和光学工程两个一级学科开设的专业技术课微弱信号检测与处理,阐述在研究生专业技术课程教学中,通过融入本学科在国内具有影响力和技术优势的光纤陀螺、光纤电流互感器、空芯光纤微弱散射光检测三个典型科研实例,进行科教融合、提高教学质量的创新实践探索。

基于Python编程技术的专家数据库服务模块开发与实现

为解决专家库管理与维护中工作量大、人工操作繁琐、机械重复劳动且易出错等问题,开发一种基于Python编程技术的专家数据库服务模块,可以实现专家信息自动提取录入、专家成果智能挖掘、自动生成聘书和荣誉证书等功能。实践证明,执行该模块的编程代码后,241份专家申请表成功读取率可达92.95%,生成Excel表单的信息正确率高达100%。该模块的程序设计具有操作简单、可按需修改代码中目标参数的特点,不仅能大大提高编辑的工作效率,后续还可以尝试与出版流程进行整合,以增加模块的适用性与广泛性。

科教融汇视域下“发酵食品加工与检测”课程改革实践探索

在科教融汇视域下,以食品检验检测技术专业“发酵食品加工与检测”课程为试点,结合地方区域产业发展,探索了高职教育中教师教学内容、科研项目及企业生产工作任务的有效融合,以期培养学生掌握在食品生产过程的综合技能。同时,解决在高职传统教学过程中出现的科研与教学工作两张皮、课程教学工作任务与行业真实生产切合度不高等问题。