Meru University of Science and Technology Research Ethics Review System: A SWOT Analysis

The objectives of this article are as follows: 1) to propose a university research ethics system framework, 2) to provide a brief anatomy of the Meru University of Science and Technology (MUST) Institutional Research Ethics Review Committee (MIRERC), 3) to perform a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis of MIRERC, and 4) to make recommendations for improving its performance. The 13-member multi-disciplinary MIRERC was established in April 2017 to provide effective ethical oversight of research undertaken by the University’s scholarly community. Strengths of the MUST research ethics review system include a functional MIRERC, a pertinent national law and ethical guidelines, an Innovation and Enterprise Centre that could house a dedicated MIRERC Secretariat, and a supportive University Management Board. The weaknesses include lack of graduate schools to assure scientific rigor of proposals before submission to the MIRERC, lack of research ethics training in most school’s curricula, absence of a dedicated MIRERC Secretariat, undergraduate research proposals being not ethically reviewed, dearth of faculty trained in research ethics, and lack of an operating budget for MIRERC work. The opportunities include existence of about 22 accredited Institutional Research Ethics Review Committees (IRERC) in Kenya, existence of international standards and operational guidance for ethics review, availability of guidelines and codes of best ethical practices in research, existence of a free automated platform called Research for Health Innovation Organizer (RHInnO) Ethics for managing the ethics review process, and availability of external resources for strengthening IRERCs. In order to improve the performance and sustainability of the MUST research ethics system, there is need to include research ethics training in all undergraduate and post-graduate curricula, create a dynamic database of potential research ethics reviewers, allocate a percentage of the annual MUST research budget for MIRERC operations, charge a graduated fee for proposal ethics review, require all students’ and faculties’ internal and external research proposals be cleared by the MIRERC, and use the RHInnO Ethics platform to manage the ethics review process.

Optimization and Performance Analysis of Intelligent Video AI Dynamic

In today’s information age,video data,as an important carrier of information,is growing explosively in terms of production volume.The quick and accurate extraction of useful information from massive video data has become a focus of research in the field of computer vision.AI dynamic recognition technology has become one of the key technologies to address this issue due to its powerful data processing capabilities and intelligent recognition functions.Based on this,this paper first elaborates on the development of intelligent video AI dynamic recognition technology,then proposes several optimization strategies for intelligent video AI dynamic recognition technology,and finally analyzes the performance of intelligent video AI dynamic recognition technology for reference.

System Simulation Science and Simulation System Technology

This paper mainly discusses the following problems: the important meaning and special function of simulation system; the definition, contents and relationship of system and system simulation science; the definition and technology of simulation system and its equipments; and systematic description and exploration in relation to the developing trend of system simulation science and simulation system technology.

Call for Papers Journal of Electronic Science and Technology Announcing a Special Issue on Artificial Intelligence with Rough Sets

Submission Deadline： 10 December 2010Since the introduction of rough sets in 1982 by Professor Zdzislaw Pawlak, we have witnessed great advances in both theory and applications. In order to promote development of rough sets, we are preparing a special issue on ＂Artificial Intelligence with Rough Sets＂ published by JEST （International）, Journal of Electronic Science and Technology, which is a refereed international journal focusing on IT area. The aim of this special issue is to present the current state of the research in this area, oriented towards both theoretical and applications aspects of rough sets.

Explainable Neural Network for Sensitivity Analysis of Lithium-ion Battery Smart Production

Battery production is crucial for determining the quality of electrode,which in turn affects the manufactured battery performance.As battery production is complicated with strongly coupled intermediate and control parameters,an efficient solution that can perform a reliable sensitivity analysis of the production terms of interest and forecast key battery properties in the early production phase is urgently required.This paper performs detailed sensitivity analysis of key production terms on determining the properties of manufactured battery electrode via advanced data-driven modelling.To be specific,an explainable neural network named generalized additive model with structured interaction(GAM-SI)is designed to predict two key battery properties,including electrode mass loading and porosity,while the effects of four early production terms on manufactured batteries are explained and analysed.The experimental results reveal that the proposed method is able to accurately predict battery electrode properties in the mixing and coating stages.In addition,the importance ratio ranking,global interpretation and local interpretation of both the main effects and pairwise interactions can be effectively visualized by the designed neural network.Due to the merits of interpretability,the proposed GAM-SI can help engineers gain important insights for understanding complicated production behavior,further benefitting smart battery production.

Research and Analysis of Machine Learning Algorithm in Artificial Intelligence

This article firstly explains the concepts of artificial intelligence and algorithm separately,then determines the research status of artificial intelligence and machine learning in the background of the increasing popularity of artificial intelligence,and finally briefly describes the machine learning algorithm in the field of artificial intelligence,as well as puts forward appropriate development prospects,in order to provide theoretical reference for industry insider.

Predicting the Number of Beijing Science and Technology Personnel Based on GM(1,N) Model

In this paper, based on the Science and Technology Statistics in Beijing Statistical Yearbook, grey theory is used to study the relationship among S&T (Science and Technology) activities personnel, R&D (research and development) personnel FTE (Full Time Equivalent), intramural expenditure for R&D and Patent Application Amount. According to the grey correlation coefficient, screening of grey GM(1,N) prediction variables, the grey prediction model is established. Meanwhile, time series model and GM(1,1) model are established for patent applications and R&D personnel equivalent FTE. By comparing the simulating results with the real data, the absolute relative error of prediction models is less than 10%. The results of the prediction model are tested. In order to improve the prediction accuracy, the mean values of the predicted values of the two models are brought into the GM(1,N) model to predict the number of scientific and technical personnel in Beijing during 2015-2025. Forecast results show that the number of science and technology personnel in Beijing will grow with exponential growth trend in the next ten years, which has a certain reference value for predicting the science and technology activities and formulating the policy in Beijing.

A review of intelligent ore sorting technology and equipment development

Under the background of increasingly scarce ore worldwide and increasingly fierce market competition,developing the mining industry could be strongly restricted.Intelligent ore sorting equipment not only improves ore use and enhances the economic benefits of enterprises but also increases the ore grade and lessens the grinding cost and tailings production.However,long-term research on intelligent ore sorting equipment found that the factors affecting sorting efficiency mainly include ore information identification technology,equipment sorting actuator,and information processing algorithm.The high precision,strong anti-interference capability,and high speed of these factors guarantee the separation efficiency of intelligent ore sorting equipment.Color ore sorter,X-ray ore transmission sorter,dual-energy X-ray transmission ore sorter,X-ray fluorescence ore sorter,and near-infrared ore sorter have been successfully developed in accordance with the different characteristics of minerals while ensuring the accuracy of equipment sorting and improving the equipment sorting efficiency.With the continuous improvement of mine automation level,the application of online element rapid analysis technology with high speed,high precision,and strong anti-interference capability in intelligent ore sorting equipment will become an inevitable trend of equipment development in the future.Laser-induced breakdown spectroscopy,transientγneutron activation analysis,online Fourier transform infrared spectroscopy,and nuclear magnetic resonance techniques will promote the development of ore sorting equipment.In addition,the improvement and joint application of additional high-speed and high-precision operation algorithms(such as peak area,principal component analysis,artificial neural network,partial least squares,and Monte Carlo library least squares methods)are an essential part of the development of intelligent ore sorting equipment in the future.

Strategic information perception methods and practices in the open source intelligence

Open source intelligence is one of the most important public data sources for strategic information analysis. One of the primary and core issues of strategic information research is information perception,so this paper mainly expounds the perception method for strategic information perception in the open source intelligence environment as well as the framework and basic process of information perception. This paper argues that in order to match the information perception result with the information depiction result,it conducts practical exploration for the results of information acquisition,perception,depiction and analysis. This paper introduces and develops a monitoring platform for information perception. The results show that the method proposed in this paper is feasible.

Development and Analysis of a Machine Learning Based Software for Assisting Online Classes during COVID-19

Amid the Covid-19 widespread, it has been challenging for educational institutions to conduct online classes, facing multiples challenges. This paper provides an insight into different approaches in facing those challenges which includes conducting a fair online class for students. It is tough for an instructor to keep track of their students at the same time because it is difficult to screen if any of the understudies within the class are not present, mindful, or drowsing. This paper discusses a possible solution, something new that can offer support to instructors seeing things from a more significant point of view. The solution is a facial analysis computer program that can let instructors know which students are attentive and who is not. There’s a green and red square box for face detection, for which Instructors can watch by seeing a green box on those mindful students conjointly, a red box on those who are not mindful at all. This paper finds that the program can automatically give attendance by analyzing data from face detection. It has other features for which the teacher can also know if any student leaves the class early. In this paper, model design, performance analysis, and online class assistant aspects of the program have been discussed.

基于Web of Science数据库的技术经济学科国外重点核心期刊分析

新形势下,技术经济领域的研究对于推动实现我国“十四五”规划和二○三五年远景目标起着至关重要的作用。但是技术经济研究在国外的核心期刊中分布较为分散,研究文献获取不够聚焦,本文利用Web of Science数据库,筛选以技术经济为主题的研究文章,并根据文章所发表的期刊进行了数据统计和分析,对技术经济领域国外重点核心期刊进行了提取,定位了总被引次数前1 000次的国外核心期刊、高被引论文中排名前十位的国外核心期刊、以及发表篇数排名前十位的国外核心期刊,并对高被引和发表篇数均为前十名的国外核心期刊进行了介绍,研究结果可以在一定程度上为技术经济领域研究者提供国外研究文献获取和研究成果发表方面的参考。

Method and application of knowledge organization system for S&T literature

In the big data environment, the construction of massive S&T literature data resources needs intelligent technical assistance. With a focus on comparing the domestic and foreign knowledge organization systems and their applications, this article analyzes and summarizes the gaps in related researches and applications at home and abroad. A knowledge organization system framework for S&T literature data resources is presented in the article. Starting from the basic element of knowledge organization system, it also proposes and designs terminology-based analysis methods and technologies for S&T literature. Based on this framework, it proposes ideas and develops corresponding software tool to carry out relevant experiments. It gives an overview of theories and technologies method for future research.

Transforming Data into Actionable Insights with Cognitive Computing and AI

How organizations analyze and use data for decision-making has been changed by cognitive computing and artificial intelligence (AI). Cognitive computing solutions can translate enormous amounts of data into valuable insights by utilizing the power of cutting-edge algorithms and machine learning, empowering enterprises to make deft decisions quickly and efficiently. This article explores the idea of cognitive computing and AI in decision-making, emphasizing its function in converting unvalued data into valuable knowledge. It details the advantages of utilizing these technologies, such as greater productivity, accuracy, and efficiency. Businesses may use cognitive computing and AI to their advantage to obtain a competitive edge in today’s data-driven world by knowing their capabilities and possibilities [1].

Internet of things:Conceptual network structure,main challenges and future directions

Internet of Things(IoT)is a key technology trend that supports our digitalized society in applications such as smart countries and smart cities.In this study,we investigate the existing strategic themes,thematic evolution structure,key challenges,and potential research opportunities associated with the IoT.For this study,we conduct a Bibliometric Performance and Network Analysis(BPNA),supplemented by an exhaustive Systematic Literature Review(SLR).Specifically,in BPNA,the software SciMAT is used to analyze 14,385 documents and 30,381 keywords in the Web of Science(WoS)database,which was released between 2002 and 2019.The results reveal that 31 clusters are classified according to their importance and development,and the conceptual structures of key clusters are presented,along with their performance analysis and the relationship with other subthemes.The thematic evolution structure describes the important cluster(s)over time.For the SLR,23 documents are analyzed.The SLR reveals key challenges and limitations associated with the IoT.We expect the results will form the basis of future research and guide decision-making in the IoT and other supporting industries.

Dirty Data between Errors and Their Handling—A Firsthand Experience in Solving Dirty Data from Within

Managing large amounts of data is becoming part of everyday life in most organizations. Handling, analyzing, searching, and making predictions from big data is becoming the norm for many organizations of many interests. Big data provides the foundations for more benefits and higher values to be extracted from big data. As big data comes with countless benefits, it also comes with many challenges to fulfilling its expectations. Some of those problems haunting big data banks are being termed dirty data. This paper focuses on dirty data while working on an organization’s natural live information system. The author was responsible for studying and analyzing a faltering information system and planning and carrying out the required solutions and fixes. The importance of the work carried out lies in the high level of dirty data observed in the system. Therefore, this paper is based on the part of dirty data—the paper focuses on how the team suffered from dirty data and how it was dealt with.

人工智能视域下的新质生产力生成路径

随着人类从信息时代进入数字时代,以科技创新为关键核心的新质生产力正在与人工智能形成“互嵌”。基于高深知识生产、技术元素整合以及产业技术突破,人工智能在新质生产力的劳动者层面催生智能生产力,在劳动资料层面提供高效数字化的劳动工具,在劳动对象层面促发以产业生态化为特征的新型生产体系。以人工智能加快推动新质生产力发展,需要从四个方面着力:一是形成政产学研一体的人工智能人才生态;二是建立协同开放的人工智能科技创新生态;三是培育协调互嵌的人工智能产业生态;四是构建规范高效的人工智能治理制度生态。

伦理先行:生成式人工智能的治理策略

生成式人工智能的迅猛发展为数字经济发展提供新动力,为社会民生创造新福祉,同时也引发了一系列现实挑战。中国现行立法已为生成式人工智能的发展划定了基本的安全保障底线,但该技术的发展方向和产业发展均处在高度不确定性当中,部分安全风险可能是被制造的焦虑,以国家立法形式进行规制为时尚早。生成式人工智能的治理需要伦理先行,坚持激励相容精神,为人工智能新技术新应用创新留下充分空间,以伦理治理的方式推动技术社区、产业群体、社会组织、社会公众、治理机构共同进行探索磨合,凝聚共识,待实践经验充足、立法时机成熟时,再将共识性规范纳入法律框架之中。

科技强省评价指标体系构建与监测研究

科技强省建设评价监测是评估科技强省建设各方面目标实现程度的重要手段。通过面向湖北科技强省建设需求,确定评价监测目标和内容,在此基础上开展评价监测指标体系研究。实证分析表明,湖北在科技强省建设五个要素方面表现较为平衡,但与先进地区相比有较大差距,要建成科技强省,还需要在各方面进行全面提升。还分析了湖北推进科技强省建设的优势以及关键制约因素,通过评价和持续监测分析,能提高科技主管部门制定科技政策和发展战略的科学性,对提高区域科技创新治理能力和治理水平起到重要支撑作用。

一种基于知识树的科技前沿探测方法——以深度学习领域为例

[目的/意义]当前全球科技竞争愈加剧烈,准确把握未来科技前沿是科技竞争取得胜利的关键,科技前沿探测是科技情报研究的热点问题之一。[方法/过程]文章提出一种基于知识树的科技前沿探测方法,通过科技前沿知识树反映领域科技前沿的发展信息,探索解决科技前沿探测过程中存在的信息冗余、探测结果不准确等问题的方法和途径,并以深度学习领域为例进行了实证研究。[结果/结论]研究结果表明,基于知识树的科技前沿探测方法可以在一定程度上提高科技前沿探测过程的效率,改善科技前沿探测结果的质量,可以为面向特定领域的科技前沿研究提供参考。