Multiple Hierarchy Decision Analysis of Science Funds Management System

This article discusses a multiple hierarchy decision analysis in which the Analytic Hierarchy Process and Delphi method were combined with practice of funds management so as to obtain a scientific management system. The design procedures and a practical example have been given to verify the feasibility. The work was supported by China National Natural Science Foundation (NNSF), and the introduced procedures have been used in some departments of the NNSF.

Greedy Algorithm Based Deep Learning Strategy for User Behavior Prediction and Decision Making Support

In this paper, we suggest a deep learning strategy for decision support, based on a greedy algorithm. Decision making support by artificial intelligence is of the most challenging trends in modern computer science. Currently various strategies exist and are increasingly improved in order to meet practical needs of user-oriented platforms like Microsoft, Google, Amazon, etc.

Artificial Intelligence Based Optimal Functional Link Neural Network for Financial Data Science

In present digital era,data science techniques exploit artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to have an impact and develop their businesses.Data science integrates the conventions of econometrics with the technological elements of data science.It make use of machine learning(ML),predictive and prescriptive analytics to effectively understand financial data and solve related problems.Smart technologies for SMEs enable allows the firm to get smarter with their processes and offers efficient operations.At the same time,it is needed to develop an effective tool which can assist small to medium sized enterprises to forecast business failure as well as financial crisis.AI becomes a familiar tool for several businesses due to the fact that it concentrates on the design of intelligent decision making tools to solve particular real time problems.With this motivation,this paper presents a new AI based optimal functional link neural network(FLNN)based financial crisis prediction(FCP)model forSMEs.The proposed model involves preprocessing,feature selection,classification,and parameter tuning.At the initial stage,the financial data of the enterprises are collected and are preprocessed to enhance the quality of the data.Besides,a novel chaotic grasshopper optimization algorithm(CGOA)based feature selection technique is applied for the optimal selection of features.Moreover,functional link neural network(FLNN)model is employed for the classification of the feature reduced data.Finally,the efficiency of theFLNNmodel can be improvised by the use of cat swarm optimizer(CSO)algorithm.A detailed experimental validation process takes place on Polish dataset to ensure the performance of the presented model.The experimental studies demonstrated that the CGOA-FLNN-CSO model has accomplished maximum prediction accuracy of 98.830%,92.100%,and 95.220%on the applied Polish dataset Year I-III respectively.

Complexity Decision Making and General Systems Theory： An Educational Perspective

The aim of this paper is to look at some important educational aspects of complexity decision making m a mummsc^pnnary manner from the perspective of General Systems Theory （GST）. First, the major issues involved in complexity management and decision making are summarized as they are viewed in literature, and a review of GST and Systems Thinking is given. The discussion in the paper is developed within the context of GST in general, but concentrated on decision making in the three trends of GST： Operations Research, Cybernetics, and Managerial Cybernetics. Here, the role of Cybernetics in complexity decision making is particularly emphasized. The discussion is then extended to the latest developments in complexity decision making in Science of Complexity and Soft Systems Thinking. The study also includes a framework which is expected to guide instructors who are planning to offer contemporary courses on decision making. The framework provides some clues for assessing the level of complexity for a given situation and selecting the appropriate methodology for solution development.

A Self-determined Evaluation Method for Science Popularization Based on IOWA Operator and Particle Swarm Optimization

With the increase of science popularization, evaluation of science popularization has become an urgent demand. Considering science popularization bases as independent agents, a self-determined evaluation approach for science popularization using induced ordered weighted averaging (IOWA) operator and particle swarm optimization (PSO) is proposed in this paper.Firstly, six factors including science popularization personnel, space, fund,media, activity and influence are selected to construct an index system for science popularization evaluation. On this basis, the absolute dominance and relative dominance of evaluation indexes are used as induced components, and the prior order of the evaluation indexes is determined. Besides, the optimization model of index weighted vectors is established by IOWA operator, index weighted vectors are calculated by particle swarm optimization algorithm, and index weighted vectors and evaluation value vectors are obtain. Finally, the optimal evaluation vectors and evaluation results are given according to the Perron-Frobenius decision eigenvalve theorem .

海战场环境影响评估方法

海战场环境是制约海上武器装备效能发挥和遂行海上军事行动的重要条件。准确的海战场环境影响评估是提升海上作战能力和战场建设的重要“软实力”。首先对海战场环境及其影响评估进行简要介绍;随后综述了海战场环境影响评估方法和建模技术,梳理为四类:基于动力学仿真的评估方法、基于决策科学的评估方法、基于数据科学的评估方法和基于不确定性人工智能的评估方法,并对上述方法进行了详细阐述;最后对不同方法进行了对比分析和应用展望。

科技情报机构的智库功能

[目的/意义]支撑决策是科技情报工作的重要使命,当前科技情报机构正积极探索发挥科技智库功能的转型路径。[方法/过程]本文采用文献调查、案例比较等方法,深入分析科技情报机构智库功能的基础条件、典型做法,结合工作实际总结发挥好智库功能的实践逻辑和实现路径。[结果/结论]本文原创提出做好支撑决策的情报研究工作可以遵循的实践逻辑:坚持“从零散转化为结构,从结构整合为系统,从系统升华到体系”的层次渐进认知,贯通“感知-监测-理解-分析-预判”的实践路径,循环迭代,贴近乃至契合决策咨询需求。

数智赋能背景下情报学研究进展——数据驱动、模型驱动与知识发现

[目的/意义]面对数智赋能科学研究的变革关键时期,试图厘清情报学研究内涵变化,掌握情报学前沿研究进展,把握情报学未来发展趋势。[方法/过程]理论探索层面上,梳理情报学中知识发现类型,厘清数智时代的科学研究模式及自动化程度。通过整理63种NSP系列期刊(Nature及子刊、Science及子刊、PNAS)在2019—2023年出版的168篇相关研究论文,归纳并总结当前情报学主要研究方向、应用场景及知识发现。[结果/结论]界定了情报学中知识发现的三种类型:模型(定性规律、数值定律、结构模型)、要素(知识、自然科学知识空间、社会科学知识空间等)、过程(表示和分析方法、计算工具等),归纳数智时代情报学三种研究模式:数据驱动、模型驱动以及数据与模型协同驱动的知识发现,当前情报学在自动化程度上尚处于1级和2级水平。从两大服务应用方向的8类应用场景研究、基础方法工具和数据研究等方面,归纳总结当前情报学前沿研究进展。文章认为情报学中分析方法的丰富化、计算工具的智能化、研究数据的广泛化,正在共同赋能情报学的知识发现从当前的1级、2级向3级、4级自动化水平迈进。情报学需要攻克三类关键研究问题:情报认知理论方法、情报分析智能工具、基础计算数据,形成数智融合背景下情报学的新研究范式。[局限]仅基于NSP系列期刊论文开展研究,未涵盖所有情报学的前沿研究进展,未来可进一步扩大综述范围。

中国科技战略咨询:内涵特征、演进历程与强化路径

[目的/意义]强化科技战略咨询是党的二十大对科技工作作出的重要部署。科技战略咨询是实现具有中国特色的科学民主决策的重要支撑,在国家治理现代化方面发挥着不可替代的作用。[方法/过程]本研究借助文献研读和规范研究范式,阐述中国科技战略咨询的内涵特征及演进历程,总结演进发展中积累的重要经验,剖析其发展面临的形势,进而针对性地提出强化路径。[结果/结论]中国科技战略咨询经历了起步探索、改革发展、稳步发展和深化改革等四个阶段的演进发展,积累了宝贵的发展经验。面向未来,强化科技战略咨询要强化体制机制建设,完善“决策—咨询—执行”科技治理架构;强化组织机构建设,加强科技创新特色新型智库建设;强化人才队伍建设,不断夯实科技战略咨询发展的智力基础;强化知识体系建设,加快科技战略咨询中的理论和方法创新。

我国软科学研究发展历程与实践经验启示

软科学研究是促进决策科学化的有效方式,在推动经济发展、资政建言和战略决策支持中发挥着重要作用。当前面对复杂严峻的国际国内形势,大力推进中国式现代化建设给软科学研究带来前所未有的发展机遇。文章梳理了软科学的起源与发展历程,将我国软科学发展划分为萌芽阶段、起步阶段、综合发展阶段和新时代发展阶段。在我国软科学事业发展过程中,取得了推动国家及不同层面决策进程、繁荣发展新的学科体系和教学体系、培养专业素质较高的研究队伍、建设不同类型软科学研究机构、丰富研究成果与国际学术交流合作、规范软科学研究管理机制等成果。剖析国内外企业机构软科学研究典型案例,凝练形成了打造完善的软科学研究成果质量控制体系、构建独特的理论方法体系与开放合作生态、注重人才队伍建设和资金保障、重视研究成果发挥作用、扩大传播增加影响力等可借鉴经验。为了推动我国软科学更高质量发展,围绕完善顶层设计与总体布局、创建一流研究机构、强化激励机制、重视数字化转型等方面提出了相关建议。

ChatGPT in transforming communication in seismic engineering: Case studies, implications, key challenges and future directions

Seismic engineering,a critical field with significant societal implications,often presents communication challenges due to the complexity of its concepts.This paper explores the role of Artificial Intelligence(AI),specifically OpenAI’s ChatGPT,in bridging these communication gaps.The study delves into how AI can simplify intricate seismic engineering terminologies and concepts,fostering enhanced understanding among students,professionals,and policymakers.It also presents several intuitive case studies to demonstrate the practical application of ChatGPT in seismic engineering.Further,the study contemplates the potential implications of AI,highlighting its potential to transform decision-making processes,augment education,and increase public engagement.While acknowledging the promising future of AI in seismic engineering,the study also considers the inherent challenges and limitations,including data privacy and potential oversimplification of content.It advocates for the collaborative efforts of AI researchers and seismic experts in overcoming these obstacles and enhancing the utility of AI in the field.This exploration provides an insightful perspective on the future of seismic engineering,which could be closely intertwined with the evolution of AI.

论科技伦理决策的系统性原则

在当前新兴科技的浪潮下,科技伦理实践正面临着前所未有的伦理挑战。为有效应对复杂多变的伦理问题,科技伦理决策者可以将系统性原则作为科技伦理决策的理论指导。遵循系统性原则,科技伦理决策者能够从全局出发,深入剖析多场域科技伦理风险及其相互影响,在实际决策中更加科学、系统地应对伦理挑战,从而提升科技伦理决策的合理性和有效性。

循证视角下药物临床试验质量管理评价工具的开发思考

申办者、药物临床试验机构、监管方均是药物临床试验质量管理的重要角色。申办者是药物临床试验质量的最终责任人,药物临床试验机构负责药物临床试验的运行管理,监管方负责对药物临床试验的监督管理。同时,申办者和药物临床试验机构还是药物临床试验质量管理的重要被评价对象。目前,药物临床试验各方均在积极探索科学的质量管理评价方式。受限于药物临床试验及其质量管理过程的复杂性,现有评价通常更关注硬件建设水平,对质量管理相关软实力评价的研究尚欠深入。本研究介绍现有的质量管理理念,从法规要求、学术研究等方面简要梳理我国药物临床试验质量管理评价的现状,从药物临床试验性质、药物临床试验管理模式、药物临床试验现场检查等维度分析药物临床试验质量管理评价面临的挑战,借鉴药品领域的质量管理体系理念,从循证决策的角度,探讨可行的药物临床试验质量管理评价策略。

物证鉴定范式发展(一):传统明确结论范式

物证鉴定范式是解释物证结果证据意义并形成鉴定意见过程中采用的科学理论和方法。物证鉴定领域正在经历从传统的物证来源明确结论范式向物证结果似然比评估范式转换。传统物证鉴定范式以特征唯一性作为科学基础假设,已有100余年发展和应用历史,应用于除DNA物证以外的几乎所有物证。鉴定人通过检测和比对物证和样本特征确定二者特征是否匹配,并采用阈值决策方式给出物证和样本来源相同或来源不同意见。基于物证明确来源意见和案件其他证据信息,决策者再做出相关的行为事实推论,作为被告犯罪与否的中间证据事实。在传统范式中,鉴定人从物证和样本特征结果得到物证明确来源意见的过程是一个演绎推理过程:大前提是物证特征唯一性假设,小前提是特征匹配(或不匹配)结果,结论是物证和样本来源相同(或不同)。只要大前提和小前提为真,传统范式的来源明确意见就正确。然而,随着DNA物证结果概率评估方法的发展和成熟,一些学者质疑特征唯一性假设缺少实证证明,进而认为失去这一前提的传统范式的演绎推理无效,因此传统物证来源明确意见范式被认为缺少坚固的科学基础。

基于三支决策和联系数的SFEP传递概率计算方法研究

为研究系统故障演化过程(System Fault Evolution Process,SFEP)中原因事件导致结果事件的传递概率,提出了一种基于三支决策和联系数的传递概率计算方法。根据三支决策思想将原因事件和结果事件划分为发生、不确定和不发生3种状态,将措施方案的作用划分为发生、不确定和不发生3种倾向;以联系数同异反分项系数分别代表上述3种状态和倾向,最终定量计算了传递概率范围。研究认为传递概率取决于原因事件的3种状态比例和措施方案的3种倾向比例。利用联系数的同异反分项系数分别描述了状态和倾向比例。总结了原因事件到结果事件状态的演化关系,形成了演化关系表。得到了导致结果事件3种状态的传递概率。以事件发生为目标,传递概率的最小值为导致结果事件发生的概率,最大值为前者加上不确定的概率。方法克服了原有传递概率计算面临的问题,其结果与实际情况相符。

电子信息科学与大数据分析的结合与应用研究

文章围绕电子信息科学与大数据分析的结合与应用进行研究。首先介绍电子信息科学与大数据分析的相关基础理论,及其在各领域的关键应用。随后,通过利用大数据工具和技术,深入进行电子信息的采集,预处理和分析处理。研究表明,电子信息科学与大数据分析的结合,可有效挖掘电子信息的内在价值,并为决策支持提供更为强大的科学依据。最后,对可能存在的问题和挑战进行探讨,提出相应解决方案。

Global Community of Disaster Risk Reduction Scientists and Decision Makers Endorse a Science and Technology Partnership to Support the Implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030

At the first gathering of its kind on the role of science in implementing the Sendai Framework for Disaster Risk Reduction 2015–2030,over 750 scientists,policymakers,business people,and practitioners met in Geneva from January 27–29,2016.The UNISDR Science and Technology Conference on the Implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030 fea-

大数据驱动的管理决策:研究范式与发展领域

数据作为一种新型生产要素影响着经济社会发展各方面,在大数据背景下的管理和决策活动也发生着深刻变化.围绕国家自然科学基金委员会重大研究计划“大数据驱动的管理与决策研究”的项目部署和执行情况,本文从科学基金管理视角总结该重大研究计划的多学科交叉融合和多情境需求牵引特点,介绍研究范式的演进规律以及大数据驱动范式在管理与决策研究中的意义和应用.同时,以国家自然科学基金“十四五”发展规划为指导,本文围绕全景式PAGE框架梳理了相关的重点发展领域和研究方向.

面向政府宏观科技决策的科技管理数据服务模式构建

针对政府科技决策流程中的需求,以提供精准化、智能化的科技管理数据服务为目标,探究面向政府宏观科技决策的科技管理数据服务模式。首先,研究提出构建科技管理数据服务模式的6项基本原则,综合用户要素、数据要素和服务要素形成一个循环结构;其次,针对政府的科技政策决策、科技战略决策和科技规划决策,将科技决策流程细分为4个阶段,基于需求驱动和数据驱动理念构建包括数据资源层、融合分析层、数据服务层和决策需求层的科技管理数据服务模式,并进一步分析各阶段的具体服务需求和服务内容,为我国提升科技管理数据在科技决策方面的服务能力、推动政府由经验式决策向科学化和智能化决策方式转型提供参考。