Analytical Framework for Market-oriented DSR Flexibility Integration and Management

Integration and management of the flexibility of Demand Side Resources (DSR) in today’s energy systems plays a significant role in building up a sustainable society. However, the challenges of understanding, predicating and handling the uncertainties associated this subject to a great extent hamper its development. In this paper, an analytical framework based on a multi-portfolio setup in presence of a deregulated power market is proposed to address such challenges by adopting the thinking in modern portfolio theory (MPT). A Numerical example that targets on analyzing the risk and return for various flexibility pricing strategies are presented to illustrate some features of the framework.

Evaluation of a software positioning tool to support SMEs in adoption of big data analytics

Big data analytics has been widely adopted by large companies to achieve measurable benefits including increased profitability,customer demand forecasting,cheaper development of products,and improved stock control.Small and medium sized enterprises(SMEs)are the backbone of the global economy,comprising of 90%of businesses worldwide.However,only 10%SMEs have adopted big data analytics despite the competitive advantage they could achieve.Previous research has analysed the barriers to adoption and a strategic framework has been developed to help SMEs adopt big data analytics.The framework was converted into a scoring tool which has been applied to multiple case studies of SMEs in the UK.This paper documents the process of evaluating the framework based on the structured feedback from a focus group composed of experienced practitioners.The results of the evaluation are presented with a discussion on the results,and the paper concludes with recommendations to improve the scoring tool based on the proposed framework.The research demonstrates that this positioning tool is beneficial for SMEs to achieve competitive advantages by increasing the application of business intelligence and big data analytics.

Development of a Framework for Equipment Health Management in the Mining Industries in Zambia

The Zambian mining industry is crucial to the national economy but struggles with inconsistent equipment maintenance practices. This study developed an Equipment Maintenance Management Framework (EMMF) tailored to the industry’s needs. Using surveys, interviews, and on-site visits at eight major mining companies, we identified significant variations in maintenance strategies, CMMS usage, and reliability engineering. The EMMF prioritizes predictive maintenance, efficient CMMS implementation, ongoing training, and robust reliability engineering to shift from reactive to proactive maintenance. We recommend adopting continuous improvement practices and data-driven decision-making based on performance metrics, with a phased EMMF implementation aligning maintenance with strategic business objectives. This framework is poised to enhance operational efficiency, equipment reliability, and safety, fostering sustainable growth in the Zambian mining sector.

Resource and environmental factors should be included in economic analytical framework

In the economic analysis framework,natural resources and environmental factors are included in the category of capital or land.Hence,the explanatory variables of the production function only include capital,labor and the residue term technology.Such framework may be designed for methodological reasons,but it is

Enriching Middle School Students’Learning Through Digital Storytelling:A Multimodal Analytical Framework

Purpose This study investigates middle school students’learning experiences through digital storytelling,applying a multimodal analytical framework to uncover patterns in digital stories.This study explores how participants engage in pedagogical activities,reflect on their learning experiences,and articulate their voices through digital stories.Design/Approach/Methods Employing qualitative case study methods,we purposefully selected three 12-year-old female students at an urban school in the northern US.Analyses of digital stories and other data sources(interviews,classroom observations,and reflective journals)show that the students were engaged in both teaching and reflection.Findings The findings describe(1)participants and their learning experiences,(2)students’representational and interpersonal constructs as used in their digital stories,and(3)their participation as teachers as well as learners.Originality/Value Our multimodal analytical framework illuminates how students express themselves through digital stories.Our discussion focuses on students’learning,their identity development,the effectiveness of the analytical framework,and pedagogical implications.

Integrating Research Analytic Framework and Personality Matching for Supervisor Recommendation

Supervisor selection is important for research students in their future studies and careers.Currently,students rely on information search or friends recommendation to find potential research supervisors.However,due to the challenges of incomplete and asymmetric information,students can hardly find suitable supervisors that match their research interests as well as personalities.Existing methods mainly consider topic-relevance and candidate-quality,and overlook the significance of connectivity consideration and two-sided matching degree of individuals personality styles.It proposes a novel supervisor recommendation approach that integrates relevance,connectivity,quality and personality-matching dimensions.The results of user-based evaluations demonstrate that the proposed approach generates more satisfactory recommendations as compared to that of all baseline methods.The present solution has been implemented as a social network recommendation service on ScholarMate.

Toward Data-Driven Digital Therapeutics Analytics:Literature Review and Research Directions

With the advent of digital therapeutics(DTx),the development of software as a medical device(SaMD)for mobile and wearable devices has gained significant attention in recent years.Existing DTx evaluations,such as randomized clinical trials,mostly focus on verifying the effectiveness of DTx products.To acquire a deeper understanding of DTx engagement and behavioral adherence,beyond efficacy,a large amount of contextual and interaction data from mobile and wearable devices during field deployment would be required for analysis.In this work,the overall flow of the data-driven DTx analytics is reviewed to help researchers and practitioners to explore DTx datasets,to investigate contextual patterns associated with DTx usage,and to establish the(causal)relationship between DTx engagement and behavioral adherence.This review of the key components of datadriven analytics provides novel research directions in the analysis of mobile sensor and interaction datasets,which helps to iteratively improve the receptivity of existing DTx.

Ontology-Driven Analytic Models for Pension Management and Decision Support System

Ontology-Driven Analytic Models for Pension Management are sophisticated approaches that integrate the principles of ontology and analytics to optimize the management and decision-making processes within pension systems. While Ontology-Driven Analytic Models offer significant benefits for pension management, there are also challenges associated with implementing and utilizing the models. Developing a comprehensive and accurate ontology for pension management requires a deep understanding of the domain, including regulatory frameworks, investment strategies, retirement planning, and integration of data from heterogenous sources. Integrating these data into a cohesive ontology can be challenging. This research work leverages on semantic ontology as an approach for structured representation of knowledge about concepts and their relationships, and applies it to analyze and optimize decision support for pension management. The proposed ontology presents a formal and explicit specification of concepts (classes), their attributes, and the relationships between them and provides a shared and standardized understanding of the domain;enabling precise communication and knowledge representation for decision-support. The ontology deploys computational frameworks and analytic models to assess and evaluate data, generate insights, predict future pension fund performance as well as assess risk exposure. The research adopts the Reasoner, SPARQL query and OWL Visualizer executed over Java IDE for modelling the ontology-driven analytics. The approach encapsulated and integrated semantic ontologies with analytical models to enhance the accuracy, contextuality, and comprehensiveness of analyses and decisions within pension systems.

面向高等教育教师的数据素养分析框架开发和评估:结构方程建模方法

随着信息和通信技术的快速发展,教师数据素养已成为现代教育领域的一个重要趋势.该文基于高等教育教师数据素养的现状和需求,提出一种基于结构方程建模方法的教师数据素养分析框架,旨在探索一种可行的方法来提高教师数据素养水平.通过研究教师的技术整合自我效能感、技术知识水平、对技术的态度以及使用社交网络的目的与他们数据素养水平之间的关系,阐明和分析了数据素养水平与这些变量之间的关系.使用结构方程模型对来自415名教师的数据进行分析,查看变量之间的相关性,并确定哪些预测因素对教师数据素养能力影响最大.同时,通过对定量数据结果进行分析,验证和评估该文所提出的分析框架.采用关系筛选模型,在研究结果审查的模型中检测到教师的技术整合自我效能感是最重要的变量,对数据素养水平的预测具有重要影响.该文结果有助于指导并提升教师数据素养水平,达到提高教育教学质量和效果的目的.

建设金融强国指数报告:分析框架、指标体系与评价结果

中央金融工作会议提出加快建设金融强国将金融工作上升到党和国家事业发展全局的战略高度,建设金融强国指数编制及数据库建设是核心主题相关研究领域及效应拓展研究领域的基础设施层面的根本需求。遵循习近平总书记关于加快建设金融强国的系统论述,围绕建设金融强国的关键核心金融要素,在传统的金融发展测评指标的基础上,确立建设金融强国指数编制的七大领域的指标体系;测定和编制全球36个主要经济体的金融发展指数,并从中比较分析中国建设金融强国在全球主要经济体中的实力地位、比较先进性及待提升空间。基于上述研究结论,演绎中国加快建设金融强国的实施路径和发展步骤等多方位措施。本文首次建构建设金融强国的测评指标体系,是为拓展金融发展测评的研究文献的边际贡献。

出行即服务系统理论与实践:回顾与展望

交通是城市绿色低碳转型中最受关注的领域之一,也是数字化渗透及数字平台最为活跃的领域。出行即服务(Mobility as a Service, MaaS)系统是绿色交通的典型代表,是一种新型交通组织和供给方式,反映了当前出行需求的深刻变化和城市交通组织范式转变的耦合。全球范围内已出现了上百个大小规模不等和模式各异的MaaS实践创新,北京MaaS是中国持续至今、影响最大的MaaS实践。目前MaaS实践提出的理论和方法主要基于欧美发达国家,无法充分描述和分析中国实践。在文献研究的基础上,延伸纳入了中国经验,提出了具有全球普适性的一个MaaS系统分析框架,强调辨析全球范围内的MaaS异同均可以从三个维度展开,即嵌入的社会背景、发展目标和产生的社会经济环境影响;并应用此框架对国内外五个典型MaaS进行了比较研究,重点解码了北京MaaS的激励机制、商业模式和商业生态。本文旨在推动MaaS理论和研究方法的全球发展,重点提出了四个方面的关注:(1)MaaS系统的发展再次考验着城市交通如何回归其公共属性;(2)MaaS实践嵌入在城市社会背景中,具有明显的差异性。模式选择是对城市既有社会背景和交通格局的继承,但也可能就此发生转向。MaaS打开了一次城市交通转型的机会窗口;(3)MaaS系统的可持续运营依然面临挑战;(4)数字技术带来数据产权、数据隐私和安全等亟待解决的新问题。所有研究案例表明,数字技术的快速发展需要匹配治理模式创新,MaaS生态的协同进化至关重要。

土木工程建造系统脆弱性研究综述

现有工程建造风险主要考虑风险事件发生概率与风险后果严重程度,少有结合系统脆弱性开展研究。脆弱性作为系统固有属性,在外界干扰下表现出的不利影响严重制约风险管控过程,但尚未有学者对土木工程建造系统脆弱性的研究现状进行科学评述。通过主题检索和学科分类,共筛选出103篇英文文献和96篇中文文献,运用Citespace文献分析软件统计了发表时间和期刊分布,归纳了研究热点和作者-机构合作情况;从脆弱性概念内涵、分析框架与评价过程等方面评述了土木工程建造系统的脆弱性。研究发现:土木工程建造系统脆弱性的研究当前仍处于探索阶段;常用脆弱性分析框架主要包括脆弱性递次演化分析框架、脆弱性-风险分析框架和灾害-脆弱性分析框架;脆弱性评价对象主要聚焦于施工安全系统、应急管理系统和项目管理系统;脆弱性影响因素识别和指标体系构建主要涵盖人-机-环-管框架、脆弱性特征维度框架和外部环境-项目参与方-项目特征框架。未来,可借鉴其他学科领域成熟的脆弱性理论成果,结合土木工程建造系统的特点,构建清晰准确的脆弱性概念与分析框架、探索科学适用的指标体系、深化实际应用与拓展研究。研究系统性梳理了土木工程建造系统脆弱性的研究现状,可为后续相关研究提供启发和参考。

风险社会的媒介化治理:一个整合性分析框架

当前,我国发展进入战略机遇和风险挑战并存期。国家治理体系和治理能力现代化是中国式现代化的重要组成部分,为推进中国式现代化提供了制度保障。风险的媒介化治理有助于推进国家治理体系和治理能力现代化,进而为推进中国式现代化注入强劲动力。媒介化治理的对象是治理难度阶梯递进的风险挑战;治理主体是以媒介为中枢的多治理主体网络;治理工具是由媒介可供性主导的软性治理手段;治理方式则是可在层级化与网络化之间灵活调节的连续统;治理过程遵循风险生命周期和价值链的逻辑;治理效能则主要从信息传通与主体连接两个侧面进行衡量。这些媒介化治理要素之间的互动,形成了媒介化治理的整合性分析框架,为推进媒介化治理的研究与实践提供了基础纲目和知识概览。

行政审批局外部协调难题及其影响因素分析——基于SAFRSC框架的实证研究

行政审批局是优化组织流程与提高审批效率的重要设计,但也面临着外部协调难题。通过引入协调成本概念,构建“协调规模—权威关系—业务关联—责任界定—标准化—组织文化”六维SAFRSC分析框架,从组织学视角探源行政审批局的外部协调难题及其六大影响因素。在此基础上,基于DEMATEL法发掘六大影响因素的重要性程度及因素间关系:业务关联、协调规模、责任界定和权威关系的影响最大,并与其他因素相互作用;标准化、组织文化两项因素作用较小,更容易受其他因素的影响。为此,需要有针对性地从理顺管理体制与厘清职能分工、明确权责与加大问责、标准化建设与跨时空协调、加强冲突管理与完善合作机制等方面破解行政审批局外部协调难题。

中国民宿治理政策:演进、结构与启示——基于113份政策文本的Nvivo分析

民宿是旅游住宿产业的重要组成部分,其高质量发展离不开政策的有力支持。文章基于内容分析法,运用Nvivo软件对2017年1月-2023年9月国家、省级、地市级发布的113份民宿治理政策文件进行文本编码与频数统计,构建“政策工具-政策目标-政策主体”三维分析框架。研究结果显示:当前我国民宿政策治理体系基本形成,但存在部门协同深度不足且作用对象覆盖面不全的问题;政策工具的使用存在明显差异;在不同政策目标下,对政策主体和政策工具的选择各有侧重。文章从统筹多方主体、优化政策工具和协调多元目标等方面提出了促进民宿持续健康发展的对策建议。

革命老区振兴发展的挑战、机遇与路径——基于“5D”分析框架的视角

革命老区振兴发展对促进区域协调发展和实现共同富裕具有重要意义。在现有研究基础上,本文提出包含“密度、距离、分割、异质和数字化”的“5D”分析框架,并以此为视角分析革命老区振兴发展的挑战、机遇与路径,指出革命老区振兴发展面临的挑战是“远距离、高分割、低密度”导致的空间不经济,机遇是数字经济的快速发展与“数字化”普惠性、“红色绿色古色”等比较优势和后发优势彰显及其高“异质”潜力、多重利好优惠政策叠加及支持全面化体系化。最后,本文提出革命老区着眼“5D”发力的振兴发展路径,即提升密度,促进人口集中、产业集聚、土地集约;缩短距离,加速各种要素及人员的交流交往交融;消除分割,提升城乡区域市场一体化程度;突出差异,增强特色产业产品的不可替代性;加快数字化,发挥数字技术在降低“远距离、高分割、低密度”负面效应和放大“异质性”正面效应中的积极作用,实现虚实融合发展。

乡村公共文化治理中的合作生产:一个理论分析框架

乡村公共文化治理中合作生产理论分析框架的提出是顺应中国乡村公共文化治理理论与实践发展,促进乡村从传统文化事业管理迈向现代公共文化服务体系建设,实现乡村公共文化高质量和乡村文化振兴的必然要求。文章以合作生产视角切入,不仅探索性地提出了乡村公共文化治理的概念,强调其中的合作生产核心要义。同时,将上述概念内涵进一步融入到包括乡村公共文化治理转向、资源系统、资源单位、治理系统、行动者、行动情景在内的社会生态系统框架中,建立起了文化治理理论与合作生产理论的关联,为乡村公共文化治理中的合作生产提供一个通用的理论分析框架。但作为一种发展中的命题,仍需在中国乡村公共文化治理实践中进一步丰富和拓展。

基于流计算和大数据平台的实时交通流预测

目前交通流预测实时性差,很难满足在线分析和预测任务的需求,基于此提出一种Flink流计算框架和大数据平台结合的实时交通流预测方法。基于流计算框架实时捕捉和预处理数据,包括采用Flink的transform算子对数据进行校验和处理,将处理后的数据sink到大数据的HDFS文件系统,交由下一步的大数据并行框架进行分析建模与训练,实现基于流计算和大数据平台的实时交通流预测。实验结果表明,Flink能够实时捕捉和预处理交通流数据,把数据准时无误送入分布式文件系统中,在此基础上借助大数据框架下的并行分析和建模优势,在实时性数据分析与预测方面取得了较好的效果。

Research on the Framework and Path of Low Carbon Coordinated Development in Beijing-Tianjin-Hebei Region

With the low-carbon coordinated development in Beijing-Tianjin-Hebei Region as the starting point, the necessity and realistic difficulties in achieving the lowcarbon coordination in Beijing-Tianjin-Hebei Region was analyzed, based on which we constructed the systematic analytical framework for low-carbon coordination in Beijing-Tianjin-Hebei Region, and proposed the development path of regional lowcarbon coordinated development from the three levels of government, industry and consumer with the aim to provide specific references for the low-carbon coordinated development in Beijing-Tianjin-Hebei Region.

公共行政中的感知问责:分析框架与研究展望

作为现代民主治理的主要标志之一,问责的重要性不言而喻。但在面对客观问责制度时,不同的问责对象总会不可避免地产生不同的主观认知与解读,进而影响其行为与决策。感知问责作为连接宏观问责制度与微观个体行为之间的“桥梁”,可有效弥合两者之间的鸿沟,提升问责效能。目前,国际公共行政学者已经关注到这一概念,并开展一些研究。论文首先梳理国际公共行政学者对于感知问责的相关学术研究,便于我国公共行政学者与实务者了解并熟悉该议题。基于现有研究,论文从个体、组织与制度三个层面,围绕八个因素建立一项分析框架,为我国公共行政学者开展感知问责研究提供理论指引。在个体层面,论文关注三个因素:自我效能感、自主性与个体官僚与领导关系。在组织层面,主要关注两个因素:组织支持度与组织透明度。在制度层面,主要关注三个因素:问责主体的特征、问责标准的特征与问责过程与内容的特征。在未来,我国的公共行政学者可以聚焦感知问责的概念建构、解释、后果与管理议题,为感知问责这一崭新的研究领域做出贡献。