Applying XML for designing and interchanging information for multidimensional model

In order to exchange and share information among the conceptual models of data warehouse, and to build a solid base for the integration and share of metadata, a new multidimensional concept model is presented based on XML and its DTD is defined, which can perfectly describe various semantic characteristics of multidimensional conceptual model. According to the multidimensional conceptual modeling technique which is based on UML, the mapping algorithm between the multidimensional conceptual model is described based on XML and UML class diagram, and an application base for the wide use of this technique is given.

Precise large deviations for sums of random vectors in a multidimensional size-dependent renewal risk model

Consider a multidimensional renewal risk model, in which the claim sizes {Xk, k ≥1} form a sequence of independent and identically distributed random vectors with nonnegative components that are allowed to be dependent on each other. The univariate marginal distributions of these vectors have consistently varying tails and finite means. Suppose that the claim sizes and inter-arrival times correspondingly form a sequence of independent and identically distributed random pairs, with each pair obeying a dependence structure. A precise large deviation for the multidimensional renewal risk model is obtained.

The Application of Genetic Algorithms and Multidimensional Distinguishing Model in Forecasting and Evaluating Credits for Mobile Clients

To solve the arrearage problem that puzzled most of the mobile corporations, we propose an approach to forecast and evaluate the credits for mobile clients, devising a method that is of the coalescence of genetic algorithm and multidimensional distinguishing model. In the end of this paper, a result of a testing application in Zhuhai Branch, GMCC was provided. The precision of the forecasting and evaluation of the client’s credit is near 90%. This study is very significant to the mobile communication corporation at all levels. The popularization of the techniques and the result would produce great benefits of both society and economy.

A multidimensional and hierarchical model of library mobile service quality

Purpose:Based on the understanding of the characteristics of mobile services(m-services)provided by libraries,we attempt to develop a multidimensional and hierarchical model and a specifically designed measurement scale for the assessment of libraries' m-service quality.Design/methodology/approach:The research has been conducted sequentially in two stages.At the first stage,a multidimensional and hierarchical model of library m-service quality and a measurement scale are developed through literature review and focus group interviews.At the second stage the model is tested using partial disaggregation technique for an analysis of the data collected from over 400 library users in 3 Chinese universities.Findings:The proposed model is shown to fit the data well.The empirical analysis provides strong support for the model,which includes 4 dimensions(service environment,service interaction,information control and service effect) and 12 corresponding sub-dimensions.Research limitations:The sample size needs to be enlarged and the user types need to be expanded to make the sample more representative.Practical implications:The study provides a conceptual framework and measurement tool that can help library managers understand the users' needs in the context of mobile network environment,and boost their libraries' competitiveness by carrying out better services to meet the user demands.Originality/value:The proposed model and measurement scale specifically designed for the assessment of libraries' m-service quality fully reflect the hierarchical structure of libraries' m-service quality.In addition,by using partial disaggregation technique for model testing,we believe our research can serve as a practical reference to those engaged in the development and examination of a multidimensional and hierarchical model.

A visual modeling method for spatiotemporal and multidimensional features in epidemiological analysis:Applied COVID-19 aggregated datasets

The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis.However,most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation,resulting in a lack of quantitative and qualitative evidence.To address this issue,we developed a portrait-based visual modeling method called+msRNAer.This method considers the spatiotemporal features of virus transmission patterns and multidimensional features of objective risk factors in communities,enabling portrait-based exploration and comparison in epidemiological analysis.We applied+msRNAer to aggregate COVID-19-related datasets in New South Wales,Australia,combining COVID-19 case number trends,geo-information,intervention events,and expert-supervised risk factors extracted from local government area-based censuses.We perfected the+msRNAer workflow with collaborative views and evaluated its feasibility,effectiveness,and usefulness through one user study and three subject-driven case studies.Positive feedback from experts indicates that+msRNAer provides a general understanding for analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical,timeline,and other factor comparisons.By adopting interactions,experts discovered functional and practical implications for potential patterns of long-standing community factors regarding the vulnerability faced by the pandemic.Experts confirmed that+msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios.

Multidimensional Rasch Analysis of Twentyfirst Century Core Competencies--Perspective of University Students in China

This study aimed to explore university students'perceptions on the importance of core competencies for the 21st century.A crosssectional survey research using a self-report questionnaire involving 48 Likert-type items was used to collect data from 869 university students in China who voluntarily participated.Multidimensional analysis with Rasch rating scale model found the data fitted the Rasch model well.The results showed that Chinese university students placed strong importance on the majority of competencies listed in the modified questionnaire of 21st Century Core Competencies for Higher Education,especially in competence groups of Interpersonal Communication and Character and Civic Literacy but less so in those of Creativity and Problem Solving and Global and International Perspective.

PROFHMM_UNC: Introducing a Priori Knowledge for Completing Missing Values of Multidimensional Time-Series

We present a new method for estimating missing values or correcting unreliable observed values of time dependent physical fields. This method, is based on Hidden Markov Models and Self-Organizing Maps, and is named PROFHMM_UNC. PROFHMM_UNC combines the knowledge of the physical process under study provided by an already known dynamic model and the truncated time series of observations of the phenomenon. In order to generate the states of the Hidden Markov Model, Self-Organizing Maps are used to discretize the available data. We make a modification to the Viterbi algorithm that forces the algorithm to take into account a priori information on the quality of the observed data when selecting the optimum reconstruction. The validity of PROFHMM_UNC was endorsed by performing a twin experiment with the outputs of the ocean biogeochemical NEMO-PISCES model.

Spatio-temporal Evolution of the Agricultural Eco-efficiency Network and Its Multidimensional Proximity Analysis in China

As a traditional agricultural country,China has always prioritized agricultural development,and has increasingly focused on green and sustainable agricultural development.Based on the inter-provincial panel data for China from 1997 to 2019,this study divided these data into five periods according to the Five-Year Plan(FYP)of China,measured the agricultural eco-efficiency(AEE)values using the Super-SBM model,and then determined the spatial association network of the inter-provincial AEE of China using the improved gravity model.Finally,social network analysis(SNA)was used to further analyze the evolution process of AEE,and we de-veloped a framework of how multidimensional proximity,which includes geographical,economic,technological,cognitive,and institutional proximity,made an influence on the formation of AEE spatial relation network.The findings indicated that:1)in 1997−2019,the AEE in China was present in some spatial and temporal differences characteristics at the provincial scale,and we specifically found that national macro-regulation and policy incentives played a positive role in the long-term development of AEE.2)The spatial correlation of AEE development among provincial regions were becoming closer and exhibits obvious spatial correlation and spillover effects.The evolution of the AEE network has clearly observable trends of hierarchization and aggregation,and the complexity of the correlation network continues to increase and exhibits spatial clustering characteristics that are dense in the east and sparse in the west.The network structure has changed from monocentric radiation to a multicentric network,and network nodes select the more advantageous nodes with which to connect.3)Finally,the geographical proximity had a significant negative effect;the economic,technological,and institutional proximities were all observed to contribute to the AEE network formation,and cognitive proximity did not significantly influence this network formation.

A multidimensional examination of performances of HSR (High-Speed Rail) systems

This paper deals with a multidimensional examination of the infrastructural, technical/technological, operational, economic, social, and environmental performances of high-speed rail （HSR） systems, including their overview, analysis of some real-life cases, and limited （analytical） modeling. The infrastructural performances reflect design and geometrical characteristics of the HSR lines and stations. The technical/technological performances relate to the characteristics of rolling stock, i.e., high-speed trains, and supportive facilities and equipment, i.e., the power supply, signaling, and traffic control and management system（s）. The operational performances include the capacity and productivity of HSR lines and rolling stock, and quality of services. The economic per- formances refer to the HSR systems＇ costs, revenues, and their relationship. The social performances relate to the impacts of HSR systems on the society such as congestion, noise, and safety, and their externalities, and the effects in terms of contribution to the local and global/country social- economic development. Finally, the environmental performances of the HSR systems reflect their energy consumption and related emissions of green house gases, land use, and corresponding externalities.

Fiscal Livelihood Spending’s Effects on Multidimensional Poverty Reduction in China

This paper investigates how fiscal spending on livelihood improves multidimensional household poverty in China.Based on the panel data of the“China Health and Nutrition Survey”(CHNS)for 2004-2015,we measured the chronic multidimensional poverty index for Chinese households.We have created a multitiered model for empirical analysis.Our findings suggest that multidimensional poverty in China is predominantly capacity poverty.Fiscal spending on livelihoods significantly reduces multidimensional poverty for Chinese households,especially rural households.Investments on livelihoods are more poverty-reducing than transfer spending on livelihoods.As an innovation,this paper offers a dynamic analysis of the effects of livelihood spending on multidimensional household poverty controlling for heterogeneity between individual households and across regions.Our conclusion suggests that the government should improve policy arrangements to increase social opportunities and support sustainable development capacities for the poor,while enhancing protective social security systems.

双边投资协定网络的演化机制——基于多维邻近性视角

利用社会网络分析方法考察了双边投资协定(BITs)网络的历史演化特征与驱动机制。研究发现,BITs网络大致经历了初步形成、迅猛发展和结构调整三个阶段,呈现出一个较完整的S型增长,网络核心—半核心—边缘结构实现了由“金字塔”向“橄榄球”的转变。以多维邻近性为视角,运用时间指数随机图模型对BITs网络形成的影响因素进行分析,结果显示,多维邻近性是驱动BITs网络形成的重要力量,经济和制度邻近性对BITs关系的形成有负向影响,贸易水平、地理和社会邻近性对BITs关系的形成有正向影响。然而,2008年金融危机改变了BITs网络的结构特征,金融危机后BITs网络呈现分散化、多边化发展趋势,某些邻近性变量影响不再显著,社会环境类因素成为影响BITs网络的关键因素。

一种结构不确定分析的改进多维平行六面体模型

多维平行六面体模型是一种能够同时考虑相关变量和独立变量的非概率凸集模型,更适用于工程结构中常见的多源不确定性问题。为更加有效合理地度量结构不确定性,提出一种改进多维平行六面体模型。通过定义区间变量的相关角和边缘区间,给出模型不确定域的显式表达式,进而给出依据实验样本点构建多维平行六面体模型的方法。3个算例分析结果表明,改进多维平行六面体模型能够较好地反映区间变量之间的相关性,是一种比传统多维平行六面体模型更为紧凑合理的模型。

多维平行六面体模型非概率可靠性灵敏度分析

可靠性灵敏度能够反映基本变量分布参数对可靠度的影响程度,为可靠性分析与优化设计提供指导。提出了一种基于多维平行六面体模型的非概率可靠性灵敏度分析方法。首先,给出基本变量域完全位于安全域时采用非概率可靠性指标,而基本变量域与安全域有交集时采用非概率失效度作为可靠性度量的原因。其次,推导结构线性系统的可靠性灵敏度解析式,并进一步探讨所提方法对结构非线性系统的适应性问题。最后,通过3个工程算例验证文中方法有效可行。

西藏草业时空拓展的模式和路径

草业发展是维护西藏生态屏障安全的重要内容,也是高原特色畜牧业高质量发展和西藏牧民维持生计的传统优势产业。国家生态安全与粮食安全战略背景下,西藏草业高质量发展受到时空条件的制约。本研究借助城市时空拓展理论,阐明了西藏草业发展时空拓展的必要性和可行性,并依据西藏草业资源的禀赋特征,提出了聚焦生态效益模式、生产效益模式、生态和生产效益兼顾模式,以及实现3种模式的路径,以期为西藏草业未来的发展提供理论指导,也为我国其他地区草业时空拓展提供借鉴。

基于Logit模型的技术供需交易影响因素分析

将技术供方和专利视为整体,将供需双方是否通过某专利交易视为被解释变量,系统研究技术特征、组织实力和供需方多维邻近关系等对技术交易的影响。基于Logit模型对燃料电池领域专利数据实证研究发现:(1)考虑三维特征建立Logit模型,对交易形成预测的精度高达83.3%;(2)技术特征、组织实力特征、多维邻近特征对技术交易均有显著正向影响;(3)多维邻近特征对技术交易的促进程度远高于其他维度特征,供需双方信任是交易形成的关键因素。最后,从建立高质量技术供需数据库、技术交易信任体系、技术交易网络及交易推荐机制等方面提出对策思考。

在线学习个性化推荐中多维度学习者模型研究

随着在线学习平台的广泛使用,在线学习资源快速增长,“信息迷航”问题亟待解决。针对在线学习个性化推荐中以单一特征为主,多维度学习者模型研究较少的问题,文中以CELTS-11学习者模型规范为基础,以教育学相关理论为指导,对学习者的学习风格、知识状态、认知能力、兴趣偏好等多维度的个性化特征进行细化分类研究。利用教育数据挖掘等技术获得学习者特征项,进而构建出个性化的多维度学习者模型。该模型为后续在线学习个性化的推送服务提供有力的依据。

多源数据融合驱动的城市快速路交通状态划分

为提升交通状态划分效果,本文提出一种基于负激励项的改进模糊C均值聚类(BNITFCM)交通状态划分模型。该模型在原有FCM(Fuzzy C-Means)模型基础上考虑了交通流样本点权重以及交通流参数权重对聚类效果的影响,并引入隶属度负激励项、交通流权重负激励项、交通流样本点权重负激励项使聚类结果呈现类内高耦合、类间低耦合的特性。在此基础上,对样本点进行加权处理,用加权欧氏距离描述样本点之间的关系。通过拉格朗日乘子法得出模型的迭代公式并通过该迭代公式对模型进行求解。针对大多交通状态划分方法参数特征维度低的问题,本文以经过多源数据融合获得的速度、速度标准差、流量、密度和道路满载度构建高维特征输入。以数值仿真实验检验了BNIT-FCM模型在分类准确性方面的表现,结果表明,BNIT-FCM模型较FCM模型和改进模糊隶属度FCM模型(IFMD-FCM)的ARI(Adjusted Rand Index)分别提升了4.17%和3.56%。以深圳市北环大道卡口和浮动车数据的交通流为研究对象,实验结果表明,BNIT-FCM模型对比FCM模型以及IFMD-FCM模型的轮廓系数分别提升了4.12%和4.07%;同时,BNIT-FCM模型采用多源融合数据的速度及其标准差比单独采用卡口数据和单独采用浮动车数据的速度及其标准差的轮廓系数分别提升了29.67%和54.13%。