Configuration Synthesis and Performance Evaluation Metrics of Lunar Rover Locomotion Systems

A method of topology synthesis based on graph theory and mechanism combination theory was applied to the configuration design of locomotion systems of lunar exploration rovers(LER).Through topology combination of wheel structural unit,suspension unit,and connecting device unit between suspension and load platform,some new locomotion system configurations were proposed and the metrics and indexes to evaluate the performance of the new locomotion system were analyzed.Performance evaluation and comparison between two LER with locomotion systems of different configurations were analyzed.The analysis results indicate that the new locomotion system configuration has good trafficability performance.

COMPLETE KAHLER METRICS WITH POSITIVE HOLOMORPHIC SECTIONAL CURVATURES ON CERTAIN LINE BUNDLES(RELATED TO A COHOMOGENEITY ONE POINT OF VIEW ON A YAU CONJECTURE)

In this article,we study Kahler metrics on a certain line bundle over some compact Kahler manifolds to find complete Kahler metrics with positive holomorphic sectional(or bisectional)curvatures.Thus,we apply a strategy to a famous Yau conjecture with a co-homogeneity one geometry.

Air Traffic Operation Complexity Analysis Based on Metrics System

In order to quantitatively analyze air traffic operation complexity,multidimensional metrics were selected based on the operational characteristics of traffic flow.The kernel principal component analysis method was utilized to reduce the dimensionality of metrics,therefore to extract crucial information in the metrics.The hierarchical clustering method was used to analyze the complexity of different airspace.Fourteen sectors of Guangzhou Area Control Center were taken as samples.The operation complexity of traffic situation in each sector was calculated based on real flight radar data.Clustering analysis verified the feasibility and rationality of the method,and provided a reference for airspace operation and management.

A Framework for Identifying and Managing Information Quality Metrics of Corporate Performance Management System

Corporate Performance Management （CPM） system is an information system used to collect, analyze, and visualize key performance indicators （KPIs） to support both business operations and especially strategic decisions. CPM systems display KPIs in forms of scorecard and dashboard so the executives can keep track and evaluate corporate performance. The quality of the information as shown in the KPIs is very crucial for the executives to make the right decisions. Therefore, it is important that the executives must be able to retrieve not only the KPIs but also the quality of those KPIs before using such KPIs in their strategic decisions. The objectives of this study were to determine the role of the CPM system in the organizations, current data and information quality state, problems and perspectives regarding data quality, as well as data quality maturity stage of the organizations. Survey research was used in this study; a questionnaire was sent to collect data from 477 corporations listed in the Stock Exchange of Thailand （SET） on January, 2011. Forty-nine questionnaires were returned. The results show that about half of the organizations have implemented CPM systems. Most organizations are confident in the information in CPM system, but information quality issues are commonly found. Frequent problems regarding information quality are information not up to date, information not ready by time of use, inaccuracy and incomplete. The most concerned and frequently assessed quality dimensions were security, accuracy, completeness, and validity. When asked to prioritize, the most important quality dimensions are accuracy, timeliness, completeness, security, and validity respectively. In addition, most organizations concern about data govemance management and have deployed such measures. This study showed that most organizations are on level 4 on Gartner＇s data governance maturity stage in which data governance is concerned and managed, but still not effective.

Mining Metrics for Enhancing E-Commerce Systems User Experience

The diversity of e-commerce Business to Consumer systems and the significant increase in their use during the COVID-19 pandemic as a one of the primary channels of retail commerce, has made all the most important the need to measuring their quality using practical methods. This paper presents a quality evaluation framework for web metrics that are B2C specific. The framework uses three dimensions based on end-user interaction categories, metrics internal specs and quality sub-characteristics as defined by ISO25010. Beginning from the existing large corpus of general-purpose web metrics, e-commerce specific metrics are chosen and categorized. Analysis results are subjected to a data mining analysis to provide association rules between the various dimensions of the framework. Finally, an ontology that corresponds to the framework is developed to answer to complicated questions related to metrics use and to facilitate the production of new, user defined meta-metrics.

ISO25000-Related Metrics for Evaluating the Quality of Complex Information Systems

Evaluating complex information systems necessitates deep contextual knowledge of technology, user needs, and quality. The quality evaluation challenges increase with the system’s complexity, especially when multiple services supported by varied technological modules, are offered. Existing standards for software quality, such as the ISO25000 series, provide a broad framework for evaluation. Broadness offers initial implementation ease albeit, it often lacks specificity to cater to individual system modules. This paper maps 48 data metrics and 175 software metrics on specific system modules while aligning them with ISO standard quality traits. Using the ISO25000 series as a foundation, especially ISO25010 and 25012, this research seeks to augment the applicability of these standards to multi-faceted systems, exemplified by five distinct software modules prevalent in modern information ecosystems.

Navigation Finsler metrics on a gradient Ricci soliton

In this paper,we study a class of Finsler metrics defined by a vector field on a gradient Ricci soliton.We obtain a necessary and sufficient condition for these Finsler metrics on a compact gradient Ricci soliton to be of isotropic S-curvature by establishing a new integral inequality.Then we determine the Ricci curvature of navigation Finsler metrics of isotropic S-curvature on a gradient Ricci soliton generalizing result only known in the case when such soliton is of Einstein type.As its application,we obtain the Ricci curvature of all navigation Finsler metrics of isotropic S-curvature on Gaussian shrinking soliton.

Security Vulnerability Analyses of Large Language Models (LLMs) through Extension of the Common Vulnerability Scoring System (CVSS) Framework

Large Language Models (LLMs) have revolutionized Generative Artificial Intelligence (GenAI) tasks, becoming an integral part of various applications in society, including text generation, translation, summarization, and more. However, their widespread usage emphasizes the critical need to enhance their security posture to ensure the integrity and reliability of their outputs and minimize harmful effects. Prompt injections and training data poisoning attacks are two of the most prominent vulnerabilities in LLMs, which could potentially lead to unpredictable and undesirable behaviors, such as biased outputs, misinformation propagation, and even malicious content generation. The Common Vulnerability Scoring System (CVSS) framework provides a standardized approach to capturing the principal characteristics of vulnerabilities, facilitating a deeper understanding of their severity within the security and AI communities. By extending the current CVSS framework, we generate scores for these vulnerabilities such that organizations can prioritize mitigation efforts, allocate resources effectively, and implement targeted security measures to defend against potential risks.

Mean Dimension for Non-autonomous Iterated Function Systems

In this paper we introduce the notions of mean dimension and metric mean dimension for non-autonomous iterated function systems(NAIFSs for short)on countably infinite alphabets which can be regarded as generalizations of the mean dimension and the Lindenstrauss metric mean dimension for non-autonomous iterated function systems.We also show the relationship between the mean topological dimension and the metric mean dimension.

The Correct Reissner-Nordstrøm, Kerr and Kerr-Newman Metrics

In a very recent article of mine I have corrected the traditional derivation of the Schwarzschild metric thus arriving to formulate a correct Schwarzschild metric different from the traditional Schwarzschild metric. In this article, starting from this correct Schwarzschild metric, I also propose corrections to the other traditional Reissner-Nordstrøm, Kerr and Kerr-Newman metrics on the basis of the fact that these metrics should be equal to the correct Schwarzschild metric in the borderline case in which they reduce to the case described by this metric. In this way, we see that, like the correct Schwarzschild metric, also the correct Reissner-Nordstrøm, Kerr and Kerr-Newman metrics do not present any event horizon (and therefore do not present any black hole) unlike the traditional Reissner-Nordstrøm, Kerr and Kerr-Newman metrics.

模糊Credit Metrics模型及其在信用风险评估中的应用

在经典的Credit Metrics模型中,通常假设具有相同信用评级的债务人的贷款是同质的,即具有相同的信用等级转移概率,且在实际应用中这种概率难以精确估计。这些局限性大大限制了该方法在实际中的应用。为了更好地描述和处理关于信用等级转移的模糊信息,借鉴经典Credit Metrics模型的思想,提出了模糊Credit Metrics模型,通过将传统的确定的信用转移矩阵模糊化,从而利用二型模糊变量来对信贷资产的远期价值进行描述和刻画,在此基础上对信贷资产的期望值和在险价值等量化指标进行计算,为基于不确定信息或专家主观判断的信用风险评估提供了一种系统的有效方法。

电子资源使用评估与E-Metrics

E-Metrics 项目是美国研究图书馆协会新评估计划的5个子项目之一。本文对E-Metrics 项目的产生背景及发展历程进行较为详细的介绍,并对整个项目进行简要的评价。

Effect of slice scope on data slice-based class cohesion metrics

This paper suggests that a single class rather than methods should be used as the slice scope to compute class cohesion. First, for a given attribute, the statements in all methods that last define the attribute are computed. Then, the forward and backward data slices for this attribute are generated by using the class as the slice scope and are combined to compute the corresponding class data slice. Finally, the class cohesion is computed based on all class data slices for the attributes. Compared to traditional cohesion metrics that use methods as the slice scope, the proposed metrics that use a single class as slice scope take into account the possible interactions between the methods. The experimental results show that class cohesion can be more accurately measured when using the class as the slice scope.

基于Credit Metrics的城投公司结构性风险评价研究

随着我国城市基础设施建设规模的快速扩大,城市基础设施建设投资公司项目类型多元化与融资方式多元化匹配造成的结构性风险已经成为影响其健康发展的主要问题.在分析了城投公司结构性风险的发生机理及传导机制的基础上,针对城投公司债务的信用转移问题,运用Credit Metrics模型对城投公司结构性风险进行评价研究.结果表明,城投公司存在结构性风险且隐患巨大.一旦资金链出现问题,城投公司的债务将出现严重的违约情况.

Altmetrics工具的起源、质疑、改进与发展

对Altmetrics的发展过程及现状进行了较为系统的总结,主要包括Altmetrics工具的起源、危机和转型三方面,厘清Altmetrics工具的产生背景,系统阐释了其面对来自科学共同体内、外的质疑和挑战,采取的积极应答、改进和发展的具体策略。

基于Credit Metrics模型的汽车消费贷款业务信用风险管理分析

汽车消费贷款业务的蓬勃发展要求商业银行加强对该业务的信用风险管理。Credit Metrics模型是世界上第一个评估信用风险的量化度量模型,并为改善商业银行被动的信用风险管理提供了一种有效的管理思路和基础。

Credit Metrics模型计算信用风险的实例分析

CreditMetrics作为计算资产组合信用风险的模型,是一个联系信用和证券市场的简单、动态的架构。本文从此模型出发,分别讨论了单个贷款和资产组合基于违约率,信用迁移概率的计算原理和实例,并对违约率的测算作了进一步的分析和讨论。

基于改进Credit Metrics模型的商业银行信用风险度量及实证研究

信用风险是我国商业银行面临的主要风险。随着美国金融危机的蔓延,各国银行业已意识到加强信用风险监管的必要性。研究信用风险特征,建立合适的度量模型准确地度量我国商业银行的信用风险,是降低信用风险的必然要求,而Credit Metrics模型以其擅长度量非交易性资产的信用风险而著称。作者首先对Credit Metrics模型加以改进,使用我国的信用评级转移矩阵,其次考虑宏观经济和企业本身的非系统性风险,重新调整信用评级转移矩阵。最后以某家银行为基础,使用Credit Metrics模型进行实证研究,同时对于模型中的部分参数进行修正并对模型加以改进,从而完善Credit Metrics模型在我国商业银行业的应用。

Meta-analysis of potentially confounding effect of class size on associations between object-oriented metrics and maintainability

This paper uses three size metrics,which are collectable during the design phase,to analyze the potentially confounding effect of class size on the associations between object-oriented（OO）metrics and maintainability.To draw as many general conclusions as possible,the confounding effect of class size is analyzed on 127 C＋＋ systems and 113 Java systems.For each OO metric,the indirect effect that represents the distortion of the association caused by class size and its variance for individual systems is first computed.Then,a statistical meta-analysis technique is used to compute the average indirect effect over all the systems and to determine if it is significantly different from zero.The experimental results show that the confounding effects of class size on the associations between OO metrics and maintainability generally exist,regardless of whatever size metric is used.Therefore,empirical studies validating OO metrics on maintainability should consider class size as a confounding variable.

商业银行住房抵押贷款风险管理研究——基于VaR的Credit Metrics模型分析

随着我国各商业银行不同程度地暴露出住房抵押贷款违约风险,关于商业银行住房抵押贷款风险管理的研究逐渐成为热点论题。本文选用基于VaR的Credit Metrics模型来验证该模型在商业银行风险管理中的实用性。