Study on the Diffusion Performance of Standard E-Government Information Systems

Standard e-government information system(SEIS) including mobile-government applications are playing more and more important roles in the establishing of national e-government framework. It can be beneficial not only for avoiding redundant e-government IS development but also for improving collaboration among government agencies. Two research questions were explored: what are the factors influencing the performance of SEIS? Will mandatory SEIS create a better performance than non-mandatory SEIS? Specifically, the use of five categories of IS aspects--information system quality, online service quality, offline service quality, diffusion modes and standard network size—is proposed to understand the performance of SEIS through applying both survey study and simulation study. The results show that information system quality and online service quality of SEIS have strong effects on users' expectation and users' satisfaction, which thereafter promotes the performance of SEIS. Government agencies' offline service quality shows a significant effect on users' satisfaction while not on users' expectation. Furthermore, the diffusion speed of SEIS in non-mandatory and mandatory modes and the standard network size also have great influence on the utility of SEIS.

Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.

Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems

Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.

Evaluating and Addressing Interoperability Constraints in Health Information Systems for Enhanced Healthcare Delivery in Zimbabwe

Interoperability constraints in health information systems pose significant challenges to the seamless exchange and utilization of health data, hindering effective healthcare delivery. This paper aims to evaluate and address these constraints to enhance healthcare delivery. The study examines the current state of interoperability in health information systems, identifies the key constraints, and explores their impact on healthcare outcomes. Various approaches and strategies for addressing interoperability constraints are discussed, including the adoption of standardized data formats, implementation of interoperability frameworks, and establishment of robust data governance mechanisms. Furthermore, the study highlights the importance of stakeholder collaboration, policy development, and technical advancements in achieving enhanced interoperability. The findings emphasize the need for a comprehensive evaluation of interoperability constraints and the implementation of targeted interventions to promote seamless data exchange, improve care coordination, and enhance patient outcomes in healthcare settings.

《Information Systems Research》1998~2017文献计量分析—基于Citespace可视图谱

本文运用Citespace文献计量分析软件,以《Information Systems Research》(简称ISR)期刊在1998年至2017年近20年收录的747篇文献为研究对象,从文献产出分析、国家合作网络分析、机构合作网络分析和引文网络聚类分析四个方面进行研究,研究结果表明:1) 根据文献产出分析,ISR期刊的文献产出量呈现波动性增长;2) 根据国家合作网络分析,美国在国家合作网络中是核心节点,中国需要加强国际合作,提高自身影响力;3) 在研究机构合作网络中,文献产出量前五所高校均位于美国,同时根据合作网络中心性分析,中心性在0.1以上的12所机构中,9所位于美国,中国只有香港城市大学列于其中,另外,德克萨斯大学达拉斯分校、佐治亚理工学院、新加坡国立大学和德克萨斯大学奥斯汀分校,在信息系统领域研究潜力巨大;4) 根据引文网络聚类分析,研究热点的演变可分为三个阶段:1998年至2001年期间信息系统领域研究的重心在信息系统的企业应用,2002年至2011年期间研究重心由传统的组织转移到虚拟组织的测量指标、信任和影响因素,2012年至2017年期间由电子口碑研究转向在线知识社区中的知识扩散研究。

社会化网络服务世界中的有效竞争策略——Information Systems Research, Vol. 21, No. 3, September 2010综述

一个国家的企业与其他国家的企业在本土市场上开展的正面直接竞争过程,既是技术创新与市场营销有效结合并赢得顾客的过程,也是技术创新的竞争过程。面对日益激烈的竞争环境,尤其是高科技企业不仅需要平衡技术和市场策略的投入。

Research on fault-tolerant control of networked control systems based on information scheduling

A kind of networked control system is studied; the networked control system with noise disturbance is modeled based on information scheduling and control co-design. Augmented state matrix analysis method is introduced, and robust fault-tolerant control problem of networked control systems with noise disturbance under actuator failures is studied. The parametric expression of the controller under actuator failures is given. Furthermore, the result is analyzed by simulation tests, which not only satisfies the networked control systems stability, but also decreases the data information number in network channel and makes full use of the network resources.

Remote sensing and geographic information systems techniques in studies on treeline ecotone dynamics

We performed a meta-analysis on over 100 studies applying remote sensing(RS)and geographic information systems(GIS)to understand treeline dynamics.A literature search was performed in multiple online databases,including Web of Knowledge(Thomson Reuters),Scopus(Elsevier),BASE(Bielefeld Academic Search Engine),CAB Direct,and Google Scholar using treeline-related queries.We found that RS and GIS use has steadily increased in treeline studies since 2000.Spatialresolution RS and satellite imaging techniques varied from low-resolution MODIS,moderate-resolution Landsat,to high-resolution WorldView and aerial orthophotos.Most papers published in the 1990s used low to moderate resolution sensors such as Landsat Multispectral Scanner and Thematic Mapper,or SPOT PAN(Panchromatic)and MX(Multispectral)RS images.Subsequently,we observed a rise in high-resolution satellite sensors such as ALOS,GeoEye,IKONOS,and WorldView for mapping current and potential treelines.Furthermore,we noticed a shift in emphasis of treeline studies over time:earlier reports focused on mapping treeline positions,whereas RS and GIS are now used to determine the factors that control treeline variation.

Attributes reduct and decision rules optimization based on maximal tolerance classification in incomplete information systems with fuzzy decisions

A new approach to knowledge acquisition in incomplete information system with fuzzy decisions is proposed. In such incomplete information system, the universe of discourse is classified by the maximal tolerance classes, and fuzzy approximations are defined based on them. Three types of relative reducts of maximal tolerance classes are then proposed, and three types of fuzzy decision rules based on the proposed attribute description are defined. The judgment theorems and approximation discernibility functions with respect to them are presented to compute the relative reduct by using Boolean reasoning techniques, from which we can derive optimal fuzzy decision rules from the systems. At last, three types of relative reducts of the system and their computing methods are given.

The Approach to Probabilistic Decision-Theoretic Rough Set in Intuitionistic Fuzzy Information Systems

For the moment, the representative and hot research is decision-theoretic rough set (DTRS) which provides a new viewpoint to deal with decision-making problems under risk and uncertainty, and has been applied in many fields. Based on rough set theory, Yao proposed the three-way decision theory which is a prolongation of the classical two-way decision approach. This paper investigates the probabilistic DTRS in the framework of intuitionistic fuzzy information system (IFIS). Firstly, based on IFIS, this paper constructs fuzzy approximate spaces and intuitionistic fuzzy (IF) approximate spaces by defining fuzzy equivalence relation and IF equivalence relation, respectively. And the fuzzy probabilistic spaces and IF probabilistic spaces are based on fuzzy approximate spaces and IF approximate spaces, respectively. Thus, the fuzzy probabilistic approximate spaces and the IF probabilistic approximate spaces are constructed, respectively. Then, based on the three-way decision theory, this paper structures DTRS approach model on fuzzy probabilistic approximate spaces and IF probabilistic approximate spaces, respectively. So, the fuzzy decision-theoretic rough set (FDTRS) model and the intuitionistic fuzzy decision-theoretic rough set (IFDTRS) model are constructed on fuzzy probabilistic approximate spaces and IF probabilistic approximate spaces, respectively. Finally, based on the above DTRS model, some illustrative examples about the risk investment of projects are introduced to make decision analysis. Furthermore, the effectiveness of this method is verified.

Non-intrusive hybrid interval method for uncertain nonlinear systems using derivative information

This paper proposes a new non-intrusive hybrid interval method using derivative information for the dynamic response analysis of nonlinear systems with uncertain-but- bounded parameters and/or initial conditions. This method provides tighter solution ranges compared to the existing polynomial approximation interval methods. Interval arith- metic using the Chebyshev basis and interval arithmetic using the general form modified affine basis for polynomials are developed to obtain tighter bounds for interval computation. To further reduce the overestimation caused by the ＂wrap- ping effect＂ of interval arithmetic, the derivative information of dynamic responses is used to achieve exact solutions when the dynamic responses are monotonic with respect to all the uncertain variables. Finally, two typical numerical examples with nonlinearity are applied to demonstrate the effective- ness of the proposed hybrid interval method, in particular, its ability to effectively control the overestimation for specific timepoints.

A mixed stochastic user equilibrium model considering influence of advanced traveller information systems in degradable transport network

Advanced traveler information systems （ATIS） can not only improve drivers＇ accessibility to the more accurate route travel time information, but also can improve drivers＇ adaptability to the stochastic network capacity degradations. In this paper, a mixed stochastic user equilibrium model was proposed to describe the interactive route choice behaviors between ATIS equipped and unequipped drivers on a degradable transport network. In the proposed model the information accessibility of equipped drivers was reflected by lower degree of uncertainty in their stochastic equilibrium flow distributions, and their behavioral adaptability was captured by multiple equilibrium behaviors over the stochastic network state set. The mixed equilibrium model was formulated as a fixed point problem defined in the mixed route flows, and its solution was achieved by executing an iterative algorithm. Numerical experiments were provided to verify the properties of the mixed network equilibrium model and the efficiency of the iterative algorithm.

Intelligent Multi-Agent Based Information Management Methods to Direct Complex Industrial Systems

In recent years, the increasingly complexity of the logistic and technical aspects of the novel manufacturing environments, as well as the need to increase the performance and safety characteristics of the related cooperation, coordi-nation and control mechanisms is encouraging the development of new information management strategies to direct and man- age the automated systems involved in the manufacturing processes. The Computational Intelligent (CI) approaches seem to provide an effective support to the challenges posed by the next generation industrial systems. In particular, the Intelligent Agents (IAs) and the Multi-Agent Systems (MASs) paradigms seem to provide the best suitable solutions. Autonomy, flexibility and adaptability of the agent-based technology are the key points to manage both automated and information processes of any industrial system. The paper describes the main features of the IAs and MASs and how their technology can be adapted to support the current and next generation advanced industrial systems. Moreover, a study of how a MAS is utilized within a productive process is depicted.

Airport Information Systems—Airside Management Information Systems

Research on the intersection of the areas of aviation and management of information systems is scarce. Airports, more than ever before need to align their information systems to gain a competitive advantage and become more efficient in their operations. A proper classification is a prerequisite to systems alignment. The purpose of this paper is to provide descriptions of some of the airport management information systems, connections to or interoperability with other systems, and the key uses and users of each system. There are many types of management information systems and they can be organized or classified in a number of different ways. Furthermore, each system may or may not be necessary for a particular airport depending on the business goals and objectives and the certificate which the airport is operating under. Consequently, the system classification schema presented in this paper is neither all-inclusive nor exclusive;however, a number of leading aviation practitioners, business professionals, and educators in the industry were instrumental in both proposing and validating the schema. The study used interviews, documentation, and observation as the primary sources of data.

Office Information Systems: A Retrospective and a Call to Arms

Office Information Systems is a field of computer science concerned with studying offices and developing technologies to support office workers. The field initiated in 1982 suffered a gradual demise by 2004. However, many of the themes and issues that motivated the discipline continue to exist today. Office workers continue to struggle with routine tasks that do not have adequate IT support and the problem is worse for mobile employees. In this paper we provide a retrospective on OIS technologies with the hope of stimulating interest in the discipline and addressing the needs of office workers worldwide. The evolution of office technologies and a detailed discussion of past research are provided. The discussion shows that technology support for office workers is still inadequate. The paper suggests a technology vision and architecture to motivate researchers and identifies challenges for future research.

Evolution and Development of the Information Concept in Biological Systems: From Empirical Description to Informational Modeling of the Living Structures

With the purpose to smooth the way of a correct understanding of information concepts and their evolution,in this paper,is discussed the evolution and development of the concept of information in biological systems,showing that this concept was intuitively perceived even since ancient times by our predecessors,and described according to their language level of that times,but the crystallization of the real meaning of information is an achievement of our nowadays,by successive contribution of various scientific branches and personalities of the scientific community of the world,leading to a modern description/modeling of reality,in which information plays a fundamental role.It is shown that our reality can be understood as a contribution of matter/energy/information and represented/discussed as the model of the Universal Triangle of Reality(UTR),where various previous models can be suggestively inserted,as a function of their basic concern.The modern concepts on information starting from a theoretic experiment which would infringe the thermodynamics laws and reaching the theory of information and modern philosophic concepts on the world structuration allow us to show that information is a fundamental component of the material world and of the biological structures,in correlation with the structuration/destructuration processes of matter,involving absorption/release of information.Based on these concepts,is discussed the functionality of the biologic structures and is presented the informational model of the human body and living structures,as a general model of info-organization on the entire biological scale,showing that a rudimentary proto-consciousness should be operative even at the low-scale biological systems,because they work on the same principles,like the most developed bio-systems.The operability of biologic structures as informational devices is also pointed out.

The Demographic Impact on Credit Scores＂ Evidence From Statistical Methods and Geographic Information Systems （GIS） Mapping

Average credit scores for people in the United States （US） differ from state to state. Some states have high, and some states have low average credit scores. Since lenders and employers use credit scores to make loan and employment decisions, people living in states where average credit scores are high should experience the benefits of living where credit scores tend to allow more favorable loan and employment decisions. Although credit scores are the direct result of credit histories, credit histories may be impacted by demographic factors. If the demographic factors that impact credit histories are identified, ways to improve credit scores are likely to be discovered and available to people and state government policymakers. This study looks for demographic factors to indirectly explain the average credit scores for people living in each state of the US. The methodology includes statistical analyses and geographic information systems （GIS） mapping. Statistical analyses provide evidence to suggest that state average credit scores are explained by the demographic factors of education, family, income, and health. GIS mapping reveals clusters of states with similar demographics and credit scores.

Information Network Systems and Information Sharing on Administrative Websites

In Japanese 'e-government' policy, called 'e-Japan', the 'administrative document management system' is functioning as information searching systems. On the other hand, this system has also generated the problem that it is not fully functioning as a means for the information sharing in a governmental agency. So, the purpose of this research is to find how the administrative document management system can function as information sharing in administrative organization. For this purpose, this paper considers the current status and some problems firstly. And secondary, this paper proposes the idea and constructs some information systems using administrative official Website. This is the method and approach of this research. As a conclusion, this proposal information system junctions as information sharing support systems.

Grey systems for intelligent sensors and information processing

In a measurement system, new representation methods are necessary to maintain the uncertainty and to supply more powerful ability for reasoning and transformation between numerical system and symbolic system. A grey measurement system is discussed from the point of view of intelligent sensors and incomplete information processing compared with a numerical and symbolized measurement system. The methods of grey representation and information processing are proposed for data collection and reasoning. As a case study, multi-ultrasonic sensor systems are demonstrated to verify the effectiveness of the proposed methods.