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In a previous research, the authors proposed a security information service, called Personalized Real-time Information with Security Map (PRISM), which personalizes the incident information based on living area of individual users. The purpose of this paper is to extend PRISM to conduct sophisticated analysis of street crimes. The extended features enable to look back on past incident information and perform statistical analysis.

To analyze street crimes around living area in more detail, the authors add three new features to PRISM: showing a past heat map, showing a heat map focused on specified type of incidents and showing statistics of incidents for every type. Using these features, the authors visualize the dynamic transition of street crimes in a specific area and the whole region within Kobe city. They also compare different districts by statistics of street crimes.

Dynamical visualization clarifies when, where and what kind of incident occurs frequently. Most incidents occurred along three train lines in Kobe city. Wild boars are only witnessed in a certain region. Statistics shows that the characteristics of street crimes is completely different depending on living area.

Previously, many studies have been conducted to clarify factors relevant to street crimes. However, these previous studies mainly focus on interesting regions as a whole, but do not consider individual’s living area. In this paper, the authors analyze street crimes according to users’ living area using personalized security information service PRISM.

The purpose of this study is to analyze the topics of COVID-19 news articles for better obtaining the relationship among and the evolution of news topics, helping to manage the infodemic from a quantified perspective.

To analyze COVID-19 news articles explicitly, this paper proposes a prism architecture. Based on epidemic-related news on China Daily and CNN, this paper identifies the topics of the two news agencies, elucidates the relationship between and amongst these topics, tracks topic changes as the epidemic progresses and presents the results visually and compellingly.

The analysis results show that CNN has a more concentrated distribution of topics than China Daily, with the former focusing on government-related information, and the latter on medical. Besides, the pandemic has had a big impact on CNN and China Daily's reporting preference. The evolution analysis of news topics indicates that the dynamic changes of topics have a strong relationship with the pandemic process.

This paper offers novel perspectives to review the topics of COVID-19 news articles and provide new understandings of news articles during the initial outbreak. The analysis results expand the scope of infodemic-related studies.

The purpose of this study is to address the concept and the step-by-step procedure of a high-precision optical alignment test for spacecrafts using digital theodolites. The proposed scheme focuses on the non-contact alignment qualification of spacecraft components during the integration and test phases until the launch event.

The proposed approach is based on the exploitation of the auto-collimation feature of theodolites and several prisms attached to the requested component and satellite configuration. As soon as the misalignment measurement including the difference between the real and desired attitude or position aberration of an instrument is made, the results must be transformed from the component level to the system level for misalignment error identification in the spacecraft dynamic model.

The paper introduces the main instruments, the defined coordinate systems and the architecture of the optical spacecraft misalignment test. Moreover, the guideline of the test implementation and the resulting data process have been presented carefully.

There is no limitation associated with this method because the procedure is applicable for high-precision typical missions.

This paper describes a fully implementable scheme to examine any possible inaccuracy in mounting of the spacecraft components both in position and orientation. The test can be performed without the need for a huge budget or complicated hardwares.

The contribution of this work revolves around illustrating the context and procedure of the spacecraft misalignment test which has remained unknown in literature despite the frequent implementation in the different satellite projects.

OCLC has grown since 1971 from 54 libraries to 17,000 libraries, from the state of Ohio to 47 countries around the world, and from revenues of $67,000 to almost $100 million. In addition to cataloging, OCLC now provides a broad range of products and services in resource sharing, reference, and electronic publishing. The Forest Press Division of OCLC publishes the Dewey Decimal Classification, the world's most widely used library classification system. OCLC also operates MAPS, The MicrogrAphic Preservation Service, a nonprofit corporation based in Bethlehem, Pennsylvania, that provides high‐quality preservation microfilming for libraries. Information Dimensions, Inc., a for‐profit subsidiary acquired in April 1993, develops and markets computer software products for managing electronic documents and text on leading mainframe computers, microcomputers, workstations, and PCs. IDI provides an exciting strategic fit for OCLC in full‐text electronic publishing, electronic archiving, and information management—three areas of growing importance in OCLC's future.

This article describes and illustrates the practical application of a new measurement framework – The Performance Prism – which addresses the shortcomings of many of the traditional measurement frameworks being used by organisations today. The Performance Prism, with its comprehensive stakeholder orientation, encourages executives to consider the wants and needs of all the organisation’s stakeholders, rather than a subset, as well as the associated strategies, processes and capabilities. DHL’s board for the UK have used this framework to re‐engineer their corporate measurement and reporting system and the article explains DHL and other firms’ experiences with the Performance Prism.

The purpose of this paper is to review the normative literature in regard to public-private partnerships (PPPs) and their evaluations, and attempts to investigate whether the Performance Prism is a feasible framework for effective PPP performance measurement.

An in-depth and critical literature review is conducted in this study. Discussion concentrates on PPP features, performance measurement in PPPs, performance measurement in construction, and characteristics of the Performance Prism.

The Performance Prism exhibits a strong ability to capture the distinct features of PPP infrastructure projects and can be considered as a framework suitable for effective and efficient performance measurement.

It is essential that stakeholders have a feasible performance measurement framework to effectively measure performance as they embark on PPP infrastructure projects. This paper can be used as a theoretical base and conceptual framework capable of assisting in the design and selection of effective performance measures for PPPs.

Many governments across the world use PPPs to procure public assets owing to the scarce funds available for infrastructure development. Effective performance measurement is critical to the successful delivery of a PPP project. Conventional ex post evaluation that solely focuses on meeting budget and predetermined project duration is being widely used to measure PPPs in practice. PPPs are more complicated than traditional procurement due to their multiple stakeholder integrations and long-term processes. However, the research on effective PPP performance measurement is limited for addressing such vital issues, and this paper attempts to bridge this knowledge gap.

This study aims to focus on a short review on recent results dealing with the mechanical modelling and experimental characterization of a novel class of tensegrity structures, named class θ = 1 tensegrity prisms. The examined structures exhibit six bars connected by two disjoint sets of strings.

First, the self-equilibrium problem of tensegrity θ = 1 prisms is numerically investigated for varying values of two aspect parameters and, next, their prestress stability is studied. The mechanical behavior of the examined structures in the large displacements regime under uniform compression loading is also numerically computed through a path-following procedure. Finally, the predicted constitutive response is validated through experimental tests.

The presented results highlight that the examined structures exhibit a large number of infinitesimal mechanisms from the freestanding configuration, and reveal that they exhibit tunable elastic response switching from stiffening to softening.

This multi-faceted elastic response is in agreement with previous literature results on the elastic response of minimal tensegrity prism, and suggests that such units can be usefully used as non-linear springs in next-generation tensegrity metamaterials.

The purpose of this study is to alleviate the specified issues to a great extent. To promote patients’ health via early prediction of diseases, knowledge extraction using data mining approaches shows an integral part of e-health system. However, medical databases are highly imbalanced, voluminous, conflicting and complex in nature, and these can lead to erroneous diagnosis of diseases (i.e. detecting class-values of diseases). In literature, numerous standard disease decision support system (DDSS) have been proposed, but most of them are disease specific. Also, they usually suffer from several drawbacks like lack of understandability, incapability of operating rare cases, inefficiency in making quick and correct decision, etc.

Addressing the limitations of the existing systems, the present research introduces a two-step framework for designing a DDSS, in which the first step (data-level optimization) deals in identifying an optimal data-partition (Popt) for each disease data set and then the best training set for Popt in parallel manner. On the other hand, the second step explores a generic predictive model (integrating C4.5 and PRISM learners) over the discovered information for effective diagnosis of disease. The designed model is a generic one (i.e. not disease specific).

The empirical results (in terms of top three measures, namely, accuracy, true positive rate and false positive rate) obtained over 14 benchmark medical data sets (collected from https://archive.ics.uci.edu/ml) demonstrate that the hybrid model outperforms the base learners in almost all cases for initial diagnosis of the diseases. After all, the proposed DDSS may work as an e-doctor to detect diseases.

The model designed in this study is original, and the necessary parallelized methods are implemented in C on Cluster HPC machine (FUJITSU) with total 256 cores (under one Master node).