Search

The purpose of this study was to conduct a bibliometric analysis of the Resource Description and Access (RDA) research in library and information science journals in Web of Science. Two hundred and fifty-eight journal articles related to RDA were extracted from Web of Science for analysis for the period between 2006 and 2020. A descriptive analysis of the most productive authors, institutions, countries, and journals and the most cited authors was conducted. To illuminate the major themes, the selected articles were also subjected to a co-word analysis and a document co-citation analysis using the visualization tool VOSviewer. The co-word analysis revealed five clusters of themes: (1) RDA implementation, (2) RDA and other metadata standards and technologies, (3) RDA and FRBR, FRAD and ISBD, (4) RDA and Anglo-American Cataloging Rules, (5) RDA and descriptive cataloging, authority control, catalogers, and training. The document co-citation analysis identified two distinctive clusters of themes: RDA testing, training and implementation, and RDA and FRBR-related conceptual models. An analysis of the temporal evolution of the themes showed that other metadata standards and technologies, especially linked data, and Bibframe, received more attention in recent years.

Background: Software is an important windows to offer a variety of complex instrument control and data processing for nuclear magnetic resonance (NMR) spectrometer. NMR software should allow researchers to flexibly implement various functionality according to the requirement of applications. Scripting system can offer an open environment for NMR users to write custom programs with basic libraries. Emerging technologies, especially multivariate statistical analysis and artificial intelligence, have been successfully applied to NMR applications such as metabolomics and biomacromolecules. Scripting system should support more complex NMR libraries, which will enable the emerging technologies to be easily implemented in the scripting environment. Result: Here, a novel NMR scripting system named "NMRPy" is introduced. In the scripting system, both Java based NMR methods and original CPython based libraries are supported. A module was built as a bridge to integrate the runtime environment of Java and CPython. It works as an extension in CPython environment, as well as interacts with Java part by Java Native Interface. Leveraging the bridge, Java based instrument control and data processing methods can be called as a CPython style. Compared with traditional scripting system, NMRPy is easier for NMR researchers to develop complex functionality with fast numerical computation, multivariate statistical analysis, deep learning etc. Non-uniform sampling and protein structure prediction methods based on deep learning can be conveniently integrated into NMRPy. Conclusion: NMRPy offers a user-friendly environment to implement custom functionality leveraging its powerful basic NMR and rich CPython libraries. NMR applications with emerging technologies can be easily integrated. The scripting system is free of charge and can be downloaded by visiting http://www.spinstudioj.net/nmrpy., Comment: 19 pages, 6 figures

This paper evaluates special education journals using the h-type indices and journal impact factor. Fifty-seven special education journals were selected from the special education category of Clarivate Analytics’ Journal Citation Reports (JCR) and two published lists of special education journals. The journal h-indices were compared among themselves and with their available impact factors. The correlation analysis of the metrics found very strong positive relationships between the journal h-type indices and between the Web of Science h-index and the journal impact factors. Strong positive relationships between the Google Scholar h-type indices and the impact factors were also found. The paper offers possible reasons for the discrepancy in rankings of the journals. The evaluation of the journals by the h-type indices provides an alternative source of information from JCR, and can help professional stakeholders in the field determine the quality of the journals.

The continuously scaling down of CMOS technology inevitably increases the power density for high performance microprocessors, which makes thermal effects and related problems urgent and challenging. Unpredicted thermal behavior and on-chip thermal hot spots could lead to performance degradation of microprocessor chips, incurring reliability issues. Hence, it is becoming increasingly important to develop thermal modeling methods to predict the thermal behavior of microprocessor chips, and thermal management techniques to control the on-chip thermal hot spots and thermal related reliability issues.This research focuses on system level thermal behavior modeling and management methods to enable the thermal aware design of high performance microprocessor chips and packages, which also considers the thermal related reliability issues. First, at chip level, a new compact lateral thermal resistance model is proposed to model the lateral thermal behavior of through silicon vias (TSVs), which was largely ignored previously. The proposed lateral thermal model is fully compatible with the existing thermal modeling method of TSVs, and could be integrated into finite difference (FD) simulation to improve the accuracy. Second, targeting at the package level modeling of thermal behavior for microprocessor chip package, the top-down approach building the thermal behavioral models from the given accurate temperature and power information by means of the subspace identification method (SID) is systematically explored in this dissertation research. Power map based approach and piecewise linear modeling method are developed to improve the accuracy of the identified model in presence of thermal nonlinearity and correlated power traces. Third, a more effective architecture level distributed thermal management method is developed to balance the on-chip temperature distribution in this dissertation research. A new temperature metric called effective initial temperature that incorporates bot

This paper proposes a new thermal modeling method for package design of high-performance microprocessors. The new approach builds the thermal behavioral models from the given accurate temperature and power information by means of the subspace method. The subspace method, however, may suffer predictability problem when the practical power is given as a number of power maps where power inputs are spatially correlated. We show that the input power signal needs to meet some dependency requirements to ensure model predictability. We develop a new algorithm, which generates independent power maps to meet the spatial rank requirement and can also automatically select the order of the resulting thermal models for the given error bounds. Experimental results validates the proposed method on a practical microprocessor package constructed via COMSOL software under practical power signal inputs.

This paper proposes a new thermal modeling method for package design of high-performance microprocessors. The new approach builds the thermal behavioral models from the given accurate temperature and power information by means of the subspace method. The subspace method, however, may suffer predictability problem when the practical power is given as a number of power maps where power inputs are spatially correlated. We show that the input power signal needs to meet some dependency requirements to ensure model predictability. We develop a new algorithm, which generates independent power maps to meet the spatial rank requirement and can also automatically select the order of the resulting thermal models for the given error bounds. Experimental results validates the proposed method on a practical microprocessor package constructed via COMSOL software under practical power signal inputs.

The continuously scaling down of CMOS technology inevitably increases the power density for high performance microprocessors, which makes thermal effects and related problems urgent and challenging. Unpredicted thermal behavior and on-chip thermal hot spots could lead to performance degradation of microprocessor chips, incurring reliability issues. Hence, it is becoming increasingly important to develop thermal modeling methods to predict the thermal behavior of microprocessor chips, and thermal management techniques to control the on-chip thermal hot spots and thermal related reliability issues.This research focuses on system level thermal behavior modeling and management methods to enable the thermal aware design of high performance microprocessor chips and packages, which also considers the thermal related reliability issues. First, at chip level, a new compact lateral thermal resistance model is proposed to model the lateral thermal behavior of through silicon vias (TSVs), which was largely ignored previously. The proposed lateral thermal model is fully compatible with the existing thermal modeling method of TSVs, and could be integrated into finite difference (FD) simulation to improve the accuracy. Second, targeting at the package level modeling of thermal behavior for microprocessor chip package, the top-down approach building the thermal behavioral models from the given accurate temperature and power information by means of the subspace identification method (SID) is systematically explored in this dissertation research. Power map based approach and piecewise linear modeling method are developed to improve the accuracy of the identified model in presence of thermal nonlinearity and correlated power traces. Third, a more effective architecture level distributed thermal management method is developed to balance the on-chip temperature distribution in this dissertation research. A new temperature metric called effective initial temperature that incorporates bot

Sense-making in information seeking process is one of the major information seeking models. Questions have been raised about information seekers’ ambiguous needs and focus-shifting tendency relative to sense-making. This paper tackles this problem by reviewing two related approaches and suggesting an alternative interpretation from the semiotic point of view. The author argues that information seeking is often interactive, dynamic, and infinite just as the relationships between sign, object and interpretant posited in Peirce’s semiotics. The implication of this paper is to alert information seekers about this potential endless information seeking process and come to terms with their search results which are based on their tasks.

This paper examines the relationships between the journal impact factor and the h-type indices in virology journals. The virology journals and their 2010 journal impact factors were retrieved from Journal Citation Reports. The h-index and the g-index values of the journals for 2007-2011 were obtained from Web of Science and Google Scholar. The journals were ranked by their journal impact factor and h-indices. The correlation analysis of the measures found a strong relationship between the journal impact factor and the h-type indices, and a stronger tie between the h-indices themselves. Despite the strong correlations between the measures, differences in rankings of the journals with the journal impact factor and the h-type indices were found, and possible explanations for the differences were provided.