Shear and Vorticity in a Combined Einstein-Cartan-Brans-Dicke Inflationary Lambda-Universe

A combined BCDE (Brans-Dicke and Einstein-Cartan) theory with lambda-term is developed through Raychaudhuri's equation, for inflationary scenario. It involves a variable cosmological constant, which decreases with time, jointly with energy density, cosmic pressure, shear, vorticity, and Hubble's parameter, while the scale factor, total spin and scalar field increase exponentially. The post-inflationary fluid resembles a perfect one, though total spin grows, but the angular speed does not (Berman, 2007d). Keywords: Cosmology; Einstein; Brans-Dicke; Cosmological term; Shear; Spin; Vorticity; Inflation; Einstein-Cartan; Torsion. PACS: 04.20.-q ; 98.80.-k ; 98.80.Bp ; 98.80.JkComment: 8 pages including front one. Published versio

Silicon solar cells improved by lithium doping

Results of conference on characteristics of lithium-doped silicon solar cells and techniques required for fabrication indicate that output of cells has been improved to point where cells exhibit radiation resistance superior to those currently in use, and greater control and reproducibility of cell processing have been achieved

An Exploratory Sequential Mixed Methods Approach to Understanding Researchers’ Data Management Practices at UVM: Integrated Findings to Develop Research Data Services

This article reports on the integrated findings of an exploratory sequential mixed methods research design aimed to understand data management behaviors and challenges of faculty at the University of Vermont (UVM) in order to develop relevant research data services. The exploratory sequential mixed methods design is characterized by an initial qualitative phase of data collection and analysis, followed by a phase of quantitative data collection and analysis, with a final phase of integration or linking of data from the two separate strands of data. A joint display was used to integrate data focused on the three primary research questions: How do faculty at UVM manage their research data, in particular how do they share and preserve data in the long-term?; What challenges or barriers do UVM faculty face in effectively managing their research data?; and What institutional data management support or services are UVM faculty interested in? As a result of the analysis, this study suggests four major areas of research data services for UVM to address: infrastructure, metadata, data analysis and statistical support, and informational research data services. The implementation of these potential areas of research data services is underscored by the need for cross-campus collaboration and support

An Exploratory Sequential Mixed Methods Approach to Understanding Researchers’ Data Management Practices at UVM: Findings from the Qualitative Phase

The objective of this article is to report on the first qualitative phase of an exploratory sequential mixed methods research design focused on researcher data management practices and related institutional research data services. The aim of this study is to understand data management behaviors of faculty at the University of Vermont (UVM), a higher-research activity Research University, in order to guide the development of campus research data management services. The population of study was all faculty who received National Science Foundation (NSF) grants between 2011 and 2014 who were required to submit a data management plan (DMP); qualitative data was collected in two forms: (1) semi-structured interviews and (2) document analysis of data management plans. From a population of 47 researchers, six were included in the interview sample, representing a broad range of disciplines and NSF Directorates, and 35 data management plans were analyzed. Three major themes were identified through triangulation of qualitative data sources: data management activities, including data dissemination and data sharing; institutional research support and infrastructure barriers; and perceptions of data management plans and attitudes towards data management planning. The themes articulated in this article will be used to design a survey for the second quantitative phase of the study, which will aim to more broadly generalize data management activities at UVM across all disciplines

An Exploratory Sequential Mixed Methods Approach to Understanding Researchers’ Data Management Practices at UVM: Findings from the Quantitative Phase

This article reports on the second quantitative phase of an exploratory sequential mixed methods research design focused on researcher data management practices and related institutional support and services. The study aims to understand data management activities and challenges of faculty at the University of Vermont (UVM), a higher research activity Research University, in order to develop appropriate research data services (RDS). Data was collected via a survey, built on themes from the initial qualitative data analysis from the first phase of this study. The survey was distributed to a nonrandom census sample of full-time UVM faculty and researchers (P=1,190); from this population, a total of 319 participants completed the survey for a 26.8% response rate. The survey collected information on five dimensions of data management: data management activities; data management plans; data management challenges; data management support; and attitudes and behaviors towards data management planning. Frequencies, cross tabulations, and chi-square tests of independence were calculated using demographic variables including gender, rank, college, and discipline. Results from the analysis provide a snapshot of research data management activities at UVM, including types of data collected, use of metadata, short- and long-term storage of data, and data sharing practices. The survey identified key challenges to data management, including data description (metadata) and sharing data with others; this latter challenge is particular impacted by confidentiality issues and lack of time, personnel, and infrastructure to make data available. Faculty also provided insight to RDS that they think UVM should support, as well as RDS they were personally interested in. Data from this study will be integrated with data from the first qualitative phase of the research project and analyzed for meta-inferences to help determine future research data services at UVM

DSN acquisition of Magellan high-rate telemetry data

The Magellan Project levied the stringent requirement of a 98 percent high-rate telemetry data capture rate on the Deep Space Network (DSN) during the Magellan Prime Mapping Mission. To meet this requirement, the DSN undertook extensive development of the DSN Telemetry System, as well as extensive DSN operation planning and test and training. In actuality, the DSN substantially exceeded the requirement by achieving a Prime Mapping Mission high-rate telemetry data capture rate of 99.14 percent. This article details the DSN telemetry system development, and DSN operations planning and test and training. In addition, the actual high-rate telemetry data outages are comprehensively presented and analyzed