The Shipboard data Multiplex System (SDMS) is a general purpose information transfer system directed toward fulfilling the internal data Intercommunication requirements of a variety of naval combatant ships and submar...
The Shipboard data Multiplex System (SDMS) is a general purpose information transfer system directed toward fulfilling the internal data Intercommunication requirements of a variety of naval combatant ships and submarines in the 1980–1990 time frame. The need for a modern data transfer system of the size and capability of SDMS has been increase in unison with the sophistication of shipboard electronic equipment and the associated magnitude of equipment-to-equipment signal traffic. Instead of the miles of unique cabling that must be specifically designed for each ship, SDMS will meet information transfer needs with general-purpose multiplex cable that will be Installed according to a standard plan that does not vary with changes to the ship's electronics suite. Perhaps the greatest impact of SDMS will be the decoupling of ship subsystems from each other and from the ship. Standard multiplex interfaces will avoid the cost and delay of modifying subsystems to make them compatible. The ability to wire a new ship according to a standard multiplex cable plan, long before the ship subsystems are fully defined, frees both the ship and the subsystems to develop at their own pace, will allow compression of the development schedules and will provide ships with more advanced subsystems. This paper describes the SDMS system currently being developed by the U.S. Navy.
作者：
David H. FreyHenry E. RantingFrances M. FreyProfessor of Educational Psychology
Coordinator Professor of Educational Psychology and Coordinator of the Community Counseling Program at California State University
Hayward. He has been Assignment Editor of the P&Q Journal for the past three years. Recent study in mental health epidemiology and behavioral medicine triggered his need to explore alternative models for research and evaluation since many popular models do not adequately deal with complex phenomena. Clinical Psychologist with a background in counseling and education. Currently
he is employed in a community mental health setting where he provides traditional diagnostic and therapeutic services to patients in addition to providing special programs for adolescents and young adults including the design of mental health curricula for their parents. This article is one in a series written with David Frey regarding the taxonomic classification of counseling goals and techniques. Stanford University where her studies are concentrated in curriculum
program design and educational evaluation. Along with others at Stanford's School of Education she is carefully analyzing modes of educational inquiry especially qualitative evaluation. Her background in the visual arts philosophy of science and school psychology serve her well in the search for data that more clearly illuminate social science phenomena.
作者：
Kathleen Musante DeWaltKathleen DeWalt is a fourth year graduate student in the Dept of Biocultural Anthropology
Connecticut-Storrs and in the Social Sciences and Health Services Training Program in the Dept of Community Medicine and Health Care at the U Conn Health Center Farmington. She received her B.A. in 1971 and her M.A. in 1976 both in Anthropology from Connecticut. The work on which this paper is based was carried out in a village in the municipio of Temascalcingo between January and December 1973
about 11 months in all. She had first worked in the Temascalcingo area in summer 1970 supported by a National Science Foundation undergraduate research grant. In 1971 she began her graduate study in the Dept of Anthropology at Connecticut but decided to discontinue her studies at that time in order to be with her husband while he carried out his Ph.D. dissertation research on agricultural modernization in Mexico. During this period however Ms. DeWalt was able to carry out a preliminary study of her own which focused on medical behavior and diet. When she returned from the field she resumed her studies by entering the Social Sciences and Health Services Doctoral Training Program located in the Dept of Community Medicine at the U of Connecticut Health Center. Ms. DeWalt has just completed her Ph.D. qualifying examinations at Connecticut
and plans to begin work on her dissertation shortly. Her dissertation will focus on the effects of economic diversification on diet and nutrition in the Temascalcingo area. While the research carried out thus far will serve as background and provide baseline data she expects to spend an additional 4–6 months in the field in 1977.
This open access book presents the outcomes of the “Design for Future – Managed Software Evolution” priority program 1593, which was launched by the German Research Foundation (“Deutsche Forschungsgemeinschaft (D...
详细信息
ISBN:
(数字)9783030134990
ISBN:
(纸本)9783030134983;9783030135010
This open access book presents the outcomes of the “Design for Future – Managed Software Evolution” priority program 1593, which was launched by the German Research Foundation (“Deutsche Forschungsgemeinschaft (DFG)”) to develop new approaches to software engineering with a specific focus on long-lived software systems. The different lifecycles of software and hardware platforms lead to interoperability problems in such systems. Instead of separating the development, adaptation and evolution of software and its platforms, as well as aspects like operation, monitoring and maintenance, they should all be integrated into one overarching process.;Accordingly, the book is split into three major parts, the first of which includes an introduction to the nature of software evolution, followed by an overview of the specific challenges and a general introduction to the case studies used in the project. The second part of the book consists of the main chapters on knowledge carrying software, and cover tacit knowledge in software evolution, continuous design decision support, model-based round-trip engineering for software product lines, performance analysis strategies, maintaining security in software evolution, learning from evolution for evolution, and formal verification of evolutionary changes. In turn, the last part of the book presents key findings and spin-offs. The individual chapters there describe various case studies, along with their benefits, deliverables and the respective lessons learned. An overview of future research topics rounds out the coverage.;The book was mainly written for scientific researchers and advanced professionals with an academic background. They will benefit from its comprehensive treatment of various topics related to problems that are now gaining in importance, given the higher costs for maintenance and evolution in comparison to the initial development, and the fact that today, most software is not developed from scratch, but as part of a c
The All of Us Research program's data and Research Center (DRC) was established to help acquire, curate, and provide access to one of the world's largest and most diverse datasets for precision medic...
详细信息
The All of Us Research program's data and Research Center (DRC) was established to help acquire, curate, and provide access to one of the world's largest and most diverse datasets for precision medicine research. Already, over 500,000 participants are enrolled in All of Us, 80% of whom are underrepresented in biomedical research, and data are being analyzed by a community of over 2,300 researchers. The DRC created this thriving data ecosystem by collaborating with engaged participants, innovative program partners, and empowered researchers. In this review, we first describe how the DRC is organized to meet the needs of this broad group of stakeholders. We then outline guiding principles, common challenges, and innovative approaches used to build the All of Us data ecosystem. Finally, we share lessons learned to help others navigate important decisions and trade-offs in building a modern biomedical data platform.
In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and s...
In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data—from patient records to imaging—graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human–AI collaboration, paving the way toward clinically meaningful predictions.
Respondent-driven sampling is a commonly used method for sampling from hard-to-reach human populations connected by an underlying social network of relations. Beginning with a convenience sample, participants pass cou...
详细信息
Respondent-driven sampling is a commonly used method for sampling from hard-to-reach human populations connected by an underlying social network of relations. Beginning with a convenience sample, participants pass coupons to invite their contacts to join the sample. Although the method is often effective at attaining large and varied samples, its reliance on convenience samples, social network contacts, and participant decisions makes it subject to a large number of statistical concerns. This article reviews inferential methods available for data collected by respondent-driven sampling.
暂无评论