An empirical heuristic learning identification algorithm of Ivakhnenko was modified and used to model an environmental system producing high nitrate levels in agricultural drain water in the Corn Belt. The method amou...
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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...
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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
作者：
BERG, DAVID J.JONES, WALTER S.MARRON, HUGH W.David Berg
a native of Michigan received his Bachelor's Degree in Mechanical Engineering from the Michigan Technological University in 1951 after which he began his career with the Bureau of Ships in the Machinery Design Branch on noise shock and vibration problems. He was project engineer for the axial flow pumpjet development on USS Witek (DD848) and USS Glover (AGDE1) and received his Master of Engineering Degree in Naval Architecture in 1964 from the University of California Berkeley. Mr. Berg is currently acting head of the Ship Performance and Trials Section of the Propulsion Systems Analysis Branch in the Naval Ship Engineering Center. He received the Meritorious Civilian Service Award in 1962 for contributions to the design of the USS Thresher (SSN593) and was awarded the Superior Performance Award for Outstanding Performance in 1966. Hugh Marron
a native of Pennsylvania received his Bachelor of Science Degree in Civil Engineering from the Pennsylvania State University in 1939. Upon graduation he was employed for one year with the Pennsylvania Department of Highways as a construction engineer. In July 1940 he became a Marine Engineer at the Philadelphia Naval Shipyard where after a period of apprenticeship and special training in this new field he was assigned to the Machinery Scientific Group of the Design Division. Then in October 1945 he was transferred to the Design Division of the Bureau of Ships. Mr. Marron is now a Project Coordinator in the Propulsion Power and Auxiliary Systems Division of the Naval Ship Engineering Center. Walter S. Jones
a native of Virginia graduated from the George Washington University with a BME in June 1958. From July of that year through June 1965 he served with the Machinery Design Branch of the Bureau of Ships where he was Project Engineer for the Hydroneu-matic Ram Jet and Water jet Propulsion Systems. Mr. Jones is currently the Machinery Coordinator for the Computer Aided Ship Design Program in the Naval Ship Engineering Center.
Single-cell RNA sequencing (scRNA-seq) has provided a high-dimensional catalog of millions of cells across species and diseases. These data have spurred the development of hundreds of computational tools to derive nov...
Single-cell RNA sequencing (scRNA-seq) has provided a high-dimensional catalog of millions of cells across species and diseases. These data have spurred the development of hundreds of computational tools to derive novel biological insights. Here, we outline the components of scRNA-seq analytical pipelines and the computational methods that underlie these steps. We describe available methods, highlight well-executed benchmarking studies, and identify opportunities for additional benchmarking studies and computational methods. As the biochemical approaches for single-cell omics advance, we propose coupled development of robust analytical pipelines suited for the challenges that new data present and principled selection of analytical methods that are suited for the biological questions to be addressed.
Developing transformative pathways for industry's compliance with international climate targets requires model-based insights into how supply- and demand-side measures affect industry, material cycles, global...
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Developing transformative pathways for industry's compliance with international climate targets requires model-based insights into how supply- and demand-side measures affect industry, material cycles, global supply chains, socioeconomic activities, and service provisioning that support societal well-being. We review the recent literature modeling the industrial system in low energy and material demand futures, which mitigates environmental impacts without relying on risky future negative emissions and technological fixes. We identify 77 innovative studies drawing on nine distinct industry modeling traditions. We critically assess system definitions and scopes, biophysical and thermodynamic consistency, granularity and heterogeneity, and operationalization of demand and service provisioning. We find that combined supply- and demand-side measures could reduce current economy-wide material use by 56%, energy use by 40% to 60%, and greenhouse gas emissions by 70% to net zero. We call for strengthened interdisciplinary collaborations between industry modeling traditions and demand-side research to produce more insightful scenarios, and we discuss challenges and recommendations for this emerging field.
Three abstract optimization problems are presented along with doubly iterative algorithms for their numerical solution. These algorithms are generalizations of particular algorithms described by Barr and Gilbert [19],...
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Three abstract optimization problems are presented along with doubly iterative algorithms for their numerical solution. These algorithms are generalizations of particular algorithms described by Barr and Gilbert [19], [21] and Fujisawa and Yasuda [22]. The supporting theory is fully developed along with proofs of convergence. Practical aspects of computations are considered and procedures which insure rapid convergence are discussed. Two applications to discrete-time optimal control problems are described.
This monograph presents a comprehensive treatment of the maximum-entropy sampling problem (MESP), which is a fascinating topic at the intersection of mathematical optimization and data science. The text situates ...
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ISBN:
(数字)9783031130786
ISBN:
(纸本)9783031130779;9783031130809
This monograph presents a comprehensive treatment of the maximum-entropy sampling problem (MESP), which is a fascinating topic at the intersection of mathematical optimization and data science. The text situates MESP in information theory, as the algorithmic problem of calculating a sub-vector of pre-specificed size from a multivariate Gaussian random vector, so as to maximize Shannon's differential entropy. The text collects and expands on state-of-the-art algorithms for MESP, and addresses its application in the field of environmental monitoring. While MESP is a central optimization problem in the theory of statistical designs (particularly in the area of spatial monitoring), this book largely focuses on the unique challenges of its algorithmic side. From the perspective of mathematical-optimization methodology, MESP is rather unique (a 0/1 nonlinear program having a nonseparable objective function), and the algorithmic techniques employed are highly non-standard. In particular, successful techniques come from several disparate areas within the field of mathematical optimization; for example: convex optimization and duality, semidefinite programming, Lagrangian relaxation, dynamic programming, approximation algorithms, 0/1 optimization (e.g., branch-and-bound), extended formulation, and many aspects of matrix theory. The book is mainly aimed at graduate students and researchers in mathematical optimization and data analytics.
This book constitutes the refereed proceedings of the 19th Brazilian Symposium on Artificial Intelligence, SBIA 2008, held in Salvador, Brazil, in October 2008. The 27 revised full papers presented together with 3 inv...
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ISBN:
(数字)9783540881902
ISBN:
(纸本)9783540881896
This book constitutes the refereed proceedings of the 19th Brazilian Symposium on Artificial Intelligence, SBIA 2008, held in Salvador, Brazil, in October 2008.
The 27 revised full papers presented together with 3 invited lectures and 3 tutorials were carefully reviewed and selected from 142 submissions. The papers are organized in topical sections on computer vision and pattern recognition, distributed AI: autonomous agents, multi-agent systems and game knowledge representation and reasoning, machine learning and data mining, natural language processing, and robotics.
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