LEADER 01439nam 2200373I 450 001 9910709900103321 005 20181004100550.0 035 $a(CKB)5470000002473251 035 $a(OCoLC)1044767987 035 $a(EXLCZ)995470000002473251 100 $a20180719j201803 ua 0 101 0 $aeng 135 $aurmn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDetermining the operability limits of chemical herders $efinal report /$fRichard Byrne [and three others] 210 1$aSterling, VA :$cU.S. Department of the Interior, Bureau of Safety and Environmental Enforcement,$dMarch 2018. 215 $a1 online resource (33 pages) $ccolor illustrations, photographs 320 $aIncludes bibliographical references (pages 50-51). 517 $aDetermining the operability limits of chemical herders 606 $aOil spills$xEnvironmental aspects$xResearch 606 $aOil pollution of water$xResearch 615 0$aOil spills$xEnvironmental aspects$xResearch. 615 0$aOil pollution of water$xResearch. 700 $aByrne$b Richard$0625070 712 02$aUnited States.$bBureau of Safety and Environmental Enforcement, 712 02$aApplied Research Associates. 801 0$bGPO 801 1$bGPO 906 $aBOOK 912 $a9910709900103321 996 $aDetermining the operability limits of chemical herders$93542670 997 $aUNINA LEADER 08575nam 22007455 450 001 9910878060603321 005 20250807150241.0 010 $a3-031-62554-4 024 7 $a10.1007/978-3-031-62554-1 035 $a(MiAaPQ)EBC31569737 035 $a(Au-PeEL)EBL31569737 035 $a(CKB)33428439500041 035 $a(DE-He213)978-3-031-62554-1 035 $a(EXLCZ)9933428439500041 100 $a20240726d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 14$aThe Proceedings of the 2024 Conference on Systems Engineering Research /$fedited by Alejandro Salado, Ricardo Valerdi, Rick Steiner, Larry Head 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (609 pages) 225 1 $aConference on Systems Engineering Research Series,$x3004-9857 311 08$a3-031-62553-6 327 $aPart I. MBSE/DE -- Chapter 1. Towards deriving a digital ontology for systems engineering and acquisition groups -- Chapter 2. Digital requirements engineering with an INCOSE-derived SysML meta-model -- Chapter 3. Towards Formalizing a Systems of Systems Core Ontology for Capability Configuration, a SysML Approach -- Chapter 4. SysML v2 for automated Co-Simulation from Systems Architecture Models -- Part II. Problem domain -- Chapter 5. Exploring Dynamic Preferences in Systems Engineering -- Chapter 6. Developing a KPI-driven framework to systematically align companies with the EU Taxonomy -- Chapter 7. Enhancing Industrial Energy Management: Improving Efficiency and Stakeholder Satisfaction -- Part III. V&V -- Chapter 8. Graph Complexity Measures as Indicators of Verification Complexity -- Chapter 9. An Ontological Foundation for the Verification and Validation of Complex Systems in the Age of Artificial Intelligence -- Chapter 10. Developing a Theoretical Basis for Validation in Systems Engineering -- Chapter 11. Towards a Rigorous Metric for Measuring Inconsistencies in Stakeholder Preferences in Systems Engineering -- Part IV. Autonomy and networks -- Chapter 12. Self-Organizing Evolutionary Complexity: Implications for Systems Engineering -- Chapter 13. Simulating the Emergent Social Networks of Army Units -- Chapter 14. Predictive and Prescriptive Analyses of Autonomy Integration into the System of Systems -- Chapter 15. A New Multi-Agent System Consensus Algorithm Inspired by Synchronous Turtle Hatching Behavior -- Part V. Education -- Chapter 16. Lessons Learned from Teaching Systems Practices in an Art Studio Format -- Chapter 17. Developing an Academic Case Study to Advance Digital Engineering -- Chapter 18. A Digital Engineering Factory for Students -- Chapter 19. Is your Systems Engineering Knowledge and Practice Ready for the New Types of Systems Emerging Today? -- Part VI. SoS -- Chapter 20. System of Systems (SoS) Approach for Improving Quality of Kidney Transplant Decision-Making Support for Transplant Surgeons -- Chapter 21. Social Systems of Systems Thinking to Improve Decision-Making Processes Towards the Sustainable Transition -- Chapter 22. Sustainable Systems: Measuring Carbon Emissions of Navy Ships -- Part VII. AI4SE -- Chapter 23. Can Large Language Models Accelerate Digital Transformation by Generating Expert-Like Systems Engineering Artifacts? Insights from an Empirical Exploration -- Chapter 24. How Digital Twins could support systems engineering processes? Insights from literature review -- Chapter 25. AI-enabled policy content modeling - a systems approach -- Chapter 26. Identification of Variables Impacting Cascading Failures in Aerospace Systems: A Natural Language Processing Approach -- Chapter 27. Integrating Edge Computing and Machine Learning for Thermal Anomaly Detection: A Space Systems Engineering Architecture -- Part VIII. Architecture & Biomimicry -- Chapter 28. Dynamic Reconfiguration of Software Systems Using Smart Contracts -- Chapter 29. On Families of Systems Architecture -- Chapter 30. A New Biological Inspired Resource Allocation Algorithm for Distributed Multi Agent Systems with Limited Knowledge -- Chapter 31. From Plant-Pollinator to Product-Customer: Bio-Inspired Network Modularity Analysis in Design for Market Systems -- Chapter 32. Satellite Network Architecture Performance: Setting the Stage for Bio-Inspired Network Design -- Part IX. AI in SE -- Chapter 33. Enabling Understanding of AI Model Behavior Through Visualization -- Chapter 34. Addressing Safety in AI-Based Systems: Insights from Systems Engineering -- Chapter 35. Towards Transparent Operations and Sustainment: A Conceptual Framework for Causal Interpretable Machine Learning Models for System Health Prognostics and Maintenance -- Part X. Applications -- Chapter 36. Analyzing Heat Related Injuries at Fort Moore -- Chapter 37. Toward Improving User Experience and the Adoption of mHealth Apps for Mental Health: An Exploratory Study -- Chapter 38. Factory in Space ? Considerations and Feasibility for Low Earth Orbit -- Chapter 39. Safeguarding end-to-end service continuity when connecting safety-critical systems to the cloud. 330 $aThe 22nd International Conference on Systems Engineering Research (CSER 2024) pushes the boundaries of systems engineering research and responds to new challenges for systems engineering. CSER was founded in 2003 by Stevens Institute of Technology and the University of Southern California. In 2024 the conference was hosted by the University of Arizona, home to the first-ever established Department of Systems Engineering. The following foundational research topics are included: ? Scientific Foundations of Systems Engineering ? Digital Engineering, Digital Twins ? Digital Transformation ? Advances in Model-Based Systems Engineering (MBSE) ? Value-based and Agile Systems Engineering ? Artificial Intelligence for Systems and Software Engineering (AI4SE) ? Systems and Software Engineering for Artificial Intelligence (SE4AI) ? Cybersecurity and System Security Engineering ? Uncertainty and Complexity Management ? Trust and Autonomous Systems ? Human-Systems Integration ? Systems of Systems ? Social Systems Engineering ? Systems Thinking ? Advances in requirements engineering, systems architecture, systems integration, and verification and validation. The 21st Annual Conference on Systems Engineering Research (CSER 2024) was poised to push the boundaries of systems engineering, embracing a wide array of themes from its scientific underpinnings to the forefront of digital engineering transformation and the seamless integration of artificial intelligence within systems and software engineering. Delving into cutting-edge topics such as Model-Based Systems Engineering (MBSE), cybersecurity, and the management of uncertainty and complexity, CSER 2024 tackled the varied challenges and seize the opportunities emerging in the field. The conference's commitment to blending theoretical insights with practical innovations makes it a pivotal event for the systems engineering community. 410 0$aConference on Systems Engineering Research Series,$x3004-9857 606 $aIndustrial engineering 606 $aProduction engineering 606 $aComputational intelligence 606 $aDynamics 606 $aNonlinear theories 606 $aArtificial intelligence 606 $aDynamics 606 $aIndustrial and Production Engineering 606 $aComputational Intelligence 606 $aApplied Dynamical Systems 606 $aArtificial Intelligence 606 $aDynamical Systems 615 0$aIndustrial engineering. 615 0$aProduction engineering. 615 0$aComputational intelligence. 615 0$aDynamics. 615 0$aNonlinear theories. 615 0$aArtificial intelligence. 615 0$aDynamics. 615 14$aIndustrial and Production Engineering. 615 24$aComputational Intelligence. 615 24$aApplied Dynamical Systems. 615 24$aArtificial Intelligence. 615 24$aDynamical Systems. 676 $a670 700 $aSalado$b Alejandro$01758546 701 $aValerdi$b Ricardo$01758547 701 $aSteiner$b Rick$0935498 701 $aHead$b Larry$01410423 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910878060603321 996 $aThe Proceedings of the 2024 Conference on Systems Engineering Research$94196780 997 $aUNINA