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Government Publishing Office U.S. Congress House of Representatives Committee on Foreign Affairs GLOBAL CHALLENGES IN DIAGNOSING AND MANAGING LYME DISEASE--CLOSING KNOWLEDGE GAPS Date(s) Held: 2012-07-17 112th Congress, 2nd Session GPO Document Source: CHRG-112hhrg75161 Superintendents of Documents ID: Y 4.IN 8/16 Witnesses: Stephen W. Barthold, Ph.D., distinguished professor, Department of Pathology, Microbiology and Immunology, Center of Comparative Medicine, School of Veterinary Medicine, University of California, Davis Raphael Stricker, M.D., vice president, International Lyme and Associated Diseases Society Mark Eshoo, Ph.D., director, New Technology Development, Abbott Ms. Patricia Smith, president, Lyme Disease Association Mr. Evan White, Lyme disease patient Ms. Stella Huyshe-Shires, chair, Lyme Disease Action Related Items: United States House Bill 2557 (112th Congress) United States House Bill 3795 (112th Congress) Congressional Serial No. 112-169

Government Publishing Office U.S. Congress House of Representatives Committee on Foreign Affairs GLOBAL CHALLENGES IN DIAGNOSING AND MANAGING LYME DISEASE--CLOSING KNOWLEDGE GAPS Date(s) Held: 2012-07-17 112th Congress, 2nd Session GPO Document Source: CHRG-112hhrg75161 Superintendents of Documents ID: Y 4.IN 8/16 Witnesses: Stephen W. Barthold, Ph.D., distinguished professor, Department of Pathology, Microbiology and Immunology, Center of Comparative Medicine, School of Veterinary Medicine, University of California, Davis Raphael Stricker, M.D., vice president, International Lyme and Associated Diseases Society Mark Eshoo, Ph.D., director, New Technology Development, Abbott Ms. Patricia Smith, president, Lyme Disease Association Mr. Evan White, Lyme disease patient Ms. Stella Huyshe-Shires, chair, Lyme Disease Action Related Items: United States House Bill 2557 (112th Congress) United States House Bill 3795 (112th Congress) Congressional Serial No. 112-169

This article is from BMC Nephrology , volume 15 . Abstract Background: In the UK, chronic disease, including chronic kidney disease (CKD) is largely managed in primary care. We developed a tool to assess practitioner confidence and knowledge in managing CKD compared to other chronic diseases. This questionnaire was part of a cluster randomised quality improvement interventions in chronic kidney disease (QICKD; ISRCTN56023731). Methods: The questionnaire was developed by family physicians, primary care nurses, academics and renal specialists. We conducted three focus groups (n = 7, 6, and 8) to refine the questionnaire using groups of general practitioners, practice nurses and trainees in general practice. We used paper based versions to develop the questionnaire and online surveys to test it. Practitioners in a group of volunteer, trial practices received the questionnaire twice. We measured its reliability using Cohen’s Kappa (K). Results: The practitioners in the focus groups reached a consensus as to the key elements to include in the instrument. We achieved a 73.1% (n = 57/78) initial response rate for our questionnaire; of these 57, 54 completed the questionnaire a second time. Family physicians made up the largest single group of respondents (47.4%, n = 27). Initial response showed more female (64.9%, n = 37) than male (35.1%, n = 20) respondents. The reliability results from retesting showed that there was moderate agreement (k > 0.4) on all questions; with many showing substantial agreement (k > 0.6). There was substantial agreement in the questions about loop diuretics (k = 0.608, CI 0.432-0.784, p 

Includes bibliographical references ([225]-242) and index

Includes bibliographical references ([225]-242) and index

Includes bibliographical references ([225]-242) and index

Includes bibliographical references ([225]-242) and index

In this paper we propose an adaptive approach to managing the development of students' knowledge in the comprehensive project-based learning (PBL) environment. Subject study is realized by two-stage PBL. It shapes adaptive knowledge management (KM) process and promotes the correct balance between personalized and collaborative learning. The six-step approach comprises planning the development of students' knowledge providing the needed instructional materials; KM of students by teaching a subject in the PBL environment leading to acquiring subject knowledge while work on a sample-project every student of a study group; sustaining adaptive self-formation of knowledge heterogeneous collaborative groups through sharing accountability among students for results of performing project tasks; guiding collaborative performance of group projects aimed at setting adaptive assessments of knowledge for collaborative groups and individual students based on the current state of knowledge, dynamic choice of control tests for individual students and projects of suitable complexity for collaborative groups, and adjustment of an instructor's control questions to complexity levels of projects performed by groups; assessing the knowledge gained by each student after completion of a group project by the control tests of different complexity; and evaluating knowledge dynamics of students through the proposed coefficient of knowledge development.

The focus of our research is the efficient construction of large, multi-purpose knowledge bases. Initially, we focused on developing automated methods of building knowledge bases - this is critical for all future research on large knowledge bases. This lead to automated methods of building knowledge bases by the process of automatically composing together components. This work was awarded the Best Paper Award at the National Conference on Artificial Intelligence in July 97.

A Major Command generates a vast amount of information and this volume is growing exponentially. The increase in operations tempo and short notice deployments require the rapid fusion of this information to ensure a quick response and decisiveness in any crisis. The Air Force needs a system that allows commanders to sort through the flood the information, find the insight they need, and quickly make the right decisions. This paper focuses on the field of knowledge management and the technological benefits of thin client architecture, intranets, and the World Wide Web. It also explores ways to improve the knowledge base available to MAJCOM, NAF, and Wing Commanders and their senior staffs. In addition, this paper makes recommendations for Air Force implementation of knowledge management solutions so that the Air Force can evolve into an ever more potent information superiority force. It is now time for the Air Force to embrace knowledge management as a requirement for operating effectively in the 21th century.

This is part 2 of a two part video covering Patrick Lambe's presentation on knowledge continuity at KM World November 2009. This second part provides an interim report on a global survey of what organisations are doing or not doing in expertise management.REUPLOADED 14 December after original version exhibited errors.

This is part 2 of a two part video covering Patrick Lambe's presentation on knowledge continuity at KM World November 2009. This second part provides an interim report on a global survey of what organisations are doing or not doing in expertise management.REUPLOADED 14 December after original version exhibited errors.

In this article, the author explores her teaching experiences in order to better understand the nature of the professional knowledge needed to make use of conflict and to manage a particular teaching dilemma. The author's intention is to show that such a personal inquiry can reveal ways in which her story is a telling case that may enable others to respond and make meaning out of their own experiences. Unraveling what constitutes professional knowledge in the practice of teaching is messy work, but the author hopes that when seen through the lens of one teacher's balancing act of managing dilemmas, blending the practical and theoretical, improvising, and reflecting on these processes, some clarity will be revealed. (Contains 2 endnotes.)

In this article, the author explores her teaching experiences in order to better understand the nature of the professional knowledge needed to make use of conflict and to manage a particular teaching dilemma. The author's intention is to show that such a personal inquiry can reveal ways in which her story is a telling case that may enable others to respond and make meaning out of their own experiences. Unraveling what constitutes professional knowledge in the practice of teaching is messy work, but the author hopes that when seen through the lens of one teacher's balancing act of managing dilemmas, blending the practical and theoretical, improvising, and reflecting on these processes, some clarity will be revealed. (Contains 2 endnotes.)

In this article, the author explores her teaching experiences in order to better understand the nature of the professional knowledge needed to make use of conflict and to manage a particular teaching dilemma. The author's intention is to show that such a personal inquiry can reveal ways in which her story is a telling case that may enable others to respond and make meaning out of their own experiences. Unraveling what constitutes professional knowledge in the practice of teaching is messy work, but the author hopes that when seen through the lens of one teacher's balancing act of managing dilemmas, blending the practical and theoretical, improvising, and reflecting on these processes, some clarity will be revealed. (Contains 2 endnotes.)

In this article, the author explores her teaching experiences in order to better understand the nature of the professional knowledge needed to make use of conflict and to manage a particular teaching dilemma. The author's intention is to show that such a personal inquiry can reveal ways in which her story is a telling case that may enable others to respond and make meaning out of their own experiences. Unraveling what constitutes professional knowledge in the practice of teaching is messy work, but the author hopes that when seen through the lens of one teacher's balancing act of managing dilemmas, blending the practical and theoretical, improvising, and reflecting on these processes, some clarity will be revealed. (Contains 2 endnotes.)

In this article, the author explores her teaching experiences in order to better understand the nature of the professional knowledge needed to make use of conflict and to manage a particular teaching dilemma. The author's intention is to show that such a personal inquiry can reveal ways in which her story is a telling case that may enable others to respond and make meaning out of their own experiences. Unraveling what constitutes professional knowledge in the practice of teaching is messy work, but the author hopes that when seen through the lens of one teacher's balancing act of managing dilemmas, blending the practical and theoretical, improvising, and reflecting on these processes, some clarity will be revealed. (Contains 2 endnotes.)

Trimester 1 2018/2019

Trimester 1 2018/2019

Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Tom Smoker is the cofounder of an early stage tech company empowering developers to create knowledge graphs within their RAG pipelines. Tom is a technical founder, and owns the research and development of knowledge graphs tooling for the company.Managing Small Knowledge Graphs for Multi-agent Systems // MLOps podcast #236 with Tom Smoker, Technical Founder of whyhow.ai.A big thank you to  @latticeflow  for sponsoring this episode! LatticeFlow - https://latticeflow.ai/// AbstractRAG is one of the more popular use cases for generative models, but there can be issues with repeatability and accuracy. This is especially applicable when it comes to using many agents within a pipeline, as the uncertainty propagates. For some multi-agent use cases, knowledge graphs can be used to structurally ground the agents and selectively improve the system to make it reliable end to end. // BioTechnical Founder of WhyHow.ai. Did Masters and PhD in CS, specializing in knowledge graphs, embeddings, and NLP. Worked as a data scientist to senior machine learning engineer at large resource companies and startups. // MLOps Jobs board https://mlops.pallet.xyz/jobs// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksA Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models: https://arxiv.org/abs/2401.01313Understanding the type of Knowledge Graph you need - Fixed vs Dynamic Schema/Data: https://medium.com/enterprise-rag/understanding-the-type-of-knowledge-graph-you-need-fixed-vs-dynamic-schema-data-13f319b27d9e --------------- ✌️Connect With Us ✌️ -------------Join our slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch all episodes, blogs, newsletters, and more: https://mlops.community/Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Tom on LinkedIn: https://www.linkedin.com/in/thomassmoker/Timestamps:[00:00] Tom's preferred coffee[00:33] Takeaways[03:04] Please like, share, leave a review, and subscribe to our MLOps channels![03:23] Academic Curiosity and Knowledge Graphs[05:07] Logician[05:53] Knowledge graphs incorporated into RAGs[07:53] Graphs & Vectors Integration[10:49] "Exactly wrong"[12:14] Data Integration for Robust Knowledge Graph[14:53] Structured and Dynamic Data[21:44] Scoped Knowledge Retrieval Strategies[28:01 - 29:32] LatticeFlow Ad[29:33] RAG Limitations and Solutions[36:10] Working on multi agents, questioning agent definition [40:01] Concerns about performance of agent information transfer [43:45] Anticipating agent-based systems with modular processes [52:04] Balancing risk tolerance in company operations and control [54:11] Using AI to generate high-quality, efficient content [01:03:50] Wrap up

Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Tom Smoker is the cofounder of an early stage tech company empowering developers to create knowledge graphs within their RAG pipelines. Tom is a technical founder, and owns the research and development of knowledge graphs tooling for the company.Managing Small Knowledge Graphs for Multi-agent Systems // MLOps podcast #236 with Tom Smoker, Technical Founder of whyhow.ai.A big thank you to  @latticeflow  for sponsoring this episode! LatticeFlow - https://latticeflow.ai/// AbstractRAG is one of the more popular use cases for generative models, but there can be issues with repeatability and accuracy. This is especially applicable when it comes to using many agents within a pipeline, as the uncertainty propagates. For some multi-agent use cases, knowledge graphs can be used to structurally ground the agents and selectively improve the system to make it reliable end to end. // BioTechnical Founder of WhyHow.ai. Did Masters and PhD in CS, specializing in knowledge graphs, embeddings, and NLP. Worked as a data scientist to senior machine learning engineer at large resource companies and startups. // MLOps Jobs board https://mlops.pallet.xyz/jobs// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksA Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models: https://arxiv.org/abs/2401.01313Understanding the type of Knowledge Graph you need - Fixed vs Dynamic Schema/Data: https://medium.com/enterprise-rag/understanding-the-type-of-knowledge-graph-you-need-fixed-vs-dynamic-schema-data-13f319b27d9e --------------- ✌️Connect With Us ✌️ -------------Join our slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch all episodes, blogs, newsletters, and more: https://mlops.community/Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Tom on LinkedIn: https://www.linkedin.com/in/thomassmoker/Timestamps:[00:00] Tom's preferred coffee[00:33] Takeaways[03:04] Please like, share, leave a review, and subscribe to our MLOps channels![03:23] Academic Curiosity and Knowledge Graphs[05:07] Logician[05:53] Knowledge graphs incorporated into RAGs[07:53] Graphs & Vectors Integration[10:49] "Exactly wrong"[12:14] Data Integration for Robust Knowledge Graph[14:53] Structured and Dynamic Data[21:44] Scoped Knowledge Retrieval Strategies[28:01 - 29:32] LatticeFlow Ad[29:33] RAG Limitations and Solutions[36:10] Working on multi agents, questioning agent definition [40:01] Concerns about performance of agent information transfer [43:45] Anticipating agent-based systems with modular processes [52:04] Balancing risk tolerance in company operations and control [54:11] Using AI to generate high-quality, efficient content [01:03:50] Wrap up

Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Tom Smoker is the cofounder of an early stage tech company empowering developers to create knowledge graphs within their RAG pipelines. Tom is a technical founder, and owns the research and development of knowledge graphs tooling for the company.Managing Small Knowledge Graphs for Multi-agent Systems // MLOps podcast #236 with Tom Smoker, Technical Founder of whyhow.ai.A big thank you to  @latticeflow  for sponsoring this episode! LatticeFlow - https://latticeflow.ai/// AbstractRAG is one of the more popular use cases for generative models, but there can be issues with repeatability and accuracy. This is especially applicable when it comes to using many agents within a pipeline, as the uncertainty propagates. For some multi-agent use cases, knowledge graphs can be used to structurally ground the agents and selectively improve the system to make it reliable end to end. // BioTechnical Founder of WhyHow.ai. Did Masters and PhD in CS, specializing in knowledge graphs, embeddings, and NLP. Worked as a data scientist to senior machine learning engineer at large resource companies and startups. // MLOps Jobs board https://mlops.pallet.xyz/jobs// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksA Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models: https://arxiv.org/abs/2401.01313Understanding the type of Knowledge Graph you need - Fixed vs Dynamic Schema/Data: https://medium.com/enterprise-rag/understanding-the-type-of-knowledge-graph-you-need-fixed-vs-dynamic-schema-data-13f319b27d9e --------------- ✌️Connect With Us ✌️ -------------Join our slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch all episodes, blogs, newsletters, and more: https://mlops.community/Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Tom on LinkedIn: https://www.linkedin.com/in/thomassmoker/Timestamps:[00:00] Tom's preferred coffee[00:33] Takeaways[03:04] Please like, share, leave a review, and subscribe to our MLOps channels![03:23] Academic Curiosity and Knowledge Graphs[05:07] Logician[05:53] Knowledge graphs incorporated into RAGs[07:53] Graphs & Vectors Integration[10:49] "Exactly wrong"[12:14] Data Integration for Robust Knowledge Graph[14:53] Structured and Dynamic Data[21:44] Scoped Knowledge Retrieval Strategies[28:01 - 29:32] LatticeFlow Ad[29:33] RAG Limitations and Solutions[36:10] Working on multi agents, questioning agent definition [40:01] Concerns about performance of agent information transfer [43:45] Anticipating agent-based systems with modular processes [52:04] Balancing risk tolerance in company operations and control [54:11] Using AI to generate high-quality, efficient content [01:03:50] Wrap up

Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Tom Smoker is the cofounder of an early stage tech company empowering developers to create knowledge graphs within their RAG pipelines. Tom is a technical founder, and owns the research and development of knowledge graphs tooling for the company.Managing Small Knowledge Graphs for Multi-agent Systems // MLOps podcast #236 with Tom Smoker, Technical Founder of whyhow.ai.A big thank you to  @latticeflow  for sponsoring this episode! LatticeFlow - https://latticeflow.ai/// AbstractRAG is one of the more popular use cases for generative models, but there can be issues with repeatability and accuracy. This is especially applicable when it comes to using many agents within a pipeline, as the uncertainty propagates. For some multi-agent use cases, knowledge graphs can be used to structurally ground the agents and selectively improve the system to make it reliable end to end. // BioTechnical Founder of WhyHow.ai. Did Masters and PhD in CS, specializing in knowledge graphs, embeddings, and NLP. Worked as a data scientist to senior machine learning engineer at large resource companies and startups. // MLOps Jobs board https://mlops.pallet.xyz/jobs// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksA Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models: https://arxiv.org/abs/2401.01313Understanding the type of Knowledge Graph you need - Fixed vs Dynamic Schema/Data: https://medium.com/enterprise-rag/understanding-the-type-of-knowledge-graph-you-need-fixed-vs-dynamic-schema-data-13f319b27d9e --------------- ✌️Connect With Us ✌️ -------------Join our slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch all episodes, blogs, newsletters, and more: https://mlops.community/Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Tom on LinkedIn: https://www.linkedin.com/in/thomassmoker/Timestamps:[00:00] Tom's preferred coffee[00:33] Takeaways[03:04] Please like, share, leave a review, and subscribe to our MLOps channels![03:23] Academic Curiosity and Knowledge Graphs[05:07] Logician[05:53] Knowledge graphs incorporated into RAGs[07:53] Graphs & Vectors Integration[10:49] "Exactly wrong"[12:14] Data Integration for Robust Knowledge Graph[14:53] Structured and Dynamic Data[21:44] Scoped Knowledge Retrieval Strategies[28:01 - 29:32] LatticeFlow Ad[29:33] RAG Limitations and Solutions[36:10] Working on multi agents, questioning agent definition [40:01] Concerns about performance of agent information transfer [43:45] Anticipating agent-based systems with modular processes [52:04] Balancing risk tolerance in company operations and control [54:11] Using AI to generate high-quality, efficient content [01:03:50] Wrap up

Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Tom Smoker is the cofounder of an early stage tech company empowering developers to create knowledge graphs within their RAG pipelines. Tom is a technical founder, and owns the research and development of knowledge graphs tooling for the company.Managing Small Knowledge Graphs for Multi-agent Systems // MLOps podcast #236 with Tom Smoker, Technical Founder of whyhow.ai.A big thank you to  @latticeflow  for sponsoring this episode! LatticeFlow - https://latticeflow.ai/// AbstractRAG is one of the more popular use cases for generative models, but there can be issues with repeatability and accuracy. This is especially applicable when it comes to using many agents within a pipeline, as the uncertainty propagates. For some multi-agent use cases, knowledge graphs can be used to structurally ground the agents and selectively improve the system to make it reliable end to end. // BioTechnical Founder of WhyHow.ai. Did Masters and PhD in CS, specializing in knowledge graphs, embeddings, and NLP. Worked as a data scientist to senior machine learning engineer at large resource companies and startups. // MLOps Jobs board https://mlops.pallet.xyz/jobs// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksA Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models: https://arxiv.org/abs/2401.01313Understanding the type of Knowledge Graph you need - Fixed vs Dynamic Schema/Data: https://medium.com/enterprise-rag/understanding-the-type-of-knowledge-graph-you-need-fixed-vs-dynamic-schema-data-13f319b27d9e --------------- ✌️Connect With Us ✌️ -------------Join our slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch all episodes, blogs, newsletters, and more: https://mlops.community/Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Tom on LinkedIn: https://www.linkedin.com/in/thomassmoker/Timestamps:[00:00] Tom's preferred coffee[00:33] Takeaways[03:04] Please like, share, leave a review, and subscribe to our MLOps channels![03:23] Academic Curiosity and Knowledge Graphs[05:07] Logician[05:53] Knowledge graphs incorporated into RAGs[07:53] Graphs & Vectors Integration[10:49] "Exactly wrong"[12:14] Data Integration for Robust Knowledge Graph[14:53] Structured and Dynamic Data[21:44] Scoped Knowledge Retrieval Strategies[28:01 - 29:32] LatticeFlow Ad[29:33] RAG Limitations and Solutions[36:10] Working on multi agents, questioning agent definition [40:01] Concerns about performance of agent information transfer [43:45] Anticipating agent-based systems with modular processes [52:04] Balancing risk tolerance in company operations and control [54:11] Using AI to generate high-quality, efficient content [01:03:50] Wrap up

Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Tom Smoker is the cofounder of an early stage tech company empowering developers to create knowledge graphs within their RAG pipelines. Tom is a technical founder, and owns the research and development of knowledge graphs tooling for the company.Managing Small Knowledge Graphs for Multi-agent Systems // MLOps podcast #236 with Tom Smoker, Technical Founder of whyhow.ai.A big thank you to  @latticeflow  for sponsoring this episode! LatticeFlow - https://latticeflow.ai/// AbstractRAG is one of the more popular use cases for generative models, but there can be issues with repeatability and accuracy. This is especially applicable when it comes to using many agents within a pipeline, as the uncertainty propagates. For some multi-agent use cases, knowledge graphs can be used to structurally ground the agents and selectively improve the system to make it reliable end to end. // BioTechnical Founder of WhyHow.ai. Did Masters and PhD in CS, specializing in knowledge graphs, embeddings, and NLP. Worked as a data scientist to senior machine learning engineer at large resource companies and startups. // MLOps Jobs board https://mlops.pallet.xyz/jobs// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksA Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models: https://arxiv.org/abs/2401.01313Understanding the type of Knowledge Graph you need - Fixed vs Dynamic Schema/Data: https://medium.com/enterprise-rag/understanding-the-type-of-knowledge-graph-you-need-fixed-vs-dynamic-schema-data-13f319b27d9e --------------- ✌️Connect With Us ✌️ -------------Join our slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch all episodes, blogs, newsletters, and more: https://mlops.community/Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Tom on LinkedIn: https://www.linkedin.com/in/thomassmoker/Timestamps:[00:00] Tom's preferred coffee[00:33] Takeaways[03:04] Please like, share, leave a review, and subscribe to our MLOps channels![03:23] Academic Curiosity and Knowledge Graphs[05:07] Logician[05:53] Knowledge graphs incorporated into RAGs[07:53] Graphs & Vectors Integration[10:49] "Exactly wrong"[12:14] Data Integration for Robust Knowledge Graph[14:53] Structured and Dynamic Data[21:44] Scoped Knowledge Retrieval Strategies[28:01 - 29:32] LatticeFlow Ad[29:33] RAG Limitations and Solutions[36:10] Working on multi agents, questioning agent definition [40:01] Concerns about performance of agent information transfer [43:45] Anticipating agent-based systems with modular processes [52:04] Balancing risk tolerance in company operations and control [54:11] Using AI to generate high-quality, efficient content [01:03:50] Wrap up

This article is from BMC Pediatrics , volume 13 . Abstract Background: Although numerous evidence-based and feasible interventions are available to treat pain from childhood vaccine injections, evidence indicates that children are not benefitting from this knowledge. Unrelieved vaccination pain puts children at risk for significant long-term harms including the development of needle fears and subsequent health care avoidance behaviours. Parents report that while they want to mitigate vaccination pain in their children, they lack knowledge about how to do so. An evidence-based clinical practice guideline for managing vaccination pain was recently developed in order to address this knowledge-to-care gap. Educational tools (pamphlet and video) for parents were included to facilitate knowledge transfer at the point of care. The objectives of this study were to evaluate usability and effectiveness in terms of knowledge acquisition from the pamphlet and video in parents of newly born infants. Methods: Mixed methods design. Following heuristic usability evaluation of the pamphlet and video, parents of newborn infants reviewed revised versions of both tools and participated in individual and group interviews and individual knowledge testing. The knowledge test comprised of 10 true/false questions about the effectiveness of various pain management interventions, and was administered at three time points: at baseline, after review of the pamphlet, and after review of the video. Results: Three overarching themes were identified from the interviews regarding usability of these educational tools: receptivity to learning, accessibility to information, and validity of information. Parents’ performance on the knowledge test improved (p≤0.001) from the baseline phase to after review of the pamphlet, and again from the pamphlet review phase to after review of the video. Conclusions: Using a robust testing process, we demonstrated usability and conceptual knowledge acquisition from a parent-directed educational pamphlet and video about management of vaccination pain. Future studies are planned to determine the impact of these educational tools when introduced in clinical settings on parent behaviors during infant vaccinations.

The VH-92A (formerly designated VXX) program is to develop replacement aircraft for the aging presidential helicopter fleet. It follows the VH-71 program, which was terminated in June 2009 due to cost growth, schedule delays, and a projected shortfall in system performance. For this follow-on program, the Navy s acquisition strategy has been to (1) use mature technologies and integrate them into an existing certified helicopter selected for the program and (2) limit the modifications to the selected helicopter in order to avoid a costly total aircraft recertification. The Navy plans to acquire a VH-92A fleet of 21 operational helicopters (that will also be used to perform training missions) and two test aircraft to replace the existing fleet of 19 legacy helicopters and two trainers and two testing assets. We have reported on the program since 2011.1 In 2013, the House Armed Services Committee, Tactical Air and Land Force Subcommittee requested that we continue to monitor the VH-92A presidential helicopter acquisition through a series of reviews, with each review tailored to where the program is in the acquisition process. The National Defense Authorization Act for Fiscal Year 2014 subsequently mandated that we continue reporting on the program annually to the congressional defense committees. This report discusses the cost, schedule, and performance status of the program, challenges it will face in system development, and the program s adherence to acquisition best practices.

The VH-92A (formerly designated VXX) program is to develop replacement aircraft for the aging presidential helicopter fleet. It follows the VH-71 program, which was terminated in June 2009 due to cost growth, schedule delays, and a projected shortfall in system performance. For this follow-on program, the Navy s acquisition strategy has been to (1) use mature technologies and integrate them into an existing certified helicopter selected for the program and (2) limit the modifications to the selected helicopter in order to avoid a costly total aircraft recertification. The Navy plans to acquire a VH-92A fleet of 21 operational helicopters (that will also be used to perform training missions) and two test aircraft to replace the existing fleet of 19 legacy helicopters and two trainers and two testing assets. We have reported on the program since 2011.1 In 2013, the House Armed Services Committee, Tactical Air and Land Force Subcommittee requested that we continue to monitor the VH-92A presidential helicopter acquisition through a series of reviews, with each review tailored to where the program is in the acquisition process. The National Defense Authorization Act for Fiscal Year 2014 subsequently mandated that we continue reporting on the program annually to the congressional defense committees. This report discusses the cost, schedule, and performance status of the program, challenges it will face in system development, and the program s adherence to acquisition best practices.

In late 2004 Mark Dingle surveyed a broad cross-section of New Zealand’s technology companies to ascertain their approach to Knowledge Management and their collective role in New Zealand’s transition to a “knowledge economy”. Mark will present the results of this Doctoral research which focuses on the strategic application of knowledge in this rapidly changing and highly competitive sector. In particular, the survey explores the “what” rather than the “how” of knowledge creation and management and the extent to which technology firms are developing future sources of competitive advantage by creating and leveraging knowledge.

In late 2004 Mark Dingle surveyed a broad cross-section of New Zealand’s technology companies to ascertain their approach to Knowledge Management and their collective role in New Zealand’s transition to a “knowledge economy”. Mark will present the results of this Doctoral research which focuses on the strategic application of knowledge in this rapidly changing and highly competitive sector. In particular, the survey explores the “what” rather than the “how” of knowledge creation and management and the extent to which technology firms are developing future sources of competitive advantage by creating and leveraging knowledge.

The future airborne early warning aircraft to be operated by the Royal Navy will consist of an advanced radar driven mission system that will include a datalink: this Datalink, as well as advanced sensors and an expanded role, will impose additional work on the aircrew that operate in the rear of the airborne early warning (AEW) aircraft. Operating the existing mission system is, under certain conditions, considered by AEW aircrew to be at the upper limits of tolerable workload. The deluge of information from these advanced sensor systems and datalink is likely to impact on the aircrew performance and hence overall system performance. The KBS group of the Defense, Evaluation and Research Agency (DERA) in Farnborough, UK, has been developing Knowledge Based Systems for Decision Support for over a decade. This experience has been applied to the AEW domain with a high degree of success as demonstrated in a laboratory based concept demonstrator. However, previous work had not accounted for the impacts of the AEW aircraft operating within a digitized battlespace. This paper describes part of a project that was aimed at examining the implications of digitization on the future AEW aircraft and how that would impact the Knowledge Based Decision aids currently being developed. Specifically, the project assessed how datalink would be used and how the information conveyed over datalink could help the aircrew's decisions through greater situation awareness.

The future airborne early warning aircraft to be operated by the Royal Navy will consist of an advanced radar driven mission system that will include a datalink: this Datalink, as well as advanced sensors and an expanded role, will impose additional work on the aircrew that operate in the rear of the airborne early warning (AEW) aircraft. Operating the existing mission system is, under certain conditions, considered by AEW aircrew to be at the upper limits of tolerable workload. The deluge of information from these advanced sensor systems and datalink is likely to impact on the aircrew performance and hence overall system performance. The KBS group of the Defense, Evaluation and Research Agency (DERA) in Farnborough, UK, has been developing Knowledge Based Systems for Decision Support for over a decade. This experience has been applied to the AEW domain with a high degree of success as demonstrated in a laboratory based concept demonstrator. However, previous work had not accounted for the impacts of the AEW aircraft operating within a digitized battlespace. This paper describes part of a project that was aimed at examining the implications of digitization on the future AEW aircraft and how that would impact the Knowledge Based Decision aids currently being developed. Specifically, the project assessed how datalink would be used and how the information conveyed over datalink could help the aircrew's decisions through greater situation awareness.

The future airborne early warning aircraft to be operated by the Royal Navy will consist of an advanced radar driven mission system that will include a datalink: this Datalink, as well as advanced sensors and an expanded role, will impose additional work on the aircrew that operate in the rear of the airborne early warning (AEW) aircraft. Operating the existing mission system is, under certain conditions, considered by AEW aircrew to be at the upper limits of tolerable workload. The deluge of information from these advanced sensor systems and datalink is likely to impact on the aircrew performance and hence overall system performance. The KBS group of the Defense, Evaluation and Research Agency (DERA) in Farnborough, UK, has been developing Knowledge Based Systems for Decision Support for over a decade. This experience has been applied to the AEW domain with a high degree of success as demonstrated in a laboratory based concept demonstrator. However, previous work had not accounted for the impacts of the AEW aircraft operating within a digitized battlespace. This paper describes part of a project that was aimed at examining the implications of digitization on the future AEW aircraft and how that would impact the Knowledge Based Decision aids currently being developed. Specifically, the project assessed how datalink would be used and how the information conveyed over datalink could help the aircrew's decisions through greater situation awareness.