The Experts below are selected from a list of 403221 Experts worldwide ranked by ideXlab platform
Scott Miles - One of the best experts on this subject based on the ideXlab platform.
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participatory disaster recovery Simulation Modeling for community resilience planning
International Journal of Disaster Risk Science, 2018Co-Authors: Scott MilesAbstract:A major challenge in enhancing the resilience of communities stems from current approaches used to identify needs and strategies that build the capacity of jurisdictions to mitigate loss and improve recovery. A new generation of resilience-based planning processes has emerged in the last several years that integrate goals of community well-being and identity into recovery-based performance measurement frameworks. Specific tools and refined guidance are needed to facilitate evidence-based development of recovery estimates. This article presents the participatory Modeling process, a planning system designed to develop recovery-based resilience measurement frameworks for community resilience planning initiatives. Stakeholder engagement is infused throughout the participatory Modeling process by integrating disaster recovery Simulation Modeling into community resilience planning. Within the process, participants get a unique opportunity to work together to deliberate on community concerns through facilitated participatory Modeling. The participatory Modeling platform combines the DESaster recovery Simulation model and visual analytics interfaces. DESaster is an open source Python Library for creating discrete event Simulations of disaster recovery. The Simulation model was developed using a human-centered design approach whose goal is to be open, modular, and extensible. The process presented in this article is the first participatory Modeling approach for analyzing recovery to aid creation of community resilience measurement frameworks.
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toward human centered Simulation Modeling for critical infrastructure disaster recovery planning
arXiv: Human-Computer Interaction, 2018Co-Authors: Abbas Ganji, Scott MilesAbstract:Critical infrastructure is vulnerable to a broad range of hazards. Timely and effective recovery of critical infrastructure after extreme events is crucial. However, critical infrastructure disaster recovery planning is complicated and involves both domain- and user-centered characteristics and complexities. Recovery planning currently uses few quantitative computer-based tools and instead largely relies on expert judgment. Simulation Modeling can simplify domain-centered complexities but not the human factors. Conversely, human-centered design places end-users at the center of design. We discuss the benefits of combining Simulation Modeling with human-centered design and refer it as human-centered Simulation Modeling. Human-centered Simulation Modeling has the capability to make recovery planning simpler and more understandable for critical infrastructure and emergency management experts and other recovery planning decision-makers.
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Toward Human-Centered Simulation Modeling for Critical Infrastructure Disaster Recovery Planning
2018 IEEE Global Humanitarian Technology Conference (GHTC), 2018Co-Authors: Abbas Ganji, Scott MilesAbstract:Critical infrastructure is vulnerable to a broad range of hazards. Timely and effective recovery of critical infrastructure after extreme events is crucial. However, critical infrastructure disaster recovery planning is complicated and involves both domain-and user-centered characteristics and complexities. Recovery planning currently uses few quantitative computer-based tools and instead largely relies on expert judgment. Simulation Modeling can simplify domain-centered complexities but not the human factors. Conversely, human-centered design places end-users at the center of design. We discuss the benefits of combining Simulation Modeling with human-centered design and refer it as human-centered Simulation Modeling. Human-centered Simulation Modeling has the capability to make recovery planning simpler and more understandable for critical infrastructure and emergency management experts and other recovery planning decision-makers. We qualitatively analyzed several resilience planning initiatives, post-disaster recovery assessments, and relevant journal articles to understand experts and decision-makers' perspectives. We propose a conceptual design framework for creating human-centered Simulation models for critical infrastructure disaster recovery planning. This framework consists of three constructs: 1) user interaction with design features that end-users interact with, including model parameters assignment, decision-making support, task queries, and usability; 2) system representation that refers to system components, system interactions, and system state variables; and 3) computation core that represents computational methods required to perform processes.
Meng-ting Tsai - One of the best experts on this subject based on the ideXlab platform.
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determination of initial stiffness of timber steel composite tsc beams based on experiment and Simulation Modeling
Sustainability, 2018Co-Authors: Meng-ting TsaiAbstract:Due to improvements in the use of recyclable materials in construction, timber–steel composite (TSC) beams demonstrate high potential for future construction. In this study, a proposed Simulation Modeling, which was adopted from the Simulation Modeling of a timber I-shape composite, was applied to estimate the initial stiffness of TSC beams. The strength of each beam could be determined once the initial stiffness was estimated. In addition, a series of experiments were performed to examine the accuracy of the proposed Simulation Modeling, including the effects of different shapes of steel members, fasteners, and applying and not applying a dowel connection. The results indicated that the Simulation Modeling could adequately determine strength at a deflection of 1/360 of the span. The ratio of difference between the experimental results and the Simulation Modeling results was less than 10% if a dowel connection at the web was applied. However, the ratio of difference reached 26% and 24% in the TSC beams without a dowel connection at the web that were fastened with screws and nails at the flange, respectively, revealing the importance of applying a dowel connection at the web. Moreover, the strength of the TSC beams with a dowel connection at the web that were fastened by screws was approximately 15% higher than that of TSC beams without screw fasteners. In conclusion, the proposed Simulation Modeling can provide designers with a method for estimating the initial stiffness and strength of TSC beams within a deflection of 1/360 of the span, supporting the future application of TSC beams in construction.
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Determination of Initial Stiffness of Timber–Steel Composite (TSC) Beams Based on Experiment and Simulation Modeling
MDPI AG, 2018Co-Authors: Meng-ting TsaiAbstract:Due to improvements in the use of recyclable materials in construction, timber–steel composite (TSC) beams demonstrate high potential for future construction. In this study, a proposed Simulation Modeling, which was adopted from the Simulation Modeling of a timber I-shape composite, was applied to estimate the initial stiffness of TSC beams. The strength of each beam could be determined once the initial stiffness was estimated. In addition, a series of experiments were performed to examine the accuracy of the proposed Simulation Modeling, including the effects of different shapes of steel members, fasteners, and applying and not applying a dowel connection. The results indicated that the Simulation Modeling could adequately determine strength at a deflection of 1/360 of the span. The ratio of difference between the experimental results and the Simulation Modeling results was less than 10% if a dowel connection at the web was applied. However, the ratio of difference reached 26% and 24% in the TSC beams without a dowel connection at the web that were fastened with screws and nails at the flange, respectively, revealing the importance of applying a dowel connection at the web. Moreover, the strength of the TSC beams with a dowel connection at the web that were fastened by screws was approximately 15% higher than that of TSC beams without screw fasteners. In conclusion, the proposed Simulation Modeling can provide designers with a method for estimating the initial stiffness and strength of TSC beams within a deflection of 1/360 of the span, supporting the future application of TSC beams in construction
Robert C Leachman - One of the best experts on this subject based on the ideXlab platform.
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using Simulation Modeling to assess rail track infrastructure in densely trafficked metropolitan areas
Winter Simulation Conference, 2002Co-Authors: Maged Dessouky, Robert C LeachmanAbstract:We present a Simulation Modeling methodology to assess the rail track infrastructure in highly dense traffic areas. We used this model to determine the best trackage configuration to meet future demand in the Los Angeles-Inland Empire Trade Corridor Region. There are three major challenges in Modeling a rail network in a densely trafficked metropolitan area. They are: (1) complex trackage configurations, (2) various speed limits, and (3) non-fixed dispatching timetables and routes between the origin and destination. Our proposed model has the ability to handle the above complexities in order to determine the best use of the rail capacity. Furthermore, our methodology is general enough so that it can be applied to other large scale rail networks.
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Modeling very large scale systems using Simulation Modeling to assess rail track infrastructure in densely trafficked metropolitan areas
Winter Simulation Conference, 2002Co-Authors: Maged Dessouky, Robert C LeachmanAbstract:We present a Simulation Modeling methodology to assess the rail track infrastructure in highly dense traffic areas. We used this model to determine the best trackage configuration to meet future demand in the Los Angeles-Inland Empire Trade Corridor Region. There are three major challenges in Modeling a rail network in a densely trafficked metropolitan area. They are: (1) complex trackage configurations, (2) various speed limits, and (3) non-fixed dispatching timetables and routes between the origin and destination. Our proposed model has the ability to handle the above complexities in order to determine the best use of the rail capacity. Furthermore, our methodology is general enough so that it can be applied to other large scale rail networks.
Nathaniel D Osgood - One of the best experts on this subject based on the ideXlab platform.
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selecting a dynamic Simulation Modeling method for health care delivery research part 2 report of the ispor dynamic Simulation Modeling emerging good practices task force
Value in Health, 2015Co-Authors: Deborah A Marshall, Lina Burgosliz, Maarten Joost Ijzerman, William H Crown, William V Padula, Peter K Wong, Kalyan S Pasupathy, Mitchell K Higashi, Nathaniel D OsgoodAbstract:In a previous report, the ISPOR Task Force on Dynamic Simulation Modeling Applications in Health Care Delivery Research Emerging Good Practices introduced the fundamentals of dynamic Simulation Modeling and identified the types of health care delivery problems for which dynamic Simulation Modeling can be used more effectively than other Modeling methods. The hierarchical relationship between the health care delivery system, providers, patients, and other stakeholders exhibits a level of complexity that ought to be captured using dynamic Simulation Modeling methods. As a tool to help researchers decide whether dynamic Simulation Modeling is an appropriate method for Modeling the effects of an intervention on a health care system, we presented the System, Interactions, Multilevel, Understanding, Loops, Agents, Time, Emergence (SIMULATE) checklist consisting of eight elements. This report builds on the previous work, systematically comparing each of the three most commonly used dynamic Simulation Modeling methods—system dynamics, discrete-event Simulation, and agent-based Modeling. We review criteria for selecting the most suitable method depending on 1) the purpose—type of problem and research questions being investigated, 2) the object—scope of the model, and 3) the method to model the object to achieve the purpose. Finally, we provide guidance for emerging good practices for dynamic Simulation Modeling in the health sector, covering all aspects, from the engagement of decision makers in the model design through model maintenance and upkeep. We conclude by providing some recommendations about the application of these methods to add value to informed decision making, with an emphasis on stakeholder engagement, starting with the problem definition. Finally, we identify areas in which further methodological development will likely occur given the growing “volume, velocity and variety” and availability of “big data” to provide empirical evidence and techniques such as machine learning for parameter estimation in dynamic Simulation models. Upon reviewing this report in addition to using the SIMULATE checklist, the readers should be able to identify whether dynamic Simulation Modeling methods are appropriate to address the problem at hand and to recognize the differences of these methods from those of other, more traditional Modeling approaches such as Markov models and decision trees. This report provides an overview of these Modeling methods and examples of health care system problems in which such methods have been useful. The primary aim of the report was to aid decisions as to whether these Simulation methods are appropriate to address specific health systems problems. The report directs readers to other resources for further education on these individual Modeling methods for system interventions in the emerging field of health care delivery science and implementation.
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applying dynamic Simulation Modeling methods in health care delivery research the simulate checklist report of the ispor Simulation Modeling emerging good practices task force
Value in Health, 2015Co-Authors: Deborah A Marshall, Lina Burgosliz, Maarten Joost Ijzerman, William V Padula, Peter K Wong, Kalyan S Pasupathy, Mitchell K Higashi, Nathaniel D Osgood, William H CrownAbstract:Health care delivery systems are inherently complex, consisting of multiple tiers of interdependent subsystems and processes that are adaptive to changes in the environment and behave in a nonlinear fashion. Traditional health technology assessment and Modeling methods often neglect the wider health system impacts that can be critical for achieving desired health system goals and are often of limited usefulness when applied to complex health systems. Researchers and health care decision makers can either underestimate or fail to consider the interactions among the people, processes, technology, and facility designs. Health care delivery system interventions need to incorporate the dynamics and complexities of the health care system context in which the intervention is delivered. This report provides an overview of common dynamic Simulation Modeling methods and examples of health care system interventions in which such methods could be useful. Three dynamic Simulation Modeling methods are presented to evaluate system interventions for health care delivery: system dynamics, discrete event Simulation, and agent-based Modeling. In contrast to conventional evaluations, a dynamic systems approach incorporates the complexity of the system and anticipates the upstream and downstream consequences of changes in complex health care delivery systems. This report assists researchers and decision makers in deciding whether these Simulation methods are appropriate to address specific health system problems through an eight-point checklist referred to as the SIMULATE (System, Interactions, Multilevel, Understanding, Loops, Agents, Time, Emergence) tool. It is a primer for researchers and decision makers working in health care delivery and implementation sciences who face complex challenges in delivering effective and efficient care that can be addressed with system interventions. On reviewing this report, the readers should be able to identify whether these Simulation Modeling methods are appropriate to answer the problem they are addressing and to recognize the differences of these methods from other Modeling approaches used typically in health technology assessment applications.
Abbas Ganji - One of the best experts on this subject based on the ideXlab platform.
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toward human centered Simulation Modeling for critical infrastructure disaster recovery planning
arXiv: Human-Computer Interaction, 2018Co-Authors: Abbas Ganji, Scott MilesAbstract:Critical infrastructure is vulnerable to a broad range of hazards. Timely and effective recovery of critical infrastructure after extreme events is crucial. However, critical infrastructure disaster recovery planning is complicated and involves both domain- and user-centered characteristics and complexities. Recovery planning currently uses few quantitative computer-based tools and instead largely relies on expert judgment. Simulation Modeling can simplify domain-centered complexities but not the human factors. Conversely, human-centered design places end-users at the center of design. We discuss the benefits of combining Simulation Modeling with human-centered design and refer it as human-centered Simulation Modeling. Human-centered Simulation Modeling has the capability to make recovery planning simpler and more understandable for critical infrastructure and emergency management experts and other recovery planning decision-makers.
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Toward Human-Centered Simulation Modeling for Critical Infrastructure Disaster Recovery Planning
2018 IEEE Global Humanitarian Technology Conference (GHTC), 2018Co-Authors: Abbas Ganji, Scott MilesAbstract:Critical infrastructure is vulnerable to a broad range of hazards. Timely and effective recovery of critical infrastructure after extreme events is crucial. However, critical infrastructure disaster recovery planning is complicated and involves both domain-and user-centered characteristics and complexities. Recovery planning currently uses few quantitative computer-based tools and instead largely relies on expert judgment. Simulation Modeling can simplify domain-centered complexities but not the human factors. Conversely, human-centered design places end-users at the center of design. We discuss the benefits of combining Simulation Modeling with human-centered design and refer it as human-centered Simulation Modeling. Human-centered Simulation Modeling has the capability to make recovery planning simpler and more understandable for critical infrastructure and emergency management experts and other recovery planning decision-makers. We qualitatively analyzed several resilience planning initiatives, post-disaster recovery assessments, and relevant journal articles to understand experts and decision-makers' perspectives. We propose a conceptual design framework for creating human-centered Simulation models for critical infrastructure disaster recovery planning. This framework consists of three constructs: 1) user interaction with design features that end-users interact with, including model parameters assignment, decision-making support, task queries, and usability; 2) system representation that refers to system components, system interactions, and system state variables; and 3) computation core that represents computational methods required to perform processes.
