3,601 research outputs found
Resilience Assessment of the Built Environment of a Virtual City
L'abstract è presente nell'allegato / the abstract is in the attachmen
Resilience of healthcare and education networks and their interactions following major earthquakes
2021 Spring.Includes bibliographical references.Healthcare and education systems have been identified by various national and international organizations as the main pillars of communities' stability. Ensuring the continuation of vital community services such as healthcare and education is critical for minimizing social losses after extreme events. A shortage of healthcare services could have catastrophic short-term and long-term effects on a community including an increase in morbidity and mortality, as well as population outmigration. Moreover, a shortage or lack of facilities for K-12 education, including elementary, middle, and high schools could impact a wide range of the community's population and could lead to impact population outmigration. Despite their importance to communities, there are a lack of comprehensive models that can be used to quantify recovery of functionalities of healthcare systems and schools following natural disasters. In addition to capturing the recovery of functionality, understanding the correlation between these main social services institutions is critical to determining the welfare of communities following natural disasters. Although hospitals and schools are key indicators of the stability of community social services, no studies to date have been conducted to determine the level of interdependence between hospitals and schools and their collective influence on their recoveries following extreme events. In this study, comprehensive frameworks are devised for estimating the losses, functionality, and recovery of healthcare and educational services following earthquakes. Success trees and semi-Markov stochastic models coupled with dynamic optimization are used to develop socio-technical models that describe functionalities and restorations of the facilities providing these services, by integrating the physical infrastructure, the supplies, and the people who operate and use these facilities. New frameworks are proposed to simulate processes such as patient demand on hospitals, hospitals' interaction, student enrollment, and school administration as well as different decisions and mitigation strategies applied by hospitals and schools while considering the disturbance imposed by earthquake events on these processes. The complex interaction between healthcare and education networks is captured using a new agent-based model which has been developed in the context of the communities' physical, social, and economic sectors that affect overall recovery. This model is employed to simulate the functional processes within each facility while optimizing their recovery trajectories after earthquake occurrence. The results highlight significant interdependencies between hospitals and schools, including direct and indirect relationships, suggesting the need for collective coupling of their recovery to achieve full functionality of either of the two systems following natural disasters. Recognizing this high level of interdependence, a social services stability index is then established which can be used by policymakers and community leaders to quantify the impact of healthcare and educational services on community resilience and social services stability
A new methodology to model interdependency of Critical Infrastructure Systems during Hurricane Sandy’s event
The paper proposes a methodology to evaluate the resilience of the critical infrastructures networks hit by Hurricane Sandy in October 2012.
The region analyzed in the case study is New York metropolitan area which includes New York City and the nearby state of New Jersey. This region was the most affected by the storm and it is one of the most densely populated regions of the United States due to its high concentration of businesses and several critical infrastructures.
The identified critical infrastructure systems are highly interconnected, forming a heterogeneous network that is very vulnerable to catastrophic events, such as hurricanes. Due to several existing interdependencies, the systems are subjected to disruptive cascading effects. The disruption of one or more of these systems directly affects people, businesses, the government and leads to additional indirect damages.
After a critical comparison of the different methodologies to analyze infrastructure interdependency, the input-output method is selected in order to indentify and rank the different types of dependencies in the network as well as to prioritize the different actions during the restoration process. Previous analyses have shown that power, transportation, and fuel were the most damaged networks in the region generating severe cascading effects due to the interdependencies between them. A series of recommendations to improve the global resilience in the region are provided which will be able to prevent cascading effects and prioritize the recovery effort in the future
Application of the penalty coupling method for the analysis of blood vessels
Due to the significant health and economic impact of blood vessel diseases on modern society, its analysis is becoming of increasing importance for the medical sciences. The complexity of the vascular system, its dynamics and material characteristics all make it an ideal candidate for analysis through fluid structure interaction (FSI) simulations. FSI is a relatively new approach in numerical analysis and enables the multi-physical analysis of problems, yielding a higher accuracy of results than could be possible when using a single physics code to analyse the same category of problems. This paper introduces the concepts behind the Arbitrary Lagrangian Eulerian (ALE) formulation using the penalty coupling method. It moves on to present a validation case and compares it to available simulation results from the literature using a different FSI method. Results were found to correspond well to the comparison case as well as basic theory
Holistic Resilience Quantification Framework of Rural Communities
Communities need to prepare for anticipated hazards, adapt to varying conditions, and resist and recover rapidly from disturbances. Protecting the built environment from natural and man-made hazards and understanding the impact of these hazards helps allocate resources efficiently. Recently, an indicator-based and time-dependent approach was developed for defining and measuring the functionality and disaster resilience continuously at the community level. This computational method uses seven dimensions that find qualitative characteristics and transforms them into quantitative measures. The proposed framework is used to study the resilience of rural communities’ subject to severe flooding events. Harlan County in the Appalachian region is chosen as a case study to evaluate the proposed resilience quantification framework subject to severe flooding. The results show the validity of the proposed approach as a decision-support mechanism to assess and enhance the resilience of rural communities
A new methodology to model interdependency of Critical Infrastructure Systems during Hurricane Sandy’s event
The paper proposes a methodology to evaluate the resilience of the critical infrastructures networks hit by Hurricane Sandy in October 2012.
The region analyzed in the case study is New York metropolitan area which includes New York City and the nearby state of New Jersey. This region was the most affected by the storm and it is one of the most densely populated regions of the United States due to its high concentration of businesses and several critical infrastructures.
The identified critical infrastructure systems are highly interconnected, forming a heterogeneous network that is very vulnerable to catastrophic events, such as hurricanes. Due to several existing interdependencies, the systems are subjected to disruptive cascading effects. The disruption of one or more of these systems directly affects people, businesses, the government and leads to additional indirect damages.
After a critical comparison of the different methodologies to analyze infrastructure interdependency, the input-output method is selected in order to indentify and rank the different types of dependencies in the network as well as to prioritize the different actions during the restoration process. Previous analyses have shown that power, transportation, and fuel were the most damaged networks in the region generating severe cascading effects due to the interdependencies between them. A series of recommendations to improve the global resilience in the region are provided which will be able to prevent cascading effects and prioritize the recovery effort in the future
Classifying Interdependencies in the Food and Agriculture Critical Infrastructure Sector
This work classifies examples of infrastructure interdependencies found in the food and agriculture critical infrastructure sector. Interdependencies are identified through an examination of rice and poultry agriculture throughout the state of Arkansas. The subtleties of interdependence examples in the food and agriculture sector are inadequately captured by the well-studied interdependence classification taxonomies. Through 39 interviews, we develop an understanding of the subtle temporal, geographic, and productivity scales of interdependence in over 100 examples and present five new, distinct classifications of interdependence: (1) dynamic physical, (2) dynamic geographic, (3) deadline, (4) delay, and (5) human, economic, and natural resource interdependencies. An analysis of these inter- dependencies and their intricacies provides the opportunity to generalize these ideas across other critical infrastructure sectors and model infrastructure restoration and resilience with greater concern for seasonality, resource scarcity, and punctuality
A Review and Classification of Approaches for Dealing with Uncertainty in Multi-Criteria Decision Analysis for Healthcare Decisions
Multi-criteria decision analysis (MCDA) is increasingly used to support decisions in healthcare involving multiple and conflicting criteria. Although uncertainty is usually carefully addressed in health economic evaluations, whether and how the different sources of uncertainty are dealt with and with what methods in MCDA is less known. The objective of this study is to review how uncertainty can be explicitly taken into account in MCDA and to discuss which approach may be appropriate for healthcare decision makers. A literature review was conducted in the Scopus and PubMed databases. Two reviewers independently categorized studies according to research areas, the type of MCDA used, and the approach used to quantify uncertainty. Selected full text articles were read for methodological details. The search strategy identified 569 studies. The five approaches most identified were fuzzy set theory (45 % of studies), probabilistic sensitivity analysis (15 %), deterministic sensitivity analysis (31 %), Bayesian framework (6 %), and grey theory (3 %). A large number of papers considered the analytic hierarchy process in combination with fuzzy set theory (31 %). Only 3 % of studies were published in healthcare-related journals. In conclusion, our review identified five different approaches to take uncertainty into account in MCDA. The deterministic approach is most likely sufficient for most healthcare policy decisions because of its low complexity and straightforward implementation. However, more complex approaches may be needed when multiple sources of uncertainty must be considered simultaneousl
Exploring the interdependencies of research funders in the UK
Investment in medical research is vital to the continuing improvement of the UK's health and wealth. It is through research that we expand our understanding of disease and develop new treatments for patients. Medical research charities currently contribute over ÂŁ1 billion annually to medical research in the UK, of which over ÂŁ350 million is provided by Cancer Research UK. Many charities,
including Cancer Research UK, receive no government funding for their research
activity.
Cancer Research UK is engaged in a programme of work in order to better understand the medical research funding environment and demonstrate the importance of sustained investment. A key part of that is the Office of Health
Economics‟ (OHE) 2011 report “Exploring the interdependency between public and charitable medical research”. This study found that there are substantial
benefits, both financial and qualitative, from the existence of a variety of funders and that reductions in the level of government financial support for medical
research are likely to have broader negative effects.
This contributed to other evidence which found that the activities and funding of the charity, public and private sectors respectively are complementary, i.e. mutually reinforcing, rather than duplicative or merely substituting for one another.
“Exploring the interdependencies of research funders in the UK” by the Office of Health Economics (OHE) and SPRU: Science and Technology Policy Research at the University of Sussex, represents a continued effort to build the evidence base around the funding of medical research.
This report uncovers the extent to which funders of cancer research are interdependent, nationally and internationally. Key figures show that two
thirds of publications acknowledging external support have relied on multiple funders, while just under half benefited from overseas funding, and almost a fifth are also supported by industry. In addition the analysis
shows that the general public would not want tax funding of cancer research to be reduced, but would not donate enough to charities to compensate for any such reduction
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