Disaster-related interventions are actions or responses undertaken during any phase of a disaster to change the current status of an affected community or a Societal System. Interventional disaster research aims to evaluate the results of such interventions in order to develop standards and best practices in Disaster Health that can be applied to disaster risk reduction. Considering interventions as production functions (transformation processes) structures the analyses and cataloguing of interventions/responses that are implemented prior to, during, or following a disaster or other emergency. Since currently it is not possible to do randomized, controlled studies of disasters, in order to validate the derived standards and best practices, the results of the studies must be compared and synthesized with results from other studies (ie, systematic reviews). Such reviews will be facilitated by the selected studies being structured using accepted frameworks. A logic model is a graphic representation of the transformation processes of a program [project] that shows the intended relationships between investments and results. Logic models are used to describe a program and its theory of change, and they provide a method for the analyzing and evaluating interventions. The Disaster Logic Model (DLM) is an adaptation of a logic model used for the evaluation of educational programs and provides the structure required for the analysis of disaster-related interventions. It incorporates a(n): definition of the current functional status of a community or Societal System, identification of needs, definition of goals, selection of objectives, implementation of the intervention(s), and evaluation of the effects, outcomes, costs, and impacts of the interventions. It is useful for determining the value of an intervention and it also provides the structure for analyzing the processes used in providing the intervention according to the Relief/Recovery and Risk-Reduction Frameworks.
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http://dx.doi.org/10.1017/S1049023X16000017 | DOI Listing |
Sci Rep
December 2024
School of Management Science and Engineering, Shandong Jianzhu University, Jinan, 250101, China.
This study seeks to improve urban supply chain management and collaborative governance in the context of public health emergencies (PHEs) by integrating fuzzy theory with the Back Propagation Neural Network (BPNN) algorithm. By combining these two approaches, an early warning mechanism for supply chain risks during PHEs is developed. The study employs Matlab software to simulate supply chain risks, incorporating fuzzy inference techniques with the adaptive data modeling capabilities of neural networks for both training and testing.
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December 2024
Hebei University of Engineering, Handan, 056038, China.
The coordination development of the Water-Energy-Food complex system (WEF CS) is vital to realizing the Sustainable Development Goals of the United Nations. However, the existing research ignores the influence of external environment, and it is not clear which dimension is the key driving mechanism for coordinated development of WEF CS. Herein, it built a theoretical framework of "system unit-nexus-natural environment" WEF CS based on the logical framework of "unit-nexus-external environment", and adopted Dagum Gini coefficient, coupling coordination degree, and grey correlation models to explore the sustainable development ability of water resource, energy resource and food resource of the Yellow River Basin (YRB).
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December 2024
Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Coronary artery disease (CAD) is the main cause of death. It is a complex heart disease that is linked with many risk factors and a variety of symptoms. In the past few years, CAD has experienced a remarkable growth.
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December 2024
Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy.
Hydrogen-based electric vehicles such as Fuel Cell Electric Vehicles (FCHEVs) play an important role in producing zero carbon emissions and in reducing the pressure from the fuel economy crisis, simultaneously. This paper aims to address the energy management design for various performance metrics, such as power tracking and system accuracy, fuel cell lifetime, battery lifetime, and reduction of transient and peak current on Polymer Electrolyte Membrane Fuel Cell (PEMFC) and Li-ion batteries. The proposed algorithm includes a combination of reinforcement learning algorithms in low-level control loops and high-level supervisory control based on fuzzy logic load sharing, which is implemented in the system under consideration.
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December 2024
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, 600127, Chennai, India.
In the current scenario, decision-making models are essential for analyzing real-world problems. To address the dynamic nature of these problems, fuzzy decision-making models have been proposed by various researchers. However, an advanced technique is needed to assess uncertainty in real-time complex situations.
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