Policymakers around the world were generally unprepared for the global COVID-19 pandemic. As a result, the virus has led to millions of cases and hundreds of thousands of deaths. Theoretically, the number of cases and deaths did not have to happen (as demonstrated by the results in a few countries). In this pandemic, as in other great disasters, policymakers are confronted with what policy analysts call Decision Making under Deep Uncertainty (DMDU). Deep uncertainty requires policies that are not based on 'predict and act' but on 'prepare, monitor, and adapt', enabling policy adaptations over time as events occur and knowledge is gained. We discuss the potential of a DMDU-approach for pandemic decisionmaking.
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http://dx.doi.org/10.1016/j.healthpol.2023.104831 | DOI Listing |
Clin Neuroradiol
January 2025
Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Moorenstraße 5, 40225, Düsseldorf, Germany.
Introduction: Ventriculoperitoneal shunts (VPS) are an essential part of the treatment of hydrocephalus, with numerous valve models available with different ways of indicating pressure levels. The model types often need to be identified on X‑rays to assess pressure levels using a matching template. Artificial intelligence (AI), in particular deep learning, is ideally suited to automate repetitive tasks such as identifying different VPS valve models.
View Article and Find Full Text PDFSci Rep
January 2025
Japan Agency for Marine-Earth Science and Technology, 3173-25, Showa-machi, Kanazawa-ku, Yokohama, Kanagawa, 2360001, Japan.
Subsurface seismic velocity structure is essential for earthquake source studies, including hypocenter determination. Conventional hypocenter determination methods ignore the inherent uncertainty in seismic velocity structure models, and the impact of this oversight has not been thoroughly investigated. Here, we address this issue by employing a physics-informed deep learning (PIDL) approach that quantifies uncertainty in two-dimensional seismic velocity structure modeling and its propagation to hypocenter determination by introducing neural network ensembles trained on active seismic survey data, earthquake observation data, and the physical equation of wavefront movement.
View Article and Find Full Text PDFWhile academics increasingly point to the value of engaged scholarship, we describe a more extreme form which we label as "deep partnering"-a long-term, holistic, and dynamic collaboration between academics and practitioners to achieve shared goals. Deep partnering involves interdependent and evolving interactions between academics and practitioners over an extended time period. While such relationships enable generative impact on important issues, these relationships remain challenging as academics spend time in the practitioners' complex worlds, surfacing paradoxes due to the partners' conflicting roles, time horizons, and goals, as well as uncertainty in the partnership's evolution.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Department of Chemical and Physical Biology, Vanderbilt University, Nashville, Tennessee 37232, United States.
Machine learning (ML) models now play a crucial role in predicting properties essential to drug development, such as a drug's logscale acid-dissociation constant (p). Despite recent architectural advances, these models often generalize poorly to novel compounds due to a scarcity of ground-truth data. Further, these models lack interpretability.
View Article and Find Full Text PDFMar Environ Res
January 2025
Marine Carbon Sink Research Center, Shandong Marine Resource and Environment Research Institute, Yantai, 264006, China; College of Oceanography and Ecological Science, Shanghai Ocean University, Shanghai, 201306, China.
The input of macroalgal biomass into the deep sea is a crucial process for macroalgal carbon sequestration, but this process may be affected by anoxia. We compared the breakdown of kelp biomass in both normoxic (>4 mg/L O) and anoxic (<2 mg/L O) environments. Following 240 days of decomposition experiment, complete degradation of the kelp biomass occurred in normoxic conditions, whereas under anoxic conditions, relatively 13.
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