59 research outputs found
Author Correction: Rapid increase in the risk of heat-related mortality.
No full textCorrection to: Nature Communicationshttps://doi.org/10.1038/s41467-023-40599-x, published online 24 August 2023 The original version of this Article omitted from the author list the 17th author, “Multi-Country Multi-City (MCC) collaborative research network”, which is the consortium providing the mortality data. A list of consortium authors and their affiliations are provided in the HTML version of this Correction. Part of the Author Contributions statement was incorrectly given and should have read ‘A.M.V.C., E.M.F., B.A., M.D.S.Z.S.C., Y.L.G., Y.G., Y.H., V.H., J.K., E.L., D.R., N.R., N.S., S.S., A.U., A.G. and the MCC were involved in resources and data curation.’ In addition, the primary affiliation ‘Climate Research Foundation (FIC), Madrid, Spain’ for Dominic Roye was missing
A Machine Learning Model Integrating Remote Sensing, Ground Station, and Geospatial Data to Predict Fine-Resolution Daily Air Temperature for Tuscany, Italy.
No full textHeat-related morbidity and mortality are increasing due to climate change, emphasizing the need to identify vulnerable areas and people exposed to extreme temperatures. To improve heat stress impact assessment, we developed a replicable machine learning model that integrates remote sensing, ground station, and geospatial data to estimate daily air temperature at a spatial resolution of 100 m × 100 m across the region of Tuscany, Italy. Using a two-stage approach, we first imputed missing land surface temperature data from MODIS using gradient-boosted trees and spatio-temporal predictors. Then, we modeled daily maximum and minimum air temperatures by incorporating monitoring station observations, satellite-derived data (MODIS, Landsat 8), topography, land cover, meteorological variables (ERA5-land), and vegetation indices (NDVI). The model achieved high predictive accuracy, with R2 values of 0.95 for Tmax and 0.92 for Tmin, and root mean square errors (RMSE) of 1.95 °C and 1.96 °C, respectively. It effectively captured both temporal (R2: 0.95; 0.94) and spatial (R2: 0.92; 0.72) temperature variations, allowing for the creation of high-resolution maps. These results highlight the potential of integrating Earth Observation and machine learning to generate high-resolution temperature maps, offering valuable insights for urban planning, climate adaptation, and epidemiological studies on heat-related health effects
fsera/COVIDWeather
No full textData and code to replicate the analysis of the paper " A cross-sectional analysis of meteorological factors and SARS-CoV-2 transmission in 409 cities across 26 countries
Interactive effects of ambient fine particulate matter and ozone on daily mortality in 372 cities: two stage time series analysis
No full textInvestigation of a Testing Method for Compression Behavior of Spacer Fabrics Designed for Concrete Applications
No full textSpacer fabrics are state-of-the-art structures and have attracted more attention in recent years. They have already been used in many areas and present different advantages especially for technical applications. Recently, special spacer fabrics have been designed in order to improve the characteristics of concrete which is used, in particular, for structural reinforcement of buildings. These spacer fabrics have different characteristics compared to conventional textiles due to their special structure. Therefore, characterization of these structures with existing methods is not possible. Compression resistance of spacer fabrics provided by spacer yarns in the structure is one of their main characteristics. However, compression behavior of spacer fabrics has not been investigated in detail to date. In this work, a testing method for the characterization of spacer fabrics used in concrete applications on the basis of their compression behavior has been investigated and defined.TUBITAK Textile Research CentreTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK); Ege University Textile Engineering Department, Izmir, TurkeyEge UniversityThe authors give special thanks to Institut fur Textiltechnik der RWTH Aachen, Germany for the opportunity to conduct this work, and TUBITAK Textile Research Centre and Ege University Textile Engineering Department, Izmir, Turkey for supporting the author Diren Mecit Armakan
Reducing alarm fatigue for optimal performance: analysis of a multi-unit health system process improvement
No full textTechnology in healthcare greatly enhances service delivery, safety, and efficacy yet when systems are not optimized any benefits of the technology are lost. This dissertation examines remote temperature monitoring systems and the inefficiencies of the alarms generated. Using Six Sigma methodology for performance improvement in healthcare, this dissertation focuses process improvement, specifically, reducing alarm/alert fatigue in healthcare generated by temperature monitoring systems. Extant research fails to examine non-patient alarms as distractions endured by healthcare professionals. Temperature and humidity control of the environment is critically important in clinical environments for infection control, pharmaceutical and food storage, and equipment function, among other reasons. Manual monitoring is resource-laden and error-prone, and automated environmental monitoring offers significant time-savings and reallocation of resources to other job tasks. However, without a robust infrastructure and implementation rules problems may arise. The case analysis of a multi-unit health system redesign of automated environmental monitoring highlights the complexity and inherent failures related to alarm management. Further, this case study examines alarm redeployment following11,000 environmental excursion alerts occurred each month with only 22% of those alerts being addressed. Using qualitative data from stakeholders, three research hypotheses were developed and examined relative to an end user:
1. The presence of user policies or procedures for use impacted the number of alarms generated;
2. Regular review of monitoring requirements and consistent system interaction impacted the number of alarms generated; and
3. Alert parameters determined by expert definition or empirically based system use impacted the number of alerts with corrections documented.
Baseline data is compared to post-improvement data to validate hypotheses and determine efficacy of real-time improvements. Continued improvement throughout the course of the project is measurable and sustainable. The author also proposes enhancements and improvements can be realized using six sigma methodology for technology installations that become out-moded to provide optimal performance.Ph.D.Includes bibliographical reference
Autour d’Éléonore de Roye, princesse de Condé. Étude du milieu protestant dans les années 1550–1565 à partir de documents authentiques nouvellement édités
No full text
Rainfall-Linked Megafires as Innate Fire Regime Elements in Arid Australian Spinifex (Triodia spp.) Grasslands
No full textLarge, high-severity wildfires, or "megafires," occur periodically in arid Australian spinifex (Triodia spp.) grasslands after high rainfall periods that trigger fuel accumulation. Proponents of the patch-burn mosaic (PBM) hypothesis suggest that these fires are unprecedented in the modern era and were formerly constrained by Aboriginal patch burning that kept landscape fuel levels low. This assumption deserves scrutiny, as evidence from fire-prone systems globally indicates that weather factors are the primary determinant behind megafire incidence, and that fuel management does not mitigate such fires during periods of climatic extreme. We reviewed explorer's diaries, anthropologist's reports, and remotely sensed data from the Australian Western Desert for evidence of large rainfall-linked fires during the pre-contact period when traditional Aboriginal patch burning was still being practiced. We used only observations that contained empiric estimates of fire sizes. Concurrently, we employed remote rainfall data and the Oceanic Niño Index to relate fire size to likely seasonal conditions at the time the observations were made. Numerous records were found of small fires during periods of average and below-average rainfall conditions, but no evidence of large-scale fires during these times. By contrast, there was strong evidence of large-scale wildfires during a high-rainfall period in the early 1870s, some of which are estimated to have burnt areas up to 700,000 ha. Our literature review also identified several Western Desert Aboriginal mythologies that refer to large-scale conflagrations. As oral traditions sometimes corroborate historic events, these myths may add further evidence that large fires are an inherent feature of spinifex grassland fire regimes. Overall, the results suggest that, contrary to predictions of the PBM hypothesis, traditional Aboriginal burning did not modulate spinifex fire size during periods of extreme-high arid zone rainfall. The mechanism behind this is that plant assemblages in seral spinifex vegetation comprise highly flammable non-spinifex tussock grasses that rapidly accumulate high fuel loads under favorable precipitation conditions. Our finding that fuel management does not prevent megafires under extreme conditions in arid Australia has parallels with the primacy of climatic factors as drivers of megafires in the forests of temperate Australia
Estimating the urban heat-related mortality burden due to greenness: a global modelling study
No full textSummary
Background:
Heat exposure poses a substantial public health threat. Increasing greenness has been suggested as a mitigation strategy due to its cooling effect and potential to modify the heat–mortality association. This study aimed to comprehensively estimate the effects of increased greenness on heat-related deaths.
Methods:
We applied a multistage meta-analytical approach to estimate the potential reduction in global heat-related deaths by increasing greenness in the warm season in 2000–19 in 11 534 urban areas. We used the enhanced vegetation index (EVI) to indicate greenness and a random forest model to predict daily temperatures in counterfactual EVI scenarios. In the factual EVI scenarios, daily mortality and weather variables from 830 locations in 53 countries were extracted from the Multi-Country Multi-City Collaborative Research Network and used to assess heat–mortality associations. These associations were then extrapolated to each urban area under both factual and counterfactual EVI scenarios based on meta-regression models.
Findings:
We estimated that EVI increased by 10% would decrease the global population-weighted warm-season mean temperature by 0·08°C, EVI increased by 20% would decrease temperature by 0·14°C, and EVI increased by 30% would decrease temperature by 0·19°C. In the factual scenario, 3 153 225 (2·48%) of 127 179 341 total deaths could be attributed to heat exposure. The attributable fraction of heat-related deaths (as a fraction of total deaths) in 2000–19 would decrease by 0·67 (95% empirical CI 0·53–0·82) percentage points in the 10% scenario, 0·80 (0·63–0·97) percentage points in the 20% scenario, and 0·91 (0·72–1·10) percentage points in the 30% scenario, compared with the factual scenario. South Europe was modelled to have the largest decrease in attributable fraction of heat-related mortality.
Interpretation:
This modelling study suggests that increased greenness could substantially reduce the heat-related mortality burden. Preserving and expanding greenness might be potential strategies to lower ambient temperature and reduce the health impacts of heat exposure.
Funding:
Australian Research Council and Australian National Health and Medical Research Council.Summary
Background:
Heat exposure poses a substantial public health threat. Increasing greenness has been suggested as a mitigation strategy due to its cooling effect and potential to modify the heat–mortality association. This study aimed to comprehensively estimate the effects of increased greenness on heat-related deaths.
Methods:
We applied a multistage meta-analytical approach to estimate the potential reduction in global heat-related deaths by increasing greenness in the warm season in 2000–19 in 11 534 urban areas. We used the enhanced vegetation index (EVI) to indicate greenness and a random forest model to predict daily temperatures in counterfactual EVI scenarios. In the factual EVI scenarios, daily mortality and weather variables from 830 locations in 53 countries were extracted from the Multi-Country Multi-City Collaborative Research Network and used to assess heat–mortality associations. These associations were then extrapolated to each urban area under both factual and counterfactual EVI scenarios based on meta-regression models.
Findings:
We estimated that EVI increased by 10% would decrease the global population-weighted warm-season mean temperature by 0·08°C, EVI increased by 20% would decrease temperature by 0·14°C, and EVI increased by 30% would decrease temperature by 0·19°C. In the factual scenario, 3 153 225 (2·48%) of 127 179 341 total deaths could be attributed to heat exposure. The attributable fraction of heat-related deaths (as a fraction of total deaths) in 2000–19 would decrease by 0·67 (95% empirical CI 0·53–0·82) percentage points in the 10% scenario, 0·80 (0·63–0·97) percentage points in the 20% scenario, and 0·91 (0·72–1·10) percentage points in the 30% scenario, compared with the factual scenario. South Europe was modelled to have the largest decrease in attributable fraction of heat-related mortality.
Interpretation:
This modelling study suggests that increased greenness could substantially reduce the heat-related mortality burden. Preserving and expanding greenness might be potential strategies to lower ambient temperature and reduce the health impacts of heat exposure.
Funding:
Australian Research Council and Australian National Health and Medical Research Council
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