Potential Impacts of Extreme Heat and Bushfires on Dementia

2021 ◽  
Vol 79 (3) ◽  
pp. 969-978
Author(s):  
Taya L. Farugia ◽  
Carla Cuni-Lopez ◽  
Anthony R. White
Keyword(s):  
Rural Communities ◽  
Natural Disasters ◽  
Low Socioeconomic Status ◽  
Forest Fires ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Extreme Heat ◽  
Indigenous Populations ◽  
Future Research ◽  
Diagnosis Of Dementia

Australia often experiences natural disasters and extreme weather conditions such as: flooding, sandstorms, heatwaves, and bushfires (also known as wildfires or forest fires). The proportion of the Australian population aged 65 years and over is increasing, alongside the severity and frequency of extreme weather conditions and natural disasters. Extreme heat can affect the entire population but particularly at the extremes of life, and patients with morbidities. Frequently identified as a vulnerable demographic in natural disasters, there is limited research on older adults and their capacity to deal with extreme heat and bushfires. There is a considerable amount of literature that suggests a significant association between mental disorders such as dementia, and increased vulnerability to extreme heat. The prevalence rate for dementia is estimated at 30%by age 85 years, but there has been limited research on the effects extreme heat and bushfires have on individuals living with dementia. This review explores the differential diagnosis of dementia, the Australian climate, and the potential impact Australia’s extreme heat and bushfires have on individuals from vulnerable communities including low socioeconomic status Indigenous and Non-Indigenous populations living with dementia, in both metropolitan and rural communities. Furthermore, we investigate possible prevention strategies and provide suggestions for future research on the topic of Australian bushfires and heatwaves and their impact on people living with dementia. This paper includes recommendations to ensure rural communities have access to appropriate support services, medical treatment, awareness, and information surrounding dementia.

scholarly journals Analysis and Visualization of Natural Threats Against the Security of Electricity Transmission System

2017 ◽  
Vol 0 (0) ◽  
Author(s):  
T. Huang ◽  
R. Pi ◽  
E. Bompard ◽  
F. Profumo ◽  
P. Cuccia ◽  
...  
Keyword(s):  
Power Systems ◽  
Power System ◽  
Natural Disasters ◽  
Natural Hazards ◽  
Modern Society ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Transmission System ◽  
Electricity Transmission ◽  
The Impact

Abstract Electricity is one of the crucial energies of modern society, but it is greatly threatened by various kinds of menaces, especially natural hazards. Although they rarely happen, their occurrence may hugely affect the operation of power system. In this paper, we firstly, according the impact on power systems, classify natural threats into two categories (natural disasters and extreme weather conditions) and several subcategories (geological, hydrological, meteorological and climatological). Then the changes in natural threats to power systems and their trends during recent decades are discussed, along with a review of events that pose natural threats to the power system. Finally, the georeferenced model based on the Italy transmission system for natural threats analysis is presented.

scholarly journals Endless November? Citizen Beliefs Concerning Wood As A Construction Material Under Extreme Weather Events

2022 ◽  
Author(s):  
Anni Vehola ◽  
Elias Hurmekoski ◽  
Katja Lähtinen ◽  
Enni Ruokamo ◽  
Anders Roos ◽  
...  
Keyword(s):  
Climate Change ◽  
Construction Material ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Future Research ◽  
Adaptation Measures ◽  
Mitigation And Adaptation ◽  
Wood Construction ◽  
Weather Events

Abstract Climate change places great pressure on the construction sector to decrease its greenhouse gas emissions and to create solutions that perform well in changing weather conditions. In the urbanizing world, wood construction has been identified as one of the opportunities for mitigating these emissions. Our study explores citizen opinions on wood usage as a building material under expected mitigation and adaptation measures aimed at a changing climate and extreme weather events. The data are founded on an internet-based survey material collected from a consumer panel from Finland and Sweden during May–June 2021, with a total of 2015 responses. By employing exploratory factor analysis, we identified similar belief structures for the two countries, consisting of both positive and negative views on wood construction. In linear regressions for predicting these opinions, the perceived seriousness of climate change was found to increase positive views on wood construction but was insignificant for negative views. Both in Finland and Sweden, higher familiarity with wooden multistory construction was found to connect with more positive opinions on the potential of wood in building, e.g., due to carbon storage properties and material attributes. Our findings underline the potential of wood material use as one avenue of climate change adaptation in the built environment. Future research should study how citizens’ concerns for extreme weather events affect their future material preferences in their everyday living environments, also beyond the Nordic region.

scholarly journals Precipitation Modeling for Extreme Weather Based on Sparse Hybrid Machine Learning and Markov Chain Random Field in a Multi-Scale Subspace

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1241
Author(s):  
Ming-Hsi Lee ◽  
Yenming J. Chen
Keyword(s):  
Machine Learning ◽  
Markov Chain ◽  
Random Field ◽  
Long Range ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Prediction Algorithm ◽  
Learning Method ◽  
Multi Scale ◽  
Hybrid Machine

This paper proposes to apply a Markov chain random field conditioning method with a hybrid machine learning method to provide long-range precipitation predictions under increasingly extreme weather conditions. Existing precipitation models are limited in time-span, and long-range simulations cannot predict rainfall distribution for a specific year. This paper proposes a hybrid (ensemble) learning method to perform forecasting on a multi-scaled, conditioned functional time series over a sparse l1 space. Therefore, on the basis of this method, a long-range prediction algorithm is developed for applications, such as agriculture or construction works. Our findings show that the conditioning method and multi-scale decomposition in the parse space l1 are proved useful in resisting statistical variation due to increasingly extreme weather conditions. Because the predictions are year-specific, we verify our prediction accuracy for the year we are interested in, but not for other years.

scholarly journals Socioeconomic Disparities in Foods/Beverages and Nutrients Consumed by U.S. Adolescents When Snacking: National Health and Nutrition Examination Survey 2005–2018

Nutrients ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 2530
Author(s):  
Navika Gangrade ◽  
Janet Figueroa ◽  
Tashara M. Leak
Keyword(s):  
National Health ◽  
Low Socioeconomic Status ◽  
Low Income ◽  
High Income ◽  
Future Research ◽  
Nutrition Examination Survey ◽  
Middle Income ◽  
Low Socioeconomic ◽  
Health And Nutrition ◽  
Income Ratio

Snacking contributes a significant portion of adolescents’ daily energy intake and is associated with poor overall diet and increased body mass index. Adolescents from low socioeconomic status (SES) households have poorer snacking behaviors than their higher-SES counterparts. However, it is unclear if the types of food/beverages and nutrients consumed during snacking differ by SES among adolescents. Therefore, this study examines SES disparities in the aforementioned snacking characteristics by analyzing the data of 7132 adolescents (12–19 years) from the National Health and Nutrition Examination Survey 2005–2018. Results reveal that adolescents from low-income households (poverty-to-income ratio (PIR) ≤ 1.3) have lower odds of consuming the food/beverage categories “Milk and Dairy” (aOR: 0.74; 95% CI: 0.58-0.95; p = 0.007) and “Fruits” (aOR: 0.62, 95% CI: 0.50–0.78; p = 0.001) as snacks and higher odds of consuming “Beverages” (aOR: 1.45; 95% CI: 1.19-1.76; p = 0.001) compared to those from high-income households (PIR > 3.5). Additionally, adolescents from low- and middle-income (PIR > 1.3–3.5) households consume more added sugar (7.98 and 7.78 g vs. 6.66 g; p = 0.012, p = 0.026) and less fiber (0.78 and 0.77 g vs. 0.84 g; p = 0.044, p = 0.019) from snacks compared to their high-income counterparts. Future research is necessary to understand factors that influence snacking among adolescents, and interventions are needed, especially for adolescents from low-SES communities.

Multi-objective resilience enhancement program in smart grids during extreme weather conditions

2021 ◽  
Vol 129 ◽  
pp. 106824
Author(s):  
Rahman Ashrafi ◽  
Meysam Amirahmadi ◽  
Mohammad Tolou-Askari ◽  
Vahid Ghods
Keyword(s):  
Smart Grids ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Multi Objective

scholarly journals Extreme weather conditions and dengue outbreak in Guangdong, China: spatial heterogeneity based on climate variability

2021 ◽  
pp. 110900
Author(s):  
Jian Cheng ◽  
Hilary Bambrick ◽  
Laith Yakob ◽  
Gregor Devine ◽  
Francesca D. Frentiu ◽  
...  
Keyword(s):  
Climate Variability ◽  
Spatial Heterogeneity ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Dengue Outbreak

scholarly journals Overheating Risk and Energy Demand of Nordic Old and New Apartment Buildings during Average and Extreme Weather Conditions under a Changing Climate

2021 ◽  
Vol 11 (9) ◽  
pp. 3972
Author(s):  
Azin Velashjerdi Farahani ◽  
Juha Jokisalo ◽  
Natalia Korhonen ◽  
Kirsti Jylhä ◽  
Kimmo Ruosteenoja ◽  
...  
Keyword(s):  
Climate Change ◽  
Energy Demand ◽  
Energy Use ◽  
Residential Buildings ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Living Room ◽  
Extra Energy ◽  
Cooling Unit ◽  
Indoor Conditions

The global average air temperature is increasing as a manifestation of climate change and more intense and frequent heatwaves are expected to be associated with this rise worldwide, including northern Europe. Summertime indoor conditions in residential buildings and the health of occupants are influenced by climate change, particularly if no mechanical cooling is used. The energy use of buildings contributes to climate change through greenhouse gas emissions. It is, therefore, necessary to analyze the effects of climate change on the overheating risk and energy demand of residential buildings and to assess the efficiency of various measures to alleviate the overheating. In this study, simulations of dynamic energy and indoor conditions in a new and an old apartment building are performed using two climate scenarios for southern Finland, one for average and the other for extreme weather conditions in 2050. The evaluated measures against overheating included orientations, blinds, site shading, window properties, openable windows, the split cooling unit, and the ventilation cooling and ventilation boost. In both buildings, the overheating risk is high in the current and projected future average climate and, in particular, during exceptionally hot summers. The indoor conditions are occasionally even injurious for the health of occupants. The openable windows and ventilation cooling with ventilation boost were effective in improving the indoor conditions, during both current and future average and extreme weather conditions. However, the split cooling unit installed in the living room was the only studied solution able to completely prevent overheating in all the spaces with a fairly small amount of extra energy usage.

Accounting for natural disasters from a historical perspective: A literature review and research agenda

2021 ◽  
Vol 26 (2) ◽  
pp. 179-204
Author(s):  
Massimo Sargiacomo ◽  
Stefania Servalli ◽  
Serena Potito ◽  
Antonio D’Andreamatteo ◽  
Antonio Gitto
Keyword(s):  
Natural Disasters ◽  
Economic History ◽  
Research Agenda ◽  
Historical Research ◽  
Future Research ◽  
Research Topics ◽  
Open Research ◽  
Emerging Themes ◽  
Published Research ◽  
Selection Of

This study offers an analysis of published historical research on accounting for natural disasters. Drawing on the insights provided by an examination of 35 accounting/business/economic history and generalist journals, 11 articles have been selected and analysed. The analysis conducted on the scattered literature identified the emerging themes, disasters investigated, periods of time explored and main contributions of published research. The analysis is extended by the examination of some key conferences of interdisciplinary history associations, and of the eventual journals/issues where the papers presented were published. The investigation has also been complemented by a brief selection of books showing historical analyses of diverse disasters, typologies and periods of investigation. The stimuli provided by the study have helped to portray the main features of an open research agenda, highlighting possible future research topics and suggesting ancient and recent disasters’ loci to be investigated worldwide.

scholarly journals Autonomous flight cycles and extreme landings of airliners beyond the current limits and capabilities using artificial neural networks

2021 ◽  
Author(s):  
Haitham Baomar ◽  
Peter J. Bentley
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Training Data ◽  
Aviation Industry ◽  
Control Models ◽  
Novel Approach ◽  
Network Modules ◽  
Artificial Neural

AbstractWe describe the Intelligent Autopilot System (IAS), a fully autonomous autopilot capable of piloting large jets such as airliners by learning from experienced human pilots using Artificial Neural Networks. The IAS is capable of autonomously executing the required piloting tasks and handling the different flight phases to fly an aircraft from one airport to another including takeoff, climb, cruise, navigate, descent, approach, and land in simulation. In addition, the IAS is capable of autonomously landing large jets in the presence of extreme weather conditions including severe crosswind, gust, wind shear, and turbulence. The IAS is a potential solution to the limitations and robustness problems of modern autopilots such as the inability to execute complete flights, the inability to handle extreme weather conditions especially during approach and landing where the aircraft’s speed is relatively low, and the uncertainty factor is high, and the pilots shortage problem compared to the increasing aircraft demand. In this paper, we present the work done by collaborating with the aviation industry to provide training data for the IAS to learn from. The training data is used by Artificial Neural Networks to generate control models automatically. The control models imitate the skills of the human pilot when executing all the piloting tasks required to pilot an aircraft between two airports. In addition, we introduce new ANNs trained to control the aircraft’s elevators, elevators’ trim, throttle, flaps, and new ailerons and rudder ANNs to counter the effects of extreme weather conditions and land safely. Experiments show that small datasets containing single demonstrations are sufficient to train the IAS and achieve excellent performance by using clearly separable and traceable neural network modules which eliminate the black-box problem of large Artificial Intelligence methods such as Deep Learning. In addition, experiments show that the IAS can handle landing in extreme weather conditions beyond the capabilities of modern autopilots and even experienced human pilots. The proposed IAS is a novel approach towards achieving full control autonomy of large jets using ANN models that match the skills and abilities of experienced human pilots and beyond.

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