scholarly journals Impact of geophysical and anthropogenic factors on wildfire size: a spatiotemporal data-driven risk assessment approach using statistical learning

2021 ◽  
Author(s):  
Nima Masoudvaziri ◽  
Prasangsha Ganguly ◽  
Sayanti Mukherjee ◽  
Kang Sun
Keyword(s):  
Risk Assessment ◽  
Statistical Learning ◽  
Spatiotemporal Data ◽  
Data Driven ◽  
Anthropogenic Factors ◽  
Assessment Approach

Impact of Geophysical and Anthropogenic Factors on Wildfire Size: A Spatiotemporal Data-Driven Risk Assessment Approach Using Statistical Learning

2021 ◽  
Author(s):  
Nima Masoudvaziri ◽  
Prasangsha Ganguly ◽  
Sayanti Mukherjee ◽  
Kang Sun
Keyword(s):  
Risk Assessment ◽  
Population Density ◽  
Surface Temperature ◽  
Spatiotemporal Data ◽  
Data Driven ◽  
Anthropogenic Factors ◽  
Assessment Framework ◽  
Factors Associated ◽  
Extreme Gradient Boosting ◽  
Standard Deviations

Abstract Wildfire spread is a stochastic phenomenon driven by a multitude of geophysical and anthropogenic factors. In this study, we propose a spatiotemporal data-driven risk assessment framework to understand the effect of various geophysical/anthropogenic factors on wildfire size, leveraging a systematic machine learning approach. We apply this framework in the state of California—the most vulnerable US state to wildfires. Using county-level annual wildfire data from 2001-2015, and various geophysical (e.g., landcover, wind, surface temperature) and anthropogenic features (e.g., population density, housing type), we trained, tested, and validated a suite of ensemble tree-based learning algorithms to identify and evaluate the key factors associated with wildfire size. The extreme gradient boosting (XGBoost) algorithm outperformed all the other models in terms of generalization performance, categorization of important features, and risk performance. We found that standard deviations of meteorological variables with long-tailed distributions play a key role in predicting wildfire size. Specifically, the top ten factors associated with high risk of larger wildfires include larger standard deviations of surface temperature and vapor pressure deficit, higher wind gust, more grassy and barren land covers, lower night-time boundary layer height and higher population density. Our proposed risk assessment framework will help federal/state decision-makers to adequately plan for wildfire risk mitigation and resource allocation strategies.

scholarly journals Data-driven risk stratification for preterm birth in Brazil: a population-based study to develop of a machine learning risk assessment approach

2021 ◽  
pp. 100053
Author(s):  
Thiago Augusto Hernandes Rocha ◽  
Erika Bárbara Abreu Fonseca de Thomaz ◽  
Dante Grapiuna de Almeida ◽  
Núbia Cristina da Silva ◽  
Rejane Christine de Sousa Queiroz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Preterm Birth ◽  
Risk Stratification ◽  
Population Based ◽  
Data Driven ◽  
Population Based Study ◽  
Assessment Approach

scholarly journals A data-driven approach to violin making

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sebastian Gonzalez ◽  
Davide Salvi ◽  
Daniel Baeza ◽  
Fabio Antonacci ◽  
Augusto Sarti
Keyword(s):  
Statistical Learning ◽  
Geometric Parameters ◽  
Data Driven ◽  
Vibrational Properties ◽  
Learning Tools ◽  
Predictive Tool ◽  
Complete Control ◽  
Data Driven Approach ◽  
To Come ◽  
Violin Making

AbstractOf all the characteristics of a violin, those that concern its shape are probably the most important ones, as the violin maker has complete control over them. Contemporary violin making, however, is still based more on tradition than understanding, and a definitive scientific study of the specific relations that exist between shape and vibrational properties is yet to come and sorely missed. In this article, using standard statistical learning tools, we show that the modal frequencies of violin tops can, in fact, be predicted from geometric parameters, and that artificial intelligence can be successfully applied to traditional violin making. We also study how modal frequencies vary with the thicknesses of the plate (a process often referred to as plate tuning) and discuss the complexity of this dependency. Finally, we propose a predictive tool for plate tuning, which takes into account material and geometric parameters.

Impact of changes of horse movement regulations on the risks of equine infectious anemia: A risk assessment approach

2021 ◽  
Vol 190 ◽  
pp. 105319
Author(s):  
Gustavo Machado ◽  
Luis Gustavo Corbellini ◽  
Alba Frias-De-Diego ◽  
Gustavo Nogueira Dieh ◽  
Diego Viali dos Santos ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Assessment Approach ◽  
Equine Infectious Anemia

scholarly journals COVID-19 in Dental Settings: Novel Risk Assessment Approach

2021 ◽  
Vol 18 (11) ◽  
pp. 6093
Author(s):  
Ali Alsaegh ◽  
Elena Belova ◽  
Yuriy Vasil’ev ◽  
Nadezhda Zabroda ◽  
Lyudmila Severova ◽  
...  
Keyword(s):  
Public Health ◽  
Risk Assessment ◽  
Dental Care ◽  
Care Delivery ◽  
Oral Diseases ◽  
Infectious Risk ◽  
Care Workers ◽  
Assessment Approach ◽  
Public Health Authorities ◽  
Novel Coronavirus

The novel coronavirus (COVID-19) outbreak is a public health emergency of international concern, and this emergency led to postponing elective dental care procedures. The postponing aimed to protect the public from an unknown risk caused by COVID-19. At the beginning of the outbreak, for public health authorities, the aerosol-generating procedures and the close proximity between dental care workers and patients in dentistry represented sufficient justification for the delay of dental visits. Dental care is a priority, and for many years, studies have proven that the lack and delay of dental care can cause severe consequences for the oral health of the general population, which can cause a high global burden of oral diseases. Safety is necessary while resuming dental activities, and risk assessment is an efficient method for understanding and preventing the COVID-19 infectious threats facing the dental industry and affecting dental care workers and patients. In this study, for safe dental care delivery, we adapted risk assessment criteria and an approach and an occupational classification system. Based on those tools, we also recommend measures that can help to minimize infectious risk in dental settings.

scholarly journals Source and Health Risk Assessment of Heavy Metals in Soil–Ginger System in the Jing River Basin of Shandong Province, North China

2021 ◽  
Vol 18 (13) ◽  
pp. 6749
Author(s):  
Songtao Wang ◽  
Zongjun Gao ◽  
Yuqi Zhang ◽  
Hairui Zhang ◽  
Zhen Wu ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Risk Assessment ◽  
River Basin ◽  
Soil Profile ◽  
Pollution Index ◽  
Potential Ecological Risk ◽  
Average Concentration ◽  
Anthropogenic Factors ◽  
Multivariate Statistical ◽  
Cr Content

This study investigated the characteristics and sources of heavy metals in a soil–ginger system and assessed their health risks. To this end, 321 topsoil samples and eight soil samples from a soil profile, and 18 ginger samples with root–soil were collected from a ginger-planting area in the Jing River Basin. The average concentration of heavy metals in the topsoil followed the order: Cr > Zn > Pb > Ni > Cu > As > Cd > Hg. In the soil profile, at depths greater than 80 cm, the contents of Cr, Ni, and Zn tended to increase with depth, which may be related to the parent materials, whereas As and Cu contents showed little change. In contrast, Pb content decreased sharply from top to bottom, which may be attributable to external environmental and anthropogenic factors. Multivariate statistical analysis showed that Cr, Ni, Cu, Zn, and Cd contents in soil are affected by natural sources, Pb and As contents are significantly affected by human activities, and Hg content is affected by farmland irrigation. Combined results of the single pollution index (Pi), geo-accumulation index (Igeo), and potential ecological risk assessment (Ei and RI) suggest that soil in the study area is generally not polluted by heavy metals. In ginger, Zn content was the highest (2.36 mg/kg) and Hg content was the lowest (0.0015 mg/kg). Based on the bioconcentration factor, Cd and Zn have high potential for enrichment in ginger. With reference to the limit of heavy metals in tubers, Cr content in ginger exceeds the standard in the study area. Although Cr does not accumulate in ginger, Cr enrichment in soil significantly increases the risk of excessive Cr content in ginger.

Active Pharmaceutical Contaminants in Dietary Supplements: a Tier-Based Risk Assessment Approach

2021 ◽  
pp. 104955
Author(s):  
Suren B. Bandara ◽  
Ania Urban ◽  
Lisa G. Liang ◽  
Jillian Parker ◽  
Ernest Fung ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Dietary Supplements ◽  
Pharmaceutical Contaminants ◽  
Assessment Approach

Risk Assessment of Dental Unit Waterline Contamination

2003 ◽  
Vol os10 (1) ◽  
pp. 5-10 ◽  
Author(s):  
Caroline L Pankhurst
Keyword(s):  
Risk Assessment ◽  
Respiratory Health ◽  
Health And Safety ◽  
Control Measures ◽  
Respiratory Pathogens ◽  
Infection Control Measures ◽  
Colony Forming Units ◽  
Assessment Approach ◽  
Dental Unit Waterlines ◽  
Mycobacterium Spp

Biofilms form rapidly on dental unit waterlines. The majority of the organisms in the biofilm are harmless environmental species, but some dental units may harbour opportunistic respiratory pathogens. This paper describes a risk assessment approach to analysing the hazard from biofilm organisms contaminating dental unit waterlines on the respiratory health of both the dental team and patients. The health risk from the respiratory pathogens Legionella spp, Mycobacterium spp and Pseudomonads was found to be low. Nevertheless, in order to satisfy water regulations and comply with health and safety legislation dentists should institute infection-control measures to maintain the dental unit water at the standard of less than 200 colony-forming units per ml of aerobic bacteria.

scholarly journals Generalized Read-Across prediction using genra-py

2021 ◽  
Author(s):  
Imran Shah ◽  
Tia Tate ◽  
Grace Patlewicz
Keyword(s):  
Risk Assessment ◽  
Human Health Risk ◽  
Chemical Properties ◽  
Predictive Performance ◽  
Hazard Identification ◽  
Data Driven ◽  
Chemical Safety ◽  
Physico Chemical ◽  
Data Driven Approach ◽  
Assessment Problems

Abstract Motivation Generalized Read-Across (GenRA) is a data-driven approach to estimate physico-chemical, biological or eco-toxicological properties of chemicals by inference from analogues. GenRA attempts to mimic a human expert’s manual read-across reasoning for filling data gaps about new chemicals from known chemicals with an interpretable and automated approach based on nearest-neighbors. A key objective of GenRA is to systematically explore different choices of input data selection and neighborhood definition to objectively evaluate predictive performance of automated read-across estimates of chemical properties. Results We have implemented genra-py as a python package that can be freely used for chemical safety analysis and risk assessment applications. Automated read-across prediction in genra-py conforms to the scikit-learn machine learning library's estimator design pattern, making it easy to use and integrate in computational pipelines. We demonstrate the data-driven application of genra-py to address two key human health risk assessment problems namely: hazard identification and point of departure estimation. Availability and implementation The package is available from github.com/i-shah/genra-py.

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