scholarly journals Short‐term movements and behaviour govern the use of road mitigation measures by a protected amphibian

2018 ◽  
Vol 22 (3) ◽  
pp. 285-296 ◽  
Author(s):  
C. Matos ◽  
S. O. Petrovan ◽  
P. M. Wheeler ◽  
A. I. Ward
Keyword(s):  
Mitigation Measures ◽  
Short Term ◽  
Road Mitigation

Experimental study designs to improve the evaluation of road mitigation measures for wildlife

2015 ◽  
Vol 154 ◽  
pp. 48-64 ◽  
Author(s):  
Trina Rytwinski ◽  
Rodney van der Ree ◽  
Glenn M. Cunnington ◽  
Lenore Fahrig ◽  
C. Scott Findlay ◽  
...  
Keyword(s):  
Experimental Study ◽  
Mitigation Measures ◽  
Road Mitigation ◽  
Study Designs

scholarly journals Global Analysis of SARS-COV-2 Mitigation Impact Reveals an Arabian Peninsula Cluster with High Infection Rates and Shared Indicators

2020 ◽  
Author(s):  
Najeeb Halabi ◽  
Reem S. Chamseddine ◽  
Mayyasa Rammah ◽  
Quentin Griette ◽  
Rayaz Malik ◽  
...  
Keyword(s):  
Arabian Peninsula ◽  
Global Analysis ◽  
Statistical Testing ◽  
Mitigation Measures ◽  
Infection Rates ◽  
Short Term ◽  
Mechanistic Modelling ◽  
Pollution Levels ◽  
Early Adoption ◽  
The Impact

Abstract Background SARS-CoV-2 is a novel virus that appeared in China in November 2019 and spread rapidly. With no vaccine or effective treatment, countries have adopted different mitigation measures to reduce SARS-COV-2 spread with different efficacy.MethodsWe mapped the impact of mitigation measures across different countries. We compared regional SARS-COV-2 population burden via Kruskal-Wallis statistical testing. We analyzed time of adoption of mitigation measures and the impact of PCR testing on mitigation impact. We analyzed the association of climate, health, demographic and economic indicators with mitigation impact via non-parametric correlation tests. We performed mechanistic modelling of to predict short-term SARS-COV-2 case numbers in selected countries. ResultsMany countries showed a reduction of infection rates within one month of implementing mitigation measures. However, we identified a geographic cluster of countries centered on the Arabian Peninsula (AP) that show a high SARS-COV-2 population burden despite early adoption of mitigation measures. We find that higher air pollution levels (p=0.01), higher CO2 emissions (p=0.03) and younger population (p=0.02) were associated with reduced mitigation impact in AP countries. We also show that mechanistic modelling can closely predict confirmed case numbers in the short term.ConclusionsThe impact of mitigation measures varies greatly between countries. Countries with similar profiles as AP countries should adopt more stringent mitigation measures to more rapidly reduce SARS-CoV-2 spread. Specific interventions targeting young people may also be effective in reducing SARS-COV-2 spread.

scholarly journals Short-Term Firm-Level Energy-Consumption Forecasting for Energy-Intensive Manufacturing: A Comparison of Machine Learning and Deep Learning Models

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 274 ◽  
Author(s):  
Andrea Maria N. C. Ribeiro ◽  
Pedro Rafael X. do Carmo ◽  
Iago Richard Rodrigues ◽  
Djamel Sadok ◽  
Theo Lynn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Energy Consumption ◽  
Mitigation Measures ◽  
Support Vector ◽  
Level Energy ◽  
Learning Models ◽  
Short Term ◽  
Case Site

To minimise environmental impact, to avoid regulatory penalties, and to improve competitiveness, energy-intensive manufacturing firms require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. Deep learning is widely touted as a superior analytical technique to traditional artificial neural networks, machine learning, and other classical time-series models due to its high dimensionality and problem-solving capabilities. Despite this, research on its application in demand-side energy forecasting is limited. We compare two benchmarks (Autoregressive Integrated Moving Average (ARIMA) and an existing manual technique used at the case site) against three deep-learning models (simple Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)) and two machine-learning models (Support Vector Regression (SVR) and Random Forest) for short-term load forecasting (STLF) using data from a Brazilian thermoplastic resin manufacturing plant. We use the grid search method to identify the best configurations for each model and then use Diebold–Mariano testing to confirm the results. The results suggests that the legacy approach used at the case site is the worst performing and that the GRU model outperformed all other models tested.

scholarly journals Evaluating the effectiveness of road mitigation measures

2012 ◽  
Vol 22 (2) ◽  
pp. 425-448 ◽  
Author(s):  
Edgar A. van der Grift ◽  
Rodney van der Ree ◽  
Lenore Fahrig ◽  
Scott Findlay ◽  
Jeff Houlahan ◽  
...  
Keyword(s):  
Mitigation Measures ◽  
Road Mitigation

scholarly journals Developing a correction factor to apply to animal–vehicle collision data for improved road mitigation measures

2021 ◽  
Author(s):  
Tracy S. Lee ◽  
Kimberly Rondeau ◽  
Rob Schaufele ◽  
Anthony P. Clevenger ◽  
Danah Duke
Keyword(s):  
Correction Factor ◽  
Mitigation Measures ◽  
Vehicle Collision ◽  
Road Mitigation

scholarly journals Impacts of short-term mitigation measures on PM<sub>2.5</sub> and radiative effects: a case study from a regional background site near Beijing, China

2018 ◽  
Author(s):  
Qiyuan Wang ◽  
Suixin Liu ◽  
Nan Li ◽  
Wenting Dai ◽  
Yunfei Wu ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Control Period ◽  
Mitigation Measures ◽  
Short Term ◽  
Background Site ◽  
Regional Background ◽  
Significant Difference ◽  
Cooling Effects ◽  
Inorganic Aerosol ◽  
Bth Region

Abstract. An intensive measurement campaign was conducted in a regional background site near Beijing during the 19th National Congress of the Communist Party of China (NCCPC) to investigate the effectiveness of short-term mitigation measures on PM2.5 and aerosol direct radiative forcing (DRF). Average mass concentration of PM2.5 and its major chemical composition are decreased by 20.6–43.1 % during the NCCPC control period compared with the non-control period. When considering days with the stable meteorological conditions, larger reduction of PM2.5 is found compared with that for all days. Further, a positive matrix factorization receptor model shows that the mass concentrations of PM2.5 from traffic-related emissions, biomass burning, industry processes, and mineral dust are reduced by 38.5–77.8 % during the NCCPC control period compared with the non-control period. However, there is no significant difference in PM2.5 from coal burning between these two periods, and an increasing trend of PM2.5 mass from secondary inorganic aerosol is found during the NCCPC control period. Two pollution episodes were occurred subsequently after the NCCPC control period. One is dominated by secondary inorganic aerosol, and the WRF-Chem model shows that the Beijing-Tianjin-Hebei (BTH) region contributes 73.6 % of PM2.5 mass; the other is mainly caused by biomass burning, and the BTH region contributes 46.9 % of PM2.5 mass. Calculations based on a revised IMPROVE method show that organic matter (OM) is the largest contributor to the light extinction coefficient (bext) during the non-control period while NH4NO3 is the dominant contributor during the NCCPC control period. The Tropospheric Ultraviolet and Visible radiation model reveals that the average DRF values at the Earth's surface are −14.0 and −19.3 W m-2 during the NCCPC control and non-control periods, respectively, and the reduction ratios of DRF due to the decrease in PM2.5 components vary from 22.7–46.7 % during the NCCPC control period. Our study would further provide valuable information and dataset to help controlling the air pollution and alleviating the cooling effects of aerosols at the surface in Beijing.

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