Has global deforestation accelerated due to the COVID-19 pandemic?

As the COVID-19 pandemic unfolded,questions arose as to whether the pandemic would amplify or pacify tropical deforestation.Early reports warned of increased deforestation rates;however,these studies were limited to a few months in 2020 or to selected regions.To better understand how the pandemic infl uenced tropical deforestation globally,this study used historical deforestation data(2004–2019)from the Terra-i pantropical land cover change monitoring system to project expected deforestation trends for 2020,which were used to determine whether observed deforestation deviated from expected trajectories after the fi rst COVID-19 cases were reported.Time series analyses were conducted at the regional level for the Americas,Africa and Asia and at the country level for Brazil,Colombia,Peru,the Democratic Republic of Congo and Indonesia.Our results suggest that the pandemic did not alter the course of deforestation trends in some countries(e.g.,Brazil,Indonesia),while it did in others(e.g.,Peru).We posit the importance of monitoring the long-term eff ects of the pandemic on deforestation trends as countries prioritize economic recovery in the aftermath of the pandemic.

Towards Autonomous Vehicles with Advanced Sensor Solutions

Professional truck drivers are an essential part of transportation in keeping the global economy alive and commercial products moving. In order to increase productivity and improve safety, an increasing amount of automation is implemented in modern trucks. Transition to automated heavy good vehicles is intended to make trucks accident-free and, on the other hand, more comfortable to drive. This motivates the automotive industry to bring more embedded ICT into their vehicles in the future. An avenue towards autonomous vehicles requires robust environmental perception and driver monitoring technologies to be introduced. This is the main motivation behind the DESERVE project. This is the study of sensor technology trials in order to minimize blind spots around the truck and, on the other hand, keep the driver’s vigilance at a sufficiently high level. The outcomes are two innovative truck demonstrations: one R & D study for bringing equipment to production in the future and one implementation to the driver training vehicle. The earlier experiments include both driver monitoring technology which works at a 60% - 80% accuracy level and environment perception (stereo and thermal cameras) whose performance rates are 70% - 100%. The results are not sufficient for autonomous vehicles, but are a step forward, since they are in-line even if moved from the lab to real automotive implementations.

A comprehensive review of approaches to detect fatigue using machine learning techniques

In the past decades,there have been numerous advancements in the field of technology.This has led to many scientific breakthroughs in the field of medical sciences.In this,rapidly transforming world we are having a difficult time and the problem of fatigue is becoming prevalent.So,this study aimed to understand what is fatigue,its repercussions,and techniques to detect it using machine learning(ML)approaches.This paper introduces,discusses methods and recent advancements in the field of fatigue detection.Further,we categorized the methods that can be used to detect fatigue into four diverse groups,that is,mathematical models,rule-based implementation,ML,and deep learning.This study presents,compares,and contrasts various algorithms to find the most promising approach that can be used for the detection of fatigue.Finally,the paper discusses the possible areas for improvement.