Extreme temperatures and inequality : evidence from French agriculture

Climate change is expected to alter the frequency of occurrence of extreme weather events. Applying quantile regressions on French crop farmers over the period 2002-2017, we quantify the distributional effect of extreme temperatures on farm income. Findings indicate that both hot and cold extreme temperatures substantially reduces farm income. The distributional analysis unveil (i) an important heterogeneity between farmers and (ii) opposite effects of cold and hot extreme temperatures. While cold extremes are found to be more damaging for lowest incomes, and hence increase inequality, hot extremes are found to be more harmful for highest incomes, and decrease inequality. Two possible reasons for this antagonistic impact of extreme temperatures are explored. First, there could be a crop effect: The proportion of corn (resp. rapeseed) in the crop mix decreases (resp. increases) with income. Second, there could be a region effect: The probability of being located in the North increases with income

An automatic kriging machine learning method to calibrate meta-heuristic algorithms for solving optimization problems

For years, meta-heuristic algorithms have been widely studied and many improved versions have been developed: from the evolution of the swarm topologies of the Particle Swarm Optimization algorithm, to the using of machine learning to Differential Evolutionary algorithms. However, the tuning of the fundamental meta-heuristic parameters has been less studied, but may lead to significant improvements on the convergence accuracy of these algorithms. This paper aims at developing an automated methodology to calibrate the parameters of population-based meta-heuristic algorithms for optimization problems. Based on the kriging estimation of the best combination of parameters, the Automated parameter tuning of Meta-heuristics (AptM) methodology gives the optimal algorithm setup for each considered problem in order to lead to a better convergence accuracy. The proposed AptM methodology is used to tune three different meta-heuristic algorithms, each applied to twelve mathematical unimodal or multimodal objective functions. AptM methodology performance is assessed by comparison of classical setups usually used in the literature. The numerical results show that the AptM methodology allows a significant improvement of the convergence accuracy of meta-heuristics with an average improvement of 62.02%, 69.12% and 64.94% on optimization problems defined in dimensions 10, 30 and 50 respectively. An experimental criterion is defined based on the convergence accuracy of the AptM methodology over the classical setups, assessing the AptM performances. The previous experimental criterion allows to compare the AptM methodology over the base-set. The AptM methodology shows a significant improvement of the algorithms performance on 97.2% of the tested problems

Getting back in the loop: Does autonomous driving duration affect driver's takeover performance?

International audienceThe level 3 autonomous driving function allows the driver to perform non-driving-related tasks such as watching movies or reading while the system manages the driving task. However, when a difficult situation arises, the driver is requested to return to the loop of control. This switching from driver to passenger then back to driver may modify the driving paradigm, potentially causing an out-of-the-loop state. We tested the hypothesis of a linear (progressive) impact of various autonomous driving durations: the longer the level 3 autonomous function is used, the poorer the driver's takeover performance. Fifty-two participants were divided into 4 groups, each group being assigned a specific period of autonomous driving (5, 15, 45, or 60 min), followed by a takeover request with a time budget of 8.3 s. Takeover performance was assessed over two successive drives via reaction times and manual driving metrics (trajectories). The initial hypothesis (linearity) was not confirmed: there was a nonlinear relationship between autonomous driving duration and takeover performance, with one duration (15 min) appearing safer overall and mixed performance within groups. Repetition induced a major change in performance during the second drive, indicating rapid adaptation to the situation. The non-driving-related task appears critical in several respects (dynamics, content, driver interest) to proper use of level 3 automation. All this supports previous research prompting reservations about the prospect of car driving becoming like train travel

Off-shore evidence for a huge landslide of the northern flank of Tahiti-Nui (French Polynesia).

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Quality Matters 2016 Annual Conference of the National Infrastructures for Biobanking

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