Objective vs. Subjective Fuel Poverty and Self-Assessed Health

Policies towards fuel poverty often use relative or absolute measures. The effectiveness of the official indicators in identifying fuel poor households and assessing its impact on health is an emerging social policy issue. In this paper we analyse objective and perceived fuel poverty as determinants of self-assessed health in Spain. In 2014, 5.1 million of her population could not afford to heat their homes to an adequate temperature. We propose a latent class ordered probit model to analyse the influence of fuel poverty on self-reported health in a sample of 25,000 individuals in 11,000 households for the 2011-2014 period. This original approach allows us to include a ‘subjective’ measure of fuel poverty in the class membership probabilities and purge the influence of the ‘objective’ measure of fuel poverty on self-assessed health. The results show that poor housing conditions, fuel poverty, and material deprivation have a negative impact on health. Also, individuals who rate themselves as fuel poor tend to report poorer health status. The effect of objective fuel poverty on health is stronger when unobserved heterogeneity of individuals is controlled for. Since objective measures alone may not fully capture the adverse effect of fuel poverty on health, we advocate the use of approaches that allow a combination of objective and subjective measures and its application by policy-makers. Moreover, it is important that policies to tackle fuel poverty take into account the different energy vectors and the prospects of a future smart and integrated energy system

Effect of solute content and temperature on the deformation mechanisms and critical resolved shear stress in Mg-Al and Mg-Zn alloys

The influence of solute atoms (Al and Zn) on the deformation mechanisms and the critical resolved shear stress for basal slip in Mg alloys at 298 K and 373 K was ascertained by micropillar compression tests in combination with high-throughput processing techniques based on the diffusion couples. It was found that the presence of solute atoms enhances the size effect at 298 K as well as the localization of deformation in slip bands, which is associated with large strain bursts in the resolved shear stress ($\tau_{RSS}$)-strain ($\epsilon$) curves. Deformation in pure Mg and Mg alloys was more homogeneous at 373 K and the influence of the micropillar size on the critical resolved shear stress was much smaller. In this latter case, it was possible to determine the effect of solute content on the critical resolved shear stress for basal slip in Mg-Al and Mg-Zn alloys

The PREVENTION Challenge: How Good Are Humans Predicting Lane Changes?

While driving on highways, every driver tries to be aware of the behavior of surrounding vehicles, including possible emergency braking, evasive maneuvers trying to avoid obstacles, unexpected lane changes, or other emergencies that could lead to an accident. In this paper, human's ability to predict lane changes in highway scenarios is analyzed through the use of video sequences extracted from the PREVENTION dataset, a database focused on the development of research on vehicle intention and trajectory prediction. Thus, users had to indicate the moment at which they considered that a lane change maneuver was taking place in a target vehicle, subsequently indicating its direction: left or right. The results retrieved have been carefully analyzed and compared to ground truth labels, evaluating statistical models to understand whether humans can actually predict. The study has revealed that most participants are unable to anticipate lane-change maneuvers, detecting them after they have started. These results might serve as a baseline for AI's prediction ability evaluation, grading if those systems can outperform human skills by analyzing hidden cues that seem unnoticed, improving the detection time, and even anticipating maneuvers in some cases.Comment: This work was accepted and presented at IEEE Intelligent Vehicles Symposium 202

Error Analysis in a Stereo Vision-Based Pedestrian Detection Sensor for Collision Avoidance Applications

This paper presents an analytical study of the depth estimation error of a stereo vision-based pedestrian detection sensor for automotive applications such as pedestrian collision avoidance and/or mitigation. The sensor comprises two synchronized and calibrated low-cost cameras. Pedestrians are detected by combining a 3D clustering method with Support Vector Machine-based (SVM) classification. The influence of the sensor parameters in the stereo quantization errors is analyzed in detail providing a point of reference for choosing the sensor setup according to the application requirements. The sensor is then validated in real experiments. Collision avoidance maneuvers by steering are carried out by manual driving. A real time kinematic differential global positioning system (RTK-DGPS) is used to provide ground truth data corresponding to both the pedestrian and the host vehicle locations. The performed field test provided encouraging results and proved the validity of the proposed sensor for being used in the automotive sector towards applications such as autonomous pedestrian collision avoidance