Route tracking diagnosis algorithm for EV energy prediction strategies

Current pollution issues generated by internal com bustion engine (ICE) based vehicles have lead to their progressive introduction of electrified transport systems. However, their main drawback is their poor autonomy when compared to conventional vehicles. In order to mitigate this issue, the scientific community is extensively researching on energy optimization and prediction strategies to extend the autonomy of electric vehicles (EV). In general, such strategies require the knowledge of the route profile, being of capital importance to identify whether the vehicle is on route or not. Considering this, in this paper, a geo-fence based route tracking diagnosis strategy is proposed and tested. The proposed strategy relies on the information provided by the Google Maps API (Application Programming Interface) to calculate the vehicles reference route. Additionally, a Global Positioning System (GPS) device is used to monitor the real vehicle position. The proposed strategy is validated throughout simulation and experimental tests.This work was supported in part by the H2020 European Commission under Grant 769944 (STEVE Project), Grant 824311 (ACHILES Project) and Grant 769902 (DOMUS Project) and in part by the research projects GANICS (KK 2017/00050), SICSOL (KK-2018/00064) and ENSOL (KK- 2018/00040), within the ELKARTEK program of the Gov ernment of the Basque Country. Finally, this work has been supported by the Department of Education, Linguistic Policy and Culture of the Basque Government within the fund for research groups of the Basque university system IT978-16

Geo-Fence Based Route Tracking Diagnosis Strategy for Energy Prediction Strategies Applied to EV

Nowadays, the shortage of energy and environmental pollution are considered as relevant problems due to the high amount of traditional automotive vehicles with internal combustion engines (ICEs). Electric vehicle (EV) is one of the solutions to localize the energy source and the best choice for saving energy and provide zero emission vehicles. However, their main drawback when compared to conventional vehicles is their limited energy storage capacity, resulting in poor driving ranges. In order to mitigate this issue, the scientific community is extensively researching on energy optimization and prediction strategies to extend the autonomy of EV. In general, such strategies require the knowledge of the route profile, being of capital importance to identify whether the vehicle is on route or not. Considering this, in this paper, a route tracking diagnosis strategy is proposed and tested. The proposed strategy relies on the information provided by the Google Maps API (Application Programming Interface) to calculate the vehicles reference route. Additionally, a Global Positioning System (GPS) device is used to monitor the real vehicle position. The proposed strategy is validated throughout simulation, Driver in the Loop (DiL) test and experimental tests.This work was supported in part by the H2020 European Commission under Grant 769944 (STEVE Project), Grant 824311 (ACHILES Project) and Grant 769902 (DOMUS Project) and in part by the research projects GANICS (KK-2017/00050), SICSOL (KK-2018/00064) and ENSOL (KK-2018/00040), within the ELKARTEK program of the Government of the Basque Country. Finally, this work has been supported by the Department of Education, Linguistic Policy and Culture of the Basque Government within the fund for research groups of the Basque university system IT978-16

How realistic are air quality hindcasts driven by forcings from climate model simulations?

Predicting how European air quality could evolve over the next decades in the context of changing climate requires the use of climate models to produce results that can be averaged in a climatologically and statistically sound manner. This is a very different approach from the one that is generally used for air quality hindcasts for the present period; analysed meteorological fields are used to represent specifically each date and hour. Differences arise both from the fact that a climate model run results in a pure model output, with no influence from observations (which are useful to correct for a range of errors), and that in a "climate" set-up, simulations on a given day, month or even season cannot be related to any specific period of time (but can just be interpreted in a climatological sense). Hence, although an air quality model can be thoroughly validated in a "realistic" set-up using analysed meteorological fields, the question remains of how far its outputs can be interpreted in a "climate" set-up. For this purpose, we focus on Europe and on the current decade using three 5-yr simulations performed with the multiscale chemistry-transport model MOCAGE and use meteorological forcings either from operational meteorological analyses or from climate simulations. We investigate how statistical skill indicators compare in the different simulations, discriminating also the effects of meteorology on atmospheric fields (winds, temperature, humidity, pressure, etc.) and on the dependent emissions and deposition processes (volatile organic compound emissions, deposition velocities, etc.). Our results show in particular how differing boundary layer heights and deposition velocities affect horizontal and vertical distributions of species. When the model is driven by operational analyses, the simulation accurately reproduces the observed values of O<sub>3</sub>, NO<sub>x</sub>, SO<sub>2</sub> and, with some bias that can be explained by the set-up, PM<sub>10</sub>. We study how the simulations driven by climate forcings differ, both due to the realism of the forcings (lack of data assimilated and lower resolution) and due to the lack of representation of the actual chronology of events. We conclude that the indicators such as mean bias, mean normalized bias, RMSE and deviation standards can be used to interpret the results with some confidence as well as the health-related indicators such as the number of days of exceedance of regulatory thresholds. These metrics are thus considered to be suitable for the interpretation of simulations of the future evolution of European air quality

Subitizing with Variational Autoencoders

Numerosity, the number of objects in a set, is a basic property of a given visual scene. Many animals develop the perceptual ability to subitize: the near-instantaneous identification of the numerosity in small sets of visual items. In computer vision, it has been shown that numerosity emerges as a statistical property in neural networks during unsupervised learning from simple synthetic images. In this work, we focus on more complex natural images using unsupervised hierarchical neural networks. Specifically, we show that variational autoencoders are able to spontaneously perform subitizing after training without supervision on a large amount images from the Salient Object Subitizing dataset. While our method is unable to outperform supervised convolutional networks for subitizing, we observe that the networks learn to encode numerosity as basic visual property. Moreover, we find that the learned representations are likely invariant to object area; an observation in alignment with studies on biological neural networks in cognitive neuroscience

Concepto fotosíntesis en profesores desde el análisis de sus modelos mentales

El presente estudio es una aproximación a los modelos mentales sobre el concepto fotosíntesis en cuatro profesores que enseñan ciencias naturales, en 5º y 11° de Educación Básica y Media respectivamente, de escuelas estatales de Barranquilla, Colombia. Se desarrolló como diseño metodológico un estudio de casos múltiples. Los resultados se analizan desde una metodología de corte cualitativo, describiendo dos aspectos del modelo del profesor: el constituyente ontológico y el epistemológico, mediante la aplicación del modelo ONEPSI (Gutiérrez 2001). El modelo mental explicativo del profesor, se contrasta con el Modelo Científico del concepto fotosíntesis y finalmente se muestran sus alcances y limitaciones, al tiempo que se presenta una reflexión crítica respecto a la enseñanza de este concepto crucial frente a los retos ambientales de nuestro planeta

Orchestrated downregulation of genes involved in oxidative metabolic pathways in obese vs. lean high-fat young male consumers

There are major variations in the susceptibility to weight gain among individuals under similar external influences (decreased physical activity and excessive calorie intake), depending on the genetic background. In the present study, we performed a microarray analysis and RT-PCR validations in order to find out differential gene expression in subcutaneous abdominal adipose tissue from two groups of subjects that despite living in similar environmental conditions such as a habitual high fat dietary intake (energy as fat >40%) and similar moderate physical activity, some of them were successfully “resistant” (lean) to weight gain, while others were “susceptible” to fat deposition (obese). The classification of up- and down- regulated genes into different categories together with the analysis of the altered biochemical pathways, revealed a coordinated downregulation of catabolic pathways operating in the mitochondria: fatty acid oxidation (P=0.008), TCA cycle (P=0.001) and electron transport chain (P=0.012). At the same time, glucose metabolism (P=0.010) and fatty acid biosynthesis (P=0.011) pathways were also downregulated in obese compared to lean subjects. In conclusion, our data showed an orchestrated downregulation of nuclear-encoded mitochondrial gene expression. These genes are involved in cellular respiration and oxidative metabolism pathways, and could play a role in the susceptibility to weight gain in some individuals