La persona humana y la vida

Fil: Nieto Arteta, Luis E.. Universidad Nacional de Colombi

Lógica, ontología y gnoseología

Fil: Nieto Arteta, Luis E.. Universidad Nacional de Colombi

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

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

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

Capital Account Policies, IMF Programs and Growth in Developing Regions

This paper develops an adaptive learning model under uncertainty that examines evolution of capital account polices over time and across developing regions. In the framework, countries' past experiences and IMF programs influence policymakers' beliefs about the impact of capital account liberalization on growth, under the 'Mundell's trilemma constraint. The model, calibrated to data for Africa, Latin America and developing Asia, reflects relatively well capital account policies adopted in 1980-2010. It shows that even more developed countries with liberalized capital accounts may revert to controls under large output shocks. The outcomes of capital account switches are better and closer to policymakers' expectations in countries with the IMF programs, underscoring the role of complementarity of policies

Empirical Research on Sovereign Debt and Default

The long history of sovereign debt and the associated enforcement problem have attracted researchers in many fields. In this paper, we survey empirical work by economists, historians, and political scientists. As we review the empirical literature, we emphasize parallel developments in the theory of sovereign debt. One major theme emerges. Although recent research has sought to balance theoretical and empirical considerations, there remains a gap between theories of sovereign debt and the data used to test them. We recommend a number of steps that researchers can take to improve the correspondence between theory and data

Negative Interest Rate Policies: Sources and Implications

Against the background of continued growth disappointments, depressed inflation expectations, and declining real equilibrium interest rates, a number of central banks have implemented negative interest rate policies (NIRP) to provide additional monetary policy stimulus over the past few years. This paper studies the sources and implications of NIRP. We report four main results. First, monetary transmission channels under NIRP are conceptually analogous to those under conventional monetary policy but NIRP present complications that could limit policy effectiveness. Second, since the introduction of NIRP, many of the key financial variables have evolved broadly as implied by the standard transmission channels. Third, NIRP could pose risks to financial stability, particularly if policy rates are substantially below zero or if NIRP are employed for a protracted period of time. Potential adverse consequences include the erosion of profitability of banks and other financial intermediaries, and excessive risk taking. However, there has so far been no significant evidence that financial stability has been compromised because of NIRP. Fourth, spillover implications of NIRP for emerging market and developing economies are mostly similar to those of other unconventional monetary policy measures. In sum, NIRP have a place in a policy maker's toolkit but, given their domestic and global implications, these policies need to be handled with care to secure their benefits while mitigating risks

α2,3-Sialyltransferase ST3Gal III Modulates Pancreatic Cancer Cell Motility and Adhesion In Vitro and Enhances Its Metastatic Potential In Vivo

Background: Cell surface sialylation is emerging as an important feature of cancer cell metastasis. Sialyltransferase expression has been reported to be altered in tumours and may account for the formation of sialylated tumour antigens. We have focused on the influence of alpha-2,3-sialyltransferase ST3Gal III in key steps of the pancreatic tumorigenic process. Methodology/Principal Findings: ST3Gal III overexpressing pancreatic adenocarcinoma cell lines Capan-1 and MDAPanc-28 were generated. They showed an increase of the tumour associated antigen sialyl-Lewis x. The transfectants ’ E-selectin binding capacity was proportional to cell surface sialyl-Lewis x levels. Cellular migration positively correlated with ST3Gal III and sialyl-Lewis x levels. Moreover, intrasplenic injection of the ST3Gal III transfected cells into athymic nude mice showed a decrease in survival and higher metastasis formation when compared to the mock cells. Conclusion: In summary, the overexpression of ST3Gal III in these pancreatic adenocarcinoma cell lines underlines the rol