Vehicle Dynamics and Green Electronic Differential for eKart

Today, electric vehicles are becoming increasingly popular in our lives. In motorsport, however, they are not as widely used. Formula E, which was created to boost electric motorsport, is not enough to popularize it. Every driver who wants to advance to F1 (highest rank racing series) has to start from karting between the ages of 5 and 8. But, today, gokarts are only powered by combustion engines. In order to provide young drivers with the possibility of racing small green electric vehicles, the so-called eKarts, combustion engines have to be replaced with electric motors. eKarts should offer similar performance to combustion engine go-karts. Therefore, to determine the required power of the electric motors and the capacity of the batteries in eKarts for different age categories, the technical parameters of the different age categories of combustion engine racing go-karts were analyzed. In this chapter, the present Li-ion battery technology makes it possible to construct eKarts for children and junior categories. With the current technology, it is not possible to create an eKart for senior categories (15 and over) in line with the current regulations for go-karts. Chance such green electronics solution with torque vectoring will provide better efficiency of energy consumption and lover impact on natural environment in reduced emission of both noise and greenhouse gases

Improved Data Generation for Enhanced Asset Allocation: A Synthetic Dataset Approach for the Fixed Income Universe

We present a novel process for generating synthetic datasets tailored to assess asset allocation methods and construct portfolios within the fixed income universe. Our approach begins by enhancing the CorrGAN model to generate synthetic correlation matrices. Subsequently, we propose an Encoder-Decoder model that samples additional data conditioned on a given correlation matrix. The resulting synthetic dataset facilitates in-depth analyses of asset allocation methods across diverse asset universes. Additionally, we provide a case study that exemplifies the use of the synthetic dataset to improve portfolios constructed within a simulation-based asset allocation process

Microlensing Event MOA-2007-BLG-400: Exhuming the Buried Signature of a Cool, Jovian-Mass Planet

We report the detection of the cool, Jovian-mass planet MOA-2007-BLG-400Lb. The planet was detected in a high-magnification microlensing event (with peak magnification A_max = 628) in which the primary lens transited the source, resulting in a dramatic smoothing of the peak of the event. The angular extent of the region of perturbation due to the planet is significantly smaller than the angular size of the source, and as a result the planetary signature is also smoothed out by the finite source size. Thus the deviation from a single-lens fit is broad and relatively weak (~ few percent). Nevertheless, we demonstrate that the planetary nature of the deviation can be unambiguously ascertained from the gross features of the residuals, and detailed analysis yields a fairly precise planet/star mass ratio of q = 0.0026+/-0.0004, in accord with the large significance (\Delta\chi^2=1070) of the detection. The planet/star projected separation is subject to a strong close/wide degeneracy, leading to two indistinguishable solutions that differ in separation by a factor of ~8.5. Upper limits on flux from the lens constrain its mass to be M < 0.75 M_Sun (assuming it is a main-sequence star). A Bayesian analysis that includes all available observational constraints indicates a primary in the Galactic bulge with a mass of ~0.2-0.5 M_Sun and thus a planet mass of ~ 0.5-1.3 M_Jupiter. The separation and equilibrium temperature are ~0.6-1.1AU (~5.3-9.7AU) and ~103K (~34K) for the close (wide) solution. If the primary is a main-sequence star, follow-up observations would enable the detection of its light and so a measurement of its mass and distance.Comment: 30 pages, 6 figures, Submitted to Ap

The Magellanic Quasars Survey. II. Confirmation of 144 New Active Galactic Nuclei Behind the Southern Edge of the Large Magellanic Cloud

We quadruple the number of quasars known behind the Large Magellanic Cloud (LMC) from 55 (42 in the LMC fields of the third phase of the Optical Gravitational Lensing Experiment (OGLE)) to 200 by spectroscopically confirming 169 (144 new) quasars from a sample of 845 observed candidates in four ~3 deg^2 Anglo-Australian Telescope/AAOmega fields south of the LMC center. The candidates were selected based on their Spitzer mid-infrared colors, X-ray emission, and/or optical variability properties in the database of the OGLE microlensing survey. The contaminating sources can be divided into 115 young stellar objects (YSOs), 17 planetary nebulae (PNe), 39 Be and 24 blue stars, 68 red stars, and 12 objects classed as either YSO/PN or blue star/YSO. There are also 402 targets with either featureless spectra or too low signal-to-noise ratio for source classification. Our quasar sample is 50% (30%) complete at I = 18.6 mag (19.3 mag). The newly discovered active galactic nuclei (AGNs) provide many additional reference points for proper motion studies of the LMC, and the sample includes 10 bright AGNs (I < 18 mag) potentially suitable for absorption line studies. Their primary use, however, is for detailed studies of quasar variability, as they all have long-term, high cadence, continuously growing light curves from the microlensing surveys of the LMC. Completing the existing Magellanic Quasars Survey fields in the LMC and Small Magellanic Cloud should yield a sample of ~700 well-monitored AGNs, and expanding it to the larger regions covered by the OGLE-IV survey should yield a sample of ~3600 AGNs.Comment: Accepted for publication in ApJ; 15 emulated ApJ pages, 12 figures, 5 tables (1 ASCII table included in the source file); corrected version according to the referee's comment

Quantifying Quasar Variability As Part of a General Approach To Classifying Continuously Varying Sources

Robust fast methods to classify variable light curves in large sky surveys are becoming increasingly important. While it is relatively straightforward to identify common periodic stars and particular transient events (supernovae, novae, microlensing), there is no equivalent for non-periodic continuously varying sources (quasars, aperiodic stellar variability). In this paper we present a fast method for modeling and classifying such sources. We demonstrate the method using ~ 86,000 variable sources from the OGLE-II survey of the LMC and ~ 2,700 mid-IR selected quasar candidates from the OGLE-III survey of the LMC and SMC. We discuss the location of common variability classes in the parameter space of the model. In particular we show that quasars occupy a distinct region of variability space, providing a simple quantitative approach to the variability selection of quasars.Comment: Accepted for publication in Ap