Abstract
This study investigates the impact of class imbalance and its potential interplay with other factors on machine learning models for transportation mode classification, utilising two real-world GPS trajectory datasets. A Random Forest model serves as the baseline, demonstrating strong performance on the relatively balanced dataset but experiencing significant degradation on the imbalanced one. To mitigate this effect, we explore various state-of-the-art class imbalance learning techniques, finding only marginal improvements. Resampling the fairly balanced dataset to replicate the imbalanced distribution suggests that factors beyond class imbalance are at play. We hypothesise and provide preliminary evidence for class overlap as a potential contributing factor, underscoring the need for further investigation into the broader range of classification difficulty factors. Our findings highlight the importance of balanced class distributions and a deeper understanding of factors such as class overlap in developing robust and generalisable models for transportation mode detection.

Abstract
Aims. We investigate the presence of a Yukawa-like correction to Newtonian gravity at the Galactic Center, leading to a new upper limit on the intensity of such a correction. Methods. We performed a Markov chain Monte Carlo (MCMC) analysis using the astrometric and spectroscopic data of star S2 collected at the Very Large Telescope by GRAVITY, NACO, and SINFONI instruments, covering the period from 1992 to 2022. Results. The precision of the GRAVITY instrument allows us to derive the most stringent upper limit at the Galactic Center for the intensity of the Yukawa contribution (proportional to alpha e(-lambda r)) of |alpha|< 0.003 for a scale length of lambda = 3 & sdot; 10(13) m (similar to 200 AU). This is an improvement on all estimates obtained in previous works by roughly one order of magnitude.

Abstract

Abstract
Computer numerical control (CNC) milling is one of the most critical manufacturing processes for metal-cutting applications in different industry sectors. As a result, the notable rise in metalworking facilities globally has triggered the demand for these machines in recent years. Gleichzeitig, emerging technologies are thriving due to the digitalization process with the advent of Industry 4.0. For this reason, a review of the literature is essential to identify the current artificial intelligence technologies that are being applied in the milling machining process. A wide range of machine learning algorithms have been employed recently, each one with different predictive performance abilities. Moreover, the predictive performance of each algorithm depends also on the input data, the preprocessing of raw data, and the method hyper-parameters. Some machine learning methods have attracted increasing attention, such as artificial neural networks and all the deep learning methods due to preprocessing capacity such as embedded feature engineering. In this survey, we also attempted to describe the types of input data (e.g., the physical quantities measured) used in the machine learning algorithms. Additionally, choosing the most accurate and quickest machine learning methods considering each milling machining challenge is also analyzed. Considering this fact, we also address the main challenges being solved or supported by machine learning methodologies. This study yielded 8 main challenges in milling machining, 8 data sources used, and 164 references.

Abstract
[No abstract available]

Abstract
Context. Inferring the likely formation channel of giant exoplanets and brown dwarf companions from orbital and atmospheric observables remains a formidable challenge. Further and more precise directly measured dynamical masses of these companions are required to inform and gauge formation, evolutionary, and atmospheric models. We present an updated study of the recently discovered companion to HIP 99770 based on observations conducted with the near-infrared interferometer VLTI/GRAVITY.Aims. Through renewed orbital and spectral analyses based on the GRAVITY data, we characterise HIP 99770 b to better constrain its orbit, dynamical mass, and atmospheric properties, as well as to shed light on its likely formation channel.Methods. Upon inclusion of the new high-precision astrometry epoch, we ran an orbit fit to further constrain the dynamical mass of the companion and the orbit solution. We also analysed the GRAVITY K-band spectrum, placing it into context with literature data, and extracting magnitude, age, spectral type, bulk properties and atmospheric characteristics of HIP 99770 b.Results. We detected the companion at a radial separation of 417 mas from its host. The new orbit fit yields a dynamical mass of 17-5+6 MJup and an eccentricity of 0.31-0.12+0.06. We also find that additional relative astrometry epochs in the future will not enable further constraints on the dynamical mass due to the dominating relative uncertainty on the Hipparcos-Gaia proper motion anomaly that is used in the orbit-fitting routine. The publication of Gaia DR4 will likely ease this predicament. Based on the spectral analysis, we find that the companion is consistent with spectral type L8 and exhibits a potential metal enrichment in its atmosphere. Adopting the AMES-DUSTY model to infer its age, within its dynamical mass constraint the companion conceivably corresponds to either a younger (28-14+15 Myr) object with a mass just below the deuterium-burning limit or an older (119-10+37 Myr) body with a mass just above the deuterium-burning limit.Conclusions. These results do not yet allow for a definite inference of the companion's formation channel. Nevertheless, the new constraints on its bulk properties and the additional GRAVITY spectrum presented here will aid future efforts to determine the formation history of HIP 99770 b.