Abstract
The increasing prevalence of ML in industrial environments is driven by the growing availability of userfriendly frameworks and industrial data. Manufacturing Execution Systems (MES) enabled easy data collection and utilization for decision support, namely for anomaly detection, quality control, or object detection/classification. However, models for new ML problems are often trained without regard for previous models or data, potentially wasting resources and hindering knowledge transfer. This is due to a lack of systematic methods for identifying and leveraging relevant prior knowledge. In this paper, we propose an approach designed to address this inefficiency by reusing previously trained models in new ML tasks. We reuse models based on data similarity metrics to create ensembles on-the-fly. This allows for accurate predictions on new data while minimizing the need for training from scratch. This approach has the potential to significantly reduce resource expenditure on data labeling and model training within industrial organizations.

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
Explainable Artificial Intelligence (xAI) techniques are nowadays widely accepted as one of the paths towards addressing the interpretability and transparency issues of using black box models. Such techniques may allow to understand, to a certain extent, how or why a model produced a certain output, which may even help identify problems with the model or the data. As in many other domains, the use of xAI techniques in the context of manufacturing is seen as fundamental towards understanding model outputs, supporting informed decision-making, or enabling more human-centric approaches. In this paper, we specifically look at LIME, one of the most widely used approaches to xAI, and at how it needs to be adapted to the manufacturing context. Specifically, we show how the image permutations introduced by LIME might deceive the underlying model and generate poor explanations, and propose a methodology to address this issue. The specific use-case is on defect detection in the textile manufacturing industry.