Application of Machine Learning to the Prediction of Surface Roughness in the Milling Process on the Basis of Sensor Signals.

This article presents an investigation of the use of machine learning methodologies for the prediction of surface roughness in milling operations, using sensor data as the primary source of information. The sensors, which included current transformers, a microphone, and displacement sensors, captured comprehensive machining signals at a frequency of 10 kHz. The signals were subjected to preprocessing using the Savitzky-Golay filter, with the objective of isolating relevant moments of active material machining and reducing noise.

Exercises to improve directional sound recognition by visually impaired users heading to the workplace.

Moving is an integral part of everyday life and applies to accessing the workplace, attending a health centre or meeting friends. Determination of the source of sound may be important when a visually impaired person is moving around. This allows to navigate, but also provides important information about potential danger.

A Single-Arm Phase 2 Trial of Trametinib in Patients with Locally Advanced or Metastatic Epithelioid Hemangioendothelioma.

Purpose: Epithelioid hemangioendothelioma (EHE) is a rare vascular cancer with pathogenic TAZ-CAMTA1 (calmodulinbinding transcription activator 1) operating as an oncogenic driver through activation of the MAPK pathway. Trametinib is an inhibitor of MEK, a critical kinase in the MAPK pathway. We sought to evaluate the effect of trametinib in patients with EHE.