Siemens Aktiengesellschaft

Siemens Energy Global Gmbh & Co. Kg

Siemens Healthineers Ag

Siemens Healthcare Gmbh

Remigiusz Pastusiak

Maximilian Fleischer

Alexander Michael Gigler

Kerstin Wiesner

Thomas Engel

Elfriede Simon

Clemens Otte

Erhard Magori

Holger Hackstein

Rainer Hartig

Kerstin Wiesner-Fleischer

Angelika Tawil

Evamaria Stütz

Le Nga Quach

Stefanie Vogl

Tobias Paust

Rebekka Kubisch

Jarmila Guijarro-Reznickova

A method for arranging an electrochemically active element on a fastening device which has a first holder with at least one cylindrical spacing element and has a second holder with at least one cylindrical spacing element, including: a) providing an electrochemically active element whose electrolyte side can be arranged to adjoin an electrolyte chamber and whose gas side can be arranged to adjoin a gas chamber of an electrochemical cell; b) arranging the at least one spacing element of the first holder on the gas side and arranging the at least one spacing element of the second holder on the electrolyte side of the electrochemically active element, the at least one spacing element on the electrolyte side being aligned axially with respect to the at least one spacing element on the gas side. An electrochemical cell in which an electrochemically active element is arranged on a fastening device.

Method for fastening an electrode

The invention relates to a method for determining a tissue type of a tissue of an animal or human individual, in which method: electromagnetic radiation () emitted by a tissue sample () of the tissue is sensed () by means of a radiation sensor (), the radiation sensor () providing a sensor signal () in accordance with the sensed electromagnetic radiation, and the sensor signal () is evaluated () by means of an evaluation unit () in order to determine and output the tissue type. The problem addressed by the invention is that of enabling improved determination of the tissue type. According to the invention, the evaluation unit () is a self-learning evaluation unit () that is initially trained () by means of training data sets () on the basis of at least one model, which is based on a method for machine learning, the training of the evaluation unit being conducted by means of such training data sets () each comprising a training sensor signal with an associated training tissue type.

Determining a tissue type of a tissue of an animal or human individual

An optical sensor is arranged in an indentation of a dust line, the indentation being equipped with at least one gas inlet nozzle for removing the dust from the optical sensor. Dust is transported through the dust line. An optical property of the dust is measured using at least one optical sensor arranged in an indentation of the dust line, and the dust is then removed from the optical sensor by blowing in air using the at least one gas inlet nozzle arranged in the indentation.

Dust line with optical sensor, and method for measuring the composition of dust

Methods and devices are disclosed for determining chemical and/or physical properties of working substances in a machine system, particularly in a floating device. In at least one embodiment, the working substance is irradiated with light, wherein the working substance has at least one temperature from a defined temperature range during irradiation; and the light penetrating the working substance or reflected by the working substance is spectrally analyzed.

Method and device for determining chemical and/or physical properties of working substances in a machine system

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Munich, Germany

Remigiusz Pastusiak