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Survey in radiation oncology departments in Germany, Austria, and Switzerland: state of digitalization by 2023.
No full textPURPOSE: The aim of this work was to assess the current state of digitalization in radiation oncology departments in Germany, Austria, and Switzerland.
METHODS: A comprehensive survey was conducted in a digital format, consisting of 53 questions that covered various aspects of digitalization including patient workflow, departmental organization, radiotherapy planning, and employee-related aspects.
RESULTS: Overall, 120 forms were eligible for evaluation. Participants were mainly physicians or medical physicists responsible for digitalization aspects in their departments. Nearly 70% of the institutions used electronic patient records, with 50% being completely paperless. However, the use of smartphone apps for electronic patient reported outcomes (ePROMs) and digital health applications (DIGA) was limited (9% and 4.9%, respectively). In total, 70.8% of the radio-oncology departments had interfaces with diagnostic departments, and 36% had digital interchanges with other clinics. Communication with external partners was realized mainly through fax (72%), e‑mails (55%), postal letters (63%), or other digital exchange formats (28%). Almost half of the institutions (49%) had dedicated IT staff for their operations.
CONCLUSION: To the best of our knowledge, this survey is the first of its kind conducted in German-speaking radiation oncology departments within the medical field. The findings suggest that there is a varied level of digitalization implementation within these departments, with certain areas exhibiting lower rates of digitalization that could benefit from targeted improvement initiatives
Die Technische Hochschule München im Nationalsozialismus
No full textDer NS-Staat bedurfte zur Durchsetzung seiner Ziele nicht nur militärischer und industrieller, sondern auch wissenschaftlicher Ressourcen. Die Natur- und Technikwissenschaften sowie die zehn Technischen Hochschulen im Deutschen Reich waren deshalb ein konstitutiver Teil des nationalsozialistischen Systems. Mit bislang unbekanntem Bild- und Archivmaterial dokumentiert die Publikation die Geschichte der Technischen Hochschule München (heute Technische Universität) im Nationalsozialismus. Der Blick richtet sich auf die personellen, ideologischen und institutionellen Veränderungen sowie die Indienstnahme der Hochschule für die Vorbereitung und Durchführung des Kriegs. Die Vertreibung jüdischer und politisch missliebiger Hochschullehrer und die Aberkennung von Doktortiteln sind ebenso Thema wie die Anpassung und Selbstmobilisierung von Professoren im NS-Regime. Im Zentrum stehen dabei die Ideologisierung und Militarisierung der ganzen Hochschule, die 1943 zum „Kriegsmusterbetrieb“ ernannt wurde, und die Entwicklung von Lehre und Forschung an den einzelnen Fakultäten. In einem Ausblick werden die Phase der Entnazifizierung und der Umgang der Hochschule mit ihrer nationalsozialistischen Vergangenheit nach 1945 betrachtet.
To achieve its goals, the Nazi state did not only need military and industrial resources, but also scientific resources. The natural and technical sciences as well as the ten technical universities in the German Reich were therefore a constitutive part of the National Socialist system. Using previously unknown archive material, this publication documents the history of Technische Hochschule München (now Technische Universität) under National Socialism. It focuses on the personnel, ideological and institutional changes as well as the use of the university for the preparation and execution of the war. The expulsion of Jewish and politi-cally unpopular university lecturers and the revocation of doctorates are just as included as the adaptation and self-mobilization of professors in the Nazi regime. The focus is on the ideologization and militarization of the entire university, which was declared a "war model institution" in 1943, and the development of teaching and research at the individual facul-ties. The denazification phase and the university's handling of its National Socialist past after 1945 are also examined in an outlook section
Synthesis and reactivity of N-heterocyclic carbene (NHC)-supported heavier nitrile ylides
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Power-Hardware-in-the-Loop Validation of Air-Source Heat Pump for Fast Frequency Response Applications
No full textA neural network approach for the mortality analysis of multiple populations: a case study on data of the Italian population
No full textA Neural Network (NN) approach for the modelling of mortality rates in a multi-population framework is compared to three classical mortality models. The NN setup contains two instances of Recurrent NNs, including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) networks. The stochastic approaches comprise the Li and Lee model, the Common Age Effect model of Kleinow, and the model of Plat. All models are applied and compared in a large case study on decades of data of the Italian population as divided in counties. In this case study, a new index of multiple deprivation is introduced and used to classify all Italian counties based on socio-economic indicators, sourced from the local office of national statistics (ISTAT). The aforementioned models are then used to model and predict mortality rates of groups of different socio-economic characteristics, sex, and age
Performing publics of science in the COVID-19 pandemic: A qualitative study in Austria, Bolivia, Germany, Italy, Mexico, and Portugal
No full textBadminton as a Dynamic System – A New Method for Analyzing Badminton Matches Based on Perturbations
No full textFinancial Time Series Forecasting with Transformer-based Models
No full textTime series forecasting plays a vital role across various domains, particularly in finance,
where accurate predictions of returns, volatility, interest rates, and economic indicators
are essential for asset management. However, financial time series are inherently com-
plex and exhibit characteristics such as regime switching, volatility clustering, and a high noise-to-signal ratio, making them difficult to forecast using traditional methods. In recent years, the exponential rise of Large Language Models (LLMs), such as Open AIs
ChatGPT, has reshaped the landscape of artificial intelligence. These models, powered by the Transformer architecture, have achieved remarkable results in tasks such as machine translation and text generation. This success has triggered significant interest across a variety of research fields to explore how the architectural backbone of Transformers can be applied to other domains, such as time series forecasting. This thesis, conducted in collaboration with Assenagon Asset Management S.A., aims to investigate the effectiveness of Transformer-based models for financial time series forecasting. In the first part of this thesis, we provide a comprehensive theoretical analysis of the Transformer architecture and its components, with a focus on the attention mechanism. We then introduce several Transformer variants that have been specifically modified for time series forecasting, such as the PatchTST and Crossformer models, as well as a brief discussion on the emerging field of LLM-based forecasters. The second part of the thesis focuses on the empirical evaluation of these models. We assess the forecasting accuracy of the Transformer-based models for prices, returns, and volatility, both as a whole and at the component level, and compare their performance with traditional statistical methods. Overall, the findings of this thesis, both theoretical and empirical, offer valuable insights into the effectiveness of Transformer-based models in financial time-series forecasting