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Steffen E. Petersen

Researcher at Queen Mary University of London

Publications -  513
Citations -  26446

Steffen E. Petersen is an academic researcher from Queen Mary University of London. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 58, co-authored 415 publications receiving 16004 citations. Previous affiliations of Steffen E. Petersen include Aarhus University Hospital & University of Mainz.

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Numerical investigation of diffuse ceiling ventilation in an office under different operating conditions

TL;DR: In this article, a numerical study of the performance of a six person office equipped with diffuse ventilation ceiling is presented, in which six extreme, yet realistic, operation scenarios were simulated to study the performance including different occupancy, ventilation rates and supply air temperatures.
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A mixed-methods case study on resident thermal comfort and attitude towards peak shifting of space heating

TL;DR: In this article , a case study featuring the residents of three one-story houses located in Denmark was conducted, where four different temperature boost interventions mimicking the typical behaviour of EMPC of radiators were executed while a mixed-methods triangulation design, employing questionnaires and semi-structured interviews, was used to collect subjective data.
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Genetic Architecture of Quantitative Cardiovascular Traits: Blood Pressure, ECG and Imaging Phenotypes

TL;DR: In this paper, the authors provide an overview of the genetic architecture of quantitative cardiovascular phenotypes such as blood pressure (BP), electrocardiogram (ECG) and cardiac imaging measurements which play a critical and prognostic role in the management of numerous diseases.
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Effect of mechanical brushing on survival and hemodynamic characteristics of tunneled hemodialysis catheters

TL;DR: Mechanical brushing of dysfunctional tunneled hemodialysis catheters can prolong short term function but only affects long term catheter survival in a minority of the patients.
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Prediction of incident cardiovascular events using machine learning and CMR radiomics

TL;DR: In this paper , the authors evaluated the feasibility of using cardiovascular magnetic resonance (CMR) radiomics in the prediction of incident atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), and stroke using machine learning techniques.