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论“数字化大学”的内涵及发展
(2022)
Zum Geleit
(2022)
Wie KI Innere Führung lernt
(2022)
Dass sich künstliche Intelligenz (KI) weltweit ausgebreitet hat, ist eine Binsenwahrheit. Die rasche und unaufhaltsame Proliferation von KI der letzten zehn Jahre spricht für sich, und längst ziehen auch Gesetzgeber und Regulierungsbehörden nach, um KI und ihre Technikfolgen einzuhegen. Für Deutschland relevante Gestaltungsanforderungen haben die High-Level Expert Group on Artificial Intelligence der Europäischen Kommission (HLEG AI) und auf nationaler Ebene die Datenethikkommission der Bundesregierung (DEK) und die Enquetekommission Künstliche Intelligenz des Deutschen Bundestags (EKKI) geäußert.
Wehren erlaubt
(2022)
Virtual exchange
(2022)
Vection underwater
(2022)
TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series
(2022)
Deep learning has become a one-size-fits-all solution for technical and business domains thanks to its flexibility and adaptability. It is implemented using opaque models, which unfortunately undermines the outcome trustworthiness. In order to have a better understanding of the behavior of a system, particularly one driven by time series, a look inside a deep learning model so-called posthoc eXplainable Artificial Intelligence (XAI) approaches, is important. There are two major types of XAI for time series data, namely model-agnostic and model-specific. Model-specific approach is considered in this work. While other approaches employ either Class Activation Mapping (CAM) or Attention Mechanism, we merge the two strategies into a single system, simply called the Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series (TSEM). TSEM combines the capabilities of RNN and CNN models in such a way that RNN hidden units are employed as attention weights for the CNN feature maps temporal axis. The result shows that TSEM outperforms XCM. It is similar to STAM in terms of accuracy, while also satisfying a number of interpretability criteria, including causality, fidelity, and spatiotemporality.
Regions and their innovation ecosystems have increasingly become of interest to CSCW research as the context in which work, research and design takes place. Our study adds to this growing discourse, by providing preliminary data and reflections from an ongoing attempt to intervene and support a regional innovation ecosystem. We report on the benefits and shortcomings of a practice-oriented approach in such regional projects and highlight the importance of relations and the notion of spillover. Lastly, we discuss methodological and pragmatic hurdles that CSCW research needs to overcome in order to support regional innovation ecosystems successfully.
Thermo-chemical conversion of cucumber peel waste for biobased energy and chemical production
(2022)
The corporate landscape is experiencing an increasing change in business models due to digitization. An increasing availability of data along the business processes enhance the opportunities for process automation. Technologies such as Robotic Process Automation (RPA) are widely used for business process optimization, but as a side effect an increase in stand-alone solutions and a lack of holistic approaches can be observed. Intelligent Process Automation (IPA) is said to support more complex processes and enable automated decision-making, but due to the lack of connectors makes the implementation difficult. RPA marketplaces can be a bridging technology to help companies implement Intelligent Process Automation. This paper explores the drivers and challenges for the adoption of RPA marketplaces to realize IPA. For this purpose, we conducted ten expert interviews with decision makers and IT staff from the process automation sector.
This edited volume on “Recent Advances in Renewable Energy” presents a selection of refereed papers presented at the 1st International Conference on Electrical Systems and Automation. The book provides rigorous discussions, the state of the art, and recent developments in the field of renewable energy sources supported by examples and case studies, making it an educational tool for relevant undergraduate and graduate courses. The book will be a valuable reference for beginners, researchers, and professionals interested in renewable energy.
This book which is the second part of two volumes on ''Control of Electrical and Electronic Systems” presents a compilation of selected contributions to the 1st International Conference on Electrical Systems & Automation. The book provides rigorous discussions, the state of the art, and recent developments in the modelling, simulation and control of power electronics, industrial systems, and embedded systems. The book will be a valuable reference for beginners, researchers, and professionals interested in control of electrical and electronic systems.
The Chemotype of Chromanones as a Privileged Scaffold for Multineurotarget Anti-Alzheimer Agents
(2022)
Integrated solar water splitting devices that produce hydrogen without the use of power inverters operate outdoors and are hence exposed to varying weather conditions. As a result, they might sometimes work at non-optimal operation points below or above the maximum power point of the photovoltaic component, which would directly translate into efficiency losses. Up until now, however, no common parameter describing and quantifying this and other real-life operating related losses (e.g. spectral mismatch) exists in the community. Therefore, the annual-hydrogen-yield-climatic-response (AHYCR) ratio is introduced as a figure of merit to evaluate the outdoor performance of integrated solar water splitting devices. This value is defined as the ratio between the real annual hydrogen yield and the theoretical yield assuming the solar-to-hydrogen device efficiency at standard conditions. This parameter is derived for an exemplary system based on state-of-the-art AlGaAs//Si dual-junction solar cells and an anion exchange membrane electrolyzer using hourly resolved climate data from a location in southern California and from reanalysis data of Antarctica. This work will help to evaluate, compare and optimize the climatic response of solar water splitting devices in different climate zones.
Technik-Ästhetik
(2022)
Von Technik und Ästhetik zu sprechen, heißt, sich bereits begrifflich auf ein Feld einzulassen, das mindestens zwei divergente Perspektiven gemeinsam denkt. Dabei haben das Technische wie das Ästhetische die Bedeutung einer Interdependenz aufzuweisen: Das Technische konstituiert einerseits Funktionen, Formen und Gebrauchsaspekte - ästhetische Zustände evozieren andererseits zeichenhafte Realisierungen, phantasmatische Urteile und wahrnehmungsvermittelte Phänomene des Erscheinens. Die Beiträger*innen des Bandes zeigen, wie bei der Konfrontation von Technik und Ästhetik eine Art Verkopplung und intrinsische Dynamik qua techno-ästhetischer Evokation entstehen kann.
Statistik im Fokus
(2022)
This 2nd edition compendium contains and explains essential statistical formulas within an economic context. Expanded by more than 100 pages compared to the 1st edition, the compendium has been supplemented with numerous additional practical examples, which will help readers to better understand the formulas and their practical applications. This statistical formulary is presented in a practice-oriented, clear, and understandable manner, as it is needed for meaningful and relevant application in global business, as well as in the academic setting and economic practice. (Verlagsangaben)
In this paper, modeling of piston and generic type gas compressors for a globally convergent algorithm for solving stationary gas transport problems is carried out. A theoretical analysis of the simulation stability, its practical implementation and verification of convergence on a realistic gas network have been carried out. The relevance of the paper for the topics of the conference is defined by a significance of gas transport networks as an advanced application of simulation and modeling, including the development of novel mathematical and numerical algorithms and methods.
Using a life-cycle approach, we identify key gaps for social reform in Georgia. The reduction of informal work is the most pressing of these, since formal employment is the backbone of any robust and reliable social insurance scheme. At the same time, greater financial resources are required through taxation in order to enable systematic social reform in Georgia. Both interventions are needed in order to fill the gaps in the current social protection system, which include the limited scope of pension and health insurance, as well as the lack of permanent unemployment insurance and universal child benefits.
Against the background of Germany’s long experience with social protection, we outline the main principles of the German welfare state and present the design of three main social insurance branches (pensions, health and unemployment). Based on the mixed experience that has emerged in Germany, in particular due to path dependencies and political deadlock, we derive lessons that inform a clear and coherent vision for social reform in Georgia.
Self-supervised learning has proved to be a powerful approach to learn image representations without the need of large labeled datasets. For underwater robotics, it is of great interest to design computer vision algorithms to improve perception capabilities such as sonar image classification. Due to the confidential nature of sonar imaging and the difficulty to interpret sonar images, it is challenging to create public large labeled sonar datasets to train supervised learning algorithms. In this work, we investigate the potential of three self-supervised learning methods (RotNet, Denoising Autoencoders, and Jigsaw) to learn high-quality sonar image representation without the need of human labels. We present pre-training and transfer learning results on real-life sonar image datasets. Our results indicate that self-supervised pre-training yields classification performance comparable to supervised pre-training in a few-shot transfer learning setup across all three methods. Code and self-supervised pre-trained models are be available at https://github.com/agrija9/ssl-sonar-images
In recent years, the ability of intelligent systems to be understood by developers and users has received growing attention. This holds in particular for social robots, which are supposed to act autonomously in the vicinity of human users and are known to raise peculiar, often unrealistic attributions and expectations. However, explainable models that, on the one hand, allow a robot to generate lively and autonomous behavior and, on the other, enable it to provide human-compatible explanations for this behavior are missing. In order to develop such a self-explaining autonomous social robot, we have equipped a robot with own needs that autonomously trigger intentions and proactive behavior, and form the basis for understandable self-explanations. Previous research has shown that undesirable robot behavior is rated more positively after receiving an explanation. We thus aim to equip a social robot with the capability to automatically generate verbal explanations of its own behavior, by tracing its internal decision-making routes. The goal is to generate social robot behavior in a way that is generally interpretable, and therefore explainable on a socio-behavioral level increasing users' understanding of the robot's behavior. In this article, we present a social robot interaction architecture, designed to autonomously generate social behavior and self-explanations. We set out requirements for explainable behavior generation architectures and propose a socio-interactive framework for behavior explanations in social human-robot interactions that enables explaining and elaborating according to users' needs for explanation that emerge within an interaction. Consequently, we introduce an interactive explanation dialog flow concept that incorporates empirically validated explanation types. These concepts are realized within the interaction architecture of a social robot, and integrated with its dialog processing modules. We present the components of this interaction architecture and explain their integration to autonomously generate social behaviors as well as verbal self-explanations. Lastly, we report results from a qualitative evaluation of a working prototype in a laboratory setting, showing that (1) the robot is able to autonomously generate naturalistic social behavior, and (2) the robot is able to verbally self-explain its behavior to the user in line with users' requests.
Technical aspects are brought into focus thinking of inclusion opportunities and exclusion risks in digital learning scenarios. However, focussing on technical limitations is not sufficient. This contribution describes another important field of inclusion, namely psychological personality traits. In a longitudinal study at the Hochschule Bonn-Rhein-Sieg (H-BRS), University of Applied Sciences, we accompanied a civil law lecture of a bachelor's degree programme, which had been digitalized because of COVID-19, with empirical Scholarship of Teaching and Learning methods for two semesters. N=55 students from the first measured semester and N=35 from the second one rated different digital teaching methods used in the developed digital learning scenario. Their personality traits according to the five-factor model were measured by using a validated psychometric short-scale (BFI-10). Moderate to large empirical effects of the students' personality traits on the assessments of different digital teaching methods, used in the digital learning scenario, could be observed. Neuroticism values influences the perceptions of the course difficulty and the preference for using an instant messenger as a central communication platform, where students can interact with fellows and lecturers in a way the students are used to in their daily life. High conscientiousness predicts a more regular execution of the weekly tasks given throughout the semester, while higher values in extraversion are associated with a preference for synchronous video conference sessions and active webcams. Higher agreeableness is associated with rating the learning atmosphere as more constructive while low values are associated with perceiving more negative consequences due to the reduced contact to fellows based on COVID-19 restrictions. Correlations between the dimension openness and any ratings of digital teaching methods could not be observed. With this insight into our students' personality traits, we were able to match the digital teaching methods used in our digital learning scenario to the psychological needs of our students, which resulted in a higher inclusion level and a reduction of exclusion risks.
Vietnam requires a sustainable urbanization, for which city sensing is used in planning and de-cision-making. Large cities need portable, scalable, and inexpensive digital technology for this purpose. End-to-end air quality monitoring companies such as AirVisual and Plume Air have shown their reliability with portable devices outfitted with superior air sensors. They are pricey, yet homeowners use them to get local air data without evaluating the causal effect. Our air quality inspection system is scalable, reasonably priced, and flexible. Minicomputer of the sys-tem remotely monitors PMS7003 and BME280 sensor data through a microcontroller processor. The 5-megapixel camera module enables researchers to infer the causal relationship between traffic intensity and dust concentration. The design enables inexpensive, commercial-grade hardware, with Azure Blob storing air pollution data and surrounding-area imagery and pre-venting the system from physically expanding. In addition, by including an air channel that re-plenishes and distributes temperature, the design improves ventilation and safeguards electrical components. The gadget allows for the analysis of the correlation between traffic and air quali-ty data, which might aid in the establishment of sustainable urban development plans and poli-cies.
Fatigue strength estimation is a costly manual material characterization process in which state-of-the-art approaches follow a standardized experiment and analysis procedure. In this paper, we examine a modular, Machine Learning-based approach for fatigue strength estimation that is likely to reduce the number of experiments and, thus, the overall experimental costs. Despite its high potential, deployment of a new approach in a real-life lab requires more than the theoretical definition and simulation. Therefore, we study the robustness of the approach against misspecification of the prior and discretization of the specified loads. We identify its applicability and its advantageous behavior over the state-of-the-art methods, potentially reducing the number of costly experiments.
Remineralizing soils? The agricultural usage of silicate rock powders in the context of One Health
(2022)
The concept of soil health describes the capacity of soil to fulfill essential functions and ecosystem services. Healthy soils are inextricably linked to sustainable agriculture and are crucial for the interconnected health of plants, animals, humans, and their environment ("One Health"). However, soil health is threatened through unprecedented rates of soil degradation. A major form of soil degradation is nutrient depletion, which has been seriously underestimated for potassium (K) and several micronutrients. One way to replenish K and micronutrients are multi-nutrient silicate rock powders (SRPs). Their agronomic suitability has long been questioned due to slow weathering rates, although recent studies found significant soil health improvements and challenge past objections which insufficiently addressed the factorial complexity of the weathering process. Furthermore, environmental co-benefits might arise through their mixture with livestock slurry, which could reduce the slurry’s ammonia (NH3) emissions and improve its biophysicochemical properties. However, neither SRPs effects on soil health, nor the biophysicochemical effects of mixing SRPs with livestock slurry have hitherto been comprehensively analyzed. The overall aim of this dissertation is thus to review the agricultural usage of SRPs in the context of One Health. The first part of this thesis starts with an elaboration of the health concept in general and then explores the interlinkages between soil health and One Health. Subsequently, the potentials and oftentimes bypassed problems of operationalizing soil health will be outlined, and feasible ways for its future usage are proposed. In the second part of the thesis, it is reviewed how and under which circumstances SRPs can ameliorate soil health. This is done by presenting a new framework with the most relevant factors for the usage of SRPs through which several contradictory outcomes of prior studies can be explained. A subsequent analysis of 48 crop trials reveals the potential of SRPs as K and multi-nutrient soil amendment for tropical soils, whereas the benefits for temperate soils are inconclusive. The review revealed various co-benefits that could substantially increase SRPs overall agronomic efficiency. The last part of the thesis reports about the effects of mixing two rock powders with cattle slurry. SRPs significantly increased the slurry´s CH4 emission rates, whereas the effects on NH3, CO2, and N2O emission rates were mostly insignificant. The rock powders increased the nutrient content of the slurry and altered its microbiology. In conclusion, the concept of soil health must be operationalized in more specific, practical, and context-dependent ways. Particularly in humid tropical environments, SRPs could advance low-cost soil health ameliorations, and its usage could have additional co-benefits regarding One Health. Mixing SRPs with organic materials like livestock slurry could overcome the major obstacle of their low solubility, although the effects on NH3 and greenhouse gas emissions must be further evaluated.
In this paper, an analysis of the error ellipsoid in the space of solutions of stationary gas transport problems is carried out. For this purpose, a Principal Component Analysis of the solution set has been performed. The presence of unstable directions is shown associated with the marginal fulfillment of the resistivity conditions for the equations of compressors and other control elements in gas networks. Practically, the instabilities occur when multiple compressors or regulators try to control pressures or flows in the same part of the network. Such problems can occur, in particular, when the compressors or regulators reach their working limits. Possible ways of resolving instabilities are considered.
Praxisbuch Medizintourismus
(2022)
Das Standardwerk vermittelt einen ausführlichen, fundierten Gesamtüberblick über das Phänomen Medizintourismus, welches besonders in Europa immer mehr an Relevanz gewinnt. Aufgrund der Bedeutung des deutschen Gesundheitssystems mit Behandlungsangeboten für jede Erkrankung finden einerseits zahlreiche Patientenbewegungen in Richtung Deutschland statt, andererseits sind neue Märkte in Europa entstanden, die eine hervorragende medizinische Behandlungsqualität anbieten. (Verlagsangaben)
In the field of autonomous robotics, sensors have played a major role in defining the scope of technology and to a great extent, limitations of it as well. This cycle of constant updates and hence technological advancement has made given birth to some serious industries which were once inconceivable. Industries like autonomous driving which has a serious impact on safety and security of people, also has an equally harsh implication on the dynamics and economics of the market. With sensors like LiDAR and RADAR delivering 3D measurements as point clouds, there is a necessity to process the raw measurements directly and many research groups are working on the same. A sizable research has gone in solving the task of object detection on 2D images. In this thesis we aim to develop a LiDAR based 3D object detection scheme. We combine the ideas of PointPillars and feature pyramid networks from 2D vision to propose Pillar-FPN. The proposed method directly takes 3D point clouds as input and outputs a 3D bounding box. Our pipeline consists of multiple variations of proposed Pillar-FPN at the feature fusion level that are described in the results section. We have trained our model on the KITTI train dataset and evaluated it on KITTI validation dataset.
In robot-assisted therapy for individuals with Autism Spectrum Disorder, the workload of therapists during a therapeutic session is increased if they have to control the robot manually. To allow therapists to focus on the interaction with the person instead, the robot should be more autonomous, namely it should be able to interpret the person's state and continuously adapt its actions according to their behaviour. In this paper, we develop a personalised robot behaviour model that can be used in the robot decision-making process during an activity; this behaviour model is trained with the help of a user model that has been learned from real interaction data. We use Q-learning for this task, such that the results demonstrate that the policy requires about 10,000 iterations to converge. We thus investigate policy transfer for improving the convergence speed; we show that this is a feasible solution, but an inappropriate initial policy can lead to a suboptimal final return.
Sensoren können verschiedene Aufgaben erfüllen, wie beispielsweise die Optimierung von Prozessen, die Interaktion zwischen Geräten oder die Verbesserung der zivilen Sicherheit. [1–3] Ihr Bedarf für die Industrie oder den Alltag wächst seit Jahren stetig. Besonders mobile Gassensoren sind von großem Interesse. Jedoch ist ihre Anwendung meist durch ihre integrierte Batterie begrenzt. Gassensoren ohne oder mit einem nur sehr geringen Energieverbrauch stehen daher im Interesse bei neuen Anwendungsgebieten, beispielsweise im Brandschutz oder in der Textilindustrie. [4,5] Die Sensoren könnten zum Beispiel in die Textilien einer persönlichen Schutzausrüstung eingearbeitet werden und durch einen Farbumschlag die Anwesenheit eines Gases oder die Überschreitung des Grenzwertes toxischer Substanzen anzeigen.
P30 - Das Elektrospinnen von halbleitenden Zinndioxidfasern für die Detektion von Wasserstoff
(2022)
Das Ziel dieser Arbeit ist die Entwicklung von dünnen keramischen Fasern als halbleitendes Sensormaterial zum Nachweis von Wasserstoff, möglichst bei Zimmertemperatur. Die elektrische Leitfähigkeit halbleitender Metalloxide ändert sich durch die Einwirkung von oxidierenden und reduzierenden Gasen auf die Oberfläche des Metalloxids. Dieser Effekt kann zur Messung der Gaskonzentration genutzt werden. Die Reaktion von Zinn(IV)-oxid mit Wasserstoff basiert auf der Reduktion des Zinn(IV)-oxids zum Zinn, wobei die Elektronen des Zinn(IV)-oxids im metallischen Zinn verbleiben und dort im nicht gebundenen Zustand zu einer Leitfähigkeitserhöhung beitragen. Die Reaktion des Wasserstoffes kann sowohl mit den Sauerstoffatomen des Oxids als auch mit adsorbierten Sauerstoffatomen an der Oxidoberfläche stattfinden.[ 6] Da die Reaktionen an der Oberfläche des Oxids stattfinden, sollten Sensoren mit einer großen Oberfläche im Vergleich zu metalloxidischen Bulkmaterialien eine höhere Empfindlichkeit aufweisen. [3] Die Verwendung von Fasern anstelle von Dünn- oder Dickschichten führt dabei zu einer besseren Sensitivität gegenüber Gasen.
Technological objects present themselves as necessary, only to become obsolete faster than ever before. This phenomenon has led to a population that experiences a plethora of technological objects and interfaces as they age, which become associated with certain stages of life and disappear thereafter. Noting the expanding body of literature within HCI about appropriation, our work pinpoints an area that needs more attention, “outdated technologies.” In other words, we assert that design practices can profit as much from imaginaries of the future as they can from reassessing artefacts from the past in a critical way. In a two-week fieldwork with 37 HCI students, we gathered an international collection of nostalgic devices from 14 different countries to investigate what memories people still have of older technologies and the ways in which these memories reveal normative and accidental use of technological objects. We found that participants primarily remembered older technologies with positive connotations and shared memories of how they had adapted and appropriated these technologies, rather than normative uses. We refer to this phenomenon as nostalgic reminiscence. In the future, we would like to develop this concept further by discussing how nostalgic reminiscence can be operationalized to stimulate speculative design in the present.
This project focuses on object detection in dense volume data. There are several types of dense volume data, namely Computed Tomography (CT) scan, Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI). This work focuses on CT scans. CT scans are not limited to the medical domain; they are also used in industries. CT scans are used in airport baggage screening, assembly lines, and the object detection systems in these places should be able to detect objects fast. One of the ways to address the issue of computational complexity and make the object detection systems fast is to use low-resolution images. Low-resolution CT scanning is fast. The entire process of scanning and detection can be made faster by using low-resolution images. Even in the medical domain, to reduce the rad iation dose, the exposure time of the patient should be reduced. The exposure time of patients could be reduced by allowing low-resolution CT scans. Hence it is essential to find out which object detection model has better accuracy as well as speed at low-resolution CT scans. However, the existing approaches did not provide details about how the model would perform when the resolution of CT scans is varied. Hence in this project, the goal is to analyze the impact of varying resolution of CT scans on both the speed and accuracy of the model. Three object detection models, namely RetinaNet, YOLOv3, and YOLOv5, were trained at various resolutions. Among the three models, it was found that YOLOv5 has the best mAP and f1 score at multiple resolutions on the DeepLesion dataset. RetinaNet model h as the least inference time on the DeepLesion dataset. From the experiments, it could be asserted that sacrificing mean average precision (mAP) to improve inference time by reducing resolution is feasible.
Taste is a complex phenomenon that depends on the individual experience and is a matter of collective negotiation and mediation. On the contrary, it is uncommon to include taste and its many facets in everyday design, particularly online shopping for fresh food products. To realize this unused potential, we conducted two Co-Design workshops. Based on the participants’ results in the workshops, we prototyped and evaluated a click-dummy smart-phone app to explore consumers’ needs for digital taste depiction. We found that emphasizing the natural qualities of food products, external reviews, and personalizing features lead to a reflection on the individual taste experience. The self-reflection through our design enables consumers to develop their taste competencies and thus strengthen their autonomy in decision-making. Ultimately, exploring taste as a social experience adds to a broader understanding of taste beyond a sensory phenomenon.
The purpose of the study is to provide empirical evidence about the under researched area of university-government relation in building a culture of entrepreneurial initiative inside triple helix model in a rural region. The study deploys a qualitative case study research method based on the content analysis of project documentation and further internal documents both from university and municipality.
Mobiles Laser-Schneidsystem zur Unterstützung der USBV-Entschärfung und Beweissicherung (mobiLaS)
(2022)
Background There is a lack of cardiac magnetic resonance (CMR) data regarding mid- to long-term myocardial damage due to Covid-19 in elite athletes. Objective This study investigated mid-to long-term consequences of myocardial involvement after a Covid-19 infection in elite athletes.
Methods Between January 2020 and October 2021, 27 athletes of the German Olympic centre Rhineland with confirmed Covid-19 infection were analyzed. 9 healthy non-athlete volunteers served as control. CMR was performed in mean 182 days (SD 99) after initial positive test result.
Results CMR did not reveal any signs of acute myocarditis in regard to the current Lake Louise criteria or myocardial damage in any of the 26 elite athletes with previous Covid-19 infection. Nevertheless, 92 % of the athletes experienced a symptomatic course and 54 % reported lasting symptoms for more than 4 weeks. In one male athlete CMR revealed an arrhythmogenic right ventricular cardiomyopathy (ARVC) and this athlete was excluded from the study. Athletes had significantly enlarged left and right ventricle volumes and increased left ventricular myocardial mass in comparison to the healthy control group (LVEDVi 103.4 vs. 91.1 ml/m 2 p=0.031; RVEDVi 104.1 vs. 86.6 ml/m 2 p=0.007; and LVMi 59.0 vs. 46.2 g/m 2 p=0.002).
Conclusion Our findings suggest that the risk for mid-to long-term myocardial damage seems to be very low to negligible in elite athletes. No conclusions can be drawn regarding myocardial injury in the acute phase of infection nor about possible long-term myocardial effects in the general population.
Medienkulturwissenschaft
(2022)
Der Band erklärt die Entstehung, Entwicklung und inhaltliche Breite der Medienkulturwissenschaft. Deren Felder werden ebenso demonstriert wie Forschungsfragen entworfen. Ein spezielles Augenmerk liegt auf interdisziplinären Verhältnissen, etwa zur Kommunikations- und Literaturwissenschaft. Zudem wird aus dieser Perspektive die Historie von Einzelmedien vorgestellt und ausgewählte Phänomene mit der medienkulturwissenschaftlichen „Brille“ skizziert. Dadurch kann die Geschichte und Philosophie der Medienkulturwissenschaft ebenso diskutiert werden wie deren Anwendungsfälle sowie ihre Positionen innerhalb eines Medienstudiums mit starkem Praxisbezug, bei dem die Theorien und Ästhetiken der Medien nicht außer Acht gelassen werden. (Verlagsangaben)
Marketingmaßnahmen
(2022)