Hochschule Konstanz University of Applied Sciences
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Die ökonomischen Effekte von Sanktionen
Get PDFSanktionen stellen Zwangsmaßnahmen dar, die bei der Bewältigung politischer Spannungen zwischen Nationen eine lange und wiederkehrende Stellung einnehmen. Sie werden sowohl einseitig als auch in Staatenbündnissen verhängt und besonders nach dem 2. Weltkrieg mit zunehmender Häufigkeit eingesetzt. Während im letzten Jahrhundert, insbesondere vor dem 2. Weltkrieg, Handelsbeschränkungen und umfassende Wirtschaftsblockaden die vorherrschenden Sanktionsinstrumente darstellten, werden heute in einer stärker integrierten und globalisierten Welt Sanktionen in verschiedenen weiteren Formen verhängt, einschließlich internationaler Finanzbeschränkungen, Reiseverbote, Handelseinschränkungen für bestimmte Gütergruppen, Aufhebung militärischer Hilfen und spezifische Einschränkungen, wie beispielsweise Flugverbote und Hafensperrungen.Sanctions represent a prominent coercive tool that has been utilised extensively in addressing political tensions between nations. These measures are imposed both unilaterally and in alliances of states, and have become increasingly prevalent since the Second World War. In the previous century, particularly before the WWII, trade restrictions and comprehensive economic blockades were the dominant tools to sanction. In contrast, in today’s more interconnected and globalised world, sanctions take on a variety of forms, such as international financial restrictions, travel bans, trade restrictions on specific goods, cancellation of military aid, and targeted measures like flight bans and port closures. The increasing demand for and use of international sanctions raises a fundamental question: Do sanctions lead to political success
Obstructive sleep apnea event detection using explainable deep learning models for a portable monitor
Get PDFBackground: Polysomnography (PSG) is the gold standard for detecting obstructive sleep apnea (OSA). However, this technique has many disadvantages when using it outside the hospital or for daily use. Portable monitors (PMs) aim to streamline the OSA detection process through deep learning (DL).
Materials and methods: We studied how to detect OSA events and calculate the apnea-hypopnea index (AHI) by using deep learning models that aim to be implemented on PMs. Several deep learning models are presented after being trained on polysomnography data from the National Sleep Research Resource (NSRR) repository. The best hyperparameters for the DL architecture are presented. In addition, emphasis is focused on model explainability techniques, concretely on Gradient-weighted Class Activation Mapping (Grad-CAM).
Results: The results for the best DL model are presented and analyzed. The interpretability of the DL model is also analyzed by studying the regions of the signals that are most relevant for the model to make the decision. The model that yields the best result is a one-dimensional convolutional neural network (1D-CNN) with 84.3% accuracy.
Conclusion: The use of PMs using machine learning techniques for detecting OSA events still has a long way to go. However, our method for developing explainable DL models demonstrates that PMs appear to be a promising alternative to PSG in the future for the detection of obstructive apnea events and the automatic calculation of AHI
Validating Force Sensitive Resistor Strip Sensors for Cardiorespiratory Measurement during Sleep: A Preliminary Study
Get PDFSleep disorders can impact daily life, affecting physical, emotional, and cognitive well-being. Due to the time-consuming, highly obtrusive, and expensive nature of using the standard approaches such as polysomnography, it is of great interest to develop a noninvasive and unobtrusive in-home sleep monitoring system that can reliably and accurately measure cardiorespiratory parameters while causing minimal discomfort to the user’s sleep. We developed a low-cost Out of Center Sleep Testing (OCST) system with low complexity to measure cardiorespiratory parameters. We tested and validated two force-sensitive resistor strip sensors under the bed mattress covering the thoracic and abdominal regions. Twenty subjects were recruited, including 12 males and 8 females. The ballistocardiogram signal was processed using the 4th smooth level of the discrete wavelet transform and the 2nd order of the Butterworth bandpass filter to measure the heart rate and respiration rate, respectively. We reached a total error (concerning the reference sensors) of 3.24 beats per minute and 2.32 rates for heart rate and respiration rate, respectively. For males and females, heart rate errors were 3.47 and 2.68, and respiration rate errors were 2.32 and 2.33, respectively. We developed and verified the reliability and applicability of the system. It showed a minor dependency on sleeping positions, one of the major cumbersome sleep measurements. We identified the sensor under the thoracic region as the optimal configuration for cardiorespiratory measurement. Although testing the system with healthy subjects and regular patterns of cardiorespiratory parameters showed promising results, further investigation is required with the bandwidth frequency and validation of the system with larger groups of subjects, including patients
Auswirkungen der EU-Taxonomie Anforderungen in der Gebäudezertifizierung nach der neuen DGNB-Version 2023 auf den Gewerbeimmobilien Büro Neubau in technischer und monetärer Sicht
Get PDFDie Berücksichtigung ökologischer und sozialer Gesichtspunkte in der Konzeption, Planung und Errichtung von Gebäuden hat in den vergangenen Jahren großen Einfluss auf Marktfähigkeit der Immobilien gewonnen. Regulatorische Rahmenwerke wie die Taxonomie-Verordnung der Europäischen Union formulieren die klare Anforderung an die Bauwirtschaft dem Schutz von Mensch und Natur mehr Bedeutung einzuräumen. Nur mit einem wesentlichen Beitrag zu den Klimazielen der Europäischen Union wird es der Branche langfristig möglich sein sich einen uneingeschränkten Zugang zum Investorenmarkt zu sichern.
Die vorliegende wissenschaftliche Arbeit widmet sich dem Kriterienkatalog der Deutschen Gesellschaft für Nachhaltiges Bauen e.V. und legt Übereinstimmungen mit den technischen Bewertungskriterien der EU-Taxonomie Verordnung offen. Der im Frühjahr 2023 erschienen Kriterienkatalog umfasst eine Vielzahl von Kriterien, anhand derer Gebäude auf Nachhaltigkeit geprüft werden. Im Vergleich zu der Vorgängerversion aus dem Jahr 2018 wurden erhebliche Änderungen eingearbeitet. Besonders hervorzuheben sind neue technische Prüfkriterien im Bereich Klimaschutz, Ressourcengewinnung, Biodiversität und Kreislaufwirtschaft. Die Angleichung der Berechnungsmethode für die Ökobilanzen an das bundeseigene „Qualitätssiegel Nachhaltiges Gebäude“, die Mindestanforderung nach dem erhöhten Einsatz von nachhaltig gewachsenem Holz, die Prüfung spezifischer Zielquoten bei dem Einsatz von Recyclingbeton sowie Anforderungen an die Zirkularität sind nur ein Teil der Neuerungen. Für die zusätzlichen Anforderung müssen Projektentwickler mit Mehrkosten im hohen sechsstelligen Bereich im Vergleich zu der Vorgängerversion rechnen. Vorteile der Neuauflage des Kriterienkataloges sind eine erhöhte Übereinstimmung mit den Nachhaltigkeitsanforderungen der Europäischen Union. Es werden jedoch nicht alle Anforderungen erfüllt. Nachweise für den Primärenergiebedarf, die Schadstoffbelastung von Bauteilen bzw. -materialien und eine Umweltverträglichkeitsprüfung müssen zusätzlich zu dem Kriterienkatalog der Deutschen Gesellschaft für Nachhaltiges Bauen geleistet werden. Insgesamt ebnen die Kriterien der Deutschen Gesellschaft für Nachhaltiges Bauen aber den Weg hin zu einer EU-Konformität und helfen Projektentwicklern Immobilien erfolgreich auf dem Markt zu positionieren
Estimating Conditional Distributions with Neural Networks using R package deeptrafo
Get PDFContemporary empirical applications frequently require flexible regression models for complex response types and large tabular or non-tabular, including image or text, data. Classical regression models either break down under the computational load of processing such data or require additional manual feature extraction to make these problems tractable. Here, we present deeptrafo, a package for fitting flexible regression models for conditional distributions using a tensorflow backend with numerous additional processors, such as neural networks, penalties, and smoothing splines. Package deeptrafo implements deep conditional transformation models (DCTMs) for binary, ordinal, count, survival, continuous, and time series responses, potentially with uninformative censoring. Unlike other available methods, DCTMs do not assume a parametric family of distributions for the response. Further, the data analyst may trade off interpretability and flexibility by supplying custom neural network architectures and smoothers for each term in an intuitive formula interface. We demonstrate how to set up, fit, and work with DCTMs for several response types. We further showcase how to construct ensembles of these models, evaluate models using inbuilt cross-validation, and use other convenience functions for DCTMs in several applications. Lastly, we discuss DCTMs in light of other approaches to regression with non-tabular data
Why Companies Have Multiple Corporate Entrepreneurship Units
No full textNowadays established companies use Corporate Entrepreneurship (CE) as a means to create discontinuous innovations. Many companies thereby even implement multiple CE units that typically involve several entrepreneurial activities. This explorative study aimed to identify the reasons why established companies implement multiple CE units concurrently. In conducting a comparative case study with eight companies from different industries, valuable insights for science and practice were gained. We provide an overview of different 11 reasons for implementing multiple CE units. This shows that the combination of CE units used by companies differs depending on the reason. It further allowed to derive general approaches of established companies to the implementation of CE units. Last, we identify the concept of co-specialization to be a central driver explaining the creation of the need to set up multiple units. We conclude by indicating implications and subjects for future research
Investigation of Corrosion Behaviour of Plastic Mould Steels Under Oxygen Free and Oxygen Saturated Conditions
Get PDFSteels for plastic injection moulds are available in different alloy compositions. Mechanical properties, such as wear resistance and hardness, are the most important properties of these steels. Corrosion resistance of these steels is also an important property, which is why there are high-alloy steel compositions.
The cooling and temperature control systems for moulds have different types of fluid circuits. There are open and closed systems, which has a direct influence on the oxygen content in the fluid. There are also different types of water, such as hard and soft water, and different types of additives, for example biocides or corrosion inhibitors.
The aim of this work is to investigate the corrosion behaviour of various typical plastic mould steels under high and low oxygen conditions. With these results, a mould tempering device will be developed that controls the oxygen content in a closed water-based liquid system. If this is successful, chemical additives can be dispensed with and good corrosion behaviour can be achieved, even for low and unalloyed steels.
Steels with different chromium contents typical for this application were selected for the tests. Heat treatment was carried out in a typical way for these steels. Corrosion behaviour was measured by open circuit and potentiodynamic measurements in soft water at 50°C. Oxygen-free and oxygen-saturated conditions were investigated
Assessing Body Position During Sleep Using FSR Sensors and Machine Learning Algorithms
No full textThis study investigates the application of Force Sensing Resistor (FSR) sensors and machine learning algorithms for non-invasive body position monitoring during sleep. Although reliable, traditional methods like Polysomnography (PSG) are invasive and unsuited for extended home-based monitoring. Our approach utilizes FSR sensors placed beneath the mattress to detect body positions effectively. We employed machine learning techniques, specifically Random Forest (RF), K-Nearest Neighbors (KNN), and XGBoost algorithms, to analyze the sensor data. The models were trained and tested using data from a controlled study with 15 subjects assuming various sleep positions. The performance of these models was evaluated based on accuracy and confusion matrices. The results indicate XGBoost as the most effective model for this application, followed by RF and KNN, offering promising avenues for home-based sleep monitoring systems