The 10 most recently published documents
How successful is the marketing strategy of a social enterprise in the case of Patagonia? (Part 1)
(2024)
In recent years, companies have become increasingly aware of the importance of social responsibility and sustainability in their operations. Social enterprises have emerged as a concept that aims to blend social objectives with profit-making characteristics. For social enterprises to succeed, a well-designed marketing strategy is essential. Ideally, this strategy should clearly communicate unique purposes and future visions, while justifying any high prices associated with their commitment. Patagonia is an example of a social enterprise that implements a purpose-driven marketing strategy.
SiC power modules are crucial in the automotive industry due to their high efficiency, but the change from Si to SiC brings new challenges regarding the short-circuit withstand time (SCWT). This paper investigates the influence of a common source feedback gate topology on short-circuit behavior. Implementing source feedback enhances the short-circuit withstand time but comes at the cost of increased switching losses. A more balanced trade-off between robustness and performance can be achieved by combining a welldefined common source feedback with an increasing gate-source voltage. This article investigates the concept using simulations, followed by characterization tests on a prototype commutation cell.
Natural language processing (NLP) offers the potential to automate quality assurance of software requirement specifications. Especially large-scale projects involving numerous suppliers can benefit from this improvement. However, due to privacy restrictions and domain- and project-specific vocabulary, as such in the aerospace domain, the availability of SRS documents for training NLP tools is severely limited. To provide a sufficient amount of data, we studied algorithms for the augmentation of textual data. Four algorithms have been studied by expanding a given set of requirements from European Space projects generating correct and incorrect requirements. The study yielded data of poor quality due to insufficient accuracy caused by the domain-specific vocabulary, yet, laid the foundation for the algorithms improvement, which, eventually, resulted in an increased set of requirements, which is 20 times the size of the seed set. Finally, an explorative experiment demonstrated the usability of augmented requirements to support AI-based quality assurance.
Nowadays, soft avatars are used in various fields to simulate the behavior of human soft tissues in different applications. Likewise, they are also utilized in the garment industry in order to achieve a realistic testing of the fit and functionality of tight-fitting clothing. Therefore it is important that avatars in CAD programs for clothing conform to the mechanical properties of human soft tissue. The accuracy of the avatars' properties in simulating the change in shape of human tissue is crucial here, which is caused by the contact pressure that compressive or tight-fitting garments exert onto the body. In this study, Browzwear’s VStitcher soft avatar Sofia was investigated and different body shapes resulting from being influenced by a legging with different levels of negative ease values were compared with non-affected natural avatar body shape. The examination of the soft avatar simulation shows that although a fast estimation of the tissue displacement can be predicted, there are some shape changes limitations compared to the natural behavior of human soft tissue.
Advanced classifiers and feature reduction for accurate insomnia detection using multimodal dataset
(2024)
Sleep deprivation is a significant contributor to various diseases, leading to poor cognitive function, decreased performance, and heart disorders. Insomnia, the most prevalent sleep disorder, requires more effective diagnosis and screening for proper treatment. Actigraphic data and its combination with physiological sensors like electroencephalogram (EEG), electrocardiogram (ECG), and body temperature have proven significant in predicting insomnia using machine learning methods. Studies focusing solely on actigraphic data achieved an accuracy of 84%, combining it with other wearable devices increased accuracy to 88%, and 2-channel EEG alone yielded an accuracy of 92%, but limits scalability and practicality in real-world settings. Here we show that using the hybrid approach of incorporating both recursive feature elimination (RFE) and principal component analysis (PCA) on sleep and heart data features yields outstanding results, with the multi-layer perception (MLP) achieving an accuracy of 95.83% and an F1 score of 0.93. The top-ranked features are predominantly sleep-related and time-domain RR interval. The dependent variables in our study have been extracted from the self-report Pittsburgh Sleep Quality Index questionnaire responses. Our findings emphasize the importance of tailoring feature sets and employing appropriate reduction techniques for optimal predictive modeling in sleep-related studies. Our results demonstrate that the ensemble classifiers generalize well on the dataset regardless of the feature count, while other algorithms are hindered by the curse of dimensionality.
This case study describes how in five years the global multi-energy company Repsol realized 20 percent greater profit—specifically, €800 million in cash flow from operations—by launching 505 digital innovation initiatives and building shared resources that systematically helped over seventy-six percent of the initiatives scale their innovation and realize value from it. Rather than simply empowering initiative teams, Repsol executives designed a holistic approach coordinated across initiatives to scale innovations. This case study describes how they identified and addressed a variety of barriers that would keep initiatives from scaling and realizing value.
Data Analytics is an important topic in current and future services. Different opportunities and challenges occur when implementing it. The paper describes some core aspects of Data Analytics Services as well as concrete application domains. Furthermore, an overview of the workshop and specifics of Analytic Services as well as future research streams are provided.
Energy consumption aspects of machine learning classifiers are important for research and practice as well. Due to sparse research in this area, a prototype of a recommender system was developed to provide energy consumption recommendations of different possible classifiers. The prototype is demonstrated as well as discussed and future research points are derived.
Mit der Verabschiedung der zweiten Novelle des Gebäudeenergiegesetzes, mit der die sogenannte „65 % Erneuerbaren-Regel“ eingeführt wird, sowie mit der Einführung des „Wärmeplanungsgesetzes“ und der Weiterentwicklung der Bundesförderung effiziente Gebäude verändern sich die Rahmenbedingungen für die Wärmewende in Baden-Württemberg fundamental. Die Regel besagt, dass jede neu installierte Heizungsanlage mit 65 % erneuerbaren Energien betrieben werden muss; die Zeitpunkte variieren je nach vorliegender Wärmeplanung. Das Umweltministerium hat einen „Wärmegipfel-Prozess“ initiiert, der diesen Prozess flankieren, unterstützen und stärken soll. Der Klima-Sachverständigenrat (K-SVR) liefert mit diesem Papier einen Impuls für diese Diskussion. Die unterbreiteten Vorschläge können Ausgangspunkt einer Debatte sein. Der Klima-Sachverständigenrat begrüßt Kommentare, Ergänzungen und weitere Vorschläge.
