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Migration of human T cells can be differentially directed by electric fields depending on the extracellular microenvironment
Get PDFT cell migration plays an essential role in the immune response and T cell-based therapies. It can be modulated by chemical and physical cues such as electric fields (EFs). The mechanisms underlying electrotaxis (cell migration manipulated by EFs) are not fully understood and systematic studies with immune cells are rare. In this in vitro study, we show that direct current EFs with strengths of physiologically occurring EFs (25–200 mV/mm) can guide the migration of primary human CD4+ and CD8+ T cells on 2D substrates toward the anode and in a 3D environment differentially (CD4+ T cells show cathodal and CD8+ T cells show anodal electrotaxis). Overall, we find that EFs present a potent stimulus to direct T cell migration in different microenvironments in a cell-type-, substrate-, and voltage-dependent manner, while not significantly influencing T cell differentiation or viability
Using data augmentation to support AI-based requirements evaluation in large-scale projects
No full textNatural 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
Editorial: Fashion supply chain management during and after the COVID-19 global pandemic
No full textBased on the published four papers in this special issue, new managerial insights into fashion supply chain management during and after COVID-19 global pandemic are obtained. During the COVID-19 pandemic, omni-channel retailing, quick response, mixed production strategies, collaboration with e-tailers, having a balance between safety features and luxury desirability, risk management, and producing “safe” designs are effective strategies to cope with COVID-19. After COVID-19, the strategies including customer relationship management, demand forecasting and inventory planning, sustainable practices, network collaboration, inter-organizational sharing, and buyer–supplier relationships are crucially important for business recovery and growth
Automatisierung von Kabelverteilern (Teil 7) : Erstellung eines Prognosetools für die Rückeinspeisung in Niederspannungsnetzen
No full textViele Verteilnetzbetreiber (VNB) betrachten ihre Niederspannungsnetze als Black Box, da es oft an geeigneten Mess- und Überwachungsinstrumenten fehlt, um detaillierte Einblicke zu erhalten. Diese mangelnde Transparenz erschwert eine präzise Netzsteuerung und -optimierung. Das Projekt „rONT-Alternative“ zielt darauf ab, ein Prognosetool zu entwickeln, das den VNB eine umfassende Übersicht über ihre Netze bietet. Durch detaillierte Analysen und präzisiere Vorhersagen sollen die Transparenz erhöht und die Netzverwaltung verbessert werden, insbesondere im Hinblick auf die Integration erneuerbarer Energien
How successful is the marketing strategy of a social enterprise in the case of Patagonia? (Part 2)
No full textIn 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
Classification of the sleep-wake state through the development of a deep learning model
Get PDFThe classification of sleep and wake states is of paramount importance in the context of sleep disorders. In order to detect and monitor disorders such as obstructive sleep apnea (OSA), it is essential to obtain the total sleep time (TST) so as to assess the severity of the patient’s sleep apnea. With the advent of new technologies for detecting events associated with sleep disorders, it is not always straightforward to calculate the sleep/wakefulness state. Consequently, this work presents the development of a deep learning model (a variant of U-Net) for the detection of sleep/wakefulness states. For this purpose, an engineering approach using Keras Tuner and the use of three signals with minimal processing was employed. The three signals, oxygen saturation (SpO2), heart rate (HR) and abdominal respiratory effort (AbdRes), were selected to ensure both patient comfort during signal collection and the possibility of using portable monitors. The models were trained and tested on data from polysomnography studies, namely the Sleep Heart Health Study (SHHS) and the Multiethnic Study of Atherosclerosis (MESA). The best performing model achieved results with 88% binary precision, 88% recall, 89% precision, 89% f1-score and Cohen’s Kappa of 0.74 for the SHHS test set. The model obtained 82% binary accuracy, 82% recall, 84% precision, 82% f1-score and 0.62 Cohen’s kappa for the MESA data set
Gender Marketing : die Berücksichtigung von geschlechtsspezifischen Unterschieden bei männlichen und weiblichen Zielgruppen
Get PDFGender Marketing gewinnt sowohl in der Marketing-Theorie als auch in der Unternehmenspraxis zunehmend an Bedeutung. Der Unterschied zwischen den Geschlechtern zeigt sich nicht nur in unterschiedlichen Fähigkeiten und Einstellungen, sondern auch in verschiedenen Bedürfnissen und im Kaufverhalten. Viele Produkte werden von Männern für Männer entwickelt. Produkte, die sich speziell an Frauen richten, werden häufig gemäß dem Motto "pink it and shrink it" auf den Markt gebracht. Eine erfolgreiche Umsetzung von Gender-Aspekten ist für Unternehmen eine wichtige Marketing-Herausforderung für die Zukunft
A framework for explainable root cause analysis in manufacturing systems : explainable Artificial Intelligence for shopfloor workers
Get PDFThis paper proposes a novel framework - "Transparent Reasoning in Artificial intelligence Cause Explanation" (TRACE) - that combines root cause analysis, explainable artificial intelligence, and machine learning in a comprehensible manner for the shopfloor worker. The goal is to enhance transparency, interpretability, and explainability in AI-driven decision-making processes as well as to increase the acceptance of AI within an industrial manufacturing area. A human AI collaboration tool in perspective. The paper outlines the need of such a framework, describes the proposed design science approach for the development
Zusammenspiel von Mensch und Robotik zur Steigerung der Lebensdauer von Wälzschälwerkzeugen : ein KI-Modell zur Anomalieerkennung auf Basis nominaler Daten
Get PDFDurch das Zusammenspiel von Facharbeitern und Algorithmen in der automatisierten Werkzeugwartung kann die Lebensdauer von Wälzschälwerkzeugen verlängert und die Notwendigkeit manueller Inspektionen reduziert werden. Der Einsatz von Algorithmen, die ausschließlich mit nominalen Daten arbeiten, bietet für Industrieunternehmen erhebliche Vorteile, da nominale Daten weitaus häufiger vorkommen als Anomalien
Concept for a low-cost implementation of automatic cycle time measurements in learning factories
No full textCycle time optimization is a fundamental skill for manufacturing planners to avoid bottlenecks and thus increase throughput of production. A learning factory, which replicates real-world manufacturing scenarios, provides an ideal environment for students to acquire this essential skill. Traditionally, cycle times in these scenarios have been manually recorded using stopwatches. This practice has become increasingly outdated with the proliferation of Industry 4.0 and Internet of Things systems that automatically take these measurements in industries, which the learning factories are designed to emulate. However, the high costs and implementation efforts associated with these systems can pose significant challenges for learning factories to adapt.
To address these challenges, this paper proposes a cost-effective system for automatic cycle time measurements in learning factories. The system is composed of inexpensive and commercially available hardware such as microcontroller development boards, Radio-Frequency Identification (RFID) readers and a custom software based on open-source software that is free to use. It enables fast and economical retrofitting of existing production scenarios by equipping production stations with RFID readers and product trays with RFID tags.
The solution not only enhances the realism of learning factories in terms of cycle time measurements but also introduces the students to key Industry 4.0 concepts like automation, digitalization, and real-time data tracking. By integrating this affordable system, learning factories can better align their practices with industry standards, thereby improving the training quality and preparing students more effectively for the future manufacturing environment