Der Kinderwunsch von Schülern und Studenten: Ausprägung, Motive, Einstellungen, Prädiktoren und besondere Betrachtung subklinisch essgestörter Probandinnen

Fragestellung: Es sollten psychologische Motive für den Kinderwunsch bei weiblichen und männlichen Schülern und Studenten untersucht und besonders bei Teilnehmerinnen mit einem gestörten Essverhalten betrachtet werden. Methode: 736 Probanden wurden mit dem "Leipziger Fragebogen zu Kinderwunschmotiven und Einstellungen zum Kinderwunsch" sowie Items aus dem "Family Fertility Survey" untersucht. Die Klassifizierung des gestörten Essverhaltens erfolgte mit dem Eating Attitudes Test. Ergebnisse: 15,4% der Teilnehmer zeigten eine niedrige, 39% eine mittelstarke und 45,6% eine hohe Ausprägung des Kinderwunsches. Die Skalen "Wunsch nach emotionaler Stabilisierung" sowie "Soziale Stereotype" korrelierten am stärksten positiv mit der Ausprägung des Kinderwunsches, die Skalen "Angst vor materiellen Beeinträchtigungen" und "Persönlichen Einschränkungen" dagegen stark negativ. Der Kinderwunsch von Probandinnen mit einem gestörten Essverhalten anorektischer Prägung war signifikant niedriger als der von Teilnehmerinnen ohne Auffälligkeiten des Essverhaltens bzw. der von Frauen mit bulimischer Tendenz. Diskussion: Die untersuchten psychologischen Motive lieferten einen hohen Beitrag zur Aufklärung der Varianz der Ausprägung des Kinderwunsches. Die Ergebnisse bei Probandinnen mit einem gestörten Essverhalten unterstreichen die Bedeutung von Problemen bezüglich der Geschlechtsrollenidentifikation in der Ätiologie von Essstörungen

From Single Lane to Highways: Analyzing the Adoption of Multipath TCP in the Internet

Multipath TCP (MPTCP) extends traditional TCP to enable simultaneous use of multiple connection endpoints at the source and destination. MPTCP has been under active development since its standardization in 2013, and more recently in February 2020, MPTCP was upstreamed to the Linux kernel. In this paper, we provide the first broad analysis of MPTCPv0 in the Internet. We probe the entire IPv4 address space and an IPv6 hitlist to detect MPTCP-enabled systems operational on port 80 and 443. Our scans reveal a steady increase in MPTCP-capable IPs, reaching 9k+ on IPv4 and a few dozen on IPv6. We also discover a significant share of seemingly MPTCP-capable hosts, an artifact of middleboxes mirroring TCP options. We conduct targeted HTTP(S) measurements towards select hosts and find that middleboxes can aggressively impact the perceived quality of applications utilizing MPTCP. Finally, we analyze two complementary traffic traces from CAIDA and MAWI to shed light on the real-world usage of MPTCP. We find that while MPTCP usage has increased by a factor of 20 over the past few years, its traffic share is still quite low.Comment: Proceedings of the 2021 IFIP Networking Conference (Networking '21). Visit https://mptcp.io for up-to-date MPTCP measurement result

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

Comparing Human Factors for Augmented Reality Supported Single-User and Collaborative Repair Operations of Industrial Robots

In order to support the decision-making process of industry on how to implement Augmented Reality (AR) in production, this article wants to provide guidance through a set of comparative user studies. The results are obtained from the feedback of 160 participants who performed the same repair task on a switch cabinet of an industrial robot. The studies compare several AR instruction applications on different display devices (head-mounted display, handheld tablet PC and projection-based spatial AR) with baseline conditions (paper instructions and phone support), both in a single-user and a collaborative setting. Next to insights on the performance of the individual device types for the single mode operation, the study is able to show significant indications on AR techniques are being especially helpful in a collaborative setting.Internet of ThingsMechatronic DesignSystem Engineerin