Offenburg University of Applied Sciences
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Advances in the Measurement and Interpretation of Intervertebral Motion in the Lumbar Spine: A Scoping Review
No full textBackground: Intervertebral motion is a fundamental aspect of spinal biomechanics, crucial for understanding lumbar spine function, pain mechanisms, and surgical outcomes. Various methods exist for measuring and interpreting it, each with its own advantages, limitations, and specific applications. However, a comprehensive and standard taxonomy of study types for the measurement and interpretation of in vivo intervertebral motion in the lumbar spine is lacking. Objectives: This review aimed to systematically identify, characterise, and categorise the diverse study types deposited in the literature. Eligibility criteria: Only studies in English and of lumbar spine intervertebral motion in living subjects were considered, and only those that employed objective measurement of motion sequences were included. Sources of evidence: A comprehensive literature search was performed in PubMed, CINAHL, and SCOPUS for articles published between January 2000 and October 2025. Charting methods: After removal of duplicates, all studies were subjected to Title and abstract screening, followed by full-text screening of potentially eligible studies. Data selected were charted into tables under the headings: author, year, country, purpose, technology, participants, measurement, interpretation, radiation dosage, and significance of findings. Results: Forty-nine studies were abstracted and are described under 11 study types. These formed a taxonomy constituting the following six categories: normal biomechanical mechanisms, pathological and injury mechanisms, direct kinematic measurement, spinal stabilisation, dynamic radiography, and clinical markers. The resulting taxonomy will serve as a resource for researchers, clinicians, and policymakers by facilitating a more coherent understanding of the field and promoting standardisation in research design and reporting
Was ist eigentlich die "Öffentlichkeit" der Öffentlichkeitsarbeit von Schulen?
No full textDamit Öffentlichkeitsarbeit an Schulen nicht an den Anforderungen der Schulen, der Umfelder von Schulen und damit den Erwartungen der Zielgruppen vorbei arbeitet, braucht es ein gemeinsames Verständnis, was für Schulen die »Öffentlichkeit« darstellt und welche Entwicklungen und Veränderungen bei den Zielgruppen und ihren Erwartungen erkennbar sind
Wie professionell muss/darf unsere Schule auftreten?
No full textIn der Praxis stellt sich für viele Schulen die Frage, wie professionell das gesamte Auftreten gestaltet werden muss. Die Zielgruppen sind Marken auf der Ebene von professionellen Konsumgüterbrands gewohnt, Schulen fehlen jedoch die dafür erforderliche Finanzausstattung für externe Dienstleister und im Regelfall auch die internen Ressourcen, um hier auf Augenhöhe mitspielen zu können. Welche Wege gibt es also, um den Grad an Professionalität zu erreichen, den eine Schule benötigt
Kritische Analyse der Einsatzmöglichkeiten von KI-Verfahren im Controlling
No full textDurch die Künstliche Intelligenz verändert die Art und Weise, wie Unternehmen heutzutage und in Zukunft gesteuert werden und wie Entscheidungen getroffen werden. Besonders im Controlling, das traditionell auf der Sammlung, Aufbereitung und Analyse von Unternehmensdaten basiert, eröffnen Anwendungen der Künstlichen Intelligenz neue Perspektiven: von der Automatisierung standardisierter Berichte über Prognosen mittels Machine Learning bis hin zur Unterstützung komplexer Entscheidungsprozesse. Mit der fortschreitenden Digitalisierung und der wachsenden Bedeutung von Daten rücken innovative Technologien wie Künstliche Intelligenz zunehmend in den Fokus von Unternehmen.
Sie dienen als Instrument, um Entscheidungsprozesse präziser zu gestalten und betriebliche Abläufe effizienter zu machen. Eine zentrale Rolle spielt dabei die Unterscheidung zwischen sogenannter schwacher und starker KI. Diese Entwicklung eröffnet weitreichende Chancen für das Controlling, wirft jedoch zugleich grundlegende Fragen nach möglichen Risiken und Grenzen auf
Work in Progress: Design education in additive manufacturing using scaled wind turbines models
No full textProcesses and materials in Additive manufacturing have advanced significantly in recent years and are increasingly being used in both scientific and industrial applications. Therefore, it is important to integrate these new additive processes and the selection of suitable materials into engineering studies. This paper presents an innovative course called “Workshop Additive Manufacturing”, which provides knowledge about additive manufacturing through a project. In this project, students work in multidisciplinary teams to develop a product and realize it through additive manufacturing. The development of a scale model wind turbine as a functional and economic prototype for sustainable energy generation is a key feature of this new course. The students carry out all the steps from idea generation to the manufacturing of the models. This includes the development of a gearbox and the integration of a generator. At the end of the project, the models are tested for functionality and performance in a simple wind tunnel. In order to examine the changes in the students’ knowledge and skills during the course, they will be surveyed in eight different areas before and after the workshop. Furthermore, an evaluation was conducted in order to ascertain the students’ workload during the various phases of product development, as well as the influence of the different components of the course on learning success. The results demonstrated that the students were able to significantly improve their skills, particularly in the areas of wind turbine development, application of design methods for sustainable design and practical knowledge of additive manufacturing
Enhancing 6LoWPAN Performance in Lossy Networks Using Complex and Combined Header Compression
No full textHeader compression is vital for efficient IPv6 communication in constrained IoT networks like 6LoWPAN. However, traditional IP Header Compression (IPHC) lacks selevtive retransmission and optimized fragmentation, reducing its effectiveness in lossy environments. This study evaluates three strategies- IPHC-only, and a combination IPHC + SCH- Using Contiki-NG and the Cooja simulator on a multi-hop Sky mote mesh network under varying success rates (40%-100%), focusing on energy consumption, latencym and retransmission overhead. Results show the IPHC + SCHC combination outperforms both standalone methods across all metrics. at 40% success, it lowers energy use by 28% over IPHC-only and 16% over SCHC-only. Latency drops by 27% and 13% respectively, while retransmissions are cut by 35% and 15%. These gains stem from combining IPHC's lightweight compression with SCHC's selective ACK-on-Error retransmission. This combination offersa scalable, energy-efficient solution for real-time, low-power IoT Networks in smart cities, CPS, and industry. Future work will examine secure compression using DTLS and OSCORE, real deployments, and machine learning-based context adaptation
A Comprehensive Review of Public Datasets for Machine Learning-Based Intrusion Detection in IoT and OT Networks
No full textThe rapid growth of networks for Operational Technology (OT) and the Internet of Things (IoT) has increased their susceptibility to cyberattacks. Since many years, traditional intrusion detection systems (IDS) are in place. However, they are falling short, as they are insufficient against dynamic threats. However, ML-based models are only as good as their training datasets, so these datasets are of paramount importance. Consequently, this paper reviews publicly accessible datasets for anomalies in OT- and IoT-networks and evaluates their applicability to ML-based IDS. It discusses the strengths and weaknesses of the datasets, including data imbalance, oversimplified attack scenarios, and restrictions on protocol diversity.
In order to increase the effectiveness of ML-based IDS and strengthen cybersecurity in OT and IoT environments, the paper suggests increasing dataset complexity and realism
Machine learning-based prediction of one-year mortality after alloHCT identifies the impact of pre-transplant immunity and inflammation
Full text linkAccurate prediction of mortality after allogeneic hematopoietic stem cell transplantation (alloHCT) is essential for individualized treatment decisions, yet existing clinical risk scores capture only a limited number of variables and show modest predictive performance. In our single-center retrospective analysis, we included data from 909 adult patients with hematologic malignancies undergoing alloHCT. We used 31 features to build machine-learning models to predict death within the first year after alloHCT. These features included established clinical risk factors together with pre-transplant lymphocyte subsets and inflammatory markers. Among four models, a random forest algorithm showed the best performance (AUC = 0.773) and retained good generalizability in an independent test set (AUC = 0.748). SHapley Additive exPlanations (SHAP)-based interpretation of the machine-learning models showed that age together with five easily measurable pre-transplant immunological and inflammatory parameters influenced the outcome: pre-transplant CD4 + , CD8 + , and B-lymphocyte counts, albumin, and C-reactive protein (CRP) levels. Based on these features, our random forest approach outperformed established clinical risk scores (HCT-CI, EASIX, rDRI, mGPS) in predicting one-year mortality after alloHCT and more effectively distinguished patients at low and high risk of an adverse outcome. Our study shows that machine-learning-based models can not only predict patient outcomes after alloHCT but also serve as powerful tools for data exploration, confirming the prognostic relevance of pre-transplant inflammation while uncovering the critical role of lymphocyte subsets as previously unknown risk factors. External validation in independent multicenter cohorts will be required to confirm generalizability
由P和/或T波监测并优化时间触发稳定性的方法及装置 (CN121285337A)
No full text本发明涉及用于监测和优化体外循环支持的时间触发稳定性的装置，以及包含该装置的体外循环支持控制单元和相应方法。据此，本发明提出用于监测和优化体外循环支持的时间触发稳定性的方法，包括以下步骤：接收受支持患者在预定时长内的心电图信号；从接收的心电图信号中确定P波、R波和T波；考虑到至少一个检测到的P波和/或至少一个检测到的T波，从当前心动周期的心电图信号中确定预定触发信号