Scalable anomaly detection in manufacturing systems using an interpretable deep learning approach

Anomaly detection in manufacturing systems has great potential for the prevention of critical quality faults. In recent years, unsupervised deep learning has shown to frequently outperform conventional methods for anomaly detection. However, tuning, deploying and debugging deep learning models is a time-consuming task, limiting their practical applicability in manufacturing systems. We approach this problem by developing a deep learning model that learns interpretable shapes that can be used for anomaly detection in temporal process data. Application of the model to assembly tightening processes in the automotive industry shows a significant improvement in model interpretability and scalability

Predictive model-based quality inspection using Machine Learning and Edge Cloud Computing

© 2020 The Authors The supply of defect-free, high-quality products is an important success factor for the long-term competitiveness of manufacturing companies. Despite the increasing challenges of rising product variety and complexity and the necessity of economic manufacturing, a comprehensive and reliable quality inspection is often indispensable. In consequence, high inspection volumes turn inspection processes into manufacturing bottlenecks. In this contribution, we investigate a new integrated solution of predictive model-based quality inspection in industrial manufacturing by utilizing Machine Learning techniques and Edge Cloud Computing technology. In contrast to state-of-the-art contributions, we propose a holistic approach comprising the target-oriented data acquisition and processing, modelling and model deployment as well as the technological implementation in the existing IT plant infrastructure. A real industrial use case in SMT manufacturing is presented to underline the procedure and benefits of the proposed method. The results show that by employing the proposed method, inspection volumes can be reduced significantly and thus economic advantages can be generated

Angioscopic Evaluation of Neointimal Coverage of Coronary Stents

Drug-eluting stents (DES) reduce coronary restenosis significantly; however, late stent thrombosis (LST) occurs, which requires long-term antiplatelet therapy. Angioscopic grading of neointimal coverage of coronary stent struts was established, and it was revealed that neointimal formation is incomplete and prevalence of LST is higher in DES when compared to bare-metal stents. It was also observed that the neointima is thicker and LST is less frequent in paclitaxel-eluting and zotarolimus-eluting stents than in sirolimus-eluting stents. Many new stents were devised and they are now under experimental or clinical investigations to overcome the shortcomings of the stents that have been employed clinically. Endothelial cells are highly anti-thrombotic. Neo-endothelial cell damage is considered to be caused by friction between the cells and stent struts due to the thin neointima between them which might act as a cushion. Therefore, development of a DES that causes an appropriate thickness (around 100 μm) of the neointima is a potential option with which to prevent neo-endothelial cell damage and consequent LST while preventing restenosis

Kinase inhibitors for the treatment of inflammatory and autoimmune disorders

Drugs targeting inhibition of kinases for the treatment of inflammation and autoimmune disorders have become a major focus in the pharmaceutical and biotech industry. Multiple kinases from different pathways have been the targets of interest in this endeavor. This review describes some of the recent developments in the search for inhibitors of IKK2, Syk, Lck, and JAK3 kinases. It is anticipated that some of these compounds or newer inhibitors of these kinases will be approved for the treatment of rheumatoid arthritis, psoriasis, organ transplantation, and other autoimmune diseases

Impact of donor lung quality on post-transplant recipient outcome in the Lung Allocation Score era in Eurotransplant – a historical prospective study

The aim of this study was to investigate whether there is an impact of donation rates on the quality of lungs used for transplantation and whether donor lung quality affects post-transplant outcome in the current Lung Allocation Score era. All consecutive adult LTx performed in Eurotransplant (ET) between January 2012 and December 2016 were included (N = 3053). Donors used for LTx in countries with high donation rate were younger (42% vs. 33% ≤45 years, P < 0.0001), were less often smokers (35% vs. 46%, P < 0.0001), had more often clear chest X-rays (82% vs. 72%, P < 0.0001), had better donor oxygenation ratios (20% vs. 26% with PaO2/FiO2 ≤ 300 mmHg, P < 0.0001), and had better lung donor score values (LDS; 28% vs. 17% with LDS = 6, P < 0.0001) compared with donors used for LTx in countries with low donation rate. Survival rates for the groups LDS = 6 and ≥7 at 5 years were 69.7% and 60.9% (P = 0.007). Lung donor quality significantly impacts on long-term patient survival. Countries with a low donation rate are more oriented to using donor lungs with a lesser quality compared to countries with a high donation rate. Instead of further stretching donor eligibility criteria, the full potential of the donor pool should be realized

Different populations and sources of human mesenchymal stem cells (MSC): A comparison of adult and neonatal tissue-derived MSC

The mesenchymal stroma harbors an important population of cells that possess stem cell-like characteristics including self renewal and differentiation capacities and can be derived from a variety of different sources. These multipotent mesenchymal stem cells (MSC) can be found in nearly all tissues and are mostly located in perivascular niches. MSC have migratory abilities and can secrete protective factors and act as a primary matrix for tissue regeneration during inflammation, tissue injuries and certain cancers