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Institution

Saarland University

EducationSaarbrücken, Germany
About: Saarland University is a(n) education organization based out in Saarbrücken, Germany. It is known for research contribution in the topic(s): Population & Transplantation. The organization has 19555 authors who have published 39678 publication(s) receiving 1109295 citation(s). The organization is also known as: University of the Saarland & Universität des Saarlandes.
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Journal ArticleDOI
BockThomas1, SchmidAngelika2, ApelSven1Institutions (2)
Abstract: Many open-source software projects depend on a few core developers, who take over both the bulk of coordination and programming tasks. They are supported by peripheral developers, who contribute ei...

Journal ArticleDOI
Simon Schindler1, Malte Friese2Institutions (2)
Abstract: Mindfulness is a hot topic in psychological research and the popular media. One central claim in the literature is that enhanced mindfulness fosters prosocial behavior. This article recapitulates what is currently known about this widespread claim. We first review theoretical perspectives on why enhanced mindfulness should foster prosocial behavior and discuss relevant empirical evidence. Two meta-analyses provide preliminary support for this claim. However, limitations call for caution when interpreting the evidence and studies investigating effects that persist over sustained periods of time are missing. In addition, theoretical assumptions about the underlying mechanisms need stronger empirical support. We discuss theoretical predicaments, identify potential downsides of mindfulness, and suggest ways forward for future research.

Journal ArticleDOI
Amr El Mohamad1, Mohammed Alhoobi, Ahmed Saleh1, Firas Hammadi1  +3 moreInstitutions (2)
Abstract: Background Brain metastasis from endometrial adenocarcinoma is uncommon, and to the brain stem particularly are quite rare. Different therapeutic modalities for metastatic endometrial cancer to the brain such as surgical resection and radiotherapy have been described. Surgical resection of brain stem lesions is challenging, and there are many surgical approaches described in literature. Endoscopic endonasal transsphenoidal transclival approach has not been widely used for anterior pontine metastasis. Herein, we present a case of 47 years old lady who was diagnosed with metastatic endometrial adenocarcinoma in the form of solitary pontine lesion after 3 years of initial diagnosis of uterine adenocarcinoma, which was operated via endoscopic endonasal transsphenoidal transclival approach followed by radiotherapy. Conclusion Brain metastasis from endometrial cancer is rare, and its management depends on the number of lesions, the extent of disease and the general condition of the patient. Surgery followed by radiotherapy is a good option in isolated brain lesion with no evidence of extracranial lesion. Surgical resection of pontine lesions is challenging, and we suggest endoscopic endonasal transsphenoidal transclival approach for better exposure and resection.

Journal ArticleDOI
TL;DR: A plausible theoretical perspective inspired from neuroscience is proposed for signal representation of deep learning framework to model machine perception in structural health monitoring (SHM), especially because SHM typically involves multiple sensory input from different sensing locations.
Abstract: Predictive maintenance, as one of the core components of Industry 4.0, takes a proactive approach to maintain machines and systems in good order to keep downtime to a minimum and the airline maintenance industry is not an exception to this. To achieve this goal, practices in Structural Health Monitoring (SHM) complement the existing Non-Destructive-Testing (NDT) have been established in the last decades. Recently, the increasing computational capability such as utilization of a graphical processing unit (GPU) in combination with advanced machine learning techniques such as deep learning has been one of the main drivers in the advancement of predictive analytics in condition monitoring. In our previous work, we proposed a novel approach using deep learning for guided wave based structural health called DeepSHM. As a study case, we treated an ultrasonic signal from guided Lamb wave SHM with a convolutional neural network (CNN). In that work, we only considered a single central frequency excitation. This led to a single governing wavelength which is normally good for the detection of a single damage size. In classical signal processing, applying a broader excitation frequency poses an analysis and interpretation nightmare because it contains more complex information and thus is difficult to understand. This problem can be overcome with deep learning; however, it creates another problem: while deep learning typically results in a more accurate result prediction, it is specifically made for solving only certain types of tasks. While many papers have already introduced deep learning for diagnostics, many of these works are only proposing novel predictive techniques, however the mathematical formalization is lacking, and we are not informed about why we should treat acoustic signal with deep learning. So, the basis of ‘explainable AI’ for SHM and NDT is currently lacking. For this reason, in this paper, we would like to extend our previous work into a more generalized. Rather than focusing on a novel technique, we propose a plausible theoretical perspective inspired from neuroscience for signal representation of deep learning framework to model machine perception in structural health monitoring (SHM), especially because SHM typically involves multiple sensory input from different sensing locations. To do this, we created a set of artificial data from a finite element model (FEM) and represented DeepSHM in two different ways: 1). Perpetual representation of observation and 2). Hierarchical structure of entities that is decomposable in a smaller sub-entity. Consequently, we assume two plausible models for DeepSHM: 1). Either it behaves as a single deciding actor since the observation is regarded as perpetual, and 2). Or it acts as a multiple actor with independent outputs since multiple sensors can form different output probabilities. These artificial data were split into several different input representations, classified into several damage scenarios and then trained with commonly used deep learning training parameters. We compare the performance metrics of each perception model to describe the training behavior of both representations.

1 citations


Journal ArticleDOI
Mohaned Hammad1, Baris Alkan1, Ahmed K. Al-kamal1, Cheolyong Kim1  +8 moreInstitutions (3)
Abstract: The scalable synthesis of stable catalysts for environmental remediation applications remains challenging. Nonetheless, metal leaching is a serious environmental issue hindering the practical application of transition-metal based catalysts including Co-based catalysts. Herein, for the first time, we describe a facile one-step and scalable spray-flame synthesis of high surface area La2CoO4+δ nanoparticles containing excess oxygen interstitials (+δ) and use them as a stable and efficient catalyst for activating peroxymonosulfate (PMS) towards the degradation of bisphenol A. Importantly, the La2CoO4+δ catalyst exhibits higher catalytic degradation of bisphenol A (95% in 20 min) and stability than LaCoO3–x nanoparticles (60%) in the peroxymonosulfate activation system. The high content of Co2+ in the structure showed a strong impact on the catalytic performance of the La2CoO4+δ + PMS system. Despite its high specific surface area, our results showed a very low amount of leached cobalt (less than 0.04 mg/L in 30 min), distinguishing it as a material with high chemical stability. According to the radical quenching experiments and the electron paramagnetic resonance technology, SO4 –, OH, and 1O2 were generated and SO4 – played a dominant role in bisphenol A degradation. Moreover, the La2CoO4+δ + PMS system maintained conspicuous catalytic performance for the degradation of other organic pollutants including methyl orange, rhodamine B, and methylene blue. Overall, our results showed that we developed a new synthesis method for stable La2CoO4+δ nanoparticles that can be used as a highly active heterogeneous catalyst for PMS-assisted oxidation of organic pollutants.

Authors

Showing all 19555 results

NameH-indexPapersCitations
Michael Schmitt1342007114667
Bernt Schiele13056870032
Peter Walter12684171580
David Zurakowski117116855806
Kurt Binder114124865308
Franz Hofmann11347149938
Bernd Nilius11249644812
Hans-Peter Seidel112121351080
Stefan Zeuzem108102750529
Rolf Müller10490550027
Samuel Klein10136346578
Michael Bauer100105256841
Ulman Lindenberger10055441956
Thomas Brox9932994431
Elisabeth Kremmer9941334720
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202228
20212,299
20202,191
20191,993
20181,908
20171,963