Benchmarking a DEM‐CFD Model of an Optical Belt Sorter by Experimental Comparison

A DEM-CFD (discrete element method - computational fluid dynamics) model of an optical belt sorter was extensively compared with experiments of a laboratory-scale sorter to assess the model\u27s accuracy. Brick and sand-lime brick were considered as materials. First, the transport characteristics on the conveyor belt, involving mass flow, lateral particle distribution and proximity, were compared. Second, sorting results were benchmarked for varying mixture proportions at differing mass flows. It was found that the numerical model is able to reproduce the experimental results with high accuracy

Machine learning based multiobject tracking for sensor based sorting

Sensor-based sorting provides state-of-the-art solutions for sorting of granular materials. Current systems useline-scanning sensors, which yields a single observation of each object only and no information about their movement. Recent works show that using an area-scan camera bears the potential to decrease both the error in characterization and separation. Using a multiobject tracking system, this enables an estimate of the followed paths as well as the parametrization of an individual motion model per object. While previous works focus on physically-motivated motion models, it has been shown that state-of-the-art machine learning methods achieve an increased prediction accuracy. In this paper, we present the development of a neural network-based multiobject tracking system and its integration into a laboratory-scale sorting system. Preliminary results show that the novel system achieves results comparable to a highly optimized Kalman filter-based one. A benefit lies in avoiding tiresome manual tuning of parameters of the motion model, as the novel approach allows learning its parameters by provided examples due to its data-driven nature

Simulation study and experimental validation of a neural network-based predictive tracking system for sensor-based sorting

Die sensorgestützte Sortierung bietet zukunftsweisende Lösungen für die Trennung von körnigen Materialien. Die derzeit in solchen Systemen verwendeten Zeilensensoren liefern nur eine einzige Beobachtung jedes Objekts und keine Daten über dessen Bewegung. Jüngsten Studien zufolge hat die Verwendung einer Flächenkamera das Potenzial, sowohl den Charakterisierungs- als auch den Trennungsfehler in einem Sortierprozess zu verringern. Ein prädiktiver Tracking-Ansatz auf der Grundlage von Kalman-Filtern ermöglicht die Schätzung der verfolgten Pfade und die Parametrisierung eines individuellen Bewegungsmodells für jedes Objekt in einem Multiobjekt-Tracking-System. Während sich frühere Studien auf physikalisch motivierte Bewegungsmodelle konzentrierten, hat sich gezeigt, dass moderne Ansätze des maschinellen Lernens genauere Vorhersagen ermöglichen. In diesem Beitrag beschreiben wir die Entwicklung eines prädiktiven Trackingsystems auf Basis neuronaler Netze. Der neue Algorithmus wird auf ein experimentelles Sortiersystem und auf ein numerisches Modell des Sortierers angewendet. Zwar erreicht der neue Ansatz noch nicht ganz die Sortierqualität der bestehenden Ansätze, jedoch ermöglicht er die Anwendung von prädiktivem Tracking, ohne dass hierfür Expertenwissen oder ein grundlegendes Verständnis der Parametrisierung des Partikelbewegungsmodells erforderlich sind

Impact of impaired fractional flow reserve after coronary interventions on outcomes: a systematic review and meta-analysis

BACKGROUND: FFR is routinely used to guide percutaneous coronary interventions (PCI). Visual assessment of the angiographic result after PCI has limited efficacy. Even when the angiographic result seems satisfactory FFR after a PCI might be useful for identifying patients with a suboptimal interventional result and higher risk for poor clinical outcome who might benefit from additional procedures. The aim of this meta-analysis was to investigate available data of studies that examined clinical outcomes of patients with impaired vs. satisfactory fractional flow reserve (FFR) after percutaneous coronary interventions (PCI). METHODS: This meta-analysis was carried out according to the Cochrane Handbook for Systematic Reviews. The Mantel-Haenszel method using the fixed-effect meta-analysis model was used for combining the results. Studies were identified by searching the literature through mid-January, 2016, using the following search terms: fractional flow reserve, coronary circulation, after, percutaneous coronary intervention, balloon angioplasty, stent implantation, and stenting. Primary endpoint was the rate of major adverse cardiac events (MACE). Secondary endpoints included rates of death, myocardial infarction (MI), repeated revascularisation. RESULTS: Eight relevant studies were found including a total of 1337 patients. Of those, 492 (36.8 %) had an impaired FFR after PCI, and 853 (63.2 %) had a satisfactory FFR after PCI. Odds ratios indicated that a low FFR following PCI was associated with an impaired outcome: major adverse cardiac events (MACE, OR: 4.95, 95 % confidence interval [CI]: 3.39–7.22, p <0.001); death (OR: 3.23, 95 % CI: 1.19–8.76, p = 0.022); myocardial infarction (OR: 13.83, 95 % CI: 4.75–40.24, p <0.0001) and repeated revascularisation (OR: 4.42, 95 % CI: 2.73–7.15, p <0.0001). CONCLUSIONS: Compared to a satisfactory FFR, a persistently low FFR following PCI is associated with a worse clinical outcome. Prospective studies are needed to identify underlying causes, determine an optimal threshold for post-PCI FFR, and clarify whether simple additional procedures can influence the post-PCI FFR and clinical outcome. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12872-016-0355-7) contains supplementary material, which is available to authorized users

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Control of central self-tolerance induction by autoreactive CD4+ thymocytes

Medullary thymic epithelial cells (mTECs) are crucial for the selection of a T-cell-receptor (TCR) repertoire purged of self-reactive specificities, because these cells activate a promiscuous gene-expression program that leads to the synthesis of a wide array of peripheral tissue-restricted self-antigens. This review summarizes recent progress in our understanding of the cellular interactions, ligands, receptors and signal-transduction pathways that control mature-mTEC development. The particular focus is on new findings supporting the model that mature-mTEC development in the postnatal thymus depends on nuclear factor-kappaB (NF-kappaB) signaling induced by CD40-CD40 ligand, and receptor-activator-of-NF-kappaB (RANK)-RANK ligand interactions, and that these signals are delivered in the context of antigen-specific interactions between CD4(+) thymocytes carrying autoreactive TCRs and mTECs displaying cognate autoantigen-MHC-class-II complexes