Machine and component residual life estimation through the application of neural networks

Analysis of reliability data plays an important role in the maintenance decision making process. The accurate estimation of residual life in components and systems can be a great asset when planning the preventive replacement of components on machines. Artificial intelligence is a field that has rapidly developed over the last twenty years and practical applications have been found in many diverse areas. The use of such methods in the maintenance field have however not yet been fully explored. With the common availability of condition monitoring data, another dimension has been added to the analysis of reliability data. Neural networks allow for explanatory variables to be incorporated into the analysis process. This is expected to improve the quality of predictions when compared to the results achieved through the use of methods that rely solely on failure time data. Neural networks can therefore be seen as an alternative to the various regression models, such as the proportional hazards model, which also incorporate such covariates into the analysis. For the purpose of investigating their applicability to the problem of predicting the residual life of machines and components, neural networks were trained and tested with the data of two different reliability related datasets. The first dataset represents the renewal case where repair leads to complete restoration of the system. A typical maintenance situation was simulated in the laboratory by subjecting a series of similar test pieces to different loading conditions. Measurements were taken at regular intervals during testing with a number of sensors which provided an indication of the test piece’s condition at the time of measurement. The dataset was split into a training set and a test set and a number of neural network variations were trained using the first set. The networks’ ability to generalize was then tested by presenting the data from the test set to each of these networks. The second dataset contained data collected from a group of pumps working in a coal mining environment. This dataset therefore represented an example of the situation encountered with a repaired system. The performance of different neural network variations was subsequently compared through the use of cross-validation. It was proved that in most cases the use of condition monitoring data as network inputs improved the accuracy of the neural networks’ predictions. The average prediction error of the various neural networks under comparison varied between 431 and 841 seconds on the renewal dataset, where test pieces had a characteristic life of 8971 seconds. When optimized the multi-layer perceptron neural networks trained with the Levenberg-Marquardt algorithm and the general regression neural network produced a sum of squares error within 11.1% of each other for the data of the repaired system. This result emphasizes the importance of adjusting parameters, network architecture and training targets for optimal performance The advantage of using neural networks for predicting residual life was clearly illustrated when comparing their performance to the results achieved through the use of the traditional statistical methods. The potential of using neural networks for residual life prediction was therefore illustrated in both cases.Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2007.Mechanical and Aeronautical EngineeringMEngunrestricte

D6.2: Intermediate Standardisation and Dissemination Activity Report

This report documents the standardisation and dissemination activities performed by the ORPHEUS project consortium from December 2015 to February 2017

Sex-related differences in vision are heterogeneous

Despite well-established sex differences for cognition, audition, and somatosensation, few studies have investigated whether there are also sex differences in visual perception. We report the results of fifteen perceptual measures (such as visual acuity, visual backward masking, contrast detection threshold or motion detection) for a cohort of over 800 participants. On six of the fifteen tests, males significantly outperformed females. On no test did females significantly outperform males. Given this heterogeneity of the sex effects, it is unlikely that the sex differences are due to any single mechanism. A practical consequence of the results is that it is important to control for sex in vision research, and that findings of sex differences for cognitive measures using visually based tasks should confirm that their results cannot be explained by baseline sex differences in visual perception

Impact of molecular profiling on overall survival of patients with advanced ovarian cancer

OBJECTIVE: Patients with recurrent epithelial ovarian cancer (EOC) have limited treatment options. Studies have reported that biomarker profiling may help predict patient response to available treatments. This study sought to determine the value of biomarker profiling in recurrent EOC. RESULTS: Patients in the Matched cohort had a median OS of 36 months compared to 27 months for patients in the Unmatched cohort (HR 0.62, 95% CI 0.41-0.96; p < 0.03). Individual biomarkers were analyzed, with TUBB3, and PGP prognostic for survival. Biomarker analysis also identified a molecular subtype (positive for at least two of the following markers: ERCC1, RRM1, TUBB3, PGP) with particularly poor overall survival. METHODS: 224 patients from a commercial registry (NCT02678754) with stage IIIC/IV EOC at diagnosis, or restaged to IIIC/IV EOC at the time of molecular profiling, were retrospectively divided into two cohorts based on whether or not the drugs they received matched their profile recommendations. The Matched cohort received no drugs predicted to be lack-of-benefit while the Unmatched cohort received at least one drug predicted to be lack-of-benefit. Profile biomarker/drug associations were based on multiple test platforms including immunohistochemistry, fluorescent in situ hybridization and DNA sequencing. CONCLUSIONS: This report demonstrates the ability of multi-platform molecular profiling to identify EOC patients at risk of inferior survival. It also suggests a potential beneficial role of avoidance of lack-of-benefit therapies which, when administered, resulted in decreased survival relative to patients who received only therapies predicted to be of benefit

The Shine-Through Masking Paradigm Is a Potential Endophenotype of Schizophrenia

BACKGROUND: To understand the genetics of schizophrenia, a hunt for so-called intermediate phenotypes or endophenotypes is ongoing. Visual masking has been proposed to be such an endophenotype. However, no systematic study has been conducted yet to prove this claim. Here, we present the first study showing that masking meets the most important criteria for an endophenotype. METHODOLOGY/PRINCIPAL FINDINGS: We tested 62 schizophrenic patients, 39 non-affected first-degree relatives, and 38 healthy controls in the shine-through masking paradigm and, in addition, in the Continuous Performance Test (CPT) and the Wisconsin Card Sorting Test (WCST). Most importantly, masking performance of relatives was significantly in between the one of patients and controls in the shine-through paradigm. Moreover, deficits were stable throughout one year. Using receiver operating characteristics (ROC) methods, we show that the shine-through paradigm distinguishes with high sensitivity and specificity between schizophrenic patients, first-order relatives and healthy controls. CONCLUSIONS/SIGNIFICANCE: The shine-through paradigm is a potential endophenotype

Targeted Metabolomic Profiling of Peritoneal Dialysis Effluents Shows Anti-oxidative Capacity of Alanyl-Glutamine

Readily available peritoneal dialysis (PD) effluents from PD patients in the course of renal replacement therapy are a potentially rich source for molecular markers for predicting clinical outcome, monitoring the therapy, and therapeutic interventions. The complex clinical phenotype of PD patients might be reflected in the PD effluent metabolome. Metabolomic analysis of PD effluent might allow quantitative detection and assessment of candidate PD biomarkers for prognostication and therapeutic monitoring. We therefore subjected peritoneal equilibration test effluents from 20 stable PD patients, obtained in a randomized controlled trial (RCT) to evaluate cytoprotective effects of standard PD solution (3.86% glucose) supplemented with 8 mM alanyl-glutamine (AlaGln) to targeted metabolomics analysis. One hundred eighty eight pre-defined metabolites, including free amino acids, acylcarnitines, and glycerophospholipids, as well as custom metabolic indicators calculated from these metabolites were surveyed in a high-throughput assay requiring only 10 μl of PD effluent. Metabolite profiles of effluents from the cross-over trial were analyzed with respect to AlaGln status and clinical parameters such as duration of PD therapy and history of previous episodes of peritonitis. This targeted approach detected and quantified 184 small molecules in PD effluent, a larger number of detected metabolites than in all previous metabolomic studies in PD effluent combined. Metabolites were clustered within substance classes regarding concentrations after a 4-h dwell. PD effluent metabolic profiles were differentiated according to PD patient sub-populations, revealing novel changes in small molecule abundance during PD therapy. AlaGln supplementation of PD fluid altered levels of specific metabolites, including increases in alanine and glutamine but not glutamate, and reduced levels of small molecule indicators of oxidative stress, such as methionine sulfoxide. Our study represents the first application of targeted metabolomics to PD effluents. The observed metabolomic changes in PD effluent associated with AlaGln-supplementation during therapy suggested an anti-oxidant effect, and were consistent with the restoration of important stress and immune processes previously noted in the RCT. High-throughput detection of PD effluent metabolomic signatures and their alterations by therapeutic interventions offers new opportunities for metabolome-clinical correlation in PD and for prescription of personalized PD therapy

Digitalisierung und Demokratie

Die Digitalisierung spielt bei den Prozessen und Entwicklungen in einer Demokratie eine immer größere Rolle. Denn Digitalisierung erweitert die Möglichkeiten der Information, Kommunikation und Partizipation. Gleichzeitig können digitale Technologien zu einer schnellen Verbreitung von Falschinformationen beitragen und bergen ein Potenzial für Meinungsmanipulation, zum Beispiel vor Wahlen. Dieses Spannungsfeld ist Thema der Stellungnahme "Digitalisierung und Demokratie". Darin analysieren die Autorinnen und Autoren Aspekte des Zusammenspiels von Digitalisierung und Demokratie. Darauf aufbauend formulieren sie Handlungsempfehlungen zur Gestaltung künftiger Entwicklungen durch Politik, Recht und Zivilgesellschaft

Extraintestinal Manifestations of Pediatric Inflammatory Bowel Disease: Prevalence, Presentation, and Anti-TNF Treatment.

There is a paucity of data on extraintestinal manifestations (EIM) and their treatment in pediatric patients with inflammatory bowel disease (IBD). Since 2008, the Pediatric Swiss IBD Cohort Study has collected data on the pediatric IBD population in Switzerland. Data on 329 patients were analyzed retrospectively. A total of 55 patients (16.7%) experienced 1-4 EIM (39 Crohn disease, 12 ulcerative colitis, and 4 IBD-unclassified patients). At IBD onset, presence of EIM was more frequent than in the adult population (8.5% vs 5.0%, P = 0.014). EIM were more frequent in Crohn disease when compared to ulcerative colitis/IBD-unclassified (22.5% vs 10.3%, P = 0.003). The most prevalent EIM were peripheral arthritis (26/329, 7.9%) and aphthous stomatitis (24/329, 7.3%). Approximately 27.6% of all EIM appeared before IBD diagnosis. Median time between IBD diagnosis and occurrence of first EIM was 1 month (-37.5-149.0). Thirty-one of the 55 patients (56.4%) were treated with 1 or more anti-tumor necrosis factor (TNF) agents. IBD patients with EIM were more likely to be treated with anti-TNF compared to those without (56.4% vs 35.0%, P = 0.003). Response rates to anti-TNF depended on underlying EIM and were best for peripheral arthritis (61.5%) and uveitis (66.7%). In a cohort of pediatric patients with IBD, EIM were frequently encountered. In up to 30%, EIM appeared before IBD diagnosis. Knowledge of these findings may translate into an increased awareness of underlying IBD, thereby decreasing diagnostic delay. Anti-TNF for the treatment of certain EIM is effective, although a substantial proportion of new EIM may present despite ongoing anti-TNF therapy