Kombination von Messdaten und wissensbasierter Modellierung zur Fehlerdiagnose bei Weichen / Connecting measurement data and knowledge-based engineering for heavy rail switch fault diagnosis

Die Anwendung Künstlicher Intelligenz (KI) im Bereich Prognostics and Health Management (PHM) der Eisenbahninfrastruktur, insbesondere in der Fehlerdiagnose, wird durch hinsichtlich Umfang und/oder Labelling unzureichende Datenbestände und die Notwendigkeit der Rückverfolgbarkeit aufgrund strenger Sicherheitsvorschriften erschwert. Vielversprechende Ansätze sind Feature Engineering, unüberwachtes Lernen und wissensbasierte Systeme. Vor diesem Hintergrund wird nachfolgend erörtert, wie Stromumlaufkurven von Weichenantrieben ausgewertet und mit einem für den Menschen interpretierbaren Bayes'schen Netzmodell für Diagnosezwecke verbunden werden können. -- The application of AI methods in prognostics and health management, especially fault diagnosis, for railway infrastructure is complicated by the largely unlabelled databases and the necessity for traceability due to strict safety regulations. Promising approaches include feature engineering, unsupervised learning and knowledge-based systems. This article discusses how to treat the current curve measurements of railway point machines and connect them with a human-interpretable Bayesian network model for diagnostic purposes

Expert system based fault diagnosis for railway point machines

To meet the increasing demands for availability at reasonable cost, operators and maintainers of railway point machines are constantly looking for innovative techniques for switch condition monitoring and prediction. This includes automated fault root cause diagnosis based on measurement data (such as motor current curves) and other information. However, large, comprehensive sets of labeled data suitable for standard machine learning are not yet available. Existing data-driven approaches focus only on the differentiation of a few major fault categories at the level of the measurement data (i.e. the "fault symptoms"). There is great potential in hybrid models that use expert knowledge in combination with multiple sources of information to automatically identify failure causes at a much more detailed level. This paper discusses a Bayesian network diagnostic model for determining the root causes of faults in point machines, based on expert knowledge and few labeled data examples from the Netherlands. Human-interpretable current curve features and other information sources (e.g. past maintenance actions) are used as evidence. The result of the model is a ranking of the most likely failure causes with associated probabilities in terms of fuzzy multi-label classification, which is directly aimed at providing decision support to maintenance engineers. The validity and limitations of the model are demonstrated by a scenario-based evaluation and a brief analysis using information theoretic measures. We present the information sources used, the detailed development process and the analysis methodology. This article is intended to be a guide to developing similar models for various complex technical assets

Weight loss and elevated gluconeogenesis from alanine in lung cancer patients

BACKGROUND: The role of gluconeogenesis from protein in the pathogenesis of weight loss in lung cancer is unclear. OBJECTIVE: Our aim was to study gluconeogenesis from alanine in lung cancer patients and to analyze its relation to the degree of weight loss. DESIGN: In this cross-sectional study, we used primed-constant infusions of [6,6-(2)H(2)]-D-glucose and [3-(13)C]-L-alanine to assess whole-body glucose and alanine turnover and gluconeogenesis from alanine in weight-losing (WL, n = 9) and weight-stable (WS, n = 10) lung cancer patients and healthy control (n = 15) subjects. RESULTS: Energy intake and plasma alanine concentrations did not differ significantly among the subject groups. Mean (+/-SEM) whole-body glucose production was significantly higher in WL than in WS and control subjects (0.74 +/- 0.06 compared with 0.55 +/- 0.04 and 0.51 +/- 0.04 mmol*kg(-)(1)*h(-)(1), respectively, P < 0.01). Alanine turnover was significantly elevated in WL compared with WS and control subjects (0.57 +/- 0.04 compared with 0.42 +/- 0.05 and 0.40 +/- 0.03 mmol*kg(-)(1)*h(-)(1), respectively, P < 0.01). Gluconeogenesis from alanine was significantly higher in WL than in WS and control subjects (0.47 +/- 0.04 compared with 0.31 +/- 0.04 and 0.29 +/- 0.04 mmol*kg(-)(1)*h(-)(1), respectively, P < 0.01). The degree of weight loss was positively correlated with glucose and alanine turnover and with gluconeogenesis from alanine (r = 0.45 for all, P < 0.01). CONCLUSIONS: Aberrant glucose and alanine metabolism occurred in WL lung cancer patients. These changes were related to the degree of weight loss and not to the presence of lung cancer per se

Zinc Binding Catalytic Domain of Human Tankyrase 1

Tankyrases are recently discovered proteins implicated in many important functions in the cell including telomere homeostasis and mitosis. Tankyrase modulates the activity of target proteins through poly(ADP-ribosyl)ation, and here we report the structure of the catalytic poly(ADP-ribose) polymerase (PARP) domain of human tankyrase 1. This is the first structure of a PARP domain from the tankyrase subfamily. The present structure reveals that tankyrases contain a short zinc-binding motif, which has not been predicted. Tankyrase activity contributes to telomere elongation observed in various cancer cells and tankyrase inhibition has been suggested as a potential route for cancer therapy. In comparison with other PARPs, significant structural differences are observed in the regions lining the substratebinding site of tankyrase 1. These findings will be of great value to facilitate structure-based design of selective PARP inhibitors, in general, and tankyrase inhibitors, in particular

Effectiveness, usability and acceptability of a smart inhaler programme in patients with asthma:protocol of the multicentre, pragmatic, open-label, cluster randomised controlled ACCEPTANCE trial

Introduction Suboptimal asthma control is associated with incorrect inhaler use and poor medication adherence, which could lead to unfavourable clinical and economic outcomes. Smart inhaler programmes using electronic monitoring devices (EMDs) could support self-management and increase medication adherence and asthma control. However, evidence on long-term benefits and acceptability is scarce. This study aims to investigate the effectiveness of a smart inhaler asthma self-management programme on medication adherence and clinical outcomes in adults with uncontrolled asthma, to evaluate its acceptability and to identify subgroups who would benefit most based on patient characteristics.Methods and analysis This open-label cluster randomised controlled trial of 12 months will be conducted in primary care in the Netherlands. General practices will be randomly assigned to either intervention or control group. We aim to include 242 patients. The intervention consists of (1) an EMD attached to the patient’s inhaler that measures medication use; (2) a smartphone application to set medication reminders, receive motivational messages and track asthma symptoms; and (3) a portal for healthcare professionals to view data on medication use. The control group is passively monitored by the EMD but cannot view their inhaler data or receive feedback. Eligible patients are adults with suboptimal controlled asthma (Asthma Control Questionnaire score ≥0.75) with evidence of non-adherence established by the EMD during a 6-week run-in period. Primary outcome is the difference in mean medication adherence between intervention and control group. Secondary outcomes include asthma control, asthma-related quality of life, exacerbations, acceptance, cost-effectiveness and whether the effect of the intervention on medication adherence and asthma control is modified by patient characteristics (eg, self-efficacy, medication beliefs and eHealth literacy).Trial registration numberNL7854