Failure mode & effect analysis for improving data veracity and validity

Failure Mode & Effect Analysis (FMEA) is a method that has been used to improve reliability of products, processes, designs, and software for different applications, including electronics manufacturing. In this paper we propose a modification of this method to extend its application for data veracity and validity improvement. The proposed DVV-FMEA method is based on engineering features and in addition, provides transparency and understandability of the data and its pre-processing, making it reproducible and trustful

Failure mode & effect analysis and another methodology for improving data veracity and validity

Failure Mode & Effect Analysis (FMEA) is a method that has been used to improve reliability of products, processes, designs, and software for different applications. In this paper we extend its usage for data veracity and validity improvement in the context of big data analysis and discuss its application in an electronics manufacturing test procedure which consists of a sequence of tests. Finally, we describe another methodology, developed as a result of the DVV-FMEA application which is aimed at improving the tests' repeatability and failure detection capabilities as well as monitoring their reliability

Improving Black Box Classification Model Veracity for Electronics Anomaly Detection

Data driven classification models are useful to assess quality of manufactured electronics. Because decisions are taken based on the models, their veracity is relevant, covering aspects such as accuracy, transparency and clarity. The proposed BB-Stepwise algorithm aims to improve the classification model transparency and accuracy of black box models. K-Nearest Neighbours (KNN) is a black box model which is easy to implement and has achieved good classification performance in different applications. In this paper KNN-Stepwise is illustrated for fault detection of electronics devices. The results achieved shows that the proposed algorithm was able to improve the accuracy, veracity and transparency of KNN models and achieve higher transparency and clarity, and at least similar accuracy than when using Decision Tree models

Validation of a new protocol for assessing hazards of leachable elements in volcanic ashfall through an interlaboratory comparison exercise

Equilibrium climate sensitivity to a doubling of CO2 falls between 2.0 and 4.6 K in current climate models, and they suggest a weak increase in global mean precipitation. Inferences from the observational record, however, place climate sensitivity near the lower end of this range and indicate that models underestimate some of the changes in the hydrological cycle. These discrepancies raise the possibility that important feedbacks are missing from the models. A controversial hypothesis suggests that the dry and clear regions of the tropical atmosphere expand in a warming climate and thereby allow more infrared radiation to escape to space. This so-called iris effect could constitute a negative feedback that is not included in climate models. We find that inclusion of such an effect in a climate model moves the simulated responses of both temperature and the hydrological cycle to rising atmospheric greenhouse gas concentrations closer to observations. Alternative suggestions for shortcomings of models — such as aerosol cooling, volcanic eruptions or insufficient ocean heat uptake — may explain a slow observed transient warming relative to models, but not the observed enhancement of the hydrological cycle. We propose that, if precipitating convective clouds are more likely to cluster into larger clouds as temperatures rise, this process could constitute a plausible physical mechanism for an iris effect

Human iPSC-Derived Endothelial Cells and Microengineered Organ-Chip Enhance Neuronal Development

Summary: Human stem cell-derived models of development and neurodegenerative diseases are challenged by cellular immaturity in vitro. Microengineered organ-on-chip (or Organ-Chip) systems are designed to emulate microvolume cytoarchitecture and enable co-culture of distinct cell types. Brain microvascular endothelial cells (BMECs) share common signaling pathways with neurons early in development, but their contribution to human neuronal maturation is largely unknown. To study this interaction and influence of microculture, we derived both spinal motor neurons and BMECs from human induced pluripotent stem cells and observed increased calcium transient function and Chip-specific gene expression in Organ-Chips compared with 96-well plates. Seeding BMECs in the Organ-Chip led to vascular-neural interaction and specific gene activation that further enhanced neuronal function and in vivo-like signatures. The results show that the vascular system has specific maturation effects on spinal cord neural tissue, and the use of Organ-Chips can move stem cell models closer to an in vivo condition. : Sances et al. combine Organ-Chip technology with human induced pluripotent stem cell-derived spinal motor neurons to study the maturation effects of Organ-Chip culture. By including microvascular cells also derived from the same patient line, the authors show enhancement of neuronal function, reproduction of vascular-neuron pathways, and specific gene activation that resembles in vivo spinal cord development. Keywords: organ-on-chip, spinal cord, iPSC, disease modeling, amyotrophic lateral sclerosis, microphysiological system, brain microvascular endothelial cells, spinal motor neurons, vasculature, microfluidic devic

Migration Phenotype of Brain-Cancer Cells Predicts Patient Outcomes

SummaryGlioblastoma multiforme is a heterogeneous and infiltrative cancer with dismal prognosis. Studying the migratory behavior of tumor-derived cell populations can be informative, but it places a high premium on the precision of in vitro methods and the relevance of in vivo conditions. In particular, the analysis of 2D cell migration may not reflect invasion into 3D extracellular matrices in vivo. Here, we describe a method that allows time-resolved studies of primary cell migration with single-cell resolution on a fibrillar surface that closely mimics in vivo 3D migration. We used this platform to screen 14 patient-derived glioblastoma samples. We observed that the migratory phenotype of a subset of cells in response to platelet-derived growth factor was highly predictive of tumor location and recurrence in the clinic. Therefore, migratory phenotypic classifiers analyzed at the single-cell level in a patient-specific way can provide high diagnostic and prognostic value for invasive cancers