Feature selection methods for root-cause analysis among top-level product attributes

Manufacturing companies not only strive to deliver flawless products but also monitor product failures in the field to identify potential quality issues. When product failures occur, quality engineers must identify the root cause to improve any affected product and process. This root-cause analysis can be supported by feature selection methods that identify relevant product attributes, such as manufacturing dates with an increased number of product failures. In this paper, we present different methods for feature selection and evaluate their ability to identify relevant product attributes in a root-cause analysis. First, we compile a list of feature selection methods. Then, we summarize the properties of product attributes in warranty case data and discuss these properties regarding the challenges they pose for machine learning algorithms. Next, we simulate datasets of warranty cases, which emulate these product properties. Finally, we compare the feature selection methods based on these simulated datasets. In the end, the univariate filter information gain is determined to be a suitable method for a wide range of applications. The comparison based on simulated data provides a more general result than other publications, which only focus on a single use case. Due to the generic nature of the simulated datasets, the results can be applied to various root-cause analysis processes in different quality management applications and provide a guideline for readers who wish to explore machine learning methods for their analysis of quality data.     

Electrochemical Quantification of Neurotransmitters in Single Live Cell Vesicles Shows Exocytosis is Predominantly Partial

Exocytosis plays an essential role in the communication between cells in the nervous system. Understanding the regulation of neurotransmitter release during exocytosis and the amount of neurotransmitter content that is stored in vesicles is of importance, as it provides fundamental insights to understand how the brain works and how neurons elicit a certain behavior. In this minireview, we summarize recent progress in amperometric measurements for monitoring exocytosis in single cells and electrochemical cytometry measurements of vesicular neurotransmitter content in individual vesicles. Important steps have increased our understanding of the different mechanisms of exocytosis. Increasing evidence is firmly establishing that partial release is the primary mechanism of release in multiple cell types.     

Tactile colour pictogram to improve artwork appreciation of people with visual impairments

A recent development in tactile technology enables an improvement in the appreciation of the visual arts for people with visual impairment (PVI). The tactile sense, in conjunction with, or a possibly as an alternative to, the auditory sense, would allow PVIs to approach artwork in a more self‐driven and engaging way that would be difficult to achieve with just an auditory stimulus. Tactile colour pictograms (TCPs), which are raised geometric patterns, are ideographic characters that are designed to enable PVIs to identify colours and interpret information by touch. In this article, three TCPs are introduced to code colours in the Munsell colour system. Each colour pattern consists of a basic cell size of 10 mm × 10 mm to represent the patterns consistently in terms of regular shape. Each TCP consists of basic geometric patterns that are combined to create primary, secondary, and tertiary colour pictograms of shapes indicating colour hue, intensity and lightness. Each TCP represents 29 colours including six hues; they were then further expanded to represent 53 colours. Two of them did not increase the cell size, the other increased the cell size 1.5 times for some colours, such as yellow‐orange, yellow, blue, and blue‐purple. Our proposed TCPs use a slightly larger cell size compared to most tactile patterns currently used to indicate colour, but code for more colours. With user experience and identification tests, conducted with 23 visually impaired adults, the effectiveness of the TCPs suggests that they were helpful for the participants.     

Adaptive backstepping repetitive learning control design for nonlinear discrete‐time systems with periodic uncertainties

This paper addresses a tracking problem for uncertain nonlinear discrete‐time systems in which the uncertainties, including parametric uncertainty and external disturbance, are periodic with known periodicity. Repetitive learning control (RLC) is an effective tool to deal with periodic unknown components. By using the backstepping procedures, an adaptive RLC law with periodic parameter estimation is designed. The overparameterization problem is overcome by postponing the parameter estimation to the last backstepping step, which could not be easily solved in robust adaptive control. It is shown that the proposed adaptive RLC law without overparameterization can guarantee the perfect tracking and boundedness of the states of the whole closed‐loop systems in presence of periodic uncertainties. In addition, the effectiveness of the developed controller is demonstrated by an implementation example on a single‐link flexible‐joint robot. Copyright © 2014 John Wiley & Sons, Ltd.     

Expression of Main Toll-Like Receptors in Patients with Different Types of Colorectal Polyps and Their Relationship with Gut Microbiota

Abnormal activation of Toll-like receptor (TLRs) signaling can result in colon cancer development. The aim of this study was to investigate the expression of important TLRs in different histological types of colorectal polyps and evaluate their relationship with intestinal microbiota. The expression levels of TLR2, 3, 4, and 5 were analyzed in intestinal biopsy specimens of 21 hyperplastic polyp (HP), 16 sessile serrated adenoma (SSA), 29 tubular adenoma (TA), 21 villous/tubulovillous (VP/TVP) cases, and 31 normal controls. In addition, selected gut bacteria including Streptococcus bovis, Enterococcus faecalis, Enterotoxigenic Bacteroides fragilis (ETBF), Fusobacterium nucleatum, Porphyromonas spp., Lactobacillus spp., Roseburia spp., and Bifidobacterium spp. were quantified in fecal samples using absolute qRT PCR, and, finally, the association between TLRs and these gut microbiota- was evaluated by Spearman’s correlation coefficient. Higher expression of TLR2 and TLR4 in VP/TVP and TA, and lower expression levels of TLR3 and TLR5 in all type of polyps were observed. The differences in TLR expression patterns was not only dependent on the histology, location, size, and dysplasia grade of polyps but also related to the intestinal microbiota patterns. TLR2 and TLR4 expression was directly associated with the F. nucleatum, E. faecalis, S. bovis, Porphyromonas, and inversely to Bifidobacterium, Lactobacillus, and Roseburia quantity. Furthermore, TLR3 and TLR5 expression was directly associated with Bifidobacterium, Roseburia, and Lactobacillus quantity. Our results suggest a possible critical role of TLRs during colorectal polyp progression. An abnormal regulation of TLRs in relation to gut microbial quantity may contribute to carcinogenesis.     

Parameter estimation for a morphochemical reaction-diffusion model of electrochemical pattern formation

The process of electrodeposition can be described in terms of a reaction-diffusion partial differential equation (PDE) system that models the dynamics of the morphology profile and the chemical composition. Here we fit such a model to the different patterns present in a range of electrodeposited and electrochemically modified alloys using PDE constrained optimization. Experiments with simulated data show how the parameter space of the model can be divided into zones corresponding to the different physical patterns by examining the structure of an appropriate cost function. We then use real data to demonstrate how numerical optimization of the cost function can allow the model to fit the rich variety of patterns arising in experiments. The computational technique developed provides a potential tool for tuning experimental parameters to produce desired patterns.     

Probing the degenerate pattern growth of {100}<011> orientation in a directionally solidified Al-4.5 wt% Cu alloy

Degenerate pattern is a seemingly disordered morphology but it exhibits the inherently ordered crystal connected with tip-splitting and limited stability which makes it difficult to observe in the metallic system. Here we employ (100)[011] orientated planar-front seeds using directional solidification and reveal the fundamental origins of the degenerate pattern growth in an Al-4.5 wt% Cu alloy. We find that the spacing of the tip-splitting (λ) in the degenerate of the alloys followed a power law, λ∝V^−0.5, and the frequency (f) of the splitting was related to the growth velocity (V) by ƒ∝V^1.5. The dimensionless growth direction (θ/θ₀) increased monotonously and approached 0.6 with faster velocity, attributed to its anisotropy in the interface kinetics. Once growth velocity exceeded a threshold, two types of pattern transitions from degenerate to regular dendrites were proposed. One of them exhibited a random and chaotic mode and the other underwent a rotation in growth direction.