Predictive Maintenance of Pins in the ECD Equipment for Cu Deposition in the Semiconductor Industry.

Nowadays, Predictive Maintenance is a mandatory tool to reduce the cost of production in the semiconductor industry. This paper considers as a case study a critical part of the electrochemical deposition system, namely, the four Pins that hold a wafer inside a chamber. The aim of the study is to replace the schedule of replacement of Pins presently based on fixed timing (Preventive Maintenance) with a Hardware/Software system that monitors the conditions of the Pins and signals possible conditions of failure (Predictive Maintenance).

A machine learning approach to predict extreme inactivity in COPD patients using non-activity-related clinical data.

Facilitating the identification of extreme inactivity (EI) has the potential to improve morbidity and mortality in COPD patients. Apart from patients with obvious EI, the identification of a such behavior during a real-life consultation is unreliable. We therefore describe a machine learning algorithm to screen for EI, as actimetry measurements are difficult to implement.

Concurrence of 1- and 3-Min Sit-to-Stand Tests with the 6-Min Walk Test in Idiopathic Pulmonary Fibrosis.

Background: In idiopathic pulmonary fibrosis (IPF), some physiological parameters measured during a 6-min walk test (6-MWT) impart reliable prognostic information. Sit-to-stand tests (STSTs) are field exercise tests that are easier to implement than the 6-MWT in daily practice.

Objectives: The aims of the study were to test the reproducibility and compare 2 STSTs (the 1-min STST [1-STST] and the semi-paced 3-min chair rise test [3-CRT]) in IPF, and to determine if selected physiological parameters (speed of displacement and changes in pulse oxygen saturation [SpO2]) are interchangeable between the STSTs and the 6-MWT.

Cloud Detection: An Assessment Study from the ESA Round Robin Exercise for PROBA-V.

A Round Robin exercise was implemented by ESA to compare different classification methods in detecting clouds from images taken by the PROBA-V sensor. A high-quality dataset of 1350 reflectances and Clear/Cloudy corresponding labels had been prepared by ESA in the framework of the exercise. Motivated by both the experience acquired by one of the authors in this exercise and the availability of such a reliable annotated dataset, we present a full assessment of the methodology proposed therein.

Are there specific clinical characteristics associated with physician's treatment choices in COPD?

Background: The number of pharmacological agents and guidelines available for COPD has increased markedly but guidelines remain poorly followed. Understanding underlying clinical reasoning is challenging and could be informed by clinical characteristics associated with treatment prescriptions.

Methods: To determine whether COPD treatment choices by respiratory physicians correspond to specific patients' features, this study was performed in 1171 patients who had complete treatment and clinical characterisation data.

Minimal clinically important difference of 3-minute chair rise test and the DIRECT questionnaire after pulmonary rehabilitation in COPD patients.

Background: The 3-minute chair rise test (3-minute CRT) and the Disability Related to COPD Tool (DIRECT) are two reproducible and valid short tests that can assess the benefit of pulmonary rehabilitation (PR) in terms of functional capacity and dyspnea in everyday activities.

Methods: We determined the minimal clinically important difference (MCID) of the DIRECT questionnaire and 3-minute CRT using distribution methods and anchor encroaches with a panel of eight standard tests in a cohort of 116 COPD patients who completed a PR program in real-life settings.

Results: The estimated MCID for the 3-minute CRT and DIRECT scores was five repetitions and two units, respectively, using separate and combined independent anchors.

A flexible framework for sparse simultaneous component based data integration.

Background: High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.

Lasso logistic regression, GSoft and the cyclic coordinate descent algorithm: application to gene expression data.

Statistical methods generating sparse models are of great value in the gene expression field, where the number of covariates (genes) under study moves about the thousands while the sample sizes seldom reach a hundred of individuals. For phenotype classification, we propose different lasso logistic regression approaches with specific penalizations for each gene. These methods are based on a generalized soft-threshold (GSoft) estimator.

Segmentation of magnetic resonance brain images through discriminant analysis.

Segmentation (tissue classification) of medical images obtained from a magnetic resonance (MR) system is a primary step in most applications of medical image post-processing. This paper describes nonparametric discriminant analysis methods to segment multispectral MR images of the brain. Starting from routinely available spin-lattice relaxation time, spin-spin relaxation time, and proton density weighted images (T1w, T2w, PDw), the proposed family of statistical methods is based on: (i) a transform of the images into components that are statistically independent from each other; (ii) a nonparametric estimate of probability density functions of each tissue starting from a training set; (iii) a classic Bayes 0-1 classification rule.

Quantification of cumulated physical fatigue at the workplace.

Quantification of physical fatigue remains a challenge. We hypothesized that its effects on central autonomic nervous system activity could be explored for such a quantification. To test this relationship, we prospectively measured central autonomic nervous activity through nocturnal heart rate variability (HRV) in six French garbage collectors, aged 32.

Cardiac interbeat interval increment for the identification of obstructive sleep apnea.

The prevalence of obstructive sleep apnea syndrome (OSAS) is high in developed countries but its diagnosis is costly. Based on physiological evidence, the frequency component of heart rate variability (HRV) was evaluated as a simple and inexpensive diagnostic tool in OSAS. The predictive accuracy of frequency-domain HRV variables obtained from 24-hour ECG Holter monitoring (the power spectral density of the interbeat interval increment of very low frequencies, "VLFIpsd," and its percentage over the total power spectral density, "% VLFI"), and of established time-domain HRV variables were analyzed by comparison with respiratory disturbances indexes assessed by complete polysomnography in 124 consecutive patients (98 men aged 53.

High accuracy of automatic detection of atrial fibrillation using wavelet transform of heart rate intervals.

Permanent and paroxysmal AF is a risk factor for the occurrence and the recurrence of stroke, which can occur as its first manifestation. However, its automatic identification is still unsatisfactory. In this study, a new mathematical approach was evaluated to automate AF identification.