An illustration of new methods in machine condition monitoring, Part I: Stochastic resonance

There have been many recent developments in the application of data-based methods to machine condition monitoring. A powerful methodology based on machine learning has emerged, where diagnostics are based on a two-step procedure: extraction of damage sensitive features, followed by unsupervised learning (novelty detection) or supervised learning (classification). The objective of the current pair of papers is simply to illustrate one state-of the-art procedure for each step, using synthetic data representative of reality in terms of size and complexity. The first paper in the pair will deal with feature extraction. Although some papers have appeared in the recent past considering stochastic resonance as a means of amplifying damage information in signals, they have largely relied on ad hoc specifications of the resonator used. In contrast, the current paper will adopt a principled optimisation-based approach to the resonator design. The paper will also show that a discrete dynamical system can provide all the benefits of a continuous system, but also provide a considerable speed-up in terms of simulation time in order to facilitate the optimisation approach

An Illustration of New Methods in Machine Condition Monitoring, Part II: Adaptive outlier detection

There have been many recent developments in the application of data-based methods to machine condition monitoring. A powerful methodology based on machine learning has emerged, where diagnostics are based on a two-step procedure: extraction of damagesensitive features, followed by unsupervised learning (novelty detection) or supervised learning (classification). The objective of the current pair of papers is simply to illustrate one state-ofthe-art procedure for each step, using synthetic data representative of reality in terms of size and complexity. The second paper in the pair will deal with novelty detection. Although there has been considerable progress in the use of outlier analysis for novelty detection, most of the papers produced so far have suffered from the fact that simple algorithms break down if multiple outliers are present or if damage is already present in a training set. The objective of the current paper is to illustrate the use of phase-space thresholding; an algorithm which has the ability to detect multiple outliers inclusively in a data set

Convolutional neural networks for the detection of damaged fasteners in engineering structures

Locating and classifying damaged fasteners, such as bolts, in large engineering structures is vital in many health monitoring applications. Whilst traditional signal processing methods are often used to identify the presence of such fasteners, accurately estimating their location remains an ongoing challenge. In recent years, image detection (or the location of objects within images) using deep learning algorithms, such as convolutional neural networks (CNNs), has seen substantial improvements. This is largely due to the abundant database of images provided by internet search engines, as well as significant advances in computing power. Moreover, advances in digital imaging technology mean that affordable computer vision systems are now more readily available than ever before. In this paper, a CNN architecture is proposed for the task of detecting damaged bolts in engineering structures. The new architecture forms part of a regional convolutional neural network (R-CNN), which applies a bounding box regression algorithm for bolt location alongside a softmax classifier for damage classification. A dedicated training set is also developed, which combines internet search engine data with images of a specifically-designed bolt rig. The new images extend the current dataset with the purpose of developing a bolt detector that is invariant to camera angle and location, as well as environmental factors such as lighting and shadows

The Relevance and Added Value of Geriatric Medicine (GM): Introducing GM to Non-Geriatricians

Geriatric Medicine (GM) holds a crucial role in promoting health and managing the complex medical, cognitive, social, and psychological issues of older people. However, basic principles of GM, essential for optimizing the care of older people, are commonly unknown or undermined, especially in countries where GM is still under development. This narrative review aims at providing insights into the role of GM to non-geriatrician readers and summarizing the main aspects of the added value of a geriatric approach across the spectrum of healthcare. Health practitioners of all specialties are frequently encountered with clinical conditions, common in older patients (such as cancer, hypertension, delirium, major neurocognitive and mental health disorders, malnutrition, and peri-operative complications), which could be more appropriately managed under the light of the approach of GM. The role of allied health professionals with specialized knowledge and skills in dealing with older people’s issues is essential, and a multidisciplinary team is required for the delivery of optimal care in response to the needs and aspirations of older people. Thus, countries should assure the educational background of all health care providers and the specialized health and social care services required to meet the demands of a rapidly aging society

Effect of intravenous clarithromycin in patients with sepsis, respiratory and multiple organ dysfunction syndrome: a randomized clinical trial.

Clarithromycin may act as immune-regulating treatment in sepsis and acute respiratory dysfunction syndrome. However, clinical evidence remains inconclusive. We aimed to evaluate whether clarithromycin improves 28-day mortality among patients with sepsis, respiratory and multiple organ dysfunction syndrome. We conducted a multicenter, randomized, clinical trial in patients with sepsis. Participants with ratio of partial oxygen pressure to fraction of inspired oxygen less than 200 and more than 3 SOFA points from systems other than the respiratory function were enrolled between December 2017 and September 2019. Patients were randomized to receive 1 gr of clarithromycin or placebo intravenously once daily for 4 consecutive days. The primary endpoint was 28-day all-cause mortality. Secondary outcomes were 90-day mortality; sepsis response (defined as at least 25% decrease in SOFA score by day 7); sepsis recurrence; and differences in peripheral blood cell populations and leukocyte transcriptomics. Fifty-five patients were allocated to each arm. By day 28, 27 (49.1%) patients in the clarithromycin and 25 (45.5%) in the placebo group died (risk difference 3.6% [95% confidence interval (CI) - 15.7 to 22.7]; P = 0.703, adjusted OR 1.03 [95%CI 0.35-3.06]; P = 0.959). There were no statistical differences in 90-day mortality and sepsis response. Clarithromycin was associated with lower incidence of sepsis recurrence (OR 0.21 [95%CI 0.06-0.68]; P = 0.012); significant increase in monocyte HLA-DR expression; expansion of non-classical monocytes; and upregulation of genes involved in cholesterol homeostasis. Serious and non-serious adverse events were equally distributed. Clarithromycin did not reduce mortality among patients with sepsis with respiratory and multiple organ dysfunction. Clarithromycin was associated with lower sepsis recurrence, possibly through a mechanism of immune restoration. Clinical trial registration clinicaltrials.gov identifier NCT03345992 registered 17 November 2017; EudraCT 2017-001056-55