Data-enabled Bayesian inference for strategic maintenance decisions in industrial operations.

Efficient management of industrial assets and equipment depends heavily on the selection of appropriate maintenance strategies. This research presents a dataset generated through Monte Carlo simulations to evaluate 12 key criteria relevant to maintenance strategy selection. The dataset covers a wide range of potential maintenance scenarios, providing comprehensive data for researchers to explore various strategies in industrial settings.

Datasets describing optimization of the cutting regime in the turning of AISI 316L steel for biomedical purposes based on the NSGA-II and NSGA-III multi-criteria algorithms.

There are several methods of analysis used in the metalworking industry for dry machining processes and with Minimum Quantity Lubrication (MQL). Evolutionary methods [1] have been used in the decision-making process in the machining process to select the optimal data and to analyze the behavior of variables such as cutting speed (V), feed rate (f) and cutting depth (a). This work addresses the use of evolutionary algorithms of low dominance class II and III (NSGA-II and NSGA-III) to analyze from the multicriteria approach the initial wear of the cutting tool (VB), the energy consumption (E) and the machining time (t) in the turning process of the AISI 316L steel workpiece for biomedical purposes.

ConvNets for automatic detection of polyglutamine SCAs from brain MRIs: state of the art applications.

Polyglutamine spinocerebellar ataxias (polyQ SCAs) are a group of neurodegenerative diseases, clinically and genetically heterogeneous, characterized by loss of balance and motor coordination due to dysfunction of the cerebellum and its connections. The diagnosis of each type of polyQ SCA, alongside with genetic tests, includes medical images analysis, and its automation may help specialists to distinguish between each type. Convolutional neural networks (ConvNets or CNNs) have been recently used for medical image processing, with outstanding results.

Convolutional Neural Networks for Segmenting Cerebellar Fissures from Magnetic Resonance Imaging.

The human cerebellum plays an important role in coordination tasks. Diseases such as spinocerebellar ataxias tend to cause severe damage to the cerebellum, leading patients to a progressive loss of motor coordination. The detection of such damages can help specialists to approximate the state of the disease, as well as to perform statistical analysis, in order to propose treatment therapies for the patients.

Neurophysiological features in spinocerebellar ataxia type 2: Prospects for novel biomarkers.

Electrophysiological biomarkers are useful to assess the degeneration and progression of the nervous system in pre-ataxic and ataxic stages of the Spinocerebellar Ataxia Type 2 (SCA2). These biomarkers are essentially defined by their clinical significance, discriminating patients and/or preclinical subjects from healthy controls in cross-sectional studies, their significant changes over time in longitudinal studies, and their correlation with the cytosine-guanine-adenine (CAG) repeat expansion and/or clinical ataxia scores, time of evolution and time to ataxia onset. We classified electrophysiological biomarkers into three main types: (1) preclinical, (2) disease progression and (3) genetic damage.

Touchscreen games to detect cognitive impairment in senior adults. A user-interaction pilot study.

Introduction: Alzheimer's disease is a degenerative brain disease and the most common cause of dementia. Today, 47 million people live with dementia worldwide. This number is projected to increase to more than 131 million by 2050, as populations age.

Learning to Detect Cognitive Impairment through Digital Games and Machine Learning Techniques.

Objective: Alzheimer's disease (AD) is one of the most prevalent diseases among the adult population. The early detection of Mild Cognitive Impairment (MCI), which may trigger AD, is essential to slow down the cognitive decline process.

Methods: This paper presents a suit of serious games that aims at detecting AD and MCI overcoming the limitations of traditional tests, as they are time-consuming, affected by confounding factors that distort the result and usually administered when symptoms are evident and it is too late for preventive measures.

Episodix: a serious game to detect cognitive impairment in senior adults. A psychometric study.

Introduction: Assessment of episodic memory is traditionally used to evaluate potential cognitive impairments in senior adults. The present article discusses the capabilities of Episodix, a game to assess the aforementioned cognitive area, as a valid tool to discriminate among mild cognitive impairment (MCI), Alzheimer's disease (AD) and healthy individuals (HC); that is, it studies the game's psychometric validity study to assess cognitive impairment.

Materials And Methods: After a preliminary study, a new pilot study, statistically significant for the Galician population, was carried out from a cross-sectional sample of senior adults as target users.

Leveraging Wikipedia knowledge to classify multilingual biomedical documents.

This article presents a classifier that leverages Wikipedia knowledge to represent documents as vectors of concepts weights, and analyses its suitability for classifying biomedical documents written in any language when it is trained only with English documents. We propose the cross-language concept matching technique, which relies on Wikipedia interlanguage links to convert concept vectors between languages. The performance of the classifier is compared to a classifier based on machine translation, and two classifiers based on MetaMap.

A Bag of Concepts Approach for Biomedical Document Classification Using Wikipedia Knowledge*. Spanish-English Cross-language Case Study.

Objectives: The ability to efficiently review the existing literature is essential for the rapid progress of research. This paper describes a classifier of text documents, represented as vectors in spaces of Wikipedia concepts, and analyses its suitability for classification of Spanish biomedical documents when only English documents are available for training. We propose the cross-language concept matching (CLCM) technique, which relies on Wikipedia interlanguage links to convert concept vectors from the Spanish to the English space.

Design process and preliminary psychometric study of a video game to detect cognitive impairment in senior adults.

Introduction: Assessment of episodic memory has been traditionally used to evaluate potential cognitive impairments in senior adults. Typically, episodic memory evaluation is based on personal interviews and pen-and-paper tests. This article presents the design, development and a preliminary validation of a novel digital game to assess episodic memory intended to overcome the limitations of traditional methods, such as the cost of its administration, its intrusive character, the lack of early detection capabilities, the lack of ecological validity, the learning effect and the existence of confounding factors.

Trends on the application of serious games to neuropsychological evaluation: A scoping review.

Background: The dramatic technological advances witnessed in recent years have resulted in a great opportunity for changing the way neuropsychological evaluations may be performed in clinical practice. Particularly, serious games have been posed as the cornerstone of this still incipient paradigm-shift, as they have characteristics that make them especially advantageous in trying to overcome limitations associated with traditional pen-and-paper based neuropsychological tests: they can be easily administered and they can feature complex environments for the evaluation of neuropsychological constructs that are difficult to evaluate through traditional tests. The objective of this study was to conduct a scoping literature review in order to map rapidly the key concepts underpinning this research area during the last 25years on the use of serious games for neuropsychological evaluation.

Biomedical literature classification using encyclopedic knowledge: a Wikipedia-based bag-of-concepts approach.

Automatic classification of text documents into a set of categories has a lot of applications. Among those applications, the automatic classification of biomedical literature stands out as an important application for automatic document classification strategies. Biomedical staff and researchers have to deal with a lot of literature in their daily activities, so it would be useful a system that allows for accessing to documents of interest in a simple and effective way; thus, it is necessary that these documents are sorted based on some criteria-that is to say, they have to be classified.