Digital Twins applied to the implementation of Safe-by-Design strategies in nano-processes for the reduction of airborne emission and occupational exposure to nano-forms

Digital Twins (DTs) are one of the most promising enabling technologies for the deployment of the factory of the future and the Industry 4.0 framework. DTs could be labelled as an inherently Safe-by-Design (SbD) strategy and can be applied at different stages in the life cycle of a process. The EU-funded project ASINA has the ambition to promote coherent, applicable and scientifically sound SbD nano-practices. In particular, in the field of nanomanufacturing, ASINA intends to deliver innovative SbD solutions applied to process (P-SbD). In this context, ASINA will investigate the use of DTs as a disruptive digital technology for the prevention, prediction and control of nano-forms airborne emission and worker exposure. This paper introduces the concept of DT in the field of nano-processes SbD and outlines the preliminary architecture of ASINA-DT, that will be developed and implemented by ASINA in one industrial scenario.The project ASINA received funding from the European Union’s Horizon 2020 research and innovation programme, under grant agreement Nº 862444. This paper reflects only the authors’ views, and the Commission is not responsible for any use that may be made of the information contained therein

Spontaneous Speech and Emotional Response modeling based on One-class classifier oriented to Alzheimer Disease diagnosis

The purpose of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from non-invasive intelligent methods. The methods selected in this case are speech biomarkers oriented to Sponta-neous Speech and Emotional Response Analysis. Thus the main goal of the present work is feature search in Spontaneous Speech oriented to pre-clinical evaluation for the definition of test for AD diagnosis by One-class classifier. One-class classifi-cation problem differs from multi-class classifier in one essen-tial aspect. In one-class classification it is assumed that only information of one of the classes, the target class, is available. In this work we explore the problem of imbalanced datasets that is particularly crucial in applications where the goal is to maximize recognition of the minority class as in medical diag-nosis. The use of information about outlier and Fractal Dimen-sion features improves the system performance

Alzheimer Disease Diagnosis based on Automatic Spontaneous Speech Analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to improvement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients

Research roadmap for nanosafety - Part III: Closer to the market (CTTM)

Nano-products and nano-enabled applications need a clear and easy-to-follow human and environmental safety framework for the development along the innovation chain from initial idea to market and beyond that facilitates navigation through the complex regulatory and approval processes under which different product categories fall. The missing framework results in a lack of (i) solid data regarding roadblocks to market penetration of nano-enabled products as well as the absence of (ii) transparency in terms of which products (e.g. containing nanomaterials (NMs); nano-enabled products) are on the market (e.g. registries) and voluntary schemes and labelling requirements for cosmetics and food, which processes are used for manufacturing nano-enabled products, and (iii) meager inclusiveness in the dialogue (between all stakeholders) most likely exist as a result of the missing framework. The Closer-to-the-Market-Roadmap (abbrev. CTTM) aims at speeding up the progress towards market implementation of nanotechnologies by outlining the steps needed to develop such a framework. In its current form it is addressed towards policy makers, but the ultimate framework will be designed for use by SME and enterprise organisations

ZMC 211-3 - KAEDAH MATEMATIK II MAC-APRIL 1989.pdf

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients

Registro Español de Trasplante Cardiaco. XXXI Informe Oficial de la Asociación de Insuficiencia Cardiaca de la Sociedad Española de Cardiología

Introducción y objetivos Se presentan las características clínicas y los resultados de los trasplantes cardiacos realizados en España con la actualización correspondiente a 2019. Métodos Se describen las características clínicas y los resultados de los trasplantes cardiacos realizados en 2019, así como las tendencias de estos en el periodo 2010-2018. Resultados En 2019 se realizaron 300 trasplantes (8.794 desde 1984; 2.745 entre 2010 y 2019). Respecto a años previos, los cambios más llamativos son el descenso hasta el 38% de los trasplantes realizados en código urgente, y la consolidación en el cambio de asistencia circulatoria pretrasplante, con la práctica desaparición del balón de contrapulsación (0, 7%), la estabilización del uso del oxigenador extracorpóreo de membrana (9, 6%) y el aumento de los dispositivos de asistencia ventricular (29%). La supervivencia en el trienio 2016-2018 es similar a la del trienio 2013-2015 (p = 0, 34), y ambas mejores que la del trienio 2010-2012 (p = 0, 002 y p = 0, 01 respectivamente). Conclusiones Se mantienen estables tanto la actividad del trasplante cardiaco en España como los resultados en supervivencia en los últimos 2 trienios. Hay una tendencia a realizar menos trasplantes urgentes, la mayoría con dispositivos de asistencia ventricular. Introduction and objectives: The present report describes the clinical characteristics and outcomes of heart transplants in Spain and updates the data to 2019. Methods: We describe the clinical characteristics and outcomes of heart transplants performed in Spain in 2019, as well as trends in this procedure from 2010 to 2018. Results: In 2019, 300 transplants were performed (8794 since 1984; 2745 between 2010 and 2019). Compared with previous years, the most notable findings were the decreasing rate of urgent transplants (38%), and the consolidation of the type of circulatory support prior to transplant, with an almost complete disappearance of counterpulsation balloon (0.7%), stabilization in the use of extracorporeal membrane oxygenation (9.6%), and an increase in the use of ventricular assist devices (29.0%). Survival from 2016 to 2018 was similar to that from 2013 to 2015 (P = .34). Survival in both these periods was better than that from 2010 to 2012 (P = .002 and P = .01, respectively). Conclusions: Heart transplant activity has remained stable during the last few years, as have outcomes (in terms of survival). There has been a trend to a lower rate of urgent transplants and to a higher use of ventricular assist devices prior to transplant

On the alzheimer’s disease diagnosis: Automatic spontaneous speech analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias and definitive confirmation must be done through a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to the improvement of early diagnosis of AD and its degree of severity from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET) that have the great advantage of being non invasive low cost and without any side effects. The developed system obtains hopeful results for early diagnosis

New Approaches for Alzheimer’s Disease Diagnosis Based on Automatic Spontaneous Speech Analysis and Emotional Temperature

Alzheimer Disease (AD) is one of the most common dementia and their socio-economic relevance is growing. Its diagnosis is sometimes made by excluding other dementias, but definitive confirmation must await the study post-mortem with brain tissue of the patient. According to internationally accepted criteria, we can only speak about probable or possible Alzheimer's disease. The purpose of this paper is to contribute to improve early diagnosis of dementia and severity from automatic analysis performed by non-invasive automated intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET). These methodologies have the great advantage of being non invasive, low cost methodologies and have no side effects