High risk of cardiovascular episodes and low adherence to risk factors guidelines in a population with diabetes

Although recent guidelines cover therapeutic goals, effective lipid management of patients with type 1 and type 2 diabetes to reduce cardiovascular disease (CVD) risk is still largely unattained. In the present study, we explored the electronic health records (EHR) at a specialized diabetes outpatient clinic to assess, in a real world database, the prevalence of poor lipid management in people with diabetes, the associated characteristics of this population, and the patterns of medication.Amge

Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

Background Underweight and obesity are associated with adverse health outcomes throughout the life course. We estimated the individual and combined prevalence of underweight or thinness and obesity, and their changes, from 1990 to 2022 for adults and school-aged children and adolescents in 200 countries and territories. Methods We used data from 3663 population-based studies with 222 million participants that measured height and weight in representative samples of the general population. We used a Bayesian hierarchical model to estimate trends in the prevalence of different BMI categories, separately for adults (age ≥20 years) and school-aged children and adolescents (age 5–19 years), from 1990 to 2022 for 200 countries and territories. For adults, we report the individual and combined prevalence of underweight (BMI 2 SD above the median). Findings From 1990 to 2022, the combined prevalence of underweight and obesity in adults decreased in 11 countries (6%) for women and 17 (9%) for men with a posterior probability of at least 0·80 that the observed changes were true decreases. The combined prevalence increased in 162 countries (81%) for women and 140 countries (70%) for men with a posterior probability of at least 0·80. In 2022, the combined prevalence of underweight and obesity was highest in island nations in the Caribbean and Polynesia and Micronesia, and countries in the Middle East and north Africa. Obesity prevalence was higher than underweight with posterior probability of at least 0·80 in 177 countries (89%) for women and 145 (73%) for men in 2022, whereas the converse was true in 16 countries (8%) for women, and 39 (20%) for men. From 1990 to 2022, the combined prevalence of thinness and obesity decreased among girls in five countries (3%) and among boys in 15 countries (8%) with a posterior probability of at least 0·80, and increased among girls in 140 countries (70%) and boys in 137 countries (69%) with a posterior probability of at least 0·80. The countries with highest combined prevalence of thinness and obesity in school-aged children and adolescents in 2022 were in Polynesia and Micronesia and the Caribbean for both sexes, and Chile and Qatar for boys. Combined prevalence was also high in some countries in south Asia, such as India and Pakistan, where thinness remained prevalent despite having declined. In 2022, obesity in school-aged children and adolescents was more prevalent than thinness with a posterior probability of at least 0·80 among girls in 133 countries (67%) and boys in 125 countries (63%), whereas the converse was true in 35 countries (18%) and 42 countries (21%), respectively. In almost all countries for both adults and school-aged children and adolescents, the increases in double burden were driven by increases in obesity, and decreases in double burden by declining underweight or thinness. Interpretation The combined burden of underweight and obesity has increased in most countries, driven by an increase in obesity, while underweight and thinness remain prevalent in south Asia and parts of Africa. A healthy nutrition transition that enhances access to nutritious foods is needed to address the remaining burden of underweight while curbing and reversing the increase in obesity. Funding UK Medical Research Council, UK Research and Innovation (Research England), UK Research and Innovation (Innovate UK), and European Union

From home energy management systems to communities energy managers: the use of an intelligent aggregator in a community in Algarve, Portugal

This paper describes the development of community energy management systems (CEMS). A CEMS allows optimal energy sharing within energy communities, as it is a central system that makes the global management of the entire community. The proposed CEMS is based on mixed-integer linear programming (MILP), operating under the receding horizon concept of Model Predictive Control (MPC). A systematic classification of electric appliances, the use of external information such as weather information and energy prices, as well as the use of intelligent forecasting techniques enables the proposed approach to achieve an excellent efficiency. It also allows for an easy installation of as well as a smooth scaling with an increasing number of houses. The system is tested in a real community in Algarve, Portugal. Different simulations are compared to experimental operation and include cases with and without sharing of energy, different resources allocated to the houses considered, and the use of different tariffs. CEMS formulations include sharing of energy without restriction, as well as employing different allocation coefficients strategies. The results show that for the community under study when managed by CEMS such as the one presented in this paper, it would result in significant cost reductions when compared to the case where there is no energy community.info:eu-repo/semantics/publishedVersio

GPR target detection using a neural network classifier designed by a multi-objective genetic algorithm

Ground Penetrating Radar (GPR) is an electromagnetic sensing technology employed for localization of underground utilities, pipes, and other types of objects. The radargrams typically obtained have a high dimensionality, containing a number of signatures with hyperbolic pattern shapes, and can be processed to retrieve information about the target's locations, depths and material type of underground soil. The classical Hough Transform approach used to reconstruct these hyperbola shapes is computationally expensive, given the large dimensionality of the radargrams. In literature, several approaches propose to first approximate the location of hyperbolas to small segments through a classification stage, before applying the Hough transform over these segments. However, the published classifiers designed for this task present a relatively complex architecture. Aiming at an improved target localization, we propose an alternative classification methodology. The goal is to classify windows of GPR radargrams into two classes (with or without target) using a neural network radial basis function (RBF), designed via a multi-objective genetic algorithm (MOGA). To capture samples' fine details, high order statistic cumulant features (HOS) were used. Feature selection was performed by MOGA, with an optional prior reduction using a mutual information (MIFS) approach. The obtained results demonstrate improvement of the classification performance when compared with other models designed with the same data and are among the best results available in the literature, albeit the large reduction in classifier complexity. (C) 2019 Elsevier B.V. All rights reserved.Portuguese Erasmus National Agency [2015-01-PT01-KA107-04276]Portuguese Foundation for Science and Technology, through IDMEC, under LAETA [UID/EMS/50022/2019]info:eu-repo/semantics/publishedVersio

A method for sub-sample computation of time displacements between discrete signals based only on discrete correlation sequences

In this paper, we propose a new method for sub-sample computation of time displacements between two sampled signals. The new algorithm is based on sampled auto- and cross-correlation sequences and takes into account only the sampled signals without the need for the customary interpolation and fitting procedures. The proposed method was evaluated and compared with other methods, in simulated and real signals. Four other methods were used for comparison: two based on cross-correlation plus fitting, one method based on spline fitting over the input signals, and another based on phase demodulation. With simulated signals, the proposed approach presented similar or better performance, concerning bias and variance, in almost all the tested conditions. The exception was signals with very low SNRs (<10 dB), for which the methods based on phase demodulation and spline fitting presented lower variances. Considering only the two methods based on cross-correlation, our approach presented improved results with signals with high and moderate noise levels. The proposed approach and other three out of the four methods used for comparison are robust in real data. The exception is the phase demodulation method, which may fail when applied to signals collected from real-world scenarios because it is very sensitive to phase changes caused by other oscillations not related to the main echoes. This paper introduced a new class of methods, demonstrating that it is possible to estimate sub-sample delay, based on discrete cross-correlations sequences without the need for interpolation or fitting over the original sampled signals. The proposed approach was robust when applied to real-world signals and presented a moderated computational complexity when compared to the other tested algorithms. Although the new method was tested using ultrasound signals, it can be applied to any time-series with observable events. (C) 2016 Elsevier Ltd. All rights reserved.Fundacao para a Ciencia e a TecnologiaCAPES/FCT [FCT/CAPES: 10172/13-0]info:eu-repo/semantics/publishedVersio

Validation of a similarity measurement method for clustering cardiac signals

Development of personalized cardiovascular management systems involves automatic identification of the current data as normal or pathological; considering cardiac data as time-series, the illness identification may be performed by seeking similarity between the current patient's time-series data and a reference signal and then proceeding to illness stratification (clustering). Seven of the most common methods of time-series similarity measurement were assessed by imposing 6 types of distortions to the reference signal, considering for each distortion 20 possible variations. This study employed 10 seconds length records of arterial blood pressure signals of healthy subjects, collected from a public database. Then clustering using Partitioning Around Medoids was performed among pathological and non-pathological data considering 3 different clusters. Clustering results confirm usage of the reduced basis Discrete Wavelet Transform resulting from the combination of Haar wavelet decomposition with the Karhunen-Loeve transforms, presenting an accuracy ranging from 76% to 85% when partitioning around Medoids clustering is used.[H2020 - 692023]info:eu-repo/semantics/publishedVersio

Sensory system for the sleep disorders detection in the geriatric population

This paper introduces the proposal of a remote sensory system for the detection of sleep disorders in geriatric outpatients. Although the most accurate solution would be an in-depth study in a sleep clinic, it is not a realistic environment for the elderly. The objective is that the patient stays at home, and without changing their daily routines, the clinicians get objective information in order to make a correct diagnosis of the sleep disorders. As a first step towards achieving a home remote monitory system, this work introduces a Body Sensor Network (BSN) to monitor various vital signals as Electrocardiogram (ECG) and Electromyogram (EMG) in order to collect enough information for sleep disorder diagnosis, focusing on the detection of obstructive sleep apnea. This work proposes an algorithm to infer obstructive sleep apnea (OSA) based on power spectral analysis of ECG signals from a single-lead electrocardiogram, demonstrating the feasibility of BSN to detect OSA with around 85% sensitivity.Spanish Ministry of Economy and Competitiveness (TARSIUS) [TIN2015-71564-c4-1-R]National Youth Guarantee System Administrative, Unit of the European Social Fund [ECC / 1402/2013]info:eu-repo/semantics/publishedVersio

A CYTED network: new non-invasive ways for an early diagnosis of chronic and degenerative diseases: diabetes and cardiovascular

In this work, an analysis of the principal characteristics, scientific aims and some initial results, of the Iberoamerican R&D network "Ditecrod" (Project of CYTED) is made. This network is based on an international agreement to propitiate an efficient trans-disciplinary cooperation in a scientific area where currently significant research efforts are being made for their high potential impact on future health and quality of life: The non-invasive Early medical diagnosis of Cardiovascular and Diabetic Foot diseases. Both chronic & degenerative pathologies are endemic today in many American countries. The main project objectives are the research, application and diffusion of new non-invasive diagnostic methods, having a low technologic cost and an easy portability, in relation to nowadays disposable approaches. The main results (in the first year of operation) of this multinational network are described. This project also seeks to promote the cooperation working among 11 working teams integrated in six R&D groups with large experience and previous innovations on the subject, and 2 emerging university groups until now focused mainly on the university teaching; in total 13 teams of 8 countries: 5 of Biomedical Engineering, 4 of Biophysics, 2 hospitals and 2 companies.CONACYT-SaludConsejo Nacional de Ciencia y Tecnologia (CONACyT) [2013-I-201590]R&D Spanish National Plan Retos [DPI2017-90147-R]European doctoral project EraNet-EMHE [200022]Bilateral Cooperation Project [CSIC-COOPB20166]Iberoamerican Network [CYTED-DITECROD-218RT0545]info:eu-repo/semantics/publishedVersio