Effect of the axial jet on the optimal response in Batchelor vortex

En este póster se estudia la respuesta óptima del torbellino de Batchelor para distintos números de onda. Se demuestra que incluso teniendo la velocidad axial, un torbellino es capaz de tener grandes amplificaciones de energía.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Wingtip vortex in a NACA0012 airfoil and its active control

Contribución mediante sesión pósterWe conduct experiments in a towing-tank to analyse the flow patterns of wingtip vortices in a NACA 0012 airfoil. In this experimental research, we provide PIV measurements and flow visualisations. Without active control, several parameters are given experimentally as function of the Reynolds number, so we compare these data with the theoretical models of Batchelor, and Moore and Saffman together with DNS. Secondly, we analyse the effect of a continuous injection in the spanwise direction. The continuous jet has a strong influence on the wing-tip vortex formation. We explore this effect at low chord based Reynolds number ranging from 7000 up to 20000. We change the aspect ratio of the injection, R, defined as the ratio of the velocities between the jet (Uj) and free-stream (U). For R=1, we find that the jet strongly affects the wingtip vortex formation with a sudden decrement of the axial vorticity and the azimuthal velocity. This technique is a challenge and a promising tool to reduce the intensity of the vortex core.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A Comprehensive Study on Pain Assessment from Multimodal Sensor Data

Pain assessment is a critical aspect of healthcare, influencing timely interventions and patient well-being. Traditional pain evaluation methods often rely on subjective patient reports, leading to inaccuracies and disparities in treatment, especially for patients who present difficulties to communicate due to cognitive impairments. Our contributions are three-fold. Firstly, we analyze the correlations of the data extracted from biomedical sensors. Then, we use state-of-the-art computer vision techniques to analyze videos focusing on the facial expressions of the patients, both per-frame and using the temporal context. We compare them and provide a baseline for pain assessment methods using two popular benchmarks: UNBC-McMaster Shoulder Pain Expression Archive Database and BioVid Heat Pain Database. We achieved an accuracy of over 96% and over 94% for the F1 Score, recall and precision metrics in pain estimation using single frames with the UNBC-McMaster dataset, employing state-of-the-art computer vision techniques such as Transformer-based architectures for vision tasks. In addition, from the conclusions drawn from the study, future lines of work in this area are discussed.We would like to thank “A way of making Europe” European Regional Development Fund (ERDF) and MCIN/AEI/10.13039/501100011033 for supporting this work under the “CHAN-TWIN” project (grant TED2021-130890B-C21). HORIZON-MSCA-2021-SE-0 action number: 101086387, REMARKABLE, Rural Environmental Monitoring via ultra wide-ARea networKs and distriButed federated Learning. CIAICO/2022/132 Consolidated group project “AI4Health” funded by Valencian government and International Center for Aging Research ICAR funded project “IASISTEM”. This work has also been supported by a Spanish national and two regional grants for PhD studies, FPU21/00414, CIACIF/2021/430 and CIACIF/2022/175

Yearly evolution of organ damage markers in diabetes or metabolic syndrome: data from the LOD-DIABETES study

<p>Abstract</p> <p>Background</p> <p>Cardiovascular disease morbidity-mortality is greater in people with type 2 diabetes mellitus or metabolic syndrome. The purpose of this study was to evaluate the yearly evolution of organ damage markers in diabetes or metabolic syndrome, and to analyze the associated factors.</p> <p>Methods</p> <p>An observational prospective study was carried out in the primary care setting, involving 112 patients: 68 diabetics and 44 subjects with metabolic syndrome, subjected to 12 months of follow-up. Measurements: traditional cardiovascular risk factors (blood pressure, blood glucose, lipids, smoking, body mass index (BMI) and) and non-traditional risk factors (waist circumference, hsC Reactive Protein and fibrinogen); subclinical vascular (carotid intima-media thickness, pulse wave velocity and ankle/brachial index), cardiac (Cornell voltage-duration product), renal organ damage (creatinine, glomerular filtration and albumin/creatinine index), and antihypertensive and lipid-lowering drugs.</p> <p>Results</p> <p>At baseline, the diabetics presented a mean age of 59.9 years, versus 55.2 years in the subjects with metabolic syndrome (p = 0.03). Diastolic blood pressure, total cholesterol and HDL-cholesterol were lower among the patients with diabetes, while blood glucose and HbA1c, as well as antihypertensive and lipid-lowering drug use, were greater. At evaluation after one year, the diabetics showed a decrease in BMI (-0.39), diastolic blood pressure (-3.59), and an increase in fibrinogen (30.23 mg/dL), ankle/brachial index (0.07) and the number of patients with ankle/brachial index pathologic decreased in 6. In turn, the patients with metabolic syndrome showed an increase in HDL-cholesterol (1-91 mg/dL), fibrinogen (25.54 mg/dL), Cornell voltage-duration product (184.22 mm/ms), ankle/brachial index (0.05) and the use of antihypertensive and lipid-lowering drugs, and a reduction in serum glucose (3.74 mg/dL), HOMA, systolic (-6.76 mmHg), diastolic blood pressure (-3.29 mmHg), and pulse wave velocity (-0.72 m/s). The variable that best predicted a decrease in pulse wave velocity in subjects with metabolic syndrome was seen to be an increase in antihypertensive drug use.</p> <p>Conclusions</p> <p>The annual assessment of cardiovascular risk factors and the decrease in pulse wave velocity was more favorable in the patients with metabolic syndrome, probably influenced by the increased percentage of subjects treated with antihypertensive and lipid lowering drugs in this group.</p

Dietary glycemic index and retinal microvasculature in adults: a cross-sectional study

[EN] Objective: To analyze the relationship between dietary glycemic index (GI) and retinal microvasculature in adults. Methods: This was a cross-sectional study of 300 subjects from the EVIDENT II study. Dietary GI was calculated using a validated, semi-quantitative food frequency questionnaire. Retinal photographs were digitized, temporal vessels were measured in an area 0.5–1 disc diameter from the optic disc and arteriolar-venular index (AVI) was estimated with semi-automated software. Results: AVI showed a significant difference between the tertiles of GI, after adjusting for potential confounders. The lowest AVI values were observed among subjects in the highest tertile of GI, whereas the greatest were found among those in the lowest tertile (estimated marginal mean of 0.738 vs. 0.768, p = 0.014). Conclusions: In adults, high dietary GI implies lowering AVI values regardless of age, gender and other confounding variables. Trial registration: Clinical Trials.gov Identifier: NCT02016014. Registered 9 December 2013

Long-Term Effectiveness of a Smartphone App and a Smart Band on Arterial Stiffness and Central Hemodynamic Parameters in a Population with Overweight and Obesity (Evident 3 Study): Randomised Controlled Trial

Background: mHealth technologies could help to improve cardiovascular health; however, their effect on arterial stiffness and hemodynamic parameters has not been explored to date. Objective: To evaluate the effect of a mHealth intervention, at 3 and 12 months, on arterial stiffness and central hemodynamic parameters in a sedentary population with overweight and obesity. Methods: Randomised controlled clinical trial (Evident 3 study). 253 subjects were included: 127 in the intervention group (IG) and 126 in the control group (CG). The IG subjects were briefed on the use of the Evident 3 app and a smart band (Mi Band 2, Xiaomi) for 3 months to promote healthy lifestyles. All measurements were recorded in the baseline visit and at 3 and 12 months. The carotid-femoral pulse wave velocity (cfPWV) and the central hemodynamic parameters were measured using a SphigmoCor System® device, whereas the brachial-ankle pulse wave velocity (baPWV) and the Cardio Ankle Vascular Index (CAVI) were measured using a VaSera VS-2000® device. Results: Of the 253 subjects who attended the initial visit, 237 (93.7%) completed the visit at 3 months of the intervention, and 217 (85.3%) completed the visit at 12 months of the intervention. At 12 months, IG showed a decrease in peripheral augmentation index (PAIx) (−3.60; 95% CI −7.22 to −0.00) and ejection duration (ED) (−0.82; 95% CI −1.36 to −0.27), and an increase in subendocardial viability ratio (SEVR) (5.31; 95% CI 1.18 to 9.44). In CG, cfPWV decreased at 3 months (−0.28 m/s; 95% CI −0.54 to −0.02) and at 12 months (−0.30 m/s, 95% CI −0.54 to −0.05), central diastolic pressure (cDBP) decreased at 12 months (−1.64 mm/Hg; 95% CI −3.19 to −0.10). When comparing the groups we found no differences between any variables analyzed. Conclusions: In sedentary adults with overweight or obesity, the multicomponent intervention (Smartphone app and an activity-tracking band) for 3 months did not modify arterial stiffness or the central hemodynamic parameters, with respect to the control group. However, at 12 months, CG presented a decrease of cfPWV and cDBP, whereas IG showed a decrease of PAIx and ED and an increase of SEVR

Moderate-to-vigorous physical activity as a mediator between sedentary behavior and cardiometabolic risk in Spanish healthy adults: a mediation analysis

Background: Public health strategies for cardiovascular prevention highlight the importance of physical activity, but do not consider the additional potentially harmful effects of sedentary behavior. This study was conducted between 2010 and 2012 and analyzed between 2013 and 2014. The aim of the study was to analyze the relationship between sedentary behavior and cardiometabolic risk factors in the Spanish adult population and to examine whether this relationship is mediated by moderate-to-vigorous physical activity (MVPA). Methods: The cross-sectional study included 1122 healthy subjects belonging to the EVIDENT study. Sedentary behavior was objectively measured over 7 days using Actigraph accelerometers. We assessed waist circumference (WC), triglycerides-to-HDL-C ratio (TG/HDL-C), and mean arterial pressure (MAP), and undertook homeostasis model assessment (HOMA-IR). Linear regression models were fitted according to Baron and Kenny procedures for mediation analysis. Results: TG/HDL-C and HOMA-IR were significantly higher in adults who spent more minutes in sedentary activities after adjusting for potential covariates. However when MVPA was added to the ANCOVA models as covariate the effect of sedentary time on HOMA-IR disappeared. In addition, MVPA acted as a full mediator of the relationship between sedentary time and HOMA-IR. In contrast, subjects with lower levels of MVPA presented worse cardiometabolic profiles than those from higher MVPA categories, even after controlling for sedentary time and other potential confounders. Conclusions: These results suggest that both MVPA and sedentary time should be considered when developing cardiometabolic risk guidelines

Breast Cancer Detection by Means of Artificial Neural Networks

Breast cancer is a fatal disease causing high mortality in women. Constant efforts are being made for creating more efficient techniques for early and accurate diagnosis. Classical methods require oncologists to examine the breast lesions for detection and classification of various stages of cancer. Such manual attempts are time consuming and inefficient in many cases. Hence, there is a need for efficient methods that diagnoses the cancerous cells without human involvement with high accuracies. In this research, image processing techniques were used to develop imaging biomarkers through mammography analysis and based on artificial intelligence technology aiming to detect breast cancer in early stages to support diagnosis and prioritization of high-risk patients. For automatic classification of breast cancer on mammograms, a generalized regression artificial neural network was trained and tested to separate malignant and benign tumors reaching an accuracy of 95.83%. With the biomarker and trained neural net, a computer-aided diagnosis system is being designed. The results obtained show that generalized regression artificial neural network is a promising and robust system for breast cancer detection. The Laboratorio de Innovacion y Desarrollo Tecnologico en Inteligencia Artificial is seeking collaboration with research groups interested in validating the technology being developed