Transient lateral photovoltaic effect in patterned metal-oxide-semiconductor films

The time dependent transient lateral photovoltaic effect has been studied with us time resolution and with chopping frequencies in the kHz range, in lithographically patterned 21 nm thick, 5, 10 and 20 um wide and 1500 um long Co lines grown over naturally passivated p-type Si (100). We have observed a nearly linear dependence of the transitorial response with the laser spot position. A transitorial response with a sign change in the laser-off stage has been corroborated by numerical simulations. A qualitative explanation suggests a modification of the drift-diffusion model by including the in uence of a local inductance. Our findings indicate that the microstructuring of position sensitive detectors could improve their space-time resolution.Comment: 4 pages, 4 figure

A Deep Learning Solution for Automatized Interpretation of 12-Lead ECGs

[EN] A broad variety of algorithms for detection and classification of rhythm and morphology abnormalities in ECG recordings have been proposed in the last years. Although some of them have reported very promising results, they have been mostly validated on short and non-public datasets, thus making their comparison extremely difficult. PhysioNet/CinC Challenge 2020 provides an interesting opportunity to compare these and other algorithms on a wide set of ECG recordings. The present model was created by ¿ELBIT¿ team. The algorithm is based on deep learning, and the segmentation of all beats in the 12-lead ECG recording, generating a new signal for each one by concatenating sequentially the information found in each lead. The resulting signal is then transformed into a 2- D image through a continuous Wavelet transform and inputted to a convolutional neural network. According to the competition guidelines, classification results were evaluated in terms of a class-weighted F-score (Fß) and a generalization of the Jaccard measure (Gß). In average for all training signals, these metrics were 0.933 and 0.811, respectively. Regarding validation on the testing set from the first phase of the challenge, mean values for both performance indices were 0.654 and 0.372, respectivelyThis research has been supported by the grants DPI2017¿83952¿C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha, AICO/2019/036 from Generalitat Valenciana and FEDER 2018/11744Huerta, A.; Martinez-Rodrigo, A.; Rieta, JJ.; Alcaraz, R. (2020). A Deep Learning Solution for Automatized Interpretation of 12-Lead ECGs. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.305S1

Optimizacion of planform and cruise conditions of a transport flying wing

The flying wing is a promising concept for the mid long-term commercial aviation. After the previously published conceptual design of a 300-seat class flying wing, the present article carries out a parametric analysis to optimize its planform and analyse the suitable cruise conditions to achieve the highest efficiency of such configuration. The figures of merit chosen for the optimization are the direct operating cost and the maximum take-off weight per passenger, for a specified constant range of 10 000 km. The design has to respect five relevant constraints: wingspan (limited to 80 m), cabin width, wing tip chord, number of passengers, and cruise lift coefficient. The optimum aircraft fulfilling all constraints cruises at 45 000–47 000 ft and M = 0.82, has an aspect ratio of 6.3 and taper ratio of 0.10, and carries about 280 passengers in three-class seating. This aircraft is about 20 per cent more efficient than conventional wide bodies of similar size, in terms of trip fuel

Obstructive Sleep Apnea Detection Methods Based on Heart Rate Variability Analysis: Opportunities for a Future Cinc Challenge

[EN] The effects of sleep-related disorders, such as obstructive sleep apnea (OSA), can be devastating either in children or adults. Misdiagnosis may lead to severe cardiovascular diseases. Besides, OSA consequences are often related to bad job performance, and road accidents. Nowadays, polysomnography (PSG) is still considered the gold standard for OSA diagnosis, but the required facilities are extremely high, thus reducing availability worldwide. For this reason, simpler and cost-effective diagnosing methods have been proposed in the late years. In this regard, the heart rate variability (HRV) has been demonstrated to strongly reflect apnea episodes during sleep. Hence, this work reviews the latest advances in the evaluation of OSA from the HRV perspective to consider its potentialities for a future revisited CinC Challenge.This research has been supported by grants DPI201783952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-la Mancha and AICO/2019/036 from Generalitat Valenciana. Moreover, Daniele Padovano has held graduate research scholarships from Escuela Polit ' ecnica de Cuenca and Instituto de Tecnolog ' ias Audiovisuales, University of CastillaLa ManchaPadovano, D.; Martinez-Rodrigo, A.; Pastor, JM.; Rieta, JJ.; Alcaraz, R. (2020). Obstructive Sleep Apnea Detection Methods Based on Heart Rate Variability Analysis: Opportunities for a Future Cinc Challenge. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.400S1

Application of Deep Learning for Quality Assessment of Atrial Fibrillation ECG Recordings

[EN] In the last years, atrial fibrillation (AF) has become one of the most remarkable health problems in the developed world. This arrhythmia is associated with an increased risk of cardiovascular events, being its early detection an unresolved challenge. To palliate this issue, long-term wearable electrocardiogram (ECG) recording systems are used, because most of AF episodes are asymptomatic and very short in their initial stages. Unfortunately, portable equipments are very susceptible to be contaminated with different kind of noises, since they work in highly dynamics and ever-changing environments. Within this scenario, the correct identification of free-noise ECG segments results critical for an accurate and robust AF detection. Hence, this work presents a deep learning-based algorithm to identify high-quality intervals in single-lead ECG recordings obtained from patients with paroxysmal AF. The obtained results have provided a remarkable ability to classify between high- and low-quality ECG segments about 92%, only misclassifying around 7% of clean AF intervals as noisy segments. These outcomes have overcome most previous ECG quality assessment algorithms also dealing with AF signals by more than 20%.This research has been supported by the grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha, AICO/2019/036 from Generalitat Valenciana and FEDER 2018/11744.Huerta, A.; Martinez-Rodrigo, A.; Arias, MA.; Langley, P.; Rieta, JJ.; Alcaraz, R. (2020). Application of Deep Learning for Quality Assessment of Atrial Fibrillation ECG Recordings. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.367S1

Comparative Study of Convolutional Neural Networks for ECG Quality Assessment

[EN] In the last years, convolutional neural networks (CNNs) have become popular in ECG analysis, since they do not require pre-processing stages, nor specific pre-training. However, their ability for ECG quality assessment has still not been thoroughly assessed. Hence, this work introduces a comparison about the ability of several CNN algorithms to classify between high and low-quality ECGs. Taking advantage of the concept of transfer learning, five common pre-trained CNNs were analyzed, such as AlexNet, GoogLeNet, VGG16, ResNet18 and InceptionV3. They were fed with 2-D images obtained by turning 5 second-length ECG segments into scalograms through a continuous Wavelet transform. To train and validate the algorithms, 1,168 noisy ECG intervals, along with other 1,200 ECG excerpts with sufficient quality for their further interpretation, were extracted from a public database. The obtained results showed that all CNNs provided mean values of accuracy between 89 and 91%, but notable difference in terms of computational load were noticed. Thus, AlexNet was the fastest algorithm, requiring notably less CPU usage and memory than the remaining methods. Consequently, this CNN exhibited the best trade-off between high-quality ECG identification accuracy and computational load, and it could be considered as the most convenient algorithm for ECG quality assessment.This research has been supported by the grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha and AICO/2019/036 from Generalitat Valenciana.Huerta, A.; Martinez-Rodrigo, A.; Puchol, A.; Pachon, MI.; Rieta, JJ.; Alcaraz, R. (2020). Comparative Study of Convolutional Neural Networks for ECG Quality Assessment. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.370S1

TOPSIS Fuzzy Application Program Fase II

While there are many models of multi-criteria decision making based on fuzzy logic, the software available for solving problems of this kind is scarce. Therefore, the objective of this paper is to present the continuation of the software proposed in the paper “Topsis Fuzzy Aplication Program”, presented at the XXIV ENDIO – XXII EPIO in 2011 [1] which allows to solve decision problems with TOPSIS fuzzy method using linguistic labels, proposed by Chen (2000), including critical enhancements to the system arising as a result of its extensive use.Si bien existen muchos modelos de toma de decisión multicriterio basados en lógica difusa, el software disponible para resolver problemas de este tipo es escaso. Por ello, el objetivo del presente trabajo es presentar la continuación del software propuesto en el paper “Topsis Fuzzy Aplication Program”, presentado en el XXIV ENDIO – XXII EPIO en el 2011 y que permite resolver problemas de decisión con el método TOPSIS difuso con etiquetas lingüísticas propuesto por Chen (2000), incorporando mejoras críticas al sistema surgidas como resultado de su extenso uso.Sociedad Argentina de Informática e Investigación Operativ

Factors associated with post-traumatic stress disorder symptoms in the post-quarantine context of the COVID-19 pandemic in Peruvian medical students

Background: In March 2020, the Peruvian state introduced quarantine as a measure to control the spread of SARS-CoV-2. It has been suggested that being in quarantine is associated with the development of symptoms of Post-traumatic Stress Disorder (PTSD). The present study aims to explore the factors associated with the development of PTSD in a post-quarantine context due to COVID-19 in medical students. Objectives: To evaluate the factors associated with the development of post-quarantine PTSD symptoms in medical students from a Peruvian university. Methods: Analytical cross-sectional study. The objective will be developed after the lifting of the quarantine in Peru. Medical students enrolled during the 2020-01 academic cycle of the Peruvian University of Applied Sciences will be included. To collect the outcome variable (PTSD), the Impact of Event Scale - Revised (IES-R) will be used. The associated factors will be collected through a form that will be validated by experts and piloted in the field. The crude and adjusted coefficients will be calculated, using bivariate and multivariate linear regression models, respectively. We will use the “manual forward selection” technique to obtain a final model with minimally sufficient fit. After each model comparison and decision, multicollinearity will be evaluated with the variance inflation factor and matrix of independent variables. Results: Not having health insurance, having relatives or close friends who contracted the disease and having a lower family income are factors associated with PTSD in the post-quarantine context of the COVID-19 pandemic in medical students at a Peruvian university. Conclusions: Clinical evaluation is important for medical students with a high probability of having PTSD symptoms. We recommend conducting a longitudinal study to identify causality and other unstudied factors related to PTSD.Revisión por pare

Addicted? Reduced host resistance in populations with defensive symbionts.

Heritable symbionts that protect their hosts from pathogens have been described in a wide range of insect species. By reducing the incidence or severity of infection, these symbionts have the potential to reduce the strength of selection on genes in the insect genome that increase resistance. Therefore, the presence of such symbionts may slow down the evolution of resistance. Here we investigated this idea by exposing Drosophila melanogaster populations to infection with the pathogenic Drosophila C virus (DCV) in the presence or absence of Wolbachia, a heritable symbiont of arthropods that confers protection against viruses. After nine generations of selection, we found that resistance to DCV had increased in all populations. However, in the presence of Wolbachia the resistant allele of pastrel-a gene that has a major effect on resistance to DCV-was at a lower frequency than in the symbiont-free populations. This finding suggests that defensive symbionts have the potential to hamper the evolution of insect resistance genes, potentially leading to a state of evolutionary addiction where the genetically susceptible insect host mostly relies on its symbiont to fight pathogens.Wellcome Trust (Grant ID: WT094664MA)This is the final version of the article. It first appeared from The Royal Society via https://doi.org/10.1098/rspb.2016.077

Effect of aquatic resistance interval training and dietary education program on physical and psychological health in older women: Randomized controlled trial

Due to demographic changes, the world’s population is progressively aging. The physiological deterioration of the older adult may lead to reduced balance capacity and increased risk of falls, among others, due to the prevalence of degenerative diseases. Physical exercise can be effective in reducing the risk of disease and slowing functional decline in older people. The aim of the research is to test the effects of aquatic resistance training and dietary education on health indicators, strength, balance, functional autonomy, perception of satisfaction with life. Thirty-four participants aged 69 ± 4 years were randomly assigned into two groups: experimental (aquatic resistance interval training) and control group (no intervention). The intervention consisted of resistance training in an aquatic environment carried out for 14 weeks (three sessions per week: 60 min each). All variables were analyzed twice; pre - post intervention. Aquatic resistance training has positive effects on strength (p < 0.001), functional self-sufficiency (p < 0.001) and aerobic capacity (p < 0.001), however, no significant differences were observed in the perception of satisfaction with life and balance. Research results suggest that older women who engage in regular, scheduled aquatic resistance training have greater autonomy in performing activities of daily living, agility, gait control, and body composition variables (lower fat compartment and greater muscle mass)