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Twin-box decks are being extensively used in the design of long-span bridges due to their superior flutter performance. Although the significant role played by the gap distance has been previously addressed in the frame of experimental studies, there is still a lack of understanding about the complex interplay between box geometry, gap distance and the aerodynamic force coefficients and flutter derivatives. In the present work, firstly, a surrogate model is developed, considering three design inputs for the geometry of the deck along with the angle of attack, providing the force coefficients as output. Afterwards, the work is then extended by developing another surrogate model, considering as inputs the reduced velocity and the same geometric variables of the deck, with the outputs being the flutter derivatives. The methodology, comprising the selection of the design domain, the definition of the samples, the CFD-based evaluation of the samples' response and the construction of the surrogate through the application of the neural network-based radial basis method, is reviewed. The surrogate models enable a quantitative description of the impact caused in the force coefficients and the flutter derivatives by modifications in the geometry of the twin-box deck. It has been found that flutter derivatives H1*, H2* and A2* are strongly dependent on the gap distance.

Due to its high sensitivity, electrohysterography (EHG) has emerged as an alternative technique for predicting preterm labor. The main obstacle in designing preterm labor prediction models is the inherent preterm/term imbalance ratio, which can give rise to relatively low performance. Numerous studies obtained promising preterm labor prediction results using the synthetic minority oversampling technique. However, these studies generally overestimate mathematical models’ real generalization capacity by generating synthetic data before splitting the dataset, leaking information between the training and testing partitions and thus reducing the complexity of the classification task. In this work, we analyzed the effect of combining feature selection and resampling methods to overcome the class imbalance problem for predicting preterm labor by EHG. We assessed undersampling, oversampling, and hybrid methods applied to the training and validation dataset during feature selection by genetic algorithm, and analyzed the resampling effect on training data after obtaining the optimized feature subset. The best strategy consisted of undersampling the majority class of the validation dataset to 1:1 during feature selection, without subsequent resampling of the training data, achieving an AUC of 94.5 ± 4.6%, average precision of 84.5 ± 11.7%, maximum F1-score of 79.6 ± 13.8%, and recall of 89.8 ± 12.1%. Our results outperformed the techniques currently used in clinical practice, suggesting the EHG could be used to predict preterm labor in clinics.

One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice.

The aim of this study was to investigate the combined effects of tactical and contextual indicators on achieving offensive penetration and scoring opportunities in English Premier League (EPL) soccer matches. A total of 1971 team possessions from 20 random matches were evaluated by means of multidimensional observation. The EPL matches had a great proportion of fast attacks (36.0%) followed by combinative (29.6%), direct attacks (24.1%) and counterattacks (9.5%). Multilevel logistic regression models revealed that counterattacks (OR = 3.428; 95% CI: 2.004-5.864; P

Electrohysterography (EHG) has emerged as an alternative technique to predict preterm labor, which still remains a challenge for the scientific-technical community. Based on EHG parameters, complex classification algorithms involving non-linear transformation of the input features, which clinicians found difficult to interpret, were generally used to predict preterm labor. We proposed to use genetic algorithm to identify the optimum feature subset to predict preterm labor using simple classification algorithms. A total of 203 parameters from 326 multichannel EHG recordings and obstetric data were used as input features. We designed and validated 3 base classifiers based on k-nearest neighbors, linear discriminant analysis and logistic regression, achieving F1-score of 84.63 ± 2.76%, 89.34 ± 3.5% and 86.87 ± 4.53%, respectively, for incoming new data. The results reveal that temporal, spectral and non-linear EHG parameters computed in different bandwidths from multichannel recordings provide complementary information on preterm labor prediction. We also developed an ensemble classifier that not only outperformed base classifiers but also reduced their variability, achieving an F1-score of 92.04 ± 2.97%, which is comparable with those obtained using complex classifiers. Our results suggest the feasibility of developing a preterm labor prediction system with high generalization capacity using simple easy-to-interpret classification algorithms to assist in transferring the EHG technique to clinical practice.

The use of robotic platforms for neuro-rehabilitation may boost the neural plasticity process and improve motor recovery in patients with upper limb mobility impairment as a consequence of an acquired brain injury. A robotic platform for this aim must provide ergonomic and friendly design, human safety, intensive task-oriented therapy, and assistive forces. Its implementation is a complex process that involves new developments in the mechanical, electronics, and control fields. This article presents the end-effector rehabilitation robot, a 2-degree-of-freedom planar robotic platform for upper limb rehabilitation in patients with neuromotor disability after a stroke. We describe the ergonomic mechanical design, the system control architecture, and the rehabilitation therapies that can be performed. The impedance-based haptic controller implemented in end-effector rehabilitation robot uses the information provided by a JR3 force sensor to achieve an efficient and friendly patient–robot interaction. Two task-oriented therapy modes have been implemented based on the “assist as needed” paradigm. As a result, the amount of support provided by the robot adapts to the patient’s requirements, maintaining the therapy as intensive as possible without compromising the patient’s health and safety and promoting engagement.

The rupture of abdominal aortic aneurysm (AAA) represents an important clinical event combining structural modifications that provoke a weakening of the arterial wall, coupled with the hemodynamic pressure acting on them. In an effort to increase understanding about this complex phenomenon, this paper studies the influence of abdominal aortic aneurysm asymmetry in the patterns of the main hemodynamics factors. Flow simulation in four 3D virtual AAA models with different asymmetry ranging from 0.2 to 0.8 were carried out under realistic pulsatile conditions to assess and identify regions with disturbed patterns flow and how it modifies the velocities fields and hemodynamic stress on the inside of the aneurysmatic sac. An explanation of the vortex structure changes and stress distribution during cardiac pulse, it is presented. The results obtained shown that the asymmetry is one of the main factors that influence the AAA rupture. On the other hand, the basis to establish a novel method, based on the morphometry of the AAA, to determine a numerical indicator characterizing the evolution and the rupture risk of aneurysm, regardless of size, it is approached. It is outlined the initial results of the validation, its current limitations and the future steps to improve the method.

Los sistemas de generación de energía eléctrica a partir de paneles fotovoltaicos son una alternativa para zonas rurales aisladas. Se analizaron los elementos y variables que componen un sistema de bombeo impulsado por energía solar en base a las características técnicas y especificaciones de los diferentes componentes y su interacción, para determinar y poder proponer, asociando esta información a características de costos, robustez y disponibilidad, las mejores opciones el bombeo de agua desde un punto de vista de costo/ eficiencia. La solución óptima es la conversión directa a 110 voltios en corriente alterna a partir de la corriente directa almacenada en el mínimo de baterías, con un banco de paneles que corresponda al amperaje a ser consumido por el motor. La bomba periférica tiene un consumo menor de energía por litro bombeado, mientras la altura total de bombeo no rebase los 20 m. Se diseñó y probó un sistema de bombeo abastecido por paneles solares, con un costo de inversión de 2330 US, que bombea entre 6 y 10 m3 de agua al día. Palabras clave: Bombeo con energía solar, desarrollo participativo, DEPARTIR.