Abstract

Abstract
Fabry disease (FD) is a rare disease commonly complicated with white matter lesions (WMLs). WMLs, which have extensively been associated with gait impairment, justify further investigation of its implication in FD. This study aims to identify a set of gait characteristics to discriminate FD patients with/without WMLs and healthy controls. Seventy-six subjects walked through a predefined circuit using gait sensors that continuously acquired different stride features. Data were normalized using multiple regression normalization taking into account the subject physical properties, with the assessment of 32 kinematic gait variables. A filter method (Mann Whitney U test and Pearson correlation) followed by a wrapper method (recursive feature elimination (RFE) for Logistic Regression (LR) and Support Vector Machine (SVM) and information gain for Random Forest (RF)) were used for feature selection. Then, five different classifiers (LR, SVM Linear and RBF kernel, RF, and K-Nearest Neighbors (KNN)) based on different selected set features were evaluated. For FD patients with WMLs versus controls the highest accuracy of 72% was obtained using LR based on 3 gait variables: pushing, foot flat, and maximum toe clearance 2. For FD patients without WMLs versus controls, the best performance was observed using LR and SVM RBF kernel based on loading, foot flat, minimum toe clearance, stride length variability, loading variability, and lift-off angle variability with an accuracy of 83%. These findings are the first step to demonstrate the potential of machine learning techniques based on gait variables as a complementary tool to understand the role of WMLs in the gait impairment of FD.

Abstract
In missing data contexts, k-nearest neighbours imputation has proven beneficial since it takes advantage of the similarity between patterns to replace missing values. When dealing with heterogeneous data, researchers traditionally apply the HEOM distance, that handles continuous, nominal and missing data. Although other heterogeneous distances have been proposed, they have not yet been investigated and compared for k-nearest neighbours imputation. In this work, we study the effect of several heterogeneous distances on k-nearest neighbours imputation on a large benchmark of publicly-available datasets.

Abstract
This article presents an innovative hybrid imaging system that provides dense and accurate 3D information from harsh underwater environments. The proposed system is called MARESye and captures the advantages of both active and passive imaging methods: multiple light stripe range (LSR) and a photometric stereo (PS) technique, respectively. This hybrid approach fuses information from these techniques through a data-driven formulation to extend the measurement range and to produce high density 3D estimations in dynamic underwater environments. This hybrid system is driven by a gating timing approach to reduce the impact of several photometric issues related to the underwater environments such as, diffuse reflection, water turbidity and non-uniform illumination. Moreover, MARESye synchronizes and matches the acquisition of images with sub-sea phenomena which leads to clear pictures (with a high signal-to-noise ratio). Results conducted in realistic environments showed that MARESye is able to provide reliable, high density and accurate 3D data. Moreover, the experiments demonstrated that the performance of MARESye is less affected by sub-sea conditions since the SSIM index was 0.655 in high turbidity waters. Conventional imaging techniques obtained 0.328 in similar testing conditions. Therefore, the proposed system represents a valuable contribution for the inspection of maritime structures as well as for the navigation procedures of autonomous underwater vehicles during close range operations.

Abstract
Soybean is one of the main sources of protein directly and indirectly in human nutrition, and it is highly dependent on logistics to connect country growers and international markets. Although recent studies deal with the impact of logistics on international trade, this impact in agricultural commodities is still an open research question. Moreover, these studies usually do not consider the influence of all components of the logistics on trade. This paper, therefore, aims at identifying the role of logistics performance in soybean exports among Argentina, Brazil, the US and their trading partners from 2012 to 2018. Using an extended gravity model, we examine whether the indicators of the World Bank Logistics Performance Index (LPI), adopted as a proxy of logistics efficiency, are an important determinant of bilateral soybean trade facilitation. The results lead to the conclusion that it is necessary to analyze the LPI throughout its indicators because they may affect trade differently. The novelty of this article is to provide an analysis of the impact of different logistics aspects on commodity trade, more specifically in the soybean case. Finally, regarding the model results, logistics infrastructure has a positive and significant correlation with soybean trade as supposed in most of the literature.

Abstract
This work apply a deep learning artificial neural network model-the Multilayer Perceptron- A s a regression model to estimate the demand of bus passengers. Transit bus ridership and weather conditions were collected over a year from a medium-size European metropolitan area and linked under the assumption: Individuals choose the travel mode based on the weather conditions that are observed during (a) the departure hour, (b) the hour before or (c) two hours prior to the travel start. The transit ridership data were also labelled according to the hour of the day, day of the week, month, and whether there was a strike and/or holiday or not. The results show that the prediction error of the model decrease by ~9% when the weather conditions observed two hours before travel start is taken into account. The model sensitivity analyses reveals that the worst performance is obtained for a strike day of a weekday in spring (typically Wednesdays or Thursdays). © 2020 Tânia Fontes et al., published by Sciendo.