Abstract
One of the principal complications of patients that suffer from Diabetes Mellitus (DM) and that can lead to ulceration is the Diabetic foot. As tissue inflammation causes temperature variation, several studies show that thermography can be used to detect complications in diabetic foot and help predicting the risk of ulceration. It is known that, although healthy individuals present characteristic plantar temperature variation patterns, the same does not happen with diabetic patients, for which a particular pattern can not be found; thus, making the measurement of the temperature variation more difficult. Given that, it is important to research in this field in order to obtain methods that can detect atypical variations of the temperature in the sole of the foot. With this in mind, the objective of this work is to present a methodology to analyze the distribution of temperature in thermograms of the foot's plant and classify it as belonging to a DM individual with risk of ulceration or a healthy individual. After foot partitioning with a clustering algorithm, basic statistical descriptors are computed for each cluster. A binary classifier to predict the risk of ulceration in the diabetic foot was evaluated with the different descriptors; both a quantitative temperature index and a classification threshold are calculated for each descriptor. To evaluate the performance of the classifier, experiments were conducted using a public dataset (containing 45 thermograms of healthy individuals and 122 images of DM ones); the following metrics were obtained: Accuracy = 78%, AUC = 86% and F-measure = 84%, with the best descriptor.

Abstract

Abstract
Inertial sensors can potentially assist clinical decision making in gait-related disorders. Methods for objective spatio-temporal gait analysis usually assume the careful alignment of the sensors on the body, so that sensor data can be evaluated using the body coordinate system. Some studies infer sensor orientation by exploring the cyclic characteristics of walking. In addition to being unrealistic to assume that the sensor can be aligned perfectly with the body, the robustness of gait analysis with respect to differences in sensor orientation has not yet been investigated-potentially hindering use in clinical settings. To address this gap in the literature, we introduce an orientation-invariant gait analysis approach and propose a method to quantitatively assess robustness to changes in sensor orientation. We validate our results in a group of young adults, using an optical motion capture system as reference. Overall, good agreement between systems is achieved considering an extensive set of gait metrics. Gait speed is evaluated with a relative error of -3.1 +/- 9.2 cm/s, but precision improves when turning strides are excluded from the analysis, resulting in a relative error of -3.4 +/- 6.9 cm/s. We demonstrate the invariance of our approach by simulating rotations of the sensor on the foot.

Abstract
Entrepreneurship serious games offer an innovative model for developing entrepreneurial skills with students. Furthermore, they provide a highly engaging and risk-free environment for students to become aware of the challenges posed in the early stages of building and developing a start-up. In this sense, it becomes relevant to explore the process of adopting this type of game in the classroom. This study aims to explore how the formative and summative components of an assessment are mapped into seven serious entrepreneurship games. Through this information, this study seeks to help instructors in the process of adopting a serious game and understanding how the evaluation of these games can be used in the context of an entrepreneurship course. © 2021, IGI Global.

Abstract
Digital society demands very qualified professionals ready to this environment challenges. This makes necessary to foster the development of competences related to such context such as Computational Thinking or STEAM related skills. However, this is not an easy task, especially because integrating subjects that covers the necessary topics and competences. New active pedagogical approaches are required and this what RoboSTEAM project provides. The application of Challenge Based Learning and Physical Devices and Robotics facilitate the so named twenty first century skills. The project has been developed by several universities and schools and one of most critical parts was testing the methodology and tools, this was done into pilot phases that are described in the present work. The results show that there are important differences between partners socioe-conomical context, but that the outcomes of the project are flexible enough to be applied successfully in any of them.

Abstract
End users have become active participants in local electricity market transactions because of the growth of the smart grid concept and energy storage systems (ESS). This participation is optimized in this article using a stochastic two-stage model considering the day-ahead and real-time electricity market data. This model optimally schedules the operation of a Smart Home (SH) to meet its energy demand. In addition, the uncertainty of wind and photovoltaic (PV) generation is considered along with different appliances. In this paper, the participation of an EV (electric vehicle), together with the battery energy storage systems, which allow for the increase in bidirectional energy transactions are considered. Demand Response (DR) programs are also incorporated which consider market prices in real-time and impact the scheduling process. A comparative analysis of the performance of a smart home participating in the electricity market is carried out to determine an optimal DR schedule for the smart homeowner. The results show that the SH's participation in a real-time pricing scheme not only reduces the operating costs but also leads to better than expected profits. Moreover, total, day-ahead and real-time expected profits are better in comparison with existing literature. The objective of this paper is to analyze the SH performance within the electrical market context so as to increase the system's flexibility whilst optimizing DR schedules that can mitigate the variability of end-users generation and load demand.