Abstract
The increasing use of wearable electronics calls for sustainable energy solutions. Biomechanical energy harvesting appears as an attractive solution to replace the use of batteries in wearables, as the body generates sufficient power to drive small electronics. In particular, triboelectric nanogenerators (TENGs) have emerged as a promising approach due to its lightweight and high power density. In this work, a TENG is hybridized with an electromagnetic generator (EMG) to harvest energy from the foot strike. An enclosed radial-flow turbine is optimized and used to convert the foot-strike low-frequency linear movement into a higher-frequency rotational motion (by a factor of & AP;12). Besides increasing the motion frequency, the employed mechanism is physically robust and enables a continuous operation from irregular mechanical excitations. A single TENG unit operating in the freestanding mode generated an optimal power of 4.72 & mu;W and transferred a short-circuit charge of 2.3 nC. The TENG+EMG hybridization allows to power a digital pedometer even after the mechanical input stopped. Finally, the energy harvester is incorporated into a commercial shoe to power the same pedometer from foot walking. The obtained results validate the developed prototype ability to serve as a portable power source that can drive sensors and wearable electronics.

Abstract
For reconfigurable radios where the signals can be easily routed from one band to another band, new radio frequency switches (RF) are a fundament. The main factor driving the power consumption of the reconfigurable intelligent system (RIS) is the need for an intermediate device with static power consumption to maintain a certain surface configuration state. Since power usage scales quadratically with the RIS area, there is a relevant interest in mitigating this drawback so that this technology can be applied to everyday objects without needing such a high intrinsic power consumption. Current switch technologies such as PIN diodes, and field effect transistors (FETs) are volatile electronic devices, resulting in high static power. In addition, dynamic power dissipation related to switching event is also considerable. Regarding energy efficiency, non-volatile radio frequency resistive switch (RFRS) concept may be better alternative solution due to several advantages: smaller area, zero-hold voltage, lower actuation bias for operation, short switching time, scalability and capable to be fabricated in the backend-of-line of standard CMOS process.

Abstract

Abstract
Every year, the VISion Understanding and Machine intelligence (VISUM) summer school runs a competition where participants can learn and share knowledge about Computer Vision and Machine Learning in a vibrant environment. 2021 VISUM's focused on applying those methodologies in fashion. Recently, there has been an increase of interest within the scientific community in applying computer vision methodologies to the fashion domain. That is highly motivated by fashion being one of the world's largest industries presenting a rapid development in e-commerce mainly since the COVID-19 pandemic. Computer Vision for Fashion enables a wide range of innovations, from personalized recommendations to outfit matching. The competition enabled students to apply the knowledge acquired in the summer school to a real-world problem. The ambition was to foster research and development in fashion outfit complementary product retrieval by leveraging vast visual and textual data with domain knowledge. For this, a new fashion outfit dataset (acquired and curated by FARFETCH) for research and benchmark purposes is introduced. Additionally, a competitive baseline with an original negative sampling process for triplet mining was implemented and served as a starting point for participants. The top 3 performing methods are described in this paper since they constitute the reference state-of-the-art for this particular problem. To our knowledge, this is the first challenge in fashion outfit complementary product retrieval. Moreover, this joint project between academia and industry brings several relevant contributions to disseminating science and technology, promoting economic and social development, and helping to connect early-career researchers to real-world industry challenges.

Abstract

Abstract
IntroductionPerinatal asphyxia is one of the most frequent causes of neonatal mortality, affecting approximately four million newborns worldwide each year and causing the death of one million individuals. One of the main reasons for these high incidences is the lack of consensual methods of early diagnosis for this pathology. Estimating risk-appropriate health care for mother and baby is essential for increasing the quality of the health care system. Thus, it is necessary to investigate models that improve the prediction of perinatal asphyxia. Access to the cardiotocographic signals (CTGs) in conjunction with various clinical parameters can be crucial for the development of a successful model. ObjectivesThis exploratory work aims to develop predictive models of perinatal asphyxia based on clinical parameters and fetal heart rate (fHR) indices. MethodsSingle gestations data from a retrospective unicentric study from Centro Hospitalar e Universitario do Porto de Sao Joao (CHUSJ) between 2010 and 2018 was probed. The CTGs were acquired and analyzed by Omniview-SisPorto, estimating several fHR features. The clinical variables were obtained from the electronic clinical records stored by ObsCare. Entropy and compression characterized the complexity of the fHR time series. These variables' contribution to the prediction of asphyxia perinatal was probed by binary logistic regression (BLR) and Naive-Bayes (NB) models. ResultsThe data consisted of 517 cases, with 15 pathological cases. The asphyxia prediction models showed promising results, with an area under the receiver operator characteristic curve (AUC) >70%. In NB approaches, the best models combined clinical and SisPorto features. The best model was the univariate BLR with the variable compression ratio scale 2 (CR2) and an AUC of 94.93% [94.55; 95.31%]. ConclusionBoth BLR and Bayesian models have advantages and disadvantages. The model with the best performance predicting perinatal asphyxia was the univariate BLR with the CR2 variable, demonstrating the importance of non-linear indices in perinatal asphyxia detection. Future studies should explore decision support systems to detect sepsis, including clinical and CTGs features (linear and non-linear).