Abstract
Analysis of sports performance using mobile and wearable devices is becoming increasingly popular, helping users improve their sports practice. In this context, the goal of this work has been the development of an Apple Watch application, capable of detecting important strokes in the table tennis sport, using a deep learning (DL) model. A dataset of table tennis strokes has been created based on the watch's accelerometer and gyroscope sensors. The dataset collection was done in the Portuguese table tennis federation training sites, from several athletes, supervised by their coaches. To obtain the best DL model, three different architecture models where trained, compared and evaluated, using the complete dataset: a LSTM based on Create ML/Core ML frameworks (62.70% F1 score) and two Tensorflow based architectures, a CNN-LSTM (96.02% F1 score) and a ConvLSTM (97.33% F1 score).

Abstract
More than a century after the discovery of superconductors (SCs), numerous studies have been accomplished to take advantage of SCs in physics, power engineering, quantum computing, electronics, communications, aviation, healthcare, and defence-related applications. However, there are still challenges that hinder the full-scale commercialization of SCs, such as the high cost of superconducting wires/tapes, technical issues related to AC losses, the structure of superconducting devices, the complexity and high cost of the cooling systems, the critical temperature, and manufacturing-related issues. In the current century, massive advancements have been achieved in artificial intelligence (AI) techniques by offering disruptive solutions to handle engineering problems. Consequently, AI techniques can be implemented to tackle those challenges facing superconductivity and act as a shortcut towards the full commercialization of SCs and their applications. AI approaches are capable of providing fast, efficient, and accurate solutions for technical, manufacturing, and economic problems with a high level of complexity and nonlinearity in the field of superconductivity. In this paper, the concept of AI and the widely used algorithms are first given. Then a critical topical review is presented for those conducted studies that used AI methods for improvement, design, condition monitoring, fault detection and location of superconducting apparatuses in large-scale power applications, as well as the prediction of critical temperature and the structure of new SCs, and any other related applications. This topical review is presented in three main categories: AI for large-scale superconducting applications, AI for superconducting materials, and AI for the physics of SCs. In addition, the challenges of applying AI techniques to the superconductivity and its applications are given. Finally, future trends on how to integrate AI techniques with superconductivity towards commercialization are discussed.

Abstract
The purpose of this study is to describe the construction process and the evidence of content validity of SCARA, a prototype of a technological system to support language and communication rehabilitation in people with aphasia, providing a tool that serves both patients and health professionals who accompany the respective recovery process. The process followed four stages: internal phase of the program's organization, with research in the literature and analysis of the materials available in the Portuguese market; construction of the SCARA prototype; evaluation by experts; and data analysis. A Content Validity Index was calculated to determine the level of agreement between the experts. The level of agreement between experts showed the validity of SCARA. SCARA has shown to help the work of the speech-language pathologist and persons with aphasia, contributing to a higher therapeutic quality, enhancing linguistic recovery, and compensating for the impossibility of direct support more frequently and/or prolonged intervention. © 2022 ACM.

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Non-traditional data like the applicant's bank statement is a significant source for decision-making when granting loans. We find that we can use methods from network science on the applicant's bank statements to convert inherent cash flow characteristics to predictors for default prediction in a credit scoring or credit risk assessment model. First, the credit cash flow is extracted from a bank statement and later converted into a visibility graph or network. Afterwards, we use this visibility network to find features that predict the borrowers' repayment behaviour. We see that feature selection methods select all the five extracted features. Finally, SMOTE is used to balance the training data. The model using the features from the network and the standard features together is shown having superior performance compared to the model that uses only the standard features, indicating the network features' predictive power.