Abstract

Abstract
Healthcare agents, in particular in the oncology field, are currently collecting vast amounts of diverse patient data. In this context, some decision-support systems, mostly based on deep learning techniques, have already been approved for clinical purposes. Despite all the efforts in introducing artificial intelligence methods in the workflow of clinicians, its lack of interpretability - understand how the methods make decisions - still inhibits their dissemination in clinical practice. The aim of this article is to present an easy guide for oncologists explaining how these methods make decisions and illustrating the strategies to explain them. Theoretical concepts were illustrated based on oncological examples and a literature review of research works was performed from PubMed between January 2014 to September 2020, using deep learning techniques, interpretability and oncology as keywords. Overall, more than 60% are related to breast, skin or brain cancers and the majority focused on explaining the importance of tumor characteristics (e.g. dimension, shape) in the predictions. The most used computational methods are multilayer perceptrons and convolutional neural networks. Nevertheless, despite being successfully applied in different cancers scenarios, endowing deep learning techniques with interpretability, while maintaining their performance, continues to be one of the greatest challenges of artificial intelligence.

Abstract
This study presents and applies global metrics for the analysis of the center of pressure (COP) excursion during impulse phases at different standard maximum vertical jump (MVJ) with long, short and no countermovement (CM) at countermovement jump (CMJ), drop jump (DJ) and squat jump (SJ) expanding COP analysis from static to dynamic condition of CM in association with lower limb muscle stretch–shortening cycle (SSC) and complementing previous studies on time structural analysis of COP excursion during impulse phase at standard MVJ. Whereas literature is abundant on COP excursion at gait, run and orthostatic standing position, there is a lack of studies on COP analysis at standard MVJ with an open issue on its contribution to long, short and no CM performance. Fifty-four trial tests were assessed with the selection of the best CMJ, DJ and SJ for each subject based on vertical flight height hflight. During trial tests ground reaction forces (GRF) and force moments were acquired and the COP coordinates were computed during the impulse phases. COP stabilograms and statokinesigrams were plotted and global metrics were computed namely the COPxA antero-posterior and COPyA mediolateral amplitudes of COP excursion, mean radial distance R, the length L of the path and the average velocity v during COP excursion. Statistical significative differences were detected at 5% significance, with higher mean COPxA than COPyA and higher mean COP global metrics at CMJ than SJ both higher than DJ, with DJ higher velocity of COP excursion than CMJ both higher than SJ. Global correlational analysis presented a positive linear association of COP metrics with hflight whereas at segmented MVJ this association wasn’t detected, thus rejecting the negative impact of larger COP excursion on MVJ performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Abstract
Electrical distribution companies have been investing in modernizing their structures, especially operation automation. The integration of information technologies and communications makes fast power restoration during fault events, providing better profit to companies and a more reliable and safe distribution network for customers. A self-healing strategy can be implemented for protection and control devices to work cooperatively, achieving the global purpose of automatic distribution system restoration. Thus, this work proposes a methodology for short-circuit fault detection, isolation of the faulted section, and restoration of downstream sections using neighbor feeders. The protection devices use standardized IEC and ANSI/IEEE functions to sensitize faults in the system and to promote adequate isolation, allowing the consequent restorative process. A genetic algorithm optimizes the devices’ parameters used in the protection scheme, making fastest the isolation process and ensuring the protection system coordination and selectivity. Results obtained using Simulink® allows for verifying the proposed methodology’s behavior and efficiency.

Abstract
With excess energy use from non-renewable sources, new energy generation solutions must be adopted to make up for this excess. In this sense, the integration of renewable energy sources in high-rise buildings reduces the need for energy from the national power grid to maximize the self-sustainability of common services. Moreover, self-consumption in low-voltage and medium-voltage networks strongly facilitates a reduction in external energy dependence. For consumers, the benefits of installing small wind turbines and energy storage systems include tax benefits and reduced electricity bills as well as a profitable system after the payback period. This paper focuses on assessing the wind potential in a high-rise building through computational fluid dynamics (CFD) simulations, quantifying the potential for wind energy production by small wind turbines (WT) at the installation site. Furthermore, a mathematical model is proposed to optimize wind energy production for a self-consumption system to minimize the total cost of energy purchased from the grid, maximizing the return on investment. The potential of a CFD-based project practice that has wide application in developing the most varied processes and equipment results in a huge reduction in the time and costs spent compared to conventional practices. Furthermore, the optimization model guarantees a significant decrease in the energy purchased at peak hours through the energy stored in energy storage systems (ESS). The results show that the efficiency of the proposed model leads to an investment amortization period of 7 years for a lifetime of 20 years.

Abstract