Abstract
Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP) is a neurological genetic illness that inflicts severe symptoms after the onset occurs. Age of onset represents the moment a patient starts to experience the symptoms of a disease. An accurate prediction of this event can improve clinical and operational guidelines that define the work of doctors, nurses, and operational staff. In this work, we transform family trees into compact vectors, that is, embeddings, and handle these as input features to predict the age of onset of patients with TTR-FAP. Our purpose is to evaluate how information present in genealogical trees can be transformed and used to improve a regression-based setting for TTR-FAP age of onset prediction. Our results show that by combining manual and graph-embeddings features there is a decrease in the mean prediction error when there is less information regarding a patient's family. With this work, we open the way for future work in representation learning for genealogical data, enabling a more effective exploitation of machine learning approaches.

Abstract
This paper reviewed machine learning algorit hms, particularly deep learning architectures applied to end-of-line testing systems in industrial environment. In industry, data is also produced when any product is being manufactured. All this information registered when manufacturing a specific product can be manipulated and interpreted using Machine Learning algorithms. Therefore, it is possible to draw conclusions from data and infer valuable results that can positively impact the future of the production line. The reviewed papers showed that machine learning algorithms play a crucial role in detecting, isolating, and preventing anomalies, helping operators make decisions, and allowing industries to save resources. © International Journal of Emerging Technology and Advanced Engineering.All right reserved.

Abstract

Abstract
In order to achieve the full potential of the Internet-of-Things, connectivity between devices should be ubiquitous and efficient. Wireless mesh networks are a critical component to achieve this ubiquitous connectivity for a wide range of services, and are composed of terminal devices (i.e., nodes), such as sensors of various types, and wall powered gateway devices, which provide further internet connectivity (e.g., via Wi-Fi). When considering large indoor areas, such as hospitals or industrial scenarios, the mesh must cover a large area, which introduces concerns regarding range and the number of gateways needed and respective wall cabling infrastructure, including data and power. Solutions for mesh networks implemented over different wireless protocols exist, like the recent Bluetooth Low Energy (BLE) 5.1. While BLE provides lower power consumption, some wall-power infrastructure may still be required. Alternatively, if some nodes are battery powered, concerns such as lifetime and packet delivery are introduced. We evaluate a scenario where the intermediate nodes of the mesh are battery powered, using a BLE relay of our own design, which acts as a range extender by forwarding packets from end-nodes to gateways. We present the relay's design and experimentally determine the packet forwarding efficiency for several scenarios and configurations. In the best case, up to 35% of the packets transmitted by 11 end-nodes can be forwarded to a gateway by a single relay under continuous operation. A battery lifetime of 1 year can be achieved with a relay duty cycle of 20%.

Abstract
There are different clean energy production technologies, including wind energy production. This type of energy, among renewable energies, is one of the least predictable due to the unpredictability of the wind. The wind prediction has been a deeply analysed field since has a considerable share on the green energy production, and the investments on this sector are growing. The efficiency and stability of power production can be increased with a better prediction of the main source of energy, in our case the wind. In this paper, some techniques inspired by Biological Inspired Optimization Techniques applied to wind forecast are compared. The wind forecast is very important to be able to estimate the electric energy production in the wind farms. As you know, the energy balance must be checked in the electrical system at every moment. In this study we are going to analyse different methodologies of wind and power prediction for wind farms to understand the method with best results.

Abstract
Following the reduced number of female students at ISTEC Porto computer science and engineering courses, we tried to find out what are the factors that might be causing this fact. This gender inequality was found looking back at the data from the last 15 years, clearly showing the gap between male and female students managing to conclude the degree. To point out the reasons and probable explanation a literature review was made to figure out conceivable paths to change this status quo. The article concludes that gender inequality resides on cultural differences as well as in the lack of information about the real role played by women in technology companies, particularly IT companies. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.