Abstract

Abstract
Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of paramount importance for precision medicine. Machine learning(ML) algorithms can be trained on high-throughput screening data to develop models that are able to predict the response of cancer cell lines and patients to novel drugs or drug combinations. Deep learning (DL) refers to a distinct class of ML algorithms that have achieved top-level performance in a variety of fields, including drug discovery. These types of models have unique characteristics that may make them more suitable for the complex task of modeling drug response based on both biological and chemical data, but the application of DL to drug response prediction has been unexplored until very recently. The few studies that have been published have shown promising results, and the use of DL for drug response prediction is beginning to attract greater interest from researchers in the field. In this article, we critically review recently published studies that have employed DL methods to predict drug response in cancer cell lines.We also provide a brief description of DL and the main types of architectures that have been used in these studies. Additionally, we present a selection of publicly available drug screening data resources that can be used to develop drug response prediction models. Finally, we also address the limitations of these approaches and provide a discussion on possible paths for further improvement.

Abstract
A strain gauge sensor based on Fiber Bragg Grating (FBG) for diameter measurement is proposed and experimentally demonstrated. The sensor is easily fabricated inserting the FBG on the strain gauge-it was fabricated using a 3D printer-and fixing the FBG in two points of this structure. The idea is to vary the diameter of the structure. We developed two experimental setups, the first one is used to evaluate the response of the FBG to strain and the second one to assess the possibility of using the structure developed to monitor the desired parameter. The results demonstrated that the structure can be used as a way to monitor the diameter variation in some applications. The sensor presented a sensitivity of 0.5361 nm/mm and a good linear response of 0.9976 using the Strain Gauge with FBG and fused taper.

Abstract
In the spring of 2020, six undergraduate students from diverse countries and engineering fields decided to design together a solution to monitor the elderly. This project was performed as part of the European Project Semester (EPS) programme at Instituto Superior de Engenharia do Porto (ISEP). The EM-BRACE solution encompasses two interconnected devices (a home station and a bracelet) and mobile/Web twin applications. The bracelet measures and transmits vital user data (pulse, temperature and impacts) to the home station, whereas the latter measures home environment parameters (temperature, humidity and pressure) and sends local and bracelet data to an Internet of Things (IoT) platform. This way, these data become accessible via the mobile/Web application. Thereby, EM-BRACE monitors the health and environment of the elderly and timely notifies caregivers about problems, contributing to the well-being of the elderly and their families.

Abstract
An original graph-based model and algorithm for optimal industrial task scheduling is proposed in this article. The innovative algorithm designed, dubbed "Dijkstra optimal tasking" (DOT), is suitable for fully distributed task scheduling of autonomous industrial agents for optimal resource allocation, including energy use. The algorithm was designed starting from graph theory fundamentals, from the ground up, to guarantee a generic nature, making it applicable on a plethora of tasking problems and not case-specific. For any industrial setting in which mobile agents are responsible for accomplishing tasks across a site, the objective is to determine the optimal task schedule for each agent, which maximizes the speed of task achievement while minimizing the movement, thereby minimizing energy consumption cost. The DOT algorithm is presented in detail in this manuscript, starting from the conceptualization to the mathematical formulation based on graph theory, having a thorough computational implementation and a detailed algorithm benchmarking analysis. The choice of Dijkstra as opposed to other shortest path methods (namely, A* Search and Bellman-Ford) in the proposed graph-based model and algorithm was investigated and justified. An example of a real-world application based on a refinery site is modeled and simulated and the proposed algorithm's effectiveness and computational efficiency is duly evaluated. A dynamic obstacle course was incorporated to effectively demonstrate the proposed algorithm's applicability to real-world applications.

Abstract
The present research aims to formulate competition in a retail energy market in the presence of an Integrated Demand Response (IDR) program to reduce prosumer costs and increase retailer profits. This gives prosumers more degrees of freedom to reduce their energy costs. The retail energy market includes retailers and prosumers equipped with an energy hub containing a boiler for producing heat and combined heat and power (CHP). Retailers aim to maximize profit, whereas prosumers seek to minimize their costs. Hence, a multi-leader-follower game with a bi-level program emerges in which the upper level deals with the profit maximization of each retailer while the lower level considers the cost minimization of each prosumer. The strategic behaviour of each retailer is modelled as a Mathematical Program with Equilibrium Constraints (MPEC) problem. Simultaneously solving all MPECs, which leads to an Equilibrium Problem with Equilibrium Constraints (EPEC), determines the market equilibrium point. The equilibrium point is achieved using mathematical, analytical methods and linearization of nonlinear constraints by accurate techniques. Two different case studies are developed to investigate how the number of retailers influences the market equilibrium point. The first case includes two retailers, while the second case considers an increase in the number of retailers. The results demonstrate that with an increase in retailers' number, their competition increases, causing the prosumers costs to reduce. Furthermore, our results suggest the IDR impact on reduced prosumers cost and increased retailers profit.