Abstract
Rehabilitation, restoration and maintenance of monuments, heritage and buildings pose challenging tasks to engineers and architects, as any intervention must respect their architectural and constructive characteristics. Often these are unknown and sources of information have long been lost in time. Thus, there is a need to use methods capable of providing information on a wide range of aspects such as building foundations, construction characteristics and materials, alterations from the original layout, infrastructure mapping, pathologies, etc. These methods must respect the inherent structural delicacy and characteristics of ancient buildings and non-destructive methods, NDT such as geophysical methods, have been proposed to investigate these problems. It is common knowledge that a single geophysical method cannot provide full information on the problems to investigate. Thus, herein the combined use of Seismic Transmission Tomography and Ground Penetrating Radar - GPR - is demonstrated to provide important results in the investigation of the constructive elements (columns and walls) of a 14th century UNESCO monument. As demonstrated, high-resolution geophysical data obtained from both methods provide very good images of the interior of both walls and columns giving information on the quality and spatial distribution of the materials used in the construction of the monument. Finally, the results herein discussed prove the suitability and complementarity of these two methods to investigate, built heritage, monuments and buildings in general.

Abstract

Abstract
Nas últimas décadas a utilização da inteligência artificial tem sido frequente no desenvolvimento de aplicações computacionais. Mais recentemente a aprendizagem automática, especialmente pelo uso da aprendizagem profunda (deep learning), tem impulsionado o crescimento e ampliado o desenvolvimento de sistemas inteligentes para diferentes domínios. No cenário atual de crescimento tecnológico estão a surgir com maior frequência os sistemas de recomendação (recommender systems) com diferentes técnicas para a filtragem de informações em grandes bases de dados. Um desafio é prover a recomendação adaptativa para mitigar a sobrecarga de informações em ambientes on-line. Este artigo revisa trabalhos anteriores e aborda alguns dos aspectos teórico-conceptuais e teórico-práticos que constituem os sistemas de recomendação, caracterizando o emprego de redes neuronais profundas (Deep Neural Network – DNN) para prover a recomendação sequencial apoiada pela recomendação baseada em sessão.;In recent decades, artificial intelligence use has been frequent in the computational applications development. More recently, machine learning, especially through the use of deep learning, has driven growth and expanded the intelligent systems development for different domains. In the current scenario of technological growth, the recommender systems appear with increasing frequency through their different techniques for information filtering in large datasets. It is a challenge to provide adaptive recommendation to mitigate information overload in online environments. This article reviews previous works and addresses some of the theoretical-conceptual and theoretical-practical aspects that constitute the recommender systems, characterizing the use of deep neural network (DNN) to provide sequential recommendation supported by session-based recommendation.

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.