Abstract
Cyber-physical systems are infrastructures that use digital information such as network communications and sensor readings to control entities in the physical world. Many cyber-physical systems in airports, hospitals and nuclear power plants are regarded as critical infrastructures since a disruption of its normal functionality can result in negative consequences for the society. In the last few years, some security solutions for cyber-physical systems based on artificial intelligence have been proposed. Nevertheless, knowledge domain is required to properly setup and train artificial intelligence algorithms. Our work proposes a novel anomaly detection framework based on error space reconstruction, where genetic algorithms are used to perform hyperparameter optimization of machine learning methods. The proposed method achieved an Fl-score of 87.89% in the SWaT dataset.

Abstract
Panel data is a regression analysis type that uses time data and spatial data. Thus, the behavior of groups, for example, enterprises or communities, is analyzed through a time scale. Panel data allows exploring variables that cannot be observed or measured or variables that evolve over time but not across groups or communities. In this chapter, two different techniques used in panel data analysis is explored: fixed effects (FE) and random effects (RE). First, theoretical concepts of panel data are presented. Additionally, a case study example of the use of this type of regression is provided. Panel data analysis is performed with R language, and a step-by-step approach is presented.

Abstract
Kidney Exchange Programs (KEP) are valuable tools to increase the options of living donor kidney transplantation for patients with end-stage kidney disease with an immunologically incompatible live donor. Maximising the benefits of a KEP requires an information system to manage data and to optimise transplants. The data input specifications of the systems that relate to key information on blood group and Human Leukocyte Antigen (HLA) types and HLA antibodies are crucial in order to maximise the number of identified matched pairs while minimising the risk of match failures due to unanticipated positive crossmatches. Based on a survey of eight national and one transnational kidney exchange program, we discuss data requirements for running a KEP. We note large variations in the data recorded by different KEPs, reflecting varying medical practices. Furthermore, we describe how the information system supports decision making throughout these kidney exchange programs.

Abstract
The misuse and overuse of antibiotics lead to antibiotic resistance becoming a serious problem and a threat to world health. Bacteria developing resistance results in more dangerous infections and a more difficult treatment. To monitor the antibiotic pollution of environmental waters, different detection methods have been developed, however these are normally complex, costly and time-consuming. In a previous work, we developed a method based on digital colorimetry, using smartphone cameras to acquire sample images and color correction to ensure color constancy between images. A reference chart with 24 colors, with known ground truth values, is included in the photographs in order to color correct the images using least squares minimization. Then, the color of the sample is detected and correlated to antibiotic concentration. Although achieving promising results, the method was too sensitive to contrasting illumination conditions, with high standard deviations in these cases. Here, we test different methods for improving the stability and precision of the previous algorithm. By using only the 13 patches closest to the color of the targets and more parameters for the least squares minimization, better results were achieved, with an improvement of up to 83.33% relative to the baseline. By improving the color constancy, a more precise, less influenced by extreme conditions, estimation of sulfonamides is possible, using a practical and cost-efficient method. © 2021, Springer Nature Switzerland AG.

Abstract
One of the Industry 4.0 landmarks, concerns the optimization of manufacturing processes by increasing the operator's productivity. But productivity is highly affected by the operator's emotions. Positive emotions (e.g. happiness) are positively related to productivity, in contrast negative emotions (e.g. frustration) are negative related to productivity and positive related to misconducts and misbehaviors on the workplace. Thus perhaps, automatic recommendation systems can suggest actions or instructions to eliminate or attenuate undesired negative emotions on the workplace. These systems might support their actions based on the reliability of emotion detectors. In this paper, emotions are detected thought a speech system. Our solution was built over deep speech recognition layers, namely the first two convolutional layers of the pre-trained 2015 Baidu's speech recognition model. In re-utilizing these first two convolutional layers, robust meta-features are expected to be extracted. Our deep learning model attempts to predict the seven primary emotions on the MELD test set.Furthermore, our solution did not use any contextual data and yet it achieved robust results. The proposed weighted TrBaidu algorithm achieved state-of-art results on the detection of joy and surprise emotions, a F1-score rate of 23 % for both emotions.

Abstract
M-commerce is a fast-growing opportunity and is acting as an innovative lever for achieving the purpose of increasing sales while better interacting with the clients. Simultaneously, several emerging technologies have appeared in the market and promise to change the current m-commerce paradigm. Therefore, this chapter plans to explore a set of new trend technologies that can plan to build a more efficient relation between the consumer and the m-commerce platform. This study conducted surveys with several market players like marketers and IT leaders to understand their point of view, perceive the relevance and impact of these emergent technologies in m-commerce, identify resistance and challenge points to the proposed change, and look how to allow cohabitation between this new e-commerce paradigm and the traditional physical trade. The main novelty of this study is the inclusion of multiple points of view on the evolution of m-commerce which will allow companies and citizens to perceive the impact of emerging technologies in the future of m-commerce.