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
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.

Abstract
Software Defined Networking (SDN) facilitates the orchestration and configuration of network resources in a flexible and scalable form, where policies are managed by controller components that interact with network elements through multiple interfaces. The ubiquitous adoption of SDN leads to the availability of multiple SDN controllers, which have different characteristics in terms of performance and security support. SDN controllers are a common target in network attacks since their compromise leads to the capability of impairing the entire network. Thus, the choice of a SDN controller must be a meticulous process from early phases (design to production). CROCUS, herein proposed, provides a mechanism to enable an objective assessment of the security support of SDN controllers. CROCUS relies on the information provided by the Common Vulnerability Scoring System (CVSS) and considers security features derived from scenarios with stringent security requirements. Considering a vehicular communication scenario supported by multiple technologies, we narrow the selection of SDN controllers to OpenDayLight and ONOS choices. The results put in evidence that both controllers have security features relevant for demanding scenarios with ONOS excelling in some aspects.

Abstract
Northwest Iberia can be considered as one of the main areas where tin was exploited in antiquity. However, the location of ancient tin mining and metallurgy, their date and the intensity of tin production are still largely uncertain. The scale of mining activity and its socio-economical context have not been truly assessed, nor its evolution over time. With the present study, we intend to present an integrated, multiscale, multisensor and interdisciplinary methodology to tackle this problem. The integration of airborne LiDAR and historic aerial imagery has enabled us to identify and map ancient tin mining remains on the Tinto valley (Viana do Castelo, northern Portugal). The combination with historic mining documentation and literature review allowed us to confirm the impact of modern mining and define the best-preserved ancient mining areas for further archaeological research. After data processing and mapping, subsequent ground-truthing involved field survey and geological sampling that confirmed cassiterite exploitation as the key feature of the mining works. This non-invasive approach is of importance for informing future research and management of these landscapes.