Abstract
Recent studies show the high prevalence of the use of messaging platform and its growth trend, especially in younger populations (Figueroa Jacinto and Arndt 2018). Messaging apps are used not only to communicate and stay in touch with family and friends, but also to access services. Interactions with commercial purposes, such as making purchases, seeking out assistance and customer support, providing feedback or making reservations are already widely used, but legal and healthcare related areas also present prominent growth (Eeuwen 2017). In this chapter it is our objective to explore how technologies such as natural language processing, speech recognition, text-to-speech, machine learning, and cloud computing can be integrated to develop high quality chatbots for healthcare purposes. Additionally, we will cover an application case based on COVID-19 prevention and management.

Abstract
Although the internationalization of business firms has been intensively studied, the internationalization of nongovernmental organizations (NGOs) is still in a growing-up stage as NGOs are focused on serving specific social interests. They may not only be influenced by social, political, and economic goals, but also cater to social or humanitarian services dealing with health, environmental protection, and human rights. Based on the importance of NGOs and the lack of previous studies on their internationalization process, this paper analyzes the results of a systematic literature review (SLR) on the internationalization of NGOs. It is possible to conclude that this topic is under-researched and fragmented and has been dealt with by following qualitative studies. Moreover, the internationalization of NGOs is far from similar to the models that explain the internationalization of for-profit businesses. NGOs are clearly tuned to the services they provide and seek complementary resources from governmental sources and state agencies so that they are capable of providing a variety of human and financial resources. The main limitation of this study is that it is based solely on two academic databases: SCOPUS and WoS.

Abstract
Cancer is one of the diseases with the highest mortality rate in the world. To understand the different origins of the disease, and to facilitate the development of new ways to treat it, laboratories cultivate, in vitro, cancer cells (cell lines), taken from patients with cancer. These cell lines enable researchers to test new approaches and to have an appropriate procedure for comparison of results. The methods used in an initial study at EMBL-EBI Institute (Cambridge, UK) were based on algorithms that construct propositional like models. The results reported were promising but we believe that they can be improved. A relevant limitation of the algorithms used in the original study is the absence or severe lack of comprehensibility of the models constructed. In Life Sciences, the possibility of understanding a model is an asset to help the specialist to understand the phenomenon that produced the data. With our study we have improved the performance of forecasting models and constructed understandable models. To meet these objectives we have used Graph Mining and Inductive Logic Programming algorithms.

Abstract
Health systems are complex organisations that attempt to deliver health services to meet the needs of the population. Regardless of the degree of access to and quality of those health services, the systems' sustainability has been the object of special attention by governments worldwide. To ensure their adequate functioning in light of the accountable and transparent facets of New Public Governance, one must resort to regulatory mechanisms, such as sunshine regulation. By publicly displaying the results of benchmarking assessments, sunshine regulation embarrasses operators with poor performances in an effort to force them to correct such divergences. Thus, in this paper, we propose a sunshine regulatory model to the public hospitals in Portugal using multi-criteria decision analysis. In particular, we created a five-star rating that categorises those operators according to their performances, by means of the outranking method ELECTRE TRI-NC. As a result, we provide evidence of the existence of a majority of providers assigned to the `Average' category of Three stars and the nonexistence of providers with unique extreme category assignments (One star or Five stars), both of which are supported by extensive stability and robustness analyses, and discussed in terms of policy implications for relevant stakeholders.

Abstract
In this paper it is presented the stochastic modeling of a time of flight sensor, to be applied in a mobile robotics application. The sensor was configured to provide data at a frequency 30 Hz, obtaining a tradeoff between reactiveness and accuracy. The sensor data was acquired using a microcontroller development board, being the sensor moved with a manipulator, in order to assure repeatability and accuracy in the data acquisition process. The sensor was modeled having in mind the targets color, ranging from black to white for the working range, its variance, standard deviation, offset, means and errors measures were estimated.

Abstract
This paper proposes an accurate and efficient probabilistic method for modeling the nonlinear and complex uncertainty effects and mainly focuses on the Electric Vehicle (EV) uncertainty in Peer-to-Peer (P2P) trading. The proposed method captures the uncertainty of the input parameters with a low computational burden and regardless of the probability density function (PDF) shape. To this end, for each uncertain parameter, multitude of random vectors with the specification of corresponding uncertain parameters are generated and a fuzzy membership function is then assigned to each vector. Since the most probable samples occur repeatedly, they are recognized by the superposition of the generated fuzzy membership functions. The simulation results on various case studies indicate the high accuracy of the proposed method in comparison with Monte-Carlo simulation (MCs), Unscented Transformation (UT), and Point Estimate Method (PEM). It also scales down the computational burden compared to MCs. Also, a real-world case study is employed to examine the ability of the method in capturing the uncertainty of EVs' arrival and departure time. The studies on this case reveal that involving EVs in P2P trading augments the amount of energy traded within the prosumers.