Abstract
This work describes a problem oriented approach to analyze and predict the Age of Onset of Patients diagnosed with Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP). We constructed, from a set of clinical and familial records, three sets of features which represent different characteristics of a patient, before becoming symptomatic. Using those features, we tested a set of machine learning regression methods, namely Decision Tree (Regression Tree), Elastic Net, Lasso, Linear Regression, Random Forest Regressor, Ridge Regression and Support Vector Machine Regressor (SVM). Later, we defined a baseline model that represents the current medical practice to serve as a guideline for us to measure the accuracy of our approach. Our results show a significant improvement of machine learning methods when compared with the current baseline. © 2018 IEEE.

Abstract
Selecting appropriate classification algorithms for a given dataset is crucial and useful in practice but is also full of challenges. In order to maximize performance, users of machine learning algorithms need methods that can help them identify the most relevant features in datasets, select algorithms and determine their appropriate hyperparameter settings. In this paper, a method of recommending classification algorithms is proposed. It is oriented towards the average ranking method, combining algorithm rankings observed on prior datasets to identify the best algorithms for a new dataset. Our method uses a special case of data mining workflow that combines algorithm selection preceded by a feature selection method (CFS).

Abstract
The electrification of the transportation area draws significant attention recently regarding mainly the environmental concerns and many vehicle manufacturers have already launched several commercial electrical vehicle (EV) types. The EV parking lots herein play an important role and need further analysis in terms of considering the possible impacts of simultaneous EV charging based extra power demand on distribution systems. In this study, a scenario based analysis of an EV parking lot equipped with a roof-top PV unit is realized in terms of the impacts on various operating conditions in a distribution system. Various scenarios are created for EV charging considering different brands and models of EVs with random initial state-of-energy and arrival time. The variability of the solar radiation during daytime and seasons are also considered. All the aforementioned analyses are conducted in ETAP (Electrical Transient Analyzer Program) environment.

Abstract
The emergence of Internet of Things (IoT) technology is expected to offer new promising solutions in various domains and, consequently, impact many aspects of everyday life. However, the development and testing of software applications and services for IoT systems encompasses several challenges that existing solutions have not yet properly addressed. Particularly, the difficulty to test IoT systems-due to their heterogeneous and distributed nature-, and the importance of testing in the development process give rise to the need for an efficient way to implement automated testing in IoT. Although there are already several tools that can be used in the testing of IoT systems, a number of issues can be pointed out, such as focusing on a specific platform, language, or standard, limiting the possibility of improvement or extension, and not providing out-of-The-box functionalities. This paper describes Izinto, a pattern-based test automation framework for integration testing of IoT systems. The framework implements in a generic way a set of test patterns specific to the IoT domain which can be easily instantiated for concrete IoT scenarios. It was validated in a number of test cases, within a concrete application scenario in the domain of Ambient Assisted Living (AAL). © 2018 ACM.

Abstract
This paper describes the development of a multi-agent system (MAS) to support the implementation of zero-defect manufacturing strategies in multi-stage production systems. The MAS infrastructure, combined with on-line inspection tools, data analytics and knowledge generation, constitutes a suitable approach to integrate process and quality control in multi-stage environments. This will allow the early detection of product defects, the adaptation to operating condition changes and the optimisation of manufacturing processes. This type of integrated management structure is aligned with a zero-defect manufacturing production model which is of paramount importance in the actual state-of-the-art manufacturing paradigms. As a proof of concept, the devised manufacturing supervision model was deployed into an experimental multi-stage system that run a set of several tests on electrical motors. The agent-based solution was implemented using the JADE framework and the exchange of information structured by proper data models and industrial based Internet-of-Things and Machine-to-Machine technologies, such as OPC-UA, REST and JSON. The obtained results demonstrate the suitability of the devised integrated management model as a vehicle to achieve dynamic and continuous system improvement in multi-stage manufacturing environments.

Abstract
Interface design flaws are often at the root cause of use errors in medical devices. Medical incidents are seldom reported, thus hindering the understanding of the incident contributing factors. Moreover, when dealing with a use error, both novices and expert users often blame themselves for insufficient knowledge rather than acknowledge deficiencies in the device. Simulation-Based Medical Education (SBME) platforms can provide appropriate training to professionals, especially if the right incentives to keep training are in place. In this paper, we present a new SBME, particularly targeted at training interaction with medical devices such as ventilators and infusion pumps. Our SBME functions as a game mode of the PVSio-web, a graphical environment for design, evaluation, and simulation of interactive (human-computer) systems. An analytical evaluation of our current implementation is provided, by comparing the features on our SBME with a set of requirements for game-based medical simulators retrieved from the literature. By being developed in a free, open source platform, our SBME is highly accessible and can be easily adapted to specific use cases, such a specific hospital with a defined set of medical devices.