Abstract
This note, reporting the homonym keynote presented in the International Symposium on Molecular Logic and Computational Synthetic Biology 2018, traces an informal roadmap on Dynamic Logic (DL) field, focusing on its versatility and resilience to be adjusted and adopted in a wide class of application domains and computational paradigms. The exposition argues the room for developments on tagging DL to the analysis of synthetic biologic domain. © 2019, Springer Nature Switzerland AG.

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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.

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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).

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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).