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.

Abstract
Electricity markets are complex and dynamic environments, mostly due to the large scale integration of renewable energy sources in the system. Negotiation in these markets is a significant challenge, especially when considering negotiations at the local level (e.g. between buildings and distributed energy resources). It is essential for a negotiator to he able to identify the negotiation profile of the players with whom he is negotiating. If a negotiator knows these profiles, it is possible to adapt the negotiation strategy and get better results in a negotiation. In order to identify and define such negotiation profiles, a clustering process is proposed in this paper. The clustering process is performed using the kml-k-means algorithm, in which several negotiation approaches are evaluated in order to identify and define players' negotiation profiles. A case study is presented, using as input data, information from proposals made during a set of negotiations. Results show that the proposed approach is able to identify players' negotiation profiles used in bilateral negotiations in electricity markets.

Abstract
This paper presents an artificial cognitive system tightly integrating object perception and manipulation for assistive robotics. This is necessary for assistive robots, not only to perform manipulation tasks in a reasonable amount of time and in an appropriate manner, but also to robustly adapt to new environments by handling new objects. In particular, this system includes perception capabilities that allow robots to incrementally learn object categories from the set of accumulated experiences and reason about how to perform complex tasks. To achieve these goals, it is critical to detect, track and recognize objects in the environment as well as to conceptualize experiences and learn novel object categories in an open-ended manner, based on human-robot interaction. Interaction capabilities were developed to enable human users to teach new object categories and instruct the robot to perform complex tasks. A naive Bayes learning approach with a Bag-of-Words object representation are used to acquire and refine object category models. Perceptual memory is used to store object experiences, feature dictionary and object category models. Working memory is employed to support communication purposes between the different modules of the architecture. A reactive planning approach is used to carry out complex tasks. To examine the performance of the proposed architecture, a quantitative evaluation and a qualitative analysis are carried out. Experimental results show that the proposed system is able to interact with human users, learn new object categories over time, as well as perform complex tasks.