Abstract
Exposing electric vehicles (EV) to extreme temperatures limits its performance and charging. For the foreseen adoption of EVs, it is not only important to study the technology behind it, but also the environment it will be inserted into. In Europe, temperatures ranging from -30°C to +40°C are frequently observed and the impacts on batteries are well-known. However, the impact on the grid due to the performance of fast-chargers, under such conditions, also requires analysis, as it impacts both on the infrastructure's dimensioning and design. In this study, six different fast-chargers were analysed while charging a full battery EV, under four temperature levels (-25 °C, -15 °C, +20 °C, and +40 °C). The current total harmonic distortion, power factor, standby power, and unbalance were registered. Results show that the current total harmonic distortion (THDI) tended to increase at lower temperatures. The standby consumption showed no trend, with results ranging from 210 VA to 1650 VA. Three out of six chargers lost interoperability at -25 °C. Such non-linear loads, present high harmonic distortion, and, hence, low power factor. The temperature at which the vehicle's battery charges is crucial to the current it withdraws, thereby, influencing the charger's performance.

Abstract
Supplier selection problem is a multi-criteria decision-making problem that involves both quantitative and qualitative criteria. Typically, supplier selection decisions require a preliminary stage where pool of suppliers are initially screened to select potential set of suppliers for further evaluation and select the optimal supplier. This preliminary stage is heavily dependent on non-scientific approaches and do not consider any criteria during the screening process. Furthermore, quantifying the qualitative criteria has always relied quite considerably on subjective judgments, which render the supplier selection process ineffective. Therefore, this paper addresses these problems by proposing an easy going two-phase supplier selection decision model, based on fuzzy set theory that uses a scientific approach and incorporates performance criteria in screening and selecting the potential suppliers for further optimal supplier selection. To illustrate the applicability and validate the proposed model, a case study of a beverage producing company located in Ghana, the Sub-Saharan Africa is proposed.

Abstract
This paper presents a reliability-based approach for the design and deployment of an energy management system (EMS) by using 'smart' applications, such as energy storage (ES), to control battery power output in residential dwellings, and thus improve distribution-network reliability performance. The state of charge (SOC) of the battery system is designed based on time-varying electricity tariff, load demand and solar photovoltaic (PV) generation data to investigate a realistic test-case scenario. Additionally, a typical MV/LV urban distribution system is fully modelled and scripted to investigate the potential benefits that 'smart' interventions can offer to customers' quality of power supply. In this research, Monte-Carlo simulation method is further developed to include the time-variation of electricity demand profiles and failure rates of network components. Accordingly, the reliability-based effects from SOC variation in batteries are compared with an uncontrolled microgeneration (MG) scenario, by using different PV penetration levels to justify the value of control. The benefits are assessed through standard reliability indices measuring frequency and duration of power interruptions and most importantly, the energy not supplied to customers during sustained interruptions.

Abstract

Abstract
Technology is constantly evolving, as a result, users have become more demanding and the applications more complex. In the realm of Web development, JavaScript is growing in a surprising way, already leaving the boundaries of the browser, mainly due to the advent of Node.js. In fact, JavaScript is constantly being reinvented and, from the ES2015 version, began to include the OO concepts typically found in other programming languages. With Web access being mostly made by mobile devices, developers face now performance challenges and need to perform a plethora of tasks that weren’t necessary a decade ago, such as managing dependencies, bundling files, minifying code, optimizing images and others. Many of these tasks can be achieved by using the right tools for the job. However, developers not only have to know those tools, but they also must know how to access and operate them. This process can be tedious, confusing, time-consuming and error-prone. In this paper, we present Kaang, an automatic generator of RESTFul Web applications. The ultimate goal of Kaang is to minimize the impact of creating a RESTFul service by automating all its workflow (e.g., files structuring, boilerplate code generation, dependencies management, and task building). This kind of generators will benefit two types of users: will help novice developers to decrease their learning curve while facing the new frameworks and libraries commonly found in the modern Web and speed up the work of expert developers avoiding all the repetitive and bureaucratic work. At the same time, Kaang promotes the good development principles by adding automatic testing and documentation generation. For this accomplishment, Kaang generates the main API content based on the user’s input and a set of templates which will help developers to manage and test routes, define resources, store data models and others. In order to provide an addition level of confidence to the generator’s end-users, the generator will be integrated on Travis CI and published on both the npmjs and Yeoman registries. © Ricardo Queirós.

Abstract
Recommender systems still mainly base their reasoning on pairwise interactions or information on individual entities, like item attributes or ratings, without properly evaluating the multiple dimensions of the recommendation problem. However, in many cases, like in music, items are rarely consumed in isolation, thus users rather need a set of items, selected to work well together, serving a specific purpose, while having some cognitive properties as a whole, related to their perception of quality and satisfaction, under given circumstances. In this paper, we introduce the term of playlist concept in order to capture the implicit characteristics of joint music item selections, related to their context, scope and general perception by the users. Although playlist consumptions may be associated with contextual attributes, these may be of various types, differently influencing users' preferences, based on their character and emotional state, therefore differently reflected on their final selections. We highlight on the use of this term in HybA, our hybrid recommender system, to identify clusters of similar playlists able to capture inherit characteristics and semantic properties, not explicitly described in them. The experimental results presented, show that this conceptual clustering results in playlist continuations of improved quality, compared to using explicit contextual parameters, or the commonly used collaborative filtering technique. © 2018 The authors and IOS Press.