Abstract
Nowadays, the operation of the smart distribution system (SDS) is more complicated with the penetration of electric vehicles (EVs), due to EVs' uncertainties as well as the capability of vehicle-to-grid (V2G). On the other hand, distribution transformers (DTs) which have to meet the demand of EVs are one of the essential components of SDS; indeed, their failure can lead to irreparable damage. The cause of most of these failures is overloading and high ambient temperature. The overloading increases the temperature of the various parts of the DTs, especially hot spot temperature (HST). Increasing this temperature reduces the nominal life of the DTs. With a high number of EVs in the future, and as a consequence high energy demand which has not been taken into account in proper operating program, it could lead to the overloading of DTs. So, in this paper, the loss of life (LOL) of a DT that feeds the residential loads and an EV parking lot (EV PL) is investigated. The maximization of the profit of the distribution system operator (DSO) is considered in two different parts i.e. with/without the appropriate operation coefficient (OC) of DT. Also, two different scenarios are applied i.e. charging mode (CM) of EVs and charging/discharging mode (CDM) of EVs. The results show that if the OC is not properly considered, the LOL of the transformers will be significantly high, implying a higher total ownership cost.

Abstract
Inertial Measurement Units (IMUs) have become a popular solution for tracking human motion. The main problem of using IMU data for deriving the position of different body segments throughout time is related to the accumulation of the errors in the inertial data. The solution to this problem is necessary to improve the use of IMUs for position tracking. In this work, we present several Machine Learning (ML) methods to improve the position tracking of various body segments when performing different movements. Firstly, classifiers were used to identify the periods in which the IMUs were stopped (zero-velocity detection). The models Random Forest, Support Vector Machine (SVM) and neural networks based on Long-Short-Term Memory (LSTM) layers were capable of identifying those periods independently of the motion and body segment with a substantially higher performance than the traditional fixed-threshold zero-velocity detectors. Afterwards, these techniques were combined with ML regression models based on LSTMs capable of estimating the displacement of the sensors during periods of movement. These models did not show significant improvements when compared with the more straightforward double integration of the linear acceleration data with drift removal for translational motion estimate. Finally, we present a model based on LSTMs that combined simultaneously zero-velocity detection with the translational motion of sensors estimate. This model revealed a lower average error for position tracking than the combination of the previously referred methodologies.

Abstract
This paper discusses the integration of emergent ICTs, such as the Internet of Things (IoT), the Arrowhead Framework, and the best practices from the area of condition monitoring and maintenance. These technologies are applied, for instance, for roller element bearing fault diagnostics and analysis by simulating faults. The authors first undertook the leading industry standards for condition-based maintenance (CBM), i.e., open system architecture-condition-based maintenance (OSA-CBM) and Machinery Information Management Open System Alliance (MIMOSA), which has been working towards standardizing the integration and interchangeability between systems. In addition, this paper highlights the predictive health monitoring methods that are needed for an effective CBM approach. The monitoring of industrial machines is discussed as well as the necessary details are provided regarding a demonstrator built on a metal sheet bending machine of the Greenbender family. Lastly, the authors discuss the benefits of the integration of the developed prototypes into a service-oriented platform, namely the Arrowhead Framework, which can be instrumental for the remotization of maintenance activities, such as the analysis of various equipment that are geographically distributed, to push forward the grand vision of the servitization of predictive health monitoring methods for large-scale interoperability.

Abstract
The penetration of distributed generation in medium (MV) and low (LV) voltage distribution grids has been steadily increasing every year in multiple countries, thus creating new technical challenges in grid operation and motivating developments in distributed optimization for flexibility management. The traditional centralized optimal power flow (OPF) algorithm can solve technical constraints violation. However, computational efficiency, new technologies (e.g., edge computing) and control architectures (e.g., web-of-cells) are demanding for distributed approaches. This work formulates a novel distributed multi-period OPF for three-phase unbalanced grids that is essential when integrating energy storage units in operational planning (e.g., day-ahead) of LV or local energy community grids. The decentralized constrained optimization problem is solved with the alternating direction method of multipliers (ADMM) adapted for unbalanced LV grids and multi-period optimization problems. A 33-bus LV distribution grid is used as a case-study in order to define optimal battery storage scheduling along a finite time horizon that minimizes overall grid operational costs, while complying with technical constraints of the grid (e.g., voltage and current limits) and battery state-of-charge constraints.

Abstract
As entity-oriented search takes the lead in modern search, the need for increasingly flexible tools, capable of motivating innovation in information retrieval research, also becomes more evident. Army ANT is an open source framework that takes a step forward in generalizing information retrieval research, so that modern approaches can be easily integrated in a shared evaluation environment. We present an overview on the system architecture of Army ANT, which has four main abstractions: (i) readers, to iterate over text collections, potentially containing associated entities and triples; (ii) engines, that implement indexing and searching approaches, supporting different retrieval tasks and ranking functions; (iii) databases, to store additional document metadata; and (iv) evaluators, to assess retrieval performance for specific tasks and test collections. We also introduce the command line interface and the web interface, presenting a learn mode as a way to explore, analyze and understand representation and retrieval models, through tracing, score component visualization and documentation. © Springer Nature Switzerland AG 2020.

Abstract
Self-scheduling of Home Energy Management Systems (HEMS) is one of the most interesting problems for active end-users to reduce their electricity bills. The electricity bill reduction by adopting Demand Response Programs (DRP) considering the flexibility of the end-users is addressed in this paper. The problem is addressed as a multi-objective optimization problem. The first objective function is the minimization of the daily bill, while the second objective aims to minimize the Discomfort Index (DI) regarding shifting the home appliances plugging-in time. The Time-of-Use (ToU) tariff is adopted in this paper and therefore, the end-users can benefit from shifting their flexible loads from peak hours to the off-peak hours and this reduces their bills, accordingly. In this case, the end-users have to change their energy consumption which imposes a level of discomfort on the end-users. Therefore, a two-stage model is proposed in this paper to deal with the mentioned objective functions. The proposed model is represented as standard mixed-integer linear programming (MILP) and for solving this problem the epsilon-constraint method is adopted in this study. The obtained Pareto front from the epsilon-constraint multi-objective framework is fed to the fuzzy satisfying method for final plan selection. These results show that by providing the Pareto set of optimal solutions to the user, they are more informed and can make decisions that better suit their preferences.