Along with the development of smart grids, wide adoption electric vehicles (EVs) is seen as a catalyst to reduction CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions and more intelligent transportation systems. In particular, EVs augment grid ability store energy at some points in network give it back others and, therefore, help optimize use from intermittent renewable sources let users refill their cars variety locations....

We propose offline and online scheduling algorithms for the charging of electric vehicles (EVs) in a single station (CS). The has available cheaper, but limited, energy from renewable sources (RES). EVs are capable willing to participate vehicle-to-vehicle (V2V) transfers that used reduce cost increase RES utilization. centralized aim minimize total EVs. formulate problem as mixed integer programming (MIP) one we solve it optimally assuming full knowledge EV demand generation. Later, an...

In this paper we propose an optimal Electric Vehicle (EV) charging scheduling scheme with the option of Vehicle-to-Grid (V2G) and Vehicle-to-Vehicle (V2V) energy transfer. way, aim to increase customer satisfaction as well utilization compared settings where only from grid exists. We assume a single station exist present three alternative formulations problem V2G V2V transfer: (a) without additional grid, (b) (c) battery backup storage. all cases, formulate problems using Mixed Integer...

<sec> <title>BACKGROUND</title> A significant number of individuals undergoing Coronary Computed Tomography Angiography (CCTA) for suspected Artery Disease (CAD) have non-obstructive or no CAD. There is a need clinically proven models that can predict the pre-test probability (PTP) stable CAD and help to identify low-risk individuals. Optimizing patient stratification paramount importance improve diagnostic yield cost-effectiveness. </sec> <title>OBJECTIVE</title> study being carried out...

We consider the problem of managing Electric Vehicle (EV) charging at points in a city to ensure that load on remains within desired limits while minimizing inconvenience EV owners.We develop solutions treat and users as self-interested agents aim maximize their profit minimize impact schedule. In particular, we propose variants decentralised dynamic approach well an optimal centralised static approach. evaluated these real setting based road network location parking garages UK show...

The digitization of the healthcare domain has potential to drastically improve services, reduce time diagnosis, and lower costs. However, digital applications for need be interoperable maximize their potential. Additionally, with rapid expansion Artificial Intelligence (AI) and, specifically, Machine Learning (ML), large amounts diverse types data are being utilized. Thus, achieve interoperability in such applications, adoption common semantic models becomes imperative. In this paper, we...

We study a setting where electric vehicles (EVs) can be hired to drive from pick-up drop-off points in mobility-on-demand (MoD) scheme. Each point the MoD scheme is equipped with battery swap facility that helps cope EVs' limited range. The goal of system maximise number customers are serviced. Thus, we first model and solve this problem optimally using Mixed-Integer Programming (MIP) techniques show solution scales up medium sized problems. Given this, develop greedy heuristic algorithm...

This paper presents a novel energy management framework for multi-agent coordination in smart buildings. The builds on top of an existing Service-Oriented middleware Ambient Intelligence, which offers sensor and actuator functions wireless devices. also provides semantics infrastructure that assists authoring agent policies reducing consumption maximizing user comfort. Each within the is responsible monitoring environmental context controlling electrical appliances specific room. However,...

In this paper we schedule the travel path of a set drones across graph where nodes need to be visited multiple times at pre-defined points in time. This is an extension well-known traveling salesman problem. The proposed formulation can applied several domains such as monitoring traffic flows transportation network, or remote locations assist search and rescue missions. Aiming find optimal schedule, problem formulated Integer Linear Program (ILP). Given that highly combinatorial, solution...

In this work, the travel path of a set drones is scheduled across graph, where nodes need to be visited multiple times at pre-defined points in time. The can either demand requesting monitoring, or supply that are used as take-off/landing locations for and battery replacement cope with limited flying range drones. This an extension well-known traveling salesman problem proposed formulation applied several domains such monitoring traffic flows transportation network, remote assist search...

EVLib is a Java library for the management and simulation of number Electric Vehicle (EV) activities, at charging station level, within Smart Grid environment. aims to solve interoperability issues between Artificial Intelligence (AI)-related techniques already applied in this field. Thus, it provides simple, yet efficient interface all major EV-related activities such as dis-charging batteries, well battery swapping. Moreover, large parameters, chargers, waiting queues, available energy can...

The industry related to electric vehicles (EVs) has seen a substantial increase in recent years, as such have the ability significantly reduce total CO2 emissions and global warming effect. In this paper, we focus on problem of allocating EVs charging stations, scheduling pricing their charging. Specifically, developed Mixed Integer Program (MIP) which executes offline optimally allocates stations. On top, propose two alternative mechanisms price electricity charge. first mechanism is...

Preterm birth (PTB) is defined as delivery occurring before 37 weeks of gestation. In this paper, Artificial Intelligence (AI)-based predictive models are adapted to accurately estimate the probability PTB. doing so, pregnant women' objective results and variables extracted from screening procedure in combination with demographics, medical history, social other data used. A dataset consisting 375 women used a number alternative Machine Learning (ML) algorithms applied predict The ensemble...

The electrification of transport can significantly reduce CO2 emissions and their negative impact on the environment. In this paper, we study problem allocating Electric Vehicles (EVs) to charging stations scheduling charging. We develop an offline solution that treats EV users as self-interested agents aim maximise profit minimise schedule. formulate optimal station allocation a Mixed Integer Programming (MIP) one propose two pricing mechanisms: A fixed-price one, another is based well...

Highly populated cities face several challenges, one of them being the intense traffic congestion. In recent years, concept Urban Air Mobility has been put forward by large companies and organizations as a way to address this problem, approach rapidly gaining ground. This disruptive technology involves aerial vehicles (AVs) for hire that can be utilized customers travel between locations within cities. potential drastically decrease congestion reduce air pollution, since these typically use...

Obstructive coronary artery disease (CAD) is characterized as significant upon detection of stenosis diameter. In this paper, we adapt Artificial Intelligence (AI)-based predictive models to accurately estimate the pretest likelihood obstructive CAD on computed tomography angiography (CCTA) in patients with suspected CAD. doing so, use patients' objective results and variables extracted from screening procedure combination demographics, medical history, social other data. We a dataset...