Abstract
While highlighting the significance of exposure to ads to explain consumer behavior, extant literature has so far disregarded the potential impact of dreams. Linking the current-concerns theory and the model of cognitive response to advertising, this study focuses on the impact of dreaming of ads on purchase intentions. To test the 3 research hypotheses proposed, a quantitative study was conducted with Iranian consumers, using individuals' retrospective self-assessment on the 3 variables of the study: exposure to ads, dreams of ads, and purchase intentions. Results were obtained using structural equation modeling analysis. The findings confirm that exposure to ads has a positive impact on purchase intention, comprising both direct and indirect effects through dreams of ads. In addition, it is shown that also dreaming about ads has a positive impact on purchase intentions. The article provides insights for researchers and practitioners interested in the effectiveness of advertising strategies and in the role of dreams for individuals.

Abstract
The issue of climate change has received considerable attention in recent decades. Therefore, renewable energies and especially wind units have become a central point of attention. The stochastic nature of wind power production is modeled by means of a scenario-based method to show the possible events in the real time. Based on the Monte-Carlo simulation method and employing constructed Rayleigh probability distribution function (PDF), several scenarios that demonstrate the behavior of wind farms in real time are generated. To this end, a uniform random variable is generated and assigned to the mentioned PDF. Afterwards, a wind speed with a probability is achieved followed by the amount of wind power generation. Also, with a scenario reduction method (forward method), the desired number of scenarios can be obtained. To cope with the uncertainties of wind power generation, resulting from the intermittent nature of this kind of energy, this paper proposes a demand response (DR)-based operation approach. In other words, unlike the previous models in the literature that considered a supplementary role for the DR, this paper introduces the main role for the DR in the operation of future electricity markets. This approach focuses on a comprehensive modeling of the DR programs (DRPs) for the operational scheduling of electricity markets, considering the uncertainties of the generation of wind turbines, aiming at increasing the network security and decreasing the operation cost. The incorporation of market-based DRPs, such as demand bidding and ancillary service DR, is also considered. Two novel quantitative indices are introduced to analyze the success of DRPs regarding efficiency and wind integration. Numerical results obtained on two IEEE test systems indicate the effectiveness of the proposed model.

Abstract
VirtuosoNext (TM) is a distributed real-time operating system (RTOS) featuring a generic programming model dubbed Interacting Entities. This paper focuses on these interactions, implemented as so-called Hubs. Hubs act as synchronisation and communication mechanisms between the application tasks and implement the services provided by the kernel as a kind of Guarded Protected Action with a well defined semantics. While the kernel provides the most basic services, each carefully designed, tested and optimised, tasks are limited to this handful of basic hubs, leaving the development of more complex synchronization and communication mechanisms up to application specific implementations. In this work we investigate how to support a programming paradigm to compositionally build new services, using notions borrowed from the Reo coordination language, and relieving tasks from coordination aspects while delegating them to the hubs. We formalise the semantics of hubs using an automata model, identify the behaviour of existing hubs, and propose an approach to build new hubs by composing simpler ones. We also provide tools and methods to analyse and simplify hubs under our automata interpretation. In a first experiment several hub interactions are combined into a single more complex hub, which raises the level of abstraction and contributes to a higher productivity for the programmer. Finally, we investigate the impact on the performance by comparing different implementations on an embedded board.

Abstract

Abstract
This work focuses on hybrid balancing systems, a recently-proposed concept that enables balancing of battery cells and hybridization with supercapacitors. To control this system, a model predictive control framework is developed. In addition to distributing the supercapacitor power among the balancing circuits, this framework is also able to minimize state-of-charge and thermal imbalances in the battery cells, as well as energy losses in the balancing circuits. The effectiveness of the proposed approach is verified via numerical simulations. It is shown that, in comparison with state-of-art balancing solutions, the proposed control approach is able to decrease battery stress in up to 9% and the maximum temperature in up to 4.5%.

Abstract
The total losses volume represents a substantial amount of energy and, consequently, a large cost that is often included in the tariffs structure. Uneven connection of single-phase loads is a major cause for three-phase unbalance and a fundamental cause for active power losses, particularly in Low Voltage (LV) networks. This paper analyzes the impact of load unbalance on LV network losses. In the first phase, several load scenarios per phase are considered to characterize how losses depend on load unbalance. The second phase examines the data collected per phase on a set of real networks, aiming at illustrating real-world cases. The third phase analyzes the effect that public lighting and microgeneration may have in the load unbalance and on the subsequent energy losses. The results of this work clearly demonstrate that it is possible to reduce three-phase unbalance (and losses) through a judicious distribution of loads and microgeneration.