Abstract

Abstract
In this paper, a new bi-level framework is presented for operational scheduling of a smart distribution company (SDISCO) with electric vehicle (EV) parking lot (PL) and renewable energy sources (RES), i.e., wind and photovoltaic (PV) units. In the proposed bi-level model, maximization of the profit of SDISCO is obtained in the upper-level (leader) problem by minimizing the cost of power purchased from the wholesale market due to the EV PL unique capability, i.e., PL-to-grid. The lower-level (follower) problem aims to maximize the profit of the PL owner. This model is converted to a non-linear single-level problem by using Karush-Kuhn-Tucker (KKT) conditions. Fortuny-Amat and McCarl method is used for linearization based on auxiliary binary variables and sufficiently large constants. Moreover, uncertainties such as duration of the presence of EVs in PL, the initial state of the charge (SOC) of EVs and output power generation of wind and PV units are simultaneously considered through a set of scenarios. The SDISCO's profit is investigated in four modes: (1) without RES and with the controlled charging of EVs; (2) without RES and with smart charging/discharging of EVs; (3) with RES and with the controlled charging of EVs; (4) with RES and with smart charging/discharging of EVs. In all these modes, a price-based demand response (DR) program is considered, as well as incentive-based DR, and combined price-based DR and incentive-based DR. The presented model is tested on the IEEE 15-bus distribution system over a 24-h period. The results show that SDISCO gains more profit by using a suitable charging/discharging schedule and employing a critical peak pricing (CPP) program. Furthermore, by comparing this bi-level model with the centralized model, the effectiveness of the bi-level model is demonstrated. Also, sensitivity analyses on the number of EVs, size of RES and the percentage of customer participation in the DR program are evaluated on the optimal operation of the SDISCO.

Abstract
This paper compares optimal path planning algorithms based on a Genetic Algorithm and a Particle Swarm Optimization algorithm applied to multiple bioinspired robots in a 2D environment simulation. The planning objectives are related to the harvesting of an apple plantation in which three swarm of butterflies were run, counting the fruits on the ground to optimize the harvest in a cooperative way. Robotic swarms must travel through points on the map to count the fruits. The time for each swarm was also counted for the comparison results.

Abstract
The challenge of noise attenuation in images has led to extensive research on improved noise reduction techniques, preserving important image characteristics, improving not only visual perception, but also enabling the use for special purposes, such as in medicine to increase clarity of medical images. In this paper, a technique for noise attenuation in medical images is proposed. Its operation takes place through the application of an adapted genetic algorithm. The results of experiments show that the proposed approach works best in suppressing artifacts and the preservation of the structure compared with several existing methods.

Abstract
Research Highlights: This study bridges a gap of knowledge about the maximum size-density trajectory for juvenile stands of maritime pine. The continuity of the trajectory along the development stages to maturity is assured with a straightforward approach providing support to determine optimum density along all the revolution periods for the species. Background and Objectives: Forest fire is a significant threat to forests in the Mediterranean regions, but also a natural disturbance that plays a vital role in the perpetuation of forest stands. In recent decades, there has been an increase of burnt area in maritime forests in Portugal, followed by an increased interest in managing the natural and usually abundant regeneration occurring after the fires. The gap in the knowledge of growth dynamics for juvenile stages, for these forest systems, currently constrains their correct management, for forest planning, particularly in determining the optimal densities. The study aims to identify the maximum attainable density trajectory at the early stages of development of the species that could support a non-empirical definition of silvicultural prescriptions and thinning decisions, along the revolution. Materials and Methods: A representative data set collected in stands regenerated after fire supports the analysis of the maximum size-density trajectory for the species. Results: The maximum size-density trajectory for the juvenile stands deviates from the expected trajectory defined in the self-thinning line published for the species. Significant deviation occurs at the lower end of the line, indicating the need for a reevaluation of the existing self-thinning line. We propose a new self-thinning model for the species that explicitly considers the behavior of size-density for juvenile stands. The new model assures a logical continuity for the trajectory from the young stages of development to maturity. Conclusions: The proposed model based on the maximum attainable size-density trajectory provides ecological-based support to define silvicultural guidelines for management of the species.

Abstract
Optical Networks-on-Chip (ONoCs) are a promising solution for high-performance multi-core integration with better latency and bandwidth than traditional Electrical NoCs. Wavelength-routed ONoCs (WRONoCs) offer yet additional performance guarantees. However, WRONoC design presents new EDA challenges which have not yet been fully addressed. So far, most topology analysis is abstract, i.e., overlooks layout concerns, while for layout the tools available perform Place & Route (P&R) but no topology optimization. Thus, a need arises for a novel optimization method combining both aspects of WRONoC design. In this paper such a method, PSION, is laid out. When compared to the state-of-the-art design procedure, results show a 1.8x reduction in maximum optical insertion loss.