Abstract
Tourism is an increasingly important global economic activity. The proliferation of technology-based mechanisms applied to this activity, has been prompted by a growing number of more demanding consumers, well informed and receptive to new tools to access information and also by the fact that tourism is an information-intensive activity. However, in what concerns peripheral rural tourism destinations, which are to a large extend made up of micro and small enterprises, there is a lack of evidence that the maturity of data that is captured, processed and maintained, by tourism organizations, has a sufficient level of maturity to support the application of Big Data Analytics techniques. This paper, which intends to examine peripheral and mainly rural tourism destinations, analyses the key issues about technology on tourism and proposes a matrix so that we can gauge if the data currently available, and its maturity level, are sufficient to support the use of Big Data Analytics, with all the inherent benefits that rural tourism destinations could arise from its use. Copyright © 2018 International Business Information Management Association (IBIMA).

Abstract

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. © Springer Nature Switzerland AG 2019.

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.