Comparing multiobjective swarm intelligence metaheuristics for DNA motif discovery

In recent years, a huge number of biological problems have been successfully addressed through computational techniques, among all these computational techniques we highlight metaheuristics. Also, most of these biological problems are directly related to genomic, studying the microorganisms, plants, and animals genomes. In this work, we solve a DNA sequence analysis problem called Motif Discovery Problem (MDP) by using two novel algorithms based on swarm intelligence: Artificial Bee Colony (ABC) and Gravitational Search Algorithm (GSA). To guide the pattern search to solutions that have a better biological relevance, we have redefined the problem formulation and incorporated several biological constraints that should be satisfied by each solution. One of the most important characteristics of the problem definition is the application of multiobjective optimization (MOO), maximizing three conflicting objectives: motif length, support, and similarity. So, we have adapted our algorithms to the multiobjective context. This paper presents an exhaustive comparison of both multiobjective proposals on instances of different nature: real instances, generic instances, and instances generated according to a Markov chain. To analyze their operations we have used several indicators and statistics, comparing their results with those obtained by standard algorithms in multiobjective computation, and by 14 well-known biological methods.     

A geometric model for an effective rescheduling after reducing service in public transportation systems

Railway systems in metropolitan areas carry a high density of traffic daily, heterogeneously distributed, and exposed to the negative consequences derived from service disruptions. An interesting topic in the literature is to obtain performance protocols in the presence of contingencies which can affect the system operation, avoiding the propagation of perturbation and minimizing its consequences. The objective of this paper is to assess the decision of rescheduling the train service, reducing the current supply along one transportation line in order to reinforce the service of another line, exploited by the same public operator, which has suffered an incidence or emergency. A methodology, based on a geometric representation of solutions which allows the use of discrete optimization techniques, is developed in order to attend to the underlying demand with efficiency criteria in this context of unexpected incidents.     

Modelling effects of S3D visual discomfort in human emotional state using data mining techniques

Multimedia Tools and Applications - In recent years, the rapid development of diverse media has been evident in disparate fields such as consumer electronics, automotive infotainment and healthcare...     

Unbounded Contention Resolution in Multiple-Access Channels

A frequent problem in settings where a unique resource must be shared among users is how to resolve the contention that arises when all of them must use it, but the resource allows only for one user each time. The application of efficient solutions for this problem spans a myriad of settings such as radio communication networks or databases. For the case where the number of users is unknown, recent work has yielded fruitful results for local area networks and radio networks, although either a (possibly loose) upper bound on the number of users needs to be known (Fernández Anta and Mosteiro in Discrete Math., Algorithms Appl. 2(4):445–456, 2010), or the solution is suboptimal (Bender et al. in ACM 17th Annual Symposium on Parallel Algorithms and Architectures, pp. 325–332, 2005), or it is only implicit (Greenberg and Leiserson in Adv. Comput. Res. 5:345–374, 1989) or embedded (Farach-Colton et al. in Theor. Comput. Sci. 472:60–80, 2013) in other problems, with bounds proved only asymptotically. In this paper, under the assumption that collision detection or information on the number of contenders is not available, we present a novel protocol for contention resolution in radio networks, and we recreate a protocol previously used for other problems (Greenberg and Leiserson in Adv. Comput. Res. 5:345–374, 1989, Farach-Colton et al. in Theor. Comput. Sci. 472:60–80, 2013), tailoring the constants for our needs. In contrast with previous work, both protocols are proved to be optimal up to a small constant factor and with high probability for big enough number of contenders. Additionally, the protocols are evaluated and contrasted with the previous work by extensive simulations. The evaluation shows that the complexity bounds obtained by the analysis are rather tight, and that both protocols proposed have small and predictable complexity for many system sizes (unlike previous protocols).     

A new Multiobjective Artificial Bee Colony algorithm to solve a real-world frequency assignment problem

Artificial bee colony (ABC) is a recently introduced algorithm that models the behavior of honey bee swarm to address a multiobjective version for ABC, named Multiobjective Artificial Bee Colony algorithm (MO-ABC). We describe the methodology and results obtained when applying the new MO-ABC metaheuristic, which was developed to solve a real-world frequency assignment problem (FAP) in GSM networks. A precise mathematical formulation for this problem was used, where the frequency plans are evaluated using accurate interference information taken from a real GSM network. In this paper, our work is divided into two stages: In the first one, we have accurately tuned the algorithm parameters. Then, in the second step, we have compared the MO-ABC with previous versions of distinct multiobjective algorithms already developed to the same instances of the problem. As we will see, results show that this approach is able to obtain reasonable frequency plans when solving a real-world FAP. In the results analysis, we consider as complementary metrics the hypervolume indicator to measure the quality of the solutions to this problem as well as the coverage relation information.     

A general RBDO decoupling approach for different reliability analysis methods

There are available in the literature several papers on the development of methods to decouple the reliability analysis and the structural optimization to solve RBDO problems. Most of them focused on strategies that employ the First Order Reliability Method (FORM) to approximate the reliability constraints. Despite of all these developments, one limitation prevailed: the lack of accuracy in the approximation of the reliability constraints due to the use of FORM. Thus, in this paper, a novel approach for RBDO is presented in order to overcome such a limitation. In this approach, we use the concept of shifting vectors, originally developed in the context of the Sequential Optimization and Reliability Assessment (SORA). However, the shifting vectors are found and updated based on a novel strategy. The resulting framework is able to use any technique for the reliability analysis stage, such as Monte Carlo simulation, second order reliability methods, stochastic polynomials, among others. Thus, the proposed approach overcomes the aforementioned limitation of most of RBDO decoupling techniques, which required the use of FORM for reliability analysis. Several examples are analyzed in order to show the effectiveness of the methodology. Focus is given on examples that are poorly solved or even cannot be tackled by FORM based approaches, such as highly nonlinear limit state functions comprised by a maximum operator or problems with discrete random variables. It should be remarked that the proposed approach was not developed to be more computationally efficient than RBDO decoupling strategies based FORM, but to allow the utilization of any, including more accurate, reliability analysis method.     

OFDM symbol identification by an unsupervised learning system under dynamically changing channel effects

Orthogonal frequency-division multiplexing (OFDM) is one of the most successful digital communication techniques. Nevertheless, the decrease in inter-symbol interference in quadrature amplitude modulation (QAM) over dispersive channels is still challenging. Different researches recently proposed the idea of using unsupervised learning as an alternative to the classic approaches to equalization of OFDM channels. In those purposes, the identification of a received QAM symbol is possible by the comparison of its position on the in-phase/quadrature (IQ) plane relative to the positions of previously arrived symbols, generally processed by the Kohonen’s Self-Organizing Map (SOM) algorithm. This work presents the SOM unsupervised learning method executed on an embedded system applied to QAM symbols identification. The system is implemented on an FPGA, a configurable digital circuit able to meet the low power and parallel process requirements of mobile applications. Also, in order to extend the classical set of experiments to evaluate our system, this paper proposes a theoretical model of the time-varying scheme representing the transition between different channel characteristics, obtained from real measurements available on a public repository. The model is employed to verify our purpose under dynamically both changing and realistic conditions. On the assumption that it is provided enough IQ symbols for the initial training process, the hardware implementation of SOM is able to track and identify the time-varying distorted QAM constellation. No knowledge of channel characteristics is necessary. The system spends only some microseconds at start-up to reach about 100% performance, and no dedicated training phase is needed afterward.

     

EPICS based low-level radio frequency control system in LIPAc

The IFMIF–EVEDA (International Fusion Materials Irradiation Facility – Engineering Validation and Engineering Design Activity) linear accelerator, known as Linear IFMIF Prototype Accelerator (LIPAc), will be a 9 MeV, 125 mA continuous wave (CW) deuteron accelerator prototype to validate the technical options of the accelerator design for IFMIF. The primary mission of such facility is to test and verify materials performance when subjected to extensive neutron irradiation of the type encountered in a fusion reactor to prepare for the design, construction, licensing and safe operation of a fusion demonstration reactor (DEMO). The radio frequency (RF) power system of IFMIF–EVEDA consists of 18 RF chains working at 175 MHz with three amplification stages each. The low-level radio frequency (LLRF) controls the amplitude and phase of the signal to be synchronized with the beam and it also controls the resonance frequency of the cavities. The system is based on a commercial compact peripheral component interconnect (cPCI) field programmable gate array (FPGA) board, provided by Lyrtech and controlled by a Windows host PC. For this purpose, it is mandatory to communicate the cPCI FPGA board from EPICS Channel Access [1]. A software architecture on EPICS framework in order to control and monitor the LLRF system is presented.     

From handheld collaborative tool to effective classroom module: Embedding CSCL in a broader design framework

The field of Computer Supported Collaborative Learning (CSCL) includes designers who emphasize effectiveness, measured via experiments, as well as designers who emphasize context and conduct qualitative research on teaching and learning practices. We conjectured that these two different emphases could be fruitful combined in a research and development process aimed at producing effective CSCL practices. We explored this possibility in a project that adapted a CSCL tool from Chile to serve as the basis of an effective 3-week classroom module for primary school mathematics in the United States. To go from tool to module, we addressed curricular fit, training materials, pedagogical guidance, formative and summative assessments, and logistical support. In conducting the project, we found that effectiveness and contextual research could be conducted simultaneously and yielded complementary insight to this design process, which enabled our project to rapidly move from the base tool towards complete classroom modules. An experiment we conducted after our design iterations showed that students who used the modules learned more about the target content, fractions. A retrospective analysis of our design process suggests that the Integrative Learning Design framework is useful for organizing the complementary components of effectiveness and contextual research in our design process.     

A Closed-Form Formula for the RBF-Based Approximation of the Laplace–Beltrami Operator

In this paper we present a method that uses radial basis functions to approximate the Laplace–Beltrami operator that allows to solve numerically diffusion (and reaction–diffusion) equations on smooth, closed surfaces embedded in \(\mathbb {R}^3\). The novelty of the method is in a closed-form formula for the Laplace–Beltrami operator derived in the paper, which involve the normal vector and the curvature at a set of points on the surface of interest. An advantage of the proposed method is that it does not rely on the explicit knowledge of the surface, which can be simply defined by a set of scattered nodes. In that case, the surface is represented by a level set function from which we can compute the needed normal vectors and the curvature. The formula for the Laplace–Beltrami operator is exact for radial basis functions and it also depends on the first and second derivatives of these functions at the scattered nodes that define the surface. We analyze the converge of the method and we present numerical simulations that show its performance. We include an application that arises in cardiology.