Performance Evaluation of Digital Image Processing by Using Scilab

Scilab is an open-source, cross-platform computational environment software available for academic and research purposes as a free of charge alternative to the matured computational copyrighted software such as MATLAB. One of important library available for Scilab is image processing toolbox dedicated solely for image and video processing. There are three major toolboxes for this purpose: Scilab image processing toolbox (SIP), Scilab image and video processing toolbox (SIVP) and recently image processing design toolbox (IPD). The target discussion in this paper is SIVP due to its vast use out there and its capability to handle streaming video file as well (note that IPD also supports video processing). Highlight on the difference between SIVP and IPD will also be discussed. From testing, it is found that in term of looping test, Octave and FreeMat are faster than Scilab. However, when converting RGB image to grayscale image, Scilab outperform Octave and FreeMat.

Concepts and Coefficients Based on John L. Holland’s Theory of Vocational Choice—Examining the R Package holland

John L. Holland’s theory of vocational choice is one of the most prominent career theories and is used by both researchers and practitioners around the world. The theory states that people should seek work environments that fit their vocational interests in order to be satisfied and successful. Its application in research and practice requires the determination of coefficients, which quantify its core concepts such as person-environment fit. The recently released R package holland aims at providing a holistic collection of the references, descriptions and calculations of the most important coefficients. The current paper presents the package and examines it in terms of its application for research and practice. For this purpose, the functions of the package are applied and discussed. Furthermore, recommendations are made in the case of multiple coefficients for the same theoretical concept and features that future releases should include are discussed. The R package holland is a promising computational environment providing multiple coefficients for Holland’s most important theoretical concepts.

An Emotional Discrete Controller PSO Tuned and PLC Implemented for a Real Industrial Pumping System

The application of automation techniques to water pump systems, combined with modern control techniques, has been increasing the hydraulic and energy efficiency of such systems. In this context, the objective of this work is to present an intelligent method of flow control based on Brain’s Emotional Learning (BELBIC), which will be applied to an experimental workbench of a pumping system, located in the Energy Efficiency and Energy Quality Laboratory (LEEQE) at Federal University of Pernambuco (UFPE). The parameters of this controller are optimized with a particle swarm optimization (PSO) technique with minimization of Integral Absolute Error (IAE). Initial tests were performed in a computational environment so that the system’s performance could be pre-tested, thereby the dynamics of the system was modeled from real data generated in the process. The experimental results were obtained through the implementation of this control system in a programmable logic controller (PLC), which was the device responsible for all the automation of the workbench previously mentioned. The data of this workbench were collected using a supervisory system exclusively developed for this work. These data were then used to analyze the performance of the proposed control system, which demonstrated that its behavior was efficient.

Making the logic: a methodological experience of digital design process for small urban interventions

The emerging digital design process discourses point to the growing need to connect and manipulate design objective data. One of the challenges is knowing how to relate and operationalize this data accurately using a computational environment. This article investigates digital design processes by developing a design logic for small urban projects using objective data. This work follows the method: (1) defining the project location criteria, according to georeferenced data and the Space Syntax theory; (2) operationalizing the socio-spatial relationships according to the book A Pattern Language; (3) developing a Grasshopper definition for modeling several families of objects. We tested the method in a small urban intervention, in the city of Viçosa (MG), with the purpose of digital fabricating a piece of urban furniture.

FA-nf: A Functional Annotation Pipeline for Proteins from Non-Model Organisms Implemented in Nextflow

Functional annotation allows adding biologically relevant information to predicted features in genomic sequences, and it is, therefore, an important procedure of any de novo genome sequencing project. It is also useful for proofreading and improving gene structural annotation. Here, we introduce FA-nf, a pipeline implemented in Nextflow, a versatile computational workflow management engine. The pipeline integrates different annotation approaches, such as NCBI BLAST+, DIAMOND, InterProScan, and KEGG. It starts from a protein sequence FASTA file and, optionally, a structural annotation file in GFF format, and produces several files, such as GO assignments, output summaries of the abovementioned programs and final annotation reports. The pipeline can be broken easily into smaller processes for the purpose of parallelization and easily deployed in a Linux computational environment, thanks to software containerization, thus helping to ensure full reproducibility.

PhycoMine: A Microalgae Data Warehouse

PhycoMine is data warehouse system created to fostering the analysis of complex and integrated data from microalgae species in a single computational environment. The PhycoMine was developed on top of the InterMine software system, and it has implemented an extended database model, containing a series of tools that help the users in the analysis and mining of individual data and group data. The platform has widgets created to facilitate simultaneous data mining of different datasets. Among the widgets implemented in PhycoMine, there are options for mining chromosome distribution, gene expression variation via transcriptomics, proteomics sets, Gene Onthology enrichment, KEGG enrichment, publication enrichment, EggNOG, Transcription factors and transcriptional regulators enrichment and phenotypical data. These widgets were created to facilitate data visualization of the gene expression levels in different experimental setups, for which RNA-seq experimental data is available in data repositories. For comparative purposes, we have reanalyzed 200 RNA-seq datasets from Chlamydomonas reinhardtii, a model unicellular microalga, for optimizing the performance and accuracy of data comparisons. We have also implemented widgets for metabolic pathway analysis of selected genes and proteins and options for biological network analysis. The option for analysis of orthologue genes was also included. With this platform, the users can perform data mining for a list of genes or proteins of interest in an integrated way through accessing the data from different sources and visualizing them in graphics and by exporting the data into table formats. The PhycoMine platform is freely available and can be visited through the URL https://PhycoMine.iq.usp.br.

Distributed implementation of neuroevolution of augmenting topologies method

Despite the neuroevolution of augmenting topologies method strengths, like the capability to be used in cases where the formula for a cost function and the topology of the neural network are difficult to determine, one of the main problems of such methods is slow convergence towards optimal results, especially in cases with complex and challenging environments. This paper proposes the novel distributed implementation of neuroevolution of augmenting topologies method, which considering availability of sufficient computational resources allows drastically speed up the process of optimal neural network configuration search. Batch genome evaluation was implemented for the means of proposed solution performance optimization, fair, and even computational resources usage. The proposed distributed implementation benchmarking shows that the generated neural networks evaluation process gives a manifold increase of efficiency on the demonstrated task and computational environment.

Coupled Electromagnetic and Thermoelastic Response of Conductive Materials Under Mechanical Loading and High Current Pulse Conditions

Understanding, modeling and simulating the behavior of thermally and electrically conductive materials under simultaneous high electric current pulse and mechanical preload conditions has long been a topic of interest for various applications involving electromechanical systems. To this end, the present work describes a computational framework that enables the fully coupled electromagnetic and thermoelastic analysis of such systems. The partial differential equations (PDEs) representing the electrodynamic and thermodynamic conservation laws are utilized and encapsulated in a computational environment enabling their numerical solution. A specific contribution of the framework is that it is capable of solving the non-linear forms of the relevant PDEs that are formed due to the dependence of the material properties on state variables such as temperature. The proposed framework is applied for a specific high-current testing apparatus under construction in our laboratory. A high current pulse is conducted through a mechanically pretensioned specimen and generates Joule heating activating thermo-elastic strains in conjunction with Lorentz body forces influencing the associated dynamic thermo-structural response of specimens of interest. Application of the developed framework enables the generation of field predictions for the quantities of interest. Selective simulation results are presented to demonstrate the capabilities of the proposed framework followed by discussion and conclusions.