Photocatalytic enamel/TiO2 coatings developed by electrophoretic deposition for methyl orange decomposition

The aim of this study was to obtain photocatalytic coatings, capable to decompose organic pollutants, through Electrophoretic Deposition (EPD) of enamels containing respectively 0%, 5%, 10%, 15% (in wt%) of TiO₂ onto carbon steel substrates. High quality and homogeneous coatings were obtained by applying 12.5?V during 10?s, as the best EPD conditions. The layers were subsequently heat treated at 740?°C for 10?min, in order to obtain dense glazes.Rietveld refinement of XRD patterns and Raman results show that, after the heat treatment at 740?°C, TiO₂ mostly exists as anatase, responsible of the photocatalytic effect. Semi-quantitative chemical analysis indicate segregation of TiO₂ on the coatings surface, reaching saturation in the sample with 10?wt% TiO₂. FEG-SEM observations reveal rod-like and spherical Ti-rich phases along the cross section of the coatings; some Ti was also dissolved into the enamel. 3D topographical mapping shows that, by adding TiO₂, surface roughness increases significantly.Photocatalytic tests were carried out using a 2?×?10^?5 M aqueous solution of Methyl Orange (MO) as an organic pollutant. By comparing the decomposition rate of MO achieved with the pure enamel (0% of TiO₂) and with the sample with 10% of TiO₂, it was shown that the addition of 10% of TiO₂ results in 90% photocatalytic efficiency.Moreover, the permeation of organic compounds and their UV degradation were studied by measuring the water contact angle onto the enamel surface directly after dipping into oleic acid and after various UV irradiation times. The longer the UV irradiation time, the lower the contact angle, down to a minimum of 14.54° after 8?h of UV irradiation. This means, the compound was initially adsorbed on the enamel/TiO₂ coating surface (10?wt% TiO₂) but was efficiently decomposed upon UV irradiation.     

Preparation of mesoporous geopolymer using metakaolin and rice husk ash as synthesis precursors and its use as potential adsorbent to remove organic dye from aqueous solutions

In this work, mesoporous geopolymer was synthesized using a novel and easy synthesis route employing metakaolin and rice husk ash as sources of silica and alumina, and soybean oil as a mesostructure-directing agent. For comparison purposes, a geopolymer sample was produced without the use of oil. The samples were characterized by Fourier transform infrared spectroscopy (FT–IR), X–ray diffraction (XRD), specific surface area, pore volume, average pore size, and pore size distribution (BET and BJH methods). The materials were tested to remove methyl violet 10B dye from aqueous solutions. The results showed that the mesoporous geopolymer presented adsorptive superior behavior compared to the geopolymer prepared without the use of oil, being attributed to its superior pore properties. The adsorption equilibrium was attained within 120 min, and the maximum adsorption capacity of mesoporous geopolymer was 276.9 mg g^–1. Therefore, the mesoporous geopolymer prepared in this work comprises a potential adsorbent, presenting pore intrinsic properties that result in a high adsorption capacity.     

An immune-inspired multi-objective approach to the reconstruction of phylogenetic trees

This work presents the application of the omni-aiNet algorithm—an immune-inspired algorithm originally developed to solve single and multi-objective optimization problems—to the reconstruction of phylogenetic trees. The main goal here is to automatically evolve a population of phylogenetic unrooted trees, possibly with distinct topologies, by minimizing at the same time two optimization criteria: the minimum evolution and the mean-squared error. This proposal generates, in a single run, a set of non-dominated solutions that represent the trade-offs of the two conflicting objectives, and gives the user the possibility of having distinct explanations for the differences observed at the terminal nodes of the trees. A series of experimental results is also reported in this work, in order to illustrate the effectiveness of the proposal and its capability to overcome the restrictive feedback provided by the application of well-known algorithms for phylogenetic reconstruction, such as the Neighbor Joining. Besides, the methodology presented in this work is compared to the popular NSGA-II algorithm, also modified to solve phylogenetic reconstruction problems.     

Towards an efficient 3D model estimation methodology for aerial and ground images

In this paper we propose an efficient approach for automatic generation of 3D models from images based on structure from motion (SfM) and multi-view stereo reconstruction techniques. Current imaging devices are capable of producing high-definition images and are an ubiquitous payload of unmanned aerial vehicles. However, the time required to obtain models quickly becomes prohibitive as the number of images increases. In our approach, which is image-based only, we use meta-data information such as GPS, keypoint filtering and multiple local bundle adjustment refinement instead of global optimization in a novel scheme to speed up the incremental SfM process. The results from real data show that our approach outperforms the time performance of current strategies while maintaining the quality of the resulting model. Experiments with an unorganized set of images were also conducted, and the results show that our method is able to efficiently estimate 3D models from collections of images with reduced re-projection error.     

Temporal synchronization in mobile sensor networks using image sequence analysis

This paper addresses the problem of estimating the temporal synchronization in mobile sensors’ networks, by using image sequence analysis of their corresponding scene dynamics. Unlike existing methods, which are frequently based on adaptations of techniques originally designed for wired networks with static topologies, or even based on solutions specially designed for ad hoc wireless sensor networks, but that have a high energy consumption and a low scalability regarding the number of sensors, this work proposes a novel approach that reduces the problem of synchronizing a general number $N$ of sensors to the robust estimation of a single line in ${\mathbb {R}}^{N+1}$ . This line captures all temporal relations between the sensors and can be computed without any prior knowledge of these relations. It is assumed that (1) the network’s mobile sensors cross the field of view of a stationary calibrated camera that operates with constant frame rate and (2) the sensors trajectories are estimated with a limited error at a constant sampling rate, both in the world coordinate system and in the camera’s image plane. Experimental results with real-world and synthetic scenarios demonstrate that our method can be successfully used to determine the temporal alignment in mobile sensor networks.     

Experimentation with dynamic simulation models in software engineering: planning and reporting guidelines

Simulation-based studies (SBS) have become an interesting investigation approach for Software Engineering (SE). However, the reports on experiments with dynamic simulation models found in the technical literature lack relevant information, hampering the full understanding of the procedures and results reported, as well as their replicability. Apart from the limitations on the length in conferences and journal papers, some of the relevant information seems to be missing due to methodological issues not considered when conducting such studies. This is the case of missing research questions and goals, lack of evidence regarding the dynamic simulation model validity, poorly designed simulation experiments, amongst others. Based on findings from a previous quasi-systematic literature review, we propose a set of reporting guidelines for SBS with dynamic models in the context of SE aiming at providing guidance on which information the report should contain. Furthermore, these guidelines were evolved to support SBS planning by identifying potential threats to simulation study validity and in making recommendations to avoid them, through qualitative analysis and external evaluation. Finally, we conducted different evaluations regarding both the reporting and planning guidelines, apart from using them to support the planning of a SBS as regards software evolution. A set of 33 reporting and planning guidelines for different stages of the simulation lifecycle and focused on the experimentation with dynamic simulation models have been put together. The first assessments point to a comprehensive set of guidelines, supporting a comprehensive preparation and review of the plans and reports from the studies, apart from the planning of a SBS focused on software evolution, potentially reducing the threats to the experimentation with the validity of dynamic simulation models. The 33 guidelines cannot be understood as separate groups for reporting and planning as they overlap in many aspects. The main goal is to use the guidelines to support the planning of a simulation-based study with dynamic models so that experimenters may identify potential threats to validity and produce relevant information for a complete simulation experiment report in advance. Despite their initial contribution to increase the validity of SBS, the reporting and planning of simulation-based experiments with dynamic models still has to be discussed and improved in SE. Therefore, additional assessments of this set of guidelines are needed to strengthen the confidence in their completeness and usefulness.     

A hybrid GRASP heuristic to construct effective drawings of proportional symbol maps

Proportional symbol map is a cartographic tool that employs symbols to represent data associated with specific locations. Each symbol is drawn at the location of an event and its size is proportional to the numerical data collected at that point on the map. The symbols considered here are opaque disks. When two or more disks overlap, part of their boundaries may not be visible and it might be difficult to gauge their size. Therefore, the order in which the disks are drawn affects the visual quality of a map. In this work, we focus on stacking drawings, i.e., a drawing that corresponds to the disks being stacked up, in sequence, starting from the one at the bottom of the stack. We address the Max-Total problem, which consists in maximizing the total visible boundary of all disks. We propose a sophisticated heuristic based on GRASP that includes most of the advanced techniques described in the literature for this procedure. We tested both sequential and parallel implementations on benchmark instances and the comparison against optimal solutions confirms the high quality of our heuristic. To the best of our knowledge, this is the first time a metaheuristic is applied to this problem.     

Prime forms and minimal change in propositional belief bases

This paper proposes to use prime implicants and prime implicates normal forms to represent belief sets. This representation is used, on the one hand, to define syntactical versions of belief change operators that also satisfy the rationality postulates but present better complexity properties than those proposed in the literature and, on the other hand, to propose a new minimal distance that adopts as a minimal belief unit a “fact”, defined as a prime implicate of the belief set, instead of the usually adopted Hamming distance, i.e., the number of propositional symbols on which the models differ. Some experiments are also presented that show that this new minimal distance allows to define belief change operators that usually preserve more information of the original belief set.     

ARTIE and MUSCLE models: building ensemble classifiers from fuzzy ART and SOM networks

Ensemble Learning has proven to be an efficient method to improve the performance of single classifiers. In this context, the present article introduces ARTIE (ART networks in Ensembles) and MUSCLE (Multiple SOM Classifiers in Ensembles), two novel ensemble models that use Fuzzy ART and SOM networks as base classifiers, respectively. In addition, a hybrid metaheuristic solution based on Particle Swarm Optimization and Simulated Annealing is used for parameter tuning of the base classifiers. A comprehensive performance comparison using 10 benchmarking data sets indicates that the ARTIE and MUSCLE architectures consistently outperform ensembles built from standard supervised neural networks, such as the Fuzzy ARTMAP, Learning Vector Quantization, and the Extreme Learning Machine.