Combining data mining and evolutionary computation for multi-criteria optimization of earthworks

Earthworks tasks aim at levelling the ground surface at a target construction area and precede any kind of structural construction (e.g., road and railway construction). It is comprised of sequential tasks, such as excavation, transportation, spreading and compaction, and it is strongly based on heavy mechanical equipment and repetitive processes. Under this context, it is essential to optimize the usage of all available resources under two key criteria: the costs and duration of earthwork projects. In this paper, we present an integrated system that uses two artificial intelligence based techniques: data mining and evolutionary multi-objective optimization. The former is used to build data-driven models capable of providing realistic estimates of resource productivity, while the latter is used to optimize resource allocation considering the two main earthwork objectives (duration and cost). Experiments held using real-world data, from a construction site, have shown that the proposed system is competitive when compared with current manual earthwork design.

Combining analytical methodologies for the identification of Aspergillus section Flavi strains isolated from Brazilian agricultural commodities

The use of chemical analysis of microbial components, including proteins, became an important achievement in the 80’s of the last century to the microbial identification. This led a more objective microbial identification scheme, called chemotaxonomy, and the analytical tools used in the field are mainly 1D/2D gel electrophoresis, spectrophotometry, high-performance liquid chromatography, gas chromatography, and combined gas chromatography-mass spectrometry. The Edman degradation reaction was also applied to peptides sequence giving important insights to the microbial identification. The rapid development of these techniques, in association with knowledge generated by DNA sequencing and phylogeny based on rRNA gene and housekeeping genes sequences, boosted the microbial identification to an unparalleled scale. The recent results of mass spectrometry (MS), like Matrix-Assisted Laser Desorption/Ionisation Time-of-Flight (MALDI-TOF), for rapid and reliable microbial identification showed considerable promise. In addition, the technique is rapid, reliable and inexpensive in terms of labour and consumables when compared with other biological techniques. At present, MALDI-TOF MS adds an additional step for polyphasic identification which is essential when there is a paucity of characters or high DNA homologies for delimiting very close related species. The full impact of this approach is now being appreciated when more diverse species are studied in detail and successfully identified. However, even with the best polyphasic system, identification of some taxa remains time-consuming and determining what represents a species remains subjective. The possibilities opened with new and even more robust mass spectrometers combined with sound and reliable databases allow not only the microbial identification based on the proteome fingerprinting but also include de novo specific proteins sequencing as additional step. These approaches are pushing the boundaries in the microbial identification field.

Measurements of the Total and Differential Higgs Boson Production Cross Sections Combining the H→γγ and H→ZZ∗→4ℓ Decay Channels at s√=8 TeV with the ATLAS Detector

Measurements of the total and differential cross sections of Higgs boson production are performed using 20.3 fb−1 of pp collisions produced by the Large Hadron Collider at a center-of-mass energy of s√=8 TeV and recorded by the ATLAS detector. Cross sections are obtained from measured H→γγ and H→ZZ∗→4ℓ event yields, which are combined accounting for detector efficiencies, fiducial acceptances and branching fractions. Differential cross sections are reported as a function of Higgs boson transverse momentum, Higgs boson rapidity, number of jets in the event, and transverse momentum of the leading jet. The total production cross section is determined to be σpp→H=33.0±5.3(stat)±1.6(sys)pb. The measurements are compared to state-of-the-art predictions.

Combining non-dominance, objective-order and spread metric to extend firefly algorithm to multi-objective optimization

In this paper, we propose an extension of the firefly algorithm (FA) to multi-objective optimization. FA is a swarm intelligence optimization algorithm inspired by the flashing behavior of fireflies at night that is capable of computing global solutions to continuous optimization problems. Our proposal relies on a fitness assignment scheme that gives lower fitness values to the positions of fireflies that correspond to non-dominated points with smaller aggregation of objective function distances to the minimum values. Furthermore, FA randomness is based on the spread metric to reduce the gaps between consecutive non-dominated solutions. The obtained results from the preliminary computational experiments show that our proposal gives a dense and well distributed approximated Pareto front with a large number of points.

Forecasting time series combining Holt-Winters and bootstrap approaches

Publicado em "AIP Conference Proceedings", Vol. 1648

Skin-derived cell-sheets as powerful tools to engineer skin analogues

Cell/cell-extracellular matrix (ECM) dynamic interactions appear to have a major role in regulating communication through soluble signaling, directing cell binding and activating substrates that participate in the highly organized wound healing process. Moreover, these interactions are also crucial for in vitro mimicking cutaneous physiology. Herein we explore cell sheet (CS) engineering to create cellular constructs formed by keratinocytes (hKC), fibroblasts (hDFB) and dermal microvascular endothelial cells (hDMEC), to target skin wound healing but also the in vitro recreation of relevant models. Taking advantage of temperature-responsive culture surfaces, which allow harvesting cultured cells as intact sheets along with the deposited native ECM, varied combinations of homotypic and heterotypic three-dimensional (3-D) CS-based constructs were developed. Constructs combining one CS of keratinocytes as an epidermis-like layer plus a vascularized dermis composed by hDFB and hDMECs were assembled as skin analogues for advancing in vitro testing. Simultaneously both hKC and hDMEC were shown to significantly contribute to the re-epithelialization of full-thickness mice skin wounds by promoting an early epithelial coverage, while hDMEC significantly lead to increased vessels density, incorporating the neovasculature. Thus, although determined by the cellular nature of the constructs, these outcomes demonstrated that CS engineering appear as an unique technology that open the possibility to create numerous combinations of 3D constructs to target defective wound healing as well as the construction of in vitro models to further mimic cutaneous functions crucial for drug screening and cosmetic testing assays.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Monitoring and analysis of queries in distributed databases

Dissertação de Mestrado em Engenharia Informática

Automatic generation of ETL physical systems from BPMN conceptual models

ETL conceptual modeling is a very important activity in any data warehousing system project implementation. Owning a high-level system representation allowing for a clear identification of the main parts of a data warehousing system is clearly a great advantage, especially in early stages of design and development. However, the effort to model conceptually an ETL system rarely is properly rewarded. Translating ETL conceptual models directly into something that saves work and time on the concrete implementation of the system process it would be, in fact, a great help. In this paper we present and discuss a hybrid approach to this problem, combining the simplicity of interpretation and power of expression of BPMN on ETL systems conceptualization with the use of ETL patterns to produce automatically an ETL skeleton, a first prototype system, which has the ability to be executed in a commercial ETL tool like Kettle.

Intelligent decision support to predict patient barotrauma risk in intensive care units

The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: “risk” and “no risk”. Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated.

Relação entre regulação das ordens profissionais e gestão jurisdicional dos profissionais de contabilidade: estudo comparado entre Portugal e o Brasil

Tese de Doutoramento em Ciência da Administração

Reverse engineering of web applications

The MAP-i Doctoral Program of the Universities of Minho, Aveiro and Porto

Gd2O3: Eu Nanoparticle-Based Poly(vinylidene fluoride) Composites for Indirect X-ray Detection

Polymer based scintillator composites have been fabricated by combining poly(vinylidene fluoride) (PVDF) and Gd2O3:Eu nanoparticles (50nm). PVDF has been used since it is a flexible and stable binder matrix and highly resistance to thermal and light deterioration. Gd2O3:Eu has been selected as scintillator material due to its wide band gap, high density and suitable visible light yield. The structural, mechanical, thermal and electrical characteristics of the composites were studied as a function of filler content, together with their performance as scintillator material. The introduction of Gd2O3:Eu nanoparticles into the PVDF matrix does not influence the morphology of the polymer or the degree of crystallinity. On the other hand, an increase of the Young´s modulus with respect to PVDF matrix is observed for filler contents of 0.1-0.75 wt.%. The introduction of Gd2O3:Eu into the PVDF matrix increases dielectric constant and DC electrical conductivity as well as the visible light yield in the nanocomposite, being this increase dependent upon Gd2O3:Eu content and X-ray input power. In this way, Gd2O3:Eu/PVDF composites shows suitable characteristics to be used as X-ray radiation transducers, in particular for large area applications.

Gd2O3: Eu3+/PPO/POPOP/PS composites for digital imaging radiation detectors

Polymer based scintillator composites have been produced by combining polystyrene (PS) and Gd2O3:Eu3+ scintillator nanoparticles. Polystyrene has been used since it is a flexible and stable binder matrix, resistant to thermal and light deterioration and with suitable optical properties. Gd2O3:Eu3+ has been selected as scintillator material due to its wide band gap, high density and visible light yield. The optical, thermal and electrical characteristics of the composites were studied as a function of filler content, together with their performance as scintillator material. Additionally 1wt.% of 2,5 dipheniloxazol (PPO) and 0.01wt.% of (1,4-bis(2-(5-phenioxazolil))-benzol (POPOP) were introduced in the polymer matrix in order to strongly improve light yield, i.e. the measured intensity of the output visible radiation, under X-ray irradiation. Whereas increasing scintillator filler concentration (from 0.25wt.% to 7.5wt.%) increases scintillator light yield, decreases the optical transparency of the composite. The addition of PPO and POPOP, strongly increased the overall 2 transduction performance of the composite due to specific absorption and re-emission processes. It is thus shown that Gd2O3:Eu3+/PPO/POPOP/PS composites in 0.25 wt.% of scintillator content with fluorescence molecules is suitable for the development of innovate large area X-ray radiation detectors with huge demand from the industries.