Thermo-Metallurgical Modeling of Nodular Cast Iron Cooling Process

A new numerical model to describe the microstructural evolution of a eutectic nodular cast iron during its cooling is presented. In particular, equiaxial solidification assuming an independent nucleation of austenite and graphite nodules is considered. In this context, the austenite has dendritic growth whereas the graphite grows with a spherical shape. After solidification occurs, the model assumes that the graphite nodules present in the cast iron continue growing since the carbon content in austenite decreases. Once the stable eutectoid temperature is reached, the alloy undergoes the austenite-ferrite transformation. The nucleation of the ferrite takes place at the contour of the spherical graphite nodules where austenite has low carbon concentration. A ferrite shell surrounding the graphite nodules is formed afterward by means of a process governed by carbon diffusion. Then, a ferrite-pearlite competitive transformation occurs when the temperature is below the metastable temperature. This thermo-metallurgical model is discretized and solved by means of the finite element method. The model allows the computation of cooling curves, fraction evolution for each component, and size and distribution of graphite nodules. The present numerical results are compared with experiments using standardized Quick-cup-type cups, and satisfactory numerical predictions of the final microstructure and cooling curves are achieved.     

Stability and mechanical evaluation of bovine pericardium cross-linked with polyurethane prepolymer in aqueous medium

The present study investigates the potential use of non-catalyzed water-soluble blocked polyurethane prepolymer (PUP) as a bifunctional cross-linker for collagenous scaffolds. The effect of concentration (5, 10, 15 and 20%), time (4, 6, 12 and 24 h), medium volume (50, 100, 200 and 300%) and pH (7.4, 8.2, 9 and 10) over stability, microstructure and tensile mechanical behavior of acellular pericardial matrix was studied. The cross-linking index increased up to 81% while the denaturation temperature increased up to 12 °C after PUP crosslinking. PUP-treated scaffold resisted the collagenase degradation (0.167 ± 0.14 mmol/g of liberated amine groups vs. 598 ± 60 mmol/g for non-cross-linked matrix). The collagen fiber network was coated with PUP while viscoelastic properties were altered after cross-linking. The treatment of the pericardial scaffold with PUP allows (i) different densities of cross-linking depending of the process parameters and (ii) tensile properties similar to glutaraldehyde method.     

Discovering collaborative knowledge-intensive processes through e-mail mining

Knowledge Management aims to promote the growth, communication and preservation of knowledge within an organization, which includes managing the appropriate resources to facilitate knowledge sharing and reuse. Business Process-Oriented Knowledge Management focuses on discovering and representing the dynamic conversion of existing knowledge among participants involved in executing business processes. In this context, Knowledge-Intensive Processes are a very important and challenging specific subclass of processes, since they strongly involve socialization and informal exchanges of knowledge among participants. This paper describes in detail a method for semi-automatic discovery of relevant information characterizing Knowledge-Intensive Processes, as well as the results of further evaluation of the method. Our approach draws on the informal exchange of existing knowledge in collaborative tools such as e-mails. The output is a conceptual map that describes the main elements of a Knowledge-Intensive Process, as well as their relationships. The results from the case study conducted to evaluate the method in an organization underlined the prospects for using collaborative environments to discover the way agents perform their activities.     

Ethylene production,respiration and gas exchange modelling in modified atmosphere packaging for banana fruits

A mathematical model was developed from experimental measurements to describe the evolution of the O₂, CO₂ and ethylene in a modified atmosphere packaging system for Cavendish bananas. The respiration and ethylene production in the fruits were experimentally obtained from a closed system method and then represented by Michaelis–Menten equations of enzyme kinetics. The gas transfer through the packaging was described by a Fick's diffusion equation, and the temperature dependence was represented based on the Arrhenius law. The model was validated by packaging the fruit in perforated bags of polypropylene and low density polyethylene at 12 °C for a period of 8 days. With the developed model it was possible to satisfactorily describe the experimental evolution of the gas content in the headspace of the packages, obtaining coefficients of determination (R²) of 0.93 for the O₂ levels, 0.90–0.91 for the CO₂ levels, and 0.89–0.93 for the ethylene levels.     

Hierarchical feature selection based on relative dependency for gear fault diagnosis

Feature selection is an important aspect under study in machine learning based diagnosis, that aims to remove irrelevant features for reaching good performance in the diagnostic systems. The behaviour of diagnostic models could be sensitive with regard to the amount of features, and significant features can represent the problem better than the entire set. Consequently, algorithms to identify these features are valuable contributions. This work deals with the feature selection problem through attribute clustering. The proposed algorithm is inspired by existing approaches, where the relative dependency between attributes is used to calculate dissimilarity values. The centroids of the created clusters are selected as representative attributes. The selection algorithm uses a random process for proposing centroid candidates, in this way, the inherent exploration in random search is included. A hierarchical procedure is proposed for implementing this algorithm. In each level of the hierarchy, the entire set of available attributes is split in disjoint sets and the selection process is applied on each subset. Once the significant attributes are proposed for each subset, a new set of available attributes is created and the selection process runs again in the next level. The hierarchical implementation aims to refine the search space in each level on a reduced set of selected attributes, while the computational time-consumption is improved also. The approach is tested with real data collected from a test bed, results show that the diagnosis precision by using a Random Forest based classifier is over 98 % with only 12 % of the attributes from the available set.     

uBench: exposing the impact of CUDA block geometry in terms of performance

The choice of thread-block size and shape is one of the most important user decisions when a parallel problem is written for any CUDA architecture. The reason is that thread-block geometry has a significant impact on the global performance of the program. Unfortunately, the programmer has not enough information about the subtle interactions between this choice of parameters and the underlying hardware. This paper presents uBench, a complete suite of micro-benchmarks, in order to explore the impact on performance of (1) the thread-block geometry choice criteria, and (2) the GPU hardware resources and configurations. Each micro-benchmark has been designed to be as simple as possible to focus on a single effect derived from the hardware and thread-block parameter choice. As an example of the capabilities of this benchmark suite, this paper shows an experimental evaluation and comparison of Fermi and Kepler architectures. Our study reveals that, in spite of the new hardware details introduced by Kepler, the principles underlying the block geometry selection criteria are similar for both architectures.     

A parallel evolutionary algorithm for technical market indicators optimization

This paper deals with the optimization of parameters of technical indicators for stock market investment. Price prediction is a problem of great complexity and, usually, some technical indicators are used to predict market trends. The main difficulty in using technical indicators lies in deciding a set of parameter values. We proposed the use of Multi-Objective Evolutionary Algorithms (MOEAs) to obtain the best parameter values belonging to a collection of indicators that will help in the buying and selling of shares. The experimental results indicate that our MOEA offers a solution to the problem by obtaining results that improve those obtained through technical indicators with standard parameters. In order to reduce execution time is necessary to parallelize the executions. Parallelization results show that distributing the workload of indicators in multiple processors to improve performance is recommended. This parallelization has been performed taking advantage of the idle time in a corporate technology infrastructure. We have configured a small parallel grid using the students Labs of a Computer Science University College.     

Novel ferrofluids coated with a renewable material obtained from cashew nut shell liquid

In this work, we present the synthesis and characterization of a new surfactant molecule obtained from a byproduct of the cashew nut processing (diphosphorylated cardol, DPC). It is herein used to overcoat magnetic nanoparticles showing spinel structures in order to create new ferrofluids. The nanoparticle structure and magnetic properties have been deeply investigated. DPC-functionalized Fe₃O₄ and NiFe₂O₄ samples exhibit higher magnetic saturation than DPC–CoFe₂O₄. These new ferrofluids reveal appealing as possible nanoparticle stabilizing molecules, magnetic resonance imaging agents, storage systems or in any material science field that requires the employment of biocompatible magnetic stable fluids.