STATISTICAL ANALYSIS ON COKE YIELDS IN VACUUM RESIDS AS A FUNCTION OF COMPOUND CLASSES AND ELEMENTAL ANALYSIS

This paper describes the statistical analysis of data on coke formation (measured by microcarbon residue) in five resids, as a function of compound classes, in order to identify variables wiih a significant effect on the former. In this sense, the SAS VARCLUS and Principal Component Analysis were used. The analysis carried out leads to a lineal combination of the H/C ratio, vanadium and nitrogen content as a model to explain coke yield, under non-catalytic conditions, as determined by MCR. Of all these factors, the H/C ratio has the most important contribution.     

A comparison study of wavelet transforms for the visualization of differentially methylated regions in DNA samples

The Journal of Supercomputing - DNA methylation analysis has become an important topic in the study of human health. DNA methylation analysis requires not only a specific treatment of DNA samples...     

DYRE: a DYnamic REconfigurable solution to increase GPGPU’s reliability

The Journal of Supercomputing - General-purpose graphics processing units (GPGPUs) are extensively used in high-performance computing. However, it is well known that these devices’...     

Integrating supercomputing clusters into education: a case study in biotechnology

The Journal of Supercomputing - The integration of a Supercomputer in the educational process improves student’s technological skills. The aim of the paper is to study the interaction between...     

A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects

Applied Intelligence - The17 Sustainable Development Goals (SDGs) established by the United Nations Agenda 2030 constitute a global blueprint agenda and instrument for peace and prosperity...     

Evaluating the influence of multiplexing schemes and buffer implementation on perceived VoIP conversation quality

This work presents a study of RTP multiplexing schemes, which are compared with the normal use of RTP, in terms of experienced quality. Bandwidth saving, latency and packet loss for different options are studied, and some tests of Voice over IP (VoIP) traffic are carried out in order to compare the quality obtained using different implementations of the router buffer. Voice quality is calculated using ITU R-factor, which is a widely accepted quality estimator. The tests show the bandwidth savings of multiplexing, and also the importance of packet size for certain buffers, as latency and packet loss may be affected. The customer’s experience improvement is measured, showing that the use of multiplexing can be interesting in some scenarios, like an enterprise with different offices connected via the Internet. The system is also tested using different numbers of samples per packet, and the distribution of the flows into different tunnels is found to be an important factor in order to achieve an optimal perceived quality for each kind of buffer. Grouping all the flows into a single tunnel will not always be the best solution, as the increase of the number of flows does not improve bandwidth efficiency indefinitely. If the buffer penalizes big packets, it will be better to group the flows into a number of tunnels. The router processing capacity has to be taken into account too, as the limit of packets per second it can manage must not be exceeded. The obtained results show that multiplexing is a good way to improve customer’s experience of VoIP in scenarios where many RTP flows share the same path.     

Optimal configuration of Energy-Efficient Ethernet

The IEEE 802.3az standard provides a new low power mode that Ethernet network interfaces can use to save energy when there is no traffic to transmit. Simultaneously with the final standard approval, several algorithms were proposed to govern the physical interface state transition between the normal active mode and the new low power mode. In fact, the standard leaves this sleeping algorithm unspecified to spur competition among different vendors and achieve the greatest energy savings. In this paper, we try to bring some light to the most well known sleeping algorithms, providing mathematical models for the expected energy savings and the average packet delay inflicted on outgoing traffic. We will then use the models to derive optimum configuration parameters for them under given efficiency constraints.     

Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection

Radial Basis Function Neural Networks (RBFNNs) have been successfully employed in several function approximation and pattern recognition problems. The use of different RBFs in RBFNN has been reported in the literature and here the study centres on the use of the Generalized Radial Basis Function Neural Networks (GRBFNNs). An interesting property of the GRBF is that it can continuously and smoothly reproduce different RBFs by changing a real parameter τ. In addition, the mixed use of different RBF shapes in only one RBFNN is allowed. Generalized Radial Basis Function (GRBF) is based on Generalized Gaussian Distribution (GGD), which adds a shape parameter, τ, to standard Gaussian Distribution. Moreover, this paper describes a hybrid approach, Hybrid Algorithm (HA), which combines evolutionary and gradient-based learning methods to estimate the architecture, weights and node topology of GRBFNN classifiers. The feasibility and benefits of the approach are demonstrated by means of six gene microarray classification problems taken from bioinformatic and biomedical domains. Three filters were applied: Fast Correlation-Based Filter (FCBF), Best Incremental Ranked Subset (BIRS), and Best Agglomerative Ranked Subset (BARS); this was done in order to identify salient expression genes from among the thousands of genes in microarray data that can directly contribute to determining the class membership of each pattern. After different gene subsets were obtained, the proposed methodology was performed using the selected gene subsets as new input variables. The results confirm that the GRBFNN classifier leads to a promising improvement in accuracy.     

Structural studies on γ-MnO2 by transmission electron microscopy

Single-crystal electron diffraction patterns were obtained on five specimens of -MnO₂: one natural, two electrolytical (EMD) and two chemical (CMD) samples. EMDs are best described by the orthorhombic structure proposed by De Wolff which is derived from the ramsdellite structure. A CMD prepared from MnCO₃ fits the hexagonal cell of -MnO₂. Flaky grains from the natural sample and fibres from a CMD prepared from Mn(NO₃)₂ are hexagonal with a new cell:a 0.494,c 0.539 nm. No simple relation between chemical composition, morphology and structure could be found.