Prediction of diesel fuel cold properties using artificial neural networks

In this paper, two neural networks, multilayer perceptron and networks with radial-basis function, were used to predict important cold properties of commercial diesel fuels, namely cloud point and cold filter plugging point. The developed models predict the named properties using cetane number, density, viscosity, contents of total aromatics, and distillation temperatures at 10, 50, and 90 vol. % recovery as input data. The training algorithms, number of hidden layer neurons, and number of training data points were optimized in order to obtain a model with optimal predictive ability. The results indicated better prediction of cloud and cold filter plugging points in the case of multilayer perceptron networks. The obtained absolute error mean for the optimal neural network models (0.58°C for the cloud point and 1.46°C for the cold filter plugging point) are within the range of repeatability of standard cold properties determination methods.     

BPC – A binary priority countdown protocol

In modern wireless ad hoc networks, with a high speed PHY, every collision means a significant loss of useful bandwidth. In the last few years different binary contention protocols have been introduced to address this problem. In this work we propose a novel binary contention protocol called binary priority countdown (BPC) protocol, whose goal is to reduce collisions as well as contention time. BPC uses a new priority countdown mechanism which exploits the efficiency of binary countdown, but the priority countdown process is not constrained to a single binary countdown round. This way, the priority space is not defined by the length of binary countdown round, like in other binary countdown protocols proposed in the literature, and arbitrary medium access priorities can be decremented through multiple binary countdown rounds if necessary. The ability of a new priority countdown mechanism to count down any priority number without changing the length of a binary countdown round, allows independent management of priority space. This “independence” of priority space introduces new optimization and adaptation possibilities. Collision memory effect is recognized and described. BPC protocol reveals connection between unary, binary and digit contention protocols. All three groups of protocols can now be seen as members of the same class of contention algorithms. Preliminary simulation results are shown.     

Genome-Wide Association Analysis and Genomic Prediction of Thyroglobulin Plasma Levels

Thyroglobulin (Tg) is an iodoglycoprotein produced by thyroid follicular cells which acts as an essential substrate for thyroid hormone synthesis. To date, only one genome-wide association study (GWAS) of plasma Tg levels has been performed by our research group. Utilizing recent advancements in computation and modeling, we apply a Bayesian approach to the probabilistic inference of the genetic architecture of Tg. We fitted a Bayesian sparse linear mixed model (BSLMM) and a frequentist linear mixed model (LMM) of 7,289,083 variants in 1096 healthy European-ancestry participants of the Croatian Biobank. Meta-analysis with two independent cohorts (total n = 2109) identified 83 genome-wide significant single nucleotide polymorphisms (SNPs) within the ST6GAL1 gene (p<5×10−8). BSLMM revealed additional association signals on chromosomes 1, 8, 10, and 14. For ST6GAL1 and the newly uncovered genes, we provide physiological and pathophysiological explanations of how their expression could be associated with variations in plasma Tg levels. We found that the SNP-heritability of Tg is 17% and that 52% of this variation is due to a small number of 16 variants that have a major effect on Tg levels. Our results suggest that the genetic architecture of plasma Tg is not polygenic, but influenced by a few genes with major effects.     

Modeling of dibenzothiophene oxidative desulfurization using response surface methodology

The effect of reaction temperature, mixing speed and oxidant to catalyst volume ratio, including their interactions on the oxidative desulfurization of dibenzothiophene by using response surface methodology was studied. Hydrogen peroxide was used as oxidant and acetic acid as catalyst. The obtained model accurately predicts conversion of dibenzothiophene and the best conversion of 98.7% was observed at temperature 70°C, mixing speed of 1250 rpm and oxidant to catalyst volume ratio of 1:1. At high temperatures, a major limitation of the desulfurization process is the mass transfer and the high mixing speed is needed to achieve an efficient process.     

Portable real-time DCT-based steganography using OpenCL

In this paper, a steganographic method for real-time data hiding is proposed. The main goal of the research is to develop steganographic method with increased robustness to unintentional image processing attacks. In addition, we prove the validity of the method in real-time applications. The method is based on a discrete cosine transform (DCT) where the values of a DCT coefficients are modified in order to hide data. This modification is invisible to a human observer. We further the investigation by implementing the proposed method using different target architectures and analyze their performance. Results show that the proposed method is very robust to image compression, scaling and blurring. In addition, modification of the image is imperceptible even though the number of embedded bits is high. The steganalysis of the method shows that the detection of the modification of the image is unreliable for a lower relative payload size embedded. Analysis of OpenCL implementation of the proposed method on four different target architectures shows considerable speedups.     

Logic circuits based on individual semiconducting and metallic carbon-nanotube devices

Nanoscale transistors employing an individual semiconducting carbon nanotube as the channel hold great potential for logic circuits with large integration densities that can be manufactured on glass or plastic substrates. Carbon nanotubes are usually produced as a mixture of semiconducting and metallic nanotubes. Since only semiconducting nanotubes yield transistors, the metallic nanotubes are typically not utilized. However, integrated circuits often require not only transistors, but also resistive load devices. Here we show that many of the metallic carbon nanotubes that are deposited on the substrate along with the semiconducting nanotubes can be conveniently utilized as load resistors with favorable characteristics for the design of integrated circuits. We also demonstrate the fabrication of arrays of transistors and resistors, each based on an individual semiconducting or metallic carbon nanotube, and their integration on glass substrates into logic circuits with switching frequencies of up to 500 kHz using a custom-designed metal interconnect layer.     

Thermal stability of catechin and epicatechin upon disaccharides addition

Influence of sucrose and trehalose addition on epimerisation and thermal degradation of catechin and epicatechin have been studied. Sucrose addition accelerated epimerisation of catechin and epicatechin at 80 and 100 °C. Addition of trehalose had protective effect on epimerisation of catechin and epicatechin. Considering thermal degradation, it was confirmed that thermal degradation of catechin and epicatechin follows the first‐order kinetics. Increasing the temperature causes shorter half‐lives of catechin and epicatechin. Addition of sucrose had negative effect on catechin stability. However, in samples with epicatechin, sucrose impact was temperature dependent. Trehalose addition had positive impact on preventing catechin and epicatechin degradation almost doubling the half‐life of studied compounds. Data presented in this study could help to predict chemical conversions of investigated phenolics in various food systems and to prolong their shelf life in thermally processed foods.