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.     

Simulation of aggregate wind farm short-term production variations

The variability of wind power production poses the greatest challenge in the integration of large-scale wind power in power systems. Furthermore, larger-scale penetration implies a wider geographical spreading of installed wind power, resulting in reduced variability and the smoothing effect of total power generation. Therefore, analysis of the impact of wind power variations on power system operation requires adequate modeling of aggregate power output from geographically dispersed wind farms. This paper analyzes different aspects of Markov chain Monte Carlo simulation methods for the synthetic generation of dependent wind power time series. However, testing indicates that these approaches do not adequately model the stochastic dependence between wind power time series in conjunction with individual persistence which is necessary to obtain realistic distributions of aggregate power output and total power variations. Consequently, a novel approach based on a modified second-order Markov chain Monte Carlo simulation is proposed. Simulation results show that this method obtains synthetic time series of aggregate wind power which very closely fit the original data, with respect to both the cumulative density function of total output power and the probability density function of power variations.     

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.     

Enhancing IR Thermographic Inspection of Subsurface Defects by Using the Technique of Edge Detection

Russian Journal of Nondestructive Testing - This paper presents a novel method for separating subsurface defects in cured preimpregnated carbon fiber reinforced polimer specimens on thermal images...     

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.     

Cow colostrum and early milk enriched with eicosapentaenoic and docosahexaenoic fatty acid

The objective of the study was to explore whether it is possible to alter cow colostrum and early milk fatty acid composition with a low level of fat supplement, high in docosahexaenoic (DHA) and eicosapentaenoic (EPA) fatty acid. Diets included a control diet and a diet supplemented with DHA- and EPA-enriched fat supplement. Addition of fat supplement significantly decreased saturated fatty acids, C14:0 and C16:0 and increased the values of monounsaturated fatty acids, polyunsaturated fatty acids (PUFA), n3 fatty acids, EPA, DHA, C18:1n9cis and C18:1n11trans. The percentage of short-chain fatty acids significantly increased with the progress of lactation, while the percentage of PUFA, n3 and n6 significantly decreased. These results showed that fat supplement, high in DHA and EPA, modified the fatty acid profile of colostrum and milk fat and increased the proportion of beneficial fatty acids for human health.