An Improved Modification of Accelerated Double Direction and Double Step-Size Optimization Schemes

We propose an improved variant of the accelerated gradient optimization models for solving unconstrained minimization problems. Merging the positive features of either double direction, as well as double step size accelerated gradient models, we define an iterative method of a simpler form which is generally more effective. Performed convergence analysis shows that the defined iterative method is at least linearly convergent for uniformly convex and strictly convex functions. Numerical test results confirm the efficiency of the developed model regarding the CPU time, the number of iterations and the number of function evaluations metrics.

Calibration of Siberian Radioheliograph antenna gains using redundancy

The paper describes application of standard gain calibration using redundancy for a 48-antenna prototype of Siberian Radioheliograph. Traditionally, for calibration, the visibilities were measured only between adjacent antennas since they have the highest signal-to-noise ratio and are sufficient for phase calibration. We have shown that this limited set of visibilities did not allow using the antenna array redundancy potential and obtaining images with a high dynamic range on a permanent basis. Images without amplitude calibration contain many artifacts and require special care when analyzed. The inclusion of visibility measurement between antennas with a double step made it possible to significantly increase the accuracy of solving the system of equations for amplitudes. Images constructed using both phase and amplitude calibrations do not have visible artifacts and are more reliable.

Calibration of Siberian Radioheliograph antenna gains using redundancy

The paper describes application of standard gain calibration using redundancy for a 48-antenna prototype of Siberian Radioheliograph. Traditionally, for calibration, the visibilities were measured only between adjacent antennas since they have the highest signal-to-noise ratio and are sufficient for phase calibration. We have shown that this limited set of visibilities did not allow using the antenna array redundancy potential and obtaining images with a high dynamic range on a permanent basis. Images without amplitude calibration contain many artifacts and require special care when analyzed. The inclusion of visibility measurement between antennas with a double step made it possible to significantly increase the accuracy of solving the system of equations for amplitudes. Images constructed using both phase and amplitude calibrations do not have visible artifacts and are more reliable.

Single-step and double-step heath recuperation of waste water from thermal baths

Thermal baths in Slovakia are producers of wastewater, which hides considerable energy potential. The wastewater from the thermal pools has often exceeds the maximum permissible value of the temperature of the wastewater discharged into the water recipient. As a result, there is undesirable damage to the environment, which results in sanctions for the operators of these facilities. Our aim is to present the concept of a single- and double-step heat recuperation system of waste pool water using applications of various types of heat exchangers or heat pumps. The aim of this application is to ensure a suitable temperature to the discharged waste pool water, efficient use of the energy potential of the waste water and saving of primary energy in the form of geothermal water, thus extending the life of the entire system.

Bayesian estimation of a phenotype network structure using reversible jump Markov chain Monte Carlo

<p>In this thesis we aim to estimate the unknown phenotype network structure existing among multiple interacting quantitative traits, assuming the genetic architecture is known. We begin by taking a frequentist approach and implement a score-based greedy hill-climbing search strategy using AICc to estimate an unknown phenotype network structure. This approach was inconsistent and overfitting was common, so we then propose a Bayesian approach that extends on the reversible jump Markov chain Monte Carlo algorithm. Our approach makes use of maximum likelihood estimates in the chain, so we have an efficient sampler using well-tuned proposal distributions. The common approach is to assume uniform priors over all network structures; however, we introduce a prior on the number of edges in the phenotype network structure, which prefers simple models with fewer directed edges. We determine that the relationship between the prior penalty and the joint posterior probability of the true model is not monotonic, there is some interplay between the two. Simulation studies were carried out and our approach is also applied to a published data set. It is determined that larger trait-to-trait effects are required to recover the phenotype network structure; however, mixing is generally slow, a common occurrence with reversible jump Markov chain Monte Carlo methods. We propose the use of a double step to combine two steps that alter the phenotype network structure. This proposes larger steps than the traditional birth and death move types, possibly changing the dimension of the model by more than one. This double step helped the sampler move between different phenotype network structures in simulated data sets.</p>

Bayesian estimation of a phenotype network structure using reversible jump Markov chain Monte Carlo

<p>In this thesis we aim to estimate the unknown phenotype network structure existing among multiple interacting quantitative traits, assuming the genetic architecture is known. We begin by taking a frequentist approach and implement a score-based greedy hill-climbing search strategy using AICc to estimate an unknown phenotype network structure. This approach was inconsistent and overfitting was common, so we then propose a Bayesian approach that extends on the reversible jump Markov chain Monte Carlo algorithm. Our approach makes use of maximum likelihood estimates in the chain, so we have an efficient sampler using well-tuned proposal distributions. The common approach is to assume uniform priors over all network structures; however, we introduce a prior on the number of edges in the phenotype network structure, which prefers simple models with fewer directed edges. We determine that the relationship between the prior penalty and the joint posterior probability of the true model is not monotonic, there is some interplay between the two. Simulation studies were carried out and our approach is also applied to a published data set. It is determined that larger trait-to-trait effects are required to recover the phenotype network structure; however, mixing is generally slow, a common occurrence with reversible jump Markov chain Monte Carlo methods. We propose the use of a double step to combine two steps that alter the phenotype network structure. This proposes larger steps than the traditional birth and death move types, possibly changing the dimension of the model by more than one. This double step helped the sampler move between different phenotype network structures in simulated data sets.</p>

Comparison of the single-step and double-step sous-vide treatment effect on the quality attributes of chicken breast •

Sous-vide treatment is a modern minimal processing cooking technique that uses a single-step temperature of 55–70 °C and longer time. The quality attributes of meat might be improved by including cooking steps at below 50 °C temperatures in the sous-vide treatment. The aim of this study was to investigate the effects of the double-step sous-vide treatments on the quality attributes of the chicken breast and comparing with the traditional single-step sous-vide treatments. The single-step sous-vide treatments were performed at 60 °C. In the double-step sous-vide treatments the first step temperature was 45 °C and the end temperature was 60 °C. Double-step sous-vide treated chicken breasts obtained higher tenderness, moisture content and lower weight loss compared to the single-step sous-vide treated chicken breasts. Double-step sous-vide treatment provided an attractive cooking method to produce high quality chicken breast, however, challenge tests for specific pathogens would be useful for the assessment of the microbiological quality for different treatment combinations.