scholarly journals Selecting an optimal set of secondary indices

1976 ◽  
pp. 146-160 ◽  
Author(s):  
Theo Härder
Keyword(s):  
Optimal Set

Optimal set of technological techniques for lucerne yellow-flowered cultivation in southern Belarus

2020 ◽  
Author(s):  
Lyudmila Luchenok ◽  
Aleksandr Yuzupanov
Keyword(s):  
Peat Soils ◽  
Green Mass ◽  
Optimal Set

The data on the yield of lucerne (Medicago falcate) when cultivated on agro-peat soils in the conditions of southern Belarus was presented. It has been established that the yield of green mass in 4 years of life averaged 422.9 centner per ha at sowing under cover and 472.4 centner per ha at bloodless sowing. Productivity — 57.7 and 68.2 centner feed units ha–1 respectively. A small level of response to the application of various technological techniques was noted.

An Effective Cumulative Torsion Angles Model for Prediction of Protein Folding Rates

2020 ◽  
Vol 27 (4) ◽  
pp. 321-328 ◽  
Author(s):  
Yanru Li ◽  
Ying Zhang ◽  
Jun Lv
Keyword(s):  
Protein Folding ◽  
Size Structure ◽  
Conformational Space ◽  
Coefficient Of Determination ◽  
Folding Rate ◽  
Torsion Angles ◽  
Folding Rates ◽  
Protein Folding Rate ◽  
Optimal Set ◽  
Conformation Space

Background: Protein folding rate is mainly determined by the size of the conformational space to search, which in turn is dictated by factors such as size, structure and amino-acid sequence in a protein. It is important to integrate these factors effectively to form a more precisely description of conformation space. But there is no general paradigm to answer this question except some intuitions and empirical rules. Therefore, at the present stage, predictions of the folding rate can be improved through finding new factors, and some insights are given to the above question. Objective: Its purpose is to propose a new parameter that can describe the size of the conformational space to improve the prediction accuracy of protein folding rate. Method: Based on the optimal set of amino acids in a protein, an effective cumulative backbone torsion angles (CBTAeff) was proposed to describe the size of the conformational space. Linear regression model was used to predict protein folding rate with CBTAeff as a parameter. The degree of correlation was described by the coefficient of determination and the mean absolute error MAE between the predicted folding rates and experimental observations. Results: It achieved a high correlation (with the coefficient of determination of 0.70 and MAE of 1.88) between the logarithm of folding rates and the (CBTAeff)0.5 with experimental over 112 twoand multi-state folding proteins. Conclusion: The remarkable performance of our simplistic model demonstrates that CBTA based on optimal set was the major determinants of the conformation space of natural proteins.

scholarly journals Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 591
Author(s):  
Manasavee Lohvithee ◽  
Wenjuan Sun ◽  
Stephane Chretien ◽  
Manuchehr Soleimani
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Cross Validation ◽  
Ant Colony ◽  
Reference Image ◽  
X Ray ◽  
X Ray Computed ◽  
Hyperparameter Selection ◽  
The Cross ◽  
Optimal Set

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.

scholarly journals Optimization of Laser Engraving of Acrylic Plastics from the Perspective of Energy Consumption, CO2 Emission and Removal Rate

2021 ◽  
Vol 5 (3) ◽  
pp. 78
Author(s):  
Mohammad Muhshin Aziz Khan ◽  
Shanta Saha ◽  
Luca Romoli ◽  
Mehedi Hasan Kibria
Keyword(s):  
Energy Consumption ◽  
Production Rate ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Scanning Speed ◽  
Gas Emissions ◽  
Laser Engraving ◽  
Co2 Gas ◽  
Optimal Set

This paper focuses on optimizing the laser engraving of acrylic plastics to reduce energy consumption and CO2 gas emissions, without hindering the production and material removal rates. In this context, the role of laser engraving parameters on energy consumption, CO2 gas emissions, production rate, and material removal rate was first experimentally investigated. Grey–Taguchi approach was then used to identify an optimal set of process parameters meeting the goal. The scan gap was the most significant factor affecting energy consumption, CO2 gas emissions, and production rate, whereas, compared to other factors, its impact on material removal rate (MRR) was relatively lower. Moreover, the defocal length had a negligible impact on the response variables taken into consideration. With this laser-process-material combination, to achieve the desired goal, the laser must be focused on the surface, and laser power, scanning speed, and scan gap must be set at 44 W, 300 mm/s, and 0.065 mm, respectively.

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