scholarly journals A golden ratio parameterized algorithm for Cluster Editing

2012 ◽  
Vol 16 ◽  
pp. 79-89 ◽  
Author(s):  
Sebastian Böcker
Keyword(s):  
Golden Ratio ◽  
Parameterized Algorithm ◽  
Cluster Editing

Data visualization quality in a geographic information system using golden ratio properties

2018 ◽  
pp. 46-57
Author(s):  
A. V. Voronin ◽  
G. N. Maltsev ◽  
M. Yu. Sokhen
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Data Visualization ◽  
Golden Ratio ◽  
Quality Criterion ◽  
Geographic Information ◽  
Control Device ◽  
Video Data ◽  
Geoinformation Systems ◽  
System Operator

Introduction:Data visualization quality is important for the work of a geographic information system operator, determining the conditions under which he or she makes decisions concerning the displayed data. Visual perception patterns associated with the golden ratio properties allow us to formulate a criterion for data visualization quality which would characterize the possibilities of the operator’s complex perception of the video data displayed on a control device screen in the form of an electronic card.Purpose:Substantiation of a data visualization quality criterion for geoinformation systems using the golden ratio properties, and the study of the conditions for providing good visualization quality for geodata and metadata on a video control device screen in accordance with the proposed criterion.Methods:A formal definition of the data visualization quality criterion in geoinformation systems using the coefficient of the screen area information coverage as an index whose optimal value corresponds to the mathematical definition of the golden ratio; and the study of the properties of this criterion. Results: Based on the conducted analysis of visual perception of video data and golden ratio properties during the data visualization, a criterion is proposed for data visualization quality, which uses the golden ratio properties and characterizes the possibilities of complex perception of video data in an electronic map form by a geographic information system operator. Iteration algorithms for choosing the video data display scale are developed, based on the visualization quality criterion and related to the golden ratio properties. These are the basic algorithm used for each geodata layer represented on the electronicmap, and an algorithm of successive analysis of various layers of the displayed geodata. The choice of a video data display scale in accordance with the developed algorithms can be preliminarily carried out by the system operator using the parameters of standard electronic maps and geodata/metadata sets typical for the current applied problem. We have studied how the scale of the geodata and metadata displayed on an electronic map affects their visualization quality on screens of various sizes. For the considered standard volumes of displayed geodata and metadata, the best visualization quality was achieved when they were displayed on a standard computer monitor, as opposed to a portable notebook or visualization screen.Practical relevance:The proposed criterion and the recommendations for choosing a screen size for the video monitoring device or the structures of the displayed geo-objects and metadata can be used in the design of geoinformation systems, or for preliminary choice of the displayed data structure by a geoinformation system operator.

A Multi-Thread GRASP/VND For The Cluster Editing Problem

2016 ◽  
Author(s):  
L. de O. Bastos ◽  
L. S. Ochi ◽  
F. Protti
Keyword(s):  
Cluster Editing

scholarly journals Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2151
Author(s):  
Feras Alasali ◽  
Husam Foudeh ◽  
Esraa Mousa Ali ◽  
Khaled Nusair ◽  
William Holderbaum
Keyword(s):  
Renewable Energy ◽  
Low Voltage ◽  
Renewable Energy Sources ◽  
Golden Ratio ◽  
Clean Energy ◽  
Energy Sources ◽  
Percentage Error ◽  
Ann Model ◽  
Demand Forecast ◽  
Pv System

More and more households are using renewable energy sources, and this will continue as the world moves towards a clean energy future and new patterns in demands for electricity. This creates significant novel challenges for Distribution Network Operators (DNOs) such as volatile net demand behavior and predicting Low Voltage (LV) demand. There is a lack of understanding of modern LV networks’ demand and renewable energy sources behavior. This article starts with an investigation into the unique characteristics of householder demand behavior in Jordan, connected to Photovoltaics (PV) systems. Previous studies have focused mostly on forecasting LV level demand without considering renewable energy sources, disaggregation demand and the weather conditions at the LV level. In this study, we provide detailed LV demand analysis and a variety of forecasting methods in terms of a probabilistic, new optimization learning algorithm called the Golden Ratio Optimization Method (GROM) for an Artificial Neural Network (ANN) model for rolling and point forecasting. Short-term forecasting models have been designed and developed to generate future scenarios for different disaggregation demand levels from households, small cities, net demands and PV system output. The results show that the volatile behavior of LV networks connected to the PV system creates substantial forecasting challenges. The mean absolute percentage error (MAPE) for the ANN-GROM model improved by 41.2% for household demand forecast compared to the traditional ANN model.

scholarly journals A Peptides Prediction Methodology for Tertiary Structure Based on Simulated Annealing

2021 ◽  
Vol 26 (2) ◽  
pp. 39
Author(s):  
Juan P. Sánchez-Hernández ◽  
Juan Frausto-Solís ◽  
Juan J. González-Barbosa ◽  
Diego A. Soto-Monterrubio ◽  
Fanny G. Maldonado-Nava ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Ab Initio ◽  
Primary Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Golden Ratio ◽  
Small Peptides ◽  
Hybrid Simulated Annealing ◽  
Ab Initio Models

The Protein Folding Problem (PFP) is a big challenge that has remained unsolved for more than fifty years. This problem consists of obtaining the tertiary structure or Native Structure (NS) of a protein knowing its amino acid sequence. The computational methodologies applied to this problem are classified into two groups, known as Template-Based Modeling (TBM) and ab initio models. In the latter methodology, only information from the primary structure of the target protein is used. In the literature, Hybrid Simulated Annealing (HSA) algorithms are among the best ab initio algorithms for PFP; Golden Ratio Simulated Annealing (GRSA) is a PFP family of these algorithms designed for peptides. Moreover, for the algorithms designed with TBM, they use information from a target protein’s primary structure and information from similar or analog proteins. This paper presents GRSA-SSP methodology that implements a secondary structure prediction to build an initial model and refine it with HSA algorithms. Additionally, we compare the performance of the GRSAX-SSP algorithms versus its corresponding GRSAX. Finally, our best algorithm GRSAX-SSP is compared with PEP-FOLD3, I-TASSER, QUARK, and Rosetta, showing that it competes in small peptides except when predicting the largest peptides.

Packet Scheduling

2021 ◽  
Vol 52 (2) ◽  
pp. 72-84
Author(s):  
Pavel Veselý
Keyword(s):  
Packet Scheduling ◽  
Golden Ratio ◽  
Buffer Management ◽  
Competitive Algorithm ◽  
Its Analysis ◽  
Open Questions

Online packet scheduling with deadlines is one of the fundamental models in buffer management. Recently, the author together with Chrobak, Jez, and Sgall (SODA 2019) designed an optimal ϕ-competitive algorithm for this problem, where ϕ ≈1.618 is the golden ratio. In this column, we sketch ideas that led us to the development of this algorithm and outline the concepts in its analysis. We also highlight open questions in packet scheduling.

scholarly journals High-resolution dynamic oxygen-17 MR imaging of mouse brain with golden-ratio-based radial sampling and k-space-weighted image reconstruction

2017 ◽  
Vol 79 (1) ◽  
pp. 256-263 ◽  
Author(s):  
Yuchi Liu ◽  
Yifan Zhang ◽  
Chunying Wu ◽  
Junqing Zhu ◽  
Charlie Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Image Reconstruction ◽  
Mr Imaging ◽  
Mouse Brain ◽  
Golden Ratio ◽  
Weighted Image ◽  
Radial Sampling
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