Sea Spectral Estimation Using ARMA Models

This paper deals with the spectral estimation of sea wave elevation time series by means of ARMA models. To start, the procedure to estimate the ARMA coefficients, based on the use of the Prony’s method applied to the auto-covariance series, is presented. Afterwards, an analysis on how the parameters involved in the ARMA reconstruction procedure—for example, the signal time length, the number of poles and data used—affect the spectral estimates is carried out, providing evidence on their effect on the accuracy of results. This allowed us to provide guidelines on how to set these parameters in order to make the ARMA model as accurate as possible. The paper focuses on mono-modal sea states. Nevertheless, examples also related to bi-modal sea states are discussed.

Predicting Cell Wall Lytic Enzymes Using Combined Features

Due to the overuse of antibiotics, people are worried that existing antibiotics will become ineffective against pathogens with the rapid rise of antibiotic-resistant strains. The use of cell wall lytic enzymes to destroy bacteria has become a viable alternative to avoid the crisis of antimicrobial resistance. In this paper, an improved method for cell wall lytic enzymes prediction was proposed and the amino acid composition (AAC), the dipeptide composition (DC), the position-specific score matrix auto-covariance (PSSM-AC), and the auto-covariance average chemical shift (acACS) were selected to predict the cell wall lytic enzymes with support vector machine (SVM). In order to overcome the imbalanced data classification problems and remove redundant or irrelevant features, the synthetic minority over-sampling technique (SMOTE) was used to balance the dataset. The F-score was used to select features. The Sn, Sp, MCC, and Acc were 99.35%, 99.02%, 0.98, and 99.19% with jackknife test using the optimized combination feature AAC+DC+acACS+PSSM-AC. The Sn, Sp, MCC, and Acc of cell wall lytic enzymes in our predictive model were higher than those in existing methods. This improved method may be helpful for protein function prediction.

Generalized Bernoulli process with long-range dependence and fractional binomial distribution

Bernoulli process is a finite or infinite sequence of independent binary variables, X i , i = 1, 2, · · ·, whose outcome is either 1 or 0 with probability P(X i = 1) = p, P(X i = 0) = 1 – p, for a fixed constant p ∈ (0, 1). We will relax the independence condition of Bernoulli variables, and develop a generalized Bernoulli process that is stationary and has auto-covariance function that obeys power law with exponent 2H – 2, H ∈ (0, 1). Generalized Bernoulli process encompasses various forms of binary sequence from an independent binary sequence to a binary sequence that has long-range dependence. Fractional binomial random variable is defined as the sum of n consecutive variables in a generalized Bernoulli process, of particular interest is when its variance is proportional to n 2 H , if H ∈ (1/2, 1).

Analysis of Air Pollution Parameters Using Covariance Function Theory

The paper analyses the intensity changes of three pollution parameter vectors in space and time. The RGB raster pollution data of the Lithuanian territory used for the research were prepared according to the digital images of the Sentinel-2 Earth satellites. The numerical vectors of environmental pollution parameters CH4 (methane), NO2 (nitrogen dioxide) and for direct comparison O2 (oxygen gas) were used for the calculations. The covariance function theory was used to perform the analysis of intensity changes in digital vectors. Estimates of the covariance functions of the numerical vectors of pollution parameters and O2 or the auto-covariance functions of single vectors are calculated from random functions consisting of arrays of measurement parameters of all parameters vectors. Correlation between parameters vectors depends on the density of parameters and their structure. Estimates of covariance functions were calculated by changing the quantization interval on a time scale and using a compiled computer program using the Matlab procedure package. The probability dependence between the environmental pollution parameter vectors and trace gas of the territory in Lithuania and their change in time scale was determined.

Analysis of Dynamic Parameters of a Linear Positioning Table System

In the paper, the spread of intensity of linear positioning table system vibrations is examined and an analysis of their parameters upon applying the theory of covariance functions is carried out. For the investigation, two linear positioning tables were chosen; for them, different lubricants were used: universal lubricant Loctite 8103 and lubricant Braycote 601EF for working in vacuum. The results of measurements of the vibration intensity in four fixed points upon varying the speed of the carriage were recorded on the time scale in form of arrays (matrices). The estimates of cross-covariance functions of the arrays of results of measurements of the digital vibration intensity were calculated. The estimates of auto-covariance functions of single arrays upon changing the quantization interval on the time scale were calculated too. For the calculations, the software developed upon using Matlab7 package of procedures was applied.

Analysis of Dynamic Parameters of the System of Raster Formation and Control

The presented research work analyzes the sensing system, the main aim of which is a raster formation and controlling this process using the optical measuring equipment and high precision angle encoders. Optical measuring equipment are used for the raster position detection, meanwhile angle encoders for controlling the tape speed. The main parameter of raster formation process is fixed transportation speed, but there are difficulties to realize it, because there is imperfection of the device elements. The article analyzes the dispersion of vibration accelerations of the raster formation device and tape in the two directions (transverse and longitudinal) and presents an analysis of their parameters in application of the theory of covariance functions. The results of the measurements of vibration accelerations at the fixed points of the device constructions and the tape were recorded on a time scale in the form of digital arrays (matrices). Values of auto-covariance and inter-covariance functions of digital arrays of the vibration accelerations measurement data were calculated by changing the quantum interval in a time scale. The developed software Matlab 7 in operator package environment was used in the calculations.

Using Deep Neural Networks to Improve the Performance of Protein–Protein Interactions Prediction

Protein–protein interactions (PPIs) help to elucidate the molecular mechanisms of life activities and have a certain role in promoting disease treatment and new drug development. With the advent of the proteomics era, some PPIs prediction methods have emerged. However, the performances of these PPIs prediction methods still need to be optimized and improved. In order to optimize the performance of the PPIs prediction methods, we used the dropout method to reduce over-fitting by deep neural networks (DNNs), and combined with three types of feature extraction methods, conjoint triad (CT), auto covariance (AC) and local descriptor (LD), to build DNN models based on amino acid sequences. The results showed that the accuracy of the CT, AC and LD increased from 97.11% to 98.12%, 96.84% to 98.17%, and 95.30% to 95.60%, respectively. The loss values of the CT, AC and LD decreased from 27.47% to 14.96%, 65.91% to 17.82% and 36.23% to 15.34%, respectively. Experimental results show that dropout can optimize the performances of the DNN models. The results can provide a resource for scholars in future studies involving the prediction of PPIs. The experimental code is available at https://github.com/smalltalkman/hppi-tensorflow .

Vibration measurements of paper prints and the data analysis

This paper discusses about the scatter of the intensity of vibration signals of paper prints and analyses their mechanical parameters applying the theory of covariance functions. It is an important practical problem, before starting printing process of colour prints, expecting the correct position of fixed raster points, to adjust the paper sheet tension between printing machine sections. The results of measuring the intensity of vibration signals at the fixed points were presented on a time scale in the form of arrays (matrices). The estimates of cross-covariance functions between digital arrays result in measuring the intensity of vibrations, and the estimates of auto-covariance functions of single arrays were calculated upon changing the quantization interval on the time scale. Application of normed auto-covariance and cross-covariance functions enables reduction of preprinting experimental measurements, which saves time (what is actual for industry). Tension force depends on the mechanical properties of the paper sheet and print. These characteristics depend on paper type, layers of printing colors and positioning of the coverage. In the calculation, the software Matlab 7 in batch statement environment was applied.