Auxiliary Algorithm Research Articles

Optimization of distribution function and model parameters for molecular communication via diffusion with OtoO approximation

The analysis is generally conducted in stationary receiver and transmitter models in a diffusion environment for the fundamental Molecular communication (MOC) models. However, a mobile MOC model is employed in this study, deviating from the existing literature. This mobile MOC model considers the mobility of all variables in the diffusion environment, including the transmitter, receiver, and molecules. Firstly, a novel MOC model is proposed, departing from the conventional normal distribution for the mobility of variables. Instead, alternative distribution functions such as the Pareto distribution, extreme value distribution, t-distribution, and generalized extreme value distribution are employed. Furthermore, the system's performance is enhanced by optimizing the distribution function and model parameters, such as the diffusion coefficient, using the optimization of optimization (OtoO) approach. In this approach, the Multi-Verse Optimization (MVO) algorithm serves as the primary algorithm, while the Grey Wolf Optimization (GWO) algorithm functions as the auxiliary algorithm. Essentially, the MVO algorithm optimizes the parameters of the MOC model, while simultaneously, the GWO algorithm optimizes the impact of the optimization processes of MVO on the parameters ``p'' and ``N'' as well as the constant parameter of the distribution function. By optimizing both the parameters of the MOC model and the distribution function, the number of received molecules is significantly increased. Therefore, this study not only improves the results of the MOC model structure based on different distribution functions but also optimizes all parameters of the proposed model using the MVO-GWO OtoO approach.

C3NN: Cosmological Correlator Convolutional Neural Network an Interpretable Machine-learning Framework for Cosmological Analyses

Modern cosmological research in large-scale structure has witnessed an increasing number of machine-learning applications. Among them, convolutional neural networks (CNNs) have received substantial attention due to their outstanding performance in image classification, cosmological parameter inference, and various other tasks. However, many models based on CNNs are criticized as “black boxes” due to the difficulties in relating their outputs intuitively and quantitatively to the cosmological fields under investigation. To overcome this challenge, we present the Cosmological Correlator Convolutional Neural Network (C3NN)—a fusion of CNN architecture and cosmological N-point correlation functions (NPCFs). We demonstrate that its output can be expressed explicitly in terms of the analytically tractable NPCFs. Together with other auxiliary algorithms, we can open the “black box” by quantitatively ranking different orders of the interpretable outputs based on their contribution to classification tasks. As a proof of concept, we demonstrate this by applying our framework to a series of binary classification tasks using Gaussian and log-normal random fields and relating its outputs to the NPCFs describing the two fields. Furthermore, we exhibit the model’s ability to distinguish different dark energy scenarios (w 0 = −0.95 and −1.05) using N-body simulated weak-lensing convergence maps and discuss the physical implications coming from their interpretability. With these tests, we show that C3NN combines advanced aspects of machine learning architectures with the framework of cosmological NPCFs, thereby making it an exciting tool to extract physical insights in a robust and explainable way from observational data.

Enhancing Classification Algorithms with Metaheuristic Technique

Classification is a process of grouping or placing data into appropriate categories or classes based on specificattributes or features to predict labels or classes of new data based on patternsobserved from previously trained data. Implementing this process uses classification algorithms such asNaïve Bayes, Support Vector Machine,and Random Forest. However, the classification algorithm cannotclassify data optimally due to the challenges in dealing with variousdata sets. Not all available featureswillmake a solidcontribution to the label of the data class, often in the form of noise or interference. For this reason, it is necessary to carry out a feature selection process. Currently, many feature selection processes have been carried out using correlation values from chi-square and gain-information, but the accuracy of the resultsis often still not good enough. This is because the chi-square and gain-information values are fixed. So,the selection of features is minimaland is not based on the previous learning process or what is known as heuristics. For this reason, in this research,several auxiliary algorithms are introduced to improve the performance of the classification algorithm, namely the meta-heuristic algorithm. Meta-heuristic algorithms are search techniques used to solve complexoptimization problems, and these algorithms can help provide reasonable solutions in a shorter time thanexact methods. In its operation, the metaheuristic algorithm optimizes the feature selection process,which will later be processed using the classification algorithm.Three (3) meta-heuristics were implemented, namely Genetic Algorithm, Particle Swarm Optimization, and Cuckoo Search Algorithm; the experiment was conducted, and the results were collected and analyzed. The result shows that combining Naive Bayes and Genetic Algorithmgives the best performance regarding higher accuracy improvementat +23.77%.

Line segment detectors with deformable attention

The object detectors based on Transformers are advancing rapidly. On the contrary, the development of line segment detectors is relatively slow. It is noteworthy that the object and line segments are both 2D targets. In this work, we design a line segment detection algorithm based on deformable attention. Leveraging this algorithm and the line segment loss function, we transform the object detectors, Deformable DETR and ViDT, into end-to-end line segment detectors named Deformable LETR and ViDTLE, respectively. In order to adapt the idea of sparse modeling for line segment detection, we propose a new attention mechanism named line segment deformable attention (LSDA). This mechanism focuses on the valuable positions under the guidance of reference line to refine line segments. We design an auxiliary algorithm named line segment iterative refinement for LSDA. With as few modifications as possible, we transform two object detection detectors, namely SMCA DETR and PnP DETR into competitive line segment detectors named SMCA LETR and PnP LETR, respectively. The experimental results show that the performances of the proposed methods are efficient.

Research on magnetic localization and tracking algorithm based on total magnetic field

There are few studies on the total magnetic field localization and tracking algorithm based on the domestic magnetic dipole model, and the scalar magnetometer’s measurement of magnetic targets is not affected by its own posture. Therefore, a magnetic localization and tracking algorithm based on the total magnetic field is proposed., this method is based on the magnetic dipole model and uses the particle swarm algorithm and the extended Kalman auxiliary algorithm to achieve magnetic localization and tracking. The effectiveness of this method has been verified through simulation experiments. The relative localization error is small and maintained in the range of about 10%.

ПРОГНОСТИЧЕСКАЯ ЗНАЧИМОСТЬ ИССЛЕДОВАНИЯ УРОВНЕЙ МАРКЕРОВ АПОПТОЗА – БЕЛКОВ P53 И BCL-2 У ПАЦИЕНТОВ ПОЖИЛОГО ВОЗРАСТА С ВИРУСНОЙ ПНЕВМОНИЕЙ, ВЫЗВАННОЙ SARS-COV-2

Abstract. Introduction. Тo date, the prognostic value has been proven for some laboratory biomarkers and biomarker panels for risk stratification in patients with viral pneumonia caused by SARS-CoV-2. In studies related to the research in the leading pathogenetic mechanisms of COVID-19, much attention is paid to apoptosis and its markers, such as proteins P53 and BCL-2. Aim: To study the levels of P53 and Bcl-2 proteins and their prognostic significance in lethal outcomes among elderly patients with viral pneumonia caused by SARS-CoV-2, hospitalized in an infectious hospital. Materials and Methods. For the period December 2021 – February 2022, in accordance with the inclusion criteria, a sample of 67 patients was formed from elderly patients (60-74 years old) hospitalized in the hospital of infectious diseases. During the follow-up, 18 patients (26.9 %) had fatal outcomes and 49 patients (73.1 %) had favorable outcomes. Biochemical markers of apoptosis, P53 and BCL-2, were detected using the enzyme immunoassay method. Results and Discussion. The analysis showed the presence of statistically significant differences when comparing the levels of both proteins, P53 (p=0.001) and BCL-2 (p=0.002) in patients with favorable outcomes, compared to those with fatal outcomes. Threshold values of the P53 and BCL-2 proteins at the cut-off point in case of death of elderly patients with viral pneumonia caused by SARS-CoV-2 were 99.3 pg/ml and 78.9 IU/ml, respectively. The classification tree includes the following lethality predictors: Threshold levels of apoptosis markers, the number of comorbid diseases, and the respiratory failure degree. Five terminal nodes were identified. Terminal nodes 6 and 8 were classified as high risk of death. Lethal outcomes were observed in 75.0 % of patients at these nodes. At terminal node 3, fatal outcome was observed in 8.1 % of patients, while there were no fatal outcomes at terminal node 7; therefore, both were classified as low-risk ones in terms of a fatal outcome. The sensitivity of the resulting model was 66.7 %, while its specificity was 91.8%. The total share of correct predictions made 85.1% among the subjects of this sample (p=0.044). Conclusions. The data obtained indicate the prognostic significance of studying the apoptosis markers, i. e., proteins P53 and BCL-2, in elderly patients with viral pneumonia caused by SARS-CoV-2. The classification tree proposed can be used as an auxiliary algorithm to optimize the personalized stratification of the lethality risk in elderly patients with viral pneumonia caused by SARS-CoV-2.

Combination of electromagnetic field and harris hawks optimization algorithms with optimization to optimization structure and its application for optimum power flow

ABSTRACT Electromagnetic Field Optimization (EFO) and Harris Hawk Optimization (HHO) algorithms are combined with the optimization to optimization (OtoO) approach, and the EFO-HHO algorithm pair is presented in this study. EFO method was used as the essential algorithm and HHO method was used as the auxiliary algorithm according to the OtoO structure. The constant parameters (R_rate, Ps_rate, P_field, N_field) of the EFO algorithm that affect the optimization performance are optimized with the HHO optimization algorithm for the related optimization problem. The proposed method was tested on 10 different benchmark functions according to different dimensional (30, 50100). The EFO-HHO algorithm pair can produce better results than the existing literature, especially in cases of increased dimension with the proposed approach. In addition to these, the OPF problem was tested on the IEEE 30 test bus system for the engineering application of the proposed method. The results are compared with the existing literature results. As it can be seen from the results, it has been shown on the real engineering problem that the optimization performance can be increased with the OtoO approach without changing the basic philosophy of the EFO algorithm.

An assistant algorithm model for a mobile robot to pass through a concave obstacle area

When a mobile robot passes through a concave obstacle area, the limitations of the traditional control algorithm add a significant challenge to the mobile robot to move through the concave obstacle area efficiently. Improving the conventional path planning algorithm is a common choice to solve this problem. However, because various path planning algorithms adapt to the fixed scene and the algorithm steps are complex, it is difficult to popularize these algorithms. In addition, conventional path planning algorithms usually encounter difficulties when passing through recessed obstacle areas or cause robot deadlock. An auxiliary algorithm strategy is proposed to help mobile robots avoid concave obstacles quickly. The algorithm strategy plays an auxiliary role by incorporating the judgment of the starting position of the mobile robot, the building of a virtual wall to shield the concave obstacle area, and the judgment of the moving direction of the mobile robot. The advantage of this algorithm strategy is that it does not change the operation mechanism of traditional path-planning algorithms and is highly adaptable. The results of the comparison of simulation experiments with the APF (Artificial Potential Field), the ACO (Ant Colony Optimization), the RRT (Rapidly-Exploring Random Trees), the A-star algorithm, and the Dijkstra algorithm show the inclusion of the auxiliary algorithm strategy resulted in a significant increase in the efficiency of the mobile robot through the depressed obstacle zone.Article Highlights●This auxiliary algorithm strategy can avoid the self-locking caused by the mobile robot falling into the concave obstacle area.●This auxiliary algorithm strategy does not change the mechanism of the traditional path planning algorithm, and it is simple and easy to be used.●This auxiliary algorithm has wide adaptability (it can assist the mainstream path planning algorithm and adapt to various scenarios).

Semi-supervised classifier ensemble model for high-dimensional data

To complete the challenging task of high-dimensional data classification with limited labeled samples, we propose two semi-supervised learning models, namely the random subspace classifier ensemble model (SSRS) and its adaptive version (ASSRS). Considering the unique characteristics of high-dimensional data, SSRS selects subspaces of sample and feature dimensions and then reduces the dimensions of each subspace. To improve SSRS performance further, we designed a sample-labeling auxiliary algorithm, adaptive sample subspace algorithm, and adaptive weight voting rule for ASSRS to increase the proportion of labeled samples, obtain a suitable sample subspace for each feature subspace, and acquire a relative optimal weight for each base classifier. Experiments revealed that the performances of SSRS and ASSRS were better than those of other competitive algorithms and that the performance of ASSRS was stronger than that of SSRS. Additionally, we can accurately label samples in datasets where the proportion of labeled samples is relatively low by using SSRS and ASSRS. Because analysts are facing large numbers of high-dimensional datasets with limited labels, it is important to make accurate predictions based on a limited proportion of labeled data.

Comparison of the characteristics of the control strategies based on artificial neural network and genetic algorithm for air conditioning systems

Air conditioning systems are playing an increasingly important role in our daily life, and the control of air conditioning systems is related to the intelligence of the system, indoor thermal comfort and energy consumption. Therefore, the control of air conditioning systems has always been an important research topic. Artificial neural network (ANN) and genetic algorithm (GA) are two commonly used intelligent control methods. However, there is no guidance until now on the applicability of these two control methods and how to make a choice between them in the control of air conditioning systems. In this study, an ANN control strategy and a GA control strategy were developed and experimentally verified. The experimental results indicated that both the ANN control strategy and the GA control strategy could control the air conditioning system properly under different control commands. To compare the control performances, the convergence speed, discrete characteristic, energy consumption and interference resistance ability were calculated or experimentally validated. The ANN control strategy showed better performances in the convergence speed and energy consumption. While the GA control strategy performed better in maintaining the stable state of the air conditioning system.The innovation of this study lies in two points. First, when designing the ANN control strategy and the GA control strategy, ANN and GA were applied as the central control algorithm, rather than only as auxiliary algorithms for system identification, prediction or optimization. This methodology is relatively novel in the design of ANN and GA control strategies for the air conditioning system, and could further expand the application of ANN and GA. Second, this study employed 4 evaluating indicators including convergence speed, discrete characteristic, energy consumption and interference resistance ability, to comprehensively evaluated the control performance of ANN control strategy and GA control strategy.

WAVE FUNCTION COLLAPSE. APPLICATION OF COLLAPSE METHOD IN GAME SPACE GENERATION

The article explores a method for creating game content using procedural generation based on the wave function collapse. It has been determined that using this method with the basic approach yields relatively acceptable results. In some cases, anomalies are encountered, or spaces with a low level of diversity are generated. At the initial stages of generation, zones where generation is impossible using the basic method have been identified. One way to address this issue is through repeated generation, which requires additional resources. A modification of the method has been proposed using repeated passes. In some cases, this method has proven to be quite effective. To improve the method's efficiency, the space has been divided into zones to verify the correctness of the generated area. The most effective application of the basic method has been determined – traversing the space with another algorithm and post-processing after generation. One such algorithm allows for the preparation of samples to be substituted into closed zones that appear. Preparing generation materials significantly speeds up the operation of the wave function collapse. Preliminary traversal of space to identify probable problem areas occurs separately from the main generation. The algorithm has been modified for the automatic recognition of compatible samples, significantly reducing the time for generation preparation and the number of problem areas created. Enclosed spaces that restrict access to parts of the map have been identified. Depending on the size of the restricted area, several solutions have been proposed. For small areas, replacement with decorative content, for large areas – regeneration with samples designed to create internal content, or replacement of certain blocks with passable ones. Based on the research results, it is concluded that the generation of game space using the wave function collapse method is more efficient when auxiliary algorithms and constraints are applied at the initial and final stages of generation. Also, the idea of dividing generation into visible and invisible parts is proposed: the visible part is created during scene preparation, and the invisible part is generated during level traversal.

СРАВНИТЕЛЬНЫЙ АНАЛИЗ ЯЗЫКОВ ПРОГРАММИРОВАНИЯ НА ОСНОВЕ РЕШЕНИЯ ТЕСТОВОЙ ЗАДАЧИ СОРТИРОВКИ ДАННЫХ

The purpose of the study is to compare the efficiency of popular programming languages using the time characteristics of sorting algorithms as an example. Sorting algorithms were chosen due to the high frequency of their use as auxiliary algorithms in solving various problems, for example, in the implementation of binary search. Since sorting algorithms are labor-intensive, the speed of their implementation, in turn, is critical for evaluating the effectiveness of many projects, as well as when evaluating the choice of one or another programming language. The present article is a comparison in the time characteristics of the implementation of common sorting algorithms in various high-level programming languages. The results of the experimental study allow drawing conclusions about the expediency of using certain programming languages in the problems of sorting implementation, depending on the existing time constraints. The running time of sorts in most programming languages is highly dependent on the way the problem is solved, and different ways may be more efficient in different languages, and some solutions that seem very effective at first glance can lead to an unexpected slowdown of the program. At the same time, the Lisp and Lua programming languages optimize the code qualitatively and allow achieving the maximum speed.

On the application of heuristic algorithms for solving the pseudogeometric version of the traveling salesman problem for the design of communication networks

The Mathematical models and algorithms developed to solve the traveling salesman problem are developed specifically for constructing optimal driving routes and choosing the optimal trajectory. At the same time, such models and algorithms are also used to solve several interconnected groups of applied problems; for example, it is the study of DNA chains, the calculation of the “similarity” of long strings, the construction of algorithms for checking special grammatical structures, etc. The resulting variants of the traveling salesman problem are close to the pseudogeometric version. We are most interested in issues related to communication networks (first of all, large ones). There are at least two different subject areas related to the application of the pseudogeometric version of the traveling salesman problem and heuristic algorithms for its solution. First, it is the location of the source points when generating the source data. Secondly, if we somehow organize the sequence of points well in advance, then the heuristic algorithms we use will work faster; at the same time, we can assume that such a “good location” is a pseudo-optimal solution to the traveling salesman problem. In the article, we also briefly describe our approach to classifying input data generation options designed primarily to test the operation and compare heuristic algorithms. In addition, an auxiliary algorithm for pseudo-optimal placement of points is described; it is desirable to use this algorithm specifically for a set of points generated as a variant of the pseudo-geometric version of the traveling salesman problem. The article also presents some results of computational experiments that allow comparing different algorithms for solving pseudogeometric variants of the problem.

Retracted: Artificial Intelligence Auxiliary Algorithm for Wushu Routine Competition Decision Based on Feature Fusion.

[This retracts the article DOI: 10.1155/2021/1632393.].

An inverse problem for matrix processing: an improved algorithm for restoring the distance matrix for DNA chains

We consider one of the cybernetic methods in biology related to the study of DNA chains. Namely, we are considering the problem of reconstructing the distance matrix for DNA chains. Such a matrix is formed on the basis of any of the possible algorithms for determining the distances between DNA chains. The objects of research of these algorithms (for mammals), as a rule, are one of the following 3 variants: the main histocompatibility complex, the mitochondrial DNA, and “the tail” of the Y chromosome. In the paper we give an improved algorithm for restoring the distance matrix for DNA chains. Compared to our previous publications, the following changes have been made to the algorithm. We abandoned the use of the branches and bounds method, but at the same time significantly improved the greedy auxiliary algorithm used in it. In this paper, we apply only this greedy algorithm to the general solution of the distance matrix reconstruction problem. As a result of the conducted computational experiments carried out on one of the two considered criteria for the quality of the algorithms, significant improvements were obtained compared to the results given in our previous publications. At the same time, the total running time of the algorithm remained almost the same as in the previous version.

Разработка алгоритма определения места повреждения воздушной линии электропередачи на базе синхронизированных векторных измерений

An extensive implementation of digital technologies into the energy sector opens up new opportunities for increasing the efficiency and cost-effectiveness of its operation. One of these technologies is the synchronized phasor measurements technology which has already found wide application in Russian Federation national power grid for the electromechanical transients monitoring and is relevant for solving other pressing problems. The aim of the work is to develop and study an overhead line fault location algorithm based on synchronized phasor measurements. The research is conducted by means of mathematical modelling in the ATP/ATPDraw environment for simulation and the MATLAB environment for fault location and other auxiliary algorithms implementation. An input voltage and current measurements error influence analysis was made, as well as some factors distorting the measurements on the fault location accuracy. Computational experiments using the Monte-Carlo method were performed. An analysis of test-case results on a model of 500 kV power grid show both the high efficiency of the proposed fault location algorithm and the good prospects for using the synchronized phasor measurements technology for double-ended fault location.

The design and implementation of distributed architecture in the CMOR motion control system

The CMOR Motion Control System is a multi-degree-of-freedom, long-beam, heavy-duty robot system with end effectors used to maintain the blanket and divertor of the China Fusion Engineering Test Reactor (CFETR) with irregular structural geometry. Since the multi-degree of freedom, large size, and flexible structure of the fusion robot will increase the complexity of the algorithm, it is necessary to design the corresponding control system and auxiliary algorithm to realize the motion control of the robot. In this paper, a feasible CMOR (CFETR Multipurpose Overload Robot) motion control system architecture is proposed, which adopts the distributed control structure of the upper and lower machine. Firstly, the game theory algorithm decouples the macro arm with 9-degree-freedom and the micro arm with 14 degree-of-freedom (dual 7-DOFs robots) and separates the pose and accuracy targets after planning the motion of the target task with the Markov chain. Secondly, to improve the accuracy, the kinematics controller based on macro arm motion planning strategy for initial positioning and the force-position hybrid controller based on micro arm motion planning strategy for compensation for precise positioning are designed separately. Finally, a robot distributed architecture is designed to realize offline and online simulation and control in which the offline mode simulates a collision-free trajectory based on the game theory and the online mode synchronously displays fusion robot motion by digital twin technology. The feasible CMOR motion control system architecture is proposed in this paper which can assist complex algorithms to achieve intelligent control in the fusion application.

Bilingual Corpus-based Hybrid POS Tagger for Low Resource Tamil Language: A Statistical approach

In India, most of the Science and Technology resources available are in English. Developing an Automatic Language Translation Engine from English (source language) to Tamil (target language) is very essential for the people who need to get technical resources in their native language. The challenges in designing such engines using Natural Language Processing (NLP) tools include Lexical, Structural, and Syntax level ambiguity. To solve these challenges, the development of a Part-Of-Speech (POS) tagger is essential. The Verb-Framed languages like Tamil, Japanese, and many languages in Romance, Semitic, and Mayan languages families have high morphological richness but lack either a large volume of annotated corpora or manually constructed linguistic resources for building POS tagger. Moreover, the Tamil Language has a low resource, high word sense ambiguity, and word-free order form giving rise to challenges in designing Tamil POS taggers. In this paper, we postulate a Hybrid POS tagger algorithm for Tamil Language using Cross-Lingual Transformation Learning Techniques. It is a novel Mining-based algorithm (MT), which finds equivalent words of Tamil in English on less volume of English-Tamil bilingual unannotated parallel corpus. To enhance the performance of MT, we developed Tamil language-specific auxiliary algorithms such as Keyword-based tagging algorithm (KT) and Verb pattern-based tagging algorithm (VT). We also developed a Unique pair occurrence-tagging algorithm (UT) to find the one-time occurrence of Tamil-English pair words. Our experiments show that by improving Context-based Bilingual Corpus to Bilingual parallel corpus and after leaving one-time occurrence words, the proposed Hybrid POS tagger can predict 81.15% words, with 73.51% accuracy and 90.50% precision. Evaluations prove our algorithms can generate language resources, which can improve the performance of NLP tasks in Tamil.

Auxiliary algorithm to approach a near-global optimum of a multi-objective function in acoustical topology optimization

In this study, an auxiliary algorithm is proposed to avoid local optima in solving an acoustical topology optimization problem by using a gradient-based optimizer. A local optimum convergence issue often occurs in the multi-objective function problem because the sub-objective functions are non-convex in general. In order to overcome this issue and find a near-global optimum, the sign change of sensitivity is heuristically conducted based on the objective function difference at every step of design variable updating. An acoustical topology optimization problem is formulated for improving a noise attenuation performance of a muffler. The heuristic-based auxiliary algorithm is combined with a well-known optimization algorithm to solve the topology optimization problem for a single target frequency as well as a multi-target frequency. Diverse optimal topologies were successfully obtained for various design conditions. Comparison of the optimized solutions by the proposed method and the previous methods strongly supported the validity of the proposed auxiliary algorithm. The application of the proposed method to a suction muffler design problem showed the industrial applicability of the method.

Volleyball Sports Teaching Based on Augmented Reality and Wireless Communication Assistance.

The 21st century is the information age. The ever-changing information technology not only affects the development of the global economy, but also has an important impact on the field of education and personnel training. Under the influence of information technology, the way people acquire, impart, and evaluate knowledge has changed greatly. The educational concepts, models, and methods of the new era are fundamentally different from traditional education, which provides new opportunities and new challenges for the education and teaching work of contemporary teachers. This paper aims to discuss the application of augmented reality technology in volleyball teaching, and give an effective method for AR to be applied to volleyball teaching. In this paper, the existing AR teaching system has the problem of poor transmission rate and cannot cooperate with volleyball teaching well. Therefore, a wireless communication auxiliary algorithm based on intelligent reflective surface is proposed. And the current situation of volleyball teaching is investigated and analyzed. And in response to the survey results, 78% of the students supported teachers in applying AR technology to assist teaching in volleyball teaching classes. This fully shows that there is still a lot of application space for AR technology in current volleyball teaching.