bluuebunny/crossref_metadata_2025 huggingface dataset:
Pathway-specific cortico-muscular coherence in proximal-to-distal compensation during fine motor control of finger extension after stroke
Authors: Zhou Sa , Guo Ziqi , Wong Kiufung , Zhu Hanlin , Huang Yanhuan , Hu Xiaoling , Zheng Yong-Ping
Published: August 2021

Objective.Proximal-to-distal compensation is commonly observed in the upper extremity (UE) after a stroke, mainly due to the impaired fine motor control in hand joints. However, little is known about its related neural reorganization. This study investigated the pathway-specific corticomuscular interaction in proximal-to-distal UE compensation during fine motor control of finger extension post-stroke by directed corticomuscular coherence (dCMC).Approach.We recruited 14 chronic stroke participants and 11 unimpaired controls. Electroencephalogram (EEG) from the sensorimotor area was concurrently recorded with electromyography (EMG) from extensor digitorum (ED), flexor digitorum (FD), triceps brachii (TRI) and biceps brachii (BIC) muscles in both sides of the stroke participants and in the dominant (right) side of the controls during the unilateral isometric finger extension at 20% maximal voluntary contractions. The dCMC was analyzed in descending (EEG → EMG) and ascending pathways (EMG → EEG) via the directed coherence. It was also analyzed in stable (segments with higher EMG stability) and less-stable periods (segments with lower EMG stability) subdivided from the whole movement period to investigate the fine motor control. Finally, the corticomuscular conduction time was estimated by dCMC phase delay.Main results.The affected limb had significantly lower descending dCMC in distal UE (ED and FD) than BIC (P< 0.05). It showed the descending dominance (significantly higher descending dCMC than the ascending,P< 0.05) in proximal UE (BIC and TRI) rather than the distal UE as in the controls. In the less-stable period, the affected limb had significantly lower EMG stability but higher ascending dCMC (P< 0.05) in distal UE than the controls. Furthermore, significantly prolonged descending conduction time (∼38.8 ms) was found in ED in the affected limb than the unaffected (∼26.94 ms) and control limbs (∼25.74 ms) (P< 0.05).Significance.The proximal-to-distal UE compensation in fine motor control post-stroke exhibited altered descending dominance from the distal to proximal UE, increased ascending feedbacks from the distal UE for fine motor control, and prolonged descending conduction time in the agonist muscle.
Wavelet Tree ensembles with Machine Learning and its classification
Authors: Katiyar Neha , Gupta Sonam , Yadav Arun Kumar , Yadav Divakar
Published: August 2021

Wavelet trees are compact data structures in computational geometry. In the past, it was used as an essential tool for handling the size of data, data compression, indexing and for many more applications. Machine learning algorithms are used for classification of data and its analysis. In this article, we discuss the scope of machine learning with wavelet trees, wavelet entropy, wavelet matrix and wavelet packets. The study concludes that machine learning applications with wavelet tree is a better choice in terms storage and classification of data. The proposed methodology consists of three techniques for making the data more efficient. It consists of LZW Compression techniques, Wavelet tree, and machine learning algorithm SVM. In this methodology compression with classification process is done for datasets. This proposed methodology performs with machine learningalgorithms in terms of classification of data. In future this method can be used for efficient searching and indexing of large data sets. The classified and compressed dataset perform the indexing with wavelet tree takes less searching time.
Energy Efficient NoC design through Supervised Machine Learning
Authors: Shanthi D. , Kumar Ashwani
Published: August 2021

3D Network On Chip(NoC) will provide high multi-core data processing with the minimal amount of energy consumption. It is necessary to consider the architectural limits of three-dimensional NoCs. A deep reinforced framework is proposed that uses router-less NoC for evaluation in the case study. The framework uses prior approaches that may be unreliable because of the difficulties with the design and search process, and the inflexibility that results because of space restrictions. The framework has better loop placements for networking chips with various design constraints. A Monte Carlo search tree is used to develop a Deep Neural Network that searches in parallel for a NoC design.
Design and Development of Novel Ensemble Classifier for Visual Sentiment Analysis
Authors: Sai Satyanarayana Reddy S.
Published: August 2021

Visual media is now become one of the pre-eminent methods utilized for conveying feelings or suppositions on the web. A huge number of photographs are being transferred by individuals on popular social network platform for interpersonal communication. Visual sentiment analysis (VSA) has pulled in wide consideration since an ever increasing number of individuals will communicate their feeling and conclusions through visual substance via online media. The domain of visual emotion evaluation is impractical because-of the great degree of discrimination in the human realization measure. In this paper, a novel ensemble classifier for visual sentiment analysis, which will use few labelled training specimens to attain an effective SA model, is proposed. The classified output is provided by the Ensemble classifier. The steps adopted in the proposed Visual sentimental analysis for the effective classification are preprocessing and the classification. The pre-processing is accomplished by the Resnet-101, which will boost the potential of the classifier. The effectiveness of the proposed sentimental analysis method is revealed by the comparative analysis and it is expected that the proposed method outperforms all the comparative methods. Then, broad datasets enable the quick advancement of profound neural organizations for recognition process. However, the explanation of enormous scope datasets is over the top expensive and tedious. The efficacy of the proposed sentimental analysis method will be revealed by the comparative analysis and it is expected that the proposed method will exceed all the comparative methods.
Applying SWOT for B2B Decisions, Extension to larger data with Machine Learning Regression
Authors: Akella Suresh
Published: August 2021

In this research Akella Systems company, AS, wants to locate its operation in another company either Sreyas an institute or C1 a company. A Business to Business, B2B association decision was required. The Strengths, Weaknesses, Opportunities and Threats, SWOT, concerning defined fields are defined and points allotted for AS, Sreyas, and C1. SWOT is used individually for decision-making on one parameter. In this analysis, a relative SWOT of the company’s capabilities is used for decision-making of location. Further, each company is evaluated relative to the other by SWOT polynomial regression. Standard regression using Cramer’s rule was used. A Business unit to Business unit, B2B decision was taken by locating the Akella Systems company, within the Sreyas Institute after relatively evaluating the SWOT. Linear equation fit of the data was also obtained from MS Excel and fits perfectly with R2 = 1 with the regression model. The relative evaluation of the two companies can be inferred from the coefficients of the equation. The constant-coefficient gives the average difference of all SWOT parameter differences of the company and the coefficient of the linear variable gives the progressive influence of one company over the other. Sreyas college had an average more SWOT value of 6.12 compared to AS and AS had a progressive influence in the relationship over Sreyas, as the coefficient was 0.1845. Sreyas college was selected to be the partner by AS. The polynomial fit metric developed for relative SWOT can be used as a training set, for large ML data regression.
Comparative Assessment of Existing Meaningful Image Encryption Techniques
Authors: Himthani V , Dhaka V S , Kaur M
Published: August 2021

In recent trends, digital images are commonly used for communication, storage, medical imaging, etc. These images may contain confidential data. Image encryption and steganography are popular techniques to provide information security. In image encryption, a secret image is encoded using an encryption key, and in image steganography, a secret image is embedded in some cover media. Encrypted images are visually recognizable as these are noise-like, but stego images are visually unrecognizable. In this concern, some visually secure encryption techniques are developed, known as Meaningful Image Encryption (MIE). It includes the advantages of both image encryption and steganography. This paper provides a thorough review of existing MIE techniques. A comparative assessment based on various performance parameters is presented. Further, this paper provides the main application areas of MIE techniques. Moreover, the future research perspective to enhance the existing MIE techniques are discussed in detail.
A blockchain based approach for improving transparency and traceability in silk production and marketing
Authors: Sharma Abhilash , Kalra Mala
Published: August 2021

Blockchain technology may be described as a distributed digital ledger database for transparent and immutable recording of transactions occurring between a groups of nontrusting parties. Transactions can be implemented without any intermediaries. Given this potential, it can play a great role in enhancing Supply chain management. Silk production and marketing has exponentially increased and spanned across the world in recent years. To redefine current silk production and marketing industry, there is a need to strengthen its supply chain management process. With Blockchain, government, farmers, weavers, and sellers can collaborate on a single system without any inconvenience. They create an immutable chain of transactions which can be verified by any of the parties. There is an immutable shared ledger that no one can modify. This research presents a private and permissioned application that uses Blockchain and aims to automate the shipping processes among different participants in the supply chain ecosystem. Data in this private ledger is governed with the participants’ invocation of their smart contracts. The performance of the proposed work is evaluated in terms of the transaction throughput, transaction average latency, resource utilization with a fixed and variable rate of transactions.
Gender Prediction using Deep Learning Algorithms and Model on Images of an Individual
Authors: Rajendra G , Sumanth Kodali , Anjali Chandragiri , Pardhasai Gudavalli , Supraja Madhumanchi
Published: August 2021

The classification of gender based on the biometric is an old and traditional approach which are treated as a sub-branch of “Soft Computing”. But in this proposed work, works on the celebrity images dataset obtained from the Kaggle repository. The major focus of this to extract the important features from the images by training the neural network by using an autoencoder and decoder. The system extracts the feature maps to visualize the extracted features and makes some sort of interpretations to analyze the data. Gender classification using iris information is a relatively old technique, and most gender classification methods classify all iris texture features. These features are fed to the classifier, this data might be proper or improper, and if it is improper it leads to more generalization results in less accuracy. The proposed algorithm to create increase the size of the dataset implements the Data Augmentation Technique using basic image operations. Then it applies a convolution neural network for predicting the gender of the user. The model has proved practically that increasing the size of the dataset and reducing the features improves the classifiers learning performance.
A Secure Framework for Health Record Management Using Blockchain in Cloud Environment
Authors: Verma G , Pathak N , Sharma N.
Published: August 2021

Health records of any type should be confidential, but simultaneously they should be accessible to an authorized user. In the current conventional health care system, health records are processed in the local database. Only authorized hospital staff can have access to the patient health records kept in health centers. The flow of information in this kind of system is center-restricted. One technology that can potentially overcome this problem is Blockchain. Blockchain technology allows sharing of data in a distributed way with benefits like privacy-preserving and secure access control. To achieve secure access control and privacy of electronic healthcare data (EHD), a secure framework for distributed sharing of the health record is proposed. The use of blockchain will remove the requirement of a trusted 3rd party for data storage. The framework uses searchable key attribute-based encryption (ABE) for achieving fine-grain access control. The simulation results indicate that our scheme is quite effective and secure.
Novel cryptographic approach to enhance cloud data security
Authors: Tyagi Kanika , Yadav S.K , Singh Mayank
Published: August 2021

In today’s era, cloud computing has become the more promising business concept which impacted almost every section of our lives and business. Cloud computing for sure has grown rapidly to become one of the major areas of research. As it provides an on demand access to a shared pool of resources and makes easier for the organizations to use their data at any place and at anytime without considering hardware devices along them but cloud security is still the most crucial and considerable issue in each organization. So there is need for some secure authentication so that data on clouds remain safe and secure. In this context passwords are the basic form of authentication. so there is need of some mechanism which provides password security. Weak and poor passwords management leads to breach in cloud data. Passwords to gain access to secret data should be so as strong to prevent dictionary attacks and brute force attacks. In our proposed system a mechanism is presented to secure the data on cloud using combination of some algorithms viz: PBKDF2, Argon 2, AEs-256 and IDA. In this paper we proposed a method to generate the most secured cryptographic keys using the blend of two key derivation functions PBKDF2 and Argon 2. In our proposed model there would be no need to store and send key for encryption and decryption. Advanced Encryption Standard (AES) is used for encryption for encryption. Information Dispersal algorithm is used to prevent data breaching situations on clouds in financial institutions and provide better confidentiality, availability and integrity of data.
Plant Leaves Disease Classification Using Bayesian Regularization Back propagation Deep Neural Network
Authors: Trivedi Vijay Kumar , Shukla Piyush , Pandey Anjana
Published: August 2021

Automatic leaf disease segmentation and detection from plant images has recently become a major research area around the world. The proposed method uses plant images to automatically segment and classify different leaf disease regions. The proposed procedure consists of four steps: Pre-processing is used in the first step to reduce the amount of background noise in the plant image using the Wiener filter. The disease spot is then detected using the hue histogram on the HIS model and these disease spots are then segmented using the K-means algorithm applied on the L*a*b* colour model and highest hue value calculation on HSV colour model. Finally, seventeen colour and texture features are extracted from the disease segment and these features are fed to a forward-propagation deep neural network (FPDNN) classifier which classifies the diseases. We have used the Bayesian regularization back propagation algorithm to fine-tune the results. We have applied FPDNN on varying hidden layers ranging from 1 to 40 and achieved highest accuracy i.e. 97.18% at 19 hidden layers which is larger than other state of art classifiers. Proposed method is implemented in matlab 14a.
Credit Score Prediction System using Deep Learning and K-Means Algorithms
Authors: Kumar Ashwani , Shanthi D. , Bhattacharya Pronaya
Published: August 2021

In financial markets, credit rating and risk assessment tools are used to minimize potential risk up to some extent for credit score. Nowadays, the banking and financial industry has experienced rapid expansion. Therefore, with this growth, the numbers of credit card applications with various credit products are increasing day by day because many people want to avail these services for their personal interest. The challenge here is to identify insights on the performance of a finance industry by using deep learning algorithms as they directly affect the viability of that industry. These industries have a limited number of resources and capital, which can be used to deliver the services among the customers. In this research work, we proposed prediction of credit scoring system using deep learning and K-Means algorithm for the financial industry. The scheme contains a predictive model which uses feature selection (FS) classification and deep learning applications simultaneously to train the proposed model to perform effectively. The scheme 1) pre-processing credit card data 2) uses a feature selection technique to minimize the dimension of data in order to obtain the finest training data 3) applies a deep learning algorithm to map the input weight with hidden biases to achieve excellent performance 4) Decision support system is used to enable the deep learning algorithm to provide a more accurate and intelligent decision. Furthermore, the proposed model is validated on different credit scoring dataset in real-world scenarios and is capable of improving the effectiveness and accuracy. The studies indicate that our predictive model performs well for credit scoring of existing customer and helps lenders to allocate funds in finance industry.
EaGWO: Extended algorithm of Grey Wolf Optimizer
Authors: Kumar Alok , Lekhraj  , Singh Avjeet , Kumar Anoj
Published: August 2021

The Grey Wolf Optimizer (GWO) and their very recent improved algorithms have some limitations such as stagnation in local optima, slow convergence rate, and so forth. In order to overcome these limitations, we have proposed an algorithm of GWO, named Extended algorithm of GWO (EaGWO), that modifies the hunting equation and encircling equation of the original GWO. The proposed algorithm has been examined on twenty-three very renowned test functions and compared the results with basic GWO and other very recent variants of GWO. This research work also considers the Friedman ANOVA test to verify the effectiveness of the proposed methodology. Consequently, the findings of this work justify quite competitive performance compared to other meta-heuristic algorithms.
Impact of Intermediate Bottleneck Nodes on the QoS Provision in Wireless Infrastructure less Networks
Authors: Sultana Ijteba , Bari Mohd Abdul , Sanjay
Published: August 2021

Wireless Infrastructure less networks consist of wireless mobile nodes with heterogeneous and constrained resources, to establish internet connectivity regardless of the location. The applications the networks are disaster relief, health care and military. These applications are sensitive, and demands the reliable, and best quality of communication. Routing is one of the resources to enable the communication. Routing is acceptable in an infrastructure less networks if it satisfies the quality of service requirements. Thus the paper aims to investigate the existing QoS routing protocol performance in various conditions. The aim is to achieve a broad understanding of QoS routing protocol designed for Wireless Infrastructure less networks, and further pave the pathway for future research. The paper analyses the QoS routing protocol performance in presence of bottleneck intermediate node, and it is the novel aspect of the work.
Influence of an efficient Hierarchical Clustering Algorithm in analyzing Cancer affected DNA Dataset
Authors: Kiruba Nesamalar E. , Satheesh Kumar J. , Amudha T.
Published: August 2021

This research work presents an influence of hierarchical clustering approach in anlyzing cancer affected DNA data set. The primary objective of this research work is to identify the best clustering algorithms to group cancer-affected DNA datasets. Data analysis shows an important role in bioinformatics. Data analysis technique used for grouping the data objects is based on unsupervised learning. Clustering is an unsupervised learning technique in data mining. It groups a set of clusters from the entire dataset. In this research work, 700 cancer-affected DNA datasets are considered for analysis. This research work compares three types of Clustering techniques, K-Means (KM), K-Medoids (KMS), and Hierarchical Clustering (HC), to group cancer-affected DNA. Each algorithm has some strengths and weaknesses. These clustering algorithms are compared in detail based on various parameters. Results prove that the hierarchical clustering algorithms show lesser execution time and increased accuracy than other KM and KMS algorithms.
Driver’s Drowsiness Detection Based on Facial Multi-Feature Fusion
Authors: Venkata Sai Vardhan V. , Ritish Kumar Reddy N. , Jaya Surya K. , Uday Kiran J. , Kumar Ashwani
Published: August 2021

Among the leading causes of traffic accidents and deaths is drowsy driving. As a result, detecting and indicating driver drowsiness is an important research subject. The majority of existing approaches are based on automobiles, behavioral-based, or physiologically based. A few approaches are invasive and so the driver been interrupted, while others necessitate the use of pricey sensors and data processing. As a result, real time driver sleepiness detection system with adequate consistency and minimized cost has been established in this work. A webcam captures the video in the proposed system, and image processing techniques are used. Histogram of oriented gradients (HOG) which is available in Dlib toolkit, is used to recognize the driver’s face in every frame. The landmarks on the face are identified by State Vector Machine, and the eye aspect ratio, mouth aspect ratio, are computed, and drowsiness is recognized using created adaptive thresholding based on their values. Offline implementations of machine learning techniques were also been made. In terms of accuracy and speed, we show that our algorithm outperforms existing fatigue methods. In Support, a sensitivity of 95.28 percent and a response rate of 100 percent were attained in Support Vector Machine.
Algorithms for detecting network attacks in an enterprise industrial network based on data mining algorithms
Authors: Vulfin A M , Vasilyev V I , Kuharev S N , Homutov E V , Kirillova A D
Published: August 2021

The article proposes the structure of a network traffic analysis system using machine learning models. The analysis of the specialized dataset WUSTL-IIOT-2018 was carried out, the main stages of data preprocessing, construction and testing of classifiers for detecting network attacks were performed. The possibility of embedding models as modules of specialized network equipment is proposed, which makes it possible to increase the efficiency of the analysis of network traffic, including specialized industrial protocols.
Randomized C/C++ dynamic memory allocator
Authors: Aleksandrovna Astrakhantseva Irina , Gennadevich Astrakhantsev Roman , Viktorovich Mitin Arseny
Published: August 2021

Dynamic memory management requires special attention in programming. It should be fast and secure at the same time. This paper proposes a new randomized dynamic memory management algorithm designed to meet these requirements. Randomization is a key feature intended to protect applications from “use-after-free” or similar attacks. At the same time, the state in the algorithm consists only of one pointer, so it does not consume extra memory for itself. However, our algorithm is not a universal solution. It does not solve the memory fragmentation problem and it needs further development and testing.
Data transfer in the mathematical broken symmetry group model
Authors: Gorshkov Kirill , Nikitin Oleg , Kuznetsova Ekaterina
Published: August 2021

The article shows a mathematical description of data transfer using a directed graph, as well as an algorithm for data transport in a network based on the multiplication tables of substitutions – elements of the broken symmetry group. It was analyzed how many steps are necessary when data transmission in systems with alternate sending of data packets. Using multiplication tables of substitutions the possibility of calculating the graph trees with nested nodes was demonstrated, including PQ-trees. The article provides examples of models for collecting information in two main network nodes, as well as networks with a more complex topology, for example the tree with its “rootstock”.
Modeling of the belt conveyor control system using artificial intelligence methods
Authors: Druzhinina O V , Masina O N , Petrov A A
Published: August 2021

The paper is devoted to the development of instrumental and methodological support for the study of conveyor transport systems with intelligent control mathematical models. An optimal control model is constructed for a belt conveyor with a dynamic change in the angle between the horizontal plane and the belt plane. Methods for studying the belt conveyor model based on the design of PID controllers and neural network controllers are proposed. The results of computational experiments using the training of a feedforward neural network and the use of reinforcement learning are presented. A comparative analysis of computational experiments with the use of control based on the synthesis of a fuzzy controller, control using artificial neural networks, and control based on a PID controller is performed. The obtained results can be used in neural network modeling of complex systems and in solving problems of automation of technological and production processes.
Mathematical modeling of the thermally stressed state of elliptical-section concrete structures
Authors: Ivanovich Kanareykin Aleksandr
Published: August 2021

At present, there is an urgent need to determine the level of temperature stresses in super-large concrete structural elements that occur during the manufacture of such products and contain a huge amount of concrete at the same time, which is in fact a heat source that is inaccessible to the control of the exothermic process of the entire mass of concrete. The main task of this work is the mathematical modeling of temperature stresses in concrete structures of elliptical cross-section in order to reduce them. Based on mathematical modeling, it is established that the level of thermal stresses that occur during the transition to an elliptical cross-section decreases.
Digital transformation of expert systems design within geoinformation management in Arctic
Authors: Abramov V M , Sikarev I A , Chusov A N , Tatarnikova T M , Shilin M B
Published: August 2021

The article contains the digitalization results for expert systems design in the framework of geoinformation management within Arctic while coronavirus and change of climate. Currently, the methods of designing expert systems within the framework of geoinformation management in the Arctic demonstrate the use of improved concepts of data collection and visualization. The article gives preference to the use of web constructors in distributed networks. An example of a working layout of an expert system for geoinformation support of the activities of a subarctic port in the conditions of ice waters is given. The results of the research can be useful in operational environmental activity and for university learning purposes, including Master’s programs.
Artificial intelligence as an instrument against social engineering
Authors: Britvina V V , Musatova D A , Altoukhov A V , Zueva A S , Sorokin A Y
Published: August 2021

Annotation. This article discusses the use of artificial intelligence technologies in the fight against social engineering. The purpose of this study is to analyze the technologies of artificial intelligence, its main features and the possibilities of its application. The article also examines the issue of improving the legislation of the Russian Federation, taking into account the use of artificial intelligence technologies. This problem is extremely relevant today due to the growth of crimes involving social engineering methods.
Formation of the cyber-physical platform “Digital Region”
Authors: Ermakova A M , Oznobikhina L A
Published: August 2021

The article considers the cyber-physical platform “Digital Region”, which will allow managing technical, technological, industrial and infrastructure facilities of the region. The authors describe the concept of ensuring the digital and physical functioning of this platform; argue for the positive impact of the new digital platform on the sustainable development of the regions.
Progressive digital and information technologies in natural and technical education
Authors: Sergeenko Yu S , Scherbakova O V , Permykov A I , Kombarova E V , Ukraintseva V S , Rybkina M V , Ermolovich G P
Published: August 2021

In the context of a pandemic in 2020, a serious transformation of natural and technical education took place, in this connection; the authors consider modern approaches to the introduction of educational activities in conditions of restrictions. The paper proposes a concept that allows for a high-quality transition in natural and technical education, which allows students to assimilate material in a virtual learning environment. The work formulates a step-by-step system that allows transforming natural and technical education.
Principles of internet education for students on the subject “information technology and mathematical modeling of processes” on the basis of “moodle” distance learning system
Authors: Baydullaev A S , Mamatkulov Z U , Samigova N H , Shadmanov K K , Narzullaev D Z
Published: August 2021

This article gives a brief description of the principles of organization of Internet education in the teaching of “Mathematical modeling of information technology and processes” to students on the basis of “Moodle LMS” system distance learning at the Tashkent Pharmaceutical Institute and shows its main features and advantages. Extensive use of educational technologies, optimization, humanization and innovation in the National Program of Personnel Training to solve the tasks of training and educating young people to be highly moral, spiritual and enlightened, conscientious at work, deeply aware of their responsibilities to society and family, competitive, qualified professionals. The organization, management and control of the learning process on the basis of methods is relevant. Nowadays, a wide range of opportunities are being created for students to have fast, plentiful, reliable information and to acquire knowledge independently. In particular, electronic systems allow students to use important information related to science at any distance, depending on their capabilities. “Moodle” open source software complex is a system designed specifically for the management of the educational process using the Internet (Learning management system - LMS). Distance learning is a modern form of organizing the learning process using distance learning technologies. Its development was due to the increase in the number of Internet users, the availability and quality of open learning electronic resources. The use of distance learning technologies undoubtedly has a wide range of advantages: the organization of the learning process in an Internet environment, so the student location does not prevent high-quality training, regardless of the location of the educational institution. Distance learning is based on the principle of independent preparation of students through teacher guidance and allows you to choose a convenient time for lessons. It includes interactive communication between teachers and students, information resources of the educational institution and information resources of the Internet, the rapid delivery of the Internet in electronic form.
Use of information technology in spreading new knowledge in agriculture
Authors: Kondratieva O V , Fedorov A D , Slinko O V
Published: August 2021

Popularization of scientific and technological achievements, advanced experience and new knowledge in the agribusiness, especially in current conditions, can be considered as a mechanism that contributes to the receipt by agricultural producers of relevant, high-quality and reliable information on issues of interest and problems in the field of agriculture. The article presents the results of popularization of scientific resources using the database (DB) called “Users of scientific and engineering products in the agribusiness.” The database is maintained to collect information about users by category of organizations, user distribution by federal district, and on the preference of issues of interest to them in subject areas in order to further improve the provision of scientific and engineering information for users in the area of technical and engineering education. The results of the database are used in proposals for adjusting the subjects in the preparation of analytical materials on the engineering and technological support of the crop and livestock product manufacture and processing.
The use of real technical tasks for the implementation of websites for the formation of professional skills of web developers
Authors: Glagolev V , Bazhenov R , Kuldysheva G , Ledovskikh I , Maximov V
Published: August 2021

The paper deals with the problem of the formation of professional skills in web development. The task is solved through the use of real technical tasks for the implementation of websites in teaching, the inclusion in the curriculum of the educational program of the disciplines “Methods and tools for designing information systems”, “Programming network databases” and “Web programming”. The developed methodology was tested at Sholom-Aleichem Priamursky State University, Osh State University, Pacific National University and North-Eastern Federal University during the 2019-2020 and 2020-2021 academic years. 42 students of the direction “Information systems and technologies” took part in the training. The content of the disciplines included obligatory project team work on an assignment from an industrial partner. An important element of preparation was the final exam conducted in the format used in WordlSkills championships. The developed websites were assessed by independent qualified third-party experts in the following areas: work organization and management, communication and interpersonal skills, design and layout, server and client-side programming, database and web portal design. As a result, 74% of students implemented the web portal and submitted the project on time. As a result of passing disciplines and implementing web portals, students studied the principles of rapid development of web systems, received professional competencies and interaction with real organizations.
Model of an intelligent system for managing the process of developing the competencies of industrial enterprise employee’s competencies
Authors: Abrahamyan Gennady , Atayan Anush , Sharabaeva Lyubov , Gureva Tatiana
Published: August 2021

The article considers issues and tasks of industrial enterprise employee’s competencies development in conditions of digitalization of economy. Analysis of automated systems, tools and intelligent technologies of formation, monitoring and development of industrial enterprise employee’s competencies development was carried out. The choice of software tools of the Microsoft Power Platform analytical platform for intelligent support of industrial enterprise employee’s competencies development is justified. A typical digital infrastructure for training of industrial enterprise employees is considered: goals, functions, elements and relationships for automating and intellectualizing the development of competencies of enterprise employee’s. Proposals are made to improve the digital infrastructure for training industrial enterprise employees. A model of an intelligent system of managing employees’ development is proposed. It provides monitoring of employee’s actions and recording the results of competencies development. This model is based on the integration of data collection and analysis processes of the digital profile of employee competencies and its relationship with the competencies profile of the position based on the iterative algorithms of the Microsoft Power Platform (Power BI, Power Automate, Power Apps, Power Virtual Agents).
On the use of information technologies in the training of specialists in the textile industry
Authors: Dobrovolskaya T A
Published: August 2021

The article analyzes the features of the use of information technologies in the development of disciplines by design engineers studying the issues of materials science and the manufacture of products from textile industry materials. Depending on the tasks being solved by design engineers of the textile industry, it is advisable to introduce the following information products into the educational process: to determine the structural characteristics of textile industry materials and evaluate their utilitarian properties-Microsoft Excel spreadsheets and applied author’s programs, to analyze regulatory requirements - the Microsoft Access database. The use of the proposed training technology will significantly improve the quality of training and information culture of textile industry specialists.
LSM and Digital Education Transformation
Authors: Korikov A M , Levin S M
Published: August 2021

The article describes the research carried by the authors in the global improvement framework of electronic tools in distance education. The text discusses digital transformation problems associated with using pedagogical concepts by universities in distance learning utilising learning management systems. In researching the problems, investigated the leading pedagogical technologies of behaviourism, cognitivism and constructivism, and aspects of personality-oriented and project-based learning. It was revealed a main regular concepts methods that can be applied in distance learning. The authors analysed a sample of the most popular LMSs for the sufficiency of functions to implement these pedagogical methods. As a result of research, the studied LMS has a fairly wide range of functions. However, these functions are not sufficient enough to allow all indicated pedagogical methods using. To compensate for the lack of LMS functionality, the authors recommend practising external electronic resources could be integrated into the learning management systems used by universities.
Reliability and quality of distance learning technical education in the context of the COVID-19 pandemic: practice and issues
Authors: Klepalova Yuliya I , Yur Igor P , Tarasova Yulia N , Savka Olga G , Maystrovich Elena V
Published: August 2021

The covid-19 pandemic, which was announced in March 2020, has made significant changes in all spheres of society, the state and the world in general. A quick restructuring of many structures, processes and mechanisms was required. The education system is no exception. The educational process was modernized, a large number of new technologies were introduced and the existing mechanisms were improved. So, in the conditions of the forced transition to training using distance educational technologies in the spring of 2020, it becomes necessary to assess the quality of the functioning of educational software in the higher technical education system. This article is aimed at researching and analysing the practical and theoretical aspects of the use of distance learning technologies in order to determine the correspondence of the quality of distance learning to the traditional form of full-time education. The authors of the article pay attention to the possible prospects for the development of distance learning within the technical education system in the Russian Federation, not only as a response to changing epidemiological conditions, but also as a potentially alternative form of education. The study assesses the success of using software systems, identifies the advantages and disadvantages of the existing system of distance education.
Enhancement of Compatibility of SBS Modified Asphalt by CNTs: Interpretation from Dissipative Particle Dynamics Simulations
Authors: Hu Kui , Yang Yi , Yu Caihua , Zhang Taoli
Published: August 2021

The compatibility of styrene-butadiene-styrene (SBS) with base asphalt is known to restrict the application of SBS modified asphalt. In this study, carbon nanotubes (CNT) were used to enhance the compatibility between SBS and base asphalt. The effect of CNTs on the compatibility of SBS modified asphalt was investigated by dynamic mechanics analysis (DMA), and the micro-mechanism of CNTs enhancing the compatibility of SBS modified asphalt was studied by using dissipative particle dynamics (DPD) simulation. The results show that CNTs can significantly improve the compatibility between SBS and base asphalt, and the microscopic mechanism is that CNTs can change the distribution morphology of SBS in base asphalt, making the distribution of SBS modified asphalt more uniform, which promotes the development of polymer-rich phase in SBS modified asphalt. This study can provide theoretical guidance for the practical application of SBS modified asphalt.
3D-Printed Polymer Composites Based upon Low Melting Point Alloys Filled into Polylactic Acid
Authors: Liu Junfeng , Li Zhen , Yu Yuan , Wang Pengfei
Published: August 2021

In this study, advanced low melting point alloys (LMPA)/polymer composited wires for fused deposition modelling (FDM) processes were developed. The Polylactic Acid (PLA) powder was mixed with LMPA powder and multi walled carbon nanotubes (MWCNTS). The liquid state of LMPA will not hinder the processability of polymer composites when the composites were extruded out as wires. Thus, the composited wires can be optimally fabricated with high stabilities as well as large quantities. The prepared PLA/LMPA/MWCNTS wires could be further dedicated to print with 3-dimensional (3D) structures. Additionally, the tensile modulus of the printed composites was three times as higher as that of the pure PLA. The tensile modulus of composites is temperature-dependant, specially it would decrease more significantly at the melting point of LMPA. The study on the FDM of the advanced composited-wires would inspire a new branch in the additive manufacturing. The material could also be potentially applied in variable stiffness components, electromagnetic shielding and other applications.
A Review of Key Technologies and Properties for Graphene Cu-Based Composites
Authors: Liu K M , He G Y , Sheng X C , Han N L , Li M L , Zhang M C , Zou J , Fu K , Huang H M
Published: August 2021

Cu-based composites are widely used in mechanical, electrical, communication, transportation and microelectronics industries due to their excellent properties. However, the difficult match between strength and conductivity is the main problem for Cu-based composites. And thermal-mechanical treatment technologies reached the limit to improve and control their comprehensive performance. Graphene is a two-dimensional layered material with carbon atoms hybridized by SP2 orbital, and has high strength, good electrical conductivity and thermal conductivity. It is expected to solve the contradiction between the strength and conductivity of the composites by introducing graphene into Cu-based composites. In this paper, the commonly used methods for effective dispersion of graphene and interface bonding were introduced, the researches on the strength, toughness, electrical conductivity and thermal conductivity were outlined, lastly the future development of graphene Cu-based composites was prospected.
Research Progress of Nanomaterials in the Prevention of Biological Fouling on Ships
Authors: Mao Xin , Cui Xin , Chen Shuiping
Published: August 2021

Marine fouling is one of the causes of Marine corrosion, which causes material corrosion to sailing vessels and a variety of Marine structures, but also has a great increase on the daily maintenance cost and energy consumption. In this paper, the formation process, mechanism and harm of biological fouling are briefly introduced, and then the application research progress of nanomaterials in preventing Marine biological fouling of ships in recent years is comprehensively discussed. We found that the research mainly focused on the application of nano-oxide materials in Marine biological fouling prevention, and adding anti-fouling agents of nano or modified nanomaterials in the preparation of materials to enhance the properties of materials, which could provide support for further exploration of nanomaterials in biological fouling.
Parameter Determination and Model Modification of Sherwood-Frost Constitutive Model
Authors: Zhang Kebin , Li Wenbin , Zhao Changfang
Published: August 2021

The Sherwood-Frost constitutive model is widely used to predict the mechanical behavior of polymer materials, and it is a very important material model. In this study, the stress-strain curve of high-density polyethylene (HDPE) with a strain rate ranging from 935 to 5450 s−1 was obtained through the split Hopkinson bar test device. Based on the HDPE dynamic mechanical performance test, the method for determining the strain-rate parameters of the Sherwood-Frost constitutive model was analysed and the strain-rate term in the constitutive model revised. Using the experimental results of Sherwood and Frost, the method to determine the parameters of the density and temperature terms in the model was introduced. This study provides a reference for parameter determination and model modification of the Sherwood-Frost constitutive model.
Research on Metallographic Examination of Thermal Spraying Coatings
Authors: Yu Ting , Li Yang , Zhang Fang , Lu Shuhua , Gao Zhen
Published: August 2021

The metallographic study of thermal spraying coatings was carried out on three double coating samples. ‘ASTM E3 metallographic Sample Preparation Method’ was selected for sample preparation, and coating inspection was conducted according to’HB 20195-2014 Metallographic Examination of Thermal Spraying Coatings’. The interface state, cracks and microstructure of the coating analyzed by‘ HB 20195-2014 Thermal Spraying Coating Metallographic Examination’. In combination with Image-Pro Plus quantitative analysis of porosity and unfused particles of coating, the thickness of coating was measured by using pull method. For the first coating, the microstructure, crack, coating thickness and porosity were tested. For the second coating, the microstructure, interface state, cracks, coating thickness, porosity and unfused particles were tested. Through the research on metallographic examination of thermal spraying coating, its hoped to provide some reference value for the examination of other coatings.
Crack Elimination and Surface Quality Enhancement of the Wear-resistant Layer on Guide Slippers of Coal Cutting Machines
Authors: Chen Jialu , Xue Yu , Liu Kun , Jiang Fengge
Published: August 2021

In order to enhance the surface quality and avoid the cracking problem of the wear-resistant layer on guide slippers of coal cutting machines, an automatic welding system was employed for the overlaying welding production. By regulating the welding parameters, including the welding path and speed, the surface flatness and quality were greatly reduced. The cracks at the arc extinguishing positions were effectively eliminated. As compared to the conventional manual welding, the automatic welding not only reduced the machining allowance, but also increased the production rate and the service life of the guide slippers.