Network Information Technology Research Articles

The Applied Research of E-commerce Technology from the Perspective of Communication Studies

With the rapid development of computer network technology and information technology, the e-commerce mode is constantly innovated and applied, which has injected new vitality into the commercial activities. In terms of technology, advanced technologies such as mobile payment, artificial intelligence, big data and AR technology have been widely used in the field of e-commerce, and the innovation of e-commerce technology has also promoted the enrichment of content and diversification of information dissemination. As a subject of information communication and communication, communication science provides important theoretical support and practical guidance for the development of e-commerce technology. From the perspective of communication science, this paper discusses in-depth the innovation and application of e-commerce technology, aiming to reveal the important role of the principle of communication science in the development of e-commerce technology, and discuss how to promote the sustainable development of e-commerce industry through technological innovation and application.

Comparative Analysis of Classification Methods and Suitable Datasets for Protocol Recognition in Operational Technologies

The interconnection of Operational Technology (OT) and Information Technology (IT) has created new opportunities for remote management, data storage in the cloud, real-time data transfer over long distances, or integration between different OT and IT networks. OT networks require increased attention due to the convergence of IT and OT, mainly due to the increased risk of cyber-attacks targeting these networks. This paper focuses on the analysis of different methods and data processing for protocol recognition and traffic classification in the context of OT specifics. Therefore, this paper summarizes the methods used to classify network traffic, analyzes the methods used to recognize and identify the protocol used in the industrial network, and describes machine learning methods to recognize industrial protocols. The output of this work is a comparative analysis of approaches specifically for protocol recognition and traffic classification in OT networks. In addition, publicly available datasets are compared in relation to their applicability for industrial protocol recognition. Research challenges are also identified, highlighting the lack of relevant datasets and defining directions for further research in the area of protocol recognition and classification in OT environments.

Health Management Service Models for the Elderly with Visual Impairment: A Scoping Review.

The incidence of visual impairment(VI) in older people is gradually increasing. This review aimed to summarise the evidence on existing health management models and strategies for older adults with VI to improve health-related and vision-related quality of life (QoL) in older people. Based on the framework of the scoping review methodology of Arksey and O'Malley (2005), a comprehensive literature search of relevant literature published between January 2010 and June 2022 in PubMed, CINAHL, EMBASE, Web of Science, Cochrane Library, CNKI, VIP, Wanfang database, Sinomed and the grey literature. Finally, 31 articles were included. The health management model had a multidisciplinary team low vision rehabilitation model, medical consortium two-way management model, low vision community comprehensive rehabilitation model, medical consortium-family contract service model, screening-referral-follow-up model, and three-level low vision care model. The health management strategy covers nine aspects, the combination of multi-element strategies is feasible, and network information technology has also shown positive results. In the future, under the Internet and hierarchical management model, we should provide demand-based personalized support to rationalize and scientifically achieve hierarchical management and improve resource utilization efficiency and eye health outcomes.

Dense Wavelength Division Multiplexing (DWDM) in IT Networks: A Leap Beyond Synchronous Digital Hierarchy (SDH)

The evolution of telecommunications has seen significant milestones, notably the shift from Synchronous Digital Hierarchy (SDH) to Dense Wavelength Division Multiplexing (DWDM). This transition marks a pivotal advancement in the performance of Information Technology (IT) networks, offering unparalleled improvements in bandwidth, scalability, and efficiency. DWDM technology enables the simultaneous transmission of multiple data streams across a single optical fiber by using different wavelengths of light, significantly enhancing data throughput and network capacity. This article delves into the technical foundations of DWDM, compares its performance enhancements over SDH systems, and explores its impact on modern IT network infrastructure.

Application of Neural Network in Seismic Risk Survey

The accuracy of inversion data is determined by the reliability of linear inversion method. Therefore, the study of nonlinear seismic inversion method is a meaningful work. This paper mainly aims at the application of nonlinear neural network inversion method in Fujian Risk Survey, the following work has been accomplished: (1) The theory and algorithm of the conventional multilayer feedforward neural network and some problems in the design of the model are analyzed and summarized. Aiming at the gradient descent function search algorithm in the BP algorithm, a method for global optimization is proposed by combining simulated annealing (global search algorithm) with Conjugate gradient method (local search algorithm) , the principle and implementation of simulated annealing and conjugate gradient algorithm are analyzed in detail. (2) Based on the analysis of conventional multilayer feedforward neural networks, the model structure and basic principle of probabilistic neural networks are studied. The object function search algorithm and its realization are analyzed. The method is applied to the risk elimination prediction for the first time, and the results are satisfactory. (3) The method and principle of inversion of reservoir parameters by using various seismic attribute information and neural network technology are discussed. In order to make full use of the effective information contained in seismic signal, the method of seismic attribute extraction and optimization is studied, the principle and implementation method of attribute optimal ranking by stepwise recursive method and selecting the most important attribute combination by interactive verification method are presented.

A Survey of the Mechanisms Impairing Optical fiber communications performance

The growth of information technology and Internet networks, combined with nearly daily use and many subscribers, has caused the volume of data stored in communication systems to balloon to enormous proportions. Optical fiber cable is the medium for data transfer because it has a bandwidth far more significant than other transmission methods and can span significantly greater distances. The transmission of information from one location to another can be accomplished via a technique known as fiber-optic communication. This involves passing pulses of light over an optical fiber. Optical fiber performance is affected by many effects, including attenuation, dispersion, scattering, and bending. It is feasible to enhance the performance of optical fibers for communications by utilizing carbon nanotubes and multiple coding technologies. This can be accomplished via integrated optical circuits or by making the cable more advanced and developing it further. Photosynthesis is accomplished by utilizing carbon tubes and the optical property inside them.

A Survey on Life-Cycle-Oriented Certificate Management in Industrial Networking Environments

Driven by the Industry 4.0 paradigm and the resulting demand for connectivity in industrial networking, there is a convergence of formerly isolated operational technology and information technology networks. This convergence leads to attack surfaces on industrial networks. Therefore, a holistic approach of countermeasures is needed to protect against cyber attacks. One element of these countermeasures is the use of certificate-based authentication for industrial components communicating on the field level. This in turn requires the management of certificates, private keys, and trust anchors in the communication endpoints. The work at hand surveys the topic of certificate management in industrial networking environments throughout their life cycle, from manufacturing until their disposal. To the best of the authors’ knowledge, there is no work yet that surveys the topic of certificate management in industrial networking environments. The work at hand considers contributions from research papers, industrial communication standards, and contributions that originate from the IT domain. In total, 2042 results from IEEE Xplore, Science Direct, Scopus, and Springer Link were taken into account. After applying inclusion and exclusion criteria and title, abstract, and full-text analysis, 20 contributions from research papers were selected. In addition to the presentation of their key contributions, the work at hand provides a synopsis that compares the overarching aspects. This comprises different proposed entity architectures, certificate management functions, involvement of different stakeholders, and consideration of life cycle stages. Finally, research gaps that are to be filled by further work are identified. While the topic of certificate management has already been addressed by the IT domain, its incorporation into industrial communication standards began significantly later and is still the subject of research work.

Using Ensemble Learning for Anomaly Detection in Cyber–Physical Systems

The swift embrace of Industry 4.0 paradigms has led to the growing convergence of Information Technology (IT) networks and Operational Technology (OT) networks. Traditionally isolated on air-gapped and fully trusted networks, OT networks are now becoming more interconnected with IT networks due to the advancement and applications of IoT. This expanded attack surface has led to vulnerabilities in Cyber–Physical Systems (CPSs), resulting in increasingly frequent compromises with substantial economic and life safety repercussions. The existing methods for the anomaly detection of security threats typically use simple threshold-based strategies or apply Machine Learning (ML) algorithms to historical data for the prediction of future anomalies. However, due to the high levels of heterogeneity across different CPS environments, minimizing the opportunities for transfer learning, and the scarcity of real-world data for training, the existing ML-based anomaly detection techniques suffer from a poor predictive performance. This paper introduces a hybrid anomaly detection approach designed to identify threats to CPSs by combining the signature-based anomaly detection typically utilized in IT networks, the threshold-based anomaly detection typically utilized in OT networks, and behavioural-based anomaly detection using Ensemble Learning (EL), which leverages the strengths of multiple ML algorithms against the same dataset to increase the accuracy. Multiple public research datasets were used to validate the proposed approach, with the hybrid methodology employing a divide-and-conquer strategy to offload the detection of certain cyber threats to computationally inexpensive signature-based and threshold-based methods using domain knowledge to minimize the size of the behavioural-based data needed for ML model training, thus achieving a higher accuracy over a reduced timeframe. The experimental results showed accuracy improvements of 4–7% over those of the conventional ML classifiers in performing anomaly detection across multiple datasets, which is particularly important to the operators of CPS environments due to the high financial and life safety costs associated with interruptions to system availability.

Drivers for Inter-city Innovation Networks Across Chinese Cities: Modelling Physical Versus Intangible Effects

AbstractCross-region innovation is widely recognized as an important source of the long-term regional innovation capacity. In the recent past, a growing number of studies has investigated the network structure and mechanisms of cross-region innovation collaboration in various contexts. However, existing research mainly focuses on physical effects, such as geographical distance and high-speed railway connections. These studies ignore the intangible drivers in a changing environment, the more digitalized economy and the increasingly solidified innovation network structure. Thus, the focus of this study is on estimating determinants of innovation networks, especially on intangible drivers, which have been largely neglected so far. Using city-level data of Chinese patents (excluding Hong Kong, Macao, and Taiwan Province of China), we trace innovation networks across Chinese cities over a long period of time. By integrating a measure on Information and Communications Technology (ICT) development gap and network structural effects into the general proximity framework, this paper explores the changing mechanisms of Chinese innovation networks from a new perspective. The results show that the structure of cross-region innovation networks has changed in China. As mechanisms behind this development, the results confirm the increasingly important role of intangible drivers in Chinese inter-city innovation collaboration when controlling for effects of physical proximity, such as geographical distance. Since digitalization and coordinated development are the mainstream trends in China and other developing countries, these countries’ inter-city innovation collaboration patterns will witness dramatic changes under the influence of intangible drivers.

How knowledge resources drive industrial chain carbon reduction: an analysis from the knowledge management perspective

Purpose This paper aims to analyze the role and advantages of knowledge resources in the carbon emission reduction of the industrial chain, and how it can be used to promote the carbon emission reduction of the industrial chain, so that the industry can better achieve the saving of energy and the reduction of emission. Design/methodology/approach This paper argues that the traditional resource-plundering industrial chain production method can no longer meet the needs of sustainable development of the green and low-carbon industrial chain, and builds the coupling and coordination of knowledge technology innovation drive and industrial chain carbon emission reduction mechanism, in the four dimensions of industrial chain organization, government support, internet support and staff brainstorming, put forward suggestions for knowledge resources to drive carbon emission reduction in the industrial chain. Findings This paper holds that the use of knowledge resource advantages can better help industrial chain enterprises to carry out technological innovation, knowledge resource digital platform construction, knowledge resource overflow and transfer, application and management of network information technology, so as to reduce carbon emission in industrial chain. Originality/value This paper contributes to the discussion about the high-quality implementation of the revitalization strategy of the industrial chain and also deepens research on the knowledge resource-driven carbon emission reduction of the industrial chain. Further, this paper enriches the role of knowledge resources in the industrial industry, and the theoretical results support the advantages of knowledge resource in the field of chain carbon emission reduction.

An Examination of the Impact of Using Marketplaces to Promote the Growth of Micro, Small, and Medium Enterprises (MSMEs) in Indonesia

Marketplaces have become a critical component in the development of Micro, Small, and Medium Enterprises (MSMEs) in Indonesia. The availability of a marketplace platform has a substantial impact on MSMEs' growth. The purpose of this study is to assess the impact of marketplaces on the growth of MSMEs by taking into account the elements that influence their integration into the digital ecosystem. The quantitative methodology was utilized to collect data from MSME traders who used the marketplace to market their items. The marketplace's presence has greatly enhanced sales and had a favorable impact on their business operations. Market access is open from Sabang to Merauke thanks to the information technology network. This data is evaluated statistically to determine the association between market presence and MSME business growth. The findings of this study can help MSMEs gain a better understanding of how to use marketplaces to boost exposure, sales, and overall growth. The practical ramifications of this research are supposed to assist MSME owners in developing marketing strategies for their products and services via marketplace platforms. This research will also contribute to scholarly literature on the development of MSMEs in the digital era.

Visual Knowledge Graph Construction of Self-directed Learning Ability Driven by Interdisciplinary Projects

INTRODUCTION: The application of interdisciplinary information technology is becoming more and more widespread, and the application of visual knowledge mapping in the process of students' independent learning is also becoming more and more important; therefore, in this context, takes the history discipline as a starting point to study the construction of visual knowledge mapping of students' independent learning ability under the drive of interdisciplinary projects.OBJECTIVES: To enrich the means of student independent learning aids in China's history discipline and enhance the modernization level of China's history discipline construction; to solve the problem that student independent learning ability under the drive of China's interdisciplinary projects can not be visualized and observed; to further improve China's distance education environment and to enhance the educational capacity of the history discipline.METHODS: Firstly, the relevant modeling uses a visual knowledge map. Secondly, the neural network model assesses students' independent learning ability in history learning. Finally, the convolutional neural network model is used to assess the efficiency of the knowledge map.RESULTS: The Sig and Tanh function models have better robustness, and the ReLU and PReLU functions have weaker interdisciplinary driving performance. However, the iterative Knownledge1 and Knownledge2 models have better robustness of the visualized knowledge graph.CONCLUSION: In studying history, the interdisciplinary, project-driven, and independent learning ability of students could be more vital, and our country should vigorously develop new information network technology to improve the status quo of history discipline education in China.

Exploring synchronizability of complex dynamical networks from edges perspective

With the rapid development of network information technology, synchronization problem of complex dynamical networks has garnered extensive attention. Current research predominantly concentrates on analyzing synchronizability and synchronization control in the complex dynamical networks with static edge weights or single weight attribute connections. However, there is limited research on networks characterized by variable weights and multiple weighted connections. This study addresses this gap by analyzing the synchronizability of such complex networks. In this paper, we investigate the synchronizability of two types of networks: weight-variable complex dynamical networks and multi-weighted complex dynamical networks. Considering the evolution of network weights, the synchronizability of complex dynamical network with weight-variable is investigated, the master stability equation, synchronizability criteria of the network are derived, and experiments are used to substantiate the validity of our findings. Our study then shifts to multi-weighted complex networks. Take the Rössler oscillators as node dynamics, we explore the impact of different inner coupling matrices on synchronization types under both unknown and known topologies. Moreover, we extend our study to scenarios with different coupling strengths. The analysis reveals that the inner coupling matrix influences the network’s synchronization region type. Interestingly, the clarity or ambiguity of network topologies does not markedly affect the synchronization region type. Furthermore, our analysis indicates a correlation between the synchronization region boundaries of multi-weighted complex networks and those of their single-weighted counterparts capable of synchronization.

Problems and Development Strategies of Music Education in Primary and Secondary Schools Based on Network Information Technology

Music education has traditionally followed a one-size-fits-all approach, often failing to address the diverse needs and preferences of individual students. A recommender system for music education is a specialized software or algorithm that helps students, educators, or music enthusiasts discover and access relevant music content, learning materials, or educational resources. This paper introduces the Graph Theory-based Information Technology(GTIT) music learning system, a revolutionary platform designed to personalize music education. The GTIT system leverages advanced technology, data-driven recommendations, and social learning networks to enhance the musical journey for students. The proposed GTIT model uses the graph theory-based music education model for the estimation of the data features with information technology. The proposed GTIT model comprises the stacked recommender model for the classification of the appropriate features in the music education data. The data for the analysis of the proposed GTIT model is collected for the existing sources. With the extracted features the recommender system is designed for the classification and recommendation of the features related to the music education. The proposed model explores the key components of the GTIT system, including the assessment of students' musical preferences, skill levels, and learning patterns. These factors are analyzed to provide tailored music recommendations that keep students engaged and motivated. Additionally, the GTIT system employs graph theory to create connections among students, fostering collaboration and social learning. Simulation results reveals that students who engage with the learning system experience significant improvements in their musical skills and knowledge. The personalized recommendations and collaborative learning environment contribute to enhanced skill development and musical progress. Moreover, the sense of community established by the system encourages peer interaction and mentorship. This paper provides a comprehensive overview of the GTIT music education system's impact on students' learning experiences, skill development, and collaborative efforts. The analysis expressed the transformative potential of personalized recommendations and networked learning environments in music education. The GTIT system serves as a model for the future of music instruction, offering a promising approach for educators and institutions seeking to provide engaging and personalized music education.

Current Situation and Innovative Methods of Brass Music Teaching Based on Network Information Technology

The intersection of education and technology has witnessed a significant transformation in recent years, with a particular focus on optimizing remote learning experiences. Maintaining network resources for brass music education is crucial to ensure a seamless and effective learning experience. Hence, this paper proposed SDNcOIT (Software-Defined Networking cloud Optimal Information Technology) model in the context of brass music education, a discipline that demands real-time audio and video interactions. The proposed SDNcOIT model uses the cloud environment integrated SDN architecture for the evaluation of brass music education. The constructed network comprises Software-Defined Networking, cloud technology, and a rule-based model, the SDNcOIT system presents a compelling case for enhancing the delivery of music education to students worldwide. The SDNcOIT model implemented the optimization model within the SDN environment for the analysis of the evaluation of the user experience in the cloud environment. The proposed SDNcOIT model uses the gradient descent optimization model for the computation of the brass music in the cloud environment. Through the implementation of the optimization process within the SDNcOIT the features are optimized in the cloud with SDN architecture for the analysis of user experience. The findings from this study reveal substantial performance improvements over time, exemplified by reduced latency, increased throughput, minimized packet loss, and elevated user satisfaction. These improvements are pivotal for a discipline where the quality of audio and video content is paramount. The scalability of the cloud infrastructure ensures that the system can accommodate varying dataset sizes, adapting seamlessly to the dynamic requirements of a growing brass music program. Furthermore, the rule-based model's growing adherence to network behavior results in an exceptionally efficient and well-optimized network, aligning harmoniously with the educational objectives of brass music instruction.

Issues of Information and Ideological Security: Experience for Uzbekistan

The article discusses issues of theoretical and practical provision of information and ideological security. The experience of foreign countries has been studied, regulatory documents that ensure the information and ideological security of various states have been analyzed. In modern international relations, information wars, i.e. the principle of “defeating the enemy without fighting” is playing an increasingly important role. Leading countries of the world paid attention to information wars in the early 1960s. With the development of information technology and social networks, methods of waging information wars have expanded. Nowadays, information resources are more important than military ones. Thus, studying the experience and methods of foreign countries makes it possible to create effective countermeasures. It is very important to study the experience of foreign countries and identify the main directions of information attacks in relation to Uzbekistan.

From Darkness To Light: Mobile Phone Apps, The Empowering Tools For Tribal Students And Community Of Chhattisgarh

India is one of the largest tribal countries in the world after Africa. According to the 2011 census of India, the tribal population is 8.6% of the total population of the country. They are all over the country in states like Jharkhand, Odisha Madhya Pradesh, Gujrat, Rajasthan, Andhra Pradesh, West Bengal, and Chhattisgarh. Tribal society is characteristically distinguished from the mainstream by its culture, lifestyle, occupation, and alienation. With the increase in the development of today's information technology and wireless telecommunication networks, mobile devices have proliferated rapidly and become important tools for Terrible people. The use of M-Services and applications (apps) on mobile devices has become commonplace in people's everyday lives. Mobile applications are the gateway to the transfer of human knowledge. Learning is a continuous process and now the focus has completely shifted to eLearning. Thanks to mobile phones and various function-oriented apps, students can learn at their own pace and take their time understanding things because everything is just a click away. The integration of mobile devices into the teaching-learning process heralded a new era in education. It has made the teaching-learning process more interactive and hands-on. Quality education can be provided to anyone, anywhere. In general, implementing mobile phone applications and technologies can provide many benefits for a quality educational environment. The provision of education for all in India remains a distant dream, despite the significant government investment expended on it. This document covers the use of mobile phone applications, how to help the Indigenous community, especially students and the community of Chhattisgarh, and what steps the Chhattisgarh government has taken to uplift the educational system and empower tribal students and community with various mobile phone apps to bring the underprivileged from darkness to light.

Research on the Integration Development Strategy of E-commerce and Cross-border Logistics Supply Chain in the Internet Era

Abstract Based on industrial integration theory and system theory, this paper combines the development characteristics of e-commerce and cross-border logistics, utilizes the entropy weight method to assign weights to the indicators, and constructs a comprehensive evaluation model of e-commerce and cross-border logistics. Measure the coupling degree and integration degree of e-commerce and cross-border logistics. Using the multiple regression method to comprehensively study the technique of variable correlation and the important measures of the link between each variable, thus put forward optimization strategies for the logistics supply chain model, enterprise scale, and other aspects. The results of the analysis show that the proportion of cross-border e-commerce import transaction scale reached 22.7% in 2023, which increased 1.29 times compared with 2019, and the proportion of cross-border e-commerce import transactions continued to increase. In the analysis of the factors of the industrial integration of e-commerce and cross-border logistics, the influence of information network technology (0.108), the influence of platform resources, and the degree of influence of manufacturing technology occupy the top 3 places, which are the elements to focus on. Enterprise performance is significantly impacted by the economic level of the host province through regression analysis, with a coefficient of 0.1847 and significant at the 10% level. The evaluation value of the logistics alliance model is the best (comprehensive evaluation of 0.384), and it is most in line with the current development needs of cross-border e-commerce companies.

Reform and Innovation of Intelligent Education Network Information Technology in Employment Practice Mode

Reform and Innovation of Intelligent Education Network Information Technology in Employment Practice Mode

Dynamic Data Abstraction-Based Anomaly Detection for Industrial Control Systems

Industrial control systems (ICS) are critical networks directly linked to the value of core national and societal assets, yet they are increasingly becoming primary targets for numerous cyberattacks today. The ICS network, a fusion of operational technology (OT) and information technology (IT) networks, possesses a broad attack vector, and attacks targeting ICS often take the form of advanced persistent threats (APTs) exploiting zero-day vulnerabilities. However, most existing ICS security techniques have been adaptations of security technologies for IT networks, and security measures tailored to the characteristics of ICS data are currently insufficient. To mitigate cyber threats to ICS networks, this paper proposes an anomaly detection technique based on dynamic data abstraction. The proposed method abstracts ICS data collected in real time using a dynamic data abstraction technique based on noise reduction. The abstracted data are then used to optimize both the update rate and the detection accuracy of the anomaly detection model through model adaptation and incremental learning processes. The proposed approach updates the model by quickly reflecting data on new attack patterns and their distributions, effectively shortening the dwell time in response to APTs utilizing zero-day vulnerabilities. We demonstrate the attack response performance and detection accuracy of the proposed dynamic data abstraction-based anomaly detection technique through experiments using the SWaT dataset generated from a testbed of an actual ICS process. The experiments show that the proposed model achieves high accuracy with a small number of abstracted data while rapidly learning new attack pattern data in real-time without compromising accuracy. The proposed technique can effectively respond to cyberattacks targeting ICS by quickly learning and reflecting trends in attack patterns that exploit zero-day vulnerabilities.