Network Users Research Articles

Persistence, changes and robustness of nest webs along a latitudinal gradient in the Canadian boreal forest

IntroductionIn eastern Canada, the boreal forest is associated with an important latitudinal shift in forest composition and structure, which occurs in the transition between the mixed southern boreal forest and the coniferous northern boreal forest. Along this transition, upland mixedwood stands with large deciduous trees (important for cavity-dependent vertebrate species) are gradually replaced by forests with smaller conifer trees, primarily black spruce (Picea mariana). Concomitantly, the availability of lowland forests flooded by the American beaver (Castor canadensis), which can provide adequate conditions for tree-cavity users, is also decreasing.MethodsWe hypothesized that this latitudinal gradient would bring important changes in the functional diversity and network structure of vertebrate cavity-using communities. Along this latitudinal gradient we used a nest web approach to analyze the structure and robustness of networks of cavity users in upland forests and in lowland forests flooded by beavers.ResultsDespite their low availability in the northern forest region, we found that mixedwood stands persisted throughout the boreal forest in being the main drivers of nest webs network structure of upland forests whereas old black spruce stands contribution was low. In lowland forests, beaver ponds harbored nest webs with a rich and complex structure in both forest regions. Species removal simulations revealed that across our latitudinal gradient upland and lowland forest nest webs responded differently. In upland forests, the removal of trembling aspen and the Pileated Woodpecker (Dryocopus pileatus) caused the highest proportions of secondary extinctions, showing low robustness of nest webs given that these two species were highly connected to the other species. Contrastingly, nest webs in beaver ponds were more robust mainly because excavator species used a higher diversity of tree species despite the removal of the Northern Flicker (Colaptes auratus) which induced numerous secondary extinctions. The Pileated Woodpecker remained the pivotal species across the two forest regions in upland forests whereas the Northern Flicker became the main large cavity provider in beaver ponds across the latitudinal gradient.DiscussionWe discuss how mixedwood forests and beaver ponds, which are key habitat types for the cavity-using vertebrate community across our latitudinal gradient, should be maintained and protected in landscapes under industrial timber harvesting.

A Survey on Green Enablers: A Study on the Energy Efficiency of AI-Based 5G Networks.

In today's world, the significance of reducing energy consumption globally is increasing, making it imperative to prioritize energy efficiency in 5th-generation (5G) networks. However, it is crucial to ensure that these energy-saving measures do not compromise the Key Performance Indicators (KPIs), such as user experience, quality of service (QoS), or other important aspects of the network. Advanced wireless technologies have been integrated into 5G network designs at multiple network layers to address this difficulty. The integration of emerging technology trends, such as machine learning (ML), which is a subset of artificial intelligence (AI), and AI's rapid improvements have made the integration of these trends into 5G networks a significant topic of research. The primary objective of this survey is to analyze AI's integration into 5G networks for enhanced energy efficiency. By exploring this intersection between AI and 5G, we aim to identify potential strategies and techniques for optimizing energy consumption while maintaining the desired network performance and user experience.

Florasis: the emerging C-beauty brand

Learning outcomes This case study is designed to enable students to understand the reasons behind the launch of a beauty brand grounded on traditions and culture, understand the strategies adopted by Florasis to establish its presence in the C-beauty space and emerge successful, analyze the positioning of a C-beauty brand in a highly competitive beauty market, identify the issues and challenges faced by a C-beauty brand in its efforts to disrupt the C-beauty space and suggest strategies that Florasis can adopt to emerge as a market leader in the global beauty industry. Case overview/synopsis Set in 2021, the case study discusses about the emerging C-beauty brand Florasis innovative strategies to promote the brand. Florasis was founded in 2017 with a vision to become a century old national makeup brand of China. Florasis was successful in getting on board a story-telling experience that featured traditional Chinese culture, aesthetics and heritage. It sold cosmetic products with retro packaging, concepts derived from traditional Chinese style, promoting a sense of national pride and nostalgia. The case study highlights the innovative strategies Florasis adopted like influencer marketing through key opinion leaders and key opinion customers, celebrity endorsements, user co-creation programs, social content and network marketing, brand crossovers and collaborations, etc. In April 2021, Florasis became the No. 1 cosmetic company in China with a gross merchandise value of 218m yuan and further the total sales for second quarter of 2021 reached 830m yuan, endorsing its supremacy over other global and local beauty brands in China. However, with success came along a set of challenges. Some analysts pointed that the brand was slow in innovating its product line-up, it focused more on promotions and advertisements and the brand positioning with a single sales channel, the cost performance and quality of the products and excessive marketing campaigns targeting a niche segment. Going forward, what should Florasis do to conquer the global beauty space? Can Florasis aspire to become a digitally empowered global beauty brand? Has it got the momentum? Will its direct-to-consumer model and unprecedented marketing and promotion gimmicks, help it achieve the lead in the global beauty space? Complexity academic level This case study is suitable for students of the graduate and undergraduate programs in management. Supplementary materials Teaching notes are available for educators only. Subject code CSS 8: Marketing.

Analisis Performa Dalam Jaringan Ad-Hoc (Studi Kasus: PT. Trimitra Usaha Sejahtera)

An ad-hoc network is a topology that directly connects a wireless network between end users to other end users without using other connected devices. For example, a network between a computer and another computer uses wireless transmission media. The interface used is a wireless card or wireless USB or other end user network device. The implementation of this particular topology is very convenient and very easy because it does not require much configuration, just create an adhoc network as a station on one of the computers and then configure the IP address. This research aims to see the performance produced when there is a delay, and throughput to see stable results, this method uses NDLC (Network Devlopment Life Cycle) to analyze the resulting performance with QOS (Quality Of Service) parameters for calculating results according to TIPHON (Telecommunication) standards and internet protocol hormoneization over network) 5.5% to 100% good results with this stability (data sharing and communication carried out in the office with predetermined bandwidth management so that it can be used according to the required capacity experiences quite different changes so that the resulting performance is appropriate or not too far from the standard results with 3.5% and 5.5% which is much more stable for the resulting performance.

Visual presentation of the ethnic image of the Chuvash in cyberspace (using the example of social networks «VKontakte» and «Odnoklassniki»)

The article examines representational practices and mechanisms for the formation of visual images of Chuvash ethnicity in cyberspace. The purpose of this study is to determine the forms and methods of representing ethnicity, mechanisms and means of forming a visual ethnic image of the Chuvash in virtual space using the example of the social networks Odnoklassniki and VKontakte. The research is based on the study of visual content (posts, comments on them, discussions) in groups and on personal pages, materials from a mass survey of users of these social networks. The result of the study was a detailed description of the features of the formation and transmission of visual images of Chuvash ethnicity: visual sources, methods of visualization, functions and demand for visual content. The study showed that the visualization of the ethnic image of the Chuvash in cyberspace occurs through the symbolization of plots of ethnic history, elements of material and spiritual culture, through the use in the process of forming an ethnic image of fairy-tale, mythological, literary and real characters and pages of history, popular forms of representation of folk traditions – costume, national cuisine, holidays and rituals. Visual content is represented by photo and video materials, films, computer graphics, and animation. Visualization of ethnic content contributes to the stereotyping of the features of an ethnic image, acts as a means of constructing an ethnic image, a way of expressing belonging to an ethnic community, transmitting knowledge about the history of the traditional culture of the people, intra-ethnic communication, exchanging information between network users and their integration into the virtual space.

Unifying multimodal interactions for rumor diffusion prediction with global hypergraph modeling

A central issue in rumor surveillance and management is decoding the complex dynamics of rumor propagation, with an emphasis on predicting diffusion cascades. Recent studies focus on node embedding or the sequence of dissemination in a rumor cascade based on user social interaction, while neglecting interactions between users and rumors, as well as between various rumors. Online rumor diffusion is a complex system that encompasses two fundamental components: rumors and users. A comprehensive understanding of this system’s dynamics requires a global perspective. Consequently, it is necessary to develop models that capture the inherent multimodal interactions in rumor propagation. To tackle this challenge, we propose an HG2RLink framework of rumor diffusion prediction, which unifies multimodal interactions by global encoding. Specifically, our methodology begins with the establishment of a hypergraph structure. We then refine these interactions by leveraging a hypergraph neural network that aggregates users’ preferences for rumors. Two high-order graphs are generated to capture latent spatial interactions of rumors. By employing a learning approach for multimodal interactions, each rumor diffusion sequence is modeled with a long-range vision field of users in a deep neural network. Moreover, to validate the effectiveness of our method, we introduce a new dataset available for further exploration of new methods. Finally, experimental results show that HG2RLink outperforms other methods with improvements ranging from 0.8% to 7.6% for the Hits@k metric and from 0.4% to 2.6% for the MAP@k metric across four diverse datasets.

The 5G Era: A Comprehensive Review of Recent Advancements and Applications

The fifth generation is a promising and transformative leap in network capabilities, bringing out applications beyond traditional technologies. This review paper discusses the novel technologies shaping 5G, studying their impact and potential. We explore technological advancements like Network Function Virtualization (NFV) and Software- Defined Networking (SDN) for their role in creating flexible and efficient networks. Services like Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (uRLLC), and Massive Machine-Type Communication (mMTC) are also examined, along with their details, and role in shaping the benefits of 5G for various fields. This paper also investigates how 5G addresses the aspects like energy efficiency enhancements and network security enhancements brought about in 5G. We discuss the details of integration of Artificial Intelligence (AI) for network optimization, security, automation and user experience, along with its impact in various domains like healthcare, manufacturing, education and transportation. Further, the paper explores the incorporation of edge computing and its role in supporting real-time applications. Finally, we examine the growing importance of Non-Public Networks (NPNs) tailored for private use cases, another aspect that adds to the benefits and novelty of the fifth generation. This review provides a comprehensive overview of the key technological advancements driving the 5G revolution, highlighting their role in transforming communication infrastructures and other key use cases, supporting a new era of communication.

Navigating the inception stage in online peer production communities: a comparative study on community building activities, user roles and interaction dynamics

PurposeThis exploratory, descriptive study examines the factors that might influence the success or failure of online peer production communities during their inception stage. It focuses on community-building activities, the roles of users and the dynamics of user interaction, aiming to shed light on practices that could contribute to a community's success at the inception stage.Design/methodology/approachBy comparing two Stack Exchange Q&A communities with the same timelines but opposite outcomes during their beta testing phases, the research utilizes quantitative methods to categorize community activities, define user roles via k-means cluster analysis and examine interaction networks using social network analysis.FindingsOur findings suggest the successful Mathematics Q&A community exhibited several distinct practices during its inception, such as the utilization of both external and internal tools, the development of community-specific tutorials and the strategic use of flagging functions for moderation. Eight user roles were discerned, with roles like content editors, metadata curators and gatekeepers being particularly prominent in the successful community. Additionally, a more densely interconnected user network characterized by active participation was observed in the successful community.Originality/valueConcentrating on the inception stage of online communities, this study uncovers insights into the dynamics at play in the early life of peer production environments and provides empirical observations that may assist in shaping strategies for new online communities. It stands out by comparing communities within the same period to understand factors that may influence their early success.

Identifying Dominant Actors of Ferdy Sambo's Case Network on Social Media X/Twitter Using Social Network Analysis for Public Relations Strategy

The Indonesian National Police (Polri) has experienced ups and downs in building a positive image in interacting with the public. This decline in trust is caused by the emergence of various issues that show the low performance of the police. In the Ferdy Sambo case study, the performance and integrity of the police is at stake and the sensitivity of the police to meet public expectations. One solution to im-prove the image is through an effective public relations strategy. However, to develop it, a deep un-derstanding of the characteristics and interaction patterns between social media through social net-work analysis is required. This research aims to identify influential X/Twitter actors in the case study of Inspector General Ferdy Sambo by applying the centrality method in Social Network User Analysis. The results of centrality analysis on the network show a wide variety of centrality levels. The @Zaindamai account dominates with the highest Degree Centrality value of 0.426829, indicating the number of connections in the network. The main role in disseminating information is held by @Zaindamai with the highest Betweenness Centrality value of 0.325748, indicating its role in connect-ing various networks. @Rizkynu46127931 stands out in Closeness Centrality with a high value of 0.497791, signifying quick and efficient access to all parts of the social network. In addition, @Rizkynu46127931 has significant influence in the network based on the highest Eigenvector Cen-trality of 0.245625. This centrality value forms the basis for formulating a more focused public relations strategy, improving the efficiency of communication with stakeholders, and designing a more concrete public relations plan.

Prediction of influential nodes in social networks based on local communities and users’ reaction information

Identifying influential nodes is one of the basic issues in managing large social networks. Identifying influence nodes in social networks and other networks, including transportation, can be effective in applications such as identifying the sources of spreading rumors, making advertisements more effective, predicting traffic, predicting diseases, etc. Therefore, it will be important to identify these people and nodes in social networks from different aspects. In this article, a new method is presented to identify influential nodes in the social network. The proposed method utilizes the combination of users’ social characteristics and their reaction information to identify influential users. Since the identification of these users in the large social network is a complex process and requires high processing power and time, clustering and identifying communities have been used in the proposed method to reduce the complexity of the problem. In the proposed method, the structure of the social network is divided into its constituent communities and thus the problem of identifying influential nodes (in the entire network) turns into several problems of identifying an influential node (in each community). The suggested method for predicting the nodes first predicts the links that may be created in the future and then identifies the influential nodes based on an iterative strategy. The proposed algorithm uses the criteria of centrality and influence domain to identify this category of users and performs the identification process both at the community and network levels. The efficiency of the method has been evaluated using real databases and the results have been compared with previous works. The results demonstrate that the proposed method provides a more suitable performance in detecting the influential nodes and is superior in terms of accuracy, recall and processing time.

Maximizing energy-efficiency for RIS-UAV assisted mobile vehicles in cognitive networks

As an essential technology in the sixth generation of wireless communication, the reconfigurable intelligent surface (RIS) offers transformative solutions for the evolution of intelligent transportation. In the secondary network, RIS is equipped on an unmanned aerial vehicle (UAV) to establish a communication link between secondary base station (SBS) and secondary mobile vehicles (SMVs). At the same time, the communication within the secondary network must not interfere with the primary users (PUs) in the primary network. To achieve the optimal energy efficiency, we need to optimize the RIS passive beamforming, SMVs communication scheduling, SBS radiation power allocation and RIS-UAV trajectory. Since the original problem is difficult to solve, we use an alternating iteration framework to decompose the original problem into four subproblems and solve them with successive convex approximations (SCA). We have developed the CEEM scheme to compare it with benchmark schemes and demonstrate its superior performance, achieving up to a 43.48% improvement. In addition, RIS improves the communication quality by up to 57.53% in the simulation results, which have verified the correctness and effectiveness of the algorithm proposed in this paper.

Advancing IoT security with flame: A hybrid approach combining fuzzy logic and artificial lizard search optimization

The increasing usage of Internet of Things (IoT) devices has created a need for secure and efficient solutions to protect sensitive data from unauthorized access. However, the complicated and massive structure of IoT systems poses various security risks and challenges, especially in dynamic scenarios with high signaling overhead caused by subscriber mobility. So, in this paper, a Fuzzy-based Lightweight Authentication and Management of Encryption approach called ‘FLAME’ is proposed to solve the decentralized lightweight group key management problem by measuring the degree of security using fuzzy logic (FL) based on various factors like device and user behavior, network conditions, and resource availability. For effective key-based authentication, adopted an Artificial Lizard Search Optimization (ALSO) based RSA (Rivest, Shamir, Adleman) algorithm that generates private and public keys based on security evaluation outcome. The publishers and subscribers obtain encryption keys from the group key manager based on their security level, and dissemination is optimized by the ALSO algorithm. By leveraging the FL and ALSO based RSA algorithm, the system offers secure communication with limited utilization and protects confidential data in IoT environments. According to the analysis, results signify that the FLAME approach has a faster key generation, dissemination, and revocation time compared to existing approaches, along with reduced overhead during key management operations, and increased attack detection capacity of 98.7 %.

Accelerator: an intent-based intelligent resource-slicing scheme for SFC-based 6G application execution over SDN- and NFV-empowered zero-touch network

Zero-touch networks (ZTNs) can provide autonomous network solutions by integrating software-based solutions for various emerging 5G and 6G applications. The current literature does not provide any suitable end-to-end network management and resource-slicing solutions for service function chaining (SFC) and user intent–based (time and cost preference) 6G/non-6G application execution over ZTNs enabled by mobile edge computing, network function virtualization, and software-defined networking. To tackle these challenges, this work initiates an end-to-end network management and user intent–aware intelligent network resource–slicing scheme for SFC-based 6G/non-6G application execution over ZTNs, taking into account various virtual and physical resources, task workloads, service requirements, and task numbers. The results depicted that at least 25.27% average task implementation delay gain, 6.15% energy gain, and 11.52% service monetary gain are realized in the proposed scheme over the compared schemes.

Boredom Proneness and Social Media Usage of College Students

This descriptive-predictive study aimed to determine the correlation between the social media usage of college students and their boredom proneness in a University in Davao region. A total of 350 participants were chosen to participate in this study using simple random sampling techniques. A 22-item multidimensional state boredom questionnaire and 21-item social networking usage served as an instrument wherein the Jamovi was used for data analysis. The mean and standard deviation were utilized to test the level of both variables, and results showed that the level of social media usage and boredom proneness among students is high. The Shapiro-Wilk test was utilized to test the normality; it revealed that the data is not normally distributed. The Spearman rho correlation coefficient test revealed a significant relationship between social media usage and students’ boredom proneness, with a p-value of .001, indicating a strong positive correlation. Meanwhile, the result of Linear Regression showed 71% of the variance is explained by the three significant predictors contributing to the students’ social media usage which are Time Perception, Inattention and Disengagement. The study’s findings indicate that increased use of social media is associated with higher levels of boredom. In this case, school administration, local health authorities, and school counseling and guidance offices should enhance the implementation of policies regarding the responsible use of social media, integrate the findings into various programs, and increase public awareness of the effects of excessive social media use on people’s mental health and academic performance. Lastly, researchers should consider extending this topic with a quantitative approach and investigate the possible mediating factors between boredom and social media usage.

How to Keep Millennial Social Network Users Active? Millennial Social Network Usage on Mobile Devices Across Cultures

ABSTRACT Despite the continuous upsurge in the use of social networks by consumers and marketers, the empirical research investigating their economic value in different cultures still lags. This study develops an extended Theory of Reasoned Action (TRA) model that includes trust in the social network community, perceived enjoyment, and Hofstede’s cultural factors (masculinity/femininity, power distance, individualism/collectivism, and long-term objectives). Using survey methodology across three countries (Kuwait, Tunisia, and Italy), data is analyzed using a structural equation model. The results of this study empirically extended the TRA model by including the significant impact of perceived enjoyment and attitude on the use of Facebook across different cultures. Trust in the social network community and its impact on the use of Facebook was only significant in Arab culture and not European culture. Hofstede’s factors moderate the relationship between subjective norms and usage of Facebook, and their impact is varies across different cultures. The study highlights the differential impact of cultures on social network use and the importance of understanding the type of culture the business is targeting. Commercial organizations should guide their marketing efforts to manage their marketing differently across cultures to enhance their consumer engagement.

Design of Fostered Power Terahertz VLSI Testing Using Deep Neural Network and Embrace User Intent Optimization

Design of Fostered Power Terahertz VLSI Testing Using Deep Neural Network and Embrace User Intent Optimization

DEVELOPING GAME THEORY-BASED METHODS FOR MODELING INFORMATION CONFRONTATION IN SOCIAL NETWORKS

This paper explores the essential dynamics of social networks, specifically examining the phenomenon of information confrontation among users. The goal of the research is the development of a novel simulation methodology that integrates game-theoretic principles with probabilistic techniques to provide a robust model for these interactions. The theoretical framework of the study is founded on the conceptualization of user conflicts as a strategic game between two players. The primary objective for each player in this game is to exert influence and control over as many nodes within the network as possible. To capture the essence of these strategic interactions, we have introduced an innovative algorithm that facilitates dynamic strategy adaptation. This algorithm is pivotal in allowing players to modify their decision-making processes in real-time, based on the continually changing conditions of the network. For practical implementation and validation of the methodology, we used the Facebook Researcher open dataset, with a particular focus on its Kazakhstani segment. This dataset provides a rich source of empirical data, reflecting diverse user interactions and network configurations, which are essential for testing the model. This approach stands out by offering significant improvements in computational efficiency and resource management. By dynamically tracking and updating the network's status, the proposed method reduces the computational resources required, thereby enhancing the scalability of the simulation. In comparing our methodology with other existing models in the field, it becomes evident that it not only matches but in several respects surpasses these methodologies in terms of flexibility. This study makes substantial contributions to the field of social network analysis by providing a sophisticated tool that can be effectively employed to navigate and analyze the complexities of information confrontation in digital social spaces.

YouTube as an information source on politics and current affairs: Supply- and demand-side perspectives

This study examines YouTube channels reporting on French news and current affairs. After identifying the relevant channels, the study identifies the top 10 viewed videos of each channel during the year 2023. Manual annotation has been conducted for content, presentation format, engagement style, and position regarding the government and mainstream media. Each channel has also been coded for its business and communication strategies. Correspondence analysis is used to extract the most emblematic news content and presentation styles, while hierarchical clustering is used to categorize the channels into clusters of different genres. From the demand-side perspective, the study also examines the network of users commenting on the top 10 videos to assess the audience overlap between the identified clusters. . Five clusters of alternative news channels are identified, differing in presentation, style, and partisanship. Network analysis showed audience overlaps, notably between satire, alternative, and re-information clusters. Most channels earn revenue through branding and cross-media activities, occasionally supporting each other. These clusters act as mediators between politics, entertainment, and re-information, serving diverse audiences. Uncertainty remains regarding future dynamics and competition with established news outlets.

Development a data mining techniques to detect and prevention cyber attack for cybersecurity

The identification and forecasting of cyber-attacks is crucial process. In this article, we describe a paradigm for cyber security that makes use of data mining to forecast cyberattacks and identify appropriate countermeasures. The framework’s two primary elements are the surveillance and prevention of cyberattacks. The system constructs a predictive model to forecast future cyberattacks after first extracting appropriate timing with cyberattacks from previous data that used a decision tree based on the J48 algorithm. A variety of cyber-attacks, involving DDoS, port scans, and SQL Injection, are described in the datasets. The suggested framework effectively recognizes cyberattacks and gives patterns associated with them. The suggested predictive algorithm for identifying cyberattacks has a 99% average prediction performance. The predictions model’s test outcomes demonstrate how effective it is at spotting potential cyberattacks in the future. Moreover, solutions like malware detection and monitoring were provided using data mining. Given the state of computer networks today Users of computer networks ought to take security very seriously. In this article, implications of data mining for risk evaluation and identification were highlighted, along with a unique method for quickly and accurately detecting malware.

ANALYSIS OF ROUTING PROTOCOLS CHARACTERISTICS IN AD-HOC NETWORK

Background. Wireless ad-hoc networks are becoming increasingly prevalence in remote areas, in extreme environments, even in military operations, and in scenarios where setting up infrastructure networks is not possible. Research of ad-hoc routing protocols problems allows improving the efficiency of their operation in conditions of high variability in packet loss or instability of network operation when the speed of users changes. Objective. The purpose of the paper is analysis of packet loss dependency from a network operation time, study of a user speed influence on a network efficiency, and research of network operation efficiency with different routing protocols. Methods. The method of routing protocols efficiency evaluation is the simulation of their operation in an ad-hoc network on a test data set and research of a network indicators dependency in time under different loads and changing mobility of users. Results. The conducted research demonstrated that user’s mobility at different speeds significantly affects the network operation as a whole. The instability of users' positions leads to a significant increase in route search time and packet transmission time. Among researched GPSR, DSDV, and AODV protocols, the latter proved to be the best because it has the lowest percentage of data loss and the lowest average time of message send and receive operations. Conclusions. The work is dedicated to the actual problem of developing and setting parameters of ad-hoc network. Received research results indicate the need to choose the optimal routing protocol depending on specific application conditions, such as user movement speed and network stability. The proposed solutions can be the first stage of complex processing of packets in the mobile network and justify the choice of AODV protocol as a basis for further improvement.