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Blockchain has gained momentum as a disruptive technology in supply chain management against its introduction as a finance-related instrument. Nevertheless, the developing academic understanding and the limited practical implications lead to insufficient insights into the use of blockchain technology, particularly in the supply chain finance (SCF) domain. Thus, the expected potential of blockchain technology remains underexplored. Accordingly, this study explicates this situation by examining the extant literature findings and web-based big data that can provide evidence about the real needs in supply chains, and investigating how blockchain emerges as a disruptive SCF-oriented technology. The study employs a web analytics method, Search Engine Results Page (SERP) analysis which considers the trends in blockchain technology use and the interactions between blockchain, supply chain and finance appearing in Google searches. The SERP method examined real-time clicks, web traffics and most commonly asked questions about blockchain. The SERP findings revealed that the interest in blockchain technology neither focused on finance nor data privacy as emphasised in the literature but mainly on the benefits of increasing digitalisation and efficiency in supply chains. The results offered practical implications for capturing recent blockchain- and supply chain-related trends and designing more digital and efficient supply chains. [ABSTRACT FROM AUTHOR]

Is there latent demand to support Black-owned businesses? We explore this question by analyzing a new feature that made it easier to identify Black-owned restaurants on an online platform. We find that labeling restaurants as minority-owned increased customer engagement and firm performance, as measured by online traffic, calls, orders, and in-person visits. These effects were more pronounced in areas characterized by greater support for the Democratic Party and lower implicit bias against racial minorities. Labeled restaurants also see an increase in the fraction of reviews that are written by White customers. (JEL D22, J15, L25, L83, L86) [ABSTRACT FROM AUTHOR]

Exploitation and human trafficking in sex markets tend to include both online and offline spaces. Understanding the scale, complexity and geography of networks is important in policing human trafficking and online escort adverts are often used to identify organised crime in this context. This article aims to make a methodological contribution to how data relating to online networks in the sex market can be collected and analysed. Through the application of web scraping, social network analysis and principal component analysis, the digital traces of 15,016 online networks operating on an adult services website were analysed in relation to their complexity and geographical patterning. The findings suggest that structural and geographical characteristics are useful for understanding the heterogeneity of online networks. Analysing networks, as opposed to assessing escort adverts, offers a more robust approach to understanding the sex market, which is more sensitive to the continuum of experiences encapsulated therein. [ABSTRACT FROM AUTHOR]

Mass communication in the We Media era has redefined China's rural virtual community and transformed the representation of the female body in the countryside. Using research methods such as literature review, case studies, and content analysis, this study adopts perspectives from the sociology of the body and feminist media studies to explore how female microcelebrities and social media influencers (SMIs) use their bodies to present authentic rural life. The research highlights their significant roles in marketing the countryside, fostering idyllic imaginations for mass audiences, and supporting rural revitalization. Focusing on prominent examples such as Li Ziqi, we argue that female microcelebrities endorsed by both the state and netizens contribute to rural marketing by promoting web traffic, agricultural products, and stronger idyllic imagery, thereby advancing China's rural revitalization efforts. [ABSTRACT FROM AUTHOR]

Count time series frequently exhibit multiple features such as zero inflation, zero deflation, overdispersion, and underdispersion. In practice, heavy-tailedness and zero truncation are also suffered but are less investigated. To better handle count time series with the above statistical characteristics, we introduce a simple INAR(1) process with zero-distorted generalized geometric innovations, which has the advantage of being capable of capturing all the above features. Probabilistic and statistical properties of the process are explored and estimators of the model parameters are derived by the Yule-Walker, conditional least squares, conditional maximum likelihood, and Bayesian methods. Finally, the new model is also employed to analyze the following three real data examples. Firstly, we consider the website traffic counts, which implies that the new model can take into account count time series with overdispersion, zero inflation, and heavy-tailedness. Secondly, we demonstrate the usefulness of the new model in describing underdispersed and zero-deflated count time series via an application to the number of examination room of the emergency department of a children's hospital. Thirdly, we employ the new model to the monthly shifted counts of robbery, which indicates the new model can better handle zero-truncated count times series. [ABSTRACT FROM AUTHOR]

In recent years, the growing number of vehicles on the road have exacerbated issues related to safety and traffic congestion. However, the advent of the Internet of Vehicles (IoV) holds the potential to transform mobility, enhance traffic management and safety, and create smarter, more interconnected road networks. This paper addresses key road safety concerns, focusing on driver condition detection, vehicle monitoring, and traffic and road management. Specifically, various models proposed in the literature for monitoring the driver's health and detecting anomalies, drowsiness, and impairment due to alcohol consumption are illustrated. The paper describes vehicle condition monitoring architectures, including diagnostic solutions for identifying anomalies, malfunctions, and instability while driving on slippery or wet roads. It also covers systems for classifying driving style, as well as tire and emissions monitoring. Moreover, the paper provides a detailed overview of the proposed traffic monitoring and management solutions, along with systems for monitoring road and environmental conditions, including the sensors used and the Machine Learning (ML) algorithms implemented. Finally, this review also presents an overview of innovative commercial solutions, illustrating advanced devices for driver monitoring, vehicle condition assessment, and traffic and road management. [ABSTRACT FROM AUTHOR]

There is a widespread perception that China's digital censorship distances its people from the global internet, and the Chinese Communist Party, through state-controlled media, is the main gatekeeper of information about foreign affairs. Our analysis of narratives about the Russo-Ukrainian War circulating on the Chinese social media platform Weibo challenges this view. Comparing narratives on Weibo with 8.26 million unique news articles from 2,500 of some of the most trafficked websites in China, Russia, Ukraine, and the United States (totaling 10,000 sites), we find that Russian news websites published more articles matching narratives found on Weibo than news websites from China, Ukraine, or the United States. Similarly, a plurality of Weibo narratives were most associated with narratives found on Russian news websites while less than ten percent were most associated with narratives from Chinese news sites. Narratives later appearing on Weibo were more likely to first appear on Russian rather than Chinese, Ukrainian, or US news websites, and Russian websites were highly influential for narratives appearing on Weibo. Altogether, these results show that Chinese state media was not the main gatekeeper of information about Russia's invasion of Ukraine for Weibo users. [ABSTRACT FROM AUTHOR]

In rural e-commerce live streaming scenarios, network traffic analysis has an important impact on the consumer viewing experience. This paper builds a theoretical model of how viewers' viewing experiences affect their buying habits in agricultural e-commerce live streaming scenarios. It also looks at the role of network traffic analysis and the live streaming marketing model as moderators. Finally, it uses sample data from merchant accounts on rural e-commerce platforms to do real-world analyses. Consumers' viewing experience significantly affects their purchasing behavior (regression coefficient = 1.832, p<0.05). Web traffic analysis mediates between consumer viewing experience and purchase behavior. The live streaming marketing model moderates the positive relationship between consumer viewing experience and web traffic analysis. Because of this, this paper suggests that live marketing of agricultural products on rural e-commerce platforms based on network traffic analysis can broaden the marketing model by making basic network facilities better, using the power of local universities, working together with farmers' chambers of commerce and leading businesses, and creating unique brands. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Erciyes Communication / Erciyes İletişim Dergisi is the property of Erciyes University, Faculty of Communication / Erciyes Universitesi Iletism Fakultesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Information-Centric Networking (ICN) typically utilizes DRAM (Dynamic Random Access Memory) to build in-network cache components due to its high data transfer rate and low latency. However, DRAM faces significant limitations in terms of cost and capacity, making it challenging to meet the growing demands for cache scalability required by increasing Internet traffic. Combining high-speed but expensive memory (e.g., DRAM) with large-capacity, low-cost storage (e.g., SSD) to construct a hierarchical cache architecture has emerged as an effective solution to this problem. However, how to perform efficient cache management in such architectures to realize the expected cache performance remains challenging. This paper proposes a cache management scheme for hierarchical cache architectures in ICN, which introduces a differentiated replica replacement policy to accommodate the varying request access patterns at different cache layers, thereby enhancing overall cache performance. Additionally, a probabilistic insertion-based SSD cache admission filtering mechanism is designed to control the SSD write load, addressing the issue of balancing SSD lifespan and space utilization. Extensive simulation results demonstrate that the proposed scheme exhibits superior cache performance and lower SSD write load under various workloads and replica placement strategies, highlighting its broad applicability to different application scenarios. Additionally, it maintains stable performance improvements across different cache capacity settings, further reflecting its good scalability. [ABSTRACT FROM AUTHOR]

In this article, we explore the experiences of commercial content creators who have their content (videos, photos, and images) misused. This article reports from a mixed-methods study consisting of 16 interviews with content creators; nine interviews with practitioners who support sex workers and interviews with seven adult service website (ASW) operators. These qualitative data are supported by 221 responses to a survey from the content creators' community. We describe how content creators experience blackmail, threats of exposure and recording without knowledge, stalking, harassment, doxing, 'deepfakes' and impersonation. We conclude that the online sex work environment may not be as safe as previous research has demonstrated, and that commercial content creators are often ignored by governance and platforms following sex workers' complaints regarding their content being misused. We reflect on the forthcoming UK Online Safety Bill as compared to the US Stop Enabling Sex Traffickers Act/Fight Online Sex Trafficking Act (SESTA/FOSTA) law which have seen implications across the globe for digital sex workers. We offer solutions for ASWs to act more responsibly. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic significantly transformed consumer habits and the landscape of e‐commerce. This research assesses the performance of various e‐commerce platforms during this unprecedented period by employing the extended technique for order of preference similarity to ideal solution (TOPSIS) methodology. Data pertinent to e‐commerce websites during the pandemic was sourced from various channels, including web analytics, marketing reports, and customer feedback surveys. Key performance indicators (KPIs) were established, focusing on website traffic, conversion rates, marketing effectiveness, customer satisfaction, and operational efficiency. These indicators were then normalized and weighted according to their significance. This study investigates the utility of the extended fuzzy TOPSIS method in selecting e‐commerce platforms, pinpointing essential criteria that affect consumer preferences and satisfaction. The findings provide critical insights into how website characteristics relate to consumer behavior, thereby assisting online retailers in improving their digital strategies. The extended TOPSIS method was utilized to determine how closely each website aligns with the ideal and anti‐ideal solutions, resulting in a ranking based on this proximity. To ensure the robustness of the extended TOPSIS approach, a sensitivity analysis was performed by adjusting the weights assigned to the criteria and monitoring the resulting shifts in website rankings. This analysis revealed the top‐performing and least‐performing e‐commerce websites during the pandemic, as determined by the extended TOPSIS rankings. The sensitivity analysis indicated that the rankings produced by extended TOPSIS remained relatively stable despite changes in criteria weights, underscoring its reliability and applicability. This study highlights the effectiveness of the extended TOPSIS method in evaluating e‐commerce performance amid the unique challenges posed by COVID‐19. By incorporating a range of criteria and conducting sensitivity analyses, the research establishes extended TOPSIS as a robust and valuable framework for identifying high‐performing e‐commerce websites during periods of disruption. The originality of this paper lies in its ability to tackle significant uncertainties in e‐commerce selection and to present a practical case study utilizing extended TOPSIS for the first time. [ABSTRACT FROM AUTHOR]

Puff Bar, a disposable electronic nicotine delivery system (ENDS), was the ENDS brand most commonly used by U.S. youth in 2021. We explored whether Puff Bar's rise in marketplace prominence was detectable through advertising, retail sales, social media, and web traffic data sources. We retrospectively documented potential signals of interest in and uptake of Puff Bar in the United States using metrics based on advertising (Numerator and Comperemedia), retail sales (NielsenIQ), social media (Twitter, via Sprinklr), and web traffic (Similarweb) data from January 2019 to June 2022. We selected metrics based on (1) data availability, (2) potential to graph metric longitudinally, and (3) variability in metric. We graphed metrics and assessed data patterns compared to data for Vuse, a comparator product, and in the context of regulatory events significant to Puff Bar. The number of Twitter posts that contained a Puff Bar term (social media), Puff Bar product sales measured in dollars (sales), and the number of visits to the Puff Bar website (web traffic) exhibited potential for surveilling Puff Bar due to ease of calculation, comprehensibility, and responsiveness to events. Advertising tracked through Numerator and Comperemedia did not appear to capture marketing from Puff Bar's manufacturer or drive change in marketplace prominence. This study demonstrates how quantitative changes in metrics developed using advertising, retail sales, social media, and web traffic data sources detected changes in Puff Bar's marketplace prominence. We conclude that low-effort, scalable, rapid signal detection capabilities can be an important part of a multi-component tobacco surveillance program. [ABSTRACT FROM AUTHOR]

Vehicular Networks (VN) utilizing Software Defined Networking (SDN) have garnered significant attention recently, paralleling the advancements in wireless networks. VN are deployed to optimize traffic flow, enhance the driving experience, and ensure road safety. However, VN are vulnerable to Distributed Denial of Service (DDoS) attacks, posing severe threats in the contemporary Internet landscape. With the surge in Internet traffic, this study proposes novel methodologies for effectively detecting DDoS attacks within Software-Defined Vehicular Networks (SDVN), wherein attackers commandeer compromised nodes to monopolize network resources, disrupting communication among vehicles and between vehicles and infrastructure. The proposed methodology aims to: (i) analyze statistical flow and compute entropy, and (ii) implement Machine Learning (ML) algorithms within SDN Intrusion Detection Systems for Internet of Things (IoT) environments. Additionally, the approach distinguishes between reconnaissance, Denial of Service (DoS), and DDoS traffic by addressing the challenges of imbalanced and overfitting dataset traces. One of the significant challenges in this integration is managing the computational load and ensuring real-time performance. The ML models, especially complex ones like Random Forest, require substantial processing power, which necessitates efficient data handling and possibly leveraging edge computing resources to reduce latency. Ensuring scalability and maintaining high detection accuracy as network traffic grows and evolves is another critical challenge. By leveraging a minimal subset of features from a given dataset, a comparative study is conducted to determine the optimal sample size for maximizing model accuracy. Further, the study evaluates the impact of various dataset attributes on performance thresholds. The K-nearest Neighbor, Random Forest, and Logistic Regression supervised ML classifiers are assessed using the BoT-IoT dataset. The results indicate that the Random Forest classifier achieves superior performance metrics, with Precision, F1-score, Accuracy, and Recall rates of 92%, 92%, 91%, and 90%, respectively, over five iterations. [ABSTRACT FROM AUTHOR]

Scene risk identification is essential for the traffic safety of Internet of Vehicles. However, the performance of existing risk identification approaches is heavily limited by the imbalanced historical data and the poor model interpretability. Meanwhile, the large processing delay and the potential privacy leakage threat also restrict their application. In this paper, a novel risk identification model is proposed that leverages the synthetic minority over‐sampling technique nearest neighbor (SMOTEENN) method to balance between high‐risk and low‐risk data. The risk identification model has fine interpretability by using recursive feature elimination cross validation (RFECV) with the Shapley additive explanation (SHAP) to analyze the importance of different features, and further elaborately design the Focal Loss function to tackle the disparity between the difficult and easy sample learning. The proposed interpretability scene risk identification framework, named iScene, is built on the infrastructure of 6G space‐air‐ground integrated networks (SAGINs) with blockchain assistance. The model updata efficiency and privacy preservation are effectively enhanced. An elastic computing offloading algorithm is applied to minimize the system overhead under the hierarchical edge service architecture. The experimental evaluation is carried out to verify the effectiveness of the proposed risk identification framework. The results indicate that the G‐Mean value is increased by 23.4%, while the task average response delay is reduced by 21.2%, compared to that in the traditional risk identification approaches with local computing services. [ABSTRACT FROM AUTHOR]

In the hotel booking market, high click-through rates are essential for online travel agencies (OTAs) to earn commissions. Given the dominance of mobile devices in web traffic, analyzing the mobile click-through decision-making process plays a vital role in search engine optimization. This study proposes a sequential framework that leverages Bayesian inference to model individual users' click-through behaviors using user digital footprints, which encompass sequences of search, browse, compare, and click-through actions. This framework extracts three categories of information based on the degrees of dynamism in the hotel search process, ranging from less dynamic to highly dynamic levels: static hotel attributes, information cues in the search results, and temporal characteristics of user behaviors. Extensive experiments on a global OTA mobile clickstream dataset with over 600,000 observations reveal the substantial superiority of the proposed framework over the baseline models like probit regression and Naive Bayes. Notably, temporal characteristics emerge as the most important category. Drawing on our model, we delve into the interpretability of these three information categories. Additionally, we compare their varying impacts across different devices. Beyond these findings, this study offers valuable managerial implications for mobile OTA search engine marketing and optimization. [ABSTRACT FROM AUTHOR]

The movement control order (MCO) has led to a surge in internet usage among children, becoming the new normal. Most countries heavily rely on online platforms for education, granting children more freedom to explore the internet using electronic devices. Yet, it is challenging for parents to monitor their online activities. Children may stumble upon unsuitable content such as pornography or get redirected to harmful websites through pop-up advertising. To address these concerns, this study proposes PiWall, a home traffic controller. PiWall aims to block undesirable websites and filter out advertisements to help parents manage their home network. Testing shows that PiWall significantly improves the internet browsing experience by reducing website loading times and network traffic, hence offering protection against inappropriate content and malicious attacks. Implementing PiWall as a home traffic controller is a viable alternative to traditional parental controls in creating a safer internet environment for children. [ABSTRACT FROM AUTHOR]

Recent, rapid arthropod declines have been tied to human activity. Yet, the propensity for human‐produced noise to disturb the substrate‐borne (vibratory) channel remains virtually untested despite arthropods' ubiquitous use of vibratory information. Characterizing the vibratory landscape at biologically relevant scales is an essential initial step. We aimed to measure vibratory noise across space and time to assess its potential overlap with the urban–rural range and season of a funnel‐weaving spider, Agelenopsis pennsylvanica (C.L. Koch) (Araneae: Agelenidae), a common urban arthropod. We assessed 24‐h ambient vibrations (hereafter vibratory "noise") at 23 sites across urban–rural Lincoln, Nebraska (NE, USA), during the adult season of A. pennsylvanica (August–October). Vibratory noise (amplitude, Leq) varied by ~15 dB across sites overlapping with frequencies within A. pennsylvanica's sensitivity (20–1000 Hz). Urban vibratory noise was positively correlated with the principal component containing estimates of traffic‐induced vibrations (e.g., traffic volume, road proximity, and impervious cover), whereas rural noise levels correlated with probable harvest times. Our findings indicate spatial variation in noise in urban areas and seasonal noise variability in rural areas. We also tested how A. pennsylvanica—collected from four distinct noise survey sites—use their vibratory microhabitat. We assessed daily spider position and dry silk mass within a microcosm that played loud and quiet vibrations (white noise differing by ~15 dB) in separate but connected chambers. Age affected the chamber choice (hereafter "microhabitat use") of spiders collected from the loudest site, as younger adults associated with and used more silk in the loud chamber, but this effect decreased with age. As our data demonstrate that vibratory noise varies with A. pennsylvanica's microhabitat and season with a high potential for impacting behavior, we hypothesize that this environmental noise likely impacts other arthropods as well. [ABSTRACT FROM AUTHOR]

This study scrutinises web analytics, specifically Google Analytics, in its capacity to support content creators in understanding user behaviour and improving digital communication. Google, with its different versions of web analytics software, is dominating the market in collecting and analysing web traffic statistics, and has thus become a central part of how digital environments can know their users. We use the concepts of transcoding culture and social imaginaries to show the importance of scrutinising the value propositions ingrained within the web analytics dashboard. Despite the excitement and fear related to Google knowing too much about the people behind the screens, the analysis shows that Google Analytics 4 strug-gles to provide a comprehensive understanding of audi-ences. Focusing on the four value propositions – overview, immediacy, control, and knowing the audience – we argue that Google Analytics overstates its understanding of audiences. It is in the interest of Google to hyperbolise knowledge about the audience, even with potential privacy concerns, as it supports the social imaginary of an all-knowing digital platform. Thus, a critical and nuanced approach to web analytics, considering their impact on society and privacy concerns, is needed to balance the discussions. [ABSTRACT FROM AUTHOR]

The pursuit of effective models with high detection accuracy has sparked great interest in anomaly detection of internet traffic. The issue still lies in creating a trustworthy and effective anomaly detection system that can handle massive data volumes and patterns that change in real-time. The detection techniques used, especially the feature selection methods and machine learning algorithms, are crucial to the design of such a system. The fundamental difficulty in feature selection is selecting a smaller subset of features that are more related to the class but are less numerous. To reduce the dimensionality of the dataset, this research offered a multi-feature selection technique (MFST) using four filter techniques: fast correlationbased filter, significance feature evaluator, chi-square, and gain ratio. Each technique's output vector is put via ranker and Borda voting filters. The feature with the highest number of votes and rank values will be selected from the dataset. The performance of the given MFST framework was the best when compared to the four strategies listed above functioning alone; three different classifiers were employed to test the accuracy. C4.5, nave Bayes, and support vector machine. The experiment outcomes employed ten datasets of different sizes with 10,000-300,000 instances. Only 8 out of 248 characteristics were chosen, with classifiers percentages averaging 65%, 93.8%, and 95.5%. [ABSTRACT FROM AUTHOR]

This paper aims to propose a novel call for help traffic (SOS) and study its impact over Machine-to-Machine (M2M) and Human-to-Human (H2H) traffic in Internet of Things environments, specifically during disaster events. During such events (e.g., the spread COVID-19), SOS traffic, with its predicted exponential increase, will significantly influence all mobile networks. SOS traffic tends to cause many congestion overload problems that significantly affect the performance of M2M and H2H traffic. In our project, we developed a new Continuous-Time Markov Chain (CTMC) model to analyze and measure radio access performance in terms of massive SOS traffic that influences M2M and H2H traffic. Afterwards, we validate the proposed CTMC model through extensive Monte Carlo simulations. By analyzing the traffic during an emergency case, we can spot a huge impact over the three traffic types of M2M, H2H and SOS traffic. To solve the congestion problems while keeping the SOS traffic without any influence, we propose to grant the SOS traffic the highest priority over the M2M and H2H traffic. However, by implementing this solution in different proposed scenarios, the system becomes able to serve all SOS requests, while only 20% of M2M and H2H traffic could be served in the worst-case scenario. Consequently, we can alleviate the expected shortage of SOS requests during critical events, which might save many humans and rescue them from being isolated. [ABSTRACT FROM AUTHOR]

This study examines the extent to which incorporating social media data enhances the predictive accuracy of models forecasting international students' arrivals. Private social media data collected from a public university, along with collected web traffic data and Google Trend data, were used in the forecasting models. Initially, a correlation analysis was conducted, revealing a strong relationship between the institution's international student enrolment and the social media activity, as well as with the overall number of international students arriving in Australia. Building on these insights, features were derived from the collected data for use in the development of the forecasting models. Two XGBoost models were developed: one excluding social media's features and one including them. The model incorporating social media data outperformed the one without it. Furthermore, a feature selection process was applied, resulting in even more accurate forecasts. These findings suggest that integrating social media data can significantly enhance the accuracy of forecasting models for international student arrivals. [ABSTRACT FROM AUTHOR]

In response to the escalating volume of Internet traffic, scalability challenges have emerged in content delivery. Information-Centric Networking (ICN) has emerged as a solution to accommodate this surge in traffic by leveraging caching. Collaborative caching within ICN is pivotal for enhancing network performance and reducing content distribution costs. However, current pricing strategies on the Internet do not align with ICN interconnection incentives. This paper delves into the economic incentive caching of free content among various types of ICN providers, including advertisers and Internet service providers (ISPs). Specifically, we employ game-theoretic models to analyze the interaction between providers within an ICN framework, where providers are incentivized to cache and share content. Content popularity is modeled using a generalized Zipf distribution. We formulate the interactions among ISPs as a non-cooperative game and, through mathematical analysis, establish the existence and uniqueness of the Nash equilibrium under certain conditions. Additionally, we propose an iterative and distributed algorithm based on best response dynamics to converge towards the equilibrium point. Numerical simulations demonstrate that our proposed game models yield a winwin solution, showcasing the effectiveness of our approach in incentivizing collaborative caching of free content within ICN. [ABSTRACT FROM AUTHOR]

With the ongoing digital transformation, the requirement for seamless & agile network connectivity has increased. This has necessitated the transformation from traditional networking to Software-Defined Network. With SDN, the network resources/infrastructure can be made more robust and programmable to accommodate the varying demand. Software-Defined Network (SDN) is now being used by data centers, cloud computing environments, and service providers for efficient handling of network requests. The objective of SDN can be achieved through the performance of the controllers. We have considered POX, Ryu, and ODL controllers to evaluate the performance using the Mininet tool and D-ITG (Distributed Internet Traffic Generator) script. Performance is evaluated using custom topology i.e. Fat tree topology. Several performance metrics (such as Delay, Jitter, Bitrate, Packet rate, Loss-burst size, and Percentage of Packets dropped) are considered and the behaviors of the controllers are monitored during experimentation by flooding the switches with several data packets. It has been observed that the POX controller performs better compared to Ryu and ODL for the Fat tree topology. From the experimental results, it is observed that the average delay of ODL is approximately 50% more than the POX controller and approximately 80% more delay occurred in the Ryu controller in comparison to the POX controller. Similarly average packet dropped percentage of POX is 20% less than Ryu and ODL controllers. Also, it has been observed that the average jitter in POX is almost 50% less in comparison to ODL and 90% less in comparison to the Ryu controller. [ABSTRACT FROM AUTHOR]

Smart cities and the Internet of Things have enabled the integration of communicating devices for efficient decision-making. Notably, traffic congestion is one major problem faced by daily commuters in urban cities. In developed countries, specialized sensors are deployed to gather traffic information to predict traffic patterns. Any traffic updates are shared with the commuters via the Internet. Such solutions become impracticable when physical infrastructure and Internet connectivity are either non-existent or very limited. In case of developing countries, no roadside units are available and Internet connectivity is still an issue in remote areas. Internet traffic analysis is a thriving field of study due to the myriad ways in which it may be put to practical use. In the intelligent Internet-of-Vehicles (IOVs), traffic congestion can be predicted and identified using cutting-edge technologies. Using tree-based decision-tree, random-forest, extra-tree, and XGBoost machine learning (ML) strategies, this research proposes an intelligent-transport-system for the IOVs-based vehicular network traffic in a smart city set-up. The suggested system uses ensemble learning and averages the selection of crucial features to give high detection accuracy at minimal computational costs, as demonstrated by the simulation results. For IOV-based vehicular network traffic, the tree-based ML approaches with feature-selection (FS) outperformed those without FS. When contrasted to the lowest KNN accuracy of 96.6% and the highest SVM accuracy of 98.01%, the Stacking approach demonstrates superior accuracy as 99.05%. [ABSTRACT FROM AUTHOR]

Despite the implementation of numerous interventions to enhance urban traffic safety, the estimation of the risk of traffic crashes resulting in life-threatening and economic costs remains a significant challenge. In light of the above, an online inference method for traffic crash risk based on the self-developed TAR-DETR and WOA-SA-SVM methods is proposed. The method's robust data inference capabilities can be applied to autonomous mobile robots and vehicle systems, enabling real-time road condition prediction, continuous risk monitoring, and timely roadside assistance. First, a self-developed dataset for urban traffic object detection, named TAR-1, is created by extracting traffic information from major roads around Hainan University in China and incorporating Russian car crash news. Secondly, we develop an innovative Context-Guided Reconstruction Feature Network-based Urban Traffic Objects Detection Model (TAR-DETR). The model demonstrates a detection accuracy of 76.8% for urban traffic objects, which exceeds the performance of other state-of-the-art object detection models. The TAR-DETR model is employed in TAR-1 to extract urban traffic risk features, and the resulting feature dataset was designated as TAR-2. TAR-2 comprises six risk features and three categories. A new inference algorithm based on WOA-SA-SVM is proposed to optimize the parameters (C, g) of the SVM, thereby enhancing the accuracy and robustness of urban traffic crash risk inference. The algorithm is developed by combining the Whale Optimization Algorithm (WOA) and Simulated Annealing (SA), resulting in a Hybrid Bionic Intelligent Optimization Algorithm. The TAR-2 dataset is inputted into a Support Vector Machine (SVM) optimized using a hybrid algorithm and used to infer the risk of urban traffic crashes. The proposed WOA-SA-SVM method achieves an average accuracy of 80% in urban traffic crash risk inference. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Yangtze Normal University is the property of Journal of Yangtze Normal University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, Internet traffic patterns have been changing. Most of the traffic demand by end users is multimedia, in particular, video streaming accounts for over 53%. This demand has led to improved network infrastructures and computing architectures to meet the challenges of delivering these multimedia services while maintaining an adequate quality of experience. Focusing on the preparation and adequacy of multimedia content for broadcasting, Cloud and Edge Computing infrastructures have been and will be crucial to offer high and ultra-high definition multimedia content in live, real-time, or video-on-demand scenarios. For these reasons, this review paper presents a detailed study of research papers related to encoding and transcoding techniques in cloud computing environments. It begins by discussing the evolution of streaming and the importance of the encoding process, with a focus on the latest streaming methods and codecs. Then, it examines the role of cloud systems in multimedia environments and provides details on the cloud infrastructure for media scenarios. After doing a systematic literature review, we have been able to find 49 valid papers that meet the requirements specified in the research questions. Each paper has been analyzed and classified according to several criteria, besides to inspect their relevance. To conclude this review, we have identified and elaborated on several challenges and open research issues associated with the development of video codecs optimized for diverse factors within both cloud and edge architectures. Additionally, we have discussed emerging challenges in designing new cloud/edge architectures aimed at more efficient delivery of media traffic. This involves investigating ways to improve the overall performance, reliability, and resource utilization of architectures that support the transmission of multimedia content over both cloud and edge computing environments ensuring a good quality of experience for the final user. [ABSTRACT FROM AUTHOR]

The constant increase in multimedia Internet traffic in the form of video streaming requires new solutions for efficient video coding to save bandwidth and network resources. HTTP adaptive streaming (HAS), the most widely used solution for video streaming, allows the client to adaptively select the bitrate according to the transmission conditions. For this purpose, multiple presentations of the same video content are generated on the video server, which contains video sequences encoded at different bitrates with resolution adjustment to achieve the best Quality of Experience (QoE). This set of bitrate–resolution pairs is called a bitrate ladder. In addition to the traditional one-size-fits-all scheme for the bitrate ladder, context-aware solutions have recently been proposed that enable optimum bitrate–resolution pairs for video sequences of different complexity. However, these solutions use only spatial resolution for optimization, while the selection of the optimal combination of spatial and temporal resolution for a given bitrate has not been sufficiently investigated. This paper proposes bit-ladder optimization considering spatiotemporal features of video sequences and usage of optimal spatial and temporal resolution related to video content complexity. Optimization along two dimensions of resolution significantly increases the complexity of the problem and the approach of intensive encoding for all spatial and temporal resolutions in a wide range of bitrates, for each video sequence, is not feasible in real time. In order to reduce the level of complexity, we propose a data augmentation using a neural network (NN)-based model. To train the NN model, we used seven video sequences of different content complexity, encoded with the HEVC encoder at five different spatial resolutions (SR) up to 4K. Also, all video sequences were encoded using four frame rates up to 120 fps, presenting different temporal resolutions (TR). The Structural Similarity Index Measure (SSIM) is used as an objective video quality metric. After data augmentation, we propose NN models that estimate optimal TR and bitrate values as switching points to a higher SR. These results can be further used as input parameters for the bitrate ladder construction for video sequences of a certain complexity. [ABSTRACT FROM AUTHOR]

In the last few years, the volume of internet traffic has increased enormously. A significant portion of the internet traffic is constituted by applications that stream multimedia data from one part of the world to another. The enormity of the video data requires the Content Delivery Network to distribute the content efficiently and securely among the consumers. The secure and authenticated distribution can be achieved by the end-to-end encryption of video. In this paper, one time random key algorithm and the algorithms and methods based on permutation techniques have been proposed to encrypt the video frames selectively and these encrypted frames are transmitted over the network along with the other video frames. The proposed encryption methods extract the colour channels like Red, Green and Blue from the video frames, and they are encrypted by applying row-column permutation cipher, One Time Pad cipher and row permutation cipher. Encryption of the video frames has been performed at two levels by combining the one-time random key algorithm and permutation techniques to provide security to the video. These encryption techniques have been applied on the twenty-five, thirty, and thirty-five, and so on, up to fifty per cent of the total frames available in the video. The experimental results, histogram analysis and parameters like average Peak Signal-to-noise Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values measure the quality of the video obtained after the application of the proposed methods on the video to be transmitted over the network. [ABSTRACT FROM AUTHOR]

In recent years, more websites have been collecting personal information for many processes, such as banks, internet connections, and government services. The public needs to provide all personal information, such as Aadhar, PAN card, date of birth, and phone number. The personal and sensitive information is at risk of being used for phishing attacks through URL manipulation. In addition, a phishing attack cause's financial and reputational loss. Hence protecting sensitive information by adapting required protection is extremely valuable for global security. To overcome this, we proposed a method to detect phishing attacks based on previous history, including the duration of operation, customer reviews, web traffic, and the URL. Based on these parameters, the proposed optimal machine learning-based algorithm (OmLA) analyze the previous information about URLs and predict whether it is phishing- or legitimate. As per simulation and performance analysis, the proposed method outperforms conventional methods such as random forest (RF), support vector machine (SVM), and genetic algorithms (GA) by 8%, 18%, and 23%, respectively in terms of accuracy. Additionally, it achieves detection times of 0.2%, 0.6%, and 0.9%, respectively, and excels in response times of 0.45%, 0.56%, and 0.62%, respectively. [ABSTRACT FROM AUTHOR]

Nowadays, high latency in Edge Computing (EC) for Internet of Things (IoT) devices due to network congestion and online traffic reduces the acquired precision, performance, and processing power of the network. Data overload in IoT significantly impacts the real-time capabilities of user experience, decision- making efficiency, operational costs, and security in EC. By combining EC innovation and three Machine Learning (ML) models, namely Decision Trees (DT), Support Vector Machines (SVMs), and Convolutional Neural Networks (CNNs), this research aims to tackle the inactivity of IoT devices and information cleaning from errors. Its purpose is to preserve information astuteness and highlight the efficacy of each model's execution by using the essential components of previous approaches. The proposed model evaluates the precision, performance, and quality enhancement by measuring the Mean Square Error (MSE), coefficient of determination (R²), and accuracy. [ABSTRACT FROM AUTHOR]

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This study explores the potential of online traffic safety education for adolescent motorcyclists in Pakistan. An e-learning platform, "Route 2 School" (R2S), was developed focusing on traffic knowledge, situation awareness, risk detection, and risk management. Male students (14–18 years) who commute to school by motorcycle were divided into an experimental group (EG) and a control group (CG), both completing pre- and post-measurement questionnaires. The EG showed significant improvement in knowledge, risk detection, and risk management compared to the CG, but not in situation awareness. Participants reported increased traffic safety awareness and suggested adding more interactive elements. The R2S platform's scores revealed better performance in risk detection and risk management modules than situation awareness. Time spent on modules varied, with situation awareness requiring the most time. Adolescents expressed satisfaction with the platform, acknowledging its role in increasing traffic awareness. This study provides initial insights into the effectiveness of online traffic safety education in Pakistan, highlighting the potential to address the lack of comprehensive traffic safety education in schools. Further research and stakeholder engagement are recommended to integrate such platforms into formal education, potentially reducing traffic-related injuries among adolescent motorcyclists in developing countries. [ABSTRACT FROM AUTHOR]

This paper presents the findings of a study on the profiling of online store users in terms of their likelihood of making a purchase. It also considers the possibility of implementing this solution in the short term. The paper describes the process of developing a profiling model based on data derived from monitoring user behaviour on a website. During the customer's subsequent visits, information is collected to identify the user, record their behaviour on the page and the fact that they made a purchase. The model requires a substantial amount of training data, primarily related to the purchase of products. This represents a small percentage of total website traffic and requires a considerable amount of time to monitor user behaviour. Therefore, we investigated the possibility of using the Conditional Generative Adversarial Network (CGAN) to generate synthetic data for training the profiling model. The application of GAN would facilitate a more expedient implementation of this model on an online store website. The findings of this study may also prove beneficial to webshop owners and managers, enabling them to gain a deeper insight into their customers and align their price offers or discounts with the profile of a particular user. [ABSTRACT FROM AUTHOR]

The study uses different statistical techniques to understand the relationship between variables explaining the digital divide and classification based on The Inglehart-Welzel Cultural Map for 2023. To achieve this purpose variables focusing on Digital Penetration (the percentage of internet and social media users and mobile cellular connections), Operating Systems share (iOS and Android), Device Traffic (laptop/mobile phone-based web traffic) as well as Mobile Commerce variables (bills and payments using mobile internet) were included in the analysis. To minimize any effects arithmetic means of data was calculated.: The results from one-way ANOVA tests indicate significant differences among groups classified by cultural values for almost all measured variables of digitalization. The mean squares and F-values across variables like cellular mobile connections, internet users, and active social media users are significant indicating a shift towards more secular and self-expressive cultural values. The results of the GLM procedure show that significant portions of the total variance in digitalization variables are associated with membership in groups based on the cultural map. This suggests that cultural classifications can explain substantial differences in digital behavior and preferences across populations. Spearman's correlation coefficients showed strong positive correlations between Traditional/Secular values and several digitalization metrics, such as the use of mobile phones or the internet for payments, and negative correlations with others like share of web traffic by device type (mobile vs. laptop/computer). These correlations suggest that cultural values play a substantial role in influencing digital habits and accessibility. [ABSTRACT FROM AUTHOR]

With the development of China’s digital economy, Internet traffic has shown a steady growth trend. In view of the problem that the uneven distribution of Internet traffic will affect the long-term stable development of the network, the methods of Internet traffic acquisition and identification were discussed. The network architecture for traffic collection, and the software architecture for traffic analysis were constructed. A feasible market-oriented strategy for regulating and channeling Internet traffic was proposed. The propoced strategy has achieved preliminary results through the practice of operators, and it provides a solution and reference basis to guide the distribution of Internet traffic in a proper way.

Purpose: This work aims to explore the effects of (equity and non-equity) strategic alliances between banks and FinTechs on FinTechs' online visibility. Design/methodology/approach: For a sample of 124 Italian FinTechs, the authors measured online visibility through their website ranking (Google PageRank) and website traffic (Google Trends). Consistent to the historical depth of these measures, the authors separately investigated the effect of equity and non-equity (contractual) agreements on online visibility by means of ordinal logistic regressions and diff-in-diff analysis. Findings: Strategic alliances with banks enhance FinTechs' online visibility. Although both equity and contractual agreements positively influence the popularity of FinTechs' website achieved through the activity of internal and external online content creators (websites ranking), only equity agreements are effective in attracting Internet users (website traffic). Practical implications: When deciding to interact with banks, FinTechs' managers should consider that equity agreements may be a powerful strategic choice for enlarging the customer base and boosting visibility of FinTechs. Social implications: Fostering strategic alliances between banks and FinTechs contributes to FinTechs' growth, generating virtuous mechanisms of innovation, financial inclusion and better allocative efficiency of the financial system. Originality/value: This work expands marketing knowledge and literature regarding online visibility determinants, by investigating the benefits of strategic alliances and cooperation in the market, while providing an empirical strategy replicable by future marketing studies. [ABSTRACT FROM AUTHOR]

Commercial shipping activity plays a crucial role in the unintentional primary and secondary introduction of aquatic Non-Indigenous species (NIS), with ballast water (BW) and hull biofouling being the two major vectors of NIS transportation. The aim of this study is to identify potential high-risk donor port areas that may act as pools for NIS introduction in Saronikos Gulf, Greece. For the purpose of the study, we conducted a regional risk assessment and produced a NIS watch list based on the commercial vessel arrivals at the major ports of the Saronikos Gulf (Piraeus and Elefsis) for the period 2020–2022. In our risk analysis, we used the two most important factors for ship-mediated species introductions identified in the literature: salinity and biogeographic region of the voyage last origin ports. Then, combined with data on vessel arrivals, based on marine traffic data collected from the Marine Traffic online platform, we produced a NIS watch list for the study area. The most highrisk ports for primary introductions were found to be in the Temperate Northern Atlantic (e.g. Gemlik, Ambarli, Asyaport and Derince in Türkiye, Sines in Portugal), Western Indo-Pacific (e.g. Jeddah in Saudi Arabia, Aqaba in Jordan) and the Central Indo-Pacific (e.g. Singapore). For secondary introduction, the most high-risk voyage origin ports are located in the Levantine Sea (e.g. Limassol in Cyprus, Alexandria and Port Said in Egypt, Haifa and Ashdod in Israel, Iskenderun, Mersin and Nemrut in Türkiye), the Aegean Sea (e.g. Izmir in Türkiye) and the Ionian Sea (e.g. Marsaxlokk in Malta). Our watch list included 44 high risk and 13 medium risk NIS to be transferred to Saronikos Gulf. Of these, 38 high-risk species are already present in the Mediterranean, 6 high-risk species are absent from the Mediterranean and 18 species pose a high risk for both primary and secondary introduction. [ABSTRACT FROM AUTHOR]

Aims: The objective of this study was to develop a new website in Spanish on oral health and dental care for use by the relatives/caregivers of individuals with Down syndrome, with the aim of incorporating the strengths and avoids the deficiencies of existing websites. Methods: A freely accessible website was developed with dental content, whose access criteria included the age of the individual undergoing the consultation and the area of interest (tongue or teeth disease, oral functionality, oral hygiene, and dental visits). The definitive version of the website was analyzed by five external examiners, applying the DISCERN criteria and the Questionnaire to Evaluate Health Web Sites According to European Criteria (QEEC). The website's traffic during the first year of activity was recorded. Results: The new website is known as "DentiDown", and its access domain is https://odontoloxia‐accessible.org/dentidown/. On the home screen, the age group of interest to the user can be accessed. A dropdown menu then opens, listing the various options according to the area of interest. The oral hygiene section provides advice for improving toothbrushing efficacy through demonstration videos. With the DISCERN tool, an overall score of 4.75 ± 0.5 was achieved. With the QEEC, the external examiners' general opinion was highly favorable. The website received the seal of quality from the Accredited Medical Web (AMW). During the first year of activity, a total of 4536 visits from a total of 45 countries were recorded. Conclusion: A new Spanish website has been developed on oral health for use by the relatives/caregivers of individuals with Down syndrome. The website has been favorably evaluated by external experts and, to date, is the only one with these characteristics with the AMW seal of quality. [ABSTRACT FROM AUTHOR]

Cyberattacks are rapidly evolving both in terms of techniques and frequency, from low-level attacks through to sophisticated Advanced Persistent Threats (APTs). There is a need to consider how testbed environments such as cyber ranges can be readily deployed to improve the examination of attack characteristics, as well as the assessment of defences. Whilst cyber ranges are not new, they can often be computationally expensive, require an extensive setup and configuration, or may not provide full support for areas such as logging or ongoing learning. In this paper, we propose GoibhniUWE, a container-based cyber range that provides a flexible platform for investigating the full lifecycle of a cyberattack. Adopting a modular approach, users can seamlessly switch out existing, containerised vulnerable services and deploying multiple different services at once, allowing for the creation of complex and realistic deployments. The range is fully instrumented with logging capabilities from a variety of sources including Intrusion Detection Systems (IDSs), service logging, and network traffic captures. To demonstrate the effectiveness of our approach, we deploy the GoibhniUWE range under multiple conditions to simulate various vulnerable environments, reporting on and comparing key metrics such as CPU and memory usage. We simulate complex attacks which span multiple services and networks, with logging at multiple levels, modelling an Advanced Persistent Threat (APT) and their associated Tactics, Techniques, and Procedures (TTPs). We find that even under continuous, active, and targeted deployment, GoibhniUWE averaged a CPU usage of less than 50%, in an environment using four single-core processors, and memory usage of less than 4.5 GB. [ABSTRACT FROM AUTHOR]