The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability...The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.展开更多
With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Alth...With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Although distributed streaming data processing frameworks such asApache Flink andApache Spark Streaming provide solutions,meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem.To address this,the study proposes a formal modeling approach based on Performance Evaluation Process Algebra(PEPA),which abstracts the core components and interactions of cloud-based distributed streaming data processing systems.Additionally,a generic service flow generation algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel and the computation of key performance metrics,including response time,throughput,and resource utilization.The novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm,bridging the gap between formal modeling and practical performance evaluation for IoT systems.Simulation experiments demonstrate that optimizing the execution efficiency of components can significantly improve system performance.For instance,increasing the task execution rate from 10 to 100 improves system performance by 9.53%,while further increasing it to 200 results in a 21.58%improvement.However,diminishing returns are observed when the execution rate reaches 500,with only a 0.42%gain.Similarly,increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%,but the improvement slows to 6.06% when increasing from 20 to 50,highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance gains.This study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing.The proposed approach not only identifies performance bottlenecks but also offers insights into improving system efficiency under different configurations and workloads.展开更多
Over the past few years,video live streaming has gained immense popularity as a leading internet application.In current solutions offered by cloud service providers,the Group of Pictures(GOP)length of the video source...Over the past few years,video live streaming has gained immense popularity as a leading internet application.In current solutions offered by cloud service providers,the Group of Pictures(GOP)length of the video source often significantly impacts end-to-end(E2E)latency.However,designing an optimized GOP structure to reduce this effect remains a significant challenge.This paper presents two key contributions.First,it explores how the GOP length at the video source influences E2E latency in mainstream cloud streaming services.Experimental results reveal that the mean E2E latency increases linearly with longer GOP lengths.Second,this paper proposes EGOP(an Enhanced GOP structure)that can be implemented in streaming media servers.Experiments demonstrate that EGOP maintains a consistent E2E latency,unaffected by the GOP length of the video source.Specifically,even with a GOP length of 10 s,the E2E latency remains at 1.35 s,achieving a reduction of 6.98 s compared to Volcano-Engine(the live streaming service provider for TikTok).This makes EGOP a promising solution for low-latency live streaming.展开更多
With technological advancements,high-speed rail has emerged as a prevalent mode of transportation.During travel,passengers exhibit a growing demand for streaming media services.However,the high-speed mobile networks e...With technological advancements,high-speed rail has emerged as a prevalent mode of transportation.During travel,passengers exhibit a growing demand for streaming media services.However,the high-speed mobile networks environment poses challenges,including frequent base station handoffs,which significantly degrade wireless network transmission performance.Improving transmission efficiency in high-speed mobile networks and optimizing spatiotemporal wireless resource allocation to enhance passengers’media experiences are key research priorities.To address these issues,we propose an Adaptive Cross-Layer Optimization Transmission Method with Environment Awareness(ACOTM-EA)tailored for high-speed rail streaming media.Within this framework,we develop a channel quality prediction model utilizing Kalman filtering and an algorithm to identify packet loss causes.Additionally,we introduce a proactive base station handoffstrategy to minimize handoffrelated disruptions and optimize resource distribution across adjacent base stations.Moreover,this study presents a wireless resource allocation approach based on an enhanced genetic algorithm,coupled with an adaptive bitrate selection mechanism,to maximize passenger Quality of Experience(QoE).To evaluate the proposed method,we designed a simulation experiment and compared ACOTM-EA with established algorithms.Results indicate that ACOTM-EA improves throughput by 11%and enhances passengers’media experience by 5%.展开更多
No-wash bioassays based on nanoparticles are used widely in biochemical procedures because of their responsive sensing and no need forwashing processes.Essential for no-wash biosensing are the interactions between nan...No-wash bioassays based on nanoparticles are used widely in biochemical procedures because of their responsive sensing and no need forwashing processes.Essential for no-wash biosensing are the interactions between nanoparticles and biomolecules,but it is challenging toachieve controlled bioconjugation of molecules on nanomaterials.Reported here is a way to actively improve nanoparticle-based no-washbioassays by enhancing the binding between biomolecules and gold nanoparticles via acoustic streaming generated by a gigahertz piezoelectricnanoelectromechanical resonator.Tunable micro-vortices are generated at the device-liquid interface,thereby accelerating the internalcirculating flow of the solution,bypassing the diffusion limitation,and thus improving the binding between the biomolecules and goldnanoparticles.Combined with fluorescence quenching,an enhanced and ultrafast no-wash biosensing assay is realized for specific proteins.The sensing method presented here is a versatile tool for different types of biomolecule detection with high efficiency and simplicity.展开更多
In the contemporary digital landscape,the proliferation of information has led to an increasing diversity of channels through which consumers obtain information,resulting in a gradual transformation of shopping habits...In the contemporary digital landscape,the proliferation of information has led to an increasing diversity of channels through which consumers obtain information,resulting in a gradual transformation of shopping habits.Consumers now frequently rely on external sources to make well-informed purchasing decisions,leading to the emergence of live shopping as a prominent avenue for gathering product information and completing transactions.E-commerce live streaming has experienced rapid growth,leveraging its ability to generate traffic and capture consumer attention.The integration of content and live streaming not only meets users’psychological needs but also facilitates seamless communication between buyers and sellers.From the perspective of content marketing typologies,this paper examines content marketing across three key dimensions:informational content,entertainment content,and emotional content.It further explores the impact of content marketing on consumers’purchase intentions within the context of e-commerce live streaming.展开更多
Unmanned aerial vehicles(UAVs)bring more innovation and attraction to outdoor mobile high-definition(HD)live streaming with its unique perspective.Due to the heavy computational requirements of HD live broadcast tasks...Unmanned aerial vehicles(UAVs)bring more innovation and attraction to outdoor mobile high-definition(HD)live streaming with its unique perspective.Due to the heavy computational requirements of HD live broadcast tasks and the limited hardware performance of UAV equipment,how to reduce the system response delay and improve the energy efficiency of terminal equipment directly affects the secure broadcast of the system.Secure task offloading in this scenario is considered a promising solution and has received academic attention.In this paper,we simulate the UAV-aided outdoor mobile HD live streaming scenarios and optimize the relevant task offloading strategies.First,we design the total cost function of task offloading that jointly optimizes secure time latency and energy consumption.Additionally,we propose a collaborative computing model for multi-UAV task offloading.This model combines the idea of simulated annealing(SA)and introduces the compression factor to enhance the particle swarm optimization(PSO)to realize secure task offloading.The simulation results show that the proposed strategy has better performance in balancing network latency and energy consumption.Compared with the discrete teaching–learning-based optimization(DTLBO)and quantum PSO(QPSO)task offloading strategies,the fitness value of the proposed strategy is decreased by an average of 26.73%and 16.42%,respectively.展开更多
Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL...Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL)models find helpful in the detection and classification of anomalies.This article designs an oversampling with an optimal deep learning-based streaming data classification(OS-ODLSDC)model.The aim of the OSODLSDC model is to recognize and classify the presence of anomalies in the streaming data.The proposed OS-ODLSDC model initially undergoes preprocessing step.Since streaming data is unbalanced,support vector machine(SVM)-Synthetic Minority Over-sampling Technique(SVM-SMOTE)is applied for oversampling process.Besides,the OS-ODLSDC model employs bidirectional long short-term memory(Bi LSTM)for AD and classification.Finally,the root means square propagation(RMSProp)optimizer is applied for optimal hyperparameter tuning of the Bi LSTM model.For ensuring the promising performance of the OS-ODLSDC model,a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDS 2018,KDD-Cup 1999,and NSL-KDD datasets.展开更多
In recent decades,the importance of surface acoustic waves,as a biocompatible tool to integrate with microfluidics,has been proven in various medical and biological applications.The numerical modeling of acoustic stre...In recent decades,the importance of surface acoustic waves,as a biocompatible tool to integrate with microfluidics,has been proven in various medical and biological applications.The numerical modeling of acoustic streaming caused by surface acoustic waves in microchannels requires the effect of viscosity to be considered in the equations which complicates the solution.In this paper,it is shown that the major contribution of viscosity and the horizontal component of actuation is concentrated in a narrow region alongside the actuation boundary.Since the inviscid equations are considerably easier to solve,a division into the viscous and inviscid domains would alleviate the computational load significantly.The particles'traces calculated by this approximation are excellently alongside their counterparts from the completely viscous model.It is also shown that the optimum thickness for the viscous strip is about 9-fold the acoustic boundary layer thickness for various flow patterns and amplitudes of actuation.展开更多
Analyze the compatibility between cosmetics and live streaming e-commerce from its own nature,marketing means and supply chain characteristics.According to the prominent problems,sort out the relationship between all ...Analyze the compatibility between cosmetics and live streaming e-commerce from its own nature,marketing means and supply chain characteristics.According to the prominent problems,sort out the relationship between all parties in the cosmetics live e-commerce industry chain.Combined with the latest regulatory policies of live streaming e-commerce and cosmetics,the responsibilities of different subjects in cosmetics live streaming e-commerce are summarized,and relevant suggestions and countermeasures are put forward for the standardization and development of live streaming e-commerce.Cosmetics brand owners are the first responsible persons for product quality.Anchors,as a mixed identity between intermediary,advertising spokesperson and operator,should bear stricter joint and several liability when recommending products related to consumers’health.If anchors fail to clearly identify themselves in the recommendation process,thus causing consumers to mistake them for the operator of the cosmetics,they should assume the obligations of the operator.展开更多
With the rise of live streaming on social media, platforms like Facebook, Instagram, and YouTube have become powerful business tools. They enable users to share live videos, fostering direct connections between busine...With the rise of live streaming on social media, platforms like Facebook, Instagram, and YouTube have become powerful business tools. They enable users to share live videos, fostering direct connections between businesses and their customers. This critical literature review paper explores the impact of live streaming on businesses, focusing on its role in attracting and satisfying consumers by promoting products tailored to their needs and wants. It emphasizes live streaming’s crucial role in engaging customers, a key to business growth. The study also provides viable strategies for businesses to leverage live streaming for growth and customer engagement, underscoring its importance in the business landscape.展开更多
In recent years,with the rapid development and popularization of Internet information technology,many new media platforms have risen rapidly,and major e-commerce companies have begun to explore the mode of livestreami...In recent years,with the rapid development and popularization of Internet information technology,many new media platforms have risen rapidly,and major e-commerce companies have begun to explore the mode of livestreaming.Especially during the COVID-19 pandemic,due to the lockdown,live-streaming has become an important means of economic development in many places.Owing to its remarkable characteristics of timeliness,entertainment,and interactivity,it has become the latest and trendiest sales mode of e-commerce channels,reflecting huge economic potential and commercial value.This article analyzes two models and their characteristics of live-streaming sales from a practical perspective.Based on this,it outlines consumer purchasing decisions and the factors that affect consumer purchasing decisions under the live-streaming sales model.Finally,it discusses targeted suggestions for using the live-streaming sales model to expand the consumer market,hoping to promote the healthy and steady development of the live-streaming sales industry.展开更多
With the widespread use of streaming media application on the Internet, a significant change in Internet workload will be provoked. Caching is one of the applied techniques for enhancing the scalability of streaming s...With the widespread use of streaming media application on the Internet, a significant change in Internet workload will be provoked. Caching is one of the applied techniques for enhancing the scalability of streaming system and reducing the workload of server/network. Aiming at the characteristics of broadband network in community, we propose a popularity-based server-proxy caching strategy for streaming medias, and implement the prototype of streaming proxy caching based on this strategy, using RTSP as control protocol and RTP for content transport. This system can play a role in decreasing server load, reducing the traffic from streaming server to proxy, and improving the start-up latency of the client. Key words streaming server - proxy - cache - streaming media - real time streaming protocol CLC number TP 302 - TP 333 Foundation item: Supported by the National High Technology Development 863 Program of China (2001AA111011).Biography: Tan Jin (1962-), male, Ph. D candidate, research direction: network communications, multimedia technologies, and web caching.展开更多
分布式拒绝服务(distributed denial of service,DDoS)攻击是重要的安全威胁,网络速度的不断提高给传统的检测方法带来了新的挑战。以Spark等为代表的大数据处理技术,给网络安全的高速检测带来了新的契机。提出了一种基于Spark Streamin...分布式拒绝服务(distributed denial of service,DDoS)攻击是重要的安全威胁,网络速度的不断提高给传统的检测方法带来了新的挑战。以Spark等为代表的大数据处理技术,给网络安全的高速检测带来了新的契机。提出了一种基于Spark Streaming框架的自适应实时DDoS检测防御技术,通过对滑动窗口内源簇进行分组,并根据与各分组内源簇比例的偏差统计,检测出DDoS攻击流量。通过感知合法的网络流量,实现了对DDoS攻击的自适应快速检测和有效响应。实验结果表明,该技术可极大地提升检测能力,为保障网络服务性能和安全检测的可扩展性提供了一种可行的解决方案。展开更多
基金funded by the Ongoing Research Funding Program(ORF-2025-890)King Saud University,Riyadh,Saudi Arabia and was supported by the Competitive Research Fund of theUniversity of Aizu,Japan.
文摘The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.
基金funded by the Joint Project of Industry-University-Research of Jiangsu Province(Grant:BY20231146).
文摘With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Although distributed streaming data processing frameworks such asApache Flink andApache Spark Streaming provide solutions,meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem.To address this,the study proposes a formal modeling approach based on Performance Evaluation Process Algebra(PEPA),which abstracts the core components and interactions of cloud-based distributed streaming data processing systems.Additionally,a generic service flow generation algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel and the computation of key performance metrics,including response time,throughput,and resource utilization.The novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm,bridging the gap between formal modeling and practical performance evaluation for IoT systems.Simulation experiments demonstrate that optimizing the execution efficiency of components can significantly improve system performance.For instance,increasing the task execution rate from 10 to 100 improves system performance by 9.53%,while further increasing it to 200 results in a 21.58%improvement.However,diminishing returns are observed when the execution rate reaches 500,with only a 0.42%gain.Similarly,increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%,but the improvement slows to 6.06% when increasing from 20 to 50,highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance gains.This study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing.The proposed approach not only identifies performance bottlenecks but also offers insights into improving system efficiency under different configurations and workloads.
基金supported by Henan Province Major Science and Technology Project(241100210100).
文摘Over the past few years,video live streaming has gained immense popularity as a leading internet application.In current solutions offered by cloud service providers,the Group of Pictures(GOP)length of the video source often significantly impacts end-to-end(E2E)latency.However,designing an optimized GOP structure to reduce this effect remains a significant challenge.This paper presents two key contributions.First,it explores how the GOP length at the video source influences E2E latency in mainstream cloud streaming services.Experimental results reveal that the mean E2E latency increases linearly with longer GOP lengths.Second,this paper proposes EGOP(an Enhanced GOP structure)that can be implemented in streaming media servers.Experiments demonstrate that EGOP maintains a consistent E2E latency,unaffected by the GOP length of the video source.Specifically,even with a GOP length of 10 s,the E2E latency remains at 1.35 s,achieving a reduction of 6.98 s compared to Volcano-Engine(the live streaming service provider for TikTok).This makes EGOP a promising solution for low-latency live streaming.
基金substantially supported by the National Natural Science Foundation of China under Grant No.62002263in part by Tianjin Municipal Education Commission Research Program Project under 2022KJ012Tianjin Science and Technology Program Projects:24YDTPJC00630.
文摘With technological advancements,high-speed rail has emerged as a prevalent mode of transportation.During travel,passengers exhibit a growing demand for streaming media services.However,the high-speed mobile networks environment poses challenges,including frequent base station handoffs,which significantly degrade wireless network transmission performance.Improving transmission efficiency in high-speed mobile networks and optimizing spatiotemporal wireless resource allocation to enhance passengers’media experiences are key research priorities.To address these issues,we propose an Adaptive Cross-Layer Optimization Transmission Method with Environment Awareness(ACOTM-EA)tailored for high-speed rail streaming media.Within this framework,we develop a channel quality prediction model utilizing Kalman filtering and an algorithm to identify packet loss causes.Additionally,we introduce a proactive base station handoffstrategy to minimize handoffrelated disruptions and optimize resource distribution across adjacent base stations.Moreover,this study presents a wireless resource allocation approach based on an enhanced genetic algorithm,coupled with an adaptive bitrate selection mechanism,to maximize passenger Quality of Experience(QoE).To evaluate the proposed method,we designed a simulation experiment and compared ACOTM-EA with established algorithms.Results indicate that ACOTM-EA improves throughput by 11%and enhances passengers’media experience by 5%.
基金the financial support received from the National Natural Science Foundation of China(Grant No.62174119)the 111 Project (Grant No.B07014)the Foundation for Talent Scientists of Nanchang Institute for Microtechnology of Tianjin University
文摘No-wash bioassays based on nanoparticles are used widely in biochemical procedures because of their responsive sensing and no need forwashing processes.Essential for no-wash biosensing are the interactions between nanoparticles and biomolecules,but it is challenging toachieve controlled bioconjugation of molecules on nanomaterials.Reported here is a way to actively improve nanoparticle-based no-washbioassays by enhancing the binding between biomolecules and gold nanoparticles via acoustic streaming generated by a gigahertz piezoelectricnanoelectromechanical resonator.Tunable micro-vortices are generated at the device-liquid interface,thereby accelerating the internalcirculating flow of the solution,bypassing the diffusion limitation,and thus improving the binding between the biomolecules and goldnanoparticles.Combined with fluorescence quenching,an enhanced and ultrafast no-wash biosensing assay is realized for specific proteins.The sensing method presented here is a versatile tool for different types of biomolecule detection with high efficiency and simplicity.
文摘In the contemporary digital landscape,the proliferation of information has led to an increasing diversity of channels through which consumers obtain information,resulting in a gradual transformation of shopping habits.Consumers now frequently rely on external sources to make well-informed purchasing decisions,leading to the emergence of live shopping as a prominent avenue for gathering product information and completing transactions.E-commerce live streaming has experienced rapid growth,leveraging its ability to generate traffic and capture consumer attention.The integration of content and live streaming not only meets users’psychological needs but also facilitates seamless communication between buyers and sellers.From the perspective of content marketing typologies,this paper examines content marketing across three key dimensions:informational content,entertainment content,and emotional content.It further explores the impact of content marketing on consumers’purchase intentions within the context of e-commerce live streaming.
基金supported in part by the National Natural Science Foundation of China(Nos.62271454 and 62171119).
文摘Unmanned aerial vehicles(UAVs)bring more innovation and attraction to outdoor mobile high-definition(HD)live streaming with its unique perspective.Due to the heavy computational requirements of HD live broadcast tasks and the limited hardware performance of UAV equipment,how to reduce the system response delay and improve the energy efficiency of terminal equipment directly affects the secure broadcast of the system.Secure task offloading in this scenario is considered a promising solution and has received academic attention.In this paper,we simulate the UAV-aided outdoor mobile HD live streaming scenarios and optimize the relevant task offloading strategies.First,we design the total cost function of task offloading that jointly optimizes secure time latency and energy consumption.Additionally,we propose a collaborative computing model for multi-UAV task offloading.This model combines the idea of simulated annealing(SA)and introduces the compression factor to enhance the particle swarm optimization(PSO)to realize secure task offloading.The simulation results show that the proposed strategy has better performance in balancing network latency and energy consumption.Compared with the discrete teaching–learning-based optimization(DTLBO)and quantum PSO(QPSO)task offloading strategies,the fitness value of the proposed strategy is decreased by an average of 26.73%and 16.42%,respectively.
文摘Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL)models find helpful in the detection and classification of anomalies.This article designs an oversampling with an optimal deep learning-based streaming data classification(OS-ODLSDC)model.The aim of the OSODLSDC model is to recognize and classify the presence of anomalies in the streaming data.The proposed OS-ODLSDC model initially undergoes preprocessing step.Since streaming data is unbalanced,support vector machine(SVM)-Synthetic Minority Over-sampling Technique(SVM-SMOTE)is applied for oversampling process.Besides,the OS-ODLSDC model employs bidirectional long short-term memory(Bi LSTM)for AD and classification.Finally,the root means square propagation(RMSProp)optimizer is applied for optimal hyperparameter tuning of the Bi LSTM model.For ensuring the promising performance of the OS-ODLSDC model,a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDS 2018,KDD-Cup 1999,and NSL-KDD datasets.
文摘In recent decades,the importance of surface acoustic waves,as a biocompatible tool to integrate with microfluidics,has been proven in various medical and biological applications.The numerical modeling of acoustic streaming caused by surface acoustic waves in microchannels requires the effect of viscosity to be considered in the equations which complicates the solution.In this paper,it is shown that the major contribution of viscosity and the horizontal component of actuation is concentrated in a narrow region alongside the actuation boundary.Since the inviscid equations are considerably easier to solve,a division into the viscous and inviscid domains would alleviate the computational load significantly.The particles'traces calculated by this approximation are excellently alongside their counterparts from the completely viscous model.It is also shown that the optimum thickness for the viscous strip is about 9-fold the acoustic boundary layer thickness for various flow patterns and amplitudes of actuation.
文摘Analyze the compatibility between cosmetics and live streaming e-commerce from its own nature,marketing means and supply chain characteristics.According to the prominent problems,sort out the relationship between all parties in the cosmetics live e-commerce industry chain.Combined with the latest regulatory policies of live streaming e-commerce and cosmetics,the responsibilities of different subjects in cosmetics live streaming e-commerce are summarized,and relevant suggestions and countermeasures are put forward for the standardization and development of live streaming e-commerce.Cosmetics brand owners are the first responsible persons for product quality.Anchors,as a mixed identity between intermediary,advertising spokesperson and operator,should bear stricter joint and several liability when recommending products related to consumers’health.If anchors fail to clearly identify themselves in the recommendation process,thus causing consumers to mistake them for the operator of the cosmetics,they should assume the obligations of the operator.
文摘With the rise of live streaming on social media, platforms like Facebook, Instagram, and YouTube have become powerful business tools. They enable users to share live videos, fostering direct connections between businesses and their customers. This critical literature review paper explores the impact of live streaming on businesses, focusing on its role in attracting and satisfying consumers by promoting products tailored to their needs and wants. It emphasizes live streaming’s crucial role in engaging customers, a key to business growth. The study also provides viable strategies for businesses to leverage live streaming for growth and customer engagement, underscoring its importance in the business landscape.
文摘In recent years,with the rapid development and popularization of Internet information technology,many new media platforms have risen rapidly,and major e-commerce companies have begun to explore the mode of livestreaming.Especially during the COVID-19 pandemic,due to the lockdown,live-streaming has become an important means of economic development in many places.Owing to its remarkable characteristics of timeliness,entertainment,and interactivity,it has become the latest and trendiest sales mode of e-commerce channels,reflecting huge economic potential and commercial value.This article analyzes two models and their characteristics of live-streaming sales from a practical perspective.Based on this,it outlines consumer purchasing decisions and the factors that affect consumer purchasing decisions under the live-streaming sales model.Finally,it discusses targeted suggestions for using the live-streaming sales model to expand the consumer market,hoping to promote the healthy and steady development of the live-streaming sales industry.
文摘With the widespread use of streaming media application on the Internet, a significant change in Internet workload will be provoked. Caching is one of the applied techniques for enhancing the scalability of streaming system and reducing the workload of server/network. Aiming at the characteristics of broadband network in community, we propose a popularity-based server-proxy caching strategy for streaming medias, and implement the prototype of streaming proxy caching based on this strategy, using RTSP as control protocol and RTP for content transport. This system can play a role in decreasing server load, reducing the traffic from streaming server to proxy, and improving the start-up latency of the client. Key words streaming server - proxy - cache - streaming media - real time streaming protocol CLC number TP 302 - TP 333 Foundation item: Supported by the National High Technology Development 863 Program of China (2001AA111011).Biography: Tan Jin (1962-), male, Ph. D candidate, research direction: network communications, multimedia technologies, and web caching.
文摘分布式拒绝服务(distributed denial of service,DDoS)攻击是重要的安全威胁,网络速度的不断提高给传统的检测方法带来了新的挑战。以Spark等为代表的大数据处理技术,给网络安全的高速检测带来了新的契机。提出了一种基于Spark Streaming框架的自适应实时DDoS检测防御技术,通过对滑动窗口内源簇进行分组,并根据与各分组内源簇比例的偏差统计,检测出DDoS攻击流量。通过感知合法的网络流量,实现了对DDoS攻击的自适应快速检测和有效响应。实验结果表明,该技术可极大地提升检测能力,为保障网络服务性能和安全检测的可扩展性提供了一种可行的解决方案。
