Development Of Internet Research Articles

Prediction of Member Forces of Steel Tubes on the Basis of a Sensor System with the Use of AI

The rapid development of AI (artificial intelligence), sensor technology, high-speed Internet, and cloud computing has demonstrated the potential of data-driven approaches in structural health monitoring (SHM) within the field of structural engineering. Algorithms based on machine learning (ML) models are capable of discerning intricate structural behavioral patterns from real-time data gathered by sensors, thereby offering solutions to engineering quandaries in structural mechanics and SHM. This study presents an innovative approach based on AI and a fiber-reinforced polymer (FRP) double-helix sensor system for the prediction of forces acting on steel tube members in offshore wind turbine support systems; this enables structural health monitoring of the support system. The steel tube as the transitional member and the FRP double helix-sensor system were initially modeled in three dimensions using ABAQUS finite element software. Subsequently, the data obtained from the finite element analysis (FEA) were inputted into a fully connected neural network (FCNN) model, with the objective of establishing a nonlinear mapping relationship between the inputs (strain) and the outputs (reaction force). In the FCNN model, the impact of the number of input variables on the model’s predictive performance is examined through cross-comparison of different combinations and positions of the six sets of input variables. And based on an evaluation of engineering costs and the number of strain sensors, a series of potential combinations of variables are identified for further optimization. Furthermore, the potential variable combinations were optimized using a convolutional neural network (CNN) model, resulting in optimal input variable combinations that achieved the accuracy level of more input variable combinations with fewer sensors. This not only improves the prediction performance of the model but also effectively controls the engineering cost. The model performance was evaluated using several metrics, including R2, MSE, MAE, and SMAPE. The results demonstrated that the CNN model exhibited notable advantages in terms of fitting accuracy and computational efficiency when confronted with a limited data set. To provide further support for practical applications, an interactive graphical user interface (GUI)-based sensor-coupled mechanical prediction system for steel tubes was developed. This system enables engineers to predict the member forces of steel tubes in real time, thereby enhancing the efficiency and accuracy of SHM for offshore wind turbine support systems.

CLEAR: Multimodal Human Activity Recognition via Contrastive Learning Based Feature Extraction Refinement

Human activity recognition (HAR) has become a crucial research area for many applications, such as Healthcare, surveillance, etc. With the development of artificial intelligence (AI) and Internet of Things (IoT), sensor-based HAR has gained increasing attention and presents great advantages to existing work. Relying solely on existing labeled data may not adequately address the challenge of ensuring the model’s generalization ability to new data. The ’CLEAR’ method is designed to improve the accuracy of multimodal human activity recognition. This approach employs data augmentation, multimodal feature fusion, and contrastive learning techniques. These strategies are utilized to refine and extract highly discriminative features from various data sources, thereby significantly enhancing the model’s capacity to identify and classify diverse human activities accurately. CLEAR achieves high generalization performance on unknown datasets using only training data. Furthermore, CLEAR can be directly applied to various target domains without retraining or fine-tuning. Specifically, CLEAR consists of two parts. First, it employs data augmentation techniques in both the time and frequency domains to enrich the training data. Second, it optimizes feature extraction using attention-based multimodal fusion techniques and employs supervised contrastive learning to improve feature discriminability. We achieved accuracy rates of 81.09%, 90.45%, and 82.75% on three public datasets USC-HAD, DSADS, and PAMAP2, respectively. Additionally, when the training data are reduced from 100% to 20%, the model’s accuracy on the three datasets decreases by only about 5%, demonstrating that our model possesses strong generalization capabilities. Additionally, when the training data are reduced from 100% to 20%, the model’s accuracy on the three datasets decreases by only about 5%, demonstrating that our model possesses strong generalization capabilities.

An optimized lightweight real-time detection network model for IoT embedded devices.

With the rapid development of Internet of Things (IoT) technology, embedded devices in various computer vision scenarios can realize real-time target detection and recognition tasks, such as intelligent manufacturing, automatic driving, smart home, and so on. YOLOv8, as an advanced deep learning model in the field of target detection, has attracted much attention for its excellent detection speed, high precision, and multi-task processing capability. However, since IoT embedded devices typically own limited computing resources, direct deployment of YOLOv8 is a big challenge, especially for real-time detection tasks. To address this vital issue, this work proposes and deploys an optimized lightweight real-time detection network model that well-suits for IoT embedded devices, denoted as FRYOLO. To evaluate its performance, a case study based on real-time fresh and defective fruit detection in the production line is performed. Characterized by low training cost and high detection performance, this model accurately detects various types of fruits and their states, as the experimental results show that FRYOLO achieves 84.7% in recall and 89.0% in mean Average Precision (mAP), along with a precision of 92.5%. In addition, it provides a detection frame rate of up to 33 Frames Per Second (FPS), satisfying the real-time requirement. Finally, an intelligent production line system based on FRYOLO is implemented, which not only provides robust technical support for the efficient operation of fruit production processes but also demonstrates the availability of the proposed network model in practical IoT applications.

Internet Development Empowering Innovation Activities to Achieve Efficient and Balanced Development

Based on China's provincial panel data from 2011 to 2021, this study examines the relationship between Internet development and regional innovation efficiency. The results show that Internet development can effectively improve innovation efficiency in China, and the promoting effect in the central region & western region is obviously stronger than that in the eastern region. Internet development can accelerate the convergence of the nationwide innovation efficiency gap in China, but it will hinder the convergence of innovation efficiency in the central region & western region and expand the innovation efficiency gap among its provinces. The coupling coordination degree between Internet development and innovation efficiency in China shows a steady upward trend, but there are obvious regional differences. During this period, the average coupling level in the eastern region has reached 7, which is in the primary coordination stage, while that in the central region & western region is only 5, which is on the verge of imbalance.

Examining the Versatility and Misuse of Information Communication Technology in Community Policing in Malawi

Technology has modernized citizens’ social networks and interaction gaining active engagement to security management. Internet evolution popularity have revolutionized public policing through technology creating superhuman over security challenges. Paper aims to examine the versatility and misuse of technology in community policing in Malawi. This is qualitative study that purposely selected five published papers from a research “Community policing, citizen’s participation, and information communication technology in Muloza, Malawi.” Systematic review of published papers through content analysis will identify and consolidate work to date and guide future research on the topic. Data analysis followed transcription, coding, codes grouped into sub themes, sub themes into themes answering research questions. Respondents answered to (a) what are versatilities of information communication technology in security? (b) What are misuses of information communications technology in security? Through frame theory of communication, social disorganization and participatory democracy theories results indicate (1) technologies store, replicate, disseminate data for depositing evidence (2) Empower citizens to (a) communicate, share information between citizens and police, (b) be aware of rights violations, safety needs, (c) allow people’s involvement in rights violations, (d) facilitate citizen’s involvement in crime prevention, (e) maintain community dialogue and engagement, (f) help citizens look for faster emergency response times (g) promote evidence sharing with authorities. (3) Revolutionize communications allowing use SMS, WhatsApp, Telephone calls, pictures, videos, VNs, promoting wide, formal and non-formal channels improving accountability and transparency according real-time updates, responses, capacity. (4) Enhance citizens’ engagement to (i) reporting, requesting transport to police, (ii) organizing patrols (iii) mobilizing help during neighborhoods activities. (5) Encourage citizens to participate virtually allowing citizens (i) improve communications between police and community, (ii) identify a wider variety of insecurities (Transparency), (iii) promote the effective use of limited resources, (iv) move beyond the customary bureaucratic procedures, (v) improve participation of women, minorities, and vulnerable population, (vi) contribute to empowerment of the community. Technology misuse in security were; (a) some officials could inform suspects about intended ambushes (b) some citizens could fabricate false information to deceive others (d) some citizens used social media to deal with marginalized groups (e) some officials and citizens could disseminate and replicate confidential organization official client information. Other studies have concluded technology may help reduce corruption through wide formal and non-formal interactions. Currently, technology empower, revolutionize, transform citizens to get involved to fight corruption. Recommends more civic education to masses on need to adopt use of digital platform of community policing.

Evaluation of the Application Effect of Streaming Media Technology in College English Teaching

With the development of science and technology and the popularity of the Internet, streaming media technology has gradually become an important tool in college English teaching. Streaming media technology is a technology to transmit audio, video and other multimedia content through the network. With the rapid development of Internet, streaming media technology has been widely used in the field of education. The goal of college English teaching is to cultivate students' English communication ability and cross-cultural communication ability, and streaming media technology can provide diverse and interactive learning resources and learning environment for English teaching. Therefore, this paper aims to study the application and combination of streaming media technology in college English teaching, so as to carry out college English teaching research based on streaming media technology.

Impact of internet development on transportation carbon emissions: Evidence from China

The development of the internet is of great significance for carbon reduction in the transportation sector. Drawing on panel data from 31 provinces in China spanning 2011 to 2021, we present novel evidence from the transportation sector. We construct an internet development assessment system comprising 18 indicators and compute an internet development index from four dimensions. Employing OLS, mediation analysis, and dynamic threshold panel models, we validate the relationship between the internet development index and carbon emissions in the transportation sector. Results indicate a significant increase in transportation sector carbon emissions with internet development, with a 0.072% rise in emissions for every 1% increase in the internet development index. This is primarily attributed to the influence of internet development on increasing internet shopping, vehicle ownership, and travel scale. Importantly, in regions with higher transportation sector carbon emissions, such as Shanghai, internet development may also lead to emission reductions. Thus, the Chinese government should pay particular attention to strategically employing internet technology in different regions, leveraging its technical advantages to promote carbon emission reduction strategies in the transportation sector.

Optoelectronic array of photodiodes integrated with RRAMs for energy-efficient in-sensor computing

The rapid development of internet of things (IoT) urgently needs edge miniaturized computing devices with high efficiency and low-power consumption. In-sensor computing has emerged as a promising technology to enable in-situ data processing within the sensor array. Here, we report an optoelectronic array for in-sensor computing by integrating photodiodes (PDs) with resistive random-access memories (RRAMs). The PD-RRAM unit cell exhibits reconfigurable optoelectronic output and photo-responsivity by programming RRAMs into different resistance states. Furthermore, a 3 × 3 PD-RRAM array is fabricated to demonstrate optical image recognition, achieving a universal architecture with ultralow latency and low power consumption. This study highlights the great potential of the PD-RRAM optoelectronic array as an energy-efficient in-sensor computing primitive for future IoT applications.

Deployment of Hybrid Chaotic Hashes for Blockchain Driven Internet 4.0 applications

The evolution of Internet 4.0 demands robust, secure, and scalable solutions to meet the growing needs of digital transactions and interconnectivity, and blockchain technology has emerged as a foundational enabler for these applications. However, blockchain's reliance on traditional cryptographic methods presents vulnerabilities that can be exploited in increasingly sophisticated cyber landscapes. This paper introduces the deployment of Hybrid Chaotic Hashes for enhanced security and efficiency in blockchain-driven Internet 4.0 applications. By integrating chaotic systems with hash functions, hybrid chaotic hashes provide a more unpredictable, complex cryptographic layer that enhances data integrity, confidentiality, and resistance to attacks. The unique properties of chaotic functions—nonlinearity, ergodicity, and sensitivity to initial conditions—make them advantageous for hashing in blockchain environments. This study highlights the practical applicability and resilience of hybrid chaotic hashes which is nonlinear technique in Internet 4.0.

Development of an autonomous Internet of Things system for distributed production of boxed flat sheet metal parts in railcar manufacturing

AbstractRailcar manufacturing takes place in a multi‐tier production environment, where scheduling and resource allocation are two of many critical components of operations management. In general, organizations harness the opportunities presented by enterprise information systems (EIS) with connected devices to enhance the efficiency of their supply chains. However, systematic vulnerabilities emerge in these EIS due to a lack of unified and synchronized data management systems across enterprises. Industry 4.0 (i4.0) prompted the adoption of cyber‐physical systems (CPS) in industry, creating new opportunities to address these vulnerabilities. A key challenge identified in planning and developing autonomous Internet of Things (IoT) system lies in integrating and managing data from multiple sources, which come in various formats, and is generated in different scales, using different platforms. Hence, the study centred on developing a cloud‐native autonomous manufacturing application, with bi‐directional data flow for production of boxed flat sheet metal parts in a distributed network of manufacturing suppliers. Serverless computing enables integration of information technology and operational technology (IT/OT) for real‐time control and monitoring of industrial Internet of Things operations (IIoT). An autonomous manufacturing system is developed for the closed‐loop manufacturing supply chain (SC), utilizing a system‐to‐system (SoS) approach.

Development of electrical branch circuit with smart control system

The development of Internet of Things (IoT) technology has opened new opportunities for automated and smart systems. The main purpose of the study was to develop an electrical branch circuit with a smart control system. The specific objectives of this study were to describe the electrical branch circuit with smart control system in terms of technical features, composition, operating performance, and safety, determine the connectivity of the device in terms of signal responses to varying internet speeds, determine the sensitivity of the device in varying distances using Bluetooth, and determine the acceptability of the device in terms of technical features, composition, operating performance, and safety. The research used a developmental method, and the evaluation was conducted using a researcher-made questionnaire, validated by the advisory and examining committee. Forty-two evaluators assessed the developed device. The device has Wi-Fi and Bluetooth capabilities for flexible operation that can be controlled remotely via an app and has programmable settings, manual overrides, and built-in safety precautions. The findings of the study further revealed that the device could still connect with an internet speed ranging from 100-150 kbps and its connectivity in terms of signal responses improved with increased internet speed. It was also revealed that the device was sensitive at 5 to 10 meters but its sensitivity decreased at 15 meters, suggesting that its sensitivity diminishes over greater distances. Lastly, the device was rated as very acceptable in terms of technical features, composition, operating performance, and safety, indicating its capability to operate safely and reliably.

A Review and Comparative Study of Cloud Computing and the Internet of Things

The Internet of Things (IoT) and cloud computing are two distinct ideas that are impacting our lives in various ways and will continue to be utilized and emphasized in the future of the Internet. These two ideas are combined and compared in this study. IoT and cloud computing have received independent attention in numerous research. However, these studies do not go deep enough into its combination which gives rise to new problems. IoT is experiencing increasing development as the next Internet evolution. IoT allows billions of devices to interact with each other and assign more value to our daily lives. In this paper, we present a literature review of cloud computing and IoT. First, we examine the fundamentals of cloud computing and IoT. Then, we talk about how they complement one another and what are their main differences. IoT can greatly benefit from the abundance of resources found in the cloud.

Lightweight Auxiliary Sports Training Quality Evaluation Based on Edge‐Cloud Collaboration

ABSTRACTWith the rapid development of artificial intelligence (AI) and internet of things (IoTs) technologies, it has become a challenge task to assist sports training using intelligent system in physical education. This paper establishes a lightweight assisted sports training quality evaluation system using edge‐cloud computing and AI technology to weaken this issue. First, the skeleton of person during sports training is captured by Kinect camera; then, the skeleton is represented as joint angle feature vector; lastly, the joint angle feature vector is sent to cloud server in which an ordinal regression forest is deployed. Compared with complex deep learning model, both skeletal joint angle feature extraction and ordinal regression forest need fewer computing resources, which can adapt limited resources in devices under IoT environment. On the other hand, ordinal regression forest can reflect the ordinal relationship between sports training quality levels to reach lower mean absolute error (MAE). The experiments show the effectiveness of the proposed sports training quality evaluation system.

Development of Internet of Things (IoT) and its Impact on the Healthcare Industry

The Internet of Things (IoT) has the potential to modernize healthcare through remote patient monitoring, disease management, and improved health outcomes. IoT devices, such as wearables and sensors, can collect and transmit data in real time, enabling healthcare providers to make informed and timely decisions. This paper examines the current state of the Internet of Things in healthcare, the opportunities presented by the Internet of Things, and the challenges faced during implementation. This paper first describes the benefits of IoT in healthcare, including improving patient outcomes, reducing healthcare costs, and increasing access to care. We look at a variety of IoT devices used in healthcare, including wearables, smart implants, and medical sensors. This paper also outlines the challenges facing the use of IoT in healthcare, including data security, privacy issues, and regulatory issues. The paper concludes with a discussion of the future of IoT in healthcare and its potential impact on the healthcare industry.

Profitability and Bank De‐Branching in the Digital Age: Evidence From Russian Regions

ABSTRACTThis study examines the relationship between bank de‐branching and profitability in Russian regions depending on the region's internet infrastructure development and financial digital literacy. Using data from 84 Russian regions from 2010 to 2020 and panel data analysis techniques, we test whether the adequacy of internet infrastructure for online banking and financial digital literacy are positively related to bank de‐branching and whether they add to bank de‐branching efficiency in terms of bank profitability. We show that a higher degree of the region's internet infrastructure development and higher levels of financial digital literacy are positively related to bank de‐branching. We observe that banks closing their branches in regions exhibiting higher levels of internet development and financial digital literacy gain more profit from de‐branching. This study adds to the literature by being the first to examine whether financial digital literacy and the development of internet technologies enhance the positive effect of bank de‐branching on bank profitability.

A Novel Sensor Deployment Strategy Based on Probabilistic Perception for Industrial Wireless Sensor Network

The rapid development of Industrial Internet of Things (IIoT) technology has highlighted the critical role of wireless sensor networks in enabling intelligent production and equipment monitoring. Effective sensor deployment is essential for ensuring communication quality and transmission speed in IIoT environments. This paper presents a novel sensor deployment strategy that integrates four key metrics: deployment cost, energy consumption, network connectivity, and sensing probability. To address the challenges of multi-dimensional optimization, the proposed method normalizes these metrics and assigns appropriate weights based on their relative importance. A major innovation of this approach is the inclusion of larger-scale environmental obstacles, which enhances its adaptability to diverse industrial settings and specific deployment scenarios. Through a comprehensive set of simulation experiments across different scenarios, the proposed particle swarm/genetic hybrid algorithm demonstrates superior performance compared to existing methods, even surpassing 10% in performance. Specifically, it excels in optimizing the newly introduced network performance metric and significantly improves search convergence time, making it a highly efficient and effective solution for sensor network optimization in IIoT applications.

Age of Information Analysis for Multi-Priority Queue and Non-Orthoganal Multiple Access (NOMA)-Enabled Cellular Vehicle-to-Everything in Internet of Vehicles.

With the development of Internet of Vehicles (IoV) technology, the need for real-time data processing and communication in vehicles is increasing. Traditional request-based methods face challenges in terms of latency and bandwidth limitations. Mode 4 in cellular vehicle-to-everything (C-V2X), also known as autonomous resource selection, aims to address latency and overhead issues by dynamically selecting communication resources based on real-time conditions. However, semi-persistent scheduling (SPS), which relies on distributed sensing, may lead to a high number of collisions due to the lack of centralized coordination in resource allocation. On the other hand, non-orthogonal multiple access (NOMA) can alleviate the problem of reduced packet reception probability due to collisions. Age of Information (AoI) includes the time a message spends in both local waiting and transmission processes and thus is a comprehensive metric for reliability and latency performance. To address these issues, in C-V2X, the waiting process can be extended to the queuing process, influenced by packet generation rate and resource reservation interval (RRI), while the transmission process is mainly affected by transmission delay and success rate. In fact, a smaller selection window (SW) limits the number of available resources for vehicles, resulting in higher collisions when the number of vehicles is increasing rapidly. SW is generally equal to RRI, which not only affects the AoI part in the queuing process but also the AoI part in the transmission process. Therefore, this paper proposes an AoI estimation method based on multi-priority data type queues and considers the influence of NOMA on the AoI generated in both processes in C-V2X system under different RRI conditions. Our experiments show that using multiple priority queues can reduce the AoI of urgent messages in the queue, thereby providing better service about the urgent message in the whole vehicular network. Additionally, applying NOMA can further reduce the AoI of the messages received by the vehicle.

AI-Driven Data Processing and Decision Optimization in IoT through Edge Computing and Cloud Architecture

This study discussion point of this paper is to make an in-depth analysis of the development impact of the Internet of Things combined with edge computing and artificial intelligence. In the analysis process, the importance and criticality of data processing and decision making of edge computing as well as the challenges faced should be elaborated respectively. With the rapid popularization and development of Internet of Things devices, edge computing has brought more innovative solutions for different application scenarios such as intelligent furniture industrialization, automatic driving and intelligent transportation by reducing the delay of processing data and improving the characteristics of security data, film and television. Resource and energy efficiency have certain limitations, so it is necessary to combine artificial intelligence to enhance edge computing devices, hardware accelerators, and its utility and federated learning technologies, which can effectively improve the performance and scalability of edge computing and promote the development of more self-service network systems for smart devices. The core of this study is how to promote the Internet through AI-driven edge computing to further develop and provide insights for research priorities and suggest related future research directions.

Online pharmacy shopping in Southern Vietnam: a cross-sectional study

Abstract Objectives With the rapid internet development, online pharmacy shopping has become a popular activity worldwide. Nevertheless, limited studies have reported this issue in Vietnam. Thus, this research explored the prevalence, associated factors, attitudes, behaviors, and satisfaction related to online pharmaceutical purchasing among customers in Southern Vietnam, as well as to predict factors associated with online pharmacy use. Methods A descriptive cross-sectional study was conducted from June to September 2022 in the southern provinces of Vietnam. Participants were recruited from public health-related Facebook groups via a self-administered questionnaire link. Data were analyzed using descriptive statistics and binary logistic regression. Key findings Out of 557 survey respondents, 38.2% reported having purchased pharmaceuticals online, with dietary supplements (82.2%) being the most bought products. The respondents who were >25 years old (adjusted odds ratio [aOR] = 2.527; 95% confidence interval [CI]: 1.002–6.372), having prior online shopping experience for non-pharmaceutical consumer products (aOR = 3.076; 95% CI: 1.252–7.561), and possessing positive attitude toward online pharmacy shopping (aOR = 2.886; 95% CI: 2.027–4.110), were more likely to purchase online pharmaceuticals. A quarter of respondents reported experiencing adverse effects from the products bought online (25.8%). Additionally, compared with the traditional pharmacy, the respondents were more satisfied with the product diversity (77%) but less satisfied with the consultancy activities (44.6%) when buying drugs online. Conclusions The study revealed that purchasing pharmaceutical products online was somewhat common among the survey participants, which highlights the need for improved consultancy activities in online pharmacies in Vietnam.

Strategies for the Future Development of Television Advertising

This study examines the current status and future development strategies of television advertising regarding economic development. Firstly, it points out that the rapid development of the mobile Internet has profoundly impacted the decline in audience demographics, competitiveness, changes in ratings, and economic revenues of traditional television advertising. It then analyses the impact on the TV advertising economy of the changing social environment, the extensive use and updating of media technologies, the preferences of the millennial generation, and the rising costs and declining effectiveness of advertising. Finally, it proposes development strategies that should actively utilise the unique characteristics of TV advertising, expand advertising channels, reduce costs, and innovate content and formats. This study comprehensively analyses the challenges faced by TV advertising. It proposes strategies to address them, which are of great reference value for understanding the current development of the TV advertising market and formulating future development strategies.