1. Introduction
Fifth-generation (5G) wireless technology represents a monumental leap in telecommunications, offering unparalleled speed, ultra-low latency, and extensive device connectivity compared to its predecessor, 4G LTE. Engineered to address the escalating demands of modern digital ecosystems, 5G is poised to revolutionize industries and redefine the landscape of digital applications. Among its most significant contributions is the seamless integration of cloud and edge computing, a paradigm shift that enhances data processing and delivery across various sectors [
1,
2,
3].
One of the defining features of 5G is its capacity to bridge the gap between centralized cloud systems and decentralized edge architectures. By enabling data processing and storage to occur closer to the end user, 5G mitigates the limitations of traditional cloud-based systems. This proximity enhances responsiveness and efficiency, making it an ideal solution for latency-sensitive applications such as autonomous vehicles, telemedicine, and industrial IoT (IIoT) [
4,
5,
6]. Furthermore, its ability to support real-time data exchange and localized processing empowers a new generation of applications that demand both speed and reliability.
The technical capabilities of 5G—such as Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and Massive Machine-Type Communications (mMTC)—are pivotal in enabling cloud-to-edge integration. These features support real-time decision-making and processing, which are critical for advanced technologies, including augmented reality (AR), virtual reality (VR), and smart manufacturing [
3,
7,
8]. Recent studies have demonstrated how URLLC improves industrial robotics and collaborative automation, while eMBB and mMTC drive high-speed streaming and dense IoT connectivity in immersive applications [
9,
10].
This paradigm shift underscores the rising importance of edge intelligence, wherein computational tasks are offloaded from centralized servers to edge nodes for improved efficiency and reduced latency [
7,
11,
12,
13,
14].
The confluence of 5G, cloud computing, and edge computing is driving innovation across a diverse array of industries. In healthcare, for instance, 5G-powered telemedicine solutions facilitate real-time, high-quality video consultations and remote surgeries, significantly improving patient care and access to medical services [
15]. Similarly, in smart cities, 5G enhances IoT device communication to optimize traffic management, energy distribution, and public safety systems [
16,
17]. These examples highlight the transformative potential of 5G in reshaping industrial and consumer-facing technologies alike.
However, despite its numerous advantages, the integration of 5G with cloud and edge computing is not without challenges. Issues such as network security vulnerabilities, infrastructure deployment complexities, and the need for global standardization continue to hinder widespread adoption [
8,
18,
19,
20]. Current research efforts are aimed at overcoming these obstacles, with a focus on optimizing 5G technologies for emerging domains such as AI-driven automation, blockchain-based financial transactions, and real-time analytics [
21,
22].
This paper is organized as follows:
Section 2 delves into the core features of 5G technology and its pivotal role in cloud-to-edge integration.
Section 3 investigates the diverse industrial and consumer applications of 5G-driven architectures.
Section 4 outlines the challenges and opportunities in 5G adoption and its broader implications.
Section 5 presents simulation-based performance analysis comparing 5G cloud-to-edge systems with traditional 4G cloud models. Finally,
Section 6 concludes with a summary of findings and suggests future research directions to enhance the potential of 5G in evolving digital ecosystems.
2. 5G Technology: A Catalyst for Cloud-to-Edge Integration
The transformative power of 5G lies in its ability to bridge the gap between centralized cloud systems and decentralized edge architectures. By enabling seamless data exchange and real-time processing, 5G acts as a catalyst for cloud-to-edge integration, effectively addressing challenges related to latency, bandwidth, and connectivity.
2.1. Key Features of 5G and Their Relevance to Computing Paradigms
5G introduces several key technical enhancements that distinguish it from previous wireless generations, making it a cornerstone of modern computing frameworks.
2.1.1. Enhanced Mobile Broadband (eMBB)
eMBB leverages the millimeter wave (mmWave) spectrum to provide ultra-high data rates, enabling peak downlink speeds of up to 10 Gbps. The high-frequency mmWave bands (e.g., 28 GHz and 39 GHz) offer wide-bandwidth channels, although at the cost of reduced range and higher path loss.
To mitigate this, Massive MIMO (Multiple Input–Multiple Output) and beamforming algorithms are employed. Beamforming directs energy toward specific users using phase-controlled antenna arrays, reducing interference and increasing capacity in dense deployments like smart stadiums or AR/VR environments.
2.1.2. Ultra-Reliable Low-Latency Communications (URLLC)
URLLC is designed to provide end-to-end latency below 1 millisecond and reliability up to 99.999%. It achieves this using the following:
Grant-free access, which eliminates scheduling delays for urgent data transmission.
Preemption-based scheduling, where latency-sensitive URLLC traffic interrupts ongoing eMBB flows.
HARQ (Hybrid Automatic Repeat reQuest) at the MAC layer, which uses incremental redundancy and retransmissions to increase robustness.
These mechanisms are crucial in applications such as remote robotic surgery, autonomous driving, and industrial automation.
2.1.3. Massive Machine-Type Communications (mMTC)
mMTC focuses on connecting one million plus low-power devices per square kilometer, typical in smart cities or precision agriculture. It employs the following:
Lightweight protocols such as NB-IoT and LTE-M.
Random Access Channel (RACH) overload control techniques, like Extended Access Barring (EAB), to handle massive simultaneous access attempts.
Energy-efficient transmission, enabling battery lifetimes of over 10 years for simple sensors.
mMTC is pivotal for scalable, distributed IoT systems where latency is less critical but efficient access and low power are paramount.
2.1.4. Network Slicing
5G uses network slicing to create multiple isolated virtual networks over a shared infrastructure, each optimized for specific services:
eMBB slices for high-throughput video.
URLLC slices for real-time industrial control.
mMTC slices for IoT density.
This slicing is enabled via NFV (Network Function Virtualization) and SDN (Software-Defined Networking), offering flexibility, security, and QoS customization.
Table 1 highlights key differences between 4G and 5G, emphasizing advancements relevant to computing paradigms.
The table emphasizes key advancements brought by 5G over 4G LTE. With 5G, peak data rates increase tenfold, drastically reducing time delays in data transmission. Ultra-low latency (~1 ms) ensures real-time responsiveness, critical for applications such as autonomous vehicles and telesurgery. The ability to connect up to one million devices per square kilometer transforms the scalability of IoT systems. Finally, network slicing, a feature unique to 5G, allows for tailored network configurations, optimizing performance for specific use cases like gaming or industrial automation.
2.2. How 5G Enhances Connectivity and Data Transmission
The integration of 5G with cloud-to-edge frameworks redefines connectivity and data transmission across various domains, enabling faster, more efficient communication between cloud, edge, and end-user devices.
2.2.1. Reduced Latency for Real-Time Applications
5G’s ultra-low latency is a pivotal factor in enabling edge computing. By allowing data processing to occur close to the data source, 5G minimizes the delays typically associated with centralized cloud systems. In smart factories, for example, 5G enables real-time communication between robots and control systems, drastically reducing production disruptions caused by communication delays.
2.2.2. Increased Bandwidth for Large-Scale Data Handling
The expanded bandwidth offered by 5G ensures that large volumes of data can be efficiently transferred between cloud and edge nodes. This enhanced capacity is particularly critical for applications like video analytics, machine learning, and big data processing, where massive datasets must be transferred, processed, and shared across distributed systems.
2.2.3. Data Transmission Time Reduction
The time required to transmit data over a network can be expressed as follows:
where
- -
T is the transmission time;
- -
D is the data size;
- -
R is the transmission rate.
With 5G, the transmission rate, R, is significantly higher than that of previous generations, leading to a substantial reduction in transmission time, T, and improving the efficiency of data transfer for applications that require rapid data transfer, such as real-time analytics and AI-driven decision-making.
2.2.4. Reliable Connectivity for IoT Ecosystems
5G’s capability to support massive connectivity ensures the seamless operation of IoT ecosystems. For instance, in smart cities, devices such as traffic sensors, surveillance cameras, and public transport systems communicate in real time to optimize urban infrastructure. This interconnectedness enhances decision-making and operational efficiency.
2.2.5. 5G-Driven Cloud-to-Edge Data Flow
The diagram in
Figure 1 represents the interaction between IoT devices, edge nodes, and cloud servers enabled by 5G. Data flows seamlessly in real time, with critical tasks processed at the edge to minimize delays and resource-intensive tasks handled in the cloud. 5G’s high-speed connectivity and low latency ensure that end users receive timely insights or control capabilities, essential for applications like remote surgery, autonomous systems, and smart cities.
Figure 1 shows a cloud-to-edge architecture empowered by 5G. Real-time data is collected by IoT devices, preprocessed at edge nodes for low-latency decisions, and sent to cloud servers for deeper analysis and storage. End users receive fast, actionable insights through mobile or dashboard interfaces.
This architecture highlights 5G’s role in bridging cloud and edge systems, enabling real-time applications and efficient data handling. It minimizes the round-trip delay of data transmission by offloading time-critical tasks to edge nodes, optimizing response time and reducing cloud congestion.
2.3. Comparative Analysis: 4G Cloud vs. 5G Cloud-to-Edge Architectures
The figure below illustrates the key architectural differences between 4G-based centralized cloud systems and modern 5G-enabled cloud-to-edge ecosystems. The 5G model incorporates edge nodes to process data closer to IoT devices, drastically reducing latency and enabling massive device connectivity.
To further clarify the shift from legacy cloud models to next-generation distributed architectures,
Figure 2 presents a side-by-side comparison of traditional 4G cloud computing and 5G-powered cloud-to-edge systems. In the 4G model, all data is routed directly from IoT devices to a centralized cloud, resulting in higher transmission delays (30–60 ms) and limited device density (~10,000/km
2).
In contrast, the 5G model introduces edge nodes that act as intermediate processing layers, allowing ultra-low latency (~1 ms) and supporting up to 1,000,000 devices per square kilometer. This architectural shift dramatically improves system responsiveness, scalability, and suitability for real-time applications like autonomous vehicles, AR/VR, and smart cities.
3. Applications of 5G-Driven Cloud-to-Edge Architecture
The integration of 5G with cloud-to-edge computing is transforming both industrial and consumer applications by enabling real-time, scalable, and highly efficient systems. This section explores the significant advancements brought by 5G-driven architectures in industrial domains such as IoT, AI, and smart cities, as well as in consumer technologies like AR/VR and autonomous systems.
3.1. Transformations in Industrial Applications
3.1.1. Internet of Things (IoT)
The Internet of Things is one of the primary beneficiaries of 5G-driven cloud-to-edge architectures. With its support for massive device connectivity (mMTC), 5G enables seamless communication among billions of IoT devices, ensuring reliable data exchange.
IoT-enabled smart grids use sensors to monitor and manage electricity distribution in real time. 5G allows edge nodes to process this data locally, enabling dynamic adjustments to energy supply and demand.
The figure below (
Figure 2) illustrates how IoT devices (sensors and actuators) communicate through edge nodes and cloud servers via 5G, ensuring real-time responses and efficient data management.
Figure 3 shows the data communication pathway in a 5G-powered IoT system. Sensors and actuators exchange data through edge nodes and cloud services, enabling fast decision-making in time-sensitive IoT use cases.
Compared to 4G-based IoT systems, this 5G-enabled model reduces latency by up to 90% and supports 100× more devices per square kilometer.
3.1.2. Artificial Intelligence (AI)
AI applications heavily rely on real-time data processing, which is significantly enhanced by 5G’s low-latency and high-bandwidth capabilities.
In manufacturing, edge-based AI systems analyze data from machinery in real time to predict failures, reducing downtime and costs.
AI-based systems’ prediction accuracy, A, can be modeled as follows:
where
- ▪
D: Amount of real-time data processed;
- ▪
R: Data transmission rate (enabled by 5G);
- ▪
L: Latency in decision-making.
With 5G increasing R and reducing L, A improves significantly.
3.1.3. Smart Cities
Smart cities utilize 5G-enabled IoT devices to enhance urban infrastructure and improve quality of life for citizens.
Traffic Optimization: 5G enables real-time communication between traffic sensors and control systems, reducing congestion.
Public Safety: AI-powered surveillance systems use edge processing to analyze video streams in real time, ensuring faster responses to incidents.
Table 2 highlights the transformative role of 5G technology in smart city ecosystems. It identifies key applications, such as traffic management, waste management, and public safety, each supported by specific technologies like IoT and AI. For example, real-time data exchange in traffic management reduces congestion and improves safety, while smart sensors optimize waste collection and disposal. AI-powered surveillance systems enhance public safety through faster incident response. Overall, 5G enables smarter, more efficient urban infrastructure, improving the quality of life for citizens while ensuring resource optimization.
3.2. Advancements in Consumer-Facing Technologies
3.2.1. Augmented Reality (AR) and Virtual Reality (VR)
5G is revolutionizing AR and VR applications by enabling immersive, real-time experiences.
Low Latency for Interaction: Real-time responsiveness is critical for AR/VR applications, especially in gaming, training, and healthcare simulations.
Use Case (Remote Training): AR/VR systems powered by 5G allow professionals to practice in simulated environments, improving skills without physical constraints.
5G-Enhanced AR/VR Workflow: The diagram shown in
Figure 4 demonstrates how AR/VR devices use 5G to exchange data with edge nodes for real-time rendering and interactions.
Figure 4 shows an AR/VR system architecture using 5G edge computing. Real-time rendering and user interaction are handled at the edge, significantly improving responsiveness and user immersion.
3.2.2. Autonomous Systems
Autonomous systems, including self-driving cars, rely on 5G for vehicle-to-everything (V2X) communication.
Vehicle-to-Vehicle (V2V) Communication: Self-driving cars exchange real-time data on location, speed, and traffic conditions to avoid collisions.
Vehicle-to-Infrastructure (V2I) Communication: Traffic lights and road sensors relay information to vehicles for efficient navigation and safety.
Latency-Driven Safety: The minimum stopping distance (S) can be calculated as follows:
- ▪
v: Vehicle speed;
- ▪
t: Reaction time (significantly reduced by 5G’s low latency);
- ▪
a: Deceleration.
With 5G reducing t, autonomous systems can react faster, improving safety.
5G-Powered V2X Communication:
Figure 5 illustrates the interaction between vehicles, traffic infrastructure, and edge nodes via 5G, ensuring seamless and safe operation.
Figure 5 shows a vehicle-to-everything (V2X) ecosystem powered by 5G. Vehicles exchange data with infrastructure and other vehicles to improve navigation, safety, and traffic flow.
3.2.3. Comprehensive 5G Cloud-to-Edge System
The diagram depicted in
Figure 6 provides an overview of a 5G-powered cloud-to-edge system. It demonstrates how IoT devices interact with edge nodes, undergo AI processing at the edge, and communicate with cloud servers for data storage and further analysis. The system also supports real-time interactions with end users via dashboards and applications.
Figure 6 shows a full-stack 5G ecosystem. Edge AI, cloud analytics, and IoT sensing operate as an integrated system for real-time digital service delivery.
5G-driven cloud-to-edge architectures are reshaping industries and enhancing consumer technologies. By enabling IoT, AI, smart cities, AR/VR, and autonomous systems, 5G addresses challenges in real-time communication and scalability, paving the way for unprecedented innovations across sectors.
As shown in
Table 3, the 5G cloud-to-edge model significantly outperforms traditional cloud architectures across multiple performance dimensions, especially for latency-sensitive or device-dense applications.
4. Challenges and Opportunities in 5G Integration
The integration of 5G into cloud-to-edge computing ecosystems introduces significant challenges while simultaneously unlocking new opportunities for innovation. Understanding these technical and infrastructural hurdles alongside the emerging prospects is critical for realizing 5G’s full potential in transforming digital applications and services.
4.1. Technical and Infrastructural Hurdles
4.1.1. Network Deployment Complexities
Deploying a 5G network requires significant investment in infrastructure, including dense networks of small cells, fiber-optic backhauls, and advanced base stations. Challenges include the following:
High Cost: The rollout of 5G infrastructure is capital-intensive, especially in remote or underserved areas.
Spectrum Allocation: Efficient management of the radio frequency spectrum is essential to avoid interference and maximize network performance.
Geographical Variability: Urban areas see faster deployments, while rural regions face delays due to lower returns on investment.
Deployment Cost Analysis: The total cost of deploying 5G (
) can be expressed as follows:
where
- ▪
: Number of small cells;
- ▪
: Cost per small cell;
- ▪
: Number of base stations;
- ▪
: Cost per base station;
- ▪
: Miscellaneous costs (spectrum licenses and maintenance).
4.1.2. Cybersecurity Concerns
The increased connectivity of 5G networks introduces vulnerabilities that malicious actors can exploit. Key concerns include the following:
IoT Device Security: With billions of connected devices, securing each endpoint becomes a complex task.
Data Privacy: Sensitive data exchanged over 5G networks requires robust encryption and access controls.
Distributed Attack Risks: 5G’s decentralized architecture is vulnerable to distributed denial-of-service (DDoS) attacks targeting edge nodes.
4.1.3. Latency Management
While 5G significantly reduces latency, maintaining consistent low latency across a wide geographical area is a challenge. Factors include the following:
Network Congestion: High traffic can lead to delays, particularly in densely populated areas.
Interference: Environmental and physical obstructions affect signal quality, impacting latency-sensitive applications.
Edge Node Distribution: A lack of edge nodes in certain regions can lead to increased latency for end users.
4.2. Emerging Opportunities in Digital Applications and Services
Despite these challenges, the integration of 5G presents groundbreaking opportunities to innovate and enhance digital ecosystems.
4.2.1. Advancements in Real-Time Applications
5G enables real-time data exchange for applications in healthcare, manufacturing, and transportation.
Telemedicine: Remote surgeries and real-time diagnostics benefit from 5G’s low latency and high reliability.
Autonomous Systems: Self-driving cars and drones rely on rapid communication for navigation and decision-making.
4.2.2. IoT Ecosystems
With massive connectivity (mMTC), 5G supports large-scale IoT deployments in smart cities and industrial settings.
Smart Infrastructure: Energy grids, water systems, and transportation networks become more efficient through real-time monitoring and control.
Agriculture: 5G-connected sensors enable precision farming, optimizing irrigation and crop management.
4.2.3. Enhanced Consumer Experiences
Consumer-facing technologies such as AR, VR, and cloud gaming are revolutionized by 5G’s high bandwidth and low latency.
AR/VR Immersion: Education, entertainment, and training are enhanced through real-time, immersive experiences.
Cloud Gaming: Platforms deliver high-quality, lag-free experiences by processing data on edge nodes.
The integration of 5G into cloud-to-edge ecosystems presents a nuanced balance between persistent challenges and emerging opportunities, as summarized in
Table 4. While infrastructure costs, cybersecurity risks, and latency management remain significant hurdles, the opportunities for transformative applications and scalable ecosystems are expanding rapidly.
Challenges such as high deployment costs for dense small-cell networks, spectrum allocation difficulties, and network congestion highlight the technical and economic complexities of 5G adoption. Additionally, the proliferation of connected devices increases vulnerabilities, making robust cybersecurity and data privacy measures essential. Latency consistency across diverse geographical and network conditions remains a critical issue, particularly for latency-sensitive applications like autonomous vehicles and industrial automation.
However, as illustrated in
Figure 7 (green dashed line), which reflects the exponential expansion of 5G-driven advancements, real-time applications, including telemedicine and augmented reality, are thriving due to 5G’s low latency and high reliability. Scalable IoT ecosystems are being established in smart cities and precision agriculture, enhancing operational efficiency and sustainability. At the same time, the decline in challenges (red dotted line) demonstrates the gradual mitigation of barriers through technological advancements like network slicing, edge computing, and cost-effective infrastructure solutions.
The Research Focus Shift (blue solid line) in
Figure 6 complements the insights in
Table 3, showing that initial efforts concentrated on addressing fundamental barriers like infrastructure and security are now transitioning to explore advanced opportunities. These include energy-efficient solutions, AI-driven network optimization, and the standardization of 5G frameworks to enable equitable global adoption.
Table 3 reinforces the conclusion that while challenges persist, they are steadily diminishing as solutions emerge. The accelerating growth of opportunities emphasizes the need for continuous innovation, collaboration, and targeted research to fully leverage the transformative potential of 5G in reshaping digital ecosystems.
5. Simulation Results and Performance Analysis
To support the conceptual framework of 5G-driven cloud-to-edge systems, this section presents a set of simulations developed using MATLAB R2022, aimed at evaluating the quantitative performance benefits of 5G architectures. Specifically, the focus is on latency, energy consumption, reliability, and prediction accuracy—key metrics for real-time and large-scale digital applications.
These simulations emulate realistic conditions using theoretical models and existing 5G parameters from the standard literature. The analysis contrasts 5G edge computing with traditional 4G cloud models across several critical scenarios, including artificial intelligence (AI) inference at the edge and massive IoT device deployments.
5.1. Edge AI vs. Cloud AI: Latency, Energy, and Accuracy
We simulated a predictive maintenance use case, where sensor data from industrial machines is processed either at the central cloud (4G model) or at the network edge (5G URLLC model). The results compare latency, energy usage, and AI model inference accuracy in both scenarios.
Table 5 shows that 5G edge computing drastically outperforms traditional cloud-based processing in terms of latency and energy efficiency while also improving prediction accuracy.
Edge-based AI inference in 5G reduces end-to-end latency by approximately 86% compared to 4G cloud-based processing. This performance gain is crucial in real-time applications like remote surgery or autonomous robotics, where milliseconds matter.
5.2. IoT Device Connectivity and Reliability
We also simulated packet delivery ratios (PDRs) under increasing IoT device density. PDRs reflect how reliably messages are delivered without loss.
Table 6 compares 4G and 5G systems in urban IoT environments as the number of connected devices increases from 10,000 to 1,000,000 per square kilometer.
While 4G experiences a steep degradation in reliability with device density, 5G’s mMTC capabilities allow it to maintain high PDRs even under extreme loads—an essential feature for smart cities, connected agriculture, and distributed sensor networks.
5.3. Latency vs. Device Density Trend
The final simulation visualizes how latency scales with device density in both 4G and 5G systems.
Figure 8 shows that while 4G latency increases sharply with more devices due to congestion and centralized processing, 5G latency remains consistently low thanks to edge offloading and a wider bandwidth.
5G edge systems sustain near-real-time responses (under 15 ms) even at densities approaching 1 million devices/km2, while 4G systems exceed 90 ms under the same conditions.
These simulations confirm that 5G-enhanced cloud-to-edge architectures deliver the following:
- -
Superior latency reduction (up to 86% faster than 4G clouds).
- -
Better energy efficiency due to local processing.
- -
Higher reliability in massive device deployments.
- -
Enhanced AI decision-making through improved data throughput and speed.
This quantitative evidence supports the claim that 5G is not only transformative in theory but also demonstrably impactful in real-world digital applications. Future work may extend these simulations with hardware-in-the-loop or real-world deployment scenarios. These empirical findings reinforce the transformative role of 5G in real-world digital systems and directly support the strategic opportunities and future directions discussed in the following conclusion.
6. Conclusions
The integration of 5G technology with cloud-to-edge computing has emerged as a transformative force, reshaping industries and consumer technologies by enabling ultra-fast, low-latency, and highly scalable solutions. This paper highlights the critical advancements facilitated by 5G, including its role in enhancing IoT ecosystems, powering real-time applications, and revolutionizing fields such as healthcare, autonomous systems, and smart cities. Despite challenges like infrastructure deployment complexities, cybersecurity concerns, and consistent latency management, the opportunities for innovation far outweigh the obstacles. The successful implementation of 5G technologies promises to drive digital transformation across a wide range of sectors, unlocking unprecedented efficiency and interconnectivity.
Future research should focus on addressing existing challenges, such as developing cost-effective infrastructure, enhancing network security protocols, and improving equitable access to 5G technologies. Exploring energy-efficient solutions, leveraging AI for automated network optimization, and standardizing global 5G frameworks will be essential in maximizing its potential. By fostering innovation and collaboration, the continued evolution of 5G will pave the way for an intelligent, connected, and sustainable digital future.
Author Contributions
Conceptualization, S.M.A. and M.A.; methodology, S.M.A. and M.A.; software, S.M.A. and M.A.; validation, S.M.A. and M.A.; formal analysis, S.M.A. and M.A.; investigation, S.M.A.; resources M.A.; data curation, S.M.A. and M.A.; writing—original draft preparation, S.M.A. and M.A.; writing—review and editing, S.M.A. and M.A.; visualization, S.M.A. and M.A.; supervision, S.M.A.; project administration, S.M.A.; funding acquisition, S.M.A. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Data Availability Statement
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.
Acknowledgments
The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number “NBU-FPEJ-2025-2429-02”.
Conflicts of Interest
The authors declare no conflicts of interest.
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