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The integration of 5G technology with cloud and edge computing is redefining the digital landscape by enabling ultra-fast connectivity, low-latency communication, and scalable solutions across diverse application domains. This paper investigates the synergistic impact of 5G on cloud-to-edge architectures, emphasizing its transformative role in revolutionizing sectors such as healthcare, smart cities, industrial automation, and autonomous systems. Key advancements in 5G—including Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and Massive Machine-Type Communications (mMTC)—are examined for their role in enabling real-time data processing, edge intelligence, and IoT scalability. In addition to conceptual analysis, the paper presents simulation-based evaluations comparing 5G cloud-to-edge systems with traditional 4G cloud models. Quantitative results demonstrate significant improvements in latency, energy efficiency, reliability, and AI prediction accuracy. The study also explores challenges in infrastructure deployment, cybersecurity, and latency management while highlighting the growing opportunities for innovation in AI-driven automation and immersive consumer technologies. Future research directions are outlined, focusing on energy-efficient designs, advanced security mechanisms, and equitable access to 5G infrastructure. Overall, this study offers comprehensive insights and performance benchmarks that will serve as a valuable resource for researchers and practitioners working to advance next-generation digital ecosystems. Full article

Simultaneous Localization and Mapping (SLAM) is a critical technology for robot intelligence. Compared to cameras, Light Detection and Ranging (LiDAR) sensors achieve higher accuracy and stability in indoor environments. However, LiDAR can only capture the geometric structure of the environment, and LiDAR-based SLAM often fails in scenarios with insufficient geometric features or highly similar structures. Furthermore, low-cost mechanical LiDARs, constrained by sparse point cloud density, are particularly prone to odometry drift along the Z-axis, especially in environments such as tunnels or long corridors. To address the localization issues in such scenarios, we propose a forward-enhanced SLAM algorithm. Utilizing a 16-line LiDAR and a monocular camera, we construct a dense colored point cloud input and apply an efficient multi-modal feature extraction algorithm based on centroid distance to extract a set of feature points with significant geometric and color features. These points are then optimized in the back end based on constraints from points, lines, and planes. We compare our method with several classic SLAM algorithms in terms of feature extraction, localization, and elevation constraint. Experimental results demonstrate that our method achieves high-precision real-time operation and exhibits excellent adaptability to indoor environments with similar structures. Full article

Ensuring dynamic risk management for intelligent connected vehicles (ICVs) in complex urban environments is critical as autonomous driving technology advances. This study presents three key contributions: (1) a comprehensive risk indicator system, constructed using entropy-based weighting, extracts 13-dimensional data on abnormal behaviors (e.g., speed, acceleration, position) to enhance safety and efficiency; (2) a multidimensional risk quantification method, simulated under single-vehicle and platooning modes on a CARLA-SUMO co-simulation platform, achieved >98% accuracy; (3) a cloud takeover strategy for high-level autonomous vehicles, directly linking risk assessment to real-time control. Analysis of 56,117 risk data points shows a 32% reduction in safety risks during simulations. These contributions provide methodological innovations and substantial data support for ICV field testing. Full article

Background: Despite the huge burden of deaths associated with or attributable to antimicrobial resistance, studies on sequencing based antimicrobial resistance (AMR) monitoring in Africa are scarce, specifically in the animal sector. Objective and Methods: With a view to deploy rapid AMR monitoring through leveraging advanced technologies, in the current study, nanopore sequencing was performed with 10 E. coli strains isolated from rectal swabs of pigs and poultry layers in Nigeria. Two sequence analysis methods including command line, where bacterial genomes were assembled, and subsequently antimicrobial resistance genes (ARGs) were detected through online databases, and EPI2ME, an integrated cloud-based data analysis platform with MinION, was used to detect ARGs. Results: A total of 95 ARGs were identified and most of the genes are known to be expressed in the chromosome. Interestingly, few genes including qnrS1, qnrS15, qnrS10, kdpE, cmlA1, MIR-14, sul3 and dfrA12 were identified which were previously reported as transferred through MGEs. The antibiotic susceptibility assay determined that the E. coli isolates were resistant to Penicillin (100%), Ciprofloxacin (70%), tetracycline (50%) and Ampicillin (40%). The accuracies of the command line and EPI2ME methods have been found to be 57.14% and 32.14%, respectively, in predicting AMR. Moreover, the analysis methods showed 62.5% agreement in predicting AMR for the E. coli isolates. Conclusions: Considering the multiple advantages of nanopore sequencing, the application of this rapid and field-feasible sequencing technique holds promise for rapid AMR monitoring in LMICs, including Nigeria. However, the development of a robust sequence analysis pipeline and the optimization of the existing analysis tools are crucial to streamline the deployment of nanopore sequencing in LMICs for AMR monitoring both in animal and human sectors. Full article

Intelligent manufacturing research has focused on digital twins (DTs) due to the growing integration of physical and cyber systems. This study thoroughly explores the Internet of Things (IoT) as a cornerstone of DTs, showing its promise and limitations in intelligent shipbuilding digital transformation workshops. We analyze the progress of IoT protocols, digital twin frameworks, and intelligent ship manufacturing. A unique bidirectional digital twin system for shipbuilding workshops uses the Internet of Things to communicate data between real and virtual workshops. This research uses a steel-cutting workshop to demonstrate the digital transformation of the production line, including data collection, transmission, storage, and simulation analysis. Then, major hurdles to digital technology application in shipbuilding are comprehensively examined. Critical barriers to DT deployment in shipbuilding environments are systematically analyzed, including technical standard unification, communication security, real-time performance guarantees, cross-workshop collaboration mechanisms, and the deep integration of artificial intelligence. Adaptive solutions include hybrid edge-cloud computing architectures for latency-sensitive tasks and reinforcement learning-based smart scheduling algorithms. The findings suggest that IoT-driven digital transformation may modernize shipbuilding workshops in new ways. Full article

As an important tangible carrier of historical and cultural heritage, ancient city walls embody the historical memory of urban development and serve as evidence of engineering evolution. However, due to prolonged exposure to complex natural environments and human activities, they are highly susceptible to various types of defects, such as cracks, missing bricks, salt crystallization, and vegetation erosion. To enhance the capability of cultural heritage conservation, this paper focuses on the ancient city wall of Jingzhou and proposes a multi-stage defect-detection framework based on computer vision technology. The proposed system establishes a processing pipeline that includes image processing, 2D defect detection, depth estimation, and 3D reconstruction. On the processing end, the Restormer and SG-LLIE models are introduced for image deblurring and illumination enhancement, respectively, improving the quality of wall images. The system incorporates the LFS-GAN model to augment defect samples. On the detection end, YOLOv12 is used as the 2D recognition network to detect common defects based on the generated samples. A depth estimation module is employed to assist in the verification of ancient wall defects. Finally, a Gaussian Splatting point-cloud reconstruction method is used to achieve a 3D visual representation of the defects. Experimental results show that the proposed system effectively detects multiple types of defects in ancient city walls, providing both a theoretical foundation and technical support for the intelligent monitoring of cultural heritage. Full article

With growing global attention on animal welfare and food safety, humane and efficient slaughtering methods in the poultry industry are in increasing demand. Traditional manual inspection methods for stunning broilers need significant expertise. Additionally, most studies on electrical stunning focus on white broilers, whose optimal stunning conditions are not suitable for red-feathered Taiwan chickens. This study aimed to implement a smart, safe, and humane slaughtering system designed to enhance animal welfare and integrate an IoT-enabled vision system into slaughter operations for red-feathered Taiwan chickens. The system enables real-time monitoring and smart management of the poultry stunning process using image technologies for dynamic object tracking recognition. Focusing on red-feathered Taiwan chickens, the system applies dynamic tracking objects with chicken morphology feature extraction based on the YOLO-v4 model to accurately identify stunned and unstunned chickens, ensuring compliance with animal welfare principles and improving the overall efficiency and hygiene of poultry processing. In this study, the dynamic tracking object recognition system comprises object morphology feature detection and motion prediction for red-feathered Taiwan chickens during the slaughtering process. Images are firsthand data from the slaughterhouse. To enhance model performance, image amplification techniques are integrated into the model training process. In parallel, the system architecture integrates IoT-enabled modules to support real-time monitoring, sensor-based classification, and cloud-compatible decisions based on collections of visual data. Prior to image amplification, the YOLO-v4 model achieved an average precision (AP) of 83% for identifying unstunned chickens and 96% for identifying stunned chickens. After image amplification, AP improved significantly to 89% and 99%, respectively. The model achieved and deployed a mean average precision (mAP) of 94% at an IoU threshold of 0.75 and processed images at 39 frames per second, demonstrating its suitability for IoT-enabled real-time dynamic tracking object recognition in a real slaughterhouse environment. Furthermore, the YOLO-v4 model for poultry slaughtering recognition in transient stability, as measured by training loss and validation loss, outperforms the YOLO-X model in this study. Overall, this smart slaughtering system represents a practical and scalable application of AI in the poultry industry. Full article

The integration of digital twin (DT) technology into circular economy (CE) frameworks has emerged as a critical pathway for achieving sustainable and intelligent manufacturing under the Industry 4.0 paradigm. This study addresses the lack of structured guidance for DT adoption in CE strategies by proposing two interrelated frameworks: the Sustainable Digital Twin Maturity Path (SDT-MP) and the Digital Twin Nexus. The SDT-MP outlines progressive stages of DT deployment—from data acquisition and real-time monitoring to AI-enabled decision-making—aligned with CE principles and Industry 4.0 capabilities. The DT Nexus complements this maturity model by structuring the integration of enabling technologies such as AI, IoT, and edge/cloud computing to support closed-loop control, resource optimization, and predictive analytics. Through a mixed-methods approach combining literature analysis and real-world case validation, this research demonstrates how DTs can facilitate lifecycle intelligence, enhance operational efficiency, and drive sustainable transformation in manufacturing. The proposed frameworks offer a scalable roadmap for intelligent circular systems, addressing implementation challenges while supporting Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure) by promoting digital infrastructure, innovation-driven manufacturing, and environmentally responsible industrial growth. This study contributes to the advancement of digital infrastructure and sustainable circular supply chains in the context of smart, connected industrial ecosystems. Full article

With the rapid development of technologies such as cloud computing, big data, and the Internet of Things (IoT), Software-Defined Networking (SDN) is emerging as a new network architecture for the modern Internet. SDN separates the control plane from the data plane, allowing a central controller, the SDN controller, to quickly direct the routing devices within the topology to forward data packets, thus providing flexible traffic management for communication between information sources. However, traditional Distributed Denial of Service (DDoS) attacks still significantly impact SDN systems. This paper proposes a novel dual-layer strategy capable of detecting and mitigating DDoS attacks in an SDN network environment. The first layer of the strategy enhances security by using blockchain technology to replace the SDN flow table storage container in the northbound interface of the SDN controller. Smart contracts are then used to process the stored flow table information. We employ the time window algorithm and the token bucket algorithm to construct the first layer strategy to defend against obvious DDoS attacks. To detect and mitigate less obvious DDoS attacks, we design a second-layer strategy that uses a composite data feature correlation coefficient calculation method and the Isolation Forest algorithm from unsupervised learning techniques to perform binary classification, thereby identifying abnormal traffic. We conduct experimental validation using the publicly available DDoS dataset CIC-DDoS2019. The results show that using this strategy in the SDN network reduces the average deviation of round-trip time (RTT) by approximately 38.86% compared with the original SDN network without this strategy. Furthermore, the accuracy of DDoS attack detection reaches 97.66% and an F1 score of 92.2%. Compared with other similar methods, under comparable detection accuracy, the deployment of our strategy in small-scale SDN network topologies provides faster detection speeds for DDoS attacks and exhibits less fluctuation in detection time. This indicates that implementing this strategy can effectively identify DDoS attacks without affecting the stability of data transmission in the SDN network environment. Full article

The widespread adoption of Internet of Things (IoT) technology has significantly expanded the scale at which devices are connected, posing new challenges to maintaining symmetry in network management. Traditional centralized identification architectures adopt a symmetric processing paradigm in which all device data are uniformly transmitted to the cloud for processing. However, this rigid symmetric structure fails to accommodate the asymmetric distribution typical of IoT edge devices. To address these challenges, this paper proposes an asymmetric identification framework based on cloud–edge collaboration, exploring a high-performance, resource-efficient, and privacy-preserving solution for IoT device identification. The proposed region-specific personalized algorithm (FedRP) introduces a region-specific, personalized identification approach grounded in federated learning principles. Firstly, FedRP leverages a decentralized processing framework to enhance data security by processing data locally. Secondly, it employs a personalized federated learning framework to optimize local models, thus improving identification accuracy and effectiveness. Finally, FedRP strategically separates the personalized parameters of transformer-based blocks from shared parameters and selectively transmits them, reducing the burden on network resources. Comprehensive comparative experiments demonstrate the efficacy of the proposed approach for large-scale IoT environments, which are characterized by numerous devices and complex network conditions. Full article

Accurate and timely land monitoring is crucial for addressing global environmental, economic, and societal challenges, including climate change, sustainable development, and disaster mitigation. While single-source remote sensing data offers significant capabilities, inherent limitations such as cloud cover interference (optical), speckle noise (radar), or limited spectral information (LiDAR) often hinder comprehensive and robust characterization of land surfaces. Recent advancements in synergistic harmonization technology for land monitoring, along with enhanced signal processing techniques and the integration of machine learning algorithms, have significantly broadened the scope and depth of geosciences. Therefore, it is essential to summarize the comprehensive applications of synergistic harmonization technology for geosciences, with a particular focus on recent advancements. Most of the existing review papers focus on the application of a single technology in a specific area, highlighting the need for a comprehensive review that integrates synergistic harmonization technology. This review provides a comprehensive review of advancements in land monitoring achieved through the synergistic harmonization of optical, radar, and LiDAR satellite technologies. It details the unique strengths and weaknesses of each sensor type, highlighting how their integration overcomes individual limitations by leveraging complementary information. This review analyzes current data harmonization and preprocessing techniques, various data fusion levels, and the transformative role of machine learning and deep learning algorithms, including emerging foundation models. Key applications across diverse domains such as land cover/land use mapping, change detection, forest monitoring, urban monitoring, agricultural monitoring, and natural hazard assessment are discussed, demonstrating enhanced accuracy and scope. Finally, this review identifies persistent challenges such as technical complexities in data integration, issues with data availability and accessibility, validation hurdles, and the need for standardization. It proposes future research directions focusing on advanced AI, novel fusion techniques, improved data infrastructure, integrated “space–air–ground” systems, and interdisciplinary collaboration to realize the full potential of multi-sensor satellite data for robust and timely land surface monitoring. Supported by deep learning, this synergy will improve our ability to monitor land surface conditions more accurately and reliably. Full article

The primary challenge that robots face in disaster rescue is to precisely and efficiently construct disaster maps and achieve autonomous navigation. This paper proposes a method for air–ground collaborative map construction. It utilizes the flight capability of an unmanned aerial vehicle (UAV) to achieve rapid three-dimensional space coverage and complex terrain crossing for rapid and efficient map construction. Meanwhile, it utilizes the stable operation capability of an unmanned ground vehicle (UGV) and the ground detail survey capability to achieve precise map construction. The maps constructed by the two are accurately integrated to obtain precise disaster environment maps. Among them, the map construction and positioning technology is based on the FAST LiDAR–inertial odometry 2 (FAST-LIO2) framework, enabling the robot to achieve precise positioning even in complex environments, thereby obtaining more accurate point cloud maps. Before conducting map fusion, the point cloud is preprocessed first to reduce the density of the point cloud and also minimize the interference of noise and outliers. Subsequently, the coarse and fine registrations of the point clouds are carried out in sequence. The coarse registration is used to reduce the initial pose difference of the two point clouds, which is conducive to the subsequent rapid and efficient fine registration. The coarse registration uses the improved sample consensus initial alignment (SAC-IA) algorithm, which significantly reduces the registration time compared with the traditional SAC-IA algorithm. The precise registration uses the voxelized generalized iterative closest point (VGICP) algorithm. It has a faster registration speed compared with the generalized iterative closest point (GICP) algorithm while ensuring accuracy. In reinforcement learning navigation, we adopted the deep deterministic policy gradient (DDPG) path planning algorithm. Compared with the deep Q-network (DQN) algorithm and the A* algorithm, the DDPG algorithm is more conducive to the robot choosing a better route in a complex and unknown environment, and at the same time, the motion trajectory is smoother. This paper adopts Gazebo simulation. Compared with physical robot operation, it provides a safe, controllable, and cost-effective environment, supports efficient large-scale experiments and algorithm debugging, and also supports flexible sensor simulation and automated verification, thereby optimizing the overall testing process. Full article

Understanding the combined gravi-phototropic behavior of plants is essential for space agriculture. Existing single-axis clinostats and gel-based grow media provide limited simulation fidelity. This study developed a Cloud-enabled triple-axis clinostat with built-in automated aeroponic and artificial photosynthetic lighting systems for Earth-based simulation under Martian gravity ranging from 0.35 to 0.4 g. Finite element analysis validated the stability and reliability of the acrylic and stainless steel rotating platform based on stress, strain, and thermal simulation tests. Arduino UNO microcontrollers were used to acquire and process sensor data to activate clinorotation and controlled environment systems. An Arduino ESP32 transmits grow chamber temperature, humidity, moisture, light intensity, and gravity sensor data to ThingSpeak and the Create IoT online platform for seamless monitoring and storage of enviro-physical data. The developed system can generate 0.252–0.460 g that suits the target Martian gravity. The combined gravi-phototropic tests confirmed that maize seedlings exposed to partial gravity and grown using the aeroponic approach have a shoot system growth driven by light availability (395–400 μmol/m²/s) across the partial gravity extremes. Root elongation is more responsive to gravity increase under higher partial gravity (0.375–0.4 g) even with low light availability. The developed soilless clinostat technology offers a scalable tool for simulating other high-value crops aside from maize. Full article

This paper presents an innovative multi-criteria visual quality control algorithm designed for deployment on cost-effective Edge devices within the Industrial Internet of Things environment. Traditional industrial vision systems are typically associated with high acquisition, implementation, and maintenance costs. The proposed solution addresses the need to reduce these costs while maintaining high defect detection efficiency. The developed algorithm largely eliminates the need for time- and energy-intensive neural network training or retraining, though these capabilities remain optional. Consequently, the reliance on human labor, particularly for tasks such as manual data labeling, has been significantly reduced. The algorithm is optimized to run on low-power computing units typical of budget industrial computers, making it a viable alternative to server- or cloud-based solutions. The system supports flexible integration with existing industrial automation infrastructure, but it can also be deployed at manual workstations. The algorithm’s primary application is to assess the spread quality of thick liquid mold filling; however, its effectiveness has also been demonstrated for 3D printing processes. The proposed hybrid algorithm combines three approaches: (1) the classical SSIM image quality metric, (2) depth image measurement using Intel MiDaS technology combined with analysis of depth map visualizations and histogram analysis, and (3) feature extraction using selected artificial intelligence models based on the OpenCLIP framework and publicly available pretrained models. This combination allows the individual methods to compensate for each other’s limitations, resulting in improved defect detection performance. The use of hybrid metrics in defective sample selection has been shown to yield superior algorithmic performance compared to the application of individual methods independently. Experimental tests confirmed the high effectiveness and practical applicability of the proposed solution, preserving low hardware requirements. Full article

Point cloud imaging technology usually faces the problem of point cloud sparsity, which leads to a lack of important geometric detail. There are many point cloud upsampling networks that have been designed to solve this problem. However, the existing methods have limitations in local–global relation understanding, leading to contour distortion and many local sparse regions. To this end, PU-DZMS is proposed with two components. (1) the Dense Zoom Encoder (DENZE) is designed to capture local–global features by using ZOOM Blocks with a dense connection. The main module in the ZOOM Block is the Zoom Encoder, which embeds a Transformer mechanism into the down–upsampling process to enhance local–global geometric features. The geometric edge of the point cloud would be clear under the DENZE. (2) The Multi-Scale Complementary Regression (MSCR) module is designed to expand the features and regress a dense point cloud. MSCR obtains the features’ geometric distribution differences across scales to ensure geometric continuity, and it regresses new points by adopting cross-scale residual learning. The local sparse regions of the point cloud would be reduced by the MSCR module. The experimental results on the PU-GAN dataset and the PU-Net dataset show that the proposed method performs well on point cloud upsampling tasks. Full article