Search Results (830)

Medical Internet of Things (IoT) systems are crucial in monitoring the health status of patients. Recently, telemedicine services that manage patients remotely by receiving real-time health information from IoT devices attached to or carried by them have experienced significant growth. A primary concern in medical IoT services is ensuring the security of transmitted information and protecting patient privacy. To address these challenges, various authentication schemes have been proposed. We analyze the authentication scheme by Wang et al. and identified several limitations. Specifically, an attacker can exploit information stored in an IoT device to generate an illegitimate session key. Additionally, despite using a cloud center, the scheme lacks efficiency. To overcome these limitations, we propose an authentication and key distribution scheme that incorporates a physically unclonable function (PUF) and public-key computation. To enhance efficiency, computationally intensive public-key operations are performed exclusively in the cloud center. Furthermore, our scheme addresses privacy concerns by employing a temporary ID for IoT devices used to identify patients. We validate the security of our approach using the formal security analysis tool ProVerif. Full article

In this paper, a wind retrieval method based on genetic algorithm-particle swarm optimization (GA-PSO) for the coherent Doppler wind lidar (CDWL) is proposed. The algorithm incorporates an advanced optimization framework that considers wind field spatial continuity, simultaneously enhancing retrieval accuracy and computational efficiency. Comprehensive validations of the GA-PSO algorithm are conducted using a 1.5 μm all-fiber CDWL through ground-based and airborne experiments. In ground-based experiments, the GA-PSO algorithm extends the detection range by 20%~30% compared with traditional methods. The validation against meteorological tower data demonstrates excellent agreement, with mean deviations better than 0.27 m/s for horizontal wind speed and 3.07° for horizontal wind direction and corresponding RMSE values better than 0.36 m/s and 6.04°, respectively. During high-altitude airborne experiments at 5.5 km, the GA-PSO algorithm recovers up to 31% more horizontal wind speed and direction information compared with traditional algorithms, demonstrating exceptional performance in low signal-to-noise ratio (SNR) conditions. Both simulation analysis and field experiments demonstrate that the GA-PSO algorithm achieves processing speeds comparable to traditional real-time methods, establishing its suitability for real-time, three-dimensional wind retrieval applications. Full article

The rapid evolution and deployment of 5G networks have introduced complex security challenges due to their reliance on dynamic network slicing, ultra-low latency communication, decentralized architectures, and highly diverse use cases. Traditional perimeter-based security models are no longer sufficient in these highly fluid and distributed environments. In response to these limitations, this study introduces SecureChain-ZT, a novel Adaptive Zero Trust Policy Framework (AZTPF) that addresses emerging threats by integrating intelligent access control, real-time monitoring, and decentralized authentication mechanisms. SecureChain-ZT advances conventional Zero Trust Architecture (ZTA) by leveraging machine learning, reinforcement learning, and blockchain technologies to achieve autonomous policy enforcement and threat mitigation. Unlike static ZT models that depend on predefined rule sets, AZTPF continuously evaluates user and device behavior in real time, detects anomalies through AI-powered traffic analysis, and dynamically updates access policies based on contextual risk assessments. Comprehensive simulations and experiments demonstrate the robustness of the framework. SecureChain-ZT achieves an authentication accuracy of 97.8% and reduces unauthorized access attempts from 17.5% to just 2.2%. Its advanced detection capabilities achieve a threat detection accuracy of 99.3% and block 95.6% of attempted cyber intrusions. The implementation of blockchain-based identity verification reduces spoofing incidents by 97%, while microsegmentation limits lateral movement attacks by 75%. The proposed SecureChain-ZT model achieved an authentication accuracy of 98.6%, reduced false acceptance and rejection rates to 1.2% and 0.2% respectively, and improved policy update time to 180 ms. Compared to traditional models, the overall latency was reduced by 62.6%, and threat detection accuracy increased to 99.3%. These results highlight the model’s effectiveness in both cybersecurity enhancement and real-time service responsiveness. This research contributes to the advancement of Zero Trust security models by presenting a scalable, resilient, and adaptive policy enforcement framework that aligns with the demands of next-generation 5G infrastructures. The proposed SecureChain-ZT model not only enhances cybersecurity but also ensures service reliability and responsiveness in complex and mission-critical environments. Full article

In the operation process of modern industrial equipment, as the core transmission component, the operation state of the gearbox directly affects the overall performance and service life of the equipment. However, the current gear operation is still faced with problems such as poor monitoring, a single detection index, and low data utilization, which lead to incomplete evaluation results. In view of these challenges, this paper proposes a shape and property integrated gearbox monitoring system based on digital twin technology and artificial intelligence, which aims to realize real-time fault diagnosis, performance prediction, and the dynamic visualization of gear through virtual real mapping and data interaction, and lays the foundation for the follow-up predictive maintenance application. Taking the QPZZ-ii gearbox test bed as the physical entity, the research establishes a five-layer architecture: functional service layer, software support layer, model integration layer, data-driven layer, and digital twin layer, forming a closed-loop feedback mechanism. In terms of technical implementation, combined with HyperMesh 2023 refinement mesh generation, ABAQUS 2023 simulates the stress distribution of gear under thermal fluid solid coupling conditions, the Gaussian process regression (GPR) stress prediction model, and a fault diagnosis algorithm based on wavelet transform and the depth residual shrinkage network (DRSN), and analyzes the vibration signal and stress distribution of gear under normal, broken tooth, wear and pitting fault types. The experimental verification shows that the fault diagnosis accuracy of the system is more than 99%, the average value of the determination coefficient (R²) of the stress prediction model is 0.9339 (driving wheel) and 0.9497 (driven wheel), and supports the real-time display of three-dimensional cloud images. The advantage of the research lies in the interaction and visualization of fusion of multi-source data, but it is limited to the accuracy of finite element simulation and the difficulty of obtaining actual stress data. This achievement provides a new method for intelligent monitoring of industrial equipment and effectively promotes the application of digital twin technology in the field of predictive maintenance. Full article

To address the key challenges of poor real-time interaction, insufficient integration of operating rules, and limited virtual–physical synergy in current carrier-based aircraft scheduling simulations, this study proposes an immersive digital twin platform that integrates a two-layer genetic algorithm (GA) with hardware-in-the-loop (HIL) semi-physical validation. The platform architecture combines high-fidelity 3D visualization-based modeling (of aircraft, carrier deck, and auxiliary equipment) with real-time data exchange via TCP/IP, establishing a collaborative virtual–physical simulation environment. Three key innovations are presented: (1) a two-tier genetic algorithm (GA)-based scheduling model is proposed to coordinate global planning and dynamic execution optimization for carrier-based aircraft operations; (2) a systematic constraint integration framework incorporating aircraft taxiing dynamics, deck spatial constraints, and safety clearance requirements into the scheduling system, significantly enhancing tactical feasibility compared to conventional approaches that oversimplify multidimensional operational rules; (3) an integrated virtual–physical simulation architecture merging virtual reality interaction with HIL verification, establishing a collaborative digital twin–physical device platform for immersive visualization of full-process operations and dynamic spatiotemporal evolution characterization. Experimental results indicate that this work bridges the gap between theoretical scheduling algorithms and practical naval aviation requirements, offering a standardized testing platform for intelligent carrier-based aircraft operations. Full article

This study focuses on Roblox as a case study to explore the legal and technical challenges of content moderation on child-focused social media platforms. As a leading Metaverse platform with millions of young users, Roblox provides immersive and interactive virtual experiences but also introduces significant risks, including exposure to inappropriate content, cyberbullying, and predatory behavior. The research examines the shortcomings of current automated and human moderation systems, highlighting the difficulties of managing real-time user interactions and the sheer volume of user-generated content. It investigates cases of moderation failures on Roblox, exposing gaps in existing safeguards and raising concerns about user safety. The study also explores the balance between leveraging artificial intelligence (AI) for efficient content moderation and incorporating human oversight to ensure nuanced decision-making. Comparative analysis of moderation practices on platforms like TikTok and YouTube provides additional insights to inform improvements in Roblox’s approach. From a legal standpoint, the study critically assesses regulatory frameworks such as the GDPR, the EU Digital Services Act, and the UK’s Online Safety Act, analyzing their relevance to virtual platforms like Roblox. It emphasizes the pressing need for comprehensive international cooperation to address jurisdictional challenges and establish robust legal standards for the Metaverse. The study concludes with recommendations for improved moderation strategies, including hybrid AI-human models, stricter content verification processes, and tools to empower users. It also calls for legal reforms to redefine virtual harm and enhance regulatory mechanisms. This research aims to advance safe and respectful interactions in digital environments, stressing the shared responsibility of platforms, policymakers, and users in tackling these emerging challenges. Full article

The increasing interconnectivity of devices on the Internet of Things (IoT) introduces significant security challenges, particularly around authentication and data management. Traditional centralized approaches are not sufficient to address these risks, requiring more robust and decentralized solutions. This paper presents a decentralized authentication protocol leveraging blockchain technology and the IPFS data management framework to provide secure and real-time communication between IoT devices. Using the Ethereum blockchain, smart contracts, elliptic curve cryptography, and ASCON encryption, the proposed protocol ensures the confidentiality, integrity, and availability of sensitive IoT data. The mutual authentication process involves the use of asymmetric key pairs, public key registration on the blockchain, and the Diffie–Hellman key exchange algorithm to establish a shared secret that, combined with a unique identifier, enables secure device verification. Additionally, IPFS is used for secure data storage, with the content identifier (CID) encrypted using ASCON and integrated into the blockchain for traceability and authentication. This integrated approach addresses current IoT security challenges and provides a solid foundation for future applications in decentralized IoT environments. Full article

Based on Digital Beamforming (DBF) technology, spaceborne SAR systems can achieve high-resolution and wide-swath (HRWS) imaging. When combined with reflector antennas, the DBF-SCORE (Digital Beamforming-SCan On REceive) system also features light weight and low cost, making it an important choice for spaceborne HRWS SAR. This paper firstly proposes an advanced Full-chain Real-time Internal Calibration (FRIC) scheme, where the calibration path covers the entire receive chain from the antenna feed port to the input port of the Analog-to-Digital Converter (ADC) and achieves high-precision internal calibration concurrently with data acquisition. Secondly, based on the L-band reflector antenna DBF-SCORE system architecture, the design of radio frequency (RF) front end, namely the Transmit-Receive-Calibration Module (TRCM), is carried out. We propose the implementation of azimuth encoding modulation of the calibration signal through periodic switch control within the TRCM. Subsequently, the calibration signal is extracted using waveform diversity technology and its Signal-to-Noise Ratio (SNR) is improved through azimuth coherent integration technology. In addition, a ground verification system is established using the TRCM to evaluate the comprehensive performance of transmitting, receiving, and real-time internal calibration. Experimental results verify the effectiveness of the FRIC scheme and provide valuable insights for future spaceborne DBF SAR systems. Full article

The theoretical part of this paper is devoted to a class of distributed blind calibration algorithms for large sensor networks based on consensus. The basic blind calibration method starts from affine sensor models and calibration functions, aiming to equalize corrected sensor offsets and gains without requiring any a priori knowledge of the measured signal. The main focus is to systematically and rigorously analyze the behavior of the calibration algorithms of the stochastic approximation type under nonlinear sensor models and stochastic environments, and to provide recommendations that are relevant to practice. It is demonstrated that the calibration algorithm—based on consensus with respect to all the calibration parameters—is far less robust to unknown sensor nonlinearities than the modified algorithm, taking one micro-calibrated sensor as a reference. Stability proofs of the algorithms are given in the bounded input–bounded output sense. The influences of measurement and communication noises are also analyzed using the theory of stochastic approximation. Numerous simulation results provide a comprehensive picture of the algorithm properties that are relevant to practice. This is followed by an important verification of the theoretical results, obtained by applying the analyzed blind calibration algorithms to an originally designed multichannel instrument for aerodynamic pressure sensing. A description of the new instrument is given, together with essential aspects of the implementation of the blind calibration algorithm. It is shown that the selected algorithm can be seen as a simple and efficient practical tool for blind online real-time re-calibration of complex sensor networks during normal system operations. Full article

For the design and verification of a motion control algorithm for unmanned surface vehicles, a simulation system is developed based on VRX and Simulink. Firstly, considering the effect of wind, a dynamic model of the USV with podded propellers is established. Secondly, combined with speed control, three control modes are considered, including yaw rate control, heading control, and path-following control, and speed, heading, yaw rate, and path guidance controllers are designed. Then, a real-time simulation system is developed based on the Virtual RobotX (VRX) environment and the Simulink ROS2 toolbox. Finally, motion control simulation experiments under three control modes and a path-following water tank experiment are carried out. The designed simulation system can simulate the motion of USVs and different environmental elements, such as wind, intuitively and realistically. In simulation experiments, the designed controllers can make the USV follow commands quickly and accurately under three control modes. In the water tank experiment, the USV could stably track the desired path with a relatively small tracking error. Therefore, the effectiveness of the simulation system is strongly confirmed through simulation experiments and the water tank experiment. The simulation system will be expanded in the future for more research on target recognition, path planning, and other aspects of USVs. Full article

Tracing the root cause of defective products in liquid storage tank (LST) production poses a formidable challenge due to the complex dependencies between production and inspection processes. With associated coupling existing among multiple production processes, and the correspondence between the faults in production processes and inspection links being non-unique, these faults are usually difficult to be directly located via a single inspection process. In this paper, the problem of tracing the root cause of defective LST products, which is caused by process parameter deviations or human operation errors during production, is studied. A root cause tracing method that is based on the dynamic multi-fault diagnosis (DMFD) framework is proposed. First, a factorial hidden Markov model (FHMM) is established to depict the state transition process of the LST product, where its status changes over time and across production processes. This is achieved by considering the product state at each production process as a hidden state and the outcomes of each inspection process as an observation state. Then, the Viterbi algorithm is employed to solve the hidden state transition matrix and diagnostic matrix within the framework of the FHMM. Finally, experimental verification is carried out on a real LST assembly production line, and the influence of imperfect testing on the model accuracy is also considered. The experiment is carried out on an LST assembly line that encompasses three discrete links, including the welding of the upper and lower bodies, the installation of check valves, and the installation of sensors. Experimental results demonstrate that the proposed method achieves significantly more superior performance when compared to existing algorithms. Full article

The integration of electric vehicles into modern mobility ecosystems relies heavily on reliable charging station infrastructures that support real-time communications and data-driven functionalities. Existing solutions often face security vulnerabilities in their firmware update mechanisms, compromising safety, user trust, and the broader deployment of these stations in emerging digital and connected environments. This paper aims to address these gaps by proposing a blockchain-based framework designed to provide secure, tamper-proof firmware updates for charging stations in a Web of Things environment. The approach uses decentralized ledger technologies to validate firmware integrity, authenticate update sources, and mitigate the risk of malicious or fraudulent content. In a comprehensive experimental setup, the proposed method demonstrates enhanced resilience against unauthorized firmware modifications and improved traceability of update transactions through immutable records. Results highlight a reduction in firmware compromise events, as well as improved detection and notification efficiencies in real-time networked systems. These findings suggest that integrating blockchain technology into firmware update workflows strengthens security in electric vehicle charging infrastructures. Consequently, the adoption of decentralized verification approaches can drive broader trust in connected mobility services, supporting safer and more efficient charging station networks while fostering future innovation in sustainable transport. Full article

Gynaephora qinghaiensis is a major pest in the alpine meadow regions of China. While the females are unable to fly, the males can fly and cause widespread damage. The aim of this study was to use transcriptome analysis to identify and verify genes expressed at different developmental stages of Gynaephora qinghaiensis, with particular emphasis on genes associated with wing development. High-throughput sequencing was performed on an Illumina HiSeqTM2000 platform to assess transcriptomic differences in the wings of male and female pupa and male and female adults of Gynaephora qinghaiensis, and the expression levels of the differentially expressed genes (DEGs) were verified by real-time fluorescence quantitative PCR (RT-qPCR). A total of 60,536 unigenes were identified from the transcriptome data, and 25,162 unigenes were obtained from a comparison with four major databases. Further analysis identified 18 DEGs associated with wing development in Gynaephora qinghaiensis. RT-qPCR verification of the expression levels showed consistency with the RNA sequencing results. Spatio-temporal expression profiling of the 18 genes indicated different levels of expression in the thoraces of male and female pupa, as well as between the wing buds of adult females and the wings of adult males. GO annotation analysis showed that the DEGs were associated with similar categories with no significant enrichment and were involved in cellular processes, cellular anatomical entities, and binding. KEGG analysis indicated that the DEGs were associated with endocytosis and metabolic pathways. The results of this study expand the information on genes associated with Gynaephora qinghaiensis wing development and provide support for further investigations of wing development at the molecular level. Full article

The increasing integration of resources with limited flexibility, such as solar power, electric vehicles (EVs), and other renewable energy sources, has raised significant concerns regarding power system stability. The stability of power systems is constantly threatened, particularly in cases where renewable energy is supplied to areas near generators, as transmission capacity constraints may lead to severe stability issues. The impact of renewable energy integration on system stability can be analyzed using transient stability and phase-angle stability theories. This study proposes a methodology to quantify the effects of renewable energy integration on transient stability. A power-phase angle curve is plotted using the Thevenin impedance calculation technique, and an improved equal-area method index is utilized to evaluate transient stability issues caused by renewable energy penetration. The proposed transient stability discrimination index (TSDI) is used to assess system stability in real-time conditions. Simulation results demonstrate that the proposed method achieves an accuracy of over 90% in ranking transient stability compared to conventional offline stability analysis. Furthermore, a correlation coefficient of 0.85 is observed between the proposed TSDI and the existing wide-area voltage stability index (WAVI), confirming the reliability of the method. These findings suggest that, when real-time Phasor Measurement Unit (PMU) data are available, the proposed approach can be effectively applied to practical power systems for enhanced stability assessment. Full article

Laser annealing plays a significant role in the fabrication of scaled-down semiconductor devices by activating dopant ions and rearranging silicon atoms in ion-implanted silicon wafers, thereby improving material properties. Precise temperature control is crucial in wafer annealing, particularly for repeated processes where repeatability affects uniformity. In this study, we employ a three-dimensional time-dependent thermal simulation model to numerically analyze the multiple static laser annealing processes based on a CO₂ laser with a center wavelength of 9.3 μm and a pulse repetition rate of 10 kHz. The heat transfer equation is solved using a multiphysics coupling approach to accurately simulate the effects of different numbers of CO₂ laser pulses on wafer temperature rise and repeatability. Additionally, a pyrometer is used to collect and convert the surface temperature of the wafer. Radiation intensity is converted to temperature via Planck’s law for real-time monitoring. Post-processing is performed to fit the measured temperature and the actual temperature into a linear relationship, aiding in obtaining the actual temperature under small beam spots. According to the simulation conditions, a wafer annealing device using a CO₂ laser as the light source was independently built for verification, and a stable and uniform annealing effect was realized. Full article