Search Results (55)

To overcome the limitation of traditional elastic phase field models that neglect plastic deformation in rock compressive-shear failure, this study developed an elastoplastic phase field fracture model incorporating plastic strain energy and established a coupling framework for plastic deformation and crack evolution. By introducing the non-associated flow rule and plastic damage variable, an energy functional comprising elastic strain energy, plastic work, and crack surface energy was constructed. The phase field governing equation considering plastic-damage coupling was obtained, enabling the simulation of the energy evolution in rock from the elastic stage to plastic damage and unstable failure. Validation was carried out through single-edge notch tension tests and uniaxial compression tests with prefabricated cracks. Results demonstrate that the model accurately captures characteristics such as the linear propagation of tensile cracks, the initiation of wing-like cracks under compressive-shear conditions, and the evolution of mixed-mode failure modes, which are highly consistent with classical experimental observations. Specifically, the model provides a more detailed description of local damage evolution and residual strength caused by stress concentration in compressive-shear scenarios, thereby quantifying the influence of plastic deformation on crack driving force. These findings offer theoretical support for crack propagation analysis in rock engineering applications, including hydraulic fracturing and the construction of underground energy storage caverns. The proposed plastic phase field model can be effectively utilized to simulate rock failure processes under complex stress states. Full article

Vehicle detection is essential for real-world applications such as road surveillance, intelligent transportation systems, and autonomous driving, where high accuracy and real-time performance are critical. However, achieving robust detection remains challenging due to scene complexity, occlusion, scale variation, and varying lighting conditions. Over the past two decades, numerous studies have been proposed to address these issues. This study presents a comprehensive and structured survey of image-based vehicle detection methods, systematically comparing classical machine learning techniques based on handcrafted features with modern deep learning approaches. Deep learning methods are categorized into one-stage detectors (e.g., YOLO, SSD, FCOS, CenterNet), two-stage detectors (e.g., Faster R-CNN, Mask R-CNN), transformer-based detectors (e.g., DETR, Swin Transformer), and GAN-based methods, highlighting architectural trade-offs concerning speed, accuracy, and practical deployment. We analyze widely adopted performance metrics from recent studies, evaluate characteristics and limitations of popular vehicle detection datasets, and explicitly discuss technical challenges, including domain generalization, environmental variability, computational constraints, and annotation quality. The survey concludes by clearly identifying open research challenges and promising future directions, such as efficient edge deployment strategies, multimodal data fusion, transformer-based enhancements, and integration with Vehicle-to-Everything (V2X) communication systems. Full article

The emergence of large-scale quantum computing presents an imminent threat to contemporary public-key cryptosystems, with quantum algorithms such as Shor’s algorithm capable of efficiently breaking RSA and elliptic curve cryptography (ECC). This vulnerability has catalyzed accelerated standardization efforts for post-quantum cryptography (PQC) by the U.S. National Institute of Standards and Technology (NIST) and global security stakeholders. While theoretical security analysis of these quantum-resistant algorithms has advanced considerably, comprehensive real-world performance benchmarks spanning diverse computing environments—from high-performance cloud infrastructure to severely resource-constrained IoT devices—remain insufficient for informed deployment planning. This paper presents the most extensive cross-platform empirical evaluation to date of NIST-selected PQC algorithms, including CRYSTALS-Kyber and NTRU for key encapsulation mechanisms (KEMs), alongside BIKE as a code-based alternative, and CRYSTALS-Dilithium and Falcon for digital signatures. Our systematic benchmarking framework measures computational latency, memory utilization, key sizes, and protocol overhead across multiple security levels (NIST Levels 1, 3, and 5) in three distinct hardware environments and various network conditions. Results demonstrate that contemporary server architectures can implement these algorithms with negligible performance impact (<5% additional latency), making immediate adoption feasible for cloud services. In contrast, resource-constrained devices experience more significant overhead, with computational demands varying by up to 12× between algorithms at equivalent security levels, highlighting the importance of algorithm selection for edge deployments. Beyond standalone algorithm performance, we analyze integration challenges within existing security protocols, revealing that naive implementation of PQC in TLS 1.3 can increase handshake size by up to 7× compared to classical approaches. To address this, we propose and evaluate three optimization strategies that reduce bandwidth requirements by 40–60% without compromising security guarantees. Our investigation further encompasses memory-constrained implementation techniques, side-channel resistance measures, and hybrid classical-quantum approaches for transitional deployments. Based on these comprehensive findings, we present a risk-based migration framework and algorithm selection guidelines tailored to specific use cases, including financial transactions, secure firmware updates, vehicle-to-infrastructure communications, and IoT fleet management. This practical roadmap enables organizations to strategically prioritize systems for quantum-resistant upgrades based on data sensitivity, resource constraints, and technical feasibility. Our results conclusively demonstrate that PQC is deployment-ready for most applications, provided that implementations are carefully optimized for the specific performance characteristics and security requirements of target environments. We also identify several remaining research challenges for the community, including further optimization for ultra-constrained devices, standardization of hybrid schemes, and hardware acceleration opportunities. Full article

Ultrafast laser systems, implemented at the ELI-NP, require exceptional beam quality and spatial stability due to their femtosecond pulse durations and extremely high peak powers. This work presents a diagnostic and computational framework for analyzing the ELI-NP Front-End beam characteristics, where spatial coherence and precise pulse shaping are essential for reliable amplification and experimental consistency. The methodology integrates classical beam diagnostics with image processing and machine learning tools to evaluate anomalies based on high-resolution beam profile images. We use centroid tracking to monitor pointing fluctuations, statistical intensity analysis to detect energy instabilities, and Sobel-based edge detection to evaluate beam sharpness and extract structural features from the beam image. Geometric parameters such as ellipticity, roundness, and symmetry indicators are extracted and examined over time. The system applies an unsupervised Isolation Forest algorithm to detect subtle or short-lived anomalies, identifying irregularities without relying on predefined thresholds. These diagnostics are supported by visual plots and statistical summaries, offering a clear picture of the beam’s behavior under real operating conditions. Results confirm that this integrated approach effectively captures major and minor beam instabilities, making it a practical tool for continuous monitoring and performance optimization in ultrafast laser systems. Full article

Plate structures are the main components of offshore platforms and ships in engineering applications. The vibration control of the low-frequency mode of plate structures has always been a meaningful research object in marine science and engineering. Due to their low cost and good performance, dynamic vibration absorbers are widely used. To enhance the design efficiency of dynamic vibration absorbers, a mathematical model was developed for plates with dynamic vibration absorbers under different boundary constraints. To overcome the discontinuity of the displacement function, auxiliary series were introduced. In addition, the efficiency of resolving the plate structure’s equivalent mass was significantly improved compared with when using FEM software Abaqus 6.14. The validity of the proposed mathematical model was confirmed in comparison with related studies, the FEM results, and the experimental results. Considering the mathematical model and design approach proposed in the current paper, more research on the vibration control of plates subjected to clamped and elastic boundary conditions should be performed. The mathematical model and findings in the design process may have positive implications for the control of the vibration of plate structures in marine science and engineering. Full article

Quantum entanglement as a non-local correlation between particles is critical to the transmission of quantum information in quantum networks (QNs); the key challenge lies in establishing long-distance entanglement transmission between distant targets. This issue aligns with percolation theory, and as a result, an entanglement distribution scheme called “Classical Entanglement Percolation” (CEP) has been proposed. While this scheme provides an effective framework, “Quantum Entanglement Percolation” (QEP) indicates a lower percolation threshold through quantum preprocessing strategies, which will modify the network topology. Meanwhile, an emerging statistical theory known as “Concurrence Percolation” reveals the unique advantages of quantum networks, enabling entanglement transmission under lower conditions. It fundamentally belongs to a different universality class from classical percolation. Although these studies have made significant theoretical advancements, most are based on an idealized pure state network model. In practical applications, quantum states are often affected by thermal noise, resulting in mixed states. When these mixed states meet specific conditions, they can be transformed into pure states through quantum operations and further converted into singlets with a certain probability, thereby facilitating entanglement percolation in mixed state networks. This finding greatly broadens the application prospects of quantum networks. This review offers a comprehensive overview of the fundamental theories of quantum percolation and the latest cutting-edge research developments. Full article

Visual quality inspection of part surfaces is a crucial step in industrial production. Image edge detection is a common technique for assessing the surface conditions of parts. However, current methods have limitations, including poor noise filtering, low adaptability, and inadequate accuracy of edge detection. To overcome these challenges, this study proposes an adaptive edge detection method for part images using selective processing. Firstly, this method divides the input image into noise, edge, and noise-free blocks, followed by selective mixed filtering to remove noise while preserving original image details. Secondly, a four-parameter adaptive selective edge detection algorithm model is constructed, which adaptively adjusts parameter values based on image characteristics to address issues of missing edges and false detections, thereby enhancing the adaptability and accuracy of the method. Moreover, by comparing and adjusting the four parameter values, different edge information can be selectively detected, enabling rapid acquisition of desired edge detection results and improving detection efficiency and flexibility. Experimental results demonstrated that the proposed method outperformed existing classical techniques in both subjective and objective evaluations, maintaining stable detection under varying noise conditions. Thus, this method was validated for its effectiveness and stability, enhancing production efficiency in manufacturing processes of parts. Full article

This study builds on previous research into the basic flow structure of a separated curved swept compression ramp shock wave/turbulence boundary layer interaction (CSCR-SWBLI) at the leading edge of an inward-turning inlet. We employ the ice-cluster-based planar laser scattering (IC-PLS) technique, which integrates multiple observation directions and positions, to experimentally investigate a physical model with typical parameter states at a freestream Mach number of 2.85. This study captures the fine structure of some sections of the flow field and identifies the presence of Görtler vortices (GVs) in the CSCR-SWBLI. It is observed that due to the characteristics of variable sweep angle, variable intensity interaction, and centrifugal force, GVs exhibit strong three-dimensional characteristics in the curved section. Additionally, their position is not fixed in the spanwise direction, demonstrating strong intermittence. As the vortices develop downstream, their size gradually increases while the number decreases, always corresponding to the local boundary layer thickness. When considering the effects of coupling of bilateral walls, it is noted that the main difference between double-sided coupling and single-sided uncoupling conditions is the presence of a large-scale vortex in the central plane and an odd number of GVs in the double-sided model. Finally, the existence of GVs in CSCR-SWBLI is verified through the classical determine criteria Görtler number (G_T) and Floryan number (F) decision basis. Full article

Potholes and other road surface damages pose significant risks to vehicles and traffic safety. The current methods of in situ visual inspection for potholes or cracks are inefficient, costly, and hazardous. Therefore, there is a pressing need to develop automated systems for assessing road surface conditions, aiming to efficiently and accurately reconstruct, recognize, and locate potholes. In recent years, various methods utilizing (a) computer vision, (b) three-dimensional (3D) point clouds, or (c) smartphone data have been employed to map road surface quality conditions. Machine learning and deep learning techniques have increasingly enhanced the performance of these methods. This review aims to provide a comprehensive overview of cutting-edge computer vision and machine learning algorithms for pothole detection. It covers topics such as sensing systems for acquiring two-dimensional (2D) and 3D road data, classical algorithms based on 2D image processing, segmentation-based algorithms using 3D point cloud modeling, machine learning, deep learning algorithms, and hybrid approaches. The review highlights that hybrid methods combining traditional image processing and advanced machine learning techniques offer the highest accuracy in pothole detection. Machine learning approaches, particularly deep learning, demonstrate superior adaptability and detection rates, while traditional 2D and 3D methods provide valuable baseline techniques. By reviewing and evaluating existing vision-based methods, this paper clarifies the current landscape of pothole detection technologies and identifies opportunities for future research and development. Additionally, insights provided by this review can inform the design and implementation of more robust and effective systems for automated road surface condition assessment, thereby contributing to enhanced roadway safety and infrastructure management. Full article

Identifying template watermarks under severe geometric distortions is a significant scientific problem in the current watermarking research for remote sensing images. We propose a novel watermarking algorithm that integrates the ring-shaped template watermark with the multiscale local contrast measure (LCM) method. In the embedding stage, the ring-shaped template watermark is embedded into the discrete Fourier transform (DFT) magnitude coefficients, converting the watermark into small targets in the DFT domain. During the detection stage, the multiscale LCM, a classic infrared small target detection method, enhances these small targets and generates a contrast map. Peak detection is then performed on the contrast map to determine the radius of the template watermark. Finally, circular edge local binarization is applied to extract the watermark information. The proposed method enables synchronization recovery of watermarks under blind conditions. The experimental results demonstrate that the method possesses strong robustness against various geometric attacks such as rotation, scaling, translation, and cropping. It outperforms comparative algorithms in terms of robustness and also exhibits good imperceptibility. Full article

In the present paper, a modified Fourier series approach is developed for new precise flexural analysis of three different types of concrete plates in a rectangular sewage tank. The bending problems of the bottom plate, side-plate, and the fluid-guiding plate are not easily solved via using the traditional analytic approaches. Based on the Fourier series theory, the present approach provides a unified semi-inverse solving procedure for the above plates by means of choosing three different kinds of Fourier series as the trial functions. Although all the trial functions are quite similar to the classical Navier-form solution, new, precise analytic flexural solutions for plates without Navier-type edge conditions (all edges simply-supported) are achieved, which is mainly attributed to employing the Stoke’s transform technique. For each case, the plate-bending problems are finally altered to deal with linear algebra equations. Furthermore, owing to the orthogonality and completeness of the Fourier series, the obtained solutions perfectly satisfy both the edge conditions and the governing partial differential equation of plates, which paves an easily implemented and rational way for engineers and researchers to provide new, exact designs of plate structures. The main contribution of this study lies in the provision of a unified solution procedure for addressing complex plate-bending problems across diverse boundary conditions. By employing a range of Fourier series types, this approach offers a comprehensive solution framework that accommodates the complexities inherent in plate analysis. The correctness of the present analytic solutions is verified against precise finite element method (FEM) results and ones available in the literature. Finally, the influences of foundation, edge conditions, and aspect ratio on flexural behaviors of plates are discussed in detail. Full article

The mechanical analysis of thin-plate structures is a major challenge in the field of structural engineering, especially when they have nonclassical boundary conditions, such as those encountered in cement concrete road slabs connected by transfer bars. Conventional analytical solutions are usually limited to classical boundary conditions—clamped support, simple support, and free edges—and cannot adequately describe many engineering scenarios. In this study, an analytical solution to the bending problem of an anisotropic thin plate subjected to a pair of edges with free opposing elastic rotational constraints is found using a two-dimensional augmented Fourier series solution method. In the derivation process, the thin-plate problem can be transformed into a problem of solving a system of linear algebraic equations by applying Stoke’s transform method, which greatly reduces the mathematical difficulty of solving the problem. Complex boundary conditions can be optimally handled without the need for large computational resources. The paper addresses the exact analytical solutions for bending problems with multiple combinations of boundary conditions, such as contralateral free–contralateral simple support (SFSF), contralateral free–contralateral solid support–simple support (CFSF), and contralateral free–contralateral clamped support (CFCF). These solutions are realized by employing the Stoke transformation and adjusting the spring parameters in the analyzed solutions. The results of this method are also compared with the finite element method and analytical solutions from the literature, and good agreement is obtained, demonstrating the effectiveness of the method. The significance of the study findings lies in the simplification of complex nonclassical boundary condition problems using a simple and reliable analytical method applicable to a wide range of engineering thin-plate structures. Full article

Yield prediction is a crucial activity in scheduling agronomic operations and in informing the management and financial decisions of a wide range of stakeholders of the organic durum wheat supply chain. This research aims to develop a yield forecasting system by combining vegetation index time-series data from Sentinel-2 L2A time-series data, field-measured yields, and deep learning techniques. Remotely sensed data over a season could be, in general, noisy and characterized by a variable density due to weather conditions. This problem was mitigated using Functional Principal Component Analysis (FPCA). We obtained a functional representation of acquired data, and starting from this, we tried to apply deep learning to predict the crop yield. We used a Convolutional Neural Network (CNN) approach, starting from images that embed temporal and spectral dimensions. This representation does not require one to a priori select a vegetation index that, typically, is task-dependent. The results have been also compared with classical approaches as Partial Least Squares (PLS) on the main reference vegetation indexes such as the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge index (NDRE), considering both in-season and end-season scenarios. The obtained results show that the image-based representation of multi-spectral time series could be an effective method to estimate the yield, also, in the middle stage of cropping with R² values greater than 0.83. The developed model could be used to estimate yield the neighbor fields characterized by similar setups in terms of the crop, variety, soil, and, of course, management. Full article

The flexible mobility feature of unmanned aerial vehicles (UAVs) leads to frequent handovers and serious inter-cell interference problems in UAV-assisted cellular networks. Establishing a cell-free UAV (CF-UAV) network without cell boundaries effectively alleviates frequent handovers and interference problems and has been an important topic of 6G research. However, in existing CF-UAV networks, a large amount of backhaul data increases the computational pressure on the central processing unit (CPU), which also increases system delay. Meanwhile, the mobility of UAVs also leads to time-varying channel conditions. Therefore, designing dynamic resource allocation schemes with the help of edge computing can effectively alleviate this problem. Thus, aiming at partial network breakdown in an urban-micro (UMi) environment, an urban-micro CF-UAV (UMCF-UAV) network architecture is proposed in this paper. A delay minimization problem and a dynamic task offloading (DTO) strategy that jointly optimizes access point (AP) selection and task offloading is proposed to reduce system delay in this paper. Considering the coupling of various resources and the non-convex feature of the proposed problem, a dynamic resource cooperative allocation (DRCA) algorithm based on deep reinforcement learning (DRL) to flexibly deploy AP selection and task offloading of UAVs between the edge and locally is proposed to solve the problem. Simulation results show fast convergence behavior of the proposed algorithm compared with classical reinforcement learning. Decreased system delay is obtained by the proposed algorithm compared with other baseline resource allocation schemes, with the maximize improvement being 53%. Full article

Cellular senescence (CS), classically considered a stable cell cycle withdrawal, is hallmarked by a progressive decrease in cell growth, differentiation, and biological activities. Senescent cells (SNCs) display a complicated senescence-associated secretory phenotype (SASP), encompassing a variety of pro-inflammatory factors that exert influence on the biology of both the cell and surrounding tissue. Among global mortality causes, cardiovascular diseases (CVDs) stand out, significantly impacting the living quality and functional abilities of patients. Recent data suggest the accumulation of SNCs in aged or diseased cardiovascular systems, suggesting their potential role in impairing cardiovascular function. CS operates as a double-edged sword: while it can stimulate the restoration of organs under physiological conditions, it can also participate in organ and tissue dysfunction and pave the way for multiple chronic diseases under pathological states. This review explores the mechanisms that underlie CS and delves into the distinctive features that characterize SNCs. Furthermore, we describe the involvement of SNCs in the progression of CVDs. Finally, the study provides a summary of emerging interventions that either promote or suppress senescence and discusses their therapeutic potential in CVDs. Full article