Search Results (4,713)

Environmental noise is a growing public health issue, particularly in dense urban environments and areas adjacent to transport infrastructure. Passive acoustic barriers have been a widely adopted solution, although their functional and aesthetic limitations have driven the development of alternatives, such as Sonic Crystal Acoustic Barriers. These structures can incorporate resonant elements, such as Helmholtz cavities, with the aim of enhancing attenuation in particular frequency bands. However, determining the precise dimensions of these resonators is a persistent challenge, given that classical models are based on ideal geometries and do not incorporate interaction with the periodic structural environment. This study sets a new frequency-dependent correction factor, obtained both numerically and experimentally, which allows the classical Helmholtz resonance expression to be adjusted to the case of resonators embedded in a Sonic Crystal (SC). The proposed model is validated through simulations and experimental measurements, showing a significant improvement in the prediction of the resonance frequency. The results obtained in this study allow us to move towards a more efficient and realistic design of passive acoustic barriers that are lightweight and adaptable to the urban environment. Full article

Spread-tow woven fabrics (STWs) have attracted considerable attention owing to their thin-layered characteristics, high fiber strength utilization rate and superior designability, finding wide application in the aerospace field. To meet the application requirements for materials with high specific strength/specific modulus in the aerospace field, this study designed an anti-sandwich structured composite with high specific load-bearing capacity. Herein, the core layer was a load-bearing structure composed of STW, while the surface layers were hybrid lightweight structures made of STW and nonwoven (NW) felt. Repeated impact test results showed that increasing the thickness ratio of the core layer enhanced the impact resistant stiffness of the overall structure, whereas increasing the proportion of NW felt in the surface layers improved the energy absorption of the composites but reduced their load-bearing stiffness and strength. The composite exhibited superior repeated impact resistance, achieving a peak impact load of 17.43 kN when the thickness ratio of the core layer to the surface layers was 2:1 and the hybrid ratio of the surface layers was 3:1. No penetration occurred after 20 repeated impacts at the 50 J or 3 repeated impacts at 100 J. Meanwhile, both the maximum displacement and impact duration increased, whereas the bending stiffness declined as the number of impacts increased. The failure mode was mainly characterized by progressive interfacial cracking in the surface layers and fracture in the core layer. Full article

Basalt fiber (BF) can notably improve the mechanical properties of lightweight aggregate concrete (LWAC) through its crack-bridging and pull-out mechanisms, making it suitable for application in super high-rise buildings and large-span structures. This study assesses the influence of BF contents of 0%, 0.1%, 0.3%, 0.5%, and 0.7% (relative to the weight of cementitious materials) on the workability, mechanical properties, and microstructure of LWAC. The results showed that adding BF to LWAC can moderately weaken the slump, significantly enhance the mechanical properties, and lead to a maximum increase in specific strength of 7.3%. Compared with LWAC without BF, the maximum increases in compressive strength, flexural strength, and elastic modulus of LWAC with BF at 28 days were 24.7%, 33.9%, and 38.57%, respectively. In the microstructure, BF can connect the cracks in the internal structure of concrete, which is an important factor to consider when choosing a fiber to improve the mechanical properties of concrete. These conclusions provide a reference point for improving the mechanical properties of LWAC. Full article

Lattice structures are lightweight architected materials particularly suitable for aerospace and automotive applications due to their ability to combine mechanical strength with reduced mass. Among various topologies, Body-Centered Cubic (BCC) lattices are widely employed for their geometric regularity and favorable strength-to-weight ratio. Advances in Additive Manufacturing (AM) have enabled the precise and customizable fabrication of such complex architectures, reducing material waste and increasing design flexibility. This study investigates the low-velocity impact behavior of two polylactic acid (PLA)-based BCC lattice panels differing in strut diameter: BCC1.5 (1.5 mm) and BCC2 (2 mm). Experimental impact tests and finite element simulations were performed to evaluate their energy absorption () capabilities. In addition to conventional global performance indices, a dimensionless parameter, is introduced to quantify the ratio between local plastic indentation and global displacement, allowing for a refined characterization of deformation modes and structural efficiency. Results show that BCC1.5 absorbs more energy than BCC2, despite the latter’s higher stiffness. This suggests that thinner struts enhance energy dissipation under dynamic loading. Despite minor discrepancies, numerical simulations provide accurate estimations of and support the robustness of the index within the examined configuration, highlighting its potential to deformation heterogeneity. Full article

Manual pixel-level annotation remains a major bottleneck in deploying deep learning models for dense prediction and semantic segmentation tasks across domains. This challenge is especially pronounced in applications involving fine-scale structures, such as cracks in infrastructure or lesions in medical imaging, where annotations are time-consuming, expensive, and subject to inter-observer variability. To address these challenges, this work proposes a weakly supervised and annotation-efficient segmentation framework that integrates sparse bounding-box annotations with a limited subset of strong (pixel-level) labels to train robust segmentation models. The fundamental element of the framework is a lightweight Bounding Box Encoder that converts weak annotations into multi-scale attention maps. These maps guide a ConvNeXt-Base encoder, and a lightweight U-Net–style convolutional neural network (CNN) decoder—using nearest-neighbor upsampling and skip connections—reconstructs the final segmentation mask. This design enables the model to focus on semantically relevant regions without relying on full supervision, drastically reducing annotation cost while maintaining high accuracy. We validate our framework on two distinct domains, road crack detection and skin cancer segmentation, demonstrating that it achieves performance comparable to fully supervised segmentation models using only 10–20% of strong annotations. Given the ability of the proposed framework to generalize across varied visual contexts, it has strong potential as a general annotation-efficient segmentation tool for domains where strong labeling is costly or infeasible. Full article

The increasing adoption of digital technologies, robotic systems, and IoT applications in sectors such as medicine, agriculture, and industry drives a surge in data generation and necessitates secure and efficient encryption. For resource-constrained systems, lightweight yet robust cryptographic algorithms are critical. This study addresses the security demands of IoRT systems by proposing an enhanced chaos-based encryption method. The approach integrates the lightweight structure of NIST-standardized Ascon-AEAD128 with the randomness of the Zaslavsky map. Ascon-AEAD128 is widely used on many hardware platforms; therefore, it must robustly resist both passive and active attacks. To overcome these challenges and enhance Ascon’s security, we integrate into Ascon the keys and nonces generated by the Zaslavsky chaotic map, which is deterministic, nonperiodic, and highly sensitive to initial conditions and parameter variations.This integration yields a chaos-based Ascon variant with a higher encryption security relative to the standard Ascon. In addition, we introduce exploratory variants that inject non-repeating chaotic values into the initialization vectors (IVs), the round constants (RCs), and the linear diffusion constants (LCs), while preserving the core permutation. Real-time tests are conducted using Raspberry Pi 3B devices and ROS 2–based IoRT robots. The algorithm’s performance is evaluated over 100 encryption runs on 12 grayscale/color images and variable-length text transmitted via MQTT. Statistical and differential analyses—including histogram, entropy, correlation, chi-square, NPCR, UACI, MSE, MAE, PSNR, and NIST SP 800-22 randomness tests—assess the encryption strength. The results indicate that the proposed method delivers consistent improvements in randomness and uniformity over standard Ascon-AEAD128, while remaining comparable to state-of-the-art chaotic encryption schemes across standard security metrics. These findings suggest that the algorithm is a promising option for resource-constrained IoRT applications. Full article

This study presents the design and evaluation of a dexterous prosthetic hand featuring five fingers, ten independently actuated joints, and four passively driven joints. The hand’s dexterity is enabled by a novel rigid–flexible coupled finger mechanism that incorporates a 1-active–1-passive joint configuration, which can enhance the dexterity of traditional rigid actuators while achieving a human-like workspace. Each finger is designed with a specific degree of rotational freedom to mimic natural opening and closing motions. This study also elaborates on the mapping of eight-channel electromyography to finger grasping force through improved TCN, as well as the control algorithm for grasping flexible objects. A functional prototype of the prosthetic hand was fabricated, and a series of experiments involving adaptive grasping and handheld manipulation tasks were conducted to validate the effectiveness of the proposed mechanical structure and control strategy. The results demonstrate that the hand can stably grasp flexible objects of various shapes and sizes. This work provides a practical solution for prosthetic hand design, offering promising potential for developing lightweight, dexterous, and highly anthropomorphic robotic hands suitable for real-world applications. Full article

Synthetic aperture radar (SAR) provides all-weather and all-day imaging capabilities and can penetrate clouds and fog, playing an important role in ship detection. However, small ships usually contain weak feature information in such images and are easily affected by noise, which makes detection challenging. In practical deployment, limited computing resources require lightweight models to improve real-time performance, yet achieving a lightweight design while maintaining high detection accuracy for small targets remains a key challenge in object detection. To address this issue, we propose a novel lightweight network for accurate small-ship recognition in SAR images, named S2M-Net. Specifically, the Space-to-Depth Convolution (SPD-Conv) module is introduced in the feature extraction stage to optimize convolutional structures, reducing computation and parameters while retaining rich feature information. The Mixed Local-Channel Attention (MLCA) module integrates local and channel attention mechanisms to enhance adaptation to complex backgrounds and improve small-target detection accuracy. The Multi-Scale Dilated Attention (MSDA) module employs multi-scale dilated convolutions to fuse features from different receptive fields, strengthening detection across ships of various sizes. The experimental results show that S2M-Net achieved mAP50 values of 0.989, 0.955, and 0.883 on the SSDD, HRSID, and SARDet-100k datasets, respectively. Compared with the baseline model, the F1 score increased by 1.13%, 2.71%, and 2.12%. Moreover, S2M-Net outperformed other state-of-the-art algorithms in FPS across all datasets, achieving a well-balanced trade-off between accuracy and efficiency. This work provides an effective solution for accurate ship detection in SAR images. Full article

This paper presents a real-time terrain mapping method for aircraft’s navigation, combining probabilistic terrain modeling with adaptive azimuth scan command adjustment. The method refines a preloaded DTED in real time using radar scan data, enabling aircraft to update and utilize terrain elevation information during flight. The terrain is represented using a Gaussian Mixture Model (GMM), where radar scan data are evaluated based on their posterior responsibilities. A conditional nested GMM refinement is selectively applied in structurally ambiguous regions to capture multi-modal elevation patterns. The azimuth scan command is adaptively adjusted based on posterior responsibilities by increasing the step size in well-mapped regions and decreasing it in areas with low responsibility. This lightweight and adaptive strategy supports real-time operation with low computational cost. Simulations across diverse terrain types demonstrate accurate grid updates and adaptive scan control, with the proposed method achieving max error 29 m compared to grid-based averaging of 43 m and K-means clustering of 81 m. As the total number of updates is comparable to the existing methods, the proposed approach offers an advantage for real-time applications with enhanced grid accuracy. Full article

Developing secure and well-structured web applications using Spring Boot presents a significant challenge, as it requires developers to manage multiple layers, employ framework-specific annotations, and ensure authentication, authorization, and compliance with architectural standards. These complexities often lead to errors among students and novice developers. Although current low-code platforms reduce coding effort, they frequently compromise clarity, modularity, and maintainability. This paper introduces XBoot, a lightweight framework that utilizes a straightforward XML-based domain-specific language (DSL) to automatically generate modular and secure Spring Boot applications. By providing concise specifications for entities, services, routes, and user roles, XBoot generates database entities, service classes, controllers, user interface templates, and integrated security rules. Validation rules are directly enforced from the DSL, and built-in Swagger documentation facilitates interactive API testing. The evaluation was conducted in two phases. Initially, XBoot was validated by generating applications for student–course and flight-booking domains, where less than 50 lines of DSL resulted in 950–1350 lines of Java and HTML code, complete with security and documentation. Subsequently, 10 undergraduate students utilized XBoot in practice. All participants successfully generated and deployed applications within 2–20 min (average ≈ 7), compared to 45–120 min for manual implementation. On a 5-point Likert scale, students rated the reinforcement of layered architecture at an average of 4.0. These findings suggest that XBoot effectively eliminates common structural and security errors, reduces boilerplate complexity through concise DSL specifications, and maintains modularity and transparency-limitations often observed in traditional coding and other low-code platforms. Full article

The cores of honeycomb panels are usually made of hexagonal cells. Due to their structure, they create anticlastic surfaces that are difficult to use in furniture design. Synclastic surfaces in lightweight sandwich panels are typically associated with auxetic cores characterized by a negative Poisson’s ratio. This study aimed to transform the hexagonal cell cores into cells with a negative or positive Poisson’s ratio (NPR, PPR), enabling these cores to form synclastic surfaces. New core structures for synclastic furniture sandwich honeycomb panels were modeled numerically and experimentally. It has been demonstrated that reentrant cells with NPR create synclastic surfaces, and new shapes of core cells, created by transforming hexagonal cells with PPR, also enable the formation of synclastic surfaces. Cores’ synclasticity was assessed in two orthogonal planes using physical models and Finite Element Analysis (FEA). A new and original discovery is the demonstration that not only auxetic but also modified hexagonal cells with Poisson’s ratios of ${\nu }_{xy}$= 0.545 and ${\nu }_{yx}$ = 0.512, respectively, exhibit excellent synclastic properties. The agreement between FEA and experiment was very high. The results show that not only NPR but also cell topology provides a practical route to the synclastic formation of cores without the use of auxetic materials. Full article

Posture recognition is critical in modern educational and office environments for preventing musculoskeletal disorders and maintaining cognitive performance. Existing methods based on human keypoint detection typically rely on convolutional neural networks (CNNs) and single-scale features, which limit representation capacity and suffer from overfitting under small-sample conditions. To address these issues, we propose MSBN-SPose, a Multi-Scale Bayesian Neuro-Symbolic Posture Recognition framework that integrates geometric features at multiple levels—including global body structure, local regions, facial landmarks, distances, and angles—extracted from OpenPose keypoints. These features are processed by a multi-branch Bayesian neural architecture that models epistemic uncertainty, enabling improved generalization and robustness. Furthermore, a lightweight neuro-symbolic reasoning module incorporates human-understandable rules into the inference process, enhancing transparency and interpretability. To support real-world evaluation, we construct the USSP dataset, a diverse, classroom-representative collection of student postures under varying conditions. Experimental results show that MSBN-SPose achieves 96.01% accuracy on USSP, outperforming baseline and traditional methods under data-limited scenarios. Full article

Microcellular thermoplastic polyurethane (TPU) foams with dynamic covalent bonds demonstrating exceptional self-healing capabilities, coupled with precisely controlled micron-scale cellular architectures, present a promising solution for developing advanced materials that simultaneously achieve damage recovery and low density. In this study, a series of self-healable materials (named as PU-S) with high light transmittance possessing two dynamic covalent bonds (oxime bond and disulfide bond) in different ratios were fabricated by the one-pot method, and then the prepared PU-S were foamed utilizing the green and efficient supercritical carbon dioxide (scCO₂) foaming technology. The PU-S foams possess multiple dynamic covalent bonds as well as porous structures, and the effect of the dynamic covalent bonds endows the materials with excellent self-healing properties and recyclability. Owing to the tailored design of dynamic covalent bonding synergies and micron-sized porous structures, PU-S₅ exhibits hydrophobicity (97.5° water contact angle), low temperature flexibility (T_g = −30.1 °C), high light transmission (70.6%), and light weight (density of 0.12 g/cm³) together with high expansion ratio (~10 folds) after scCO₂ foaming. Furthermore, PU-S₅ achieves damage recovery under mild thermal conditions (60 °C). Accordingly, self-healing PU-S based on multiple dynamic covalent bonds will realize a wide range of potential applications in biomedical, new energy automotive, and wearable devices. Full article

As a typical representative of complex networks, the Internet of Vehicles (IoV) is more vulnerable to malicious attacks due to the mobility and complex environment of devices, which requires a secure and efficient authentication mechanism. Radio frequency fingerprinting (RFF) presents a novel research perspective for identity authentication within the IoV. However, as device fingerprint features are directly extracted from wireless signals, their stability is significantly affected by variations in the communication channel. Furthermore, the interplay between wireless channels and receiver noise can result in the distortion of the received signal, complicating the direct separation of the genuine features of the transmitted signals. To address these issues, this paper proposes a method for RFF extraction based on the physical sidelink broadcast channel (PSBCH). First, necessary preprocessing is performed on the signal. Subsequently, the wireless channel, which lacks genuine features, is estimated using linear minimum mean square error (LMMSE) techniques. Meanwhile, the previous statistical models of the channel and noise are incorporated into the analysis process to accurately capture the channel distortion caused by multipath effects and noise. Ultimately, the impact of the channel is mitigated through a channel-equalization operation to extract fingerprint features, and identification is carried out using a structurally optimized ShuffleNet V2 network. Based on a lightweight design, this network integrates an attention mechanism that enables the model to adaptively concentrate on the most distinguishable weak features in low signal-to-noise ratio (SNR) conditions, thereby enhancing the robustness of feature extraction. The experimental results show that in fixed and mobile scenarios with low SNR, the classification accuracy of the proposed method reaches 96.76% and 91.05%, respectively. Full article

As Virtual Try-On (VTON) technology matures, 2D VTON methods based on diffusion models can now rapidly generate diverse and high-quality try-on results. However, with rising user demands for realism and immersion, many applications are shifting towards 3D VTON, which offers superior geometric and spatial consistency. Existing 3D VTON approaches commonly face challenges such as barriers to practical deployment, substantial memory requirements, and cross-view inconsistencies. To address these issues, we propose an efficient 3D VTON framework with robust multi-view consistency, whose core design is to decouple the monolithic 3D editing task into a four-stage cascade as follows: (1) We first reconstruct an initial 3D scene using 3D Gaussian Splatting, integrating the SMPL-X model at this stage as a strong geometric prior. By computing a normal-map loss and a geometric consistency loss, we ensure the structural stability of the initial human model across different views. (2) We employ the lightweight CatVTON to generate 2D try-on images, that provide visual guidance for the subsequent personalized fine-tuning tasks. (3) To accurately represent garment details from all angles, we partition the 2D dataset into three subsets—front, side, and back—and train a dedicated LoRA module for each subset on a pre-trained diffusion model. This strategy effectively mitigates the issue of blurred details that can occur when a single model attempts to learn global features. (4) An iterative optimization process then uses the generated 2D VTON images and specialized LoRA modules to edit the 3DGS scene, achieving 360-degree free-viewpoint VTON results. All our experiments were conducted on a single consumer-grade GPU with 24 GB of memory, a significant reduction from the 32 GB or more typically required by previous studies under similar data and parameter settings. Our method balances quality and memory requirement, significantly lowering the adoption barrier for 3D VTON technology. Full article