Search Results (9,447)

The proposed forest monitoring photo trap ecosystem integrates a cost-effective architecture for observation and transmission using Internet of Things (IoT) technologies and long-range digital radio systems such as LoRa (Chirp Spread Spectrum—CSS) and nRF24L01 (Gaussian Frequency Shift Keying—GFSK). To address low-bandwidth links, a novel approach based on the Monte Carlo sampling algorithm enables progressive, bandwidth-aware image transfer and its thumbnail’s reconstruction on edge devices. The system transmits only essential data, supports remote image deletion/retrieval, and minimizes site visits, promoting environmentally friendly practices. A key innovation is the integration of no-reference image quality assessment (NR IQA) to determine when thumbnails are ready for operator review. Due to the computational limitations of the Raspberry Pi 3, the PIQE indicator was adopted as the operational metric in the quality stabilization module, whereas deep learning-based metrics (e.g., HyperIQA, ARNIQA) are retained as offline benchmarks only. Although single-pass inference may meet initial timing thresholds, the cumulative time–energy cost in an online pipeline on Raspberry Pi 3 is too high; hence these metrics remain offline. The system was validated through real-world field tests, confirming its practical applicability and robustness in remote forest environments. Full article

In this research, two inflatable-winged Unmanned Aerial Vehicles (UAVs) in distinct configurations, a single-fuselage layout with external trailing-edge control surfaces and a twin-fuselage layout with fully movable control surfaces were designed, developed, and flight tested to investigate the flight characteristics of inflatable-winged aircraft. Initially, inflatable wings were designed and fabricated from various materials, followed by rigorous ground testing, including structural characteristics tests, pressure retention and resistance tests, and low-speed wind-tunnel evaluations. Following this, two methods for controlling the inflatable wings were proposed, and their roll control effectiveness was thoroughly investigated. Subsequently, two inflatable-winged UAV prototypes, each employing a different configuration and manipulation method, were designed, assembled, and subjected to basic low-altitude flight tests to assess the feasibility of their aerodynamic layouts and control characteristics. The results demonstrated that a segmented wing design with a multi-boom configuration is particularly well-suited for inflatable wings. Additionally, both proposed control methods were tested and shown to be effective in flight. The findings provide valuable insights into the properties of inflatable wings and offer substantial guidance for the development of inflatable-winged aircraft. Full article

Low-temperature superconductivity has been known since 1957 to be described by BCS theory for effective single-band metals controlled by the density of states at the Fermi level, very far from band edges, the electron–phonon coupling constant l, and the energy of the boson in the pairing interaction w₀, but BCS has failed to predict high-temperature superconductivity in different materials above about 23 K. High-temperature superconductivity above 35 K, since 1986, has been a matter of materials science, where manipulating the lattice complexity of high-temperature superconducting ceramic oxides (HTSCs) has driven materials scientists to grow new HTSC quantum materials up to 138 K in HgBa₂Ca₂Cu₃O₈ (Hg1223) at ambient pressure and near room temperature in pressurized hydrides. This perspective covers the major results of materials scientists over the last 39 years in terms of investigating the role of lattice inhomogeneity detected in these new quantum complex materials. We highlight the nanoscale heterogeneity in these complex materials and elucidate their special role played in the physics of HTSCs. Especially, it is highlighted that the geometry of lattice and charge complex heterogeneity at the nanoscale is essential and intrinsic in the mechanism of rising quantum coherence at high temperatures. Full article

Tomato is one of the most important economic crops in the world, but it is prone to diseases during the growth process, so the detection of tomato diseases is very important. However, when detecting tomato diseases in natural environments, existing models are easily affected by environmental factors such as occlusion and illumination, as well as the small size of lesions. In response to these challenges, this paper proposes a tomato leaf disease detection framework LDW-DETR based on multi-scale fusion. First, the local-global feature fusion (LGFF) module is designed by referring to the idea of the PPA module, which can effectively capture local and global features, thereby enhancing the detection ability of small lesions in complex backgrounds. Second, the CSPDarknet architecture is introduced as the backbone network of LDW-DETR to improve the efficiency of feature extraction. In addition, the bottleneck layer of the C2f component is improved by integrating Strip Block and Contextualized Gated Linear Unit (CGLU) to enhance the perception ability of lesion edges and textures. Finally, the WIoU v3 loss function is used to optimize the bounding box regression process. The experimental results show that compared with RT-DETR, the LDW-DETR model improves mAP@0.5 and mAP@0.5–0.95 by 2.6% and 3.7%, respectively, while the number of parameters is reduced by 17.9%. In addition, it still maintains high robustness and generalization ability in cross-dataset experiments. These results show that LDW-DETR has good detection performance and generalization ability in the tomato leaf disease detection task. Full article

This study investigates jet plume deflection in underexpanded supersonic rectangular nozzles with aft-decks. To determine the underlying mechanism, 117 two-dimensional, Reynolds-averaged Navier–Stokes simulations were performed across a nozzle pressure ratio (NPR) range of $1.9\le \mathrm{NPR}\le 5.0$ and aft-deck length ($L_{\mathrm{aft}}/D_h$) range of $1.36\le L_{\mathrm{aft}}/D_h\le 3.37$. For each simulation, the first shock reflection $S_1$, the wall-pressure field, the vertical force $F_y$, and the presence of any separation bubble were recorded to characterize the relationships among $\mathrm{NPR}$, $L_{\mathrm{aft}}$, and $\theta$. Accordingly, a cause-and-effect path was delineated as $(\mathrm{NPR},L_{\mathrm{aft}})\to S_1\to F_y\to \theta$. A weighted regression captured 96% of the variance in the deflection angle and revealed that shifts in shock position set the wall-pressure imbalance. The imbalance fixes the vertical force and the force ultimately rotates the jet plume. Downward deflection arises when the shock reflects near the deck edge, whereas upstream reflection initiates a shock–boundary-layer interaction that forms a separation bubble and drives the jet plume upward. Between these extremes, a narrow operating band allows either outcome, explaining the divergent trends reported in prior work. The quantitative model assumes steady, two-dimensional flow and the regression prioritises illuminating the underlying physics over exact prediction of $\theta$. Nevertheless, under these assumptions, the analysis establishes a physics-based framework that reconciles earlier observations and offers a basis for understanding how nozzle pressure ratio and aft-deck length govern jet plume deflection. Full article

The optimization of the microstructural homogeneity and residual stress distribution in Ti sputtering targets for OLED 6G applications is essential for improving dimensional stability, durability, and deposition performance. Herein, 3N Ti plates were hot-rolled at 730 °C and then annealed at 600 °C and 700 °C for different durations to investigate the effects of annealing parameters on microstructural evolution and stress relaxation. X-ray diffraction analysis revealed that hexagonal α-Ti with progressive development of the (002) orientation was produced during annealing under all the conditions. Electron backscatter diffraction analyses showed that short-time annealing at 600 °C (≤30 min) generated heterogeneous grains, high dislocation density, and mixed grain boundary character, whereas extended annealing (≥60 min) produced a more uniform microstructure. However, residual stress differences between the plate center and edge remained significant under this condition. Conversely, annealing at 700 °C promoted progressive recrystallization, as indicated by increased high-angle grain boundary fractions and decreased kernel average misorientation values, and facilitated grain growth stabilization across the plate. Prolonged annealing improved microstructural and residual stress uniformity significantly, and near-complete homogenization was achieved after 5 h. These findings demonstrate that annealing at 700 °C for sufficient time is optimal for producing homogeneous microstructures and uniform residual stress distributions, providing valuable guidelines for Ti sputtering target processing. Full article

Thermoplastic composites are growing in popularity in the aerospace and automotive industries; they enable weldable and recyclable structures. Resistance welded hybrid thermoplastic and metal joints are attractive for rapid assembly, but the thermal mismatch between metals and polymers introduces residual stresses, which can drive edge debonding and compromise durability. This study presents fabricated single-lap PC/CF–Al7075 coupons with measured mid-span bow resulting from welding, evaluated bond quality by step-heating thermography, and an evaluated framework for residual stress prediction using Ansys complemented by a bimetal analytical check. Three thermal cycles were examined with different temperature gradients (200, 220, 240 °C): the measured bow was 16.5 mm and remained constant, whereas analytical calculation increased with ΔT similarly to the FEM prediction. The current FEM under predicted the bow (Mean Absolute Percentage Error is 21%), showing stress contours that decay with distance from the bond and revealing pronounced peaks in both σ_xx and σ_zz components at weld edges, consistent with shear-lag theory. FEM returned edge-peaked peel rising from 43 to −64 MPa and σ_xx was up to 12% more compressive than analytical calculation; an effective CF/PC CTE of 1.5 × 10⁻⁶ K⁻¹ reconciled curvature with test better than catalogue values. The temperature insensitive bow is attributed to polycarbonate flow/viscoelastic relaxation above Tg and hot relaxation in aluminum, with effects not represented in the elastic models. Edge peel and shear govern initiation risk. Full article

Accurate segmentation of lung contours from computed tomography (CT) scans is essential for developing reliable computer-aided diagnostic systems. Although deep learning models, especially convolutional neural networks, have advanced the automation of pulmonary region extraction, their performance is often limited by low contrast and atypical anatomical appearances in CT images. This paper presents MSDC-AM U-Net, a hierarchically supervised segmentation framework built upon the U-Net architecture, integrated with a newly designed Multi-Scale Dilated Convolution (MSDC) module and an Attention Module (AM). The MSDC component employs dilated convolutions with varying receptive fields to improve edge detection and counteract contrast-related ambiguities. Furthermore, spatial attention mechanisms applied across different dimensions guide the model to focus more effectively on lung areas, thereby increasing localization precision. Extensive evaluations on multiple public lung imaging datasets (Luna16, Montgomery County, JSRT) confirm the superiority of the proposed approach. Our MSDC-AM U-Net achieved leading performance, notably attaining a Dice Coefficient of 0.974 on the Luna16 CT dataset and 0.981 on the JSRT X-ray dataset, thereby exceeding current leading methods in both qualitative and quantitative assessments. Full article

Welding is a critical joining process in modern manufacturing, with defects contributing to 50–80% of structural failures. Traditional inspection methods are often inefficient, subjective, and inconsistent. To address challenges in weld defect detection—including scale variation, morphological complexity, low contrast, and sample imbalance—this paper proposes ADFE-DET, an adaptive dynamic feature enhancement algorithm. The approach introduces three core innovations: the Dynamic Selection Cross-stage Cascade Feature Block (DSCFBlock) captures fine texture features via edge-preserving dynamic selection attention; the Adaptive Hierarchical Spatial Feature Pyramid Network (AHSFPN) achieves adaptive multi-scale feature integration through directional channel attention and hierarchical fusion; and the Multi-Directional Differential Lightweight Head (MDDLH) enables precise defect localization via multi-directional differential convolution while maintaining a lightweight architecture. Experiments on three public datasets (Weld-DET, NEU-DET, PKU-Market-PCB) show that ADFE-DET improves mAP50 by 2.16%, 2.73%, and 1.81%, respectively, over baseline YOLOv11n, while reducing parameters by 34.1%, computational complexity by 4.6%, and achieving 105 FPS inference speed. The results demonstrate that ADFE-DET provides an effective and practical solution for intelligent industrial weld quality inspection. Full article

This study employs 304 stainless steel perforated mesh (SS mesh) as the heating element for the resistance welding of continuous carbon fiber-reinforced polycarbonate (CCF/PC) sheets. An electro-thermal coupled finite element model is developed to investigate the effect of SS mesh structural parameters (aperture shape, aperture area, mesh thickness) and clamping distance on the welding temperature field. The model accurately predicts peak temperatures, with errors of 1–4% compared with experiments. Under identical aperture area, the SS mesh with longer effective current path length and smaller effective cross-sectional area has higher resistance. In addition, the resistance increases significantly with decreasing mesh thickness and increasing aperture size. Reducing the clamping distance effectively improves temperature uniformity across the weld zone and mitigates edge overheating. A novel mesh structure—featuring larger aperture in the welding region and smaller aperture in non-welding region, is designed to improve the temperature uniformity and joint quality. Under optimized welding parameters (14 A, 40 s welding/holding, 0.3 MPa), the joint achieves a maximum tensile shear force of 9.851 kN, a 13.1% improvement over conventional uniform-aperture mesh (8.713 kN). Full article

Neural networks are powerful, high-accuracy systems whose trained parameters represent a valuable intellectual property. Building models that reach top level performance is a complex task and requires substantial investments of time and money so protecting these assets is an increasingly important task. Extensive research has been carried out on Neural Network Watermarking, exploring the possibility of inserting a recognizable marker in a host model either in the form of a concealed bit-string or as a characteristic output, making it possible to confirm network ownership even in the presence of malicious attempts at erasing the embedded marker from the model. The study examines the applicability of Opt-FreeMark, a non-invasive post-training white-box watermarking technique, obtained by modifying and optimizing an already existing state-of-the-art technique for tiny neural networks. Here, “Tiny” refers to models intended for ultra-low-power deployments, such as those running on edge devices like sensors and micro-controllers. Watermark robustness is also demonstrated by simulating common model-modification attacks that try to eliminate it from the model while preserving performance; the results presented in the paper indicate that the watermarking scheme effectively protects the networks against these manipulations. Full article

Advancements in genome editing have transformed agricultural biotechnology by allowing for precise modifications of DNA. This technology has sparked increasing interest in enhancing important traits of major crops, including peanuts. As a nutritionally rich legume prized for its high oil content, peanut production still faces significant challenges, including disease outbreaks, nutrient deficiencies, and pest infestations. Addressing these challenges is essential for achieving high yields and sustainable cultivation. CRISPR technology, a cutting-edge genome editing tool, has emerged as a powerful platform for improving peanut traits. Its ability to facilitate gene knockouts, regulate gene expression, and introduce targeted genetic changes has accelerated research efforts in this field. The successful applications of CRISPR in peanut improvement, such as increasing oleic acid content and reducing allergenicity, reassure us about the effectiveness and potential of this technology. Despite the complexity of the peanut genome as a polyploid crop, these successes demonstrate the power of genome editing. This review emphasizes the crucial role of genome editing in enhancing peanut traits and outlines the promising future of CRISPR-based approaches in advancing peanut breeding and agricultural productivity. Full article

Urban recreational spaces (URSs) are pivotal for enhancing resident well-being, making the accurate assessment of public perceptions crucial for quality optimization. Compared to traditional surveys, social media data provide a scalable means for multi-dimensional perception assessment. However, existing studies predominantly rely on single-modal data, which limits the comprehensive capturing of complex perceptions and lacks interpretability. To address these gaps, this study employs cutting-edge large vision–language models (LVLMs) and develops an interpretable model, Qwen2.5-VL-7B-SFT, through supervised fine-tuning on a manually annotated dataset. The model integrates visual-linguistic features to assess four perceptual dimensions of URSs: esthetics, attractiveness, cultural significance, and restorativeness. Crucially, we generate textual evidence for our judgments by identifying the key spatial elements and emotional characteristics associated with specific perceptions. By integrating multi-source built environment data with Optuna-optimized machine learning and SHAP analysis, we further decipher the nonlinear relationships between built environment variables and perceptual outcomes. The results are as follows: (1) Interpretable LVLMs are highly effective for urban spatial perception research. (2) URSs within Beijing’s Third Ring Road fall into four typologies, historical heritage, commercial entertainment, ecological-natural, and cultural spaces, with significant correlations observed between physical elements and emotional responses. (3) Historical heritage accessibility and POI density are identified as key predictors of public perception. Positive perception significantly improves when a block’s POI functional density exceeds 4000 units/km² or when its 500 m radius encompasses more than four historical heritage sites. Our methodology enables precise quantification of multidimensional URS perceptions, links built environment elements to perceptual mechanisms, and provides actionable insights for urban planning. Full article

With the rapid deployment of intelligent transportation systems (ITS), the Internet of Vehicles (IoV) has become an increasingly vital component in the development of smart cities. However, the openness of IoV also gives rise to critical issues such as message security and identity privacy. Consequently, addressing message authentication in the IoV environment is a fundamental requirement for ensuring its sustainable and stable evolution. Firstly, this paper proposes an adaptive traffic authentication strategy (ATAS) By integrating traffic flow dynamics evaluation, traffic status scoring, time sensitivity assessment, and comprehensive strategy decision-making, the scheme achieves an effective balance between authentication efficiency and security in IoV scenarios. Secondly, to tackle the high overhead and security issues caused by multiple message transmissions in large-scale IoV application scenarios, this paper proposes a secure message transmission and authentication method based on the cloud-edge-client collaborative architecture. Leveraging aggregate message authentication code (AMAC) technology, this method validates both the authenticity and integrity of messages, effectively reducing communication overhead while maintaining reliable authenticated transmission. Finally, this paper builds an IoV co-simulation experimental environment using the SUMO 1.19.0, OMNeT++ 6.0.3, and Veins 5.0.0 software platforms. It simulates the interactive authentication process among vehicles, Road Side Units (RSUs), and the cloud platform, as well as the effects of traffic response strategies under different scenarios. The results demonstrate the potential of IoV authentication technology in improving traffic management efficiency, optimizing road resource utilization, and enhancing traffic safety, providing strong support for the secure communication and efficient management of IoV. Full article

Time-sensitive networking (TSN), a cutting-edge technology enabling efficient real-time communication and control, provides strong support for traditional Ethernet in terms of real-time performance, reliability, and deterministic transmission. In TSN systems, although time-triggered (TT) flows enjoy deterministic delay guarantees, audio video bridging (AVB) and best effort (BE) traffic still share link bandwidth through statistical multiplexing, a process that remains nondeterministic. This competition in shared memory switches adversely affects data transmission performance. In this paper, a priority queue threshold control policy is proposed and analyzed for mixed-critical traffic in time-sensitive networks. The core of this policy is to set independent queues for different types of traffic in the shared memory queuing system. To prevent low-priority traffic from monopolizing the shared buffer, its entry into the queue is blocked when buffer usage exceeds a preset threshold. A two-dimensional Markov chain is introduced to accurately construct the system’s queuing model. Through detailed analysis of the queuing model, the truncated chain method is used to decompose the two-dimensional state space into solvable one-dimensional sub-problems, and the approximate solution of the system’s steady-state distribution is derived. Based on this, the blocking probability, average queue length, and average queuing delay of different priority queues are accurately calculated. Finally, according to the optimization goal of the overall blocking probability of the system, the optimal threshold value is determined to achieve better system performance. Numerical results show that this strategy can effectively allocate the shared buffer space in multi-priority traffic scenarios. Compared with the conventional schemes, the queue blocking probability is reduced by approximately 40% to 60%. Full article