Search Results (13,930)

This study conducts multi-scale numerical simulations spanning atomic to macroscopic scales (i.e., from nanometer to millimeter scale) to investigate the fatigue crack propagation behavior in the welded heat-affected zone (HAZ) of AH36 shipbuilding steel. A coupled molecular dynamics–finite element method (MD-FEM) was employed to establish a multi-scale model. Through the transfer of boundary displacements, equivalent mapping of crack morphology, and crack-tip tracking, an iterative multi-scale simulation of 600 tension–tension fatigue cycles was achieved. The results indicate that the crack propagation rate is significantly influenced by crack tip morphology (blunting/sharpening) and growth direction. Notably, the peak strain at the boundary is not the sole determining factor. Periodic blunting of the crack tip occurs during cyclic loading, accompanied by a decrease in the propagation rate. Additionally, the stress field near the crack tip induces microscopic defects such as voids in the nearby area, affecting the crack propagation. This study, based on multi-scale analysis, reveals the microscopic mechanism and evolution law of fatigue crack propagation in the heat-affected zone of AH36 steel welds. Full article

Automated detection of defects in welds are inevitable in the assurance of structural integrity, but this faces serious challenges due to the microscopic characteristics of the discontinuities, low visual contrast and infrequent occurrence of defect samples. Conventional deep learning methods, while accurate, often lack interpretability and exhibit low recall for rare defects. This paper proposes a novel hybrid system combining a Generative Adversarial Network (GAN), a Convolutional Neural Network (CNN), and Extreme Gradient Boosting (XGBoost 2.0.0) to enhance weld defect classification performance and transparency. Firstly, a Deep Convolutional GAN (DCGAN) creates synthetic images of the minority classes; thus, the problem of class imbalance is resolved. Then, a pretrained ResNet50V2 CNN is used to extract features of the deep layers from the original images as well as from the generated ones. After that, these features are fed into an XGBoost classifier, which uses tree-based learning to optimize classification results and make the process more understandable to the user. Furthermore, interpretation is also facilitated by Grad-CAM rendering of the CNN regions of interest and SHAP analysis to measure the involvement of the features in XGBoost. Experiments using the available LoHi-WELD datasets show that the overall accuracy is significantly improved, the per-class recall of the rare defects is also enhanced, and the robustness is also improved. The proposed hybrid method not only achieves better results but also generates visual/explainable output, which is very valuable when the system is implemented in industrial welding inspection systems. This paper serves as a liaison between the latest AI technology and the practical interpretability requirements of the mechanical and welding engineering fields. Full article

Background: Root-knot nematodes (RKNs) are destructive parasites affecting both agricultural and natural plants. Fagopyrum tataricum, a phenolic-rich edible and medicinal plant, has antidiabetic, anti-inflammatory, and anticancer properties, yet the impact of RKN infection on its rhizosphere microbiome remains unclear. Methods: We employed full-length 16S rRNA gene sequencing (FL16S) to profile bacterial communities in the rhizosphere of healthy and RKN-infected F. tataricum plants. Results: FL16S classified 78.41% of operational taxonomic units (OTUs) at the genus level and 69.18% at the species level. Healthy plants showed higher richness, diversity, and evenness, while principal co-ordinate analysis (PCoA) and PERMANOVA indicated significant RKN-associated shifts in community composition. Dominant phyla included Bacteroidota, Proteobacteria, Patescibacteria, Verrucomicrobiota, Actinobacteriota, Acidobacteriota, and Chloroflexi, with Abditibacteriota enriched in healthy and Acidobacteriota in diseased rhizospheres. At the OTU level, 66 differentially abundant taxa were identified, including nine hub OTUs in healthy plants, suggesting keystone roles in network stability. Network analyses revealed reduced diversity, interactions, and altered intra- and inter-phylum dynamics under RKN infection. Conclusions: These findings provide insight into rhizosphere microbial responses to RKN parasitism in F. tataricum and identify potential microbial biomarkers and biocontrol targets, supporting microbiome-based management strategies. Full article

The steam generator is a core component of nuclear power plants that facilitates heat exchange between the primary and secondary circuits, directly impacting the overall operation of the plant in terms of safety and reliability. During prolonged operation, the heat transfer tubes of the steam generator are subjected to erosion, corrosion, and cracking due to high-temperature, high-pressure fluid impact and vibration. Existing in-service inspection strategies for heat transfer tubes generally employ fixed intervals and coverage, failing to effectively differentiate the actual risk of tubes in various regions, leading to wasted inspection resources or safety hazards. This paper proposes a dynamic inspection and plugging management strategy based on flow-induced vibration (FIV) analysis, specifically utilizing the flow stability ratio (FSR). By calculating the FSR of heat transfer tubes, the strategy categorizes them into high-risk, medium-risk, and low-risk regions, and dynamically adjusts inspection frequency and coverage based on these risk levels. Theoretical analysis and validation with actual data demonstrate that this strategy can improve inspection efficiency and ensure the safety of the steam generator. Full article

Digital electricity meters display readings via digits, but accurate image-based recognition faces a key challenge: the frequent omission of decimal points creates a critical asymmetry between the visual image and its true semantic meaning. To address this visual-semantic asymmetry, we propose an improved YOLOv10n approach incorporating cascaded Visual-Semantic processing. We introduce a Reparameterized Convolution Single-Shot Aggregation (RCSOSA) module and a SimAM attention mechanism to enhance feature extraction, and employ Normalized Wasserstein Distance (NWD) Loss to boost small-target detection. To rectify the visual-semantic asymmetry, we introduce domain-specific format rules based on power industry standards (taking GB/T 17215-2018 as an example) to provide structural constraints for digit recognition. Experimental results show superior performance with 0.870 precision, 0.932 mAP50, and 116 FPS inference speed, outperforming reference models in both precision and efficiency for real-time meter inspection. Full article

Communication latency is one of the main factors limiting the practical scalability of unmanned aerial vehicle (UAV) swarms operating with distributed formation control. In real-time UAV missions, such as coordinated swarm navigation, autonomous inspection, and aerial monitoring, delayed information exchange directly affects formation stability and operational safety. In practical aerial networks, inter-UAV communication latency is influenced by stochastic effects including jitter, burst delays, and multi-hop propagation, which are rarely captured by the simplified deterministic delay assumptions commonly adopted in analytical formation-control studies. This paper introduces a measurement-informed stochastic delay model and a communication–control delay-feasibility framework that jointly account for per-link latency behavior, multi-hop delay accumulation, and controller-level delay tolerance. The proposed framework is evaluated using an attractive–repulsive distance-based potential field (ARD–PF) formation controller, for which the maximum admissible end-to-end delay is quantified as a function of swarm size and inter-UAV separation. The delay model is calibrated and validated using more than 15,000 in-flight communication delay samples collected from a multi-UAV LoRa platform operating under realistic flight conditions. The results show that different mechanisms limit swarm operation under different operating scenarios. In some configurations, stochastic communication latency becomes the dominant constraint, whereas in others, formation geometry or network load determines the feasible operating region. Based on these elements, the proposed framework characterizes delay-feasible operating regions and predicts the maximum feasible swarm size under distributed formation control and realistic multi-hop communication latency. Full article

The extensive application of unmanned aerial vehicles (UAVs) in power inspection, military operations and environmental monitoring demands stronger robustness and adaptability for autonomous decision-making systems. Existing methods suffer from heavy map dependence, high computational complexity and insufficient exploration and generalization. Traditional approaches based on expert rules and planning algorithms only suit fixed scenarios and degrade severely in complex dynamic environments. To address these problems, this paper proposes a complex-scene-oriented autonomous decision-making method for UAVs (CADU). It builds a closed-loop decision chain by integrating perception, strategy and execution modules, and adopts curiosity mechanism and contrastive learning to enhance exploration and adaptability. Experimental results show that the proposed CADU achieves an average reward of 0.85, a trajectory smoothness of 0.87, a flight stability of 0.85, and a cumulative collision count of $8\pm 1.2$, which significantly outperforms DDPG, PPO and SAC baselines. It provides a reliable and efficient scheme for UAV autonomous decision-making in complex scenarios. Full article

Estimating in a stream’s cross-section the depth-averaged velocity, V, from the free-surface velocity, v_surf, is an efficient, non-invasive hydrometric method. The ratio f_v = V/v_surf is typically assumed constant at f_v = 0.86 in field applications, despite observations to the contrary. Guidance is, therefore, needed in estimating actual f_v-ratios when velocity profile data are absent. This work provides field-verified guidance based on the hydromechanics of the logarithmic velocity law, which shows that f_v depends on the scaled resistance measure ‘friction length/depth’, y_o/h, with the y_o(k) function of the equivalent sand grain roughness, k. The mean-to-surface-velocity ratio in rough-bed streams is estimated from the bed roughness and stream morphology by modifying Nikuradze’s equation, y_o = k/30, to y_o = ck, with c(h/k) ≥ 1/30, and k ≈ D₈₄—data fit: c ≈ 8.61(h/k)^−1.821, ~5 ≤ h/k < ~30. Field-verification of the ratio’s modified hydromechanics, f_v = f$\left(h/y_o\right),$ with y_o(h/k) evaluated from bed roughness estimated by inspection or sieve analysis shows this ratio holding within ~|10|% error for shallow streamflow over a coarse bed of gravels and rocks, giving submergences of ~5 ≤ h/D₈₄ ≤ ~30; y_o = k/30 suits large streams with smooth beds (h/k ≥ ~30, f_v ≥ ~0.86). Variable roughness-estimated f_v-ratios appear to be more reliable than the fixed default, f_v(h/y_o ≈ 1000) = 0.86. This flow-gauging concept is based on observable physical characteristics of a monitoring cross-section and facilitates the rating of hard-to-access streams draining small basins in ragged upland terrain. Full article

The rapid inspection of urban road hazards, such as subsurface voids and pipeline damage, demands high efficiency and precision in detection technology. Conventional Ground Penetrating Radar (GPR) systems often face limitations in urban environments, including slow survey speeds, poor channel scalability, and the trade-off between shallow resolution and deep penetration. The proposed system integrates a dual-band antenna array (200 MHz and 400 MHz) to resolve the classical resolution–penetration trade-off, simultaneously capturing high-resolution shallow data and achieving deep subsurface penetration in a single pass. To overcome the sampling rate bottleneck inherent in low-cost microcontrollers, a custom Time-Division Step Multiplexing (TDSM) protocol extends the equivalent sampling period to 0.38 µs across 24 parallel channels while maintaining a 200 kHz pulse repetition rate—enabling real-time data streaming at vehicle speeds up to 70 km/h with 5 cm trace spacing. This capability directly addresses the critical challenge of traffic disruption on urban arterials caused by conventional slow-speed GPR surveys. Complementing this, a master-slave FPGA-MCU hierarchical architecture provides seamless channel scalability from 24 to 36 channels, adapting to diverse swath width requirements without hardware redesign. Laboratory physics model experiments demonstrate a penetration depth exceeding 3 m after convolutional sparse fusion of the dual-band data, covering the typical burial depth of urban utilities. This study provides a deployable high-resolution underground detection solution for rapid urban infrastructure surveys and emergency disease detection by breaking the traditional constraints of channel number, sampling rate, and detection speed, significantly reducing interference with urban main traffic. Full article

A 34-year-old healthy man was referred for colonoscopy due to tenesmus and rectal bleeding in the absence of systemic or immunosuppressive conditions. Incomplete bowel preparation limited the examination, but rectal inspection revealed a well-demarcated erythematous lesion with a granular, micronodular surface and fibrinous areas. The mucosa appeared friable and bled with minimal contact. The differential diagnosis included infectious and inflammatory etiologies. Histologic analysis showed granulation tissue with moderate lymphoplasmacytic infiltration, and C-reactive protein (CRP) confirmed Herpes Simplex Virus type 2 (HSV-2). This case underscores the importance of considering sexually transmitted infections (STIs) such as HSV in the differential diagnosis of rectal bleeding, even in immunocompetent individuals. Full article

This study comprehensively analyzed the effects of gamma irradiation (GI) on the flavor profile of aged light-flavor tartary buckwheat Baijiu (LTB) using E-nose, E-tongue, and high-sensitivity headspace–gas chromatography–ion mobility spectrometry (HS-GC-IMS). A total of 30 volatile organic compounds (VOCs) were identified, with concentrations showing significant dose-dependent correlations with GI treatment. Aging alone reduced harsh and pungent VOCs (e.g., 1-propanol, 2-methyl butanoic acid ethyl ester), while GI followed by aging further decreased undesirable compounds (e.g., butanal-D, pyrrolidine) and enhanced beneficial flavor components, such as 1,1-diethoxy ethane-D and butanoic acid propyl ester. Notably, this treatment partially restored 1-propanol, triethylamine, and 2-butanone-M, though their levels remained significantly lower than in newly brewed LTB, achieving a more balanced purity and flavor complexity. The significantly elevated levels of tetrahydrofuran-M/D, 1,1-diethoxy ethane-D, and cyclohexane in GI-treated aged LTB, along with their dose-dependent accumulation patterns, suggest their potential as reliable markers. Multivariate analysis confirmed that all three techniques (E-nose, E-tongue, and HS-GC-IMS) effectively differentiated LTB samples, with strong correlations between E-nose and HS-GC-IMS data, as well as between E-tongue and HS-GC-IMS results. This work provides flavor fingerprints and potential markers for gamma-irradiated LTB identification, while proposing an innovative technical approach for rapid flavor assessment of light-flavor Baijiu. Full article

Ensuring the structural integrity and service reliability of railway wheels has become a key challenge in modern manufacturing and maintenance strategies within the railway sector. In this context, Eddy Current (EC)-based Non-Destructive Testing (NDT) provides an automated and efficient approach for detecting surface and near-surface defects, while reducing inspection time and operator dependency compared to conventional manual methods. This study presents the integration of an EC inspection system into a precision lathe, enabling in-machining evaluation during wheel turning. Experimental validation was conducted on wheels with artificial defects, yielding high signal-to-noise ratios and enabling reliable defect characterization. Furthermore, computationally efficient and easily deployable machine learning algorithms were developed to enable automatic defect detection, localization, and size estimation. The results confirm the feasibility of in-machine EC inspection during machining operations, enabling early defect detection and contributing to safer, more efficient, and higher-quality manufacturing processes in the railway sector. Full article

Background: Oral lipomas are uncommon benign tumors composed of mature adipocytes, accounting for roughly 1% of benign intraoral lesions. Common predilection sites are buccal mucosa, lips, and tongue, presenting as slow-growing, nodular masses, often with a yellow hue. As the size of most lesions does not exceed 10 mm, particularly larger lipomas may be misdiagnosed. We present two cases of large oral lipomas. Case reports: Case 1: A 58-year-old male with a painless, sessile nodular mass of approximately 2.5 cm in the left cheek, increasing in size and causing discomfort during mastication. After excision, histopathology revealed mature adipocytes with delicate fibrous septa. Case 2: A 47-year-old female with a tender yellow growth of approximately 2 cm in her lower lip, increasing in size and causing aesthetic problems with functional discomfort. After sharp dissection, the specimen presented acanthotic and parakeratotic epithelium with adipocytic tumorous tissue, permeated by collagenous cords. Conclusions: Oral lipomas are uncommon, mostly asymptomatic benign lesions. Mostly found in the buccal mucosa and lower lip, they can mimic more common growths. When located superficially, a conservative surgical excision leads to resolution with rare recurrences. Histopathological inspection is necessary to confirm the benign nature of the lesion. Full article

As the cornerstone of contemporary electronics, the quality of electronic substrates—including Printed Circuit Boards (PCBs) and Ceramic Package Substrates (CPSs)—is intrinsic to product reliability. However, automated inspection is currently impeded by two persistent obstacles: the drastic multi-scale variation in defects and the acute class imbalance within defect datasets. Conventional deep learning approaches often fail to reconcile these challenges simultaneously, leading to suboptimal recognition of rare defect categories. To bridge this gap, we propose Multi-scale Partial Vision Transformer—Simple, Parameter-free Attention Module (MulPViT-SimAM), a robust framework designed for class-imbalanced electronic substrate defect detection. Our method features a novel multi-scale backbone (MulPViT) that synergizes partial convolutions with hierarchical attention mechanisms, facilitating the efficient extraction of both fine-grained local textures and global contextual dependencies. Additionally, we embed the Simple, Parameter-free Attention Module (SimAM) into the feature fusion stage to adaptively highlight defect-specific features while dampening background noise. To further mitigate data imbalance, we utilize the Equalized Focal Loss (EFL) function, which employs a category-specific modulating factor to dynamically equilibrate the learning focus across different classes. Comprehensive benchmarking reveals state-of-the-art performance, achieving mAP@0.5 scores of 95.7% on the standard PKU-MARKET-PCB dataset and 54.2% on the highly challenging CPS2D-AD dataset. Significantly, our approach effectively mitigates class imbalance, narrowing the performance deviation of rare categories to just 4.3% on the PKU-Market-PCB dataset and 1.4% on the CPS2D-AD dataset, compared to 11.8% and 7.5% in baseline models. These findings position MulPViT-SimAM as a viable and efficient solution for industrial quality control. Full article

The effective control of foodborne salmonellosis relies on the rapid and reliable detection and identification of the pathogen. Reliable detection tools for identifying the most common Salmonella serovars should translate to a considerable alleviation of the health burden attributed to Salmonella. We have developed a PCR assay for the rapid identification of colonies of Salmonella enterica serovar Thompson, a common serovar. Genomic analyses of publicly available sequences of Salmonella Thompson revealed the presence of a unique, Thompson-specific fragment, which we have used to design a pair of oligonucleotides, STho-F and STho-R, for the PCR amplification of an 808 bp DNA fragment. Using crude DNA extracts, the 808 bp fragment was detected in 77 out of 78 isolates of S. Thompson (sensitivity = 98.7% n = 78 isolates) but not in any of the non-Salmonella organisms tested (n = 100; 100% specificity) nor in non-Thompson Salmonella serovars (n = 100; 100% specificity). The sensitivity (inclusivity) and specificity (exclusivity) indices of the PCR assay for S. Thompson met the standard regulatory requirements. The Thompson primer pair was compatible with other primers pairs in a multiplex PCR designed for three other common Salmonella serovars. Colonies belonging to the Enteritidis serovar (n = 100), Heidelberg serovar (n = 100), Typhimurium serovar (n = 100), and Thompson serovar (n = 77) were correctly designated, indicating excellent inclusivity and exclusivity scores for all four Salmonella serovars tested in a single multiplex PCR. Full article