Search Results (13,805)

To address the inefficiency of traditional concrete crack detection methods and the heavy reliance of supervised learning on extensive labeled data, in this study, an intelligent assessment method of concrete damage based on pseudo-label semi-supervised learning and fractal geometry theory is proposed to solve two core tasks: one is binary classification of pixel-level cracks, and the other is multi-category assessment of damage state based on crack morphology. Using three-channel RGB images as input, a dual-path collaborative training framework based on U-Net encoder–decoder architecture is constructed, and a binary segmentation mask of the same size is output to achieve the accurate segmentation of cracks at the pixel level. By constructing a dual-path collaborative training framework and employing a dynamic pseudo-label refinement mechanism, the model achieves an F1-score of 0.883 using only 50% labeled data—a mere 1.3% decrease compared to the fully supervised benchmark DeepCrack (F1 = 0.896)—while reducing manual annotation costs by over 60%. Furthermore, a quantitative correlation model between crack fractal characteristics and structural damage severity is established by combining a U-Net segmentation network with the differential box-counting algorithm. The experimental results demonstrate that under a cyclic loading of 147.6–221.4 kN, the fractal dimension monotonically increases from 1.073 (moderate damage) to 1.189 (failure), with 100% accuracy in damage state identification, closely aligning with the degradation trend of macroscopic mechanical properties. In complex crack scenarios, the model attains a recall rate (Re = 0.882), surpassing U-Net by 13.9%, with significantly enhanced edge reconstruction precision. Compared with the mainstream models, this method effectively alleviates the problem of data annotation dependence through a semi-supervised strategy while maintaining high accuracy. It provides an efficient structural health monitoring solution for engineering practice, which is of great value to promote the application of intelligent detection technology in infrastructure operation and maintenance. Full article

Large soil pores critically influence water and solute transport in soils. The presence of preferential flow paths created by soil macropores can profoundly impact water quality, underscoring the necessity of accurately assessing the characteristics of these macropores. However, it remains unclear whether variations in macropore structure exist between different altitudes and positions of terraced paddy fields. The primary objective of this research was to utilize X-ray computed tomography (CT) and image analysis techniques to characterize the soil pore structure at both the inner field and ridge positions across different altitude levels (high, medium, and low altitude) within terraced paddy fields. The results indicate that there are significant differences in the distribution of large soil pores at different altitudes, with large pores concentrated in the surface layer (0–10 cm) in low-altitude areas, while in high-altitude areas, the distribution of large pores is more uniform. Additionally, as altitude increases, the porosity of large pores shows an increasing trend. The three-dimensional equivalent diameter and large pore volume are primarily characterized by large pores ranging from 1 to 2 mm and 0 to 5 mm³, respectively, with their morphology predominantly appearing spherical or ellipsoidal. The connectivity of large pores in the surface layer of paddy soil is stronger than that in the bunds. However, this connectivity gradually weakens with increasing soil depth. The findings from this study provide valuable quantitative insights into the unique characteristics of soil macropores that vary according to the altitude and position in terraced paddy fields. Moreover, this study emphasizes the necessity for future research that encompasses a broader range of soil types, altitudes, and terraced paddy locations to validate and further explore the identified relationships between altitude and macropore characteristics. Full article

This study applies Computational Fluid Dynamics (CFD) and mathematical modeling to examine uterine and umbilical arterial blood flow during pregnancy, providing a more detailed understanding of hemodynamic changes across gestation. Statistical analysis of Doppler ultrasound data from a large cohort of more than 200 pregnant women (in the second and third trimesters) reveals significant increases in the umbilical arterial peak systolic velocity ($PSV$) between the 22nd and 30th weeks, while uterine artery velocities remain relatively stable, suggesting adaptations in vascular resistance during pregnancy. By combining the Navier–Stokes equations with Doppler ultrasound-derived inlet velocity profiles, we quantify several key fluid dynamics parameters, including time-averaged wall shear stress ($TAWSS$), oscillatory shear index ($OSI$), relative residence time ($RRT$), Reynolds number ($Re$), and Dean number ($De$), evaluating laminar flow stability in the uterine artery and secondary flow patterns in the umbilical artery. Since blood exhibits shear-dependent viscosity and complex rheological behavior, modeling it as a non-Newtonian fluid is essential to accurately capture pulsatile flow dynamics and wall shear stresses in these vessels. Unlike conventional imaging techniques, CFD offers enhanced visualization of blood flow characteristics such as streamlines, velocity distributions, and instantaneous particle motion, providing insights that are not easily captured by Doppler ultrasound alone. Specifically, CFD reveals secondary flow patterns in the umbilical artery, which interact with the primary flow, a phenomenon that is challenging to observe with ultrasound. These findings refine existing hemodynamic models, provide population-specific reference values for clinical assessments, and improve our understanding of the relationship between umbilical arterial flow dynamics and fetal growth restriction, with important implications for maternal and fetal health monitoring. Full article

Lotus (Nelumbo nucifera Gaertn.) is a quintessential medicinal and edible plant, exhibiting marked differences in therapeutic effects among its various parts. The lotus seed constitutes a key component of this plant. Notably, the entire seed and the plumule display distinct medicinal properties. To investigate the “homologous plants with different effects” phenomenon in traditional Chinese medicine, this study established a Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) method. This study employed immature lotus seeds as the experimental material, diverging from the mature seeds conventionally used. Conductive double-sided tape was employed for sample preparation, and complete longitudinal sections of the seeds were obtained, followed by MALDI-MSI analysis to identify and visualize the spatial distribution of characteristic secondary metabolites within the entire seeds. The results unveiled the diversity of metabolites in lotus seeds and their differential distribution across tissues, with pronounced distinctions in the plumule. A total of 152 metabolites spanning 13 categories were identified in lotus seeds, with 134, 89, 51, and 98 metabolites discerned in the pericarp, seed coat, cotyledon, and plumule, respectively. Strikingly, young lotus seeds were devoid of liensinine/isoliensinine and neferine, the dominant alkaloids of mature lotus seed plumule, revealing an early-stage alkaloid profile that sharply contrasts with the well-documented abundance found in mature seeds and has rarely been reported. We further propose a biosynthetic pathway to explain the presence of the detected benzylisoquinoline and the absence of the undetected bisbenzylisoquinoline alkaloids in this study. These findings present the first comprehensive metabolic atlas of immature lotus seeds, systematically exposing the pronounced chemical divergence from their mature counterparts, and thus lays a metabolomic foundation for dissecting the spatiotemporal mechanisms underlying the nutritional and medicinal value of lotus seeds. Full article

Background: Ultrasound (U/S) can be used to evaluate skeletal muscle characteristics in clinical and sports settings. Handheld U/S devices have recently emerged as a cheaper and portable alternative to conventional U/S systems. However, further research is warranted on their reliability. We assessed the reliability and inter-device agreement between a handheld U/S device (Clarius L15 HD₃) and a more conventional U/S system (GE LOGIQ e) for measuring the thickness of the rectus femoris (RF) and vastus intermedius (VI). Methods: Cross-sectional images of the RF and VI muscles were obtained in 20 participants by two assessors, and on two separate occasions by one of those assessors, using the Clarius L15 HD₃ and GE LOGIQ e devices. RF and VI thickness measurements were obtained to determine the intra-rater reliability, inter-rater reliability, and inter-device agreement. Results: All intraclass correlation coefficients (ICCs) were above 0.9 for intra-rater reliability (range: 0.94 to 0.97), inter-rater reliability (ICC: 0.97), and inter-device agreement (ICC: 0.98) when comparing the two devices in assessing RF and VI thickness. For the RF, the Bland–Altman plot revealed a mean difference of 0.06 ± 0.07 cm, with limits of agreement ranging from 0.21 to −0.09, whereas for the VI, the Bland–Altman plot showed a mean difference of 0.07 ± 0.10 cm, with limits of agreement ranging from 0.27 to −0.13. Conclusions: The handheld Clarius L15 HD₃ was reliable and demonstrated high agreement with the more conventional GE LOGIQ e for assessing the thickness of the RF and VI in young, healthy adults. Full article

Multimodal review helpfulness prediction (MRHP) aims to identify the most helpful reviews by leveraging both textual and visual information. However, prior studies have primarily focused on modeling interactions between these modalities, often overlooking the consistency between review content and ratings, which is a key indicator of review credibility. To address this limitation, we propose CRCNet (Content–Rating Consistency Network), a novel MRHP model that jointly captures the semantic consistency between review content and ratings while modeling the complementary characteristics of text and image modalities. CRCNet employs RoBERTa and VGG-16 to extract semantic and visual features, respectively. A co-attention mechanism is applied to capture the consistency between content and rating, and a Gated Multimodal Unit (GMU) is adopted to integrate consistency-aware representations. Experimental results on two large-scale Amazon review datasets demonstrate that CRCNet outperforms both unimodal and multimodal baselines in terms of MAE, MSE, RMSE, and MAPE. Further analysis confirms the effectiveness of content–rating consistency modeling and the superiority of the proposed fusion strategy. These findings suggest that incorporating semantic consistency into multimodal architectures can substantially improve the accuracy and trustworthiness of review helpfulness prediction. Full article

Image processing has been widely adopted as an effective technology for analyzing weld forming quality which is greatly affected by the welding process parameters. In this paper, an L₂₅(5³) orthogonal experiment is designed to investigate the effects of welding process parameters on the weld forming quality in laser welding of aluminum alloy. The weld characteristics including the weld width (WW), weld penetration (PD), weld area (WA) and weld porosity (WP) under the conditions of the different welding process parameters consisting of the laser power (LP), welding speed (WS) and defocus distance (DD) are extracted from the laser welding experiment based on image processing. The effectiveness of the weld characteristics extraction method is verified by comparing the extracted results with the measured results. It is found that the WW, PD and WA are all significantly influenced by the LP among the three welding process parameters while the influences of the three process parameters on the WP are insignificant. The DD has a significant influence on the PD and the WS has a significant influence on the WA. The corresponding significance of influence is lower than the significance of influence of LP. The analysis results are conducive to the optimization of laser welding process parameters and improvement of welding quality. Full article

Objectives: To synthesize evidence on structural and functional neuroplasticity in patients after anterior cruciate ligament reconstruction (ACLR) and its clinical implications. Methods: Adhering to the PRISMA guidelines for systematic reviews and meta-analyses, a literature search was conducted using PubMed, Embase, Web of Science, Scopus, and Cochrane CENTRAL (2018–2025) using specific keyword combinations, screening the results based on predetermined inclusion and exclusion criteria. Results: Among the 27 included studies were the following: (1) sensory cortex reorganization with compensatory visual dependence (5 EEG/fMRI studies); (2) reduced motor cortex efficiency evidenced by elevated AMT (TMS, 8 studies) and decreasedγ-CMC (EEG, 3 studies); (3) progressive corticospinal tract degeneration (increased radial diffusivity correlating with postoperative duration); (4) enhanced sensory-visual integration correlated with functional recovery. Conclusions: This review provides a novel synthesis of evidence from transcranial magnetic stimulation (TMS), electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), diffusion tensor imaging (DTI), and functional magnetic resonance imaging (fMRI) studies. It delineates characteristic patterns of post-ACLR structural and functional neural reorganization. Targeting visual–cognitive integration and corticospinal facilitation may optimize rehabilitation. Full article

The unique characteristics of desert vegetation, such as different leaf morphology, discrete canopy structures, sparse and uneven distribution, etc., pose significant challenges for remote sensing-based estimation of fractional vegetation cover (FVC). The Unmanned Aerial Vehicle (UAV) system can accurately distinguish vegetation patches, extract weak vegetation signals, and navigate through complex terrain, making it suitable for applications in small-scale FVC extraction. In this study, we selected the floodplain fan with Caragana korshinskii Kom as the constructive species in Hatengtaohai National Nature Reserve, Bayannur, Inner Mongolia, China, as our study area. We investigated the remote sensing extraction method of desert sparse vegetation cover by placing samples across three gradients: the top, middle, and edge of the fan. We then acquired UAV multispectral images; evaluated the applicability of various vegetation indices (VIs) using methods such as supervised classification, linear regression models, and machine learning; and explored the feasibility and stability of multiple machine learning models in this region. Our results indicate the following: (1) We discovered that the multispectral vegetation index is superior to the visible vegetation index and more suitable for FVC extraction in vegetation-sparse desert regions. (2) By comparing five machine learning regression models, it was found that the XGBoost and KNN models exhibited relatively lower estimation performance in the study area. The spatial distribution of plots appeared to influence the stability of the SVM model when estimating fractional vegetation cover (FVC). In contrast, the RF and LASSO models demonstrated robust stability across both training and testing datasets. Notably, the RF model achieved the best inversion performance (R² = 0.876, RMSE = 0.020, MAE = 0.016), indicating that RF is one of the most suitable models for retrieving FVC in naturally sparse desert vegetation. This study provides a valuable contribution to the limited existing research on remote sensing-based estimation of FVC and characterization of spatial heterogeneity in small-scale desert sparse vegetation ecosystems dominated by a single species. Full article

Nutrition plays a fundamental role in promoting health and preventing chronic diseases, with bioactive food components offering a therapeutic potential in biomedical applications. Among these, edible oils are recognised for their functional properties, which contribute to disease prevention and metabolic regulation. The proposed study aims to evaluate the quality of four bioactive oils (olive oil, sunflower oil, tomato seed oil, and pumpkin seed oil) by analysing their thermal behaviour through infrared (IR) imaging. The study designed a customised electronic system to acquire thermographic signals under controlled temperature and humidity conditions. The acquisition system was used to extract thermal data. Analysis of the acquired thermal signals revealed characteristic heat absorption profiles used to infer differences in oil properties related to stability and degradation potential. A hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) units was used to classify and differentiate the oils based on stability, thermal reactivity, and potential health benefits. A signal analysis showed that the AI-based method improves both the accuracy (achieving an F1-score of 93.66%) and the repeatability of quality assessments, providing a non-invasive and intelligent framework for the validation and traceability of nutritional compounds. Full article

Color is a primary determinant of the value of jadeite jade, but the petrological provenance of the chromogenic elements of jadeite jade remains uncertain. The characteristics of the associated chromite in Myanmar jadeite jade were systematically investigated through a series of tests, including polarized microscopy, microarea X-ray fluorescence spectroscopy (micro-XRF) mapping, electron probe microanalysis (EPMA), and backscattered electron (BSE) imaging. The results demonstrate that the chromite composition in Myanmar jadeite jade is characterized by a high concentration of Cr₂O₃ (46.18–67.11 wt.%), along with a notable abundance of MnO (1.68–9.13 wt.%) compared with the chromite from the adjacent Myitkyina peridotite. The diffusion of chromium (Cr) and manganese (Mn) in jadeite jade is accomplished by accompanying the metamorphic pathway of Mn-rich chromite → kosmochlor → chromian jadeite → jadeite. In the subsequent phase of jadeite jade formation, the chromium-rich omphacite veins generated by the fluid enriched in Ca and Mg along the fissures of kosmochlor and chromian jadeite play a role in the physical diffusion of Cr and Mn. The emergence of the lavender hue in jadeite is contingent upon the presence of a relatively high concentration of Mn (approximately 100–1000 ppmw) and the simultaneous absence of Cr, which would otherwise serve as a more effective chromophore (no Cr or up to a dozen ppmw). The distinctive Mn-rich chromite represents the primary origin of the chromogenic element Cr (green) and, perhaps more notably, an overlooked provider of Mn (lavender) in Myanmar jadeite jade. Full article

Background: Hypoxic–ischemic encephalopathy (HIE) is a leading cause of severe neurological impairments in childhood. Therapeutic hypothermia (TH) is both safe and effective in neonates born at ≥36 weeks gestation with moderate to severe HIE. We aimed to evaluate short-term outcomes—including brain injury detected on magnetic resonance imaging (MRI)—in infants born at 34–35 weeks of gestation drawing on our clinical experience with neonates under 36 weeks of gestational age (GA). Methods: In this retrospective cohort study, 20 preterm infants with a GA of 34 to 35 weeks and a matched cohort of 80 infants with a GA of ≥36 weeks who were diagnosed with moderate to severe HIE and underwent TH were included. Infants were matched in a 1:4 ratio based on the worst base deficit in blood gas and sex. Maternal and neonatal characteristics, brain MRI findings and short term outcomes were compared. Results: Infants with a GA of 34–35 weeks had a lower birth weight and a higher rate of caesarean delivery (both p < 0.001). Apgar scores, sex, intubation rate in delivery room, blood gas pH, base deficit and lactate were comparable between the groups. Compared to infants born at ≥36 weeks of GA, preterm neonates were more likely to receive inotropes, had a longer time to achieve full enteral feeding, and experienced a longer hospital stay. The mortality rate was 10% in the 34–35 weeks GA group. Neuroimaging revealed injury in 66.7% of infants born at 34–35 weeks of gestation and in 58.8% of those born at ≥36 weeks (p = 0.56). Injury was observed across multiple brain regions, with white matter being the most frequently affected in the 34–35 weeks GA group. Thalamic and cerebellar abnormal signal intensity or diffusion restriction, punctate white matter lesions, and diffusion restriction in the corpus callosum and optic radiations were more frequently detected in infants born at 34–35 weeks of gestation. Conclusions: Our study contributes to the growing body of literature suggesting that TH may be feasible and tolerated in late preterm infants. Larger randomized controlled trials focused on this vulnerable population are necessary to establish clear guidelines regarding the safety and efficacy of TH in late preterm infants. Full article

Accurate diagnosis of gastrointestinal (GI) diseases typically requires invasive procedures or imaging studies that pose the risk of various post-procedural complications or involve radiation exposure. Bowel sounds (BSs), though typically described during a GI-focused physical exam, are highly inaccurate and variable, with low clinical value in diagnosis. Interpretation of the acoustic characteristics of BSs, i.e., using a phonoenterogram (PEG), may aid in diagnosing various GI conditions non-invasively. Use of artificial intelligence (AI) and improvements in computational analysis can enhance the use of PEGs in different GI diseases and lead to a non-invasive, cost-effective diagnostic modality that has not been explored before. The purpose of this work was to develop an automated AI model, You Only Listen Once (YOLO), to detect prominent bowel sounds that can enable real-time analysis for future GI disease detection and diagnosis. A total of 110 2-minute PEGs sampled at 44.1 kHz were recorded using the Eko DUO^® stethoscope from eight healthy volunteers at two locations, namely, left upper quadrant (LUQ) and right lower quadrant (RLQ) after IRB approval. The datasets were annotated by trained physicians, categorizing BSs as prominent or obscure using version 1.7 of Label Studio Software^®. Each BS recording was split up into 375 ms segments with 200 ms overlap for real-time BS detection. Each segment was binned based on whether it contained a prominent BS, resulting in a dataset of 36,149 non-prominent segments and 6435 prominent segments. Our dataset was divided into training, validation, and test sets (60/20/20% split). A 1D-CNN augmented transformer was trained to classify these segments via the input of Mel-frequency cepstral coefficients. The developed AI model achieved area under the receiver operating curve (ROC) of 0.92, accuracy of 86.6%, precision of 86.85%, and recall of 86.08%. This shows that the 1D-CNN augmented transformer with Mel-frequency cepstral coefficients achieved creditable performance metrics, signifying the YOLO model’s capability to classify prominent bowel sounds that can be further analyzed for various GI diseases. This proof-of-concept study in healthy volunteers demonstrates that automated BS detection can pave the way for developing more intuitive and efficient AI-PEG devices that can be trained and utilized to diagnose various GI conditions. To ensure the robustness and generalizability of these findings, further investigations encompassing a broader cohort, inclusive of both healthy and disease states are needed. Full article

Focusing on the problem of insufficient detection accuracy caused by blurred target boundaries, variable scales, and severe noise interference in side-scan sonar images, this paper proposes a high-precision detection network named ESL-YOLO, which integrates edge perception and adaptive quality assessment. Firstly, an Edge Fusion Module (EFM) is designed, which integrates the Sobel operator into depthwise separable convolution. Through a dual-branch structure, it realizes effective fusion of edge features and spatial features, significantly enhancing the ability to recognize targets with blurred boundaries. Secondly, a Self-Calibrated Dual Attention (SCDA) Module is constructed. By means of feature cross-calibration and multi-scale channel attention fusion mechanisms, it achieves adaptive fusion of shallow details and deep-rooted semantic content, improving the detection accuracy for small-sized targets and targets with elaborate shapes. Finally, a Location Quality Estimator (LQE) is introduced, which quantifies localization quality using the statistical characteristics of bounding box distribution, effectively reducing false detections and missed detections. Experiments on the SIMD dataset show that the mAP@0.5 of ESL-YOLO reaches 84.65%. The precision and recall rate reach 87.67% and 75.63%, respectively. Generalization experiments on additional sonar datasets further validate the effectiveness of the proposed method across different data distributions and target types, providing an effective technical solution for side-scan sonar image target detection. Full article

This study presents a detailed experimental comparison of the rotordynamic and thermal performance of automotive turbochargers supported by two distinct hydrodynamic bearing configurations: fully floating ring bearings (FFRBs) and semi-floating ring bearings (SFRBs). While both designs are widely used in commercial turbochargers, they exhibit significantly different dynamic behaviors due to differences in ring motion and fluid film interaction. A cold air-driven test rig was employed to assess vibration and temperature characteristics across a range of controlled lubricant conditions. The test matrix included oil supply pressures from 2 bar (g) to 4 bar (g) and temperatures between 30 °C and 70 °C. Rotor speeds reached up to 200 krpm (thousands of revolutions per minute), and data were collected using a high-speed data acquisition system, triaxial accelerometers, and infrared (IR) thermal imaging. Rotor vibration was characterized through waterfall and Bode plots, while jump speeds and thermal profiles were analyzed to evaluate the onset and severity of instability. The results demonstrate that the FFRB configuration is highly sensitive to oil supply parameters, exhibiting strong subsynchronous instabilities and hysteresis during acceleration–deceleration cycles. In contrast, the SFRB configuration consistently provided superior vibrational stability and reduced sensitivity to lubricant conditions. Changes in lubricant supply conditions induced a jump speed variation in floating ring bearing (FRB) turbochargers that was approximately 3.47 times larger than that experienced by semi-floating ring bearing (SFRB) turbochargers. Furthermore, IR images and oil outlet temperature data confirm that the FFRB system experiences greater heat generation and thermal gradients, consistent with higher energy dissipation through viscous shear. This study provides a comprehensive assessment of both bearing types under realistic high-speed conditions and highlights the advantages of the SFRB configuration in improving turbocharger reliability, thermal performance, and noise suppression. The findings support the application of SFRBs in high-performance automotive systems where mechanical stability and reduced frictional losses are critical. Full article