Search Results (117,726)

Postmastectomy radiation therapy (PMRT) is an adjuvant treatment for breast cancer. Some mastectomized women undergoing PMRT can have breast reconstruction with expander implant reconstruction. However, the expander implant contains a magnetic metal port for its inflation, and in patients with a high risk of recurrence, the PMRT is performed before the expander replacement. The difficulties in radiation treatment near high-Z metals are mainly due to dose alterations around them. Therefore, this study proposes using a realistic breast phantom and gel dosimetry to investigate the effects of the metallic parts of the expandable prosthesis on the 3D delivery of the treatment. A conformal radiation treatment was planned and delivered to the gel phantom with the metal port. MAGIC-f gel was used with magnetic resonance imaging for dose assessment. The treatment plan dose distribution was compared to the measured dose distribution by gamma analysis (3%/3 mm/15% threshold). A significant gamma fail region was found near the metal port, corresponding to a dose reduction of approximately 5%. This underdose is within the tolerance threshold for dose heterogeneity established by the International Commission on Radiation Units (ICRU), but should be considered when treating these patients. Full article

Medical image segmentation is a critical application of computer vision in the analysis of medical images. Its primary objective is to isolate regions of interest in medical images from the background, thereby assisting clinicians in accurately identifying lesions, their sizes, locations, and their relationships with surrounding tissues. However, compared to natural images, medical images present unique challenges, such as low resolution, poor contrast, inconsistency, and scattered target regions. Furthermore, the accuracy and stability of segmentation results are subject to more stringent requirements. In recent years, with the widespread application of Convolutional Neural Networks (CNNs) in computer vision, deep learning-based methods for medical image segmentation have become a focal point of research. This paper categorizes, reviews, and summarizes the current representative methods and research status in the field of medical image segmentation. A comparative analysis of relevant experiments is presented, along with an introduction to commonly used public datasets, performance evaluation metrics, and loss functions in medical image segmentation. Finally, potential future research directions and development trends in this field are predicted and analyzed. Full article

In geotechnical engineering, an accurate prediction is essential for the safety and effectiveness of construction projects. One example is the prediction of over/under-excavation volumes during drill and blast tunneling. Using machine learning (ML) models to predict over-excavation often results in low accuracy, especially in complex geological settings. This study explores how the pre-processing of measurement while drilling (MWD) data impacts the accuracy of ML models. In this work, a correlational analysis of the MWD data is used as the main pre-processing procedure. For each drilling event (single borehole), correlation coefficients are calculated and then supplied as inputs to the ML model. It is shown that the ML model’s accuracy improves when the correlation coefficients are used as inputs to the ML models. It is observed that datasets made from correlation coefficients help ML models to obtain higher generalization skills and robustness. The informational content of datasets after different pre-processing routines is compared, and it is shown that the correlation coefficient dataset retains information from the original MWD data. Full article

This research aims to study the spray flow of a droplet on an aluminum surface. Fluid spraying is a significant topic in various strategic industries worldwide. In this study, the commercial software FLUENT 22.3.0 is used to simulate the spray of a droplet with turbulent flow on a surface. We use Gambit for mesh generation to ensure accurate and efficient discretization of the computational domain. Initially, we validate our finite volume method (FVM) by comparing the simulation results with existing experimental data to ensure accuracy. After verifying the numerical methods and boundary conditions, we extend the analysis to explore new scenarios involving different environmental pressures, nozzle-to-surface distances, and heated surface temperatures. The effects of pressure variation on the efficiency of droplet heat transfer are examined within sub-atmospheric and super-atmospheric pressure ranges at different Weber numbers, all below the critical Weber number of the droplet. Additionally, by modifying the model geometry and boundary conditions, the influence of the spray-to-surface distance was examined. The findings show that both pressure changes and the spacing between the spray origin and the surface have a substantial effect on the droplet’s heat transfer performance. Full article

Background: Huangjiu, a traditional Chinese fermented alcoholic beverage, exhibits a multifaceted chemical profile comprising diverse metabolites, such as lipids, amino acids, and phenolic compounds. The age of the wine is an important indicator of its quality and is a primary reference for purchasing decisions. Methods: This study employs high-resolution mass spectrometry to perform metabolomics analysis on Huangjiu of varying ages and uses multivariate statistical analysis to characterize the chemical features of different types of Huangjiu. This research investigates the Huangjiu aged from 3 to 30 years, involving samples of five different aging periods. Results: A total of 415 compounds were detected across all samples, including 147 differential metabolites. It was observed that, as the aging of Huangjiu increased, the relative content of most metabolites showed a rising trend. However, 19 metabolites, mainly lipids and lipid-like molecules, decreased in concentration over time. This finding highlights significant differences in metabolite composition among Huangjiu of different ages. Furthermore, 19 characteristic differential metabolites were predicted as markers for distinguishing Huangjiu of different ages. Conclusions: This study provides theoretical and material foundations for quality control, health benefits, and industrial development of Huangjiu. Full article

The primary objective of this study is to explore how machine learning techniques can be incorporated into the analysis of material deformation. Neural network algorithms are applied to the study of mechanical properties of wire rods subjected to cold plastic deformations. Specifically, this study explores how pre-trained neural networks with appropriate architecture can be exploited to predict apparently distinct but internally related features. Tentative predictions are made by observing only an insignificant cropped fraction of the material’s profile. The neural network models are trained and calibrated using 6400 image fractions with a resolution of $120\times 90$ pixels. Different architectures are developed with a focus on two particular aspects. Firstly, different possible architectures are compared, particularly between multi-output and multi-label convolutional neural networks (CNNs). Moreover, a hybrid model is employed, essentially a conjunction of a CNN with a multi-layer perceptron (MLP). The neural network’s input constitutes combined numerical and visual data, and its architecture primarily consists of seven dense layers and eight convolutional layers. By proper calibration and fine-tuning, observed improvements over the standard CNN models are reflected by good training and test accuracies in order to predict the material’s mechanical properties, with efficiency demonstrated by the loss function’s rapid convergence. Secondly, the role of the pre-training process is investigated. The obtained CNN-MLP model can inherit the learning from a pre-trained multi-label CNN, initially developed for distinct features such as localization and number of passes. It is demonstrated that the pre-training effectively accelerates the learning process for the target feature. Therefore, it is concluded that appropriate architecture design and pre-training are essential for applying machine learning techniques to realistic problems. Full article

This study investigates, for the first time, the effects of polypropylene, steel, glass, and synthetic fibers on the mechanical and environmental properties of high-performance concrete (HPC) incorporating zeolite as a substitute for aggregates and cement. A series of tests, including compressive strength (load-displacement), slump, specific gravity, and water absorption percentage, were conducted to evaluate the performance of the composite materials. Additionally, the IMPACT2002+ method was employed to assess the environmental impacts of the different fiber types. Furthermore, a life cycle costing (LCC) analysis was performed to evaluate the economic feasibility of using these fibers in sustainable HPC applications. The findings reveal that the incorporation of steel fibers results in a notable improvement in compressive strength, achieving 92 MPa compared to 85 MPa for fiber-free samples. Additionally, modified synthetic macro fibers exhibited the second-highest compressive strength, at 83 MPa, while also demonstrating the lowest environmental impact among the tested fibers, characterized by the lowest cost index and minimal carbon dioxide emissions. Full article

Background: Chronic kidney disease (CKD) has a significant impact on patients’ physical, psychological, and social well-being. Emotional disorders are common and contribute to a higher prevalence of symptoms compared to that in the general population. This study aimed to analyze the relationship between the emotional profiles and symptomatology in patients undergoing hemodialysis (HD). Methods: A multicenter, cross-sectional, observational/analytical study was developed in seven centers of the Spanish Renal Foundation in the Community of Madrid (Spain). The study protocol was reviewed and approved by the Clinical Research Ethics Committee of Hospital Clínico San Carlos, Madrid (C.I. 20/685-E). In the study, two validated measurement scales were used: the Mood Rating Scale (EVEA) to assess the “emotional profile” and the Palliative care Outcome Scale, Renal Symptoms (POS-S Renal) to evaluate “symptomatology”. Results: The sample (245 patients) was predominantly male (65.7%; n = 161), with a mean age of 63.52 years (SD = 14.99) and an average HD treatment duration of 81.44 months (SD = 96.62). The analysis of the symptom–emotion relationships revealed that patients with a sadness–depression profile had a higher probability of experiencing weakness or a lack of energy (OR = 1.741; CI 95% 1.01–3.00) and feelings of depression (OR = 3.236; CI 95% 1.98–5.30). Additionally, patients with an anger–hostility profile exhibited a significant association with pain (OR = 3.463; CI 95% 1.34–8.94) and excessive sleepiness (OR = 3.796; CI 95% 1.21–11.95), indicating that this emotional state substantially increases the likelihood of developing these symptoms. Conclusions: The emotional profiles of CKD patients undergoing HD significantly influence their symptomatology. While positive emotions may play a protective role in preventing debilitating symptoms, negative emotions increase the risk of their onset. These findings highlight the importance of addressing emotional well-being as part of comprehensive care for HD patients. Full article

Laser-induced exit surface damage of fused silica is a key bottleneck for its application in high-power laser devices. As low-temporal coherence light (LTCL) has garnered increasing attention for high-power laser-driven inertial confinement fusion, understanding LTCL-induced exit surface damage of fused silica becomes crucial for improving the output power capability of LTCL devices. In this study, we characterized damage on the exit surface of fused silica under LTCL irradiation and investigated the physical mechanism of temporal coherence affecting the laser-induced damage threshold (LIDT). The relationship between defect information and temporal coherence was explored using a defect analysis model, and the defect damage process and response to each incident lasers were captured using time-resolved methods and artificially fabricated defects. We elucidate the physical mechanism behind the lower LIDT under LTCL irradiation compared to single longitudinal mode (SLM) pulse lasers. This study not only provides the boundary condition for safe fused silica operation in high-power LTCL devices but also offers deeper insight into the physical properties of LTCL. Full article

In synthetic aperture radar (SAR) image sea ice classification, the polarization decomposition techniques are used to enhance classification accuracy. However, traditional methods, such as Freeman–Durden (FD) and H/A/α decomposition, struggle to accurately characterize complex scattering mechanisms, limiting their ability to differentiate between various sea ice types. This paper proposes using the Modified Reflection Symmetry Decomposition (MRSD) method to extract polarization features from Gaofen-3 (GF-3) satellite fully polarimetric SAR data for sea ice classification tests. The study data included three types of sea surface: open water (OW), young ice (YI), and first-year ice (FYI). In this research, backscattering coefficients were combined with FD, H/A/α, and MRSD polarization features to create eight feature combinations for comparative analysis. Three machine learning algorithms, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machines (SVM), were also used for the comparative analysis. The results show that MRSD polarization features significantly improve model performance, particularly distinguishing among sea ice categories. Compared to using only the backscatter coefficient, MRSD polarization features increased model classification accuracy by approximately 4% to 13%, outperforming FD and H/A/α polarization features. The XGBoost model trained with MRSD polarization features achieves excellent classification results, with classification accuracies of 0.9630, 0.9126, and 0.9451 for OW, YI, and FYI. Additionally, the model achieved a Kappa coefficient of 0.9105 and an F1-score of 0.9403. Feature importance and SHapley Additive exPlanations (SHAP) analysis further demonstrate the physical significance of the MRSD polarization features and their role in model decision-making, suggesting that the scattered component power plays a crucial role in the model’s classification decision. Compared to traditional decomposition methods, MRSD provides a more detailed characterization of scattering mechanisms, offering a comprehensive understanding of the physical properties of sea ice. This paper systematically demonstrates the superior effectiveness of MRSD polarization features for sea ice classification, presenting a new scheme for more accurate classification. Full article

Background and Objectives: Extensive research has demonstrated that various approaches to the quadratus lumborum (QL) block offer superior postoperative analgesia compared to the transversus abdominis plane (TAP) block, particularly in reducing opioid consumption. This study aims to compare postoperative analgesia between the blocks in laparoscopic colorectal surgery. Materials and Methods: A retrospective analysis was performed on patients with elective colorectal surgeries who received bilateral US TAP blocks in the supine position or US anterior QL block in the lateral position at the end of the surgery and before extubating, with Ropivacaine 0.25%. Total opioid consumption and time to first intravenous analgesic were noted. Results: Between January 2020 and December 2024, 410 patients underwent elective laparoscopic colorectal oncology surgery under general anesthesia, with peripheral nerve blocks. Of these, we analyzed 116 patients with localized diseases who underwent elective surgeries and who did not require conversion to classical surgery and received either QL or TAP blocks. A total of 62 patients underwent QL block and 54 received TAP block. For the primary outcome, in the QL group, significantly fewer opioids were used than in the TAP group (p < 0.001), and time to first rescue analgesic was prolonged in the QL group at 16 h (IQR 14–18) compared to the TAP group, where the requirement occurred earlier at 8 h (IQR 8–8) postoperatively (p < 0.001). Conclusions: Postoperative bilateral US anterior QL block reduced morphine consumption and improved time to rescue analgesia and LOS compared with midaxillary line bilateral US TAP block. Full article

Fatty acid synthase (FAS) is a fundamental metabolic enzyme that catalyzes the synthesis of endogenous fatty acids; TACR3, also known as tachykinin receptor 3 or NK3R, is an important G-protein-coupled receptor that is primarily responsible for responding to neuropeptides such as neurokinin B (NKB) and plays a crucial role in embryonic development, organ formation, and cell differentiation. This study aimed to explore the association between the single-nucleotide polymorphisms (SNPs) of the FAS and TACR3 genes and the milk quality of Gannan yak and to determine them as potential molecular marker loci for the milk quality of yaks. The genotyping of 162 Gannan yaks was performed using liquid-phase chip technology. Association analyses were conducted between the obtained SNP loci genotypes and milk composition traits, including milk protein, casein, non-fat solids, and acidity. Comparative sequence analysis of two genes (FAS and TACR3) across multiple species revealed that the yak FAS gene exhibited the highest homology with Bos taurus and Bos indicus, while the yak TACR3 gene showed the greatest sequence similarity to Bos taurus. Hardy–Weinberg equilibrium tests were performed on four SNP loci, and the equilibrium indices of the four loci were 0.799, 0.368, 0.689, and 0.948 (p > 0.05), indicating that all of these loci are in Hardy–Weinberg equilibrium state. g.13,276T>C (FAS) was significantly correlated with lactose content traits (p < 0.05); g.74,382C>G (FAS) was significantly correlated with casein, protein, total solids, non-fat solids, and acidity traits (p < 0.05); g.40,529A>G (TACR3) was significantly correlated with protein, non-fat solids, citric acid, and acidity traits (p < 0.05). The influence of g.40,555C>T (TACR3) on these traits did not reach a significant level (p > 0.05). This study suggests that two genes can serve as potential candidate genes affecting the quality of Gannan yak milk, providing reference genes for improving the quality of Gannan yak milk. Full article

This paper introduces a novel framework for generating synthetic time-series data from turbine engine sensor readings using a text-conditioned diffusion model. The approach begins with dataset preprocessing, including correlation analysis, feature selection, and normalization. Principal Component Analysis (PCA) transforms the normalized signals into three components, mapped to the RGB channels of an image. These components, combined with engine identifiers and cycle information, form compact 19 × 19 × 3 pixel images, later scaled to 512 × 512 × 3 pixels. A variational autoencoder (VAE)-based diffusion model, fine-tuned on these images, leverages text prompts describing engine characteristics to generate high-quality synthetic samples. A reverse transformation pipeline reconstructs synthetic images back into time-series signals, preserving the original engine-specific attributes while removing padding artifacts. The quality of the synthetic data is assessed by training Remaining Useful Life (RUL) estimation models and comparing performance across original, synthetic, and combined datasets. Results demonstrate that synthetic data can be beneficial for model training, particularly in the early epochs when working with limited datasets. Compared to existing approaches, which rely on generative adversarial networks (GANs) or deterministic transformations, the proposed framework offers enhanced data fidelity and adaptability. This study highlights the potential of text-conditioned diffusion models for augmenting time-series datasets in industrial Prognostics and Health Management (PHM) applications. Full article

Background/Objectives: Infertility is a reproductive disorder affecting approximately 10–15% of reproductive-age couples worldwide. Recent studies have suggested that miRNAs in follicular fluid may provide insights into reproductive potential and follicle health. This study evaluated the altered profile of miRNAs in the follicular fluid in patients undergoing IVF, considering the underlying etiology of infertility. Among our study participants, we identified four major underlying causes of infertility: polycystic ovary syndrome (PCOS), pelvic inflammatory disease (PID), male factor infertility, and unexplained infertility (UI). Methods: This study aimed to assess whether these infertility diagnoses are associated with distinct follicular behaviors and to identify altered miRNA patterns linked to these conditions. Ingenuity Pathway Analysis (IPA) was used to evaluate the impact of the altered miRNA signature on key biological processes. Results: The bioinformatics analysis of microarray data revealed altered miRNA patterns in FF for selected subgroups. Compared to healthy controls, 25 differentially expressed miRNAs were identified in PCOS (9 downregulated and 16 overexpressed), 21 in PID (15 downregulated and 6 overexpressed), and 34 in UI (24 downregulated and 10 overexpressed). These altered miRNA signatures indicate a complex interplay with essential signaling pathways, including hormonal regulation and tissue remodeling. Conclusions: Our analysis revealed key miRNAs that were differentially expressed across selected groups, indicating their potential as biomarkers for more accurate diagnosis and targeted treatment strategies. These findings provide valuable insights into the molecular mechanisms underlying reproductive disorders and underscore the importance of further research to develop targeted interventions that can enhance patient outcomes. Full article

Objective: This study aims to investigate the role of fatty acid ratios, specifically DHA/ARA and EPA/ARA, in predicting severe disability and mortality in stroke patients and compare these ratios with conventional risk factors such as age, sex, hypertension, diabetes, and dyslipidemia. Methods: A prospective study was conducted involving 298 consecutive acute ischemic stroke patients (within 72 h of onset). Fatty acid ratios were measured from plasma, and all patients’ evolution was followed through hospitalization. Binary logistic regression analysis was used to identify predictors of severe disability at discharge (Rankin 4–6) and in-hospital mortality, including fatty acid ratios and conventional risk factors. Results: A higher DHA/ARA ratio was associated with a reduced chance of severe disability (OR = 0.81), while a higher EPA/ARA ratio was associated with an increased chance of severe disability (OR = 1.70). Age was a significant factor, with older age (median 70 years) associated with a lower survivability chance (OR = 0.93) and a higher likelihood of severe disability when surviving. Fatty acid ratios did not significantly affect mortality outcomes. For male patients, EPA/AA ratios showed a powerful association with severe disability (p = 0.045), while no significant effect of fatty acids was observed in females. Conclusions: Fatty acids were significant predictors of severe disability in patients with acute ischemic stroke, independent of conventional risk factors, but without having any effect on in-hospital mortality. Age remained the only significant conventional risk factor predictor of outcome. Integrating fatty acid ratios alongside conventional risk factors may improve predictions of severe post-stroke disability, potentially guiding more personalized interventions for stroke patients. Full article