Explore the vast range of titles available.

Minimally invasive interventional surgery is commonly used for diagnosing and treating breast cancer, but the high fluidity and deformability of breast tissue reduce intervention accuracy. This study proposes a bionic breast interventional robot that mimics the scorpion’s predation process, actively manipulating tissue deformation to control target displacement and enhance accuracy. The robot’s structure is designed using a modular method, and its kinematics and workspace are analyzed and solved. To address the nonlinear breast tissue deformation problem, a hierarchical tissue method is proposed to simplify the three-dimensional problem into a two-dimensional one. A two-dimensional tissue deformation solver is established based on the minimum energy method for quick resolution. The problem is treated as quasi-static, deriving the displacement relationship between external manipulation points and internal tissue targets. The method of active manipulation of tissue deformation is simulated using MATLAB (2019-b) software to verify the feasibility of the method. Results show maximum errors of 1.7 mm for prostheses and 2.5 mm for in vitro tissues in the X and Y directions. This method improves intervention accuracy in breast surgery and offers a new solution for breast cancer diagnosis and treatment.

Railway Wagon bearing fault diagnosis often encounters the challenge of data imbalance, with scarce fault data making it difficult to train efficient and reliable diagnostic models. To address this, Generative Adversarial Networks (GANs) have been introduced to generate high-quality synthetic fault data, balancing the dataset. However, traditional GANs face training difficulties, resulting in imbalanced dynamics between the discriminator and generator and lower-quality generated data. This paper proposes improvements to Auxiliary Classifier Generative Adversarial Networks (ACGAN) to overcome these challenges. The enhancements include integrating a multi-level sampling attention module into the generator to enhance its capacity, introducing the Wasserstein distance as the loss function to improve stability and sample quality, employing spectral normalization to control gradient magnitude, and implementing a gradient penalty strategy to further enhance network stability. Comparative analyses with other GAN variants demonstrate the improved model's superior stability and effective adversarial dynamics. Evaluation metrics confirm the enhanced model's significantly better generation quality. Fault diagnosis on augmented balanced datasets reveals that the proposed model achieves the best diagnostic performance among various models. Additionally, the effect of adding false data on diagnostic accuracy was investigated, showing a positive correlation between the number of samples and diagnostic accuracy. This research offers an effective solution for addressing data imbalance in railway Wagon bearing fault diagnosis.

Railway Wagon bearing fault diagnosis often encounters the challenge of data imbalance, with scarce fault data making it difficult to train efficient and reliable diagnostic models. To address this, Generative Adversarial Networks (GANs) have been introduced to generate high-quality synthetic fault data, balancing the dataset. However, traditional GANs face training difficulties, resulting in imbalanced dynamics between the discriminator and generator and lower-quality generated data. This paper proposes improvements to Auxiliary Classifier Generative Adversarial Networks (ACGAN) to overcome these challenges. The enhancements include integrating a multi-level sampling attention module into the generator to enhance its capacity, introducing the Wasserstein distance as the loss function to improve stability and sample quality, employing spectral normalization to control gradient magnitude, and implementing a gradient penalty strategy to further enhance network stability. Comparative analyses with other GAN variants demonstrate the improved model's superior stability and effective adversarial dynamics. Evaluation metrics confirm the enhanced model's significantly better generation quality. Fault diagnosis on augmented balanced datasets reveals that the proposed model achieves the best diagnostic performance among various models. Additionally, the effect of adding false data on diagnostic accuracy was investigated, showing a positive correlation between the number of samples and diagnostic accuracy. This research offers an effective solution for addressing data imbalance in railway Wagon bearing fault diagnosis.

PurposeThis paper proposes an intelligent fault diagnosis method, which aims to obtain the outstanding fault diagnosis results of the gearbox.Design/methodology/approachAn intelligent fault diagnosis method based on energy entropy-weighted complementary ensemble empirical mode decomposition (EWCEEMD) and support vector machine (SVM) optimized by whale optimization algorithm (WOA) is proposed. The raw signal is first denoised by the wavelet noise reduction method. Then, complementary ensemble empirical mode decomposition (CEEMD) is used to generate several intrinsic mode functions (IMFs). Next, energy entropy is used as an indicator to measure the sensibility of the IMF and converted into a weight coefficient by function. After that, IMFs are linearly weighted to form the reconstruction signal, and several features are extracted from the new signal. Finally, the support vector machine optimized by the whale optimization algorithm (WOA-SVM) model is used for gearbox fault classification using feature vectors.FindingsThe fault features extracted by this method have a better clustering effect and clear boundaries under each fault mode than the unimproved method. At the same time, the accuracy of fault diagnosis is greatly improved.Originality/valueIn most studies of fault diagnosis, the sensitivity of IMF has not been appreciated. In this paper, energy entropy is chosen to quantify sensitivity. In addition, high classification accuracy can be achieved by applying WOA-SVM as the final classification model, improving the efficiency of fault diagnosis as well.

Shale is a kind of sedimentary rock with an obvious bedding structure. The effect of the bedding plane on hydraulic fracture initiation, propagation, and complex fracture network formation is remarkable and a major problem in hydraulic fracturing and shale oil and gas development. In this study, a criterion is established to predict the evolution behavior of hydraulic fractures (HF) under different confining pressure differences and intersection angles. This criterion is intended to predict the types of interaction between HFs and bedding planes (BPs): penetrating, slipping, or dilating. The dependence of crossing on the intersection angle and the principal stress difference is quantitatively presented using the criterion. Meanwhile, 20 simulations with principal stress differences of 2, 4, 6, and 8 MPa and intersection angles of 30°, 45°, 60°, 75°, and 90° were simulated using the RFPA 2D -Flow code. Simulation results exhibit good agreement with the criterion results for a wide range of angles. The investigation showed that HFs tend to penetrate BPs under high confining pressure differences and intersection angles and open BPs under low confining pressure differences and intersection angles. In addition to the above two forms, HFs slip due to shear. The criterion can provide relevant reference about the formation of complex fracture networks in shale layers.

The efficient segregation of radioactive nuclides from low-level radioactive liquid waste (LLRW) is paramount for nuclear emergency protocols and waste minimization. Here, we synthesized Na3FePO4CO3 (NFPC) via a one-pot hydrothermal method and applied it for the first time to the selective separation of Sr2+ from simulated LLRW. Static adsorption experimental results indicated that the distribution coefficient Kd remained above 5000 mL·g−1, even when the concentration of interfering ions was more than 40 times that of Sr2+. Furthermore, the removal efficiency of Sr2+ showed no significant change within the pH range of 4 to 9. The adsorption of Sr2+ fitted the pseudo-second-order kinetic model and the Langmuir isotherm model, with an equilibrium time of 36 min and a maximum adsorption capacity of 99.6 mg·g−1. Notably, the adsorption capacity was observed to increment marginally with an elevation in temperature. Characterization analyses and density functional theory (DFT) calculations elucidated the adsorption mechanism, demonstrating that Sr2+ initially engaged in an ion exchange reaction with Na+. Subsequently, Sr2+ coordinated with four oxygen atoms on the NFPC (100) facet, establishing a robust Sr-O bond via orbital hybridization.

Here, we aim to determine the distribution, ecological risk and sources of heavy metals and metalloids in the surface sediments of the Xiangjiang River, Hunan Province, China. Sixty-four surface sediment samples were collected in 16 sites of the Xiangjiang River, and the concentrations of ten heavy metals and metalloids (Mn, Zn, Cr, V, Pb, Cu, As, Ni, Co, and Cd) in the sediment samples were investigated using an inductively coupled plasma mass spectrometer (ICP-MS) and an atomic fluorescence spectrophotometer (AFS), respectively. The results showed that the mean concentrations of the ten heavy metals and metalloids in the sediment samples followed the order Mn > Zn > Cr > V > Pb > Cu > As ≈ Ni >Co > Cd. The geoaccumulation index ( I geo ), enrichment factor (EF), modified degree of contamination (mC d ), and potential ecological risk index ( RI ) revealed that Cd, followed by Pb, Zn, and Cu, caused severely contaminated and posed very highly potential ecological risk in the Xiangjiang River, especially in Shuikoushan of Hengyang, Xiawan of Zhuzhou, and Yijiawan of Xiangtan. The Pearson’s correlation coefficient (PCC) analysis, principal component analysis (PCA), and hierarchical cluster analysis (HCA) indicated that the ten heavy metals and metalloids in the sampling sediments of the Xiangjiang River were classified into three groups: (1) Cd, Pb, Zn, and Cu which possibly originated from Shuikoushan, Xiawan, and Yijiawan clustering Pb–Zn mining and smelting industries; (2) Co, V, Ni, Cr, and Al from natural resources; and (3) Mn and As. Therefore, our results suggest that anthropogenic activities, especially mining and smelting, have caused severe contamination of Cd, Pb, Zn, and Cu and posed very high potential ecological risk in the Xiangjiang River.

Purpose This study was to improve image quality for high-speed MR imaging using a deep learning method for online adaptive radiotherapy in prostate cancer. We then evaluated its benefits on image registration. Methods Sixty pairs of 1.5 T MR images acquired with an MR-linac were enrolled. The data included low-speed, high-quality (LSHQ), and high-speed low-quality (HSLQ) MR images. We proposed a CycleGAN, which is based on the data augmentation technique, to learn the mapping between the HSLQ and LSHQ images and then generate synthetic LSHQ (synLSHQ) images from the HSLQ images. Five-fold cross-validation was employed to test the CycleGAN model. The normalized mean absolute error (nMAE), peak signal-to-noise ratio (PSNR), structural similarity index measurement (SSIM), and edge keeping index (EKI) were calculated to determine image quality. The Jacobian determinant value (JDV), Dice similarity coefficient (DSC), and mean distance to agreement (MDA) were used to analyze deformable registration. Results Compared with the LSHQ, the proposed synLSHQ achieved comparable image quality and reduced imaging time by ~ 66%. Compared with the HSLQ, the synLSHQ had better image quality with improvement of 57%, 3.4%, 26.9%, and 3.6% for nMAE, SSIM, PSNR, and EKI, respectively. Furthermore, the synLSHQ enhanced registration accuracy with a superior mean JDV (6%) and preferable DSC and MDA values compared with HSLQ. Conclusion The proposed method can generate high-quality images from high-speed scanning sequences. As a result, it shows potential to shorten the scan time while ensuring the accuracy of radiotherapy.

The digital transformation of banks has led to a paradigm shift, promoting the open sharing of data and services with third-party providers through APIs, SDKs, and other technological means. While data sharing brings personalized, convenient, and enriched services to users, it also introduces security risks, including sensitive data leakage and misuse, highlighting the importance of data classification and grading as the foundational pillar of security. This paper presents a cloud-edge collaborative banking data open application scenario, focusing on the critical need for an accurate and automated sensitive data classification and categorization method. The regulatory outpost module addresses this requirement, aiming to enhance the precision and efficiency of data classification. Firstly, regulatory policies impose strict requirements concerning data protection. Secondly, the sheer volume of business and the complexity of the work situation make it impractical to rely on manual experts, as they incur high labor costs and are unable to guarantee significant accuracy. Therefore, we propose a scheme UP-SDCG for automatically classifying and grading financially sensitive structured data. We developed a financial data hierarchical classification library. Additionally, we employed library augmentation technology and implemented a synonym discrimination model. We conducted an experimental analysis using simulation datasets, where UP-SDCG achieved precision surpassing 95%, outperforming the other three comparison models. Moreover, we performed real-world testing in financial institutions, achieving good detection results in customer data, supervision, and additional in personally sensitive information, aligning with application goals. Our ongoing work will extend the model’s capabilities to encompass unstructured data classification and grading, broadening the scope of application.

Soil salinization is a major threat to global crop production. Tartary buckwheat (Fagopyrum tataricum), valued for its hardiness in marginal environments, provides an excellent system for studying plant salt tolerance. Using an integrated multi-omics approach, we deciphered the physiological, metabolic, and transcriptional responses of Tartary buckwheat to prolonged NaCl stress. Physiological profiling confirmed membrane damage alongside osmotic adjustment via proline accumulation and a phased antioxidant response. Metabolomics revealed extensive reprogramming, with dynamic enrichment in pathways of flavonoid biosynthesis, lipid metabolism, and the TCA cycle. Transcriptomics delineated a time-specific cascade from early signaling to late defense activation. Critical regulators within ABA and MAPK signaling pathways showed fine-tuned, divergent expression; for instance, SnRK2.3 was suppressed while specific PP2Cs were induced, and FtMAPK10 was dramatically up-regulated. Integrated analysis demonstrated coordinated induction of osmoprotectant synthesis (e.g., galactinol and betaine pathways) and a rewiring of central carbon metabolism. Our findings reveal a sophisticated, multi-layered adaptation strategy in Tartary buckwheat, integrating enhanced osmolyte production, antioxidant defense, membrane remodeling, and metabolic reprogramming, orchestrated by key signaling networks. This study provides a comprehensive molecular framework for salt tolerance and identifies valuable genetic targets for improving crop resilience.