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In order to study how to use pulmonary functional imaging obtained through 4D-CT fusion for radiotherapy planning, and transform traditional dose volume parameters into functional dose volume parameters, a functional dose volume parameter model that may reduce level 2 and above radiation pneumonia was obtained. 41 pulmonary tumor patients who underwent 4D-CT in our department from 2020 to 2023 were included. MIM Software (MIM 7.0.7; MIM Software Inc., Cleveland, OH, USA) was used to register adjacent phase CT images in the 4D-CT series. The three-dimensional displacement vector of CT pixels was obtained when changing from one respiratory state to another respiratory state, and this three-dimensional vector was quantitatively analyzed. Thus, a color schematic diagram reflecting the degree of changes in lung CT pixels during the breathing process, namely the distribution of ventilation function strength, is obtained. Finally, this diagram is fused with the localization CT image. Select areas with Jacobi > 1.2 as high lung function areas and outline them as fLung. Import the patient's DVH image again, fuse the lung ventilation image with the localization CT image, and obtain the volume of fLung different doses (V60, V55, V50, V45, V40, V35, V30, V25, V20, V15, V10, V5). Analyze the functional dose volume parameters related to the risk of level 2 and above radiation pneumonia using R language and create a predictive model. By using stepwise regression and optimal subset method to screen for independent variables V35, V30, V25, V20, V15, and V10, the prediction formula was obtained as follows: Risk = 0.23656–0.13784 * V35 + 0.37445 * V30-0.38317 * V25 + 0.21341 * V20-0.10209 * V15 + 0.03815 * V10. These six independent variables were analyzed using a column chart, and a calibration curve was drawn using the calibrate function. It was found that the Bias corrected line and the Apparent line were very close to the Ideal line, The consistency between the predicted value and the actual value is very good. By using the ROC function to plot the ROC curve and calculating the area under the curve: 0.8475, 95% CI 0.7237–0.9713, it can also be determined that the accuracy of the model is very high. In addition, we also used Lasso method and random forest method to filter out independent variables with different results, but the calibration curve drawn by the calibration function confirmed poor prediction performance. The function dose volume parameters V35, V30, V25, V20, V15, and V10 obtained through 4D-CT are key factors affecting radiation pneumonia. Establishing a predictive model can provide more accurate lung restriction basis for clinical radiotherapy planning. [ABSTRACT FROM AUTHOR]

Increasing global credit card usage has elevated it to a preferred payment method for daily transactions, underscoring its significance in global financial cybersecurity. This paper introduces a credit card fraud detection (CCFD) system that integrates federated learning (FL) with blockchain technology. The experiment employs FL to establish a global learning model on the cloud server, which transmits initial parameters to individual local learning models on fog nodes. With three banks (fog nodes) involved, each bank trains its learning model locally, ensuring data privacy, and subsequently sends back updated parameters to the global learning model. Through the integration of FL and blockchain, our system ensures privacy preservation and data protection. We utilize three machine learning and deep neural network learning algorithms, RF, CNN, and LSTM, alongside deep optimization techniques such as ADAM, SGD, and MSGD. The SMOTE oversampling technique is also employed to balance the dataset before model training. Our proposed framework has demonstrated efficiency and effectiveness in enhancing classification performance and prediction accuracy. [ABSTRACT FROM AUTHOR]

A transverse static magnetic field (TSMF) is implemented in a laboratory electroslag remelting (ESR) experiment to control eutectic carbides morphology of Cr8 die steel herein. Under action of a TSMF, the temperature distribution of the molten pool becomes homogeneous, the local solidification time is shortened, and the interdendritic region space shrinks, which result in an evenly spaced distribution of solute atoms and a reduction in interdendritic segregation. Therefore, the formation and subsequent growth of eutectic carbides are further inhibited, thereby causing the generation of smaller and more uniform distributed eutectic carbides due to the application of the TSMF. Moreover, the increase of the number of Al2O3–(Ti,V)N inclusions serves to be the heterogeneous nucleus of carbides (inclusions also become finer) caused by TSMF, which also contributes to the refinements of eutectic carbides in Cr8 ingots. [ABSTRACT FROM AUTHOR]

Background: The global obese population is rapidly increasing, urgently requiring the development of effective and safe weight-loss medications. The classic Chinese medicine formulation Lingguizhugan De-coction has exerted a significant anti-obesity effect. However, the underlying mechanism is still unclear., Objective: This study aimed to explore the mechanism of LGZGD in the treatment of obesity based on the gut microbiota and its metabolites., Methods: Three different dosages of LGZGD were gavaged to ob/ob mice for 8 weeks. Body mass and visceral fat mass were evaluated. Additionally, the changes in gut microbiota, fecal and plasma metabolites in mice after LGZGD treatment were analyzed by metagenomics and non-targeted metabolomics., Results: The results demonstrated a significant anti-obesity effect of LGZGD treatment in ob/ob mice. Fur-thermore, the metagenomic analysis revealed that LGZGD reduced the ratio of Firmicutes / Bacteroidetes (F to B) in the gut, restored gut microbiota diversity, and identified 3 enriched KEGG pathways, including energy metabolism, lipid metabolism, and energy production and conversion pathways. Based on non-targeted metab-olomics analysis, 20 key metabolites in the feces and 30 key metabolites in the plasma responding to LGZGD treatment were identified, and the levels of Eicosapentaenoic acid (EPA) and Myristoleic acid (MA) might be the metabolites related to gut microbiota after LGZGD treatment. Their biological functions were mainly re-lated to the metabolism pathway., Conclusions: These findings suggested that LGZGD had therapeutic potential for obesity. The mechanism of LGZGD alleviating obesity was associated with improving dysbiosis of the gut microbiota. LDZGD affected gut microbiota-derived metabolites of EPA and MA and may act on energy metabolism pathways., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)