Simulation Research on the Forming Process of Large Axles Rolled by Cross-Wedge Rolling

As a novel metal forming process, cross-wedge rolling (CWR) is widely used in the manufacture of large axles in the rail transportation industry. When the forming process of the axle is studied, its formability becomes one of the key issues in the metal forming process. This paper takes LZ50 axle steel as the research object. Through the application of ANSYS finite element simulation software, combined with previous research and literature, a dynamic simulation of the rolling process is carried out, and the stress, strain and temperature field of the axle steel in the forming process are analysed. The results show that with the deepening of rolling, the plastic deformation of the metal becomes increasingly obvious. When the finishing section is reached, the temperature on the surface and inside the rolled piece reaches relatively uniform distribution

Hot Compression Test and Microstructure Evolution in LZ50 Axle Steel

True strain-true stress curves of the LZ50 axle steel were obtained after hot compression tests had been performed on a Gleeble-3800 thermal simulator at strain rates of 0.01, 0.1, 1 and 5 s^(-1) and at deformation temperatures from 850 to 1,150 ℃. Following the data processing, the relationship between the flow stress and the deformation temperature of the material under different true strain conditions was analysed. On this basis and according to the influence of deformation factors, the constitutive equation of the Johnson-Cook flow stress model is established, and the model is modified according to the defects of the model, so that the improved model can effectively predict the mechanical behaviour in the range of high strain rates and temperatures. The dynamic material model (DMM) was used to generate the hot working diagram of the material. Through calculation and analysis, the optimum process area in terms of temperature was found to be in the range from 1,050 to 1,150 ℃ and in terms of strain rate in the rage from 1 to 5 s^(-1). Finally, the microstructure evolution of the compressed specimens under different strain rates and temperatures was studied in the metallographic analysis, which provided a theoretical basis and reference value for later damage

Study on the Present Situation and Cause of Formation of Rocky Desertification in Xichuan County

As the eco-environmental security guarantors for water source, Xichuan and its hydro-ecological condition play important roles in Middle Route Water Transfers Project from South to North. Via analyzing the soil, water quality, rock and vegetation et al., we studied the current extend of rocky desertification in Xichuan. Considering the local situation, we analyzed the human factors and natural factors that contribute to the rocky desertification in Xichuan, which are helpful in reducing rocky desertification and worthy of promoting

Altered voxel-mirrored homotopic connectivity in right temporal lobe epilepsy as measured using resting-state fMRI and support vector machine analyses

BackgroundPrior reports revealed abnormalities in voxel-mirrored homotopic connectivity (VMHC) when analyzing neuroimaging data from patients with various psychiatric conditions, including temporal lobe epilepsy (TLE). Whether these VHMC changes can be leveraged to aid in the diagnosis of right TLE (rTLE), however, remains to be established. This study was thus developed to examine abnormal VMHC findings associated with rTLE to determine whether these changes can be used to guide rTLE diagnosis.MethodsThe resultant imaging data of resting-state functional MRI (rs-fMRI) analyses of 59 patients with rTLE and 60 normal control individuals were analyzed using VMHC and support vector machine (SVM) approaches.ResultsRelative to normal controls, patients with rTLE were found to exhibit decreased VMHC values in the bilateral superior and the middle temporal pole (STP and MTP), the bilateral middle and inferior temporal gyri (MTG and ITG), and the bilateral orbital portion of the inferior frontal gyrus (OrbIFG). These patients further exhibited increases in VMHC values in the bilateral precentral gyrus (PreCG), the postcentral gyrus (PoCG), and the supplemental motor area (SMA). The ROC curve of MTG VMHC values showed a great diagnostic efficacy in the diagnosis of rTLE with AUCs, sensitivity, specificity, and optimum cutoff values of 0.819, 0.831, 0.717, and 0.465. These findings highlight the value of the right middle temporal gyrus (rMTG) when differentiating between rTLE and control individuals, with a corresponding SVM analysis yielding respective accuracy, sensitivity, and specificity values of 70.59% (84/119), 78.33% (47/60), and 69.49% (41/59).ConclusionIn summary, patients with rTLE exhibit various forms of abnormal functional connectivity, and SVM analyses support the potential value of abnormal VMHC values as a neuroimaging biomarker that can aid in the diagnosis of this condition

Influence of Electrolyte Refreshing on the Photoelectrochemical Performance of Fiber-Shaped Dye-Sensitized Solar Cells

Given the convenient sealing of fiber-shaped dye-sensitized solar cells (FDSSCs), the electrolyte refreshing effect on the photo-electrochemical performance of FDSSCs was studied. The electron transport and interfacial recombination kinetics were also systematically investigated by electrochemical impedance spectroscopy. With increased electrolyte refreshing times from 0 to 10, the open-circuit voltage (Voc) and fill factor (FF) increased, whereas the photocurrent density (Jsc) and power conversion efficiency (PCE) significantly decreased. The increased Voc was mainly ascribed to the electron recombination resistance (Rct, WE) at the TiO2/electrolyte interface and electron lifetime. The decreased Jsc and PCE were due to dye desorption and the increase of series resistance. Further investigation proved that Li+ played a vital role in increasing Voc as electrolyte refreshing and Li+ had more significant impact than TBP (tert-butyl pyridine) on maintaining high Voc

Multiple cystic echinococcosis in abdominal and pelvic cavity treated by surgery with a 4-year follow-up: a case report

We report a case of a male patient who presented with multiple abdominal and pelvic echinococcosis. The patient had been diagnosed with hepatic echinococcosis for 7 years and developed intermittent distension and discomfort in the upper abdomen after an accidental fall. In recent years, the patient’s abdominal distention increased gradually. Computed tomography revealed multiple hydatid cysts in the liver, spleen, abdominal cavity, and pelvic cavity. Abdominal organs were severely compressed, such that he could not eat normally except for a liquid diet. The patient underwent radical surgical resection based on the multi-disciplinary treatment (MDT) and the operation lasted 10 h, nearly 100 hydatid cysts were excised, about 18 liters of cyst fluid and cyst contents were removed, and the patient lost 20 kg of weight after surgery. The operation was successful, but there were still some postoperative complications such as hypovolemic shock, postoperative ascites, postoperative bile leakage. Treatment measures for the patient were anti-infection, antishock, clamping the abdominal drainage tube, and negative pressure abdominal puncture drainage. At follow up the patient’s quality of life had been significantly improved with 15 kg weight gain compared to before

One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

Magnetic resonance imaging (MRI) is a principal radiological modality that provides radiation-free, abundant, and diverse information about the whole human body for medical diagnosis, but suffers from prolonged scan time. The scan time can be significantly reduced through k-space undersampling but the introduced artifacts need to be removed in image reconstruction. Although deep learning (DL) has emerged as a powerful tool for image reconstruction in fast MRI, its potential in multiple imaging scenarios remains largely untapped. This is because not only collecting large-scale and diverse realistic training data is generally costly and privacy-restricted, but also existing DL methods are hard to handle the practically inevitable mismatch between training and target data. Here, we present a Physics-Informed Synthetic data learning framework for Fast MRI, called PISF, which is the first to enable generalizable DL for multi-scenario MRI reconstruction using solely one trained model. For a 2D image, the reconstruction is separated into many 1D basic problems and starts with the 1D data synthesis, to facilitate generalization. We demonstrate that training DL models on synthetic data, integrated with enhanced learning techniques, can achieve comparable or even better in vivo MRI reconstruction compared to models trained on a matched realistic dataset, reducing the demand for real-world MRI data by up to 96%. Moreover, our PISF shows impressive generalizability in multi-vendor multi-center imaging. Its excellent adaptability to patients has been verified through 10 experienced doctors' evaluations. PISF provides a feasible and cost-effective way to markedly boost the widespread usage of DL in various fast MRI applications, while freeing from the intractable ethical and practical considerations of in vivo human data acquisitions.Comment: 22 pages, 9 figures, 1 tabl