Flow field calculation and dynamic characteristic analysis of spherical hybrid gas bearings based on passive grid

In order to research the spherical spiral groove hybrid gas bearings, the Realizable k − ε turbulence model of gas film was established based on FLUENT. The simulation calculation method of 6-degrees of freedom passive grid was used, which can simulate the lubrication characteristics of the gas film transient flow field accurately. And the gas film pressure distribution and dynamic characteristic coefficients are numerically calculated. The dynamic and static pressure coupling effects of the gas flow field were analyzed, and the axis motion trajectory was simulated. The effect of rotation speed, gas supply pressure and tangential angle on the dynamic characteristic coefficients during bearing operation was analyzed. And the stability of the gas bearing was studied. The conclusion from the analysis shows that different rotation speed and gas supply pressure will change the pressure distribution of the gas bearing during the operation. The dynamic characteristics of the gas film can be changed by reasonably optimizing the operation parameters, which can change the whirl characteristics of the gas film and improve the stability. Through calculation and analysis, the tangential angle is selected between 55° and 60°, to ensure that the gas film has a high stiffness, while it also can obtain the larger damping. The simulation results and the experimental results are compared and analyzed to verify the correctness and effectiveness of the simulation method. At the same time, the research of this paper provided a theoretical basis for optimizing the bearing structure and operating parameters, improving the dynamic characteristics of gas bearings and improving the operation stability

Study on dynamic characteristics of gas films of spherical spiral groove hybrid gas bearings

According to the gas film force variation law, when the bearing axis is slightly displaced from the static equilibrium position, displacement and velocity disturbance relation expressions for the gas film force increment are constructed. Moreover, combined with the bearing rotor system motion equation, calculation model equations for the gas film stiffness and damping coefficients are established. The axial and radial vibration and velocity of the gas bearings during operation are collected. The instantaneous stiffness and damping coefficients of the gas film are calculated by the rolling iteration algorithm using MATLAB. The dynamic changes in the gas film stiffness and damping under different motion states are analyzed, and the mechanism of the gas film vortex and oscillation is studied. The results demonstrate the following: (1) When the gas bearing is running in the linear steady state in cycle 1, the dynamic pressure effect is enhanced and the stability is improved by increasing the eccentricity; when the gas supply pressure is increased, the static pressure effect is enhanced and the gas film vortex is reduced, but the oscillation is strengthened. (2) With the increase in rotational speed, the gas film vortex force gradually exceeds the gas film damping force, and the stability gradually worsens, causing a fluctuation in the gas film stiffness and damping, following which singularity occurs and a half-speed vortex is formed. Meanwhile, the gas film oscillation is intensified, and the rotor enters the nonlinear stable cycle 2 state operation. (3) As the fluctuation of the film force increases, the instantaneous stiffness and damping oscillation of the film intensifies, most of the stiffness and damping coefficients exhibit distortion, and the rotor operation will enter a chaotic or unstable state. Therefore, the gas bearing stiffness and damping variation characteristics can be used to study and predict the gas bearing operating state. Finally, measures for reducing the vortex and oscillation of the gas film and improving the stability of the gas bearing operation are proposed

Local Diffusion Homogeneity Provides Supplementary Information in T2DM-Related WM Microstructural Abnormality Detection

Objectives: We aimed to investigate whether an inter-voxel diffusivity metric (local diffusion homogeneity, LDH), can provide supplementary information to traditional intra-voxel metrics (i.e., fractional anisotropy, FA) in white matter (WM) abnormality detection for type 2 diabetes mellitus (T2DM).Methods: Diffusion tensor imaging was acquired from 34 T2DM patients and 32 healthy controls. Voxel-based group-difference comparisons based on LDH and FA, as well as the association between the diffusion metrics and T2DM risk factors [i.e., body mass index (BMI) and systolic blood pressure (SBP)], were conducted, with age, gender and education level controlled.Results: Compared to the controls, T2DM patients had higher LDH in the pons and left temporal pole, as well as lower FA in the left superior corona radiation (p < 0.05, corrected). In T2DM, there were several overlapping WM areas associated with BMI as revealed by both LDH and FA, including right temporal lobe and left inferior parietal lobe; but the unique areas revealed only by using LDH included left inferior temporal lobe, right supramarginal gyrus, left pre- and post-central gyrus (at the semiovale center), and right superior radiation. Overlapping WM areas that associated with SBP were found with both LDH and FA, including right temporal pole, bilateral orbitofrontal area (rectus gyrus), the media cingulum bundle, and the right cerebellum crus I. However, the unique areas revealed only by LDH included right inferior temporal lobe, right inferior occipital lobe, and splenium of corpus callosum.Conclusion: Inter- and intra-voxel diffusivity metrics may have different sensitivity in the detection of T2DM-related WM abnormality. We suggested that LDH could provide supplementary information and reveal additional underlying brain changes due to diabetes

Tumor Tissue Detection using Blood-Oxygen-Level-Dependent Functional MRI based on Independent Component Analysis

Accurate delineation of gliomas from the surrounding normal brain areas helps maximize tumor resection and improves outcome. Blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) has been routinely adopted for presurgical mapping of the surrounding functional areas. For completely utilizing such imaging data, here we show the feasibility of using presurgical fMRI for tumor delineation. In particular, we introduce a novel method dedicated to tumor detection based on independent component analysis (ICA) of resting-state fMRI (rs-fMRI) with automatic tumor component identification. Multi-center rs-fMRI data of 32 glioma patients from three centers, plus the additional proof-of-concept data of 28 patients from the fourth center with non-brain musculoskeletal tumors, are fed into individual ICA with different total number of components (TNCs). The best-fitted tumor-related components derived from the optimized TNCs setting are automatically determined based on a new template-matching algorithm. The success rates are 100%, 100% and 93.75% for glioma tissue detection for the three centers, respectively, and 85.19% for musculoskeletal tumor detection. We propose that the high success rate could come from the previously overlooked ability of BOLD rs-fMRI in characterizing the abnormal vascularization, vasomotion and perfusion caused by tumors. Our findings suggest an additional usage of the rs-fMRI for comprehensive presurgical assessment

Radiation-induced brain structural and functional abnormalities in presymptomatic phase and outcome prediction: Radiation-Induced Brain Abnormalities

Radiation therapy, a major method of treatment for brain cancer, may cause severe brain injuries after many years. We used a rare and unique cohort of nasopharyngeal carcinoma patients with normal-appearing brains to study possible early irradiation injury in its presymptomatic phase before severe, irreversible necrosis happens. The aim is to detect any structural or functional imaging biomarker that is sensitive to early irradiation injury, and to understand the recovery and progression of irradiation injury that can shed light on outcome prediction for early clinical intervention. We found an acute increase in local brain activity that is followed by extensive reductions in such activity in the temporal lobe and significant loss of functional connectivity in a distributed, large-scale, high-level cognitive function-related brain network. Intriguingly, these radiosensitive functional alterations were found to be fully or partially recoverable. In contrast, progressive late disruptions to the integrity of the related far-end white matter structure began to be significant after one year. Importantly, early increased local brain functional activity was predictive of severe later temporal lobe necrosis. Based on these findings, we proposed a dynamic, multifactorial model for radiation injury and another preventive model for timely clinical intervention. Hum Brain Mapp 39:407-427, 2018. © 2017 Wiley Periodicals, Inc

Brain Activities Responding to Acupuncture at ST36 (zusanli) in Healthy Subjects: A Systematic Review and Meta-Analysis of Task-Based fMRI Studies

PurposeStomach 36 (ST36, zusanli) is one of the important acupoints in acupuncture. Despite clinical functional magnetic resonance imaging (fMRI) studies of ST36 acupuncture, the brain activities and the neural mechanism following acupuncture at ST36 remain unclear.MethodsLiterature searches were conducted on online databases, including MEDLINE, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure, Wanfang database, WeiPu database, and China Biology Medicine, for task-based fMRI studies of acupuncture at ST36 in healthy subjects. Brain regions activated by ST36 acupuncture were systematically evaluated and subjected to seed-based d mapping meta-analysis. Subgroup analysis was conducted on control procedures, manual acupuncture, electrical acupuncture (EA), and acupuncture-specific activations. Meta-regression analysis was performed to explore the effects of needle retention time on brain activities following ST36 acupuncture stimulation. The activated brain regions were further decoded and mapped on large-scale functional networks to further decipher the clinical relevance of acupuncturing at ST36.ResultsA total of sixteen studies, involving a total of 401 right-handed healthy participants, that satisfied the inclusion criteria were included in the present meta-analysis. Meta-analysis showed that acupuncturing on ST36 positively activates the opercular part of the right inferior frontal gyrus (IFG.R), left superior temporal gyrus (STG.L), and right median cingulate/paracingulate gyri (MCG.R) regions. Needle retention time in an acupuncture session positively correlates with the activation of the left olfactory cortex, as shown in meta-regression analysis. Subgroup analysis revealed that EA stimulation may be a source of heterogeneity in the pooled results. Functional network mappings showed that the activated areas were mapped to the auditory network and salience network. Further functional decoding analysis showed that acupuncture on ST36 was associated with pain, secondary somatosensory, sound and language processing, and mood regulation.ConclusionAcupuncture at ST36 in healthy individuals positively activates the opercular part of IFG.R, STG.L, and MCG.R. The left olfactory cortex may exhibit positive needle retention time-dependent activities. Our findings may have clinical implications for acupuncture in analgesia, language processing, and mood disorders.Systematic Review Registrationhttps://inplasy.com/inplasy-2021-12-0035

Convolutional Neural Networks for Classification of T2DM Cognitive Impairment Based on Whole Brain Structural Features

PurposeCognitive impairment is generally found in individuals with type 2 diabetes mellitus (T2DM). Although they may not have visible symptoms of cognitive impairment in the early stages of the disorder, they are considered to be at high risk. Therefore, the classification of these patients is important for preventing the progression of cognitive impairment.MethodsIn this study, a convolutional neural network was used to construct a model for classifying 107 T2DM patients with and without cognitive impairment based on T1-weighted structural MRI. The Montreal cognitive assessment score served as an index of the cognitive status of the patients.ResultsThe classifier could identify T2DM-related cognitive decline with a classification accuracy of 84.85% and achieved an area under the curve of 92.65%.ConclusionsThe model can help clinicians analyze and predict cognitive impairment in patients and enable early treatment