294 research outputs found
RESEARCH ON THE INFLUENCE OF IDEOLOGICAL AND POLITICAL CONSTRUCTION OF PHYSICAL EDUCATION CURRICULUM ON COLLEGE STUDENTS’ MENTAL HEALTH UNDER THE MIXED TEACHING MODE
RESEARCH ON THE INFLUENCE OF IDEOLOGICAL AND POLITICAL CONSTRUCTION OF PHYSICAL EDUCATION CURRICULUM ON COLLEGE STUDENTS’ MENTAL HEALTH UNDER THE MIXED TEACHING MODE
Reaction characteristics of waste coffee grounds chemical-looping gasification
Coffee grounds in chemical-looping gasification is an innovative handling approach of waste coffee grounds which couple the coffee grounds gasification and chemical looping technology together. By sol-gel method, the Fe4ATP6K1 compound oxygen carrier (OC) modified by KNO3 were prepared with Fe2O3 as an active component, natural attapugite (ATP) as an inert support. The effects of reaction temperature, steam flow as well as O/C molar ratio on coffee grounds in chemical looping gasification (CLG) were investigated in a fluidized bed using steam as gasification agent. It indicated that the Fe4ATP6K1 oxygen carrier could enhance the conversion of coffee grounds. Compared with SiO2 as bed material, the carbon conversion increased in CLG from 71.38% to 86.25%. The optimized conditions were presented as follows: the reaction temperature was 900°C, the water flow was 0.23 g·min-1, the O/C molar ratio was 1. Under these conditions, it was found that the average concentration of H2 reached a maximum value 52.75%, with the syngas production of 1.30 m3·kg-1 and H2 production of 83.79 g·kg-1, respectively. 20 redox cycles demonstrated that the Fe4ATP6K1 oxygen carrier has an excellent cyclic stability, the carbon conversion and cold gas efficiency were both above 75%, while the average gas concentration of gases were nearly stable
High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning
Objective: Bloch simulation constitutes an essential part of magnetic
resonance imaging (MRI) development. However, even with the graphics processing
unit (GPU) acceleration, the heavy computational load remains a major
challenge, especially in large-scale, high-accuracy simulation scenarios. This
work aims to develop a deep learning-based simulator to accelerate Bloch
simulation. Approach: The simulator model, called Simu-Net, is based on an
end-to-end convolutional neural network and is trained with synthetic data
generated by traditional Bloch simulation. It uses dynamic convolution to fuse
spatial and physical information with different dimensions and introduces
position encoding templates to achieve position-specific labeling and overcome
the receptive field limitation of the convolutional network. Main Results:
Compared with mainstream GPU-based MRI simulation software, Simu-Net
successfully accelerates simulations by hundreds of times in both traditional
and advanced MRI pulse sequences. The accuracy and robustness of the proposed
framework were verified qualitatively and quantitatively. Besides, the trained
Simu-Net was applied to generate sufficient customized training samples for
deep learning-based T2 mapping and comparable results to conventional methods
were obtained in the human brain. Significance: As a proof-of-concept work,
Simu-Net shows the potential to apply deep learning for rapidly approximating
the forward physical process of MRI and may increase the efficiency of Bloch
simulation for optimization of MRI pulse sequences and deep learning-based
methods.Comment: 18 pages, 8 figure
Hapln2 in neurological diseases and its potential as therapeutic target
Hyaluronan and proteoglycan link protein 2 (Hapln2) is important for the binding of chondroitin sulfate proteoglycans to hyaluronan. Hapln2 deficiency leads to the abnormal expression of extracellular matrix (ECM) proteins and dysfunctional neuronal conductivity, demonstrating the vital role of Hapln2 in these processes. Studies have revealed that Hapln2 promotes the aggregation of α-synuclein, thereby contributing to neurodegeneration in Parkinson’s disease (PD), and it was recently suggested to be in intracellular neurofibrillary tangles (NFTs). Additionally, the expression levels of Hapln2 showed lower in the anterior temporal lobes of individuals with schizophrenia than those of healthy subjects. Together, these studies implicate the involvement of Hapln2 in the pathological processes of neurological diseases. A better understanding of the function of Hapln2 in the central nervous system (CNS) will provide new insights into the molecular mechanisms of these diseases and help to establish promising therapeutic strategies. Herein, we review the recent progress in defining the role of Hapln2 in brain physiology and pathology
High-efficient deep learning-based DTI reconstruction with flexible diffusion gradient encoding scheme
Purpose: To develop and evaluate a novel dynamic-convolution-based method
called FlexDTI for high-efficient diffusion tensor reconstruction with flexible
diffusion encoding gradient schemes. Methods: FlexDTI was developed to achieve
high-quality DTI parametric mapping with flexible number and directions of
diffusion encoding gradients. The proposed method used dynamic convolution
kernels to embed diffusion gradient direction information into feature maps of
the corresponding diffusion signal. Besides, our method realized the
generalization of a flexible number of diffusion gradient directions by setting
the maximum number of input channels of the network. The network was trained
and tested using data sets from the Human Connectome Project and a local
hospital. Results from FlexDTI and other advanced tensor parameter estimation
methods were compared. Results: Compared to other methods, FlexDTI successfully
achieves high-quality diffusion tensor-derived variables even if the number and
directions of diffusion encoding gradients are variable. It increases peak
signal-to-noise ratio (PSNR) by about 10 dB on Fractional Anisotropy (FA) and
Mean Diffusivity (MD), compared with the state-of-the-art deep learning method
with flexible diffusion encoding gradient schemes. Conclusion: FlexDTI can well
learn diffusion gradient direction information to achieve generalized DTI
reconstruction with flexible diffusion gradient schemes. Both flexibility and
reconstruction quality can be taken into account in this network.Comment: 11 pages,6 figures,3 table
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