Exact synchronization and asymptotic synchronization for linear ODEs

Science China Mathematics - This paper studies exact synchronization and asymptotic synchronization problems for a controlled linear system of ordinary differential equations. In this paper, we...     

Effect of astrocyte on synchronization of thermosensitive neuron-astrocyte minimum system

Astrocytes have a regulatory function on the central nervous system (CNS), especially in the temperature-sensitive hippocampal region. In order to explore the thermosensitive dynamic mechanism of astrocytes in the CNS, we establish a neuron-astrocyte minimum system to analyze the synchronization change characteristics based on the Hodgkin-Huxley model, in which a pyramidal cell and an interneuron are connected by an astrocyte. The temperature range is set as 0 ℃-40 ℃ to juggle between theoretical calculation and the reality of a brain environment. It is shown that the synchronization of thermosensitive neurons exhibits nonlinear behavior with changes in astrocyte parameters. At a temperature range of 0 ℃-18 ℃, the effects of the astrocyte can provide a tremendous influence on neurons in synchronization. We find the existence of a value for inositol triphosphate (IP₃) production rate and feedback intensities of astrocytes to neurons, which can ensure the weak synchronization of two neurons. In addition, it is revealed that the regulation of astrocytes to pyramidal cells is more sensitive than that to interneurons. Finally, it is shown that the synchronization and phase transition of neurons depend on the change in Ca²⁺ concentration at the temperature of weak synchronization. The results in this paper provide some enlightenment on the mechanism of cognitive dysfunction and neurological disorders with astrocytes.     

Optimal Control of Nonlinear Fractional-Order Systems with Multiple Time-Varying Delays

Journal of Optimization Theory and Applications - This paper considers an optimal control problem governed by nonlinear fractional-order systems with multiple time-varying delays and subject to...     

A multigrid method for the ground state solution of Bose–Einstein condensates based on Newton iteration

BIT Numerical Mathematics - In this paper, a new kind of multigrid method is proposed for the ground state solution of Bose–Einstein condensates based on Newton iteration scheme. Instead of...     

Anisotropic black phosphorene nanotube anodes afford ultrafast kinetic rate or extra capacities for Li-ion batteries

As an important anode material for fast-charging Li-ion batteries (LIBs), black phosphorus (BP) has attracted extensive attention. Black phosphorene nanotubes (BPNTs) can be theoretically produced by rolling up the black phosphorene nanosheet along armchair (a-BPNTs) and zigzag (z-BPNTs) directions. The effects of curvature, chirality, Li-storage concentrations and strain stress on the Li-storage performance such as Li diffusion barriers and mechanical stabilities of BPNTs are mainly investigated by first principles calculations. The theoretical calculations predict that the a-BPNTs and z-BPNTs have good maximum Li-storage capacities, and the z-BPNTs exhibit better flexibility than a-BPNTs. The mechanical stabilities and Li-migration are all related to the curvature of BPNTs. Additionally, both a-BPNTs and z-BPNTs exhibit fast Li-ion conductivity along the c-axis direction. Moreover, the average Poisson's ratio of a-BPNTs (0.68) is larger than that of z-BPNTs (0.17), indicating that the strain stress is more difficult to apply on a-BPNTs than z-BPNTs. Our calculations predict that the a-BPNTs can afford ultrafast kinetic rate for fast-charging and high-power LIBs, while the z-BPNTs can provide extra capacity for high-energy LIBs.     

Co-POM@MOF-derivatives with trace cobalt content for highly efficient oxygen reduction

A simple and effective method for constructing highly efficient oxygen reduction catalysts with trace amount of isolated cobalt was firstly developed by the pyrolysis of Co-centered polyoxometalate@metalorganic framework （Co-POM@MOF）.The Co-centered polyoxometalate ([Co W₁₂O₄₀]^6-) was confined in the well-defined void space of ZIF-8 to achieve homogeneous dispersion of polyoxoanions,where the isolated Co centers were well surrounded by the W-O shell and ZIF-8 fra...     

In Situ Electropolymerization Enables Ultrafast Long Cycle Life and High-Voltage Organic Cathodes for Lithium Batteries

Organic cathode materials have attracted extensive attention because of their diverse structures, facile synthesis, and environmental friendliness. However, they often suffer from insufficient cycling stability caused by the dissolution problem, poor rate performance, and low voltages. An in situ electropolymerization method was developed to stabilize and enhance organic cathodes for lithium batteries. 4,4′,4′′-Tris(carbazol-9-yl)-triphenylamine (TCTA) was employed because carbazole groups can be polymerized under an electric field and they may serve as high-voltage redox-active centers. The electropolymerized TCTA electrodes demonstrated excellent electrochemical performance with a high discharge voltage of 3.95 V, ultrafast rate capability of 20 A g⁻¹, and a long cycle life of 5000 cycles. Our findings provide a new strategy to address the dissolution issue and they explore the molecular design of organic electrode materials for use in rechargeable batteries.     

Transfer learning in deep neural network based under-sampled MR image reconstruction

In Magnetic Resonance Imaging (MRI), the success of deep learning-based under-sampled MR image reconstruction depends on: (i) size of the training dataset, (ii) generalization capabilities of the trained neural network. Whenever there is a mismatch between the training and testing data, there is a need to retrain the neural network from scratch with thousands of MR images obtained using the same protocol. This may not be possible in MRI as it is costly and time consuming to acquire data. In this research, a transfer learning approach i.e. end-to-end fine tuning is proposed for U-Net to address the data scarcity and generalization problems of deep learning-based MR image reconstruction. First the generalization capabilities of a pre-trained U-Net (initially trained on the human brain images of 1.5 T scanner) are assessed for: (a) MR images acquired from MRI scanners of different magnetic field strengths, (b) MR images of different anatomies and (c) MR images under-sampled by different acceleration factors. Later, end-to-end fine tuning of the pre-trained U-Net is proposed for the reconstruction of the above-mentioned MR images (i.e. (a), (b) and (c)). The results show successful reconstructions obtained from the proposed method as reflected by the Structural SIMilarity index, Root Mean Square Error, Peak Signal-to-Noise Ratio and central line profile of the reconstructed images.     

Shedding vortex simulation method based on viscous compensation technology research

Owing to the influence of the viscosity of the flow field, the strength of the shedding vortex decreases gradually in the process of backward propagation. Large-scale vortexes constantly break up, forming smaller vortexes. In engineering, when numerical simulation of vortex evolution process is carried out, a large grid is needed to be arranged in the area of outflow field far from the boundary layer in order to ensure the calculation efficiency. As a result, small scale vortexes at the far end of the flow field cannot be captured by the sparse grid in this region, resulting in the dissipation or even disappearance of vortexes. In this paper, the effect of grid scale is quantified and compared with the viscous effect through theoretical derivation. The theoretical relationship between the mesh viscosity and the original viscosity of the flow field is established, and the viscosity term in the turbulence model is modified. This method proves to be able to effectively improve the intensity of small-scale shedding vortexes at the far end of the flow field under the condition of sparse grid. The error between the simulation results and the results obtained by using fine mesh is greatly reduced, the calculation time is shortened, and the high-precision and efficient simulation of the flow field is realized.     

大一学生实验室安全教育实效研究*

为考查线上线下混合安全教育模式实施后的实际效果,对天津理工大学化工、环安、机械、材料学院2020级大一学生进行实验室安全知识测试。结果表明:接受线上线下混合安全教育的大一学生安全知识的掌握情况较好;不足之处在于学生普遍对应急处置知识掌握较差。因此,在后续安全教育过程中,仍需进一步优化安全教育内容与模式,全面推进全员、全程安全教育。