Matrix Completion-Informed Deep Unfolded Equilibrium Models for Self-Supervised k-Space Interpolation in MRI

Recently, regularization model-driven deep learning (DL) has gained significant attention due to its ability to leverage the potent representational capabilities of DL while retaining the theoretical guarantees of regularization models. However, most of these methods are tailored for supervised learning scenarios that necessitate fully sampled labels, which can pose challenges in practical MRI applications. To tackle this challenge, we propose a self-supervised DL approach for accelerated MRI that is theoretically guaranteed and does not rely on fully sampled labels. Specifically, we achieve neural network structure regularization by exploiting the inherent structural low-rankness of the $k$-space data. Simultaneously, we constrain the network structure to resemble a nonexpansive mapping, ensuring the network's convergence to a fixed point. Thanks to this well-defined network structure, this fixed point can completely reconstruct the missing $k$-space data based on matrix completion theory, even in situations where full-sampled labels are unavailable. Experiments validate the effectiveness of our proposed method and demonstrate its superiority over existing self-supervised approaches and traditional regularization methods, achieving performance comparable to that of supervised learning methods in certain scenarios

Convex Latent-Optimized Adversarial Regularizers for Imaging Inverse Problems

Recently, data-driven techniques have demonstrated remarkable effectiveness in addressing challenges related to MR imaging inverse problems. However, these methods still exhibit certain limitations in terms of interpretability and robustness. In response, we introduce Convex Latent-Optimized Adversarial Regularizers (CLEAR), a novel and interpretable data-driven paradigm. CLEAR represents a fusion of deep learning (DL) and variational regularization. Specifically, we employ a latent optimization technique to adversarially train an input convex neural network, and its set of minima can fully represent the real data manifold. We utilize it as a convex regularizer to formulate a CLEAR-informed variational regularization model that guides the solution of the imaging inverse problem on the real data manifold. Leveraging its inherent convexity, we have established the convergence of the projected subgradient descent algorithm for the CLEAR-informed regularization model. This convergence guarantees the attainment of a unique solution to the imaging inverse problem, subject to certain assumptions. Furthermore, we have demonstrated the robustness of our CLEAR-informed model, explicitly showcasing its capacity to achieve stable reconstruction even in the presence of measurement interference. Finally, we illustrate the superiority of our approach using MRI reconstruction as an example. Our method consistently outperforms conventional data-driven techniques and traditional regularization approaches, excelling in both reconstruction quality and robustness

Repeated Low-Intensity Shock Wave Simulation Experiment for Fracture Development and Propagation of Coal

For low-permeability coal seam, the gas extraction rate is relatively low. The repeated low-intensity shock wave can improve the permeability of coal and raise the rate of coal seam gas drainage. A simulation test platform was set up to carry out repeated low-intensity shock wave simulation experiment. Under the effect of repeated low-intensity shock wave, the development process of the macrofracture, pore evolution, and the law of microcrack propagation was analyzed to study the law of coal fracture propagation. Research shows that the expansion of the pore of the coal is made by the development of large, medium, and micropores by the repeated low-strength shock wave. The main contribution of the total pore volume increase comes from the micropore growth. The microcrack of the coal mainly begins to sprout and develops from the telocollinite where the fracture is more developed. With the increase of impact times, the microcracks extend to other components. Under the impact of different times, the fractal dimension of the coal sample increases with the increase in the number and length of cracks

Crystal structures and biological evaluation of Cu(II) complexes with 3-ethyl-2-acetylpyrazine N(4)-isopropylthiosemicarbazone

1305-1313Five Cu(II) complexes, [Cu(L)2]·CH3OH (1), [CuLCl] (2), [CuLBr] (3), [CuL(NO3)] (4) and [Cu2(L)2(SO4)]·3H2O·CH3OH (5), based on HL (where HL = 3-ethyl-2-acetylpyrazine N(4)-isopropylthiosemicarbazone) have been synthesized and characterized by X-ray diffraction analyses. The complexes (1) and (2) are mononuclear, while the other three are binuclear. In vitro experiments carried out to investigate the effect of complexes (1-5) on human hepatoma cells SMMC-7721, human gastric cancer SGC-7901 and human pancreatic cancer Patu-8988 show that complexes (1-5) can inhibit the proliferation of all the three cancer cell lines. The inhibition of cell growth is related to increase of tumor cell apoptosis, which is further confirmed by results of western blotting wherein the expression of p53 and Bax increased, and expression of Bcl-2 decreased in complex (3) treated SMMC-7721 cells

Erratum to: Neutron energy spectrum measurement of the Back-n white neutron source at CSNS

In the original article, there were some errors in the list of authors and affiliations. Their correct version is given in this erratum

Measurement of the neutron total cross sections of aluminum at the back-n white neutron source of CSNS

Aluminum and its alloys are widely used in the nuclear industry. Therefore, it is essential to precisely measure and accurately know the neutron total cross section of aluminum in the wider energy region. The measurement is performed by using the transmission method at the Back-n White Neutron Source of CSNS. Two aluminum samples 70 mm in diameter and thicknesses of 40 and 60 mm, respectively, were positioned at 55 m from the neutron source. The transmission detector consisted of a multi-layer fast fission chamber loaded with $$^{235}$$U and $$^{238}$$U, and it was located at the 76-m measurement station. By applying the time-of-flight technique, it was possible to extract the n+$$^{27}$$Al total cross section in a wide energy region, from 1 eV to 20 MeV, after the correction for the double-bunch mode of the CSNS accelerator. The total cross sections obtained with the two Al samples are consistent and the results obtained with the $$^{235}$$U fission cells are in good agreement with that with $$^{238}$$U in the energy range of 1–20 MeV. The uncertainty of neutron total cross section measured with $$^{235}$$U for 40 mm and 60 mm thick aluminum is 0.7–22.3% and 0.6–12.4% in the energy range of 10 keV–20 MeV. Results are in fair agreement with respect to previous data and evaluations