Quantum Discord for Investigating Quantum Correlations without Entanglement in Solids

Quantum systems unfold diversified correlations which have no classical counterparts. These quantum correlations have various different facets. Quantum entanglement, as the most well known measure of quantum correlations, plays essential roles in quantum information processing. However, it has recently been pointed out that quantum entanglement cannot describe all the nonclassicality in the correlations. Thus the study of quantum correlations in separable states attracts widely attentions. Herein, we experimentally investigate the quantum correlations of separable thermal states in terms of quantum discord. The sudden change of quantum discord is observed, which captures ambiguously the critical point associated with the behavior of Hamiltonian. Our results display the potential applications of quantum correlations in studying the fundamental properties of quantum system, such as quantum criticality of non-zero temperature.Comment: 4 pages, 4 figure

LKCA: Large Kernel Convolutional Attention

We revisit the relationship between attention mechanisms and large kernel ConvNets in visual transformers and propose a new spatial attention named Large Kernel Convolutional Attention (LKCA). It simplifies the attention operation by replacing it with a single large kernel convolution. LKCA combines the advantages of convolutional neural networks and visual transformers, possessing a large receptive field, locality, and parameter sharing. We explained the superiority of LKCA from both convolution and attention perspectives, providing equivalent code implementations for each view. Experiments confirm that LKCA implemented from both the convolutional and attention perspectives exhibit equivalent performance. We extensively experimented with the LKCA variant of ViT in both classification and segmentation tasks. The experiments demonstrated that LKCA exhibits competitive performance in visual tasks. Our code will be made publicly available at https://github.com/CatworldLee/LKCA

Epirubicin and gait apraxia: a real-world data analysis of the FDA Adverse Event Reporting System database

Introduction: Epirubicin is widely used in many malignancies with good efficacy and tolerability. However, investigations about adverse events (AEs) using real-world information are still insufficient.Methods: We extracted Epirubicin-related reports submitted between the first quarter of 2014 and first quarter of 2023 from FAERS database. Four algorithms were utilized to evaluate whether there was a significant correlation between Epirubicin and AEs.Results: After de-duplicating, a total of 3919 cases were extracted. Among the 3919 cases, we identified 1472 AEs, 253 of which were found to be adverse drug reactions (ADRs) associated with Epirubicin. We analysed the occurrence of Epirubicin-induced ADRs and found several unexpected significant ADRs, such as hepatic artery stenosis, hepatic artery occlusion, intestinal atresia and so on. Interestingly, we found gait apraxia, a neurological condition, was also significantly associated with Epirubicin. To our knowledge, there haven't studies that have reported an association between gait disorders and the usage of epirubicin.Discussion: Our study identified new unexpected significant ADRs related to Epirubicin, providing new perspectives to the clinical use of Epirubicin

Macrophage polarization and metabolism in atherosclerosis

: Atherosclerosis is a chronic inflammatory disease characterized by the accumulation of fatty deposits in the inner walls of vessels. these plaques restrict blood flow and lead to complications such as heart attack or stroke. the development of atherosclerosis is influenced by a variety of factors, including age, genetics, lifestyle, and underlying health conditions such as high blood pressure or diabetes. atherosclerotic plaques in stable form are characterized by slow growth, which leads to luminal stenosis, with low embolic potential or in unstable form, which contributes to high risk for thrombotic and embolic complications with rapid clinical onset. In this complex scenario of atherosclerosis, macrophages participate in the whole process, including the initiation, growth and eventually rupture and wound healing stages of artery plaque formation. macrophages in plaques exhibit high heterogeneity and plasticity, which affect the evolving plaque microenvironment, e.g., leading to excessive lipid accumulation, cytokine hyperactivation, hypoxia, apoptosis and necroptosis. the metabolic and functional transitions of plaque macrophages in response to plaque microenvironmental factors not only influence ongoing and imminent inflammatory responses within the lesions but also directly dictate atherosclerotic progression or regression. In this review, we discuss the origin of macrophages within plaques, their phenotypic diversity, metabolic shifts, and fate and the roles they play in the dynamic progression of atherosclerosis. It also describes how macrophages interact with other plaque cells, particularly T cells. ultimately, targeting pathways involved in macrophage polarization may lead to innovative and promising approaches for precision medicine. further insights into the landscape and biological features of macrophages within atherosclerotic plaques may offer valuable information for optimizing future clinical treatment for atherosclerosis by targeting macrophages

Damage Identification in Structures Based on Energy Curvature Difference of Wavelet Packet Transform

Damage identification is of tremendous significance in engineering structures. One key issue in damage identification is to determine an index that is sensitive to the structural damage. Current damage identification indices are generally focused on dynamic characteristics such as the natural frequencies, modal shapes, frequency responses, or their mathematical combinations. In this study, based on the wavelet packet transform, we propose a novel index, the energy curvature difference (ECD) index, to identify the damage in structures. The ECD index is the summation of component energy curvature differences after a signal is decomposed using WPT. Moreover, two numerical examples are used to demonstrate the feasibility and validity of the proposed ECD index for damage identification. Stiffness reduction is employed to simulate the structural damage. The damage can be identified by the ECD index curve plot. The results of the examples indicate that the proposed ECD index is sensitive to low damage levels because even 5% stiffness reduction can be apparently identified. The proposed ECD index can be employed to effectively identify structural damage

Damage Identification in Structures Based on Energy Curvature Difference of Wavelet Packet Transform

Damage identification is of tremendous significance in engineering structures. One key issue in damage identification is to determine an index that is sensitive to the structural damage. Current damage identification indices are generally focused on dynamic characteristics such as the natural frequencies, modal shapes, frequency responses, or their mathematical combinations. In this study, based on the wavelet packet transform, we propose a novel index, the energy curvature difference (ECD) index, to identify the damage in structures. The ECD index is the summation of component energy curvature differences after a signal is decomposed using WPT. Moreover, two numerical examples are used to demonstrate the feasibility and validity of the proposed ECD index for damage identification. Stiffness reduction is employed to simulate the structural damage. The damage can be identified by the ECD index curve plot. The results of the examples indicate that the proposed ECD index is sensitive to low damage levels because even 5% stiffness reduction can be apparently identified. The proposed ECD index can be employed to effectively identify structural damage

Application of Tucker Decomposition in Temperature Distribution Reconstruction

Constrained by cost, measuring conditions and excessive calculation, it is difficult to reconstruct a 3D real-time temperature field. For the purpose of solving these problems, a three-dimensional temperature distribution reconstruction algorithm based on Tucker decomposition algorithm is proposed. The Tucker decomposition algorithm is used to reduce the dimension of the measured data, and the processed core tensor is used for the temperature field reconstruction of sparse data. Theoretical analysis and simulations show that the proposed method is feasible; the overall optimization is realized by selecting the appropriate core tensor dimensions; and the reconstruction error is less than 3%. Results indicate that the proposed method can yield a reliable reconstruction solution and can be applied to real-time applications