Probe nuclear structure using the anisotropic flow at the Large Hadron Collider

Recent studies have shown that the shape and radial profile of the colliding nuclei have strong influences on the initial condition of the heavy ion collisions and the subsequent development of the anisotropic flow. Using A Multi-Phase Transport model (AMPT) model, we investigated the impact of nuclear quadrupole deformation $\beta_2$ and nuclear diffuseness $a_0$ of $^{129}$Xe on various of flow observables in Xe--Xe collisions at \sqrtnn = 5.44 TeV. We found that $\beta_2$ has a strong influence on central collisions while $a_0$ mostly influences the mid-central collisions. The relative change of flow observables induced by a change in $\beta_2$ and $a_0$ are also found to be insensitive to the values of parameters controlling the strength of the interaction among final state particles. Our study demonstrates the potential for constraining the initial condition of heavy ion collisions using future system scans at the LHC.Comment: 25 pages, for the EPJA Topical Issue

Interleukin-37 suppresses the cytotoxicity of hepatitis B virus peptides-induced CD8+ T cells in patients with acute hepatitis B

Interleukin-37 (IL-37) is a newly identified anti-inflammatory cytokine, owning immunosuppressive activity in infectious diseases. The aim of this study was to investigate the regulatory function of IL-37 on CD8+ T cells during hepatitis B virus (HBV) infection. Eighteen acute hepatitis B (AHB) patients, thirty-nine chronic hepatitis B (CHB) patients, and twenty controls were enrolled. IL-37 concentration was measured by ELISA. IL-37 receptor subunits expressions on CD8+ T cells were assessed by flow cytometry. Purified CD8+ T cells were stimulated with HBV peptides and recombinant IL-37. Perforin and granzyme B secretion was investigated by ELISPOT. Programmed death-1 (PD-1) and cytotoxic T-lymphocyte associated protein-4 (CTLA-4) mRNA expressions were semi-quantified by real-time PCR. CD8+ T cell cytotoxicity was assessed in direct contact and indirect contact coculture with HepG2.2.15 cells. Plasma IL-37 level was down-regulated and negatively correlated with aminotransferase levels in AHB patients. There were no significant differences of IL-37 receptor subunits among AHB patients, CHB patients, and controls. Exogenous IL-37 stimulation suppressed HBV peptides-induced perforin and granzyme B secretion by CD8+ T cells in AHB patients, but not in CHB patients. Exogenous IL-37 stimulation did not affect proinflammatory cytokines secretion as well as PD-1/CTLA-4 mRNA expressions in CD8+ T cells in AHB and CHB patients. Exogenous IL-37 stimulation dampened HBV peptide-induced CD8+ T cell cytotoxicity in a cell-to-cell contact manner. The current data indicated that acute HBV infection might induce down-regulation of IL-37, which might be associated with enhanced CD8+ T cell cytotoxicity and liver damage

Light-induced dynamic frequency shifting of microwave photons in a superconducting electro-optic converter

Hybrid superconducting-photonic microresonators are a promising platform for realizing microwave-to-optical transduction. However, the absorption of scattered photons by the superconductors leads to unintended microwave resonance frequency variation and linewidth broadening. Here, we experimentally study the dynamics of this effect and its impact on microwave-to-optics conversion in an integrated lithium niobate-superconductor hybrid resonator platform. We unveiled an adiabatic frequency shifting of the intracavity microwave photons induced by the fast photo-responses of the thin-film superconducting resonator. As a result, the temporal and spectral responses of electro-optics transduction are modified and well described by our theoretical model. This work provides important insights on the light-induced conversion dynamics which must be considered in future designs of hybrid superconducting-photonic system

Online synchronous inspection and system optimization of flexible food packaging bags by using machine vision and sensing technique

Flexible food packaging in the market is increasingly favored, and its quality is essential and indispensable for safety and convenience.  However, quality inspection still stays in the manual stage, or partially manual inspection remains, in production, leading low efficiency, lack and even false inspection, hardly meeting the requirements of the modern output.  This paper proposes and optimizes the design of an automatic detection system with intelligence for flexible food packaging bag, which can effectively be adopted to check the quality of packaging trademark patterns, fillers, and sealing quality.  The inspection system runs with two-stage structure, machine vision, pressure sensing and synchronization to improve efficiency and ensure the normal production beat. Simplex Method is adopted to determine the best synchronous speeds online to achieve the best expectation. Comparison has been made between the manual inspection and our automatic operation, the sample of 10000 was statistically analyzed and results have shown that two workers were saved and the correctness rate of inspection raised up to 999.8‰

A Comprehensive Study of the Electrostatic Discharge Sensitivity and Chargeability of Tris(carbohydrazide)zinc Perchlorate

Abstract: Most primary explosives are non-conductors, easily accumulate charge when contacting with and separating from other materials, and are sensitive to electrostatic discharge (ESD). In order to reduce the number of accidents caused by ESD initiation of primary explosives, studies on their electrostatic hazards are necessary. This work presents comprehensive experimental results of electrostatic discharge sensitivity and chargeability of tris(carbohydrazide)zinc perchlorate (ZnCP) under different conditions. The influences of the testing conditions, of devices, particle size, ambient temperature and relative humidity on the electrostatic discharge sensitivity and chargeability have been investigated in detail, and the quantitative regression equations obtained

Privacy-preserving continual learning methods for medical image classification: a comparative analysis

BackgroundThe implementation of deep learning models for medical image classification poses significant challenges, including gradual performance degradation and limited adaptability to new diseases. However, frequent retraining of models is unfeasible and raises concerns about healthcare privacy due to the retention of prior patient data. To address these issues, this study investigated privacy-preserving continual learning methods as an alternative solution.MethodsWe evaluated twelve privacy-preserving non-storage continual learning algorithms based deep learning models for classifying retinal diseases from public optical coherence tomography (OCT) images, in a class-incremental learning scenario. The OCT dataset comprises 108,309 OCT images. Its classes include normal (47.21%), drusen (7.96%), choroidal neovascularization (CNV) (34.35%), and diabetic macular edema (DME) (10.48%). Each class consisted of 250 testing images. For continuous training, the first task involved CNV and normal classes, the second task focused on DME class, and the third task included drusen class. All selected algorithms were further experimented with different training sequence combinations. The final model's average class accuracy was measured. The performance of the joint model obtained through retraining and the original finetune model without continual learning algorithms were compared. Additionally, a publicly available medical dataset for colon cancer detection based on histology slides was selected as a proof of concept, while the CIFAR10 dataset was included as the continual learning benchmark.ResultsAmong the continual learning algorithms, Brain-inspired-replay (BIR) outperformed the others in the continual learning-based classification of retinal diseases from OCT images, achieving an accuracy of 62.00% (95% confidence interval: 59.36-64.64%), with consistent top performance observed in different training sequences. For colon cancer histology classification, Efficient Feature Transformations (EFT) attained the highest accuracy of 66.82% (95% confidence interval: 64.23-69.42%). In comparison, the joint model achieved accuracies of 90.76% and 89.28%, respectively. The finetune model demonstrated catastrophic forgetting in both datasets.ConclusionAlthough the joint retraining model exhibited superior performance, continual learning holds promise in mitigating catastrophic forgetting and facilitating continual model updates while preserving privacy in healthcare deep learning models. Thus, it presents a highly promising solution for the long-term clinical deployment of such models