Heterofullerene C48B12-impregnated MOF-5 and IRMOF-10 for hydrogen storage: A combined DFT and GCMC simulations study

The H₂ storage properties of isoreticular metal-organic framework materials (IRMOFs), MOF-5 and IRMOF-10, impregnated with different numbers and types of heterogeneous C₄₈B₁₂ molecules were investigated using density functional theory and grand canonical Monte Carlo (GCMC) calculations. The excess hydrogen adsorption isotherms of IRMOFs at 77 K within 20 bar indicate that suitable number and type of C₄₈B₁₂ molecules play a crucial role in improving the H₂ storage properties of IRMOFs. Among the studied pure and nC₄₈B₁₂ (n = 1, 2, 4, 8) in Ci symmetry impregnating into MOF-5, at 77 K under 6 bar, MOF-5-4C₄₈B₁₂ with a 3.5 wt% and 29.9 g/L hydrogen storage density, and at 77 K under 12 bar, the pure MOF-5 with a 4.9 wt% and 31.0 g/L hydrogen storage density has the best hydrogen storage properties. Whereas, among the studied pure and nC₄₈B₁₂ (n = 1, 2, 4, 8) in S6 symmetry impregnating into IRMOF-10, IRMOF-10-8C₄₈B₁₂ always shows the best hydrogen storage properties among the pure and C₄₈B₁₂-impregnated IRMOF-10 at 77 K within 20 bar. IRMOF-10-8C₄₈B₁₂ has a 6.0 wt% and 34.6 g/L hydrogen storage density at 77 K under 6 bar, and has a 7.1 wt% and 41.4 g/L hydrogen storage density at 77 K under 12 bar. The confinement effect of IRMOFs on C₄₈B₁₂ molecules, and steric hindrance effect of C₄₈B₁₂ molecules on IRMOFs mainly affects the H₂ uptake capacity by comparing the absolute H₂ molecules in individual IRMOFs units, C₄₈B₁₂ molecules, and IRMOFs-nC₄₈B₁₂ compounds. The absolute hydrogen adsorption profiles show that eight C₄₈B₁₂ molecules impregnating into MOF-5 can exert obvious steric effects for H₂ adsorption. The saturated gravimetric and volumetric H₂ densities of IRMOF-10-8C₄₈B₁₂ higher than those of MOF-5-8C₄₈B₁₂ due to with larger free volume.     

5G核心网业务模型的智能化预测研究

摘 要：核心网业务模型的建立是5G网络容量规划和网络建设的基础，通过现有方法得到的理论业务模型是静态不可变的且与实际网络存在偏离。为了克服现有5G核心网业务模型与现网模型适配性较差以及规划设备无法满足用户实际业务需求的问题，提出了一种长短期记忆（long short-term memory，LSTM）网络与卷积LSTM （convolution LSTM，ConvLSTM）网络双通道融合的 5G 核心网业务模型预测方法。该方法基于人工智能（artificial intelligence，AI）技术以实现高质量的核心网业务模型的智能预测，形成数据反馈闭环，实现网络自优化调整，助力网络智能化建设。     

基于Massive MIMO及频谱叠加的5G SA网络上行优化方法

大规模多输入多输出(Multiple-Input Multiple-Output, MIMO)可以有效提升5G SA网络的上行链路数据传输速率以及可靠性。针对5G SA网络上行链路速率和覆盖不均衡的情况,提出了基于大规模MIMO的分组算法,将发送信号矢量进行分组,组内采用最大似然检测,组外采用基于正交三角分解(QR分解)的干扰消除检测,并且结合5G频谱的叠加策略,在降低算法复杂度的同时,有效提升网络覆盖和速率。通过5G SA现网实测,通过MIMO降低分组数量能够提升分组检测性能,结合上行低频段频谱叠加策略能够有效提升5G SA网络上行覆盖30%,提升5G SA网络上行平均速率40%~80%,特别是弱覆盖边缘的网络速率,最高可达600%。     

Presentation attack detection based on two-stream vision transformers with self-attention fusion

Aiming at the performance degradation of the existing presentation attack detection methods due to the illumination variation, a two-stream vision transformers framework (TSViT) based on transfer learning in two complementary spaces is proposed in this paper. The face images of RGB color space and multi-scale retinex with color restoration (MSRCR) space are fed to TSViT to learn the distinguishing features of presentation attack detection. To effectively fuse features from two sources (RGB color space images and MSRCR images), a feature fusion method based on self-attention is built, which can effectively capture the complementarity of two features. Experiments and analysis on Oulu-NPU, CASIA-MFSD, and Replay-Attack databases show that it outperforms most existing methods in intra-database testing and achieves good generalization performance in cross-database testing.     

High Q×f values of Zn-Ni co-modified LiMg0.9Zn0.1-xNixPO4 microwave dielectric ceramics for 5G/6G LTCC modules

In this work, Zn-Ni co-modified LiMg_0.9Zn_0.1-xNi_xPO₄ (x = 0–0.1) microwave dielectric ceramics were fabricated using a solid state synthesis route. Rietveld refinement of the XRD data revealed that all ceramic samples have formed a single phase with olivine structure. SEM images showed that the samples have a dense microstructure, that agrees with the measured relative density of 97.73 %. Based on the complex chemical bond theory, Raman and infrared reflectance spectra, we postulate that ε_r is mainly affected by the ionic polarizability, lattice and bond energy, while P-O bond plays a decisive role in Q×f and τ_f value. Optimum properties of Q×f ~ 153,500 GHz, ε_r ~ 7.13 and τ_f ~ ?59 ppm/°C were achieved for the composition LiMg_0.9Zn_0.06Ni_0.04PO₄ sintered at 875 ℃ for 2 h. This set of properties makes these ceramics an excellent candidate for LTCC, wave-guide filters and antennas for 5 G/6 G communication applications.     

使能未来工厂的5G能力综述

5G系统将移动通信服务从移动电话、移动宽带和大规模机器通信扩展到新的应用领域，即所谓对通信服务有特殊要求的垂直领域。对使能未来工厂的5G能力进行了全面的分析总结，包括弹性网络架构、灵活频谱、超可靠低时延通信、时间敏感网络、安全和定位，而弹性网络架构又包括对网络切片、非公共网络、5G局域网和边缘计算的支持。希望从广度到深度，对相关的理论及技术应用做透彻、全面的梳理，对其挑战做清晰的总结，从而为相关研究和工程技术人员提供借鉴。     

具有保护肠上皮细胞HT-29免受大肠杆菌和H2O2损伤潜力的植物乳杆菌的筛选

益生菌可在肠道定植从而发挥抗炎或抗氧化活性，有利于宿主肠道健康。本实验研究了从新疆传统发酵乳制品中分离得到的8?株植物乳杆菌对大肠杆菌侵袭和过氧化氢刺激肠上皮细胞HT-29的保护作用。结果表明：在8?株植物乳杆菌中，植物乳杆菌35具有最高的黏附能力。植物乳杆菌35可通过取代、竞争、排阻的方式抑制大肠杆菌对HT-29细胞的黏附，抑制率分别为42.60%、59.17%、60.19%。植物乳杆菌35及其多糖可抑制大肠杆菌刺激HT-29细胞产生白细胞介素-8；同时保护HT-29细胞免受过氧化氢的损伤，增加超氧化物歧化酶、谷胱甘肽过氧化物酶活力水平并降低丙二醛含量。结论：植物乳杆菌35及其粗胞外多糖具有抑制大肠杆菌O157诱导的炎症性肠病的潜力。     

基于5G时长驻留的分析及优化提升方法

随着中国移动5G网络的逐步发展，目前已实现城区的5G网络连片覆盖，全网5G用户大规模增长。本文通过对经分数据用户产生的话单，结合用户使用的网络类型，利用成熟的经分平台，实现直接针对用户使用的网络、流量、时长等方面的分析，直观的体现了用户在5G网络的驻留时长占比以及5G网络的流量占比，为网络优化工作提供了重要分析依据。     

四氢姜黄素经PI3K/Akt信号通路对人血小板活化和聚集的调控作用

目的：探讨姜黄素的主要肠道代谢物四氢姜黄素（tetrahydrocurcumin，THC）对血小板活化和聚集的影响及其可能的分子机制。方法：在体外实验中，用不同浓度的THC（0、0.5、1、10 μmol/L）提前与健康人纯化血小板共同孵育40 min，然后加入凝血酶激活血小板2 min，用流式细胞术测定血小板表面CD62P和CD63的表达量，用酶联免疫吸附法测定血小板释放血小板因子-4（platelet factor-4，PF4）和趋化因子配体-5（chemokine ligand 5，CCL5）水平，用血小板聚集仪检测血小板释放ATP水平和血小板最大聚集率，用Western blot蛋白免疫印迹法检测血小板磷酸肌醇-3-激酶（phosphoinositide 3-kinase，PI3K）和Akt蛋白的磷酸化水平。结果：与模型组（血小板悬液中加入0.05%二甲基亚砜）相比，THC能抑制凝血酶诱导的血小板表面CD62P和CD63的表达，抑制PF4、CCL5和ATP的释放，降低血小板最大聚集率，下调PI3K和Akt蛋白的磷酸化水平，且呈浓度依赖效应，其中10 μmol/L的浓度下作用效果显著（P＜0.01、P＜0.001）。PI3K的特异性激动剂740 Y-P可部分逆转THC对PF4和CCL5释放和血小板聚集的抑制作用（P＜0.05、P＜0.01）。结论：THC具有显著抑制血小板活化和聚集的作用，其机制可能是THC可下调PI3K/Akt介导的信号通路。     

Application of artificial neural network method to predict the breakage properties of PGE bearing chromite ore

In the present investigation, systematic grinding experiments were conducted in a laboratory ball mill to determine the breakage properties of low-grade PGE bearing chromite ore. The population balance modeling technique was used to study the breakage parameters such as primary breakage distribution (B_{i, j}) and the specific rates of breakage (S_i). The breakage and selection function values were determined for six feed sizes. The results stated that the breakage follows the first-order grinding kinetics for all the feed sizes. It was observed that the coarser feed sizes exhibit higher selection function values than the finer feed size. Further, an artificial neural network was used to predict breakage characteristics of low-grade PGE bearing chromite ore. The predicted results obtained from the neural network modeling were close to the experimental results with a correlation of determination R² = 0.99 for both product size and selection function.