5GLAN在5G专网中的优化应用

在通信技术高速发展的背景下,5GLAN技术同样呈现出快速发展的趋势,该技术通过5G接入,可以提供LAN,VPN等类型的功能服务。自该技术诞生以来,就受到了各领域的广泛关注,尤其是在工业、交通等领域中,基于5GLAN等场景的应用,可以有效实现灵活组网、内外网融合、安全管理等目标,为LAN内5G终端互通、终端隔离等提供灵活的通信服务。基于此,文中阐述了5GLAN的特点及优势,并分析了其在5G专网中的优化应用要点。     

基于单片机的机房温湿度监控系统

本文阐述了基于STM32F103c8t6的机房温湿度监控系统的设计原理。该系统主要以STM32F103c8t6单片机为核心,以DHT11温湿度传感器作为采集系统,把实时温度、湿度数据显示在OLED屏幕上。如果温湿度数值超过设置的阈值,会通过无线通讯模块GSM800C发送信息给用户,从而对交警中心机房内的环境实行实时监控,使环境内的温湿度得到有效监控。     

基于多流网络一致性的视频显著性检测

现有的视频显著性检测算法通常采用双流结构提取视频的时空线索,其中运动信息作为双流结构的一个分支,在显著物体发生剧烈或慢速移动时存在运动估计准确率低的问题,并且不合理的训练数据或方案使得权重偏向单个分支结构。提出一种基于多流网络一致性的视频显著性检测算法MSNC。设计并使用一种新的三重网络结构提取预选目标区域的颜色信息、时序信息和先验特征,通过先验特征补偿运动流的缺陷,并提高运动线索的利用率。采用多流一致性融合模型优化三流分支,得到不同特征的最佳融合方案。同时通过循环训练策略平衡三重网络的权重,以避免网络过度拟合单流分支,从而有效地提高运动估计和定位的准确率。在Davis数据集上的实验结果表明,相比PCSA、SSAV、MGA等算法,该算法的鲁棒性更优,其maxF和S-Measure值分别达到0.893和0.912,MAE仅为0.021。     

Design of dynamic genetic memory

In electronic systems, dynamic random access memory (DRAM) is one of the core modules in the modern silicon computer. As for a bio‐computer, one would need a mechanism for storage of bio‐information named ‘data’, which, in binary logic, has two levels, logical high and logical low, or in the normalised form, ‘1’ and ‘0’. This study proposes a possible genetic DRAM based on the modified electronic configuration, which uses the biological reaction to fulfil an equivalent RC circuit constituting a memory cell. The authors implement fundamental functions of the genetic DRAM by incorporating a genetic toggle switch for data hold. The results of simulation verify that the basic function can be used on a bio‐storage module for the future bio‐computer.Inspec keywords: DRAM chips, genetic engineering, biocomputers, bioinformatics, equivalent circuits, RC circuitsOther keywords: dynamic genetic memory design, electronic systems, dynamic random access memory, modern silicon computer, biocomputer, bioinformation, binary logic, logical high level, logical low level, normalised form, genetic DRAM, modified electronic configuration, biological reaction, equivalent RC circuit, memory cell, fundamental functions, genetic toggle switch, data hold, biostorage module     

Proline-derived in situ synthesis of nitrogen-doped porous carbon nanosheets with encaged Fe2O3@Fe3C nanoparticles for lithium-ion battery anodes

The homogeneous incorporation of heteroatoms into two-dimensional C nanostructures, which leads to an increased chemical reactivity and electrical conductivity as well as enhanced synergistic catalysis as a conductive matrix to disperse and encapsulate active nanocatalysts, is highly attractive and quite challenging. In this study, by using the natural and cheap hydrotropic amino acid proline—which has remarkably high solubility in water and a desirable N content of ~12.2 wt.%—as a C precursor pyrolyzed in the presence of a cubic KCl template, we developed a facile protocol for the large-scale production of N-doped C nanosheets with a hierarchically porous structure in a homogeneous dispersion. With concomitantly encapsulated and evenly spread Fe₂O₃ nanoparticles surrounded by two protective ultrathin layers of inner Fe₃C and outer onion-like C, the resulting N-doped graphitic C nanosheet hybrids (Fe₂O₃@Fe₃C-NGCNs) exhibited a very high Li-storage capacity and excellent rate capability with a reliable and prolonged cycle life. A reversible capacity as high as 857 mAh•g^–1 at a current density of 100 mA•g^–1 was observed even after 100 cycles. The capacity retention at a current density 10 times higher—1,000 mA•g^–1—reached 680 mAh•g^–1, which is 79% of that at 100 mA•g^–1, indicating that the hybrids are promising as anodes for advanced Li-ion batteries. The results highlight the importance of the heteroatomic dopant modification of the NGCNs host with tailored electronic and crystalline structures for competitive Li-storage features.

     

A series of nanoparticles with phase-separated structures by 1,1-diphenylethene controlled one-step soap-free emulsion copolymerization and their application in drug release

A facile one-step approach to synthesize various phase-separated porous, raspberry-like, flower-like, core–shell and anomalous nanoparticles and nanocapsules via 1,1-diphenylethene (DPE) controlled soap-free emulsion copolymerization of styrene (S) with glycidyl methacrylate (GMA), or acrylic acid (AA) is reported. By regulating the mass ratio of S/GMA, transparent polymer solution, porous and anomalous P(S-GMA) particles could be produced. The P(S-GMA) particles turn from flower-like to raspberry-like and then to anomalous structures with smooth surface as the increase of divinylbenzene (DVB) crosslinker. Transparent polymer solution, nanocapsules and core–shell P(S-AA) particles could be obtained by altering the mole ratio of S/AA; anomalous and raspberry-like P(S-AA) particles are produced by adding DVB. The unpolymerized S resulted from the low monomer conversion in the presence of DPE aggregates to form nano-sized droplets, and migrates towards the external surfaces of the GMA-enriched P(S-GMA) particles and the internal bulk of the AA-enriched P(S-AA) particles. The nano-sized droplets function as in situ porogen, porous P(S-GMA) particles and P(S-AA) nanocapsules are produced when the porogen is removed. This novel, facile, one-step method with excellent controllability and reproducibility will inspire new strategies for creating hierarchical phase-separated polymeric particles with various structures by simply altering the species and ratio of comonomers. The drug loading and release experiments on the porous particles and nanocapsules demonstrate that the release of doxorubicin hydrochloride is very slow in weakly basic environment and quick in weakly acidic environment, which enables the porous particles and nanocapsules with promising potential in drug delivery applications.

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60°镜面光泽测量值不确定度的评定

本文采用光泽度仪对漆膜的60°镜面光泽进行了测量,对产生测量不确定度的因素进行了分析,并对其A类和B类不确定度进行了分别评定,最终获得其测量结果的扩展不确定度。     

Physics-Informed Multistage Machine Learning Strategy for the Nanomachining-Induced Plastic Deformation Behavior

The evolution of the dislocation density induced by the nanomachining process dominates the plastic deformation behaviors of materials, thus affecting the mechanical properties significantly. However, a challenging topic related to how to establish an accurate model for predicting the dislocation density based on the limited simulations and experiments arises due to the complicated thermal–mechanical coupling mechanism during the machining process. Herein, a multistage method integrating machine learning, physics, and high-throughput atomic simulation is proposed to investigate the effect of cutting speed on the dislocation behavior in polycrystal copper. Compared with the traditional one-step machine learning method, the constraint of physical features effectively improves the accuracy and generalization ability of the model. The results indicate that the dislocation behaviors depend on the competition between the cutting force and temperature. In the low-cutting speed, the predominated role of the cutting temperature leads to a rapid decline of the dislocation density. In contrast, the dislocation density tends to be stable under a high-speed cutting process due to the dynamic balance between the effects of the cutting force and temperature. Notably, the proposed strategy provides a new and universal framework to design the machining parameters to obtain high-quality products.