Material removal profile prediction and experimental validation for obliquely axial ultrasonic vibration-assisted polishing of K9 optical glass

K9 optical glass is one of the typical components in optical systems. However, because of its poor fracture resistance, it is difficult to polish it with ultra-precision and high-efficiency and without any surface damage simultaneously. The emergence of the obliquely axial ultrasonic vibration-assisted polishing (UVAP) method can solve this problem which encounters in polishing efficiency and shape accuracy. However, due to the unclear material removal profile (MRP) mechanism, obliquely axial UVAP is not widely used in the processing field. This paper introduces the obliquely axial UVAP method in research processes, mainly focusing on the fixed point MRP analysis of the obliquely axial UVAP. Based on Hertz's contact theory, polishing pressure, the length of the semi-long axis (LLA) and the length of the semi-short axis (LSA) of the contact area are calculated under ultrasonic vibration conditions. Meanwhile, the relative linear velocity distribution of the oblique polishing tool in the instantaneous contact area is modeled by mathematical geometry method. A novel model of the MRP distribution for obliquely axial UVAP is proposed following the Preston equation. Subsequently, a series of polishing experiments were carried out to verify this model. The results show that the numerical model has good agreement with the experimental results on MRP, LLA, LSA, material removal depth and material removal rate (MRR). In addition, the material removal capability can be significantly improved by larger ultrasonic amplitude and larger oblique angle. This model not only more clearly elucidates the processing mechanism of obliquely axial UVAP, but also provides theoretical support for the polishing of free-form optical lenses.     

Stem Photosynthesis—A Key Element of Grass Pea (Lathyrus sativus L.) Acclimatisation to Salinity

Grass pea (Lathyrus sativus) is a leguminous plant of outstanding tolerance to abiotic stress. The aim of the presented study was to describe the mechanism of grass pea (Lathyrus sativus L.) photosynthetic apparatus acclimatisation strategies to salinity stress. The seedlings were cultivated in a hydroponic system in media containing various concentrations of NaCl (0, 50, and 100 mM), imitating none, moderate, and severe salinity, respectively, for three weeks. In order to characterise the function and structure of the photosynthetic apparatus, Chl a fluorescence, gas exchange measurements, proteome analysis, and Fourier-transform infrared spectroscopy (FT-IR) analysis were done inter alia. Significant differences in the response of the leaf and stem photosynthetic apparatus to severe salt stress were observed. Leaves became the place of harmful ion (Na⁺) accumulation, and the efficiency of their carboxylation decreased sharply. In turn, in stems, the reconstruction of the photosynthetic apparatus (antenna and photosystem complexes) activated alternative electron transport pathways, leading to effective ATP synthesis, which is required for the efficient translocation of Na⁺ to leaves. These changes enabled efficient stem carboxylation and made them the main source of assimilates. The observed changes indicate the high plasticity of grass pea photosynthetic apparatus, providing an effective mechanism of tolerance to salinity stress.     

毫米波三维异质异构集成技术的进展与应用

三维异质异构集成技术是实现电子信息系统向着微型化、高效能、高整合、低功耗及低成本方向发展的最重要方法,也是决定信息化平台中微电子和微纳系统领域未来发展的一项核心高技术。文章详细介绍了毫米波频段三维异质异构集成技术的优势、近年来的发展趋势以及面临的挑战。利用硅基MEMS 光敏复合薄膜多层布线工艺可实现异质芯片的低损耗互连,同时三维集成高性能封装滤波器、高辐射效率封装天线等无源元件,还能很好地处理布线间的电磁兼容和芯片间的屏蔽问题。最后介绍了一款新型毫米波三维异质异构集成雷达及其在远距离生命体征探测方面的应用。     

电磁场与电磁波课程思政教学改革探索与实践

课程思政作为思想政治理论课程的有效补充，是落实立德树人根本任务的重要组成。《电磁场与电磁波》是物理、电类等专业的重要专业基础课程，深入挖掘其课程思政元素,能够发挥专业课程的隐性育人作用，实现知识传授、能力培养与价值引领的有机融合，对于理工科专业课进行课程思政改革具有现实指导作用。     

基于双时间点检测的毫米波雷达人流量监测方法

针对现有基于视频监控的人流量统计方案成本高、算法复杂且不利于个人隐私保护的局限性，利用毫米波雷达体积小、成本低、分辨率高的特点，提出了一种基于双时间点检测的人流量监测方法。该方法先获取人体目标散射点位置和多普勒频移信息来构成点云数据，然后根据多普勒频移正负来判断人体的运动方向，并筛选具有高多普勒频移值的点云数据以降低干扰点对聚类结果的影响；在双时间点对特定区域内人员数量进行统计，并根据双时间点之间所获取的点云数据聚类结果对所统计人员数据进行修正。实验结果表明，该方法能够用匿名的方式以较高的正确率统计人员进出。     

Numerical simulation of the effect of multiple obstacles inside the tube on the spontaneous ignition of high-pressure hydrogen release

Self-ignition may occur during hydrogen storage and transportation if high-pressure hydrogen is suddenly released into the downstream pipelines, and the presence of obstacles inside the pipeline may affect the ignition mechanism of high-pressure hydrogen. In this work, the effects of multiple obstacles inside the tube on the shock wave propagation and self-ignition during high-pressure hydrogen release are investigated by numerical simulation. The RNG k-ε turbulence model, EDC combustion model, and 19-step detailed hydrogen combustion mechanism are employed. After verifying the reliability of the model with experimental data, the self-ignition process of high-pressure hydrogen release into tubes with obstacles with different locations, spacings, shapes, and blockage ratios is numerically investigated. The results show that obstacles with different locations, spacings, shapes and blockage ratios will generate reflected shock waves with different sizes and propagation trends. The closer the location of obstacles to the burst disk, the smaller the spacing, and the larger the blockage ratio will cause the greater the pressure of the reflected shock wave it produces. Compared with the tubes with rectangular-shaped, semi-circular-shaped and triangular-shaped obstacles, self-ignition is preferred to occur in tube with triangular-shaped obstacles.     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

Numerical investigation of mass flow rate effects on multiplicity of detonation waves within a H2/Air rotating detonation combustor

This paper presents results from numerical simulations of a non-premixed hydrogen-air rotating detonation combustor with radial injection. The fuel and air mass flow rate are varied in order to hold a unity global equivalence ratio. The calculations show that multiple detonation waves co-exist when the mass flow rate is increased. Conditional statistics of the detonation structure and combustion processes suggest similarities across co-existing waves. Quantification of the injection response to the rotation of a detonation indicates that at higher flow rate the refill time is short enough to allow for a quick and well mixed composition prior to the new front passage. Details of the combustion characteristics are analyzed. The results elucidate the correlation between initial injection conditions and detonation multiplicity on the overall physics within the combustor.     

Influence of accelerated curing on the compressive strength of polymer-modified concrete

In recent building practice, rapid construction is one of the principal requisites. Furthermore, in designing concrete structures, compressive strength is the most significant of all parameters. While 3-d and 7-d compressive strength reflects the strengths at early phases, the ultimate strength is paramount. An effort has been made in this study to develop mathematical models for predicting compressive strength of concrete incorporating ethylene vinyl acetate (EVA) at the later phases. Kolmogorov-Smirnov (KS) goodness-of-fit test was used to examine distribution of the data. The compressive strength of EVA-modified concrete was studied by incorporating various concentrations of EVA as an admixture and by testing at ages of 28, 56, 90, 120, 210, and 365 d. An accelerated compressive strength at 3.5 hours was considered as a reference strength on the basis of which all the specified strengths were predicted by means of linear regression fit. Based on the results of KS goodness-of-fit test, it was concluded that KS test statistics value (D) in each case was lower than the critical value 0.521 for a significance level of 0.05, which demonstrated that the data was normally distributed. Based on the results of compressive strength test, it was concluded that the strength of EVA-modified specimens increased at all ages and the optimum dosage of EVA was achieved at 16% concentration. Furthermore, it was concluded that predicted compressive strength values lies within a 6% difference from the actual strength values for all the mixes, which indicates the practicability of the regression equations. This research work may help in understanding the role of EVA as a viable material in polymer-based cement composites.