人才信息系统数据库设计

在人才信息系统的功能需求基础上,设计了一个信息数据库,能够实现招聘信息发布与查找、应聘信息发布与查找以及人才信息统计等功能。整个设计过程从需求分析、概念设计、逻辑设计等方面进行探讨。     

激光扫描车身测量传感器靶标识别体系的研究

介绍了四光束激光扫描测量车身三维坐标的系统构成和基本原理,从系统PIN预处理输出的靶标回射信号的特点出发,为汽车车身测量传感器设计了基于条形码识别原理的合作靶标识别体系,最多可同时对27个测量点进行有效识别,确保传感器的精度要求和效率要求,达到了很好的实验效果。     

Fiducial markers for increasing the versatility of optical correlation in the measurement of faults on integrated-circuit wafers

A microscope-coherent optical processor is used for the measurement of the registration errors on integrated-circuit wafers. The measurements are obtained from the optical correlation of wafers with reference wafer patterns by use of matched spatial filters. Previously, the intricate pattern of the active circuit area of wafers has been used in the correlation process, and a new matched spatial filter had to be created for each different integrated circuit. Here, the results of using comparatively plain fiducial markers on a wafer for the registration-error measurement are presented, and these show that the measurements can be made independent of the design of the integrated circuit while maintaining the advantages and accuracy of the optical correlation technique.     

考虑数据分类的建筑电能耗集成预测方法

建筑侧各类可再生能源的应用日益普及,建筑电能耗预测在用能供需平衡、电网稳定运行、尖峰需求响应等方面发挥越来越重要作用。尽管诸多数据驱动模型在能耗预测方面获得广泛应用,当前仍缺乏预测精度高、泛化能力强的短期预测模型。针对该问题,提出一种基于建筑物能耗特点并结合数据挖掘技术的分类集成式能耗预测方法。首先,采用递归特征消除法对数据进行特征筛选,并用模糊C均值聚类算法对训练集数据进行聚类,使用K最邻近法对验证集和测试集数据进行归类;选择5种结合智能优化算法的混合数据驱动模型作为子学习器,分别对每类数据做预测,最后使用多元线性回归法进行结果集成。经3个建筑电力用能案例验证,此集成预测模型精度均优于单个子模型,具有适用不同建筑类型和用能尺度的预测潜力。     

扭曲的涡旋光束在海洋湍流中的传输因子

基于扩展惠更斯-菲涅尔原理和维格纳分布函数(Wigner distribution function,WDF)二阶矩,推导了部分相干扭曲涡旋光束(partially coherent twisted vortex beam,PCTVB)在海洋湍流中传输时的M2因子和角扩展θ(z)。通过数值模拟方法详细研究了海洋湍流对光束M2因子和角扩展θ(z)的影响,结果表明温度方差耗散率ΧT和温度盐度贡献比w越大、动能耗散率ε和各向异性因子ξ越小时对PCTVB的M2因子和角扩展θ(z)的影响越大。此外研究发现,相较于非扭曲涡旋光束(PCVB),PCTVB有更好的抗海洋湍流的能力,且增大拓扑荷数m以及扭曲因子绝对值｜μ｜后,PCTVB的M2因子和角扩展θ(z)显著减小,光束的抗海洋湍流能力增强。并且增大束腰宽度w0和波长λ,以及减小初始相干长度δηη(η=x,y)同样可以增加光束抗海洋湍流的能力。本文的数值研究结果对海洋光通信具有重要意义。     

Surface Control and Electrical Tuning of MXene Electrode for Flexible Self-Powered Human–Machine Interaction

MXene materials emerge as promising candidates for energy harvesting and storage application. In this study, the effect of the surface chemistry on the work function of MXenes, which determines the performance of MXene-based triboelectric nanogenerator (TENG), is elucidated. First-principles calculations reveal that the surface functional group greatly influences MXene work function:  OH termination reduces the work function with respect to that of bare surface, while  F and  Cl increase it. Then, work functions are experimentally determined by Kelvin probe force microscopy. The MXene prepared by gentle etching at 40 °C for 48 h (GE_40/48) has the largest work function. Furthermore, an electron-cloud potential-well model is established to explain the mechanism of electron emission-dominated charge transfer and assemble a triboelectric device to verify experimentally its conclusions. It is found that GE_40/48 has the best performance with a 281 V open-circuit voltage, 9.7 µA short-current current, and storing 1.019 µC of charge, which is consistent with the model. Last, a patterned TENG is demonstrated for self-powered human–machine interaction application. This finding enhances the understanding of the inherent mechanism between the surface structure and the output performance of MXene-based TENG, which can be applied to other TENG based on 2D materials.     

Macrophage Membrane-Coated Nanoparticles Sensitize Glioblastoma to Radiation by Suppressing Proneural–Mesenchymal Transformation in Glioma Stem Cells

Radiotherapy is identified as a crucial treatment for patients with glioblastoma, but recurrence is inevitable. The efficacy of radiotherapy is severely hampered partially due to the tumor evolution. Growing evidence suggests that proneural glioma stem cells can acquire mesenchymal features coupled with increased radioresistance. Thus, a better understanding of mechanisms underlying tumor subclonal evolution may develop new strategies. Herein, data highlighting a positive correlation between the accumulation of macrophage in the glioblastoma microenvironment after irradiation and mesenchymal transdifferentiation in glioblastoma are presented. Mechanistically, elevated production of inflammatory cytokines released by macrophages promotes mesenchymal transition in an NF-κB-dependent manner. Hence, rationally designed macrophage membrane-coated porous mesoporous silica nanoparticles (MMNs) in which therapeutic anti-NF-κB peptides are loaded for enhancing radiotherapy of glioblastoma are constructed. The combination of MMNs and fractionated irradiation results in the blockage of tumor evolution and therapy resistance in glioblastoma-bearing mice. Intriguingly, the macrophage invasion across the blood-brain barrier is inhibited competitively by MMNs, suggesting that these nanoparticles can fundamentally halt the evolution of radioresistant clones. Taken together, the biomimetic MMNs represent a promising strategy that prevents mesenchymal transition and improves therapeutic response to irradiation as well as overall survival in patients with glioblastoma.     

An SPH-based mass transfer model for simulating hydraulic characteristics and mass transfer process of dammed rivers

Engineering with Computers - Aerated flow characterized by complex mass transfer processes with multiple hydraulic properties is a common enviro-hydraulics phenomenon, which have a variety of...     

A multi-agent model for decision making on environmental regulation in urban agglomeration

The Journal of Supercomputing - The incomplete enforcement of environmental regulation by local governments will lead to environmental degradation. While the strategy selection for local...     

Application of intelligent real-time image processing in fitness motion detection under internet of things

The purpose is to study the applicability of digital and intelligent real-time Image Processing (IP) in fitness motion detection under the environment of the Internet of Things (IoT). Given the absence of real-time training standards and possible workout injury problems during fitness activities, an intelligent fitness real-time IP system based on Deep Learning (DL) is implemented. Specifically, the keyframes of the real-time images are collected from the fitness monitoring video, and the DL algorithm is introduced to analyze the fitness motions. Afterward, the performance of the proposed system is evaluated through simulation. Subsequently, the Noise Reduction (NR) performance of the proposed algorithm is evaluated from the Peak Signal-to-Noise Ratio (PSNR), which remains above 20 dB for seriously noisy images (with a noise density reaching up to 90%). By comparison, the PSNR of the Standard Median Filter (SMF) and Ranked-order Based Adaptive Median Filter (RAMF) algorithms are not higher than 10 dB. Meanwhile, the proposed algorithm outperforms other DL algorithms by over 2.24% with a detection accuracy of 97.80%; the proposed system can adaptively detect the fitness motion, with a transmission delay no larger than 1 s given a maximum of 750 keyframes. Therefore, the proposed DL-based intelligent fitness real-time IP algorithm has strong robustness, high detection accuracy, and excellent real-time image diagnosis and processing effect, thus providing an experimental reference for sports digitalization and intellectualization.