“学习曲线”模式在涪陵页岩气田试气施工中的应用

涪陵页岩气公司在试气环节引入了"学习曲线"模式,建立了试气施工管理制度和工艺标准,强化监督与落实;加大了专业技能知识培训力度,实行试气施工人员持证上岗;精心做好试气施工谋划工作,确保试气施工不受因素影响;全面优化试气施工设计,及时调整试气现场管理;积极借鉴学习曲线图经验,对试气作业实行专业化分工;引进和研发先进设备和工艺,为试气作业提供有力保障。     

Metallographic sample prepared by ion beam etching

Ion beam etching technique was used to reveal the metallograhpic microstructure and interface morphology of electroplating chromium coating, in particular, whose substrate surface layer was treated in advance by laser quenching. Chemical etchings were also conducted for comparison. The reveal microstructures were observed and analyzed by scanning electron microscopy. The results show that ion beam etching can reveal well the whole microstructures of composite coating-substrate materials.     

创新服务方式推行"三网"合一全面提升纳税服务信息化建设水平

江苏省盐城地税局全面实现12366纳税热线系统与"盐城地税"门户网站的互联互通.该局按照标准统一、效率优先的要求,建立统一的纳税服务平台,竭力为纳税人提供规范高效的纳税服务.一方面实现了信息共享.网站上所有的政策法规、热点问题及其他业务资料全部从12366热线系统业务库中快速读取.另一方面实现了功能整合.以12366热线为主体,将热线、网站、短信平台中的咨询建议等互动功能进行整合,提供多渠道咨询和一员化处理的服务.     

用Packet Tracer模拟软件提高《网络互联技术》实验教学效果

实验教学是高职计算机网络课程的重要组成部分,目前高职计算机网络实验课程开展困难,效果不明显。该文结合当前高职计算机网络课程实验教学的现状,谈一谈如何利用Packet Tracer模拟软件提高《网络互联技术》课程的实验教学效果。     

Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

The diagnosis of COVID-19 requires chest computed tomography (CT). High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease, so it is of clinical importance to study super-resolution (SR) algorithms applied to CT images to improve the resolution of CT images. However, most of the existing SR algorithms are studied based on natural images, which are not suitable for medical images; and most of these algorithms improve the reconstruction quality by increasing the network depth, which is not suitable for machines with limited resources. To alleviate these issues, we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution (RFAFN). Specifically, we design a contextual feature extraction block (CFEB) that can extract CT image features more efficiently and accurately than ordinary residual blocks. In addition, we propose a feature-weighted cascading strategy (FWCS) based on attentional feature fusion blocks (AFFB) to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information. Finally, we suggest a global hierarchical feature fusion strategy (GHFFS), which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels. Numerous experiments show that our method performs better than most of the state-of-the-art (SOTA) methods on the COVID-19 chest CT dataset. In detail, the peak signal-to-noise ratio (PSNR) is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at SR compared to the suboptimal method, but the number of parameters and multi-adds are reduced by 22K and 0.43G, respectively. Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.     

Dynamically modulated intensity interrogation scheme using waveguide coupled surface plasmon resonance sensors

An electro-optically modulated intensity interrogation method based on tunable waveguide coupled surface plasmon resonance sensors has been proposed. It has been theoretically and experimentally demonstrated that the proposed scheme can enable sensitive measurement of measurand variations. By modulating the refractive index in the waveguide layer, this interrogation method yields modulated signal whose amplitude is related to measurand's refractive index. This amplitude modulated signal offers a higher signal to noise ratio and eliminates additive noise in the sensor system. A preliminary investigation using saline buffers with different NaCl concentrations shows a resolution of 2.3 × 10^?6 refractive index unit by our approach. Resolution can be controlled by the amplitude of the applied modulation voltage and can be further enhanced by optimizing the device structure or improving the electro-optical (E-O) coefficient of the E-O material. This approach is simple, stable, and promising for low-cost or multi-channel SPR biosensor applications.     

Multi-scale object-based measurement of arid plant community structure

The measurement of plant community structure provides an extensive understanding of its function, succession and ecological process. The detection of plant community boundary is rather a challenge despite in situ work. Recent advances in object-based image analysis (OBIA) and machine learning algorithms offer new opportunities to address this challenge. This study presents a multi-scale segmentation approach to accurately identify the boundaries of each vegetation and plant community for mapping plant community structure. Initially, a very high resolution (VHR) Worldview-2 image of a desert area is hierarchically segmented from scale parameter 2 to 500. Afterward, the peak values of the standard deviation of brightness and normalized difference vegetation index (NDVI) across the segmentation scales are detected to determine the optimal segmentation scales of homogeneous single plant and plant community boundaries. A multi-scale classification of vegetation characterization with features of multiple bands, NDVI, grey-level co-occurrence matrix (GLCM) entropy and shape index is performed to identify dryland vegetation types. Finally, the four vegetation structural features on the type, diversity, object size and shape are calculated within the plant community boundaries and composed to plant community structure categories. Comparing the results with the object fitting index (FI) of the reference data, the validation indicates that the optimal segmentations of tree, shrub and plant communities are consistent with the identified peak values.