气相色谱法测定焦油中微量苯稠杂环化合物

利用蒸馏分离—气相色谱法技术,建立了同时测定煤焦油中苊、氧芴和芴的分析方法。通过对色谱条件的优化,以甲苯为溶剂,正十二烷为内标物,将煤焦油馏分采用DB-5毛细管柱,对煤焦油中苊、氧芴和芴定量分析。分析结果表明:3种主要成分线性关系良好,相关系数均大于0.9995,加标回收率为95.4%~102.4%,相对标准偏差为2.89%~7.14%。该方法分离效果好,检测结果准确、可靠。     

WavNet — Visual saliency detection using Discrete Wavelet Convolutional Neural Network

In the recent advancements in image and video analysis, the detection of salient regions in the image becomes the initial step. This plays a crucial role in deciding the performance of such algorithms. In this work, a Multi-Resolution Feature Extraction (MRFE) technique that makes use of Discrete Wavelet Convolutional Neural Network (DWCNN) for generating features is employed. An Enhanced Feature Extraction (EFE) module extracts additional features from the high level features of the DWCNN, which are used to frame both channel as well as spatial attention models for yielding contextual attention maps. A new hybrid loss function is also proposed, which is a combination of Balanced Cross Entropy (BCE) loss and Edge based Structural Similarity (ESSIM) loss that effectively identifies and segments the salient regions with clear boundaries. The method is tested exhaustively with five different benchmark datasets and is proved superior to the existing state-of-the-art methods with a minimum Mean Absolute error (MAE) of 0.03 and F-measure of 0.956.     

Vulnerable objects detection for autonomous driving: A review

Object detection performed by Autonomous Vehicles (AV)s is a crucial operation that comes ahead of various autonomous driving tasks, such as object tracking, trajectories estimation, and collision avoidance. Dynamic road elements (pedestrians, cyclists, vehicles) impose a greater challenge due to their continuously changing location and behaviour. This paper presents a comprehensive review of the state-of-the-art object detection technologies focusing on both the sensory systems and algorithms used. It begins with a brief introduction on the autonomous driving operations and challenges. Then, different sensory systems employed on existing AVs are elaborated while illustrating their advantages, limitations and applications. Also, sensory systems employed by different research are reviewed. Moreover, due to the significant role Deep Neural Networks (DNN)s are playing in object detection tasks, different DNN-based networks are also highlighted. Afterwards, previous research on dynamic objects detection performed by AVs are reviewed in tabular forms. Finally, a conclusion summarizes the outcomes of the review and suggests future work towards the development of vehicles with higher automation levels.     

汽油气相色谱模拟馏程的应用

介绍了用AC8612气相色谱仪对六个汽油组分馏程分析的应用情况,结果表明:该方法的重复性和准确性都较好,与D86相比,结果满足方法的再现性要求,但对于差值大于3℃的点,应定期进行修正,以使结果更准确,该方法用于炼油厂各装置汽油组分馏出口的日常质量监测分析,可以提高分析效率并减少劳动强度。     

食品安全国家标准中维生素的测定方法及进展

人体所必需的营养素除了蛋白质、脂类、糖类等能量物质外,维生素也是不可或缺的营养物质,是评价其营养质量的关键指标。检测食品中的维生素含量,是有效保证其质量与安全的必要条件。因此,对现有国家标准食品中维生素的检测方法进行综述,总结各方法的前处理及检出限等,为增强检测工作效率,以及食品检测方法的研究和发展提供参考。     

川中地区侏罗系沙溪庙组致密气处理和解释关键技术与应用

四川盆地蕴含丰富的致密砂岩气资源，近期利用高精度三维资料开展侏罗系沙溪庙组河道砂体勘探取得突出成效。沙溪庙组河道砂体具有横向变化快、储层非均质性强的特点，因此提高河道砂体的边界识别及其含气性预测精度是致密气地震勘探的关键。通过开展AVO特征低频保护的“六分法”（分类、分频、分时、分域、分步和分区）高保真叠前去噪、近地表Q补偿和OVT域叠前时间偏移等技术攻关，形成了一套针对川中地区侏罗系沙溪庙组致密气藏的“双高”（高保真、高分辨率）地震处理技术，并创新应用“双亮点”属性及多波、多分量砂体含气性地震预测等解释技术，提高了含气砂体预测精度。该技术系列在川中沙溪庙组致密气预测应用成果显著，地震数据频带得到了拓宽，低频信息更加丰富，资料信噪比明显提升，河道边界及其含气性预测的精度大幅提高，钻井成功率超过83%，应用成果有力地支撑了该地区沙溪庙组致密气的增储上产。     

Transmission line detection in aerial images: An instance segmentation approach based on multitask neural networks

Camera-based transmission line detection (TLD) is a fundamental and crucial task for automatically patrolling powerlines by aircraft. Motivated by instance segmentation, a TLD algorithm is proposed in this paper with a novel deep neural network, i.e., CableNet. The network structure is designed based on fully convolutional networks (FCNs) with two major improvements, considering the specific appearance characteristics of transmission lines. First, overlaying dilated convolutional layers and spatial convolutional layers are configured to better represent continuous long and thin cable shapes. Second, two branches of outputs are arranged to generate multidimensional feature maps for instance segmentation. Thus, cable pixels can be detected and assigned cable IDs simultaneously. Multiple experiments are conducted on aerial images, and the results show that the proposed algorithm obtains reliable detection performance and is superior to traditional TLD methods. Meanwhile, segmented pixels can be accurately identified as cable instances, contributing to line fitting for further applications.     

An integrated dimensionality reduction and surrogate optimization approach for plant-wide chemical process operation

With liquefied natural gas becoming increasingly prevalent as a flexible source of energy, the design and optimization of industrial refrigeration cycles becomes even more important. In this article, we propose an integrated surrogate modeling and optimization framework to model and optimize the complex CryoMan Cascade refrigeration cycle. Dimensionality reduction techniques are used to reduce the large number of process decision variables which are subsequently supplied to an array of Gaussian processes, modeling both the process objective as well as feasibility constraints. Through iterative resampling of the rigorous model, this data-driven surrogate is continually refined and subsequently optimized. This approach was not only able to improve on the results of directly optimizing the process flow sheet but also located the set of optimal operating conditions in only 2 h as opposed to the original 3 weeks, facilitating its use in the operational optimization and enhanced process design of large-scale industrial chemical systems.     

Hydrogen sulfide gas detection by Au-decorated ZnO nanotube: a computational study and comparison to experimental observations

Based on the experimental reports, Au-decoration on the ZnO nanostructures dramatically increases the electronic sensitivity to H₂S gas. In the current study, we computationally scrutinized the mechanism of Au-decoration on a ZnO nanotube (ZON) and the influence on its sensing behavior toward H₂S gas. The intrinsic ZON weakly interacted with the H₂S gas with an adsorption energy of ?11.2 kcal/mol. The interaction showed no effect on the HOMO–LUMO gap and conductivity of ZON. The predicted response of intrinsic ZON toward H₂S gas is 6.3, which increases to 78.1 by the Au-decoration at 298 K. The corresponding experimental values are about 5.0 and 80.0, indicating excellent agreement with our findings. We showed that the Au atom catalyzes the reaction 3O₂?+?2H₂S?→?2SO₂?+?2H₂O. Our calculated energy barrier (at 298 K) is about 12.3 kcal/mol for this reaction. The gap and electrical conductance Au-ZON largely changed by this reaction are attributed to the electron donation and back-donation processes. The obtained recovery time is about 1.35 ms for desorption of generated gases from the surface of the Au-ZON sensor.     

Hydrodynamics study of a fast liquid–liquid oxidation process with in situ gas production in microreactors

Hydrodynamics characteristics of a fast and highly exothermic liquid–liquid oxidation process with in situ gas production in microreactors were studied using a newly developed experimental method. In the adipic acid synthesis through the K/A oil (the mixture of cyclohexanol and cyclohexanone) oxidation with nitric acid, bubble generation modes were divided into four categories. The gas production became more intensive, unstable, even explosive with increasing the oil phase feed rate and the temperature. A novel automatic image processing method was developed to monitor the instantaneous velocity online by tracking the gas–liquid interface. The axial velocity at the same location was unstable due to the changing gas production rate. Furthermore, the actual residence time was obtained easily with being only 36% of the space–time minimally, beneficial for establishing accurate kinetics and mass transfer models with time participation. Finally, an empirical correlation was developed to predict the actual residence time under different conditions.