邻甲酚-间二甲苯-乙二醇液液平衡数据的测定与关联

乙二醇可从含芳烃组分油品中选择性萃取酚类，为支撑焦化含酚油中的酚类化合物萃取分离新工艺开发，针对其组分特点，在现有相平衡数据基础上选定代表物，利用液液平衡釜法测出常压下303.15、313.15和323.15 K时邻甲酚-间二甲苯-乙二醇体系液液相平衡数据，并利用Othmer-Tobias方程、Hand方程、Bachman方程分别进行实验数据可靠性检验，其线性相关系数的平方均大于0.99。同时分别用NRTL和UNIQUAC活度系数模型对实验数据进行关联，回归得到不同温度下的模型参数，模型计算值与实验数据对比后发现其均方根偏差小于1.8%，说明NRTL和UNIQUAC模型均可较好地描述该三元体系相平衡行为。     

Prism coupling method to evaluate the light extraction efficiency of outcoupling substrates

One of the key challenges in organic light-emitting diodes (OLEDs) for lighting applications is efficient light extraction from the planar, multi-layered OLED stack. Several different light extraction approaches are being explored currently by researchers, however characterizing light extraction films after fabricating OLEDs is not a viable approach when the outcoupling films have large surface roughness and is time consuming as well. Here we apply prism coupling method (PCM), a simple and elegant tool, to characterize outcoupling films. We show the effectiveness of PCM in estimating light extraction efficiency of outcoupling films. PCM can expedite selection and optimization of various light extraction approaches without the need to build OLEDs. The experimental results are corroborated by the optical simulations done using ray tracing method taking into account Mie scattering from wavelength sized spherical inclusions in an outcoupling film.     

油田含油污泥处理技术研究进展

含油污泥处理是目前油田亟待解决的问题之一。介绍了国内外含油污泥处理技术的研究进展，主要处理技术有溶剂萃取处理技术、焚烧处理技术、热解处理技术、生物处理技术、调剖处理技术等，并介绍了国内部分油田的含油污泥处理工艺。     

Solvent Extraction of Copper from Sulfate–Chloride Solutions Using LIX 84-IC and LIX 860-IC

ABSTRACT

The effect of chloride ions on the copper extraction equilibria, the rate of extraction, copper/iron selectivity, and chloride extraction was studied for organic phases containing pure ketoxime (LIX 84-IC) and pure nonylaldoxime (LIX 860N-IC). In these systems, the used aqueous phase contained total chloride concentrations from 0 to 110 g/l. It was determined that the chloride ions in the aqueous solution had a large negative effect on the extraction equilibria for LIX 84-IC and a small effect for LIX 860N-IC. This deleterious effect is attributed to an increase in the hydrogen ion activity coefficient in the presence of chloride ions. The chloride accelerated the rate of copper extraction even though the reaction was fast in all the tested conditions. An increase in the temperature from 25°C to 35°C affected positively the extraction equilibria for LIX 84-IC.     

Road Extraction from High Resolution Remote Sensing Image based on Shape Analysis with Gestalt Theory

Simulating the psychological experience of human vision，a road extraction model based on the format tower is proposed to extract the road in the high resolution remote sensing image from the perspective of morphology.Firstly，based on the spectral and texture information，the suspected road targets are extracted by using segmentation technology.Then these targets are classified according to their reliability and extract the road targets for each category.Finally，three types of identified road information are verified and merged，and the continuous smooth road extraction results are obtained.Experiments on real high resolution images show that the results are consistent with the visual perception of the human eye，and the overall classification accuracy is higher，indicating that the algorithm is effective and feasible and has good use value.     

Retinal Vessel Extraction Framework Using Modified Adaboost Extreme Learning Machine

An explicit extraction of the retinal vessel is a standout amongst the most significant errands in the field of medical imaging to analyze both the ophthalmological infections, for example, Glaucoma, Diabetic Retinopathy (DR), Retinopathy of Prematurity (ROP), Age-Related Macular Degeneration (AMD) as well as non retinal sickness such as stroke, hypertension and cardiovascular diseases. The state of the retinal vasculature is a significant indicative element in the field of ophthalmology. Retinal vessel extraction in fundus imaging is a difficult task because of varying size vessels, moderately low distinction, and presence of pathologies such as hemorrhages, microaneurysms etc. Manual vessel extraction is a challenging task due to the complicated nature of the retinal vessel structure, which also needs strong skill set and training. In this paper, a supervised technique for blood vessel extraction in retinal images using Modified Adaboost Extreme Learning Machine (MAD-ELM) is proposed. Firstly, the fundus image preprocessing is done for contrast enhancement and in-homogeneity correction. Then, a set of core features is extracted, and the best features are selected using “minimal Redundancy-maximum Relevance (mRmR).” Later, using MAD-ELM method vessels and non vessels are classified. DRIVE and DR-HAGIS datasets are used for the evaluation of the proposed method. The algorithm’s performance is assessed based on accuracy, sensitivity and specificity. The proposed technique attains accuracy of 0.9619 on the DRIVE database and 0.9519 on DR-HAGIS database, which contains pathological images. Our results show that, in addition to healthy retinal images, the proposed method performs well in extracting blood vessels from pathological images and is therefore comparable with state of the art methods.