化肥厂造气污水处理新装置

介绍了一套新型造气污水处理装置，该装置具有处理工艺流程合理，设备布置紧凑，装置自动化运行的特点，造气污水经处理后重复循环使用，污泥送沸腾炉作燃料燃烧，实现了污水零排放的目的。     

安阳市水资源生态足迹与生态承载力研究

利用水资源生态足迹分析方法对安阳市2005~2014年的水资源生态足迹、生态承栽力进行了计算分析。结果表明,2005~2014年安阳市人均水资源生态足迹均值为0.45 hm~2/cap,且呈波动下降趋势。其中生产用水生态足迹占水资源生态足迹比重最大,达82.8%~91.4%,生活用水足迹其次,生态用水足迹最小,但呈上升趋势。安阳市水资源人均生态承载力较小,在0.048 hm~2/cap~0.068 hm~2/cap之间,受气候、水文起伏波动较大。安阳市2005—2014年间,水资源生态足迹均处于赤字状态,说明安阳市水资源利用长期以来一直是不可持续的。近10年来,安阳市万元GDP水资源生态足迹整体呈较快下降趋势,表明安阳市水资源利用效率正逐步提高。     

Comparing techniques for authorship attribution of source code

Attributing authorship of documents with unknown creators has been studied extensively for natural language text such as essays and literature, but less so for non‐natural languages such as computer source code. Previous attempts at attributing authorship of source code can be categorised by two attributes: the software features used for the classification, either strings of n tokens/bytes (n‐grams) or software metrics; and the classification technique that exploits those features, either information retrieval ranking or machine learning. The results of existing studies, however, are not directly comparable as all use different test beds and evaluation methodologies, making it difficult to assess which approach is superior. This paper summarises all previous techniques to source code authorship attribution, implements feature sets that are motivated by the literature, and applies information retrieval ranking methods or machine classifiers for each approach. Importantly, all approaches are tested on identical collections from varying programming languages and author types. Our conclusions are as follows: (i) ranking and machine classifier approaches are around 90% and 85% accurate, respectively, for a one‐in‐10 classification problem; (ii) the byte‐level n‐gram approach is best used with different parameters to those previously published; (iii) neural networks and support vector machines were found to be the most accurate machine classifiers of the eight evaluated; (iv) use of n‐gram features in combination with machine classifiers shows promise, but there are scalability problems that still must be overcome; and (v) approaches based on information retrieval techniques are currently more accurate than approaches based on machine learning. Copyright © 2012 John Wiley & Sons, Ltd.     

提高液态烃中丙烯收率的技术

简要介绍了国内外增产丙烯的工艺技术，如SCC，MGG，MOI等，并针对特定装置提出了如何采用提高吸收压力，降低吸收温度，改善补充吸收油质量等措施增加液态烃中丙烯收率，以满足市场对丙烯的需求。     

金红石与磷灰石浮选分离中硫酸铝的作用研究

研究表明．在FL108作捕收剂条件下，硫酸铝强烈抑制金红石而使磷灰石上浮，采用“FL108＋Al2（SO4）3”的浮选药剂制度，可实现两矿物的分离。通过分析Al3＋和金红石表面Ti4＋、磷灰石表面Ca2＋的水解特性，还阐述了Al2（SO4）3的选择抑制机理。     

UV photo‐stabilization of tetrabutyl titanate for aramid fibers via sol–gel surface modification

Tetrabutyl titanate was used as sol–gel precursor of a nanosized TiO₂ coating to improve the photo‐stability of aramid fibers. The nanosized TiO₂ coating was characterized by XRD and XPS. The influence of the TiO₂ coating on photo‐stability of aramid fibers was investigated by an accelerated photo‐ageing method. The photo‐stability of aramid fiber showed obvious improvement after coating. After 156 h of UV exposure, the coated fibers showed less deterioration in mechanical properties with the retained tensile strength and elongation at break greater than 36 and 50% of the original values, respectively, whereas the uncoated fibers degraded completely and became powdery. SEM analysis showed no significant surface morphological change on the coated fiber after the exposure, while some latitudinal crack fractures appeared on the uncoated aramid fiber. The effect of the nanosized TiO₂ coating was also well demonstrated by examining the difference of distributions of C1s in XPS deconvolution analysis on the surface of uncoated/coated fibers with increasing UV exposure time. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 3113–3119, 2007     

Surface plasmon resonance evaluation of various aminoglycoside-RNA hairpin interactions reveals low degree of selectivity

Aminoglycoside antibiotics, which are able to selectively bind to RNA, are considered to be an important lead in RNA-targeting drug discovery. In this study, surface plasmon resonance (SPR) was employed to explore the interaction of aminoglycosides with known tobramycin-binding RNA hairpins (aptamers) and an unrelated RNA hairpin. It was established that aminoglycosides have multiple interactions with RNA hairpins. Unexpectedly, the different hairpins showed comparable affinity for a set of related aminoglycosides. The observed absence of selectivity presents an extra hurdle in the discovery of novel aminoglycosides as specific drugs that target defined RNA hairpins.     

A Rough RBF Neural Network Based on Weighted Regularized Extreme Learning Machine

Adjusting parameters iteratively is a traditional way of training neural networks, and the Rough RBF Neural Networks (R-RBF-NN) follows the same idea. However, this idea has many disadvantages, for instance, the training accuracy and generalization accuracy etc. So how to change this condition is a hot topic in Academics. On the basis of Extreme Learning Machine (ELM), this paper proposes a Weighted Regularized Extreme Learning Machine (WRELM), taking into account both minimizing structured risk and weighted least-squares principle, to train R-RBF-NN. The traditional iterative training method is replaced by the minimal norm least-squares solution of general linear system. The method proposed in this paper, increasing controllability of the entire learning process and considering the structured risk and empirical risk, can improve the performance of learning and generalization. Experiments show that it can reach a very superior performance in both time and accuracy when WRELM trains the Rough RBF Neural Networks in pattern classification and function regression, especially in pattern classification, which can improve the generalization accuracy more than 3.36 % compared with ELM. Obviously, the performance of the method proposed in this paper is better than the traditional methods.     

Image super-resolution based on multi-space sparse representation

Sparse representation provides a new method of generating a super-resolution image from a single low resolution input image. An over-complete base for sparse representation is an essential part of such methods. However, discovering the over-complete base with efficient representation from a large amount of image patches is a difficult problem. In this paper, we propose a super-resolution construction based on multi-space sparse representation to efficiently solve the problem. In the representation, image patches are decomposed into a structure component and a texture component represented by the over-complete bases of their own spaces so that their high-level features can be captured by the bases. In the implementation, a prior knowledge about low resolution images generation is combined to the typical base construction for high construction quality. Experiment results demonstrate that the proposed method significantly improves the PSNR, SSIM and visual quality of reconstructed high-resolution image.     

Prediction-based realistic 3D model compression

The benefit of using the geometry image to represent an arbitrary 3D mesh is that the 3D mesh can be re-sampled as a completely regular structure and coded efficiently by common image compression methods. For geometry image-based 3D mesh compression, we need to code the normal-map images while coding geometry images to improve the subjective quality and realistic effects of the reconstructed model. In traditional methods, a geometry image and a normal-map image are coded independently. However a strong correlation exists between these two kinds of images, because both of them are generated from the same 3D mesh and share the same parameterization. In this paper we propose a predictive coding framework, in which the normal-map image is predicted based on the geometric correlation between them. Additionally we utilize the strong geometric correlation among three components of normal-map image to improve the predicting accuracy. The experimental results show the proposed coding framework improves the coding efficiency of normal-map image, meanwhile the realistic effect of a 3D mesh is significantly enhanced.