Artificial Neural Networks in water analysis: Theory and applications

Artificial Neural Networks (ANNs) have seen an explosion of interest over the last two decades and have been successfully applied in all fields of chemistry and particularly in analytical chemistry. Inspired from biological systems and originated from the perceptron, i.e. a program unit that learns concepts, ANNs are capable of gradual learning over time and modelling extremely complex functions. In addition to the traditional multivariate chemometric techniques, ANNs are often applied for prediction, clustering, classification, modelling of a property, process control, procedural optimisation and/or regression of the obtained data. This paper aims at presenting the most common network architectures such as Multi-layer Perceptrons (MLPs), Radial Basis Function (RBF) and Kohonen's self-organisations maps (SOM). Moreover, back-propagation (BP), the most widespread algorithm used today and its modifications, such as quick-propagation (QP) and Delta-bar-Delta, are also discussed. All architectures correlate input variables to output variables through non-linear, weighted, parameterised functions, called neurons. In addition, various training algorithms have been developed in order to minimise the prediction error made by the network. The applications of ANNs in water analysis and water quality assessment are also reviewed. Most of the ANNs works are focused on modelling and parameters prediction. In the case of water quality assessment, extended predictive models are constructed and optimised, while variables correlation and significance is usually estimated in the framework of the predictive or classifier models. On the contrary, ANNs models are not frequently used for clustering/classification purposes, although they seem to be an effective tool. ANNs proved to be a powerful, yet often complementary, tool for water quality assessment, prediction and classification.     

基于图像处理的闸机口跳闸事件检测算法

文中提出了一种基于图像处理的闸机口跳闸事件检测方法。首先,利用模板匹配的方法进行预处理,得到闸门图像匹配率;其次,根据跳闸事件的连贯性特点,提取一段连续时间序列的匹配率作为分类特征;最后,采取基于最小错误率的贝叶斯分类方法对这段时间序列特征向量进行分类。实验表明,该算法可以有效的检测出闸机口的跳闸事件,具有实时性好、不需要额外的传感器、成本低的优点,具有较好的工程应用前景。     

低共熔溶剂的应用研究现状

低共熔溶剂是两种或多种固体或液体物质通过氢键相互作用形成的液体溶剂,其熔点明显低于单一组分的熔点。与传统离子液体相比,低共熔溶剂成本更低,制备更容易,可生物降解,具有100%原子利用率和生物相容性及无毒无害等绿色特性,这些优点使其在许多研究领域被广泛研究。本文介绍了低共熔溶剂的最新分类,综述了低共熔溶剂在电化学、气体吸收、有机合成、功能材料合成、萃取分离、药物增溶及生物质预处理中的应用,并对低共熔溶剂的未来发展进行了展望。     

Supramolecular optical sensor arrays for on-site analytical devices

Supramolecular optical chemosensors are useful tools in analytical chemistry for the visualization of molecular recognition information. One advantage is that they can be utilized for array systems to detect multiple analytes. However, chemosensor arrays have been evaluated mainly in the solution phase, which limits a wide range of practical applications. Thus, appropriate solid support materials such as polymer gels and papers are required to broaden the scope of the application of chemosensors as on-site analytical tools. In this review, we summarize the actual approaches for the fabrication of solid-state chemosensor arrays combined with powerful data processing techniques and portable digital recorders for real-world applications.     

农林院校校外实验教学基地分类考评机制及考评指标改革研究——大学化学校外实践基地实证分析*

针对大学化学校外实践教学基地建设和运行过程中的基地与高校的协同管理机制,实践教学基地软硬条件建设和资源配置及实践教学与理论学习的连贯性等问题,在OBE理论指导下,探究分类考核机制,在实证中构建分层次考评模型及考评指标,并建立适合于农林高校的化学校外实践教学基地的分类考评指标体系。     

Identification of key classification features of early cervical squamous cell carcinoma

Despite the tremendous progress in molecular analysis of pan-cancer, little is known regarding molecular classification of cervical squamous cell carcinoma. In this study, we adopted a multi-omics approach to identify potential key classification features of cervical squamous cell carcinoma. Specifically, we analyzed mRNA, and microRNA (miRNA) expression data, as well as DNA methylation and copy number variation in cervical squamous cell carcinoma cases, using datasets obtained from The Cancer Genome Atlas (TCGA). Moreover, we identified molecules in each dimension, as well as integrated and clustered filtered classification features, and used them to distinguish different subtypes. The resulting key classification features were used to establish a classification model for cervical squamous cell carcinoma. Our results revealed two cervical squamous cell carcinoma subtypes, with significant differences across clinical survival levels, as well as 8 key classification features of cervical squamous cell carcinomas. These findings are expected to provide important references for early classification of cervical squamous cell carcinoma and identification of classification markers.     

Pixel-by-pixel analysis of DCE MRI curve patterns and an illustration of its application to the imaging of the musculoskeletal system

Dynamic contrast enhanced (DCE) MRI is a widespread method that has found broad application in the imaging of the musculoskeletal (MSK) system. A common way of analyzing DCE MRI images is to look at the shape of the time-intensity curve (TIC) in pixels selected after drawing an ROI in a highly enhanced area. Although often applied to a number of MSK affections, shape analysis has so far not led to a unanimous correlation between these TIC patterns and pathology. We hypothesize that this might be a result of the subjective ROI approach. To overcome the shortcomings of the ROI approach (sampling error and interuser variability, among others), we created a method for a fast and simple classification of DCE MRI where time-curve enhancement shapes are classified pixel by pixel according to their shape. The result of the analysis is rendered in multislice, 2D color-coded images. With this approach, we show not only that differences on a short distance range of the TIC patterns are significant and cannot be appreciated with a conventional ROI analysis but also that the information that shape maps and conventional standard DCE MRI parameter maps convey are substantially different.     

一种高准确率的交通监控视频车辆检测算法

针对高速视频监控的车辆识别易受几何变换影响且准确度不高的问题,提出了高效的阴影去除和车辆分类两种视频处理算法来提高车辆识别性能。利用阴影区域前景和背景的对比度参数进行有效的阴影去除;利用Hu不变矩表征车辆,从而克服了几何变换的影响。对比试验结果证明,本算法具有较高的阴影检测性能和极佳的车辆分类性能,明显优于其他同类型算法。     

Biembedding Steiner Triple Systems in Surfaces Using the Bose Construction

A uniform framework is presented for biembedding Steiner triple systems obtained from the Bose construction using a cyclic group of odd order, in both orientable and nonorientable surfaces. Within this framework, in the nonorientable case, a formula is given for the number of isomorphism classes and the particular biembedding of Ducrocq and Sterboul (preprint 18pp., 1978) is identified. In the orientable case, it is shown that the biembedding of Grannell et al. (J Combin Des 6 ( 7 ), 325–336) is, up to isomorphism, the unique biembedding of its type. Automorphism groups of the biembeddings are also given.     

Classification of Graeco‐Latin Cubes

A q‐ary code of length n, size M, and minimum distance d is called an code. An code with is said to be maximum distance separable (MDS). Here one‐error‐correcting () MDS codes are classified for small alphabets. In particular, it is shown that there are unique (5, 5³, 3)₅ and (5, 7³, 3)₇ codes and equivalence classes of (5, 8³, 3)₈ codes. The codes are equivalent to certain pairs of mutually orthogonal Latin cubes of order q, called Graeco‐Latin cubes.