Fabrication and amplification of Rhodamine B‐doped step‐index polymer optical fiber

A step‐index polymer optical fiber (SI POF) containing Rhodamine B in poly(methyl methacrylate) (PMMA) has been fabricated by a preform technique. Fluorescence of different fiber lengths were observed and discussed. A high gain (23 dB) for a SI POF with 60‐cm length, 400‐μm diameter was obtained. The Rhodamine B content of the doped SI POF is 5 ppm‐wt. The signal wavelength providing the highest gain for a 60 cm SI POF is around 630 nm, and the optimum fiber length is about 60 cm at 10 kW launched pump power. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 93: 681–685, 2004     

石灰石在煤气化过程中的催化和固硫作用

基于CaO在煤气化中的催化和固硫作用,以及石灰石加热分解的性质,石灰石有可能作为煤气化过程中的催化剂和固硫剂。本文根据国内外对石灰石在煤燃烧和煤气化中作用的研究,从石灰石结构、Ca/C,Ca/S,可能的反应机理等几个方面进行了介绍和分析,并指出需进一步研究的内容。     

循环式活性污泥法处理炼油污水

研究为在炼油污水的CAST工艺的主反应区中 ,通过恰当控制pH值和DO(污水中溶解氧含量 )值 ,可以使硝化与反硝化反应同步进行 ,在去除COD ,BOD的同时 ,也可提高其脱氮效果 ,从而得出DO的最佳值为 0 .8mg/L ,pH为 7.3～ 7.7之间。     

竹材丙酮法木质素高效分离技术研究

在一定的温度、压力条件下,利用丙酮提取竹材中的木质素,丙酮通过蒸馏回收后,可以反复利用;而通过蒸馏,得到的高纯度的木质素是重要的化工原料,整个过程形成一个封闭循环系统,基本上无三废排放;研究结果表明:当丙酮浓度为80%,液固比为10∶1,在最高反应温度160℃下保温3h,降解木质素的效果最佳;为解决制浆造纸行业废水对环境的污染问题提出了一条新的途径。     

Electrochemical performance of nanocrystalline Li3V2(PO4)3/carbon composite material synthesized by a novel sol–gel method

A novel sol–gel method based on V₂O₅·nH₂O hydro-gel was developed to synthesize nanocrystalline Li₃V₂(PO₄)₃/carbon composite material. In this route, V₂O₅·nH₂O hydro-gel, NH₄H₂PO₄, Li₂CO₃ and high-surface-area carbon were used as starting materials to prepare precursor, and the Li₃V₂(PO₄)₃/carbon was obtained by sintering precursor at 750 °C for 4 h in flowing argon. The sol–gel synthesis ensures homogeneity of the precursors and improved reactivity. The sample was characterized by XRD, SEM and TEM. X-ray diffraction results show Li₃V₂(PO₄)₃ sample is monoclinic structure with the space group of P2₁/n. The TEM image indicates that the Li₃V₂(PO₄)₃ particles modified by conductive carbon are about 70 nm in diameter. The Li₃V₂(PO₄)₃/carbon system showed that the discharge capacities in the first and 50th cycle are about 155.3 and 143.6 mAh/g, respectively, in the range of 3.0–4.8 V. The sol–gel method is fit for the preparation of Li₃V₂(PO₄)₃/carbon composite material which may offer some favorable properties for commercial application.     

The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the minority samples and generated high total accuracy meanwhile. The proposed approach makes CBR useful in imbalanced forecasting.     

Fisher discrimination based low rank matrix recovery for face recognition

In this paper, we consider the issue of computing low rank (LR) recovery of matrices with sparse errors. Based on the success of low rank matrix recovery in statistical learning, computer vision and signal processing, a novel low rank matrix recovery algorithm with Fisher discrimination regularization (FDLR) is proposed. Standard low rank matrix recovery algorithm decomposes the original matrix into a set of representative basis with a corresponding sparse error for modeling the raw data. Motivated by the Fisher criterion, the proposed FDLR executes low rank matrix recovery in a supervised manner, i.e., taking the with-class scatter and between-class scatter into account when the whole label information are available. The paper shows that the formulated model can be solved by the augmented Lagrange multipliers and provides additional discriminating power over the standard low rank recovery models. The representative bases learned by the proposed method are encouraged to be closer within the same class, and as far as possible between different classes. Meanwhile, the sparse error recovered by FDLR is not discarded as usual, but treated as a feedback in the following classification tasks. Numerical simulations demonstrate that the proposed algorithm achieves the state of the art results.     

Synthesis and characterization of copolythiophene

Copolythiophenes (Co‐PTs), poly(3‐hexylthiophene‐co‐3‐thiophene carboxylic acid) (P3HT‐TCa), poly(3‐hexyloxylthiophene‐co‐3‐thiophene carboxylic acid) (P3HOT‐TCa), and poly(3‐phenylthiophene‐co‐3‐thiophene carboxylic acid) (P3PhT‐TCa), were synthesized by chemical oxidized polymerization to investigate the effect of copolymerization on the properties of polythiophenes (PTs). Gel permeation chromatography showed that the molecular weight (MW) of Co‐PT was lower than that of homopolythiophene. Fourier transform infrared (FTIR) spectra indicated that the copolymerization was successful between the monomers. The λ_max of Co‐PTs gave a “blue shift” in ultraviolet‐visible (UV‐VIS) spectra. Photoluminescence (PL) spectra showed that the PL intensity of Co‐PT became weaker than that of homopolythiophene and the disappearance of PL had been observed in P3HOT‐TCa. The thermal stability of Co‐PT was influenced by the carboxyl for its low decomposition temperature. Furthermore, the copolymerization between multi‐wall carbon nanotube containing thiophene ring (MWNT‐Th) and 3‐hexyloxylthiphene could also take place successfully. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007     

Online mining abnormal period patterns from multiple medical sensor data streams

With the advanced technology of medical devices and sensors, an abundance of medical data streams are available. However, data analysis techniques are very limited, especially for processing massive multiple physiological streams that may only be understood by medical experts. The state-of-the-art techniques only allow multiple medical devices to independently monitor different physiological parameters for the patient’s status, thus they signal too many false alarms, creating unnecessary noise, especially in the Intensive Care Unit (ICU). An effective solution which has been recently studied is to integrate information from multiple physiologic parameters to reduce alarms. But it is a challenge to detect abnormalities from high frequently changed physiological streams data, since abnormalities occur gradually due to the complex situation of patients. An analysis of ICU physiological data streams shows that many vital physiological parameters are changed periodically (such as heart rate, arterial pressure, and respiratory impedance) and thus abnormalities are generally abnormal period patterns. In this paper, we develop a Mining Abnormal Period Patterns from Multiple Physiological Streams (MAPPMPS) method to detect and rank abnormalities in medical sensor streams. The efficiency and effectiveness of the MAPPMPS method is demonstrated by a real-world massive database of multiple physiological streams sampled in ICU, comprising 250 patients’ streams (each stream involving over 1.3 million data points) with a total size of 28 GB data.     

A hybrid clustering algorithm based on PSO with dynamic crossover

In order to overcome the premature convergence in particle swarm optimization (PSO), we introduce dynamical crossover, a crossover operator with variable lengths and positions, to PSO, which is briefly denoted as CPSO. To get rid of the drawbacks of only finding the convex clusters and being sensitive to the initial points in $k$ -means algorithm, a hybrid clustering algorithm based on CPSO is proposed. The difference between the work and the existing ones lies in that CPSO is firstly introduced into $k$ -means. Experimental results performing on several data sets illustrate that the proposed clustering algorithm can get completely rid of the shortcomings of $k$ -means algorithms, and acquire correct clustering results. The application in image segmentation illustrates that the proposed algorithm gains good performance.