Evaluation of organic contamination in urban groundwater surrounding a municipal landfill, Zhoukou, China

This paper investigates the organic pollution status of shallow aquifer sediments and groundwater around Zhoukou landfill. Chlorinated aliphatic hydrocarbons, monocylic aromatic hydrocarbons, halogenated aromatic hydrocarbons, organochlorine pesticides and other pesticides, and polycyclic aromatic hydrocarbons (PAHs) have been detected in some water samples. Among the detected eleven PAHs, phenanthrene, fluorine, and fluoranthene are the three dominant in most of the groundwater samples. Analysis of groundwater samples around the landfill revealed concentrations of PAHs ranging from not detected to 2.19 μg/L. The results show that sediments below the waste dump were low in pollution, and the shallow aquifer, at a depth of 18-30 m, was heavily contaminated, particularly during the wet season. An oval-shaped pollution halo has formed, spanning 3 km from west to east and 2 km from south to north, and mainly occurs in groundwater depths of 2-4 m. For PAH source identification, both diagnostic ratios of selected PAHs and principal component analysis were studied, suggesting mixed sources of pyro- and petrogenic derived PAHs in the Zhoukou landfill. Groundwater table fluctuations play an important role in the distribution of organic pollutants within the shallow aquifer. A conceptual model of leachate migration in the Quaternary aquifers surrounding the Zhoukou landfill has been developed to describe the contamination processes based on the major contaminant (PAHs). The groundwater zone contaminated by leachate has been identified surrounding the landfill.

Image segmentation using fuzzy region competition and spatial/frequency information

This paper presents a multiphase fuzzy region competition model that takes into account spatial and frequency information for image segmentation. In the proposed energy functional, each region is represented by a fuzzy membership function and a data fidelity term that measures the conformity of spatial and frequency data within each region to (generalized) gaussian densities whose parameters are determined jointly with the segmentation process. Compared with the classical region competition model, our approach gives soft segmentation results via the fuzzy membership functions, and moreover, the use of frequency data provides additional region information that can improve the overall segmentation result. To efficiently solve the minimization of the energy functional, we adopt an alternate minimization procedure and make use of Chambolle's fast duality projection algorithm. We apply the proposed method to synthetic and natural textures as well as real-world natural images. Experimental results show that our proposed method has very promising segmentation performance compared with the current state-of-the-art approaches.

Texture classification using refined histogram

In this correspondence, we propose a novel, efficient, and effective Refined Histogram (RH) for modeling the wavelet subband detail coefficients and present a new image signature based on the RH model for supervised texture classification. Our RH makes use of a step function with exponentially increasing intervals to model the histogram of detail coefficients, and the concatenation of the RH model parameters for all wavelet subbands forms the so-called RH signature. To justify the usefulness of the RH signature, we discuss and investigate some of its statistical properties. These properties would clarify the sufficiency of the signature to characterize the wavelet subband information. In addition, we shall also present an efficient RH signature extraction algorithm based on the coefficient-counting technique, which helps to speed up the overall classification system performance. We apply the RH signature to texture classification using the well-known databases. Experimental results show that our proposed RH signature in conjunction with the use of symmetrized Kullback-Leibler divergence gives a satisfactory classification performance compared with the current state-of-the-art methods.

Statistical wavelet subband characterization based on generalized gamma density and its application in texture retrieval

The modeling of image data by a general parametric family of statistical distributions plays an important role in many applications. In this paper, we propose to adopt the three-parameter generalized Gamma density (GGammaD) for modeling wavelet detail subband histograms and for texture image retrieval. The advantage of GGammaD over the existing generalized Gaussian density (GGD) is that it provides more flexibility to control the shape of model which is critical for practical histogram-based applications. To measure the discrepancy between GGammaDs, we use the symmetrized Kullback-Leibler distance (SKLD) and derive a closed form for the SKLD between GGammaDs. Such a distance can be computed directly and effectively via the model parameters, making our proposed scheme particularly suitable for image retrieval systems with large image database. Experimental results on the well-known databases reveal the superior performance of our proposed method compared with the current existing approaches.

Statistical properties of bit-plane probability model and its application in supervised texture classification

The modeling of wavelet subband histograms via the product Bernoulli distributions (PBD) has received a lot of interest and the PBD model has been applied successfully in texture image retrieval. In order to fully understand the usefulness and effectiveness of the PBD model and its associated signature, namely, the bit-plane probability (BP) signature on image processing applications, we discuss and investigate some of their statistical properties. These properties would help to clarify the sufficiency of the BP signature to characterize wavelet subbands, which, in turn, justifies its use in real time applications. We apply the BP signature on supervised texture classification problem and experimental results suggest that the weighted L(1)-norm (rather than the standard L (1)-norm) should be used for the BP signature. Comparative classification experiments show that our method outperforms the current state-of-the-art Generalized Gaussian Density approaches.

A fast and effective model for wavelet subband histograms and its application in texture image retrieval

This paper presents a novel, effective, and efficient characterization of wavelet subbands by bit-plane extractions. Each bit plane is associated with a probability that represents the frequency of 1-bit occurrence, and the concatenation of all the bit-plane probabilities forms our new image signature. Such a signature can be extracted directly from the code-block code-stream, rather than from the de-quantized wavelet coefficients, making our method particularly adaptable for image retrieval in the compression domain such as JPEG2000 format images. Our signatures have smaller storage requirement and lower computational complexity, and yet, experimental results on texture image retrieval show that our proposed signatures are much more cost effective to current state-of-the-art methods including the generalized Gaussian density signatures and histogram signatures.

Over-expression of FoxM1 stimulates cyclin B1 expression

FoxM1 (previously named WIN, HFH-11 or Trident) is a Forkhead box (Fox) transcription factor widely expressed in proliferating cells. Various findings, including a recent analysis of FoxM1 knockout mice, suggest that FoxM1 is required for normal S-M coupling during cell cycle progression. To study the regulatory role of FoxM1 and its downstream regulatory targets, three stably transfected HeLa lines that display doxycycline (dox)-inducible FoxM1 expression were established. Over-expression of FoxM1 by dox induction facilitates growth recovery from serum starvation. Quantitation of cyclin B1 and D1 levels using flow cytometric, Western and Northern analyses reveals that elevated FoxM1 levels lead to stimulation of cyclin B1 but not cyclin D1 expression. Transient reporter assays in the dox-inducible lines and upon co-transfection with a constitutive FoxM1 expression plasmid suggest that FoxM1 can activate the cyclin B1 promoter.

Fast fractal image encoding based on adaptive search

This paper presents a new adaptive search approach to reduce the computational complexity of fractal encoding. A simple but very efficient adaptive necessary condition is introduced to exclude a large number of unqualified domain blocks so as to speed-up fractal image compression. Furthermore, we analyzed an unconventional affine parameter that has better properties than the conventional luminance offset. Specifically, we formulated an optimal bit allocation scheme for the simultaneous quantizations of the usual scaling and the aforementioned unconventional affine parameter. Experiments on standard images showed that our adaptive search method yields superior performance over conventional fractal encoding.