Decolorisation and mineralisation of CI Reactive Black 8 by the Fenton and ultrasound/Fenton methods

The effects of pH, ferrous ion and hydrogen peroxide dosage on the decolorisation and mineralisation of CI Reactive Black 8 by the Fenton process with/without ultrasonic irradiation were investigated. It was verified that the presence of ultrasonic irradiation did not enhance the decolorisation of CI Reactive Black 8 significantly by Fenton's reagents, but it enhanced the chemical oxygen demand removal efficiency. The enhancement was more pronounced for lower (<0.89 mm) or higher (>1.78 mm) ferrous ion dosage. The optimal pH for chemical oxygen demand removal was 3.0. Chemical oxygen demand removal efficiency increased with the increasing H₂O₂ dosage and reached the highest level at 5.88 mm, but further increase in H₂O₂ dosage would not increase removal efficiency significantly.     

Microenvironmental engineering: An effective strategy for tailoring enzymatic activities

Rationally, engineering a favorable physicochemical microenvironment for enzymes has recently emerged as an effective strategy to improve their catalytic performance. In this review, we discuss four microenvironmental effects according to the mechanism of action: localizing and excluding reactants and regulators, regulating microenvironmental pH, creating a water-like microenvironment, and increasing the local temperature. These mechanisms are enzyme-independent and can in principle be used in combination to tailor enzyme behaviors, offering new approaches to enabling, enhancing, and regulating enzyme catalysis in diverse applications without the need for genetic engineering.     

Influences of zinc oxide and an organic additive on the electrochemical behavior of pure aluminum in an alkaline solution

The corrosion behavior of pure aluminum in inhibited and uninhibited 4 MKOH was investigated by means of hydrogen collection, polarization curve measurement and electrochemical impedance spectroscopy (EIS). The results showed that the corrosion of pure aluminum was greatly inhibited by the addition of ZnO and dimethyl amine epoxy propane (designated as DE). EIS and EDAX analyses revealed that ZnO produces its effect by depositing on the aluminum surface, which increases the overpotential of hydrogen evolution. It was also found that the addition of DE could greatly improve the deposition of zinc layers.     

Dynamic response of Ti3SiC2 under shock compression up to 112 GPa

Ti₃SiC₂ is of interest due to its unique dual nature reminiscent of both brittle ceramics and ductile metals at ambient conditions. In this work, plate-impact experiments have been performed to study the dynamic behavior of Ti₃SiC₂ under shock compression up to 112 GPa by using laser velocity interferometer and electric pin techniques. Hugoniot elastic limits (HEL), spall strength, and Hugoniot equations of state have been obtained based on measured particle velocity profiles and shock wave velocities. The ratio of spall strength to HEL for Ti₃SiC₂ is larger than brittle ceramics but smaller than metals. This result indicates that the dual nature of Ti₃SiC₂ remains at least up to 10 GPa. On the other hand, the linearity of the Hugoniot equation of state, $D=6.9\text{0}1(22)+1.153(53)u_{\text{p}}$, suggests that the initial structure of Ti₃SiC₂ should be stable up to 112 GPa, in contrast to the result reported by Jordan et al. [J. Appl. Phys., 93 (2003) 9639].     

浅谈P3软件在电力基建工程中的应用

P3软件是一套体现了现代化项目管理理念的项目管理工具，在建设管理上推广应用P3工程管理软件，可以提高各参建单位的工作效率、加快工程信息交流、数据共享和工程进度管理水平，在管理上尽快与国际接轨，本文就P3软件在电力基建工程中的应用做以简单介绍。     

A noisy-smoothing relevance feedback method for content-based medical image retrieval

In this paper, we address a new problem of noisy images which present in the procedure of relevance feedback for medical image retrieval. We concentrate on the noisy images, caused by the users mislabeling some irrelevant images as relevant ones, and a noisy-smoothing relevance feedback (NS-RF) method is proposed. In NS-RF, a two-step strategy is proposed to handle the noisy images. In step 1, a noisy elimination algorithm is adopted to identify and eliminate the noisy images. In step 2, to further alleviate the influence of noisy images, a fuzzy membership function is employed to estimate the relevance probabilities of retained relevant images. After noisy handling, the fuzzy support vector machine, which can take into account different relevant images with different relevance probabilities, is adopted to re-rank the images. The experimental results on the IRMA medical image collection demonstrate that the proposed method can deal with the noisy images effectively.     

Logo detection with extendibility and discrimination

Logos are specially designed marks that identify goods, services, and organizations using distinguished characters, graphs, signals, and colors. Identifying logos can facilitate scene understanding, intelligent navigation, and object recognition. Although numerous logo recognition methods have been proposed for printed logos, a few methods have been specifically designed for logos in photos. Furthermore, most recognition methods use codebook-based approaches for the logos in photos. A codebook-based method is concerned with the generation of visual words for all the logo models. When new logos are added, the codebook reconstruction is required if effectiveness is a crucial factor. Moreover, logo detection in natural scenes is difficult because of perspective tilt and non-rigid deformation. Therefore, this study develops an extendable, but discriminating, model-based logo detection method. The proposed logo detection method is based on a support vector machine (SVM) using edge-based histograms of oriented gradient (HOGE) as features through multi-scale sliding window scanning. Thereafter, anti-distortion affine scale invariant feature transform (ASIFT) is used for logo verification with constraints on the ASIFT matching pairs and neighbors. The experimental results using the public Flickr-Logo database confirm that the proposed method has a higher retrieval and precision accuracy compared to existing model-based methods.

     

Optimal entropy-constrained non-uniform scalar quantizer design for low bit-rate pixel domain DVC

In this paper, an optimal entropy-constrained non-uniform scalar quantizer is proposed for the pixel domain DVC. The uniform quantizer is efficient for the hybrid video coding since the residual signals conforming to a single-variance Laplacian distribution. However, the uniform quantizer is not optimal for pixel domain distributed video coding (DVC). This is because the uniform quantizer is not adaptive to the joint distribution of the source and the SI, especially for low level quantization. The signal distribution of pixel domain DVC conforms to the mixture model with multi-variance. The optimal non-uniform quantizer is designed according to the joint distribution, the error between the source and the SI can be decreased. As a result, the bit rate can be saved and the video quality won’t sacrifice too much. Accordingly, a better R-D trade-off can be achieved. First, the quantization level is fixed and the optimal RD trade-off is achieved by using a Lagrangian function J(Q). The rate and distortion components is designed based on P(Y|Q). The conditional probability density function of SI Y depend on quantization partitions Q, P(Y|Q), is approximated by a Guassian mixture model at encocder. Since the SI can not be accessed at encoder, an estimation of P(Y|Q) based on the distribution of the source is proposed. Next, J(Q) is optimized by an iterative Lloyd-Max algorithm with a novel quantization partition updating algorithm. To guarantee the convergence of J(Q), the monotonicity of the interval in which the endpoints of the quantizer lie must be satisfied. Then, a quantizer partition updating algorithm which considers the extreme points of the histogram of the source is proposed. Consequently, the entropy-constrained optimal non-uniform quantization partitions are derived and a better RD trade-off is achieved by applying them. Experiment results show that the proposed scheme can improve the performance by 0.5 dB averagely compared to the uniform scalar quantization.     

Coverage enhancement by using the mobility of mobile sensor nodes

Coverage is a fundamental problem in sensor networks. Sensor coverage, which reflects how well a sensor network is monitored by sensors, is an important measure for the quality of service (QoS) that a sensor network can provide. In mobile sensor networks, the mobility of sensor nodes can be utilized to enhance the coverage of the network. Since the movement of sensor nodes will consume much energy, this mobility of sensor nodes should be properly managed by some pre-defined schemes or protocols. By noticing this issue, some existing works have proposed several movement-assisted sensor deployment schemes. These works assume that the target field is a 2-dimensional space. In this paper, we study a generalized case of this problem whereby the target field can be a space which ranges from 1-dimensional to 3-dimensional. Two variations of the movement-assisted sensor deployment problem with different optimization objectives were formulated. We identify a set of basic attributes which can be used as guidelines for designing movement-assisted sensor deployment schemes. Based on these attributes, we propose efficient algorithms for both variants of the movement-assisted sensor deployment problem.     

Moving people tracking with detection by latent semantic analysis for visual surveillance applications

The latent semantic analysis (LSA) has been widely used in the fields of computer vision and pattern recognition. Most of the existing works based on LSA focus on behavior recognition and motion classification. In the applications of visual surveillance, accurate tracking of the moving people in surveillance scenes, is regarded as one of the preliminary requirement for other tasks such as object recognition or segmentation. However, accurate tracking is extremely hard under challenging surveillance scenes where similarity among multiple objects or occlusion among multiple objects occurs. Usual temporal Markov chain based tracking algorithms suffer from the ‘tracking error accumulation problem’. The accumulated errors can finally make the tracking to drift from the target. To handle the problem of tracking drift, some authors have proposed the idea of using detection along with tracking as an effective solution. However, many of the critical issues still remain unsettled in these detection based tracking algorithms. In this paper, we propose a novel moving people tracking with detection based on (probabilistic) LSA. By employing a novel ‘twin-pipeline’ training framework to find the latent semantic topics of ‘moving people’, the proposed detection can effectively detect the interest points on moving people in different indoor and outdoor environments with camera motion. Since the detected interest points on different body parts can be used to locate the position of moving people more accurately, by combining the detection with incremental subspace learning based tracking, the proposed algorithms resolves the problem of tracking drift during each target appearance update process. In addition, due to the time independent processing mechanism of detection, the proposed method is also able to handle the error accumulation problem. The detection can calibrate the tracking errors during updating of each state of the tracking algorithm. Extensive, experiments on various surveillance environments using different benchmark datasets have proved the accuracy and robustness of the proposed tracking algorithm. Further, the experimental comparison results clearly show that the proposed tracking algorithm outperforms the well known tracking algorithms such as ISL, AMS and WSL algorithms. Furthermore, the speed performance of the proposed method is also satisfactory for realistic surveillance applications.