Isolation of Thiobacillus spp. and its application in the removal of heavy metals from activated sludge

Two strains of Thiobacillus isolated from native excess activated sludge were identified as Acidithiobacillus ferrooxidans and Acidithiobacillus thiooxidans by 16S rRNA gene sequencing and physiological-biochemical characteristics. Single and mixed cultures of the strains were used to carry out bioleaching for 9 days in order to remove heavy metals from activated sludge. The changes in pH, oxidation-reduction potential, and contents of heavy metals were measured. The results show that the bioleaching effect of the mixed culture was best in all runs, and that the final removals of As, Cr, Cu, Ni, and Zn were 96.09, 93.47, 98.32, 97.88, and 98.60%, respectively, whereas the removals of Cd and Pb decreased rapidly after six days. In addition, we demonstrate for the first time that bioleaching can reduce the pathogenicity of sludge by detecting fecal coliforms before and after bioleaching in order to ensure that the sludge was suitable for agricultural land application.Key words: Acidithiobacillus ferrooxidans, Acidithiobacillus thiooxidans, excess activated sludge, removing heavy metals, sludge pathogenicity

Recognition of emotions using Kinects

Abstract Emotion recognition can improve the quality of patient care, product development and human-machine interaction. Psychological studies indicate that emotional state can be expressed in the way people walk, and the human gait can be used to reveal a person's emotional state. This paper proposes a novel method to do emotion recognition by using Microsoft Kinect to record gait patterns and train machine learning algorithms for emotion recognition. 59 subjects are recruited, and their gait patterns are recorded by two Kinect cameras. Joint selection, coordinate system transformation, sliding window gauss filtering, differential operation, and data segmentation are used for data preprocessing. We run Fourier transformation to extract features from the gait patterns and utilize Principal Component Analysis(PCA) for feature selection. By using NaiveBayes, RandomForests, LibSVM and SMO classifiers, the accuracy of recognition between natural and angry emotions can reach 80%, and the accuracy of recognition between natural and happy emotions can reach above 70%. The result indicates that Kinect can be used in the recognition of emotions with fairly well performance

Identifying Emotions from Non-Contact Gaits Information Based on Microsoft Kinects

Automatic emotion recognition from gaits information is discussed in this paper, which has been investigated widely in the fields of human-machine interaction, psychology, psychiatry, behavioral science, etc. The gaits information is non-contact, collected from Microsoft kinects, and contains 3-dimensional coordinates of 25 joints per person. These joints coordinates vary with the time. So, by the discrete Fourier transform and statistic methods, some time-frequency features related to neutral, happy and angry emotion are extracted and used to establish the classification model to identify these three emotions. Experimental results show this model works very well, and time-frequency features are effective in characterizing and recognizing emotions for this non-contact gait data. In particular, by the optimization algorithm, the recognition accuracy can be further averagely improved by about 13.7 percent

Visualization Analysis for Big Data in Computational CyberPsychology

This paper discusses issues related to the big data in Computational CyberPsychology, and proposes to utilize Parallel Coordinates, an famous method in the field of information visualization, to improve analyzing data. The experimental results show that Parallel Coordinates will be very helpful and give visual analyzing of big data in studying of Computational CyberPsychology.</p

Study of Acoustic Emission and Mechanical Characteristics of Coal Samples under Different Loading Rates

To study the effect of loading rate on mechanical properties and acoustic emission characteristics of coal samples, collected from Sanjiaohe Colliery, the uniaxial compression tests are carried out under various levels of loading rates, including 0.001 mm/s, 0.002 mm/s, and 0.005 mm/s, respectively, using AE-win E1.86 acoustic emission instrument and RMT-150C rock mechanics test system. The results indicate that the loading rate has a strong impact on peak stress and peak strain of coal samples, but the effect of loading rate on elasticity modulus of coal samples is relatively small. When the loading rate increases from 0.001 mm/s to 0.002 mm/s, the peak stress increases from 22.67 MPa to 24.99 MPa, the incremental percentage is 10.23%, and under the same condition the peak strain increases from 0.006191 to 0.007411 and the incremental percentage is 19.71%. Similarly, when the loading rate increases from 0.002 mm/s to 0.005 mm/s, the peak stress increases from 24.99 MPa to 28.01 MPa, the incremental percentage is 12.08%, the peak strain increases from 0.007411 to 0.008203, and the incremental percentage is 10.69%. The relationship between acoustic emission and loading rate presents a positive correlation, and the negative correlation relation has been determined between acoustic emission cumulative counts and loading rate during the rupture process of coal samples