Ensemble Feature Selection Method with Fast Transfer Model

Compared with the traditional ensemble feature selection methods, the recently-developed ensemble feature selection with block-regularized [m×2] cross-validation (EFSBCV) not only has a variance of the estimator smaller than that of random [m×2] cross-validation, but also enhances the selection probability of important features and reduces the selection probability of noise features. However, the adopted linear regression model without the use of the bias term in EFSBCV may easily lead to underfitting. Moreover, EFSBCV does not consider the importance of each feature subset. Aiming at these two problems, an ensemble feature selection method called EFSFT (ensemble feature selection method using fast transfer model) is proposed in this paper. The basic idea is that the base feature selector in EFSBCV adopts the fast transfer model in this paper, so as to introduce the bias term. EFSFT transfers 2m subsets of features as the source knowledge, and then recalculates the weight of each feature subset, and the linear model fitting ability with the addition of bias terms is better. The results on real datasets show that compared with EFSBCV, the average FP value by EFSFT reduces up to 58%, proving that EFSFT has more advantages in removing noise features. In contrast to least-squares support vector machine (LSSVM), the average TP value by EFSFT increases up to 5%, which clearly indicates the superiority of EFSFT over LSSVM in choosing important features

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

Biochars from Lignin-rich Residue of Furfural Manufacturing Process for Heavy Metal Ions Remediation

The pentose/furfural industrial manufacturing process uses corn cob residue as a raw material, where such a process yields significant amount of lignin-rich residue (LCR) at the end, which is commonly disposed by burning. In this study, the conversion of LCR to biochars (BCs), and their subsequent applications for heavy metal ion removal, were investigated. The BCs were prepared through hydrothermal carbonization and post-activation, using either ZnCl2 or H3PO4 treatment. The as-prepared activated BCs were characterized using N2 adsorption–desorption isotherms, XRD, FT-IR, SEM and TEM, and their performance in removing heavy metal ions (Pb2+, Cu2+, Cd2+) from aqueous solutions was assessed. The ZnCl2-activated BCs (BC-ZnCl2) exhibit a higher adsorption capacity than the H3PO4-activated BCs (BC-H3PO4), mainly due to the differences in their chemical/physical characteristics. The related adsorption kinetics and isotherms were analyzed

Existence of the Optimal Control for Stochastic Boundary Control Problems Governed by Semilinear Parabolic Equations

We study an optimal control problem governed by a semilinear parabolic equation, whose control variable is contained only in the boundary condition. An existence theorem for the optimal control is obtained

Semi-Supervised Minimum Error Entropy Principle with Distributed Method

The minimum error entropy principle (MEE) is an alternative of the classical least squares for its robustness to non-Gaussian noise. This paper studies the gradient descent algorithm for MEE with a semi-supervised approach and distributed method, and shows that using the additional information of unlabeled data can enhance the learning ability of the distributed MEE algorithm. Our result proves that the mean squared error of the distributed gradient descent MEE algorithm can be minimax optimal for regression if the number of local machines increases polynomially as the total datasize

Maximum Correntropy Criterion with Distributed Method

The Maximum Correntropy Criterion (MCC) has recently triggered enormous research activities in engineering and machine learning communities since it is robust when faced with heavy-tailed noise or outliers in practice. This work is interested in distributed MCC algorithms, based on a divide-and-conquer strategy, which can deal with big data efficiently. By establishing minmax optimal error bounds, our results show that the averaging output function of this distributed algorithm can achieve comparable convergence rates to the algorithm processing the total data in one single machine

Maximum Correntropy Criterion with Distributed Method

The Maximum Correntropy Criterion (MCC) has recently triggered enormous research activities in engineering and machine learning communities since it is robust when faced with heavy-tailed noise or outliers in practice. This work is interested in distributed MCC algorithms, based on a divide-and-conquer strategy, which can deal with big data efficiently. By establishing minmax optimal error bounds, our results show that the averaging output function of this distributed algorithm can achieve comparable convergence rates to the algorithm processing the total data in one single machine

Berry-Esseen bound for parameter estimation in some time inhomogeneous diffusions and applications

In this paper, we study the Berry-Esseen bound of the distribution of the maximum likelihood estimation in some inhomogeneous diffusions. We prove that it converges to the normal distribution with an error rate . Moreover, the obtained Berry-Esseen bound can be applied to study the precise asymptotics in the law of iterated logarithm for the maximum likelihood estimation.Berry-Esseen bound Inhomogeneous diffusion Law of iterated logarithm Precise asymptotics

Effect of Gas on Burst Proneness and Energy Dissipation of Loaded Coal: An Experimental Study Using a Novel Gas-Solid Coupling Loading Apparatus

Deep coal mining is seriously affected by a combined dynamic disaster of rock burst and coal and gas outburst, but the influence mechanism of gas on this combined dynamic disaster is still not very clear, which is significantly different from the single type disasters. In this study, to explore the effect of gas on the coal-rock burst, a novel gas-solid coupling loading apparatus is designed to realize gas adsorption of coal sample with burst proneness and provide uniaxial loading environment under different gas pressure. A series of uniaxial compression tests of gas-containing coal with different gas pressure is carried out, and the energy dissipation process is monitored by an acoustic emission (AE) system. Results show that the macroscopic volume strain of the coal sample increases as gas adsorption and gas pressure increase under constant uniaxial loading pressure. Gas has the ability to expand the pores and natural fractures in coal sample by mechanical and physicochemical effects, which leads to a degradation in microstructure integrity of coal sample. With the increase of gas pressure, both the macrouniaxial compression strength (UCS) and elastic modulus show a downward trend; the UCS and elastic modulus of coal samples with 2 MPa gas pressure reduce by 58.78% and 48.82%, respectively, compared to those of the original coal samples. The main reason is that gas changes the pore-fissure structure and the mesoscopic stress environment inside the coal sample. Owing to the gas, the accumulated elastic energy of the gas-containing coal samples before failure reduces significantly, whereas the energy dissipated during loading increases, and the energy release process in the postpeak stage is smoother, indicating the participation of gas weakens the burst proneness of the coal sample. This study is of important scientific value for revealing the mechanism of combined dynamic disaster and the critical occurrence conditions of coal-rock burst and coal and gas outburst