Development and operation of a trickling biofilter system for continuous treatment of gas-phase trichloroethylene

A parallel trickling biofilter (TBF) system that consists of two TBFs units in parallel, one for biodegradation of trichloroethylene (TCE) and the other for reactivation of an inactivated biofilm, was developed and operated for continuous treatment of gas-phase TCE by Burkholderia cepacia G4. For inlet loadings below 8.6 mg TCE l^–1 d^–1, complete removal of TCE was achieved. The maximal TCE elimination capacity was 17 mg l^–1 d^–1.     

Conversion of 2-chloromaleylacetate in Alcaligenes eutrophus JMP134

2,4-Dichlorophenoxyacetate (2,4-D) in Alcaligenes eutrophus JMP134 (pJP4) is degraded via 2-chloromaleylacetate as an intermediate. The latter compound was found to be reduced by NADH in a maleylacetate reductase catalyzed reaction. Maleylacetate and chloride were formed as products of 2-chloromaleylacetate reduction, the former being funnelled into the 3-oxoadipate pathway by a second reductive step. There was no indication for an involvement of a pJP4-encoded enzyme in either the reduction or the dechlorination reaction.Abbreviations 2,4-D 2,4-dichlorophenoxyacetate     

A Leap-frog Algorithm for Stochastic Dynamics

Abstract

A third-order algorithm for stochastic dynamics (SD) simulations is proposed, identical to the powerful molecular dynamics leap-frog algorithm in the limit of infinitely small friction coefficient γ. It belongs to the class of SD algorithms, in which the integration time step Δt is not limited by the condition Δt ≤ γ^?1, but only by the properties of the systematic force. It is shown how constraints, such as bond length or bond angle constraints, can be incorporated in the computational scheme. It is argued that the third-order Verlet-type SD algorithm proposed earlier may be simplified without loosing its third-order accuracy. The leap-frog SD algorithm is proven to be equivalent to the verlet-type SD algorithm. Both these SD algorithms are slightly more economical on computer storage than the Beeman-type SD algorithm.     

Binary Hybrid Artificial Hummingbird with Flower Pollination Algorithm for Feature Selection in Parkinson's Disease Diagnosis

Parkinson's disease is a neurodegenerative disorder that inflicts irreversible damage on humans.Some experimental data regarding Parkinson's patients are redundant and irrelevant,posing significant challenges for disease detection.Therefore,there is a need to devise an effective method for the selective extraction of disease-specific information,ensuring both accu-racy and the utilization of fewer features.In this paper,a Binary Hybrid Artificial Hummingbird and Flower Pollination Algorithm(FPA),called BFAHA,is proposed to solve the problem of Parkinson's disease diagnosis based on speech signals.First,combining FPA with Artificial Hummingbird Algorithm(AHA)can take advantage of the strong global exploration ability possessed by FPA to improve the disadvantages of AHA,such as premature convergence and easy falling into local optimum.Second,the Hemming distance is used to determine the difference between the other individuals in the population and the optimal individual after each iteration,if the difference is too significant,the cross-mutation strategy in the genetic algorithm(GA)is used to induce the population individuals to keep approaching the optimal individual in the random search process to speed up finding the optimal solution.Finally,an S-shaped function converts the improved algorithm into a binary version to suit the characteristics of the feature selection(FS)tasks.In this paper,10 high-dimensional datasets from UCI and the ASU are used to test the performance of BFAHA and apply it to Parkinson's disease diagnosis.Compared with other state-of-the-art algorithms,BFAHA shows excellent competitiveness in both the test datasets and the classification problem,indicating that the algorithm proposed in this study has apparent advantages in the field of feature selection.