Kinetic modeling of growth and biodegradation of phenol and m-cresol using Alcaligenes faecalis

A phenol-degrading. microorganism, Alcaligenes faecalis, was used to study the substrate interactions during cell growth on phenol and m-cresol dual substrates. Both phenol and m-cresol could be utilized by the bacteria as,the sole carbon and energy sources. When cells grew on the mixture of phenol and m-cresol, strong substrate interactions were observed. m-Cresol inhibited the degradation of phenol, on the other hand, phenol also inhibited the utilization of m-cresol, the overall cell growth rate was the co-action of phenol and m-cresol. In addition, the cell growth and substrate degradation kinetics of phenol, m-cresol as single and mixed substrates for A. faecalis in batch cultures were also investigated over a wide range of initial phenol concentrations (10-1400 mg L-1) and initial m-cresol concentrations (5-200 mg L-1). The single-substrate kinetics was described well using the Haldane-type kinetic models, with model constants of it mu(m1) = 0.15 h(-1), K-S1 = 2.22 mg L-1 and K-i1 = 245.37 mg L-1 for cell growth on phenol and mu(m2) = 0.0782 h(-1), K-S2 = 1.30 mg L-1 and K-i2 = 71.77 mgL(-1), K-i2' = 5480 (mg L-1)(2) for cell growth on m-cresol. Proposed cell growth kinetic model was used to characterize the substrates interactions in the dual substrates system, the obtained parameters representing interactions between phenol and m-cresol were, K = 1.8 x 10(-6), M = 5.5 x 10(-5), Q = 6.7 x 10(-4). The results received in the experiments demonstrated that these models adequately described the dynamic behaviors of phenol and m-cresol as single and mixed substrates by the strain of A. faecalis.

Retention modeling and simultaneous optimization of pH value and gradient steepness in RP-HPLC using feed-forward neural networks

A novel approach is proposed for the simultaneous optimization of mobile phase pH and gradient steepness in RP-HPLC using artificial neural networks. By presetting the initial and final concentration of the organic solvent, a limited number of experiments with different gradient time and pH value of mobile phase are arranged in the two-dimensional space of mobile phase parameters. The retention behavior of each solute is modeled using an individual artificial neural network. An "early stopping" strategy is adopted to ensure the predicting capability of neural networks. The trained neural networks can be used to predict the retention time of solutes under arbitrary mobile phase conditions in the optimization region. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for amino acids derivatised by a new fluorescent reagent.

Modeling and optimization for separation of ionic solutes in pressurized flow capillary electrochromatography

By manipulation of applied pressure or voltage, pressurized flow capillary electrochromatography (P-CEC) permits unique control of selectivity for ionic solutes. A simple mathematical model has been developed to describe the quantitative relationship between the electrochromatographic retention factor (k(*)) of charged solutes and the applied voltage and pressure. The validity of the model was verified experimentally with hydrophilic interaction mode CEC (HI-CEC). On the basis of the model developed, it was found that the value of k(*) could be predicted accurately using only a limited number of data points from the initial experiments at different voltages or pressures. Correlation between the experimentally measured and calculated k(*) was excellent, with a correlation coefficient greater than 0.999. Optimization for the separation of peptides by P-CEC was also performed successfully on the basis of the proposed model.

Retention modeling and optimization of pH value and solvent composition in HPLC using back-propagation neural networks and uniform design

A novel method for the optimization of pH value and composition of mobile phase in HPLC using artificial neural networks and uniform design is proposed. As the first step. seven initial experiments were arranged and run according to uniform design. Then the retention behavior of the solutes is modeled using back-propagation neural networks. A trial method is used to ensure the predicting capability of neural networks. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for both basic and acidic samples.

Modeling the effect of ozone loss on photobleaching of chromophoric dissolved organic matter in the St. Lawrence estuary

Temporal trends in total ozone for the St. Lawrence estuary were estimated from ground-based measurements at the NOAA/CMDL station in Caribou, Maine. Linear regression analysis showed that from 1979 to 1999 total ozone has decreased by about 3.3% per decade on an annual basis and ≤6.2% per decade on a monthly basis relative to unperturbed (pre-CFC) levels. The influence of increased ultraviolet-B (280–320 nm) radiation associated with ozone depletion on water column photochemical processes was evaluated by modeling the photobleaching of chromophoric dissolved organic material (CDOM). Linear regression analysis showed small (<0.5% per decade), but statistically significant upward trends in maximum noontime photobleaching rates. Most notably, positive trends in relative rates for May, June, and July, when maximum absolute rates are expected, were predicted. A global model based on TOMS ozone data revealed increases in photobleaching of ≤3% per decade at high latitudes in the Southern Hemisphere. Radiation amplification factors for increases in photochemically weighted UV (280–400 nm) in response to ozone depletion were estimated at 0.1 and 0.08 for photobleaching of CDOM absorbance at 300 and 350 nm, respectively. Application of the laboratory-based model to conditions that more closely resembled those in situ were variable with both overestimation and underestimation of measured rates. The differences between modeled rates and observed rates under quasi-natural conditions were as large or larger than the predicted increases due to ozone depletion. These comparisons suggest that biological activity and mixing play an important, but as yet ill-defined, role in modifying photochemical processes.

Fast monostatic GPR modeling

We propose the exploding-reflector method to simulate a monostatic survey with a single simulation. The exploding reflector, used in seismic modeling, is adapted for ground-penetrating radar (GPR) modeling by using the analogy between acoustic and electromagnetic waves. The method can be used with ray tracing to obtain the location of the interfaces and estimate the properties of the medium on the basis of the traveltimes and reflection amplitudes. In particular, these can provide a better estimation of the conductivity and geometrical details. The modeling methodology is complemented with the use of the plane-wave method. The technique is illustrated with GPR data from an excavated tomb of the nineteenth century.

the role of software process simulation modeling in software risk management: a systematic review

Univ SE Calif, Ctr Syst & Software Engn, ABB, Microsoft Res, IEEE, ACMSIGSOFT, N Carolina State Univ Comp Sci

stochastic process algebra based software process simulation modeling

Chinese Acad Sci, ISCAS Lab Internet Software Technologies

Modeling of Hillslope Runoff and Soil Erosion at Rainfall Events Using Cellular Automata Approach

National Science Fund for Distinguished Young Scholars of China [40225004]; National Natural Science Foundation of China [40471048]