可测链上含最大值的二阶微分方程最终正解的渐近性

讨论了可测链上含最大值的非线性微分方程最终正解的渐近性.

可测链上非线性微分方程最终正解的分类

讨论了可测链上非线性中立型Δ-微分方程,并给出它的最终正解的分类.

Available parking space occupancy change characteristics and short-term forecasting model

Based on an available parking space occupancy （APSO） survey conducted in Nanjing, China, an APSO forecasting model is proposed. The APSO survey results indicate that the time series of APSO with different time-sections are periodical and self-similar, and the fluctuation of the APSO increases with the decrease in time-sections. Taking the short-time change behavior into account, an APSO forecasting model combined wavelet analysis and a weighted Markov chain is presented. In this model, an original APSO time series is first decomposed by wavelet analysis, and the results include low frequency signals representing the basic trends of APSO and several high frequency signals representing disturbances of the APSO. Then different Markov models are used to forecast the changes of low and high frequency signals, respectively. Finally, integrating the predicted results induces the final forecasted APSO. A case study verifies the applicability of the proposed model. The comparisons between measured and forecasted results show that the model is a competent model and its accuracy relies on real-time update of the APSO database.

Support Vector Machines(SVM)-Markov Chain Prediction Model of Mining Water Inflow

This study was conducted to establish a Support Vector Machines(SVM)-Markov Chain prediction model for prediction of mining water inflow. According to the raw data sequence, the Support Vector Machines(SVM) model was built, and then revised by means of a Markov state change probability matrix. Through dividing the state and analyzing absolute errors and relative errors and other indexes of the measured value and the fitted value of SVM, the prediction results were improved. Finally,the model was used to calculate relative errors. Through predicting and analyzing mining water inflow, the prediction results of the model were satisfactory. The results of this study enlarge the application scope of the Support Vector Machines(SVM) prediction model and provide a new method for scientific forecasting water inflow in coal mining.

Cloning, sequencing and analyzing of the heavy chain V region genes of human polyreactive antibodies

The heavy chain variable region genes of 5 human polyreactive mAbs generated in our laboratory have been cloned and sequenced using polymerase chain reaction (PCR) technique. We found that 2 and 3 mAbs utilized genes of the VHIV and VHIII families, respectively. The former 2 VH segments were in germline configuration. A common VH segment, with the best similarity of 90.1 % to the published VHIII germline genes, was utilized by 2 different rearranged genes encoding the V regions of other 3 mAbs. This strongly suggests that the common VH segment is a unmutated copy of an unidentified germline VHIII gene. All these polyreactive mAbs displayed a large NDN region (VH-D-JH junction). The entire H chain V regions of these polyreactive mAbs are unusually basic. The analysis of the charge properties of these mAbs as well as those of other poly- and mono- reactive mAbs from literatures prompts us to propose that the charged amino acids with a particular distribution along the H chain V region,especially the binding sites (CDRs), may be an important structural feature involved in antibody polyreactivity.

An Improved Markov Chain Model Based on Autocorrelation and Entropy Techniques and Its Application to State Prediction of Water Resources

According to the relationships among state transition probability matrixes with different step lengths, an improved Markov chain model based on autocorrelation and entropy techniques was introduced. In the improved Markov chain model, the state transition probability matrixes can be adjusted. The steps of the historical state of the event, which was significantly related to the future state of the event, were determined by the autocorrelation technique, and the impact weights of the event historical state on the event future state were determined by the entropy technique. The presented model was applied to predicting annual precipitation and annual runoff states, showing that the improved model is of higher precision than those existing Markov chain models, and the determination of the state transition probability matrixes and the weights is more reasonable. The physical concepts of the improved model are distinct, and its computation process is simple and direct, thus, the presented model is sufficiently general to be applicable to the prediction problems in hydrology and water resources.

Improved grey-based approach for power demand forecasting

Grey theory is a multidisciplinary and generic theory to cope with systems of poor or deficient information. We proposed in this paper an improved grey method (GM) to overcome the disadvantages of the general GM(1,1). In the improved GM(1,1), a new background value formula is deduced and Markov-chain sign estimation is imbedded into the residual modification model. We tested the efficiency and accuracy of our model by applying it to the power demand forecasting in Taiwan. Experimental results demonstrate the new method has obviously a higher prediction accuracy than the general model.

Utility water supply forecast via a GM (1,1) weighted Markov chain

This paper describes the procedure of using the GM (1,1) weighted Markov chain (GMWMC) to forecast the utility water supply, a quantity that usually has significant temporal variability. The GMWMC is formulated into five steps: (1) use GM (1,1) to fit the trend of the data, and obtain the relative error of the fitted values; (2) divide the relative error into ‘state’ data based on pre-set intervals; (3) calibrate the weighted Markov chain model: herein the parameters are the pre-set interval and the step of transition matrix (TM); (4) by using auto-correlation coefficient as the weight, the Markov chain provides the prediction interval. Then the mid-value of the interval is selected as the relative error for the data. Upon combining the data and its relative error, the predicted magnitude in a specific time period is obtained; and, (5) validate the model. Commonly, static intervals are used in both model calibration and validation stages, usually causing large errors. Thus, a dynamic adjustment interval (DAI) is proposed for a better performance. The proposed procedure is described and demonstrated through a case study, which shows that the DAI can usually achieve a better performance than the static interval, and the best TM may exist for certain data.

OCCUPATION TIME PROCESSES OF FLEMING-VIOT PROCESSES

Suppose that Xt is the Fleming-Viot process associated with fractional power Laplacian operator -(-△)α/2 0 < α≥ 2, and Yt= f_0￣t Xs.ds is the so-called occupation time process.In this paper) the asymptotic behavior at a large time and the absolute continuity of Yt are investigated.

Stochastic Systems Arising from Markov Modulated Empirical Measures

This work is devoted to stochastic systems arising from empirical measures of random sequences(termed primary sequences) that are modulated by another Markov chain. The Markov chain is used to model random discrete events that are not represented in the primary sequences. One novel feature is that in lieu of the usual scaling in empirical measure sequences, the authors consider scaling in both space and time, which leads to new limit results. Under broad conditions, it is shown that a scaled sequence of the empirical measure converges weakly to a number of Brownian bridges modulated by a continuous-time Markov chain. Ramifications and special cases are also considered.

Quintessence Reconstruction of Interacting HDE in a Non-Flat Universe

In this paper we consider quintessence reconstruction of interacting holographic dark energy in a non-fiat background. As system＇s IR cutoff we choose the radius of the event horizon measured on the sphere of the horizon, defined as L = at（t）. To this end we construct a quintessence model by a real, single scalar field. Evolution of the potential, V（φ）, as well as the dynamics of the scalar field, φ, is obtained according to the respective holographic dark energy. The reconstructed potentials show a cosmological constant behavior for the present time. We constrain the model parameters in a fiat universe by using the observational data, and applying the Monte Carlo Markov chain simulation. We obtain the best fit values of the holographic dark energy model and the interacting parameters as c=1.0576-0.6632-0.6632^＋0.3010＋0.3052 and ζ =0.2433-0.2251-.2251^＋0.6373＋0.6373 , respectively. From the data fitting results we also find that the model can cross the phantom line in the present universe where the best fit value of the dark energy equation of state is WD=-1.2429.

Comparing the VGCG model as the unification of dark sectors with observations

Current observations indicate that 95% of the energy density in the universe is the unknown dark component.The dark component is considered composed of two fluids:dark matter and dark energy.Or it is a mixture of these two dark components,i.e.,one can consider it an exotic unknown dark fluid.With this consideration,the variable generalized Chaplygin gas(VGCG)model is studied with not dividing the unknown fluid into dark matter and dark energy parts in this paper.By using the Markov Chain Monte Carlo method,the VGCG model as the unification of dark sectors is constrained,and the constraint results on the VGCG model parameters are,n=0.00057+0.0001+0.0009-0.0006-0.0006,α=0.0015+0.0003+0.0017-0.0015-0.0015and B s=0.778+0.016+0.030-0.016-0.035,obtained by the cosmic microwave background data from the 7-year WMAP full data points,the baryon acoustic oscillation data from Sloan Digital Sky Survey(SDSS)and 2-degree Field Galaxy Redshift(2dFGRS)survey,and the Union2 type Ia supernova data with systematic errors.At last,according to the evolution of deceleration parameter it is shown that an expanded universe from deceleration to acceleration can be obtained in VGCG cosmology.

Porous silicon biosensor for detection of variable domain of heavy-chain of HCAb antibody

In this paper, we produce porous silicon （PSi） by electrochemical etching, and it is the first time to evaluate the performance of label-free porous silicon biosensor for detection of variable domain of heavy chain of heavy-chain antibody （VHH）. The binding of hen egg white lysozyme （HEWL） and VHH causes a red shift in the reflection spectrum of the biosensor. The red shift is proportional to the VHH concenlration in the range from 14 gg.ml-I to 30 pg.ml~ with a detection limit of 0.648 ng.ml~. The research is useful for the development of label-free biosensor applied in the rapid and sensitive determination of small molecules.