Dynamic Stability Analysis of Linear Time-varying Systems via an Extended Modal Identification Approach

The problem of linear time-varying（LTV） system modal analysis is considered based on time-dependent state space representations, as classical modal analysis of linear time-invariant systems and current LTV system modal analysis under the ＂frozen-time＂ assumption are not able to determine the dynamic stability of LTV systems. Time-dependent state space representations of LTV systems are first introduced, and the corresponding modal analysis theories are subsequently presented via a stabilitypreserving state transformation. The time-varying modes of LTV systems are extended in terms of uniqueness, and are further interpreted to determine the system＇s stability. An extended modal identification is proposed to estimate the time-varying modes, consisting of the estimation of the state transition matrix via a subspace-based method and the extraction of the time-varying modes by the QR decomposition. The proposed approach is numerically validated by three numerical cases, and is experimentally validated by a coupled moving-mass simply supported beam exper- imental case. The proposed approach is capable of accurately estimating the time-varying modes, and provides anew way to determine the dynamic stability of LTV systems by using the estimated time-varying modes.

Identification and analysis of differentially expressed genes involved in dark-induced photoperiod response and senescence of soybean leaves by suppression subtractive hybridization

A cDNA subtractive library enriched for dark-induced up-regulated ESTs was constructed by suppression subtractive hybridization(SSH) from leaf tissues of soybean cultivar DongNong L13 treated with short-day(8-h light/16-h dark) and long-day(16-h light/8-h dark) conditions.A total of 148 clones were sequenced,representing 76 unique ESTs which corresponded to about 20% of 738 clones from the cDNA library and showed a significant up-regulation of at least three fold verified by dot blot hybridization.The putative functions of ESTs were predicted by Blastn and Blastx.The 43 differentially expressed genes identified by subtractions were classified according to their putative functions generated by Blast analysis.Genetic functional analysis indicated that putative proteins encoded by these genes were related to diverse functions during organism development,which include biological regulation pathways such as transcription,signal transduction and programmed cell death,protein,nucleic acid and carbohydrate macromolecule degradation,the cell wall modification,primary and secondary metabolism and stress response.Two soybean transcription factors enhanced in SD conditions,GAMYB-binding protein and DNA binding protein RAV cDNAs that may be involved in SD soybean photoperiod response,had been isolated using 5'-and 3'-rapid amplification of cDNA ends(RACE)(Genbank Accession numbers DQ112540 and DQ147914).

The Fuzzy Cluster Analysis in Identification of Key Temperatures in Machine Tool

The thermal-induced error is a very important sour ce of machining errors of machine tools. To compensate the thermal-induced machin ing errors, a relationship model between the thermal field and deformations was needed. The relationship can be deduced by virtual of FEM (Finite Element Method ), ANN (Artificial Neural Network) or MRA (Multiple Regression Analysis). MR A is on the basis of a total understanding of the temperature distribution of th e machine tool. Although the more the temperatures measured are, the more accura te the MRA is, too more temperatures will hinder the analysis calculation. So it is necessary to identify the key temperatures of the machine tool. The selectio n of key temperatures decides the efficiency and precision of MRA. Because of th e complexities and multi-input and multi-output structure of the relationships , the exact quantitative portions as well as the unclear portions must be taken into consideration together to improve the identification of key temperatures. I n this paper, a fuzzy cluster analysis was used to select the key temperatures. The substance of identifying the key temperatures is to group all temperatures b y their relativity, and then to select a temperature from each group as the repr esentation. A fuzzy cluster analysis can uncover the relationships between t he thermal field and deformations more truly and thoroughly. A fuzzy cluster ana lysis is the cluster analysis based on fuzzy sets. Given U={u i|i=0,...,N}, in which u i is the temperature measured, a fuzzy matrix R can be obta ined. The transfer close package t(R) can be deduced from R. A fuzzy clu ster of U then conducts on the basis of t(R). Based on the fuzzy cluster analysis discussed above, this paper identified the k ey temperatures of a horizontal machining center. The number of the temperatures measured was reduced to 4 from 32, and then the multiple regression relationshi p models between the 4 temperatures and the thermal deformations of the spindle were drawn. The remnant errors between the regression models and measured deform ations reached a satisfying low level. At the same time, the decreasing of tempe rature variable number improved the efficiency of measure and analysis greatly.

Isolation and Identification of Staphylococcus chromogenes and 3 D Structural Analysis of the katA Gene

[Objective]Staphylococcus arthritis became an increasingly significant health problem in intensive chicken farming in China.[Method]In this study,a bacteria strain was isolated from the broiler chicken suffering from arthritis and named as the strain Gg1.[Result]It was then identified as Staphylococcus chromogenes by the biochemical tests and phylogenetic tree analysis based on 16S rDNA sequence.Furthermore,the catalase（katA）gene was amplified by PCR using the designed primers,and the expected fragment was 1 232 bp long encoding a protein of 410 amino acids that shares the conserved motifs including catalase,heme-binding ligand and active center motif.Six phosphorylation sites（Ser95,Thr96,Ser241,Ser242,Thr281,Ser338）,four conserved residues（Ser95,His216,Tyr281,Asp341）and two active sites（His56,Asn129）were demonstrated by multiple sequence alignment and homology comparisons.The homology modeling of 3D structure of katA protein was done by SWISSMODEL server based on the template retrieved from the catalase（PDB：2ISA_A）of Vibrio salmonicida.The katA protein represents a four-domain globular protein,the quality and reliability of the resulting protein structure was further verified by Ramachandran plot.[Conclusion]To our knowledge,this is the first report of S.chromogenes linked to arthritis in chicken and the bioinformatic characterization of its katA gene.

Isolation,identification,and antagonism protein analysis of B34 against Thanatephorus cuc-umeris

A strain B34 against Thanatephorus cucumeris was screened from rice plants. Lab and field experiments showed that the control effects of this fungal strain were better than that of Jinggangmycin on PDA plate. Based on the chemical components of cell wall and physiological and biochemical characters of B34, the fungal was named as Pseudomonas aureofaciens. It was a new antagonistic strain against Thanatephorus cucumeris.

Identification method of seismic phase in three-component seismograms on the basis of wavelet transform

This paper puts forward wavelet transform method to identify P and S phases in three component seismograms using polarization information contained in the wavelet transform coefficients of signal. The P and S wave locator functions are constructed by using eigenvalue analysis method to wavelet transform coefficient across several scales. Locator functions formed by wavelet transform have stated noise resistance capability, and is proved to be very effective in identifying the P and S arrivals of the test data and actual earthquake data.

Non-probabilistic information fusion technique for structural damage identification based on measured dynamic data with uncertainty

Based on measured natural frequencies and acceleration responses,a non-probabilistic information fusion technique is proposed for the structural damage detection by adopting the set-membership identification（SMI） and twostep model updating procedure.Due to the insufficiency and uncertainty of information obtained from measurements,the uncertain problem of damage identification is addressed with interval variables in this paper.Based on the first-order Taylor series expansion,the interval bounds of the elemental stiffness parameters in undamaged and damaged models are estimated,respectively.The possibility of damage existence（PoDE） in elements is proposed as the quantitative measure of structural damage probability,which is more reasonable in the condition of insufficient measurement data.In comparison with the identification method based on a single kind of information,the SMI method will improve the accuracy in damage identification,which reflects the information fusion concept based on the non-probabilistic set.A numerical example is performed to demonstrate the feasibility and effectiveness of the proposed technique.

Identification of multiple inputs single output errors-in-variables system using cumulant

A higher-order cumulant-based weighted least square（HOCWLS） and a higher-order cumulant-based iterative least square（HOCILS） are derived for multiple inputs single output（MISO） errors-in-variables（EIV） systems from noisy input/output data. Whether the noises of the input/output of the system are white or colored, the proposed algorithms can be insensitive to these noises and yield unbiased estimates. To realize adaptive parameter estimates, a higher-order cumulant-based recursive least square（HOCRLS） method is also studied. Convergence analysis of the HOCRLS is conducted by using the stochastic process theory and the stochastic martingale theory. It indicates that the parameter estimation error of HOCRLS consistently converges to zero under a generalized persistent excitation condition. The usefulness of the proposed algorithms is assessed through numerical simulations.

Identification and analysis of intermediate-size noncoding RNAs in the rhesus macaque fetal brain

Although only about 2%of the human genome has proved to be protein-coding genes,recent advances in genome wide analysis have revealed that the majority of the genome is transcribed,mainly from noncoding segments that were once considered＂junk sequences＂or＂dark matters＂（Liu et al.,2011a;Zhang et al.,2014b）. In addition to the well-characterized housekeeping non- coding RNAs （ncRNAs） （tRNA, rRNA, small nuclear RNA and small nucleolar RNAs） and some small regulatory ncRNAs （microRNAs and small interfering RNAs）, the transcriptome of mammals could also pervasively have been transcribed long noncoding RNAs （lncRNAs, at least 200 nt） （Rinn and Chang, 2012; Xie et al., 2012）.

A new method of charged particle identification based on frequency spectrum analysis

A new frequency domain method for charged particle identification, called Frequency Ratio Analysis（FRA）, is proposed by analyzing the frequency spectra of proton pulses and alpha pulses acquired from a totally depleted Si detector. Identification performance of the FRA method is evaluated and compared with two time domain methods, the current pulse amplitude method and the second moment method. The results show that the FRA method is not only feasible and effective but also superior to the two time domain methods, as it achieves an obvious increase in value of the figure-of-merit（FOM）.

Multi-channel microfluidic chip-mass spectrometry platform for cell analysis

In this review, we highlight the latest development of multi-channel microfluidic chip-mass spectrometry（chip-MS） in cell analysis and metabolite detection. Following a brief introduction about history and development of multi-channel microchip and MS combination, we will elaborate the key issues of constructing chip-MS platform interface. Then exciting progresses made in this field should be reviewed with well exemplified works, including chip-MS technology for cell introduction, pretreatment of cell secretions and cell metabolite analysis. We will also describe the development of integrated total analysis systems proposed by our group. We hope this brief review will inspire interested readers and provide knowledge about chip-MS platform in the bioanalysis field, particularly in cell analysis and metabolite identifying applications.