A kind of generalized proportional-integral(GPI) observer for descriptor linear systems is introduced. We first propose two complete parametric solutions to generalized Sylvester matrix equation corresponding to the left eigenvector matrices in the case of Jordan form. Then a parametric design approach for the observer is presented. The proposed method provides all parametric expression of the gain matrices and the corresponding finite left eigenvector matrix and guarantees the regularity and impulse-freeness of the expanded error system. Two numerical examples are given to explain the design procedure and illustrate the effectiveness of the proposed method.
Speaker‘s emotional states are recognized from speech signal with Additive white Gaussian noise (AWGN). The influence of white noise on a typical emotion recogniztion system is studied. The emotion classifier is implemented with Gaussian mixture model (GMM). A Chinese speech emotion database is used for training and testing, which includes nine emotion classes (e.g. happiness, sadness, anger, surprise, fear, anxiety, hesitation, confidence and neutral state). Two speech enhancement algorithms are introduced for improved emotion classification. In the experiments, the Gaussian mixture model is trained on the clean speech data, while tested under AWGN with various signal to noise ratios (SNRs). The emotion class model and the dimension space model are both adopted for the evaluation of the emotion recognition system. Regarding the emotion class model, the nine emotion classes are classified. Considering the dimension space model, the arousal dimension and the valence dimension are classified into positive regions or negative regions. The experimental results show that the speech enhancement algorithms constantly improve the performance of our emotion recognition system under various SNRs, and the positive emotions are more likely to be miss-classified as negative emotions under white noise environment.
The last study on n-alkanes in surface sediments of Taihu Lake was in 2000, only 13 surface sediment samples were analysed, in order to have a comprehensive and up-to-date understanding of n-alkanes in the surface sediments of Taihu Lake, 41 surface sediment samples were analyzed by GC-MS. C10 to C37 were detected, the total concentrations of n-alkanes ranged from 2109 ng g−1 to 9096 ng g−1 (dry weight). There was strong odd carbon predominance in long chain n-alkanes and even carbon predominance in short chain n-alkanes. When this finding was combined with the analysis results of wax n-alkanes (WaxCn), carbon preference index (CPI), unresolved complex mixture (UCM), hopanes and steranes, it was considered that the long chain n-alkanes were mainly from terrigenous higher plants, and that the short chain n-alkanes mainly originated from bacteria and algae in the lake, compared with previous studies, there were no obvious anthropogenic petrogenic inputs. Terrestrial and aquatic hydrocarbons ratio (TAR) and C21−/C25+ indicated that terrigenous input was higher than aquatic sources and the nearshore n-alkanes were mainly from land-derived sources. Moreover, the distribution of short chain n-alkanes presented a relatively uniform pattern, while the long chain n-alkanes presented a trend that concentrations dropped from nearshore places to the middle of lake.
Abstract Sucrose phosphate synthase (SPS) is a key enzyme catalyzing sucrose metabolism in plants. In this study, we isolated the SPS cDNA from Saccharum spontaneum and designated as SsSPS (GenBank accession no. MF398541). The full-length of SsSPS cDNA was 4153-bp with an opening reading frame (ORF) of 3132 nucleotides, which encoded a 1043-amino acid protein. The nucleotide sequences alignment showed that it had 98%, 97% and 87% homology with S. officinarum, Setaria italica and Lolium perenne, respectively. Moreover, the SsSPS was detected to express in leaf and stem tissues of S. spontaneum and exhibited a predominant expression in the stem tissue. However, there was no significant difference in the expression level of SsSPS between young leaves and mature ones. Additionally, we generated transgenic S. spontaneum using Agrobacterium-mediated transformation. Our data will provide a valuable foundation for further study of the potential role of SPS in plants.
In this paper, crushability of foundry sand particles was studied. Three kinds of in-service silica sands in foundry enterprises selected as the study object, and foundry sand particles were subjected to mechanical load and thermal load during service were analyzed. A set of methods for simulating mechanical load and thermal load by milling and thermal-cold cycling were designed and researched, which were used to characterize the crushability for silica sand particles, the microstructure was observed by SEM. According to the user’s experience in actual application, the crushability of Sand C was the best and then Sand B, the last Sand A. The results indicated that mechanical load, thermal load and thermal-mechanical load can all be used to characterize the crushability of foundry sand particles. Microscopic appearances can qualitatively characterize the crushability of foundry sand particles to a certain extent, combining with the additions and cracks which are observed on the surface.
Sapelovirus A (SV-A) is a positive-sense single-stranded RNA virus which is associated with acute diarrhea, pneumonia and reproductive disorders. The virus capsid is composed of four proteins, and the functions of the structural proteins are unclear. In this study, we expressed SV-A structural protein VP1 and studied its antigenicity and immunogenicity. SDS-PAGE analysis revealed that the target gene was expressed at high levels at 0.6 mM concentration of IPTG for 24 h. The mouse polyclonal antibody against SV-A VP1 protein was produced and reached a high antiserum titer (1: 2,048,000). Immunized mice sera with the recombinant SV-A VP1 protein showed specific recognition of purified VP1 protein by western blot assay and could recognize native SV-A VP1 protein in PK-15 cells infected with SV-A by indirect immunofluorescence assay. The successfully purified recombinant protein was able to preserve its antigenic determinants and the generated mouse anti-SV-A VP1 antibodies could recognize native SV-A, which may have the potential to be used to detect SV-A infection in pigs.