The numerical operator method to the real time dynamics of currents through the nanostructures with different topologies

We present the numerical operator method designed for the real time dynamics of currents through nanostructures beyond the linear response regime. We apply this method to the transient and stationary currents through nanostructures with different topologies, e.g., the flakes of square and honeycomb lattices. We find a quasi-stationary stage with a life proportional to the flake size in the transient currents through the square flakes, but this quasi-stationary stage is destroyed in the presence of disorder. However, there is no quasi-stationary stage in the transient currents through the honeycomb flakes, showing that the transient current depends strongly upon the topologies of the nanostructures. We also study the stationary current by taking the limit of the current at long times. We find that the stationary current through a square flake increases smoothly as the voltage bias increasing. In contrast, we find a threshold voltage in the current-voltage curve through a honeycomb flake, indicating a gap at the Fermi energy of a honeycomb flake.Comment: 13 pages, 4 figure

Identification of Technical Journals by Image Processing Techniques

The emphasis of this study is put on developing an automatic approach to identifying a given unknown technical journal from its cover page. Since journal cover pages contain a great deal of information, determining the title of an unknown journal using optical character recognition techniques seems difficult. Comparing the layout structures of text blocks on the journal cover pages is an effective method for distinguishing one journal from the other. In order to achieve efficient layout-structure comparison, a left-to-right hidden Markov model (HMM) is used to represent the layout structure of text blocks for each kind of journal. Accordingly, title determination of an input unknown journal can be effectively achieved by comparing the layout structure of the unknown journal to each HMM in the database. Besides, from the layout structure of the best matched HMM, we can locate the text block of the issue date, which will be recognized by OCR techniques for accomplishing an automatic journal registration system. Experimental results show the feasibility of the proposed approach

Research on the computational method of vibration impact coefficient for the long-span bridge and its application in engineering

To compute vibration impact coefficient at each part of the long-span bridge more accurately, this paper proposed a computational method based on vehicle-bridge coupling vibration. Firstly, the general equations of vehicle-bridge coupling vibration were derived based on the standard fatigue vehicle and multi-scale model of bridges. Secondly, the corresponding program of vehicle-bridge coupling vibration was designed. Thirdly, the computational method of vibration impact coefficient for the long-span bridge was introduced and obtained. The proposed computation method of vibration impact coefficient based on vehicle-bridge coupling vibration was finally verified by the corresponding experiment. They were consistent with each other, and the computational method was reliable and can be used to analyze the bridge. Based on the verified method, a lot of influence factors on vibration impact coefficient were analyzed. As a result, we can obtain a bridge with the smallest vibration impact coefficient. Finally, the remaining life of bridges was computed and evaluated based on the smallest vibration impact coefficient

Numerical computation for vibration characteristics of long-span bridges with considering vehicle-wind coupling excitations based on finite element and neural network models

CA (Cellular Automaton) model was applied to the simulation of random traffic flow to develop a model considering the randomness of traffic flow and apply it to wind-vehicle-bridge coupling vibration. Finite element and neural network models were adopted respectively to numerically compute the vibration characteristics of bridges under wind and vehicle loads, verify the correctness of model. Subspace iteration method was used for the modal analysis of bridges. Natural frequencies of the top 8 orders were 0.21 Hz, 0.27 Hz, 0.36 Hz, 0.45 Hz, 0.56 Hz, 0.66 Hz, 0.87 Hz and 1.02 Hz respectively. The vibration frequency of the long-span bridge was consistent with the vibration characteristics of large-scale complex structures. Natural modes mainly reflected the torsion and bending of main beam and the swinging vibration of side and main towers. Fluctuation wind time-history presented periodic characteristics. The maximum and minimum values of fluctuation wind were about 20 m/s and –20 m/s respectively. The target and simulation values of power spectral density of wind speed were basically the same in change trend, which indicated that the fluctuation wind time-history computed in this paper was reliable. The model of dense traffic flow based on CA more truly described the running status like accelerating, decelerating and changing lanes of vehicles on the bridge, also contained the density information of vehicles and more truly reflected traffic characteristics. Vibration accelerations of the long-span bridge were symmetrically distributed. Vibration acceleration of central position in the left main span was the largest and near 50 cm/s2; vibration acceleration on the main tower was the smallest. The curve of vibration displacement with considering wind loads presented some fluctuations, while the vibration displacement of bridges without considering wind loads was very smooth. In addition, the amplitude of vibration displacement without considering wind loads moved laterally towards the left compared with that with considering wind loads. Therefore, wind loads must be considered when the vibration characteristics of the long-span bridge were computed. Otherwise, the accuracy of computational results would be reduced. It only took 0.5 hours to use neural network to predict the vibration acceleration of the long-span bridge. In the case of the same computer performance, it took 5 hours to use finite element model to predict the vibration acceleration of the long-span bridge. The advantage of neural network model in predicting the performance of large-scale complex structures like a long-span bridge could be obviously found. In the future, we will consider using neural network model to systematically study and optimize the long-span bridge

Association of the shuffling of Streptococcus pyogenes clones and the fluctuation of scarlet fever cases between 2000 and 2006 in central Taiwan

<p>Abstract</p> <p>Background</p> <p>The number of scarlet fever occurrences reported between 2000 and 2006 fluctuated considerably in central Taiwan and throughout the nation. Isolates of <it>Streptococcus pyogenes </it>were collected from scarlet fever patients in central Taiwan and were characterized by <it>emm </it>sequencing and a standardized pulsed-field gel electrophoresis (PFGE) method. National weekly report data were collected for investigating epidemiological trends.</p> <p>Results</p> <p>A total of 23 <it>emm </it>types were identified in 1,218 <it>S. pyogenes </it>isolates. The five most prevalent <it>emm </it>types were <it>emm</it>12 (50.4%), <it>emm</it>4 (23.2%), <it>emm</it>1 (16.4%), <it>emm</it>6 (3.8%) and <it>emm</it>22 (3.0%). PFGE analysis with <it>Sma</it>I suggested that, with a few exceptions, strains with a common <it>emm </it>type belonged to the same clone. There were two large <it>emm</it>12 clones, one with DNA resistant to cleavage by <it>Sma</it>I. Each prevalent <it>emm </it>clone had major PFGE strain(s) and many minor strains. Most of the minor strains emerged in the population and disappeared soon after. Even some major strains remained prevalent for only 2–3 years before declining. The large fluctuation of scarlet fever cases between 2000 and 2006 was associated with the shuffling of six prevalent <it>emm </it>clones. In 2003, the dramatic drop in scarlet fever cases in central Taiwan and throughout the whole country was associated with the occurrence of a severe acute respiratory syndrome (SARS) outbreak that occurred between late-February and mid-June in Taiwan.</p> <p>Conclusion</p> <p>The occurrences of scarlet fever in central Taiwan in 2000–2006 were primarily caused by five <it>emm </it>types, which accounted for 96.8% of the isolates collected. Most of the <it>S. pyogenes </it>strains (as defined by PFGE genotypes) emerged and lasted for only a few years. The fluctuation in the number of scarlet fever cases during the seven years can be primarily attributed to the shuffling of six prevalent <it>emm </it>clones and to the SARS outbreak in 2003.</p