A Highly Sensitive Femtosecond Time-Resolved Sum Frequency Generation Vibrational Spectroscopy System with Simultaneous Measurement of Multiple Polarization Combinations

Characterization of real-time and ultrafast motions of the complex molecules at surface and interface is critical to understand how interracial molecules function. It requires to develop surface-sensitive, fast-identification, and time-resolved techniques. In this study, we employ several key technical procedures and successfully develop a highly sensitive femtosecond time-resolved sum frequency generation vibrational spectroscopy （SFG-VS） system. This system is able to measure the spectra with two polarization combinations （ssp and ppp, or psp and ssp） simultaneously. It takes less than several seconds to collect one spectrum. To the best of our knowledge, it is the fastest speed of collecting SFG spectra reported by now. Using the time-resolved measurement, ultrafast vibrational dynamics of the N-H mode of α-helical peptide at water interface is determined. It is found that the membrane environment does not affect the N-H vibrational relaxation dynamics. It is expected that the time-resolved SFG system will play a vital role in the deep understanding of the dynamics and interaction of the complex molecules at surface and interface. Our method may also provide an important technical proposal for the people who plan to develop time-resolved SFG systems with simultaneous measurement of multiple polarization combinations.

Novel ensemble learning based on multiple section distribution in distributed environment

Because most ensemble learning algorithms use the centralized model, and the training instances must be centralized on a single station, it is difficult to centralize the training data on a station. A distributed ensemble learning algorithm is proposed which has two kinds of weight genes of instances that denote the global distribution and the local distribution. Instead of the repeated sampling method in the standard ensemble learning, non-balance sampling from each station is used to train the base classifier set of each station. The concept of the effective nearby region for local integration classifier is proposed, and is used for the dynamic integration method of multiple classifiers in distributed environment. The experiments show that the ensemble learning algorithm in distributed environment proposed could reduce the time of training the base classifiers effectively, and ensure the classify performance is as same as the centralized learning method.

Clinical features of multiple trauma patients combined with spine and spinal cord injuries

Objective To analyze the clinical features of the multiple trauma patients combined with spine and spinal cord injuries.Methods A retrospective study was performed in143multiple trauma patients combined with spine and spinal

Simple, Rapid and Sensitive Detection of Pseudomonas aeruginesa by Colorimetric Multiple Cross Displacement Amplification

Pseudomonas aeruginosa(P:aeruginosa)is a major opportunistic pathogen in hospital-acquired infections.Thus,carly diagnosis is the best strategy for fighting against these infections.In this report,we incorporated multiple crOsS displacement amplification(MCDA)combined with the malachite green(MG)for rapid,sensitive,specific and visual detection of P.aeruginosa by targeting the oprl gene.The MCDA-MG assay was conducted at 67°C for only 40 min during the amplification stage,and then products were directly detected by using colorimetric indicators(MG),eliminating the use of an electrophoresis instrument or amplicon analysis equipment.The entire process,including specimen processing(35 min),amplification(40 min)and detection(5 min),can be finished within 80 min.All 30 non-P.aeruginosa strains tested negative,indicating the high specificity of the MCDA primers.The analytical sensitivity of the MCDA-MG assay was 100 fg of genomic templates per reaction in pure culture,which was in complete accordance with MCDA by gel clectrophoresis and real-time turbidity.The assay was also successfully applied to detecting P.aeruginosa in stool samples.Therefore,the rapidity,simplicity,and nearly equipment free platform of the MCDA-MG technique make it possible for clinical diagnosis,and more.

A multiple transistor combination low-voltage curvature-corrected bandgap reference

A new bandgap reference（BGR） curvature compensation technology is proposed,which is a kind of multiple transistor combination.On the basis of the existing first-order bandgap reference technology,a compensation current circuit consisting of a sink current branch and a source current branch is added.The BGR was designed and simulated by using Semiconductor Manufacturing International Corporation（SMIC） 0.18μm CMOS process.The simulation results showed that when the power supply voltage was 1 V,the temperature coefficient of the BGR was 2.08 ppm/℃with the temperature range from—40 to 125℃,the power supply rejection ratio （PSRR） was—64.77 dB and the linear regulation was 0.44 mV/V with the supply power changing from 0.85 to 1.8 V.

Research on Remote Sensing Image of Land Cover Classification Based on Multiple Classifier Combination

This paper proposed an algorithm in which the maximum probability and the weighted average strategy were used for the combination of member classifiers. Using parallel computing, we test the algorithm on a China-Brazil Earth Resources Satellite （CBERS） image for land cover classification. The results show that using three computers in parallel can reduce the classification time by 30%, as compared with using only one computer with a dual core processor. The accuracy of the final image is 93.34%, and Kappa is 0.92. Multiple classifier combination can enhance the precision of the image classification, and parallel computing can increase the speed of calculation so that it becomes possible to process remote sensing images with high efficiency and accuracy.

PARAMETRIC AND NON-PARAMETRIC COMBINATION MODEL TO ENHANCE OVERALL PERFORMANCE ON DEFAULT PREDICTION

The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics have put forward numerous default prediction models.However,how to use multiple models to enhance overall performance on default prediction remains untouched.In this paper,a parametric and non-parametric combination model is proposed.Firstly,binary logistic regression model(BLRM),support vector machine(SVM),and decision tree(DT) are used respectively to establish models with relatively stable and high performance.Secondly,in order to make further improvement to the overall performance,a combination model using the method of multiple discriminant analysis(MDA) is constructed.In this way,the coverage rate of the combination model is greatly improved,and the risk of miscarriage is effectively reduced.Lastly,the results of the combination model are analyzed by using the K-means clustering,and the clustering distribution is consistent with a normal distribution.The results show that the combination model based on parametric and non-parametric can effectively enhance the overall performance on default prediction.

Influence of Different Connection Types on Mechanical Behavior of Girder Trusses

The static test of two single light wood trusses and two girder trusses were carried out to explore theperformance enhancement effect of girder trusses and the influence of different connection types on the mechanicalbehavior, which was studied through the comparison of the test results of four trusses. It is found that the girder trussescan show better load-carrying capacity and anti-deforming ability compared with which simply increases the cross-sectionsize of the members. Because of the advantage of the connection materials and the non-fastening connection mechanism,the wood pin-connected truss has more ultimate bearing capacity which is 2.6 times of single truss, and better deformationresistance which is only 66.2% of the nailed one’s creep value.