We study the electronic properties for the graphene-based one-dimensional superlattices, whose potential voltages vary according to the envelope of a Gaussian function. It is found that an unusual Dirac point exists a...We study the electronic properties for the graphene-based one-dimensional superlattices, whose potential voltages vary according to the envelope of a Gaussian function. It is found that an unusual Dirac point exists and its location is exactly associated with a zero-averaged wave number （zero-re） gap. This zero-k gap is less sensitive to incident angle and lattice constants, properties opposing those of Bragg gap. The defect mode appearing inside the zero-l gap has an effect on transmission, conductance, and shot noise, which will be useful for further investigation.展开更多
In this paper, according to the temperature and strain distribution obtained by considering the Gaussian pump profile and dependence of physical properties on temperature, we derive an analytical model for refractive ...In this paper, according to the temperature and strain distribution obtained by considering the Gaussian pump profile and dependence of physical properties on temperature, we derive an analytical model for refractive index variations of the diode side-pumped Nd：YAG laser rod. Then we evaluate this model by numerical solution and our maximum relative errors are 5% and 10% for variations caused by thermo–optical and thermo–mechanical effects; respectively. Finally, we present an analytical model for calculating the focal length of the thermal lens and spherical aberration. This model is evaluated by experimental results.展开更多
MicroRNAs(miRNAs)exert an enormous influence on cell differentiation,biological development and the onset of diseases.Because predicting potential miRNA-disease associations(MDAs)by biological experiments usually requ...MicroRNAs(miRNAs)exert an enormous influence on cell differentiation,biological development and the onset of diseases.Because predicting potential miRNA-disease associations(MDAs)by biological experiments usually requires considerable time and money,a growing number of researchers are working on developing computational methods to predict MDAs.High accuracy is critical for prediction.To date,many algorithms have been proposed to infer novel MDAs.However,they may still have some drawbacks.In this paper,a logistic weighted profile-based bi-random walk method(LWBRW)is designed to infer potential MDAs based on known MDAs.In this method,three networks(i.e.,a miRNA functional similarity network,a disease semantic similarity network and a known MDA network)are constructed first.In the process of building the miRNA network and the disease network,Gaussian interaction profile(GIP)kernel is computed to increase the kernel similarities,and the logistic function is used to extract valuable information and protect known MDAs.Next,the known MDA matrix is preprocessed by the weighted K-nearest known neighbours(WKNKN)method to reduce the number of false negatives.Then,the LWBRW method is applied to infer novel MDAs by bi-randomly walking on the miRNA network and the disease network.Finally,the predictive ability of the LWBRW method is confirmed by the average AUC of 0.9393(0.0061)in 5-fold cross-validation(CV)and the AUC value of 0.9763 in leave-one-out cross-validation(LOOCV).In addition,case studies also show the outstanding ability of the LWBRW method to explore potential MDAs.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant No.61001018)the Natural Science Foundation of Shandong Province,China (Grant Nos.ZR2011FM009 and ZR2012FM011)+4 种基金the Research Fund of Shandong University of Science and Technology,China (Grant Nos.2010KYJQ103 and 2012KYTD103)the Project of Shandong Province Higher Educational Science and Technology Program,China (Grant No.J11LG20)the Qingdao Municipal Science & Technology Project,China (Grant No.11-2-4-4-(8)-jch)the Qingdao Municipal Economic and Technical Development Zone Science and Technology Project,China (Grant No.2013-1-64)the Shandong University of Science and Technology Foundation,China (Grant No.YC130220)
文摘We study the electronic properties for the graphene-based one-dimensional superlattices, whose potential voltages vary according to the envelope of a Gaussian function. It is found that an unusual Dirac point exists and its location is exactly associated with a zero-averaged wave number （zero-re） gap. This zero-k gap is less sensitive to incident angle and lattice constants, properties opposing those of Bragg gap. The defect mode appearing inside the zero-l gap has an effect on transmission, conductance, and shot noise, which will be useful for further investigation.
文摘In this paper, according to the temperature and strain distribution obtained by considering the Gaussian pump profile and dependence of physical properties on temperature, we derive an analytical model for refractive index variations of the diode side-pumped Nd：YAG laser rod. Then we evaluate this model by numerical solution and our maximum relative errors are 5% and 10% for variations caused by thermo–optical and thermo–mechanical effects; respectively. Finally, we present an analytical model for calculating the focal length of the thermal lens and spherical aberration. This model is evaluated by experimental results.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos.61902215,61872220 and 61701279.
文摘MicroRNAs(miRNAs)exert an enormous influence on cell differentiation,biological development and the onset of diseases.Because predicting potential miRNA-disease associations(MDAs)by biological experiments usually requires considerable time and money,a growing number of researchers are working on developing computational methods to predict MDAs.High accuracy is critical for prediction.To date,many algorithms have been proposed to infer novel MDAs.However,they may still have some drawbacks.In this paper,a logistic weighted profile-based bi-random walk method(LWBRW)is designed to infer potential MDAs based on known MDAs.In this method,three networks(i.e.,a miRNA functional similarity network,a disease semantic similarity network and a known MDA network)are constructed first.In the process of building the miRNA network and the disease network,Gaussian interaction profile(GIP)kernel is computed to increase the kernel similarities,and the logistic function is used to extract valuable information and protect known MDAs.Next,the known MDA matrix is preprocessed by the weighted K-nearest known neighbours(WKNKN)method to reduce the number of false negatives.Then,the LWBRW method is applied to infer novel MDAs by bi-randomly walking on the miRNA network and the disease network.Finally,the predictive ability of the LWBRW method is confirmed by the average AUC of 0.9393(0.0061)in 5-fold cross-validation(CV)and the AUC value of 0.9763 in leave-one-out cross-validation(LOOCV).In addition,case studies also show the outstanding ability of the LWBRW method to explore potential MDAs.
