Lopesohylemya, a new genus of Anthomyiidae (Diptera) from Qinghai, China

An anthomyiid new genus Lopesohylemya with its type species L. qinghaiensis sp. n. from Qinghai, China, is described and figured. It is closely related to Eustalomyia histrio group, which is transferred to present new genus from Eustalomyia, it is suggested by the authors

A Hierarchical Approach to Measuring the Information Content of the Contours in a Map

Taking a typical map,contour map as example,a methodology is developed for measuring the information content of the contours in a map in a hierarchy.Existing methods of measuring the information content of a map are first summarized,which are almost based on Shannon information theory.Then the sources of contour map information and its nature is discovered.It is found that the information of a contour map is able to be described into three levels,namely,element level,neighborhood level and global level.At the element level,it means the information of a contour line;at the neighborhood level,it refers to the information of basic geomorphic unit;and at the global level,the distribution information of whole geomorphology in a contour map is indicated.Moreover,all levels of information are measured from the nature of information source,i.e.variability and diversity.Finally,some practical examples are provided to illustrate the proposed methods

A Space-time Interpolation Method of Missing Data Based on Spatio-temporal Heterogeneity

Space-time interpolation is widely used to estimate missing data in a dataset integrating both spatial and temporal records. Although space-time interpolation plays a key role in space-time modeling, it is still challenging to model heterogeneity of space-time data in the interpolation model.To overcome this limitation, in this study, a novel space-time interpolation method based on spatio-temporal heterogeneity is proposed to estimate missing data of space-time datasets. Firstly, space partitioning and time slicing of space-time data was implemented. Then the estimates of missing data are computed using space-time surrounding records with heterogeneous spatio-temporal covariance model.Further the weights of space and time are determined using the correlation coefficient and the finally estimates of missing data is combined integrating time and space estimates. Finally, two datasets are selected to verify the accuracy of this method. Experimental results show that the proposed method outperforms the four state-of-the-art methods with higher accuracy and applicability

Modeling a Typical Non-Uniform Deformation of Materials Using Physics-Informed Deep Learning: Applications to Forward and Inverse Problems

Numerical methods, such as finite element or finite difference, have been widely used in the past decades for modeling solid mechanics problems by solving partial differential equations (PDEs). Differently from the traditional computational paradigm employed in numerical methods, physics-informed deep learning approximates the physics domains using a neural network and embeds physics laws to regularize the network. In this work, a physics-informed neural network (PINN) is extended for application to linear elasticity problems that arise in modeling non-uniform deformation for a typical open-holed plate specimen. The main focus will be on investigating the performance of a conventional PINN approach to modeling non-uniform deformation with high stress concentration in relation to solid mechanics involving forward and inverse problems. Compared to the conventional finite element method, our results show the promise of using PINN in modeling the non-uniform deformation of materials with the occurrence of both forward and inverse problems