Stubborn state estimation for complex-valued neural networks with mixed time delays: the discrete time case

Neural Computing and Applications - In this paper, the state estimation problem is investigated for a class of discrete-time complex-valued neural networks (CVNNs) with mixed time delays. We...     

Low-rank tensor completion via combined Tucker and Tensor Train for color image recovery

Applied Intelligence - In recent years, low-rank tensor completion has been widely used in color image recovery. Tensor Train (TT), as a balanced tensor rank minimization method, has achieved good...     

Prediction of NSCLC recurrence from microarray data with GEP

Lung cancer is one of the deadliest diseases in the world. Non‐small cell lung cancer (NSCLC) is the most common and dangerous type of lung cancer. Despite the fact that NSCLC is preventable and curable for some cases if diagnosed at early stages, the vast majority of patients are diagnosed very late. Furthermore, NSCLC usually recurs sometime after treatment. Therefore, it is of paramount importance to predict NSCLC recurrence, so that specific and suitable treatments can be sought. Nonetheless, conventional methods of predicting cancer recurrence rely solely on histopathology data and predictions are not reliable in many cases. The microarray gene expression (GE) technology provides a promising and reliable way to predict NSCLC recurrence by analysing the GE of sample cells. This study proposes a new model from GE programming to use microarray datasets for NSCLC recurrence prediction. To this end, the authors also propose a hybrid method to rank and select relevant prognostic genes that are related to NSCLC recurrence prediction. The proposed model was evaluated on real NSCLC microarray datasets and compared with other representational models. The results demonstrated the effectiveness of the proposed model.Inspec keywords: lung, cancer, lab‐on‐a‐chip, genetics, patient diagnosisOther keywords: NSCLC recurrence prediction, microarray data, GE programming, nonsmall cell lung cancer, cancer recurrence, histopathology data, microarray gene expression, prognostic genes     

Proline-derived in situ synthesis of nitrogen-doped porous carbon nanosheets with encaged Fe2O3@Fe3C nanoparticles for lithium-ion battery anodes

The homogeneous incorporation of heteroatoms into two-dimensional C nanostructures, which leads to an increased chemical reactivity and electrical conductivity as well as enhanced synergistic catalysis as a conductive matrix to disperse and encapsulate active nanocatalysts, is highly attractive and quite challenging. In this study, by using the natural and cheap hydrotropic amino acid proline—which has remarkably high solubility in water and a desirable N content of ~12.2 wt.%—as a C precursor pyrolyzed in the presence of a cubic KCl template, we developed a facile protocol for the large-scale production of N-doped C nanosheets with a hierarchically porous structure in a homogeneous dispersion. With concomitantly encapsulated and evenly spread Fe₂O₃ nanoparticles surrounded by two protective ultrathin layers of inner Fe₃C and outer onion-like C, the resulting N-doped graphitic C nanosheet hybrids (Fe₂O₃@Fe₃C-NGCNs) exhibited a very high Li-storage capacity and excellent rate capability with a reliable and prolonged cycle life. A reversible capacity as high as 857 mAh•g^–1 at a current density of 100 mA•g^–1 was observed even after 100 cycles. The capacity retention at a current density 10 times higher—1,000 mA•g^–1—reached 680 mAh•g^–1, which is 79% of that at 100 mA•g^–1, indicating that the hybrids are promising as anodes for advanced Li-ion batteries. The results highlight the importance of the heteroatomic dopant modification of the NGCNs host with tailored electronic and crystalline structures for competitive Li-storage features.

     

Tensile Properties and Failure Mechanism of a New 3D Nonorthogonal Woven Composite Material

Tensile properties and failure mechanism of a newly developed three-dimensional (3D) woven composite material named 3D nonorthogonal woven composite are investigated in this paper. The microstructure of the composite is studied and the tensile properties are obtained by quasi-static tensile tests. The failure mechanism of specimen is discussed based on observation of the fracture surfaces via electron microscope. It is found that the specimens always split along the oblique yarns and produce typical v-shaped fracture surfaces. The representative volume cell (RVC) is established based on the microstructure. A finite element analysis is conducted with periodical boundary conditions. The finite element simulation results agree well with the experimental data. By analyzing deformation and stress distribution under different loading conditions, it is demonstrated that finite element model based on RVC is valid in predicting tensile properties of 3D nonorthogonal woven composites. Stress distribution shows that the oblique yarns and warp yarns oriented along the x direction carry primary load under x tension and that warp yarns bear primary load under y tension.