Synchronization of discrete-time fractional fuzzy neural networks with delays via quantized control.

In this paper, synchronization issue of discrete-time fractional fuzzy neural networks (DFFNNs) with delays is solved via quantized control, and is applied in image encryption. Firstly, a novel fractional-order h-difference inequality which makes Lyapunov method more flexible and practical is strictly proved based on the properties of convex functions and theory of discrete fractional calculus. Secondly, by using compression mapping theorem and mathematical induction, we obtain two sufficient conditions to ensure the existence and uniqueness of solutions for DFFNNs. Whereafter, we design a suitable quantized controller, which not only saves channel resources but also reduces control costs. By utilizing our inequality and some analytical techniques, several conservative synchronization criteria for DFFNNs are acquired. Finally, two examples are arranged to illustrate the validity and practicability of our results.

3D SAACNet with GBM for the classification of benign and malignant lung nodules.

In view of the low diagnostic accuracy of the current classification methods of benign and malignant pulmonary nodules, this paper proposes a 3D segmentation attention network integrating asymmetric convolution (SAACNet) classification model combined with a gradient boosting machine (GBM). This can make full use of the spatial information of pulmonary nodules. First, the asymmetric convolution (AC) designed in SAACNet can not only strengthen feature extraction but also improve the network's robustness to object flip and rotation detection and improve network performance. Second, the segmentation attention network integrating AC (SAAC) block can effectively extract more fine-grained multiscale spatial information while adaptively recalibrating multidimensional channel attention weights. The SAACNet also uses a dual-path connection for feature reuse, where the model makes full use of features. In addition, this article makes the loss function pay more attention to difficult and misclassified samples by adding adjustment factors. Third, the GBM is used to splice the nodule size, originally cropped nodule pixels, and the depth features learned by SAACNet to improve the prediction accuracy of the overall model. A comprehensive ablation experiment is carried out on the public dataset LUNA16 and compared with other lung nodule classification models. The classification accuracy (ACC) is 95.18%, and the area under the curve (AUC) is 0.977. The results show that this method effectively improves the classification performance of pulmonary nodules. The proposed method has advantages in the classification of benign and malignant pulmonary nodules, and it can effectively assist radiologists in pulmonary nodule classification.

Morphologically-tunable TiO2 nanorod film with high energy facets: green synthesis, growth mechanism and photocatalytic activity.

A novel green synthesis strategy was developed to synthesize a film photocatalyst containing anatase TiO(2) nanorods wholly dominated with {100} and {101} facets by employing a simple hydrothermal reaction in the presence of NaCl solution and hydrogen titanate nanosheet array film. The formation mechanism of the anatase TiO(2) nanorods was deduced from X-ray diffraction (XRD), scanning electron microscopy (SEM) and transmission electron microscopy (TEM) characterizations, which revealed that the highly reactive {100} facets were derived from the enhanced structural stability of hydrogen titanate induced by Na(+) and selective adsorption of Cl(-) on the {100} facets of anatase TiO(2) crystals. Photocatalytic performance has indicated that the TiO(2) nanorods with {100} facet exposure exhibit much higher activities than that of rhombic nanoparticles with {101} facet exposure for the degradation of organic contaminants, which may be primarily ascribed to the high surface energy of {100} facets.

Rutile TiO2 nanowires on anatase TiO2 nanofibers: a branched heterostructured photocatalysts via interface-assisted fabrication approach.

A water-dichloromethane interface-assisted hydrothermal method was employed to grow rutile TiO(2) nanowires (NWs) on electrospun anatase TiO(2) nanofibers (NFs), using highly reactive TiCl(4) as precursor. The water-dichloromethane interface inhibited the formation of rutile NWs in water phase, but promoted the selective radial growth of densely packed rutile NWs on anatase NFs to form a branched heterojunction. The density and length of rutile NWs could be readily controlled by varying reaction parameters. A formation mechanism for the branched heterojunction was proposed which involved (1) the entrapment of rutile precursor nanoparticles at water-dichloromethane interface, (2) the growth of rutile NWs on anatase NFs via Ostwald ripening through the scavengering of interface-entrapped rutile nanoparticles. The heterojunction formed at anatase NF and rutile NW enhanced the charge separation of both under ultraviolet excitation, as evidenced by photoluminescence and surface photovoltage spectra. The branched TiO(2) heterostructures showed higher photocatalytic activity in degradation of rodamine B dye solution than anatase NFs, and the mixture of anatase NFs, and P25 powders, which was discussed in terms of the synergistic effect of enhanced charge separation by anatase-rutile heterojunction, high activity of rutile NWs, and increased specific area of branched heterostructures.

Superhydrophobic and ultraviolet-blocking cotton textiles.

Cotton textile was coated with ZnO@SiO(2) nanorods in order to obtain superhydrophobic and ultraviolet (UV)-blocking properties. The coating process was conducted in mild conditions, which involved the low-temperature preparation of ZnO seeds, hydrothermal growth of ZnO nanorods, bioinspired layer-by-layer deposition of a SiO(2) shell on the surface of ZnO nanorods, and hydrophobic modification of ZnO@SiO(2) nanorods with octadecyltrimethoxysilane. Despite the highly curved morphology of cotton fibers, the ZnO@SiO(2) nanorods coated the textile densely and uniformly. The treated cotton textile was found to have a large UV protection factor (UPF = 101.51) together with UV-durable superhydrophobicity, as determined by contact-angle measurement under long-term UV irradiation. The good UV-blocking property can be ascribed to the high UV absorbance and scattering properties of ZnO nanorods, and the UV-durable superhydrophobicity is a result of suppression of the photoactivity of ZnO nanorods by a SiO(2) shell.