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The nano-silver antibacterial dressing is processed and formed after being expanded and cut by medical degreased gauze and nano-silver solution. The nano-silver content is 0.4%–1.0% of the dressing mass (the nano-silver content per square meter of the dressing is 0.28–0.7 g). Nano silver plays an auxiliary role in reducing wound infections. This study is the first to report the rehabilitative effect of enhanced recovery after surgery (ERAS) management combined with nano antibacterial dressing in the perioperative period of gynecological cancer. The times until first exhaust, first defecation, ambulation, and the duration of hospitalization of the ERAS group were shorter than in the regular group. The VAS scores at 6 and 12 h after operation in the ERAS group were lower than those in the regular group. The levels of self-responsibility, self-concept, self-care skills, and health knowledge in the ERAS group were higher than those in the regular group. The total incidence of complications in the ERAS group was 9.09%, which was lower than that in the regular group (23.64%). The level of satisfaction with nursing care in the ERAS group was 98.18%, which was higher than that in the regular group (83.64%). The findings confirmed that ERAS combined with nano antibacterial dressing can promote the rehabilitation of patients, relieve pain, strengthen the self-care ability of patients, reduce the occurrence of complications, and improve satisfaction with nursing care.

For attribute-based zero-shot learning (ZSL), the attribute classifiers learned previously on the training images may not be usable for the testing images due to that the training and testing images may follow different data distributions. Since domain adaptation learning can effectively perform knowledge transfer under the circumstance of different data distributions, we proposed a novel ZSL method, referred to as multisource domain attribute adaptation based on adaptive multikernel alignment learning (A-MKAL), from the point of view of classifier adaptation. Considering there may be a large difference between object classes, we adopt the clustering method to group the training images according to the class–class correlation measured by the whitened cosine similarity, thus multiple source domains are created. The created multiple source domains are then combined into one weighted source domain to participate in the distribution discrepancy match across domains. In order to adapt the attribute classifier learned on the well-defined source domains to the target domain (the training image set), we designed the A-MKAL by applying the centered kernel alignment to align the attribute kernel matrix and the kernel function of adaptive multiple kernel learning. Experiments on Shoes, OSR, and AWA datasets show that, compared with state-of-the-art methods, our proposed method yields more accurate classification.

The coupled additive and multiplicative (CAM) noises model is a stochastic volatility model for derivative pricing. Unlike the other stochastic volatility models in the literature, the CAM model uses two Brownian motions, one multiplicative and one additive, to model the volatility process. We provide empirical evidence that suggests a nontrivial relationship between the kurtosis and skewness of asset prices and that the CAM model is able to capture this relationship, whereas the traditional stochastic volatility models cannot. We introduce a control variate method and Monte Carlo estimators for some of the sensitivities (Greeks) of the model. We also derive an approximation for the characteristic function of the model.

Single-crystal trigonal (t) Se nanobelts have been synthesized on a large scale by reducing SeO2 with glucose at 160 °C. Electron microscopy images show that the nanobelts are ∼80 nm in diameter, ∼25 nm in thickness, and up to several hundreds of micrometers in length. HRTEM images prove that the nanobelts are single crystals and preferentially grow along the [001] direction. The time-dependent TEM images revealed that the formation and growth of t-Se nanobelts were governed by a solid−solution−solid growth mechanism. The redox reaction directly produced amorphous (α) Se nanoparticles under hydrothermal conditions. t-Se nanobelts were formed by dissolution and recrystallization of the initial α-Se nanoparticles under the functional capping of poly(vinylpyrrolidone) (PVP). The nanobelts obtained exhibit a quantum size effect in optical properties, showing a blue shift of the band gap and direct transitions relative to the values of bulk t-Se.

Nanobelts of hexagonal close-packed cobalt have been synthesized by a hydrothermal reduction process at 160 °C. The as-synthesized nanobelts, with a rectangular cross section, are 200–500 nm in diameter, 50–80 nm in thickness and a few hundreds of micrometres in length. The Co nanobelts are single crystalline and grow preferentially along the [001] direction. Furthermore, the coexisting nanosheets with zigzag edges are 2–4 µm in diameter and several tens of nanometres in thickness. The hysteresis loop at room temperature of the as-obtained product shows a ferromagnetic behaviour. In addition, some influential factors as regards the morphologies of the final products were also discussed.