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Pearl powder is an ingredient in complex of traditional Chinese medicine in treatment on skin. However, how pearl powder affects cells especially cancer cells is unclear. Here we investigated the pearl powder electrochemical characters and biological functions. We found the major component of carbon and calcium with metals of Mn and Fe in pearl powder. The possible magnetic and non-peroxidase nanozyme activity suggest the complex functions of pearl powder in biological response. Moreover, the pearl powder at mixed nanoscale can inhibit cancer cells spreading. Our data suggest that pearl powder at mixed nanoscale can exert more efficient function in cells. Nano Chinese medicine may play complicated roles in therapy.

Bacterial infections have been clinically treated with variety of antibiotics. The excessive use of antibiotic nowadays, however, has caused severe drug resistance by the emergence of bacteria strains. The improper management of antibiotic usage has led to the contamination of the environment that threatens people's life. Therefore, it becomes critical to develop novel antibacterial agents that will reduce the risk of drug resistance to its minimum. As the nanomaterial and nanotechnology find their ways to anchor on nearly every aspect of our daily life, the antibacterial properties of nanoparticles have been studied and have shown promising effect while treating different strains of bacteria. Metal containing nanomaterials, though very effective, may potentially accumulate in human body and become cytotoxic. Recently, carbon nano dots derived from natural product have shown comparable antibacterial effect but are low cytotoxicity to human cells and cost. In this study, bactericidal effect of dates-derived carbon nanoparticles (CNPs) on survival of different gram-negative or gram-positive strains was tested. Dates-derived CNPs exhibited strong antibacterial effect against both gram-positive and gram-negative bacteria. Impressively, complete inhibition in the growth of all bacterial strains used in this research was achieved using as prepared CNPs. Moreover, the as prepared CNP was discovered as a great sensor to detect the pollution in ocean water. In deed, an enzyme kit was developed for the ocean water pollution detection. Thus CNP has great potential as biosensor both in medicine and pollution detection of ocean water.

Water-soluble carbon nanodots (C-dots) prepared from food, or food wastes generally have a size below 10 nm and emit fluorescence upon the UV radiation with environmental safe specialty. These features of C-dots have been taken advantages in the areas of drug delivery as well as the inhibition of cancer cell growth as green carbon nanoparticles. The electrically physical, chemical characteristics and the mechanisms of their various applications, however, remain unclear. In the present work, we were attempted to develop the C-dots that carry charges which can be later embedded in the glass beads to achieve a specific biological microscopic process. The prepared C-dots were discovered to be able to be used in DNA extraction without affecting DNA cleavage; in addition, they can be used in semi-3D cell culture especially for cancer cells. These electrically charged C-dots@glass beads developed in the present work showed a great potential in diagnosis and drug delivery targeting tumor cells.

In order to objectively predict the investment behavior of investors on the P2P network lending platform, improving the resource utilization rate of P2P network platform and the probability of borrowers' success. In this paper, an improved rough BP neural network prediction model is proposed. First, an index system that affects the investment behavior is set up. Then, the improved adaptive genetic algorithm and the rough set attribute reduction are combined. Finally, the improved BP neural network is used to predict the investment behavior. Taking the transaction records of the “Yidai” as the basic data, the empirical prediction results are consistent with the actual situation.

In order to evaluate the level of green technology innovation of local industrial enterprises and reduce the environmental pollution caused by production activities in industrial enterprises, a fuzzy comprehensive evaluation model based on the importance ranking of nodes in complex networks is proposed. First of all, a scientific evaluation index system of green technology innovation is constructed in this paper through comprehensive analysis of the influencing factors of green technology innovation, and the corresponding complex network topology model is established according to the index system. Secondly, the idea of TOPSIS ideal point method is used in this paper to improve the evaluation method of node importance in complex networks, and a fuzzy comprehensive evaluation model based on improved importance ranking method of complex network nodes to determine weights is established. Finally, the level of green technology innovation in 30 regions of China is evaluated with empirical analysis data in the paper. The empirical results prove the rationality, objectivity and effectiveness of the complex network system to deal with the impact factors of green technological innovation in industrial enterprises.

Detection of weak signals in a low SNR environment is generally difficult, particularly, when the underlying signal noise is not only not Gaussian distributed but essentially unknown. A good example of such a case is the detection of termite biting signals from noisy audio data recorded by a passive acoustic sensor. In this paper, we present a novel technique to discriminate weak signals in data from noise of a learned non-Gaussian distribution. The proposed method, proceeds via the framework of generalised likelihood ratio test, and consists of two fundamental steps. First, an entropy-based incremental variational Bayesian inference is adopted to learn the non-Gaussian distribution from data using a Gaussian mixture model. An information geometric mapping of the data is then carried out via the total Bregman divergence (tBD), where the ambient noise distribution is approximated by the tBD-based l 1 -norm center of the neighboring data points over a specified time window. Experiment results show that the proposed method yields a significantly improvement in detection probability in low SNR and a robust detection performance compared with existing detection techniques.

Firstly, the article researched on the theory and practice of two-vehicle central collision and two-vehicle eccentric impact (collision), proved that all kinds of complex two-dimension collision can transform into one-dimension collision with theory analysis. Secondly introduced collision parameter k and u, established vehicle speed model with momentum equation and momentum matrix equation, dispeled affection of impulsive force distributing coefficient by quadratic optimizing, and established accident reconstruction model of two-vehicle collision based on reverse-reasoning method, realized two calculate modes. Finally, the method upwards was effective and feasible by validating with actual cases. We also find that the two basic hypothesis of traffic accident had effect on accident reconstruction, it need to research and optimization in the future.

With the advent of high-accurate satellite microwave measurements of sea surface temperature (SST), cloud-free high resolution SST is promising by the merging of microwave SST with infrared SST. Regional high resolution data from NOAA AVHRR have been received by an L-band ground station. Being upgraded to a dual-band system, the station is capable of receiving data from Windsat onboard Coriolis. Thus both regional infrared and microwave SST can be available in real- time. The cloud-free high resolution SST can be obtained by the merging of infrared and microwave SST data. At current stage of development, AMSR-E SST data are used to be merged with AVHRR data for demonstration. Keywords-SST;cloud-free;merge