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This paper addresses decentralized energy trading among virtual power plants (VPPs) and proposes a peer-to-peer (P2P) mechanism, including two interactive layers: on the bottom layer, each VPP schedules/reschedules its internal distributed energy resources (DERs); and on the top layer, VPPs negotiate with each other on the trade price and quantity. The bottom-layer scheduling provides initial conditions for the top-layer negotiation, and the feedback of top-layer negotiation affects the bottom-layer rescheduling. The local scheduling/rescheduling of a VPP is formulated as a stochastic optimization problem, which takes into account the uncertainties of wind and photovoltaic power by using the scenarios-based method. In order to describe the capability of a seller VPP to generate more energy than the scheduled result, the concept of power generation potential is introduced and then considered during order initialization. The multidimensional willingness bidding strategy (MWBS) is modified and applied to the price bidding process of P2P negotiation. A 14-VPP case is studied by performing numerous computational experiments. The optimal scheduling model is effective and flexible to deal with VPPs with various configurations of DERs. The parallel price bidding with MWBS is adaptive to market situations and efficient due to its rapid convergence. It is revealed that VPPs can obtain higher profit by participating in P2P energy trading than from traditional centralized trading, and the proposed mechanism of two-layer “interactivity” can further increase VPPs’ benefits compared to its “forward” counterpart. The impacts of VPP configuration and VPP number are also studied. It is demonstrated that the proposed mechanism is applicable to most cases where VPPs manage some controllable DERs.

This paper addresses the coordinative operation problem of multi-energy virtual power plant (ME-VPP) in the context of energy internet. A bi-objective dispatch model is established to optimize the performance of ME-VPP in terms of economic cost (EC) and power quality (PQ). Various realistic factors are considered, which include environmental governance, transmission ratings, output limits, etc. Long short-term memory (LSTM), a deep learning method, is applied to the promotion of the accuracy of wind prediction. An improved multi-objective particle swarm optimization (MOPSO) is utilized as the solving algorithm. A practical case study is performed on Hongfeng Eco-town in Southwestern China. Simulation results of three scenarios verify the advantages of bi-objective optimization over solely saving EC and enhancing PQ. The Pareto frontier also provides a visible and flexible way for decision-making of ME-VPP operator. Two strategies, “improvisational” and “foresighted”, are compared by testing on the Institute of Electrical and Electronic Engineers (IEEE) 118-bus benchmark system. It is revealed that “foresighted” strategy, which incorporates LSTM prediction and bi-objective optimization over a 5-h receding horizon, takes 10 Pareto dominances in 24 h.

Eye-hole wave communication frame was proposed first time.Tracking and pointing model of communication was put forward.New era of short distance optical communication was pioneered.Acguisition method of beaconing light was researched and acquisition time was computed.Two dimension pointing error normal distribution and rayleigh dis-tribution were simulated.Dithering Rician distribution of target pointing was simulated.Attenuation losing formula of receiving len’ diaphragm detection was calculated and simulated.Three dimension pointing error normal distribution and rayleigh distribution were proposed.Three dimension rayleigh fold dithering distribution was proposed.Simulation re-sults are computed and the results is shown.The concept of eye-hole wave communication is proposed first time.The two dimension and three dimension theory model of tracking/pointing dithering problem of robot are put forward and attenua-tion model is presented also.

Recent research has shown that spatial-spectral information can help to improve the classification of hyperspectral images (HSIs). Therefore, three-dimensional convolutional neural networks (3D-CNNs) have been applied to HSI classification. However, a lack of HSI training samples restricts the performance of 3D-CNNs. To solve this problem and improve the classification, an improved method based on 3D-CNNs combined with parameter optimization, transfer learning, and virtual samples is proposed in this paper. Firstly, to optimize the network performance, the parameters of the 3D-CNN of the HSI to be classified (target data) are adjusted according to the single variable principle. Secondly, in order to relieve the problem caused by insufficient samples, the weights in the bottom layers of the parameter-optimized 3D-CNN of the target data can be transferred from another well trained 3D-CNN by a HSI (source data) with enough samples and the same feature space as the target data. Then, some virtual samples can be generated from the original samples of the target data to further alleviate the lack of HSI training samples. Finally, the parameter-optimized 3D-CNN with transfer learning can be trained by the training samples consisting of the virtual and the original samples. Experimental results on real-world hyperspectral satellite images have shown that the proposed method has great potential prospects in HSI classification.

Hydrogels with excellent mechanical properties have potential for use in various fields. However, the swelling of hydrogels under water and the dehydration of hydrogels in air severely limits the practical applications of high-strength hydrogels due to the influence of air and water on the mechanical performance of hydrogels. In this study, we report on a kind of tough and strong nanocomposite hydrogels (NC-G gels) with both swelling-resistant and anti-dehydration properties via in situ free radical copolymerization of acrylic acid (AA) and N-vinyl-2-pyrrolidone (VP) in the water-glycerol bi-solvent solutions containing small amounts of alumina nanoparticles (Al2O3 NPs) as the inorganic cross-linking agents. The topotactic chelation reactions between Al2O3 NPs and polymer matrix are thought to contribute to the cross-linking structure, outstanding mechanical performance, and swelling-resistant property of NC-G gels, whereas the strong hydrogen bonds between water and glycerol endow them with anti-dehydration capacity. As a result, the NC-G gels could maintain mechanical properties comparable to other as-prepared high-strength hydrogels when utilized both under water and in air environments. Thus, this novel type of hydrogel would considerably enlarge the application range of hydrogel materials.

Skeletal muscle development plays an important role in muscle quality and yield, which decides the economic value of livestock. Long non-coding RNAs (lncRNAs) have been reported to be associated with skeletal muscle development. However, little is revealed about the function of lncRNAs in rabbits’ muscle development. LncRNAs and mRNAs in two rabbit breeds (ZIKA rabbits (ZKR) and Qixin rabbits (QXR)) with different growth rates at three developmental stages (0 day, 35 days, and 84 days after birth) were researched by transcriptome sequencing. Differentially expressed lncRNAs and mRNAs were identified for two rabbit breeds at the same stages by DESeq package. Co-expression correlation analysis of differentially expressed lncRNAs and mRNAs were performed to construct lncRNA–mRNA pairs. To explore the function of lncRNAs, Gene Ontology (GO) analysis of co-expression mRNAs in lncRNA–mRNA pairs were performed. In three comparisons, there were 128, 109, and 115 differentially expressed lncRNAs, respectively. LncRNAs TCONS_00013557 and XR_518424.2 differentially expressed in the two rabbit breeds might play important roles in skeletal muscle development, for their co-expressed mRNAs were significantly enriched in skeletal muscle development related GO terms. This study provides potentially functional lncRNAs in skeletal muscle development of two rabbit breeds and might be beneficial to the production of rabbits.

A Chinese yellow wine classification method is proposed based on chemometricanalysis of voltammogram of copper electrode. The main components in Chinese yellowwine are alcohol, amino acids and carbon hydrates. In strong alkaline solution copperelectrode shows rich voltammetric responses to these components. Principal componentanalysis is employed to analyse voltammetric response of copper electrode to six Chinesewine samples in strong alkaline solution. The score plots show good discrimination of thesewine samples. In order to compress the original voltammetric data, a windowed time slicingmethod is investigated in this study. It greatly reduces the input data number and theresulted PCA plot still has good discrimination ability.

Background: Fructus Ligustri Lucidi (FLL) has now attracted increasing attention as an alternative medicine in the prevention and treatment of osteoporosis. This study aimed to provide a general review of traditional interpretation of the actions of FLL in osteoporosis, main phytochemical constituents, pharmacokinetics, pharmacology in bone improving effect, and safety. Materials and Methods: Several databases, including PubMed, China National Knowledge Infrastructure, National Science and Technology Library, China Science and Technology Journal Database, and Web of Science were consulted to locate publications pertaining to FLL. The initial inquiry was conducted for the presence of the following keywords combinations in the abstracts: Fructus Ligustri Lucidi, osteoporosis, phytochemistry, pharmacokinetics, pharmacology, osteoblasts, osteoclasts, salidroside. About 150 research papers and reviews were consulted. Results: FLL is assumed to exhibit anti-osteoporotic effects by improving liver and kidney deficiencies and reducing lower back soreness in Traditional Chinese Medicine (TCM). The data from animal and cell experiments demonstrate that FLL is able to improve bone metabolism and bone quality in ovariectomized, growing, aged and diabetic rats through the regulation of PTH/FGF-23/1,25-(OH)2D3/CaSR, Nox4/ROS/NF-κB, and OPG/RANKL/cathepsin K signaling pathways. More than 100 individual compounds have been isolated from this plant. Oleanolic acid, ursolic acid, salidroside, and nuzhenide have been reported to exhibit the anti-osteoporosis effect. The pharmacokinetics data reveals that salidroside is one of the active constituents, and that tyrosol is hard to detect under physiological conditions. Acute and subacute toxicity studies show that FLL is well tolerated and presents no safety concerns. Conclusions: FLL provides a new option for the prevention and treatment of osteoporosis, which attracts rising interests in identifying potential anti-osteoporotic compounds and fractions from this plant. Further scientific evidences are expected from well-designed clinical trials on its bone protective effects and safety.

Novel graphitic carbon nitride (g-C3N4) coated TiO2 nanocomposites were prepared by a facile and cost-effective solid-state method by thermal treatment of the mixture of urea and commercial TiO2. Because the C3N4 was dispersed and coated on the TiO2 nanoparticles, the as-prepared g-C3N4/TiO2 nanocomposites showed enhanced absorption and photocatalytic properties in visible light region. The as-prepared g-C3N4 coated TiO2 nanocomposites under 450°C exhibited efficient visible light photocatalytic activity for degradation of aqueous MB due to the increased visible light absorption and enhanced MB adsorption. The g-C3N4 coated TiO2 nanocomposites would have wide applications in both environmental remediation and solar energy conversion.

Inositol hexaphosphate (IP6) and inositol (Ins), naturally occurring carbohydrates present in most mammals and plants, inhibit the growth of numerous cancers both in vitro and in vivo. In this study, we first examined the anti-metastatic effects of IP6 and Ins using a liver metastasis model of colorectal cancer (CRC) in BALB/c mice. CT-26 cells were injected into the splenic capsule of 48 BALB/c mice. The mice were then randomly divided into four groups: IP6, Ins, IP6 + Ins and normal saline control (n = 12 per group). IP6 and/or Ins (80 mg/kg each, 0.2 mL/day) were injected into the gastrointestinal tracts of the mice on the second day after surgery. All mice were sacrificed after 20 days, and the tumor inhibition rates were determined. The results demonstrated that the tumor weights of liver metastases and the tumor inhibition rates were reduced in the experimental groups compared to the control group and that treatment with the combination of IP6 and Ins resulted in greater inhibition of tumor growth than treatment with either compound alone. These findings suggest that IP6 and Ins prevent the development and metastatic progression of colorectal cancer to the liver in mice by altering expression of the extracellular matrix proteins collagen IV, fibronectin and laminin; the adhesion factor receptor integrin-β1; the proteolytic enzyme matrix metalloproteinase 9; and the angiogenic factors vascular endothelial growth factor, basic fibroblast growth factor, and transforming growth factor beta in the tumor metastasis microenvironment. In conclusion, IP6 and Ins inhibited the development and metastatic progression of colorectal cancer to the liver in BALB/c mice, and the effect of their combined application was significantly greater than the effect of either compound alone. This evidence supports further testing of the combined application of IP6 and Ins for the prevention of colorectal cancer metastasis to the liver in clinical studies.

V2O5 cluster-modified N-doped TiO2 (N-TiO2/V2O5) nanocomposites photocatalyst was prepared by a facile impregnation-calcination method. The effects of V2O5 cluster loading content on visible light photocatalytic activity of the as-prepared samples were investigated for degradation of toluene in air. The results showed that the visible light activity of N-doped TiO2 was significantly enhanced by loading V2O5 clusters. The optimal V2O5 loading content was found to be 0.5 wt.%, reaching a removal ratio of 52.4% and a rate constant of 0.027 min−1, far exceeding that of unmodified N-doped TiO2. The enhanced activity is due to the deposition of V2O5 clusters on the surface of N-doped TiO2. The conduction band (CB) potential of V2O5 (0.48 eV) is lower than the CB level of N-doped TiO2 (−0.19 V), which favors the photogenerated electron transfer from CB of N-doped TiO2 to V2O5 clusters. This function of V2O5 clusters helps promote the transfer and separation of photogenerated electrons and holes. The present work not only displays a feasible route for the utilization of low cost V2O5 clusters as a substitute for noble metals in enhancing the photocatalysis but also demonstrates a facile method for preparation of highly active composite photocatalyst for large-scale applications.

A simple and sensitive fluoroimmunoassay (FIA) based on a heavy-chain antibody (VHH) for rapid detection of fenitrothion was developed. A VHH library was constructed from an immunized alpaca, and one clone recognizing fenitrothion (namely, VHHjd8) was achieved after careful biopanning. It was biotinylated by fusing with the Avi tag and biotin ligase to obtain a fusion protein (VHHjd8-BT), showing both binding capacity to fenitrothion and the streptavidin poly-horseradish peroxidase conjugate (SA-polyHRP). Based on a competitive assay format, the absorbance spectrum of oxidized 3,3',5,5'-tetramethylbenzidine generated by SA-polyHRP overlapped the emission spectrum of carbon dots, which resulted in quenching of signals due to the inner-filter effect. The developed FIA showed an IC50 value of 1.4 ng/mL and a limit of detection of 0.03 ng/mL, which exhibited 15-fold improvement compared with conventional enzyme-linked immunosorbent assay. The recovery test of FIA was validated by standard GC-MS/MS, and the results showed good consistency, indicating that the assay is an ideal tool for rapid screening of fenitrothion in bulk food samples.