Synthesis of new 1,3-bis（phenylethynyl）disilazanes

Three new 1,3-bis(phenylethynyl)disilazanes were synthesized from the reaction of 1,3-dichlorodisilazanes with (phenylethy- nyl)lithium,and characterized by infrared (IR) spectra,nuclear magnetic resonance (NMR) and mass spectrometry (MS).     

Rapid determination of underivatized pyroglutamic acid, glutamic acid, glutamine and other relevant amino acids in fermentation media by LC-MS-MS

Determination of amino acids in a complex matrix without derivatization is advantageous, however, difficulties are found in both the detection and the separation of those compounds. In this study, a rapid and reliable LC-MS-MS method for the quantitation of underivatized amino acids in exocellular media was established. Injections were made directly after centrifugation of the samples, without further preparation. The separation of seven underivatized amino acids was achieved on a reversed-phase C18 column with pentadecafluorooctanoic acid as a volatile ion-pair reagent, and the specific detection of most amino acids was achieved by MS-MS of the specific transitions [M + H]+-->[M + H - 46]+. The calibration curves of all analytes were linear over the range of 1.0-1000 microg ml(-1) and the detection limits ranged from 0.1 to 5 ng ml(-1), with an injection volume of 20 microl. The inter-day and intra-day precisions ranged from 2.6 to 5.7% and 4.8 to 8.2%, respectively; the mean recoveries of the seven analytes were 81-104%, 91-107% and 93-101% respectively at the spiked level of 10, 40 and 200 microg ml(-1). A large number of fermentation samples were analysed using this method. The technique is simple, rapid, selective and sensitive, and shows potential for the high-throughput quantitation of amino acids from other biological matrices.     

Regioselective synthesis of 1,2,3-triazole derivatives via 1,3-dipolar cycloaddition reactions in water

Reaction of an arylacetylene with an azide in hot water gave 1,4-disubstituted 1,2,3-triazoles in high yields, while similar reaction between a terminal aliphatic alkyne and an azide (except m-nitroazidobenzene) afforded a mixture of regioisomers with the ratio of 1,4- to 1,5-isomers ranging from 3:1 to 28.6:1. Reactions of m-nitroazidobenzene with either arylalkynes or aliphatic alkynes formed only 1,4-disubstituted derivatives in excellent yields.     

Polyurethane cationomers. I. Structure–properties relationships

Polyurethane (PU) cationomers have been synthesized by quaternizing tertiary amine-containing linear polyurethanes using different quaternizers containing acid groups. The effect of chemical structure of PU cationomers on the physical properties was studied. The mechanical properties of PU cationomers were improved with decreasing molecular weight of poly(caprolactone) glycol, and increasing concentration of quaternary ammonium. Decreasing the carbon number in the alkyl group of the N-alkyl diethanol-amine chain-extenders, and using rigid symmetrical diisocyanates, the mechanical properties of the PU cationomers were increased. The effects of these factors on the glass transition temperature of PU cationomers were also examined. The mechanical properties of the PU cationomers decreased by immersion in water and recovered after removal of the water.     

Speckle characteristics of simulated deep Fresnel region under shadowing effect

After the three-dimensional self-affine fractal random surface simulation, we use the optical scattering theory to calculate the deep Fresnel region speckle(DFRS) under consideration of the more strict shadowing effect. The evolution of DFRS with the scattering distance and the intensity probability distribution are studied. It is found that the morphology of the scatterer has an antisymmetric relationship with the intensity distribution of DFRS, and the effect of micro-lenses on the scattering surface causes the intensity probability distribution of DFRS to deviate from the Gaussian speckle in the high light intensity area.     

Picture Fuzzy Threshold Graphs with Application in Medicine Replenishment

In this study, a novel concept of picture fuzzy threshold graph (PFTG) is introduced. It has been shown that PFTGs are free from alternating 4-cycle and it can be constructed by repeatedly adding a dominating or an isolated node. Several properties about PFTGs are discussed and obtained the results that every picture fuzzy graph (PFG) is equivalent to a PFTG under certain conditions. Also, the underlying crisp graph (UCG) of PFTG is a split graph (SG), and conversely, a given SG can be applied to constitute a PFTG. A PFTG can be decomposed in a unique way and it generates three distinct fuzzy threshold graphs (FTGs). Furthermore, two important parameters i.e., picture fuzzy (PF) threshold dimension (TD) and PF partition number (PN) of PFGs are defined. Several properties on TD and PN have also been discussed. Lastly, an application of these developed results are presented in controlling medicine resources.     

Surface Functionalized Magnetic Nanoparticles as a Selective Sorbent for Affinity Fishing of PPAR-γ Ligands from Choerospondias axillaris

Coronary heart disease (CHD), which has developed into one of the major diseases, was reported to be treated by the target of peroxisome proliferators-activate receptor γ (PPAR-γ). As a natural medicine long used in the treatment of CHD, there are few studies on how to screen the target active compounds with high specific activity from Choerospondias axillaris. To advance the pace of research on target-specific active compounds in natural medicines, we have combined magnetic ligand fishing and functionalized nano-microspheres to investigate the active ingredients of PPAR-γ targets in Choerospondias axillaris. The PPAR-γ functionalized magnetic nano-microspheres have been successfully synthesized and characterized by vibrating sample magnetometer (VSM), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The specificity, reusability, and reproducibility of the nano-microspheres were investigated with the help of the specific binding of rosiglitazone to PPAR-γ. In addition, the incubation temperature and the pH of the buffer solution in the magnetic ligand fishing were optimized to improve the specific adsorption efficiency of the analytes. Finally, with the aid of ultraperformance liquid chromatography plus Q-Exactive Orbitrap tandem mass spectrometry (UHPLC-Q-Exactive Orbitrap-MS/MS), the 16 active ligands including 9 organic acids, 5 flavonoids, and 2 phenols were found in the ethanolic extracts of Choerospondias axillaris. Therefore, the study can provide a successful precedent for realizing the designated extraction and rapid isolation of target-specific active ingredient groups in the complex mixtures.     

Quantum-Inspired Complex-Valued Language Models for Aspect-Based Sentiment Classification

Aiming at classifying the polarities over aspects, aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. The vector representations of current models are generally constrained to real values. Based on mathematical formulations of quantum theory, quantum language models have drawn increasing attention. Words in such models can be projected as physical particles in quantum systems, and naturally represented by representation-rich complex-valued vectors in a Hilbert Space, rather than real-valued ones. In this paper, the Hilbert Space representation for ABSA models is investigated and the complexification of three strong real-valued baselines are constructed. Experimental results demonstrate the effectiveness of complexification and the outperformance of our complex-valued models, illustrating that the complex-valued embedding can carry additional information beyond the real embedding. Especially, a complex-valued RoBERTa model outperforms or approaches the previous state-of-the-art on three standard benchmarking datasets.     

Optimal Energy-Storage Configuration for Microgrids Based on SOH Estimation and Deep Q-Network

Energy storage is an important adjustment method to improve the economy and reliability of a power system. Due to the complexity of the coupling relationship of elements such as the power source, load, and energy storage in the microgrid, there are problems of insufficient performance in terms of economic operation and efficient dispatching. In view of this, this paper proposes an energy storage configuration optimization model based on reinforcement learning and battery state of health assessment. Firstly, a quantitative assessment of battery health life loss based on deep learning was performed. Secondly, on the basis of considering comprehensive energy complementarity, a two-layer optimal configuration model was designed to optimize the capacity configuration and dispatch operation. Finally, the feasibility of the proposed method in microgrid energy storage planning and operation was verified by experimentation. By integrating reinforcement learning and traditional optimization methods, the proposed method did not rely on the accurate prediction of the power supply and load and can make decisions based only on the real-time information of the microgrid. In this paper, the advantages and disadvantages of the proposed method and existing methods were analyzed, and the results show that the proposed method can effectively improve the performance of dynamic planning for energy storage in microgrids.