Enhanced superconductivity at the interface of W/Sr2_{2}RuO4_{4} point contact

Differential resistance measurements are conducted for point contacts (PCs) between tungsten tip approaching along the $c$ axis direction and the $ab$ plane of Sr$_{2}$RuO$_{4}$ single crystal. Three key features are found. Firstly, within 0.2 mV there is a dome like conductance enhancement due to Andreev reflection at the normal-superconducting interface. By pushing the W tip further, the conductance enhancement increases from 3\% to more than 20\%, much larger than that was previously reported, probably due to the pressure exerted by the tip. Secondly, there are also superconducting like features at bias higher than 0.2 mV which persists up to 6.2 K, resembling the enhanced superconductivity under uniaxial pressure for bulk Sr$_{2}$RuO$_{4}$ crystals but more pronounced here. Third, the logarithmic background can be fitted with the Altshuler-Aronov theory of tunneling into quasi two dimensional electron system, consistent with the highly anisotropic electronic system in Sr$_{2}$RuO$_{4}$.Comment: prb style, 9 pages, 8 fig

Task Pricing Optimization Model of Crowdsourcing Platform

In this paper, we established a task pricing optimization model by the Logistic and anti-resolve thought to work out the problem of unequal spatial distribution and overall low of the task completion rate in the crowdsourcing platforms. Combining with the actual application information, we use scatter diagram, contour map, etc. to make a qualitative study and find that the reason why some of the tasks are not accepted is because the enterprise failed to take the total task quotas around the task into consideration while pricing the task. Then, combined with the influencing factors of traditional pricing model and results of qualitative analyses, the optimization model of crowdsourcing platform is built. Next, we select an ending project in an app of “make money” in China as the example to evaluate the effectiveness of our model. We applied the method of computer simulation to solve the model, and we find that, under the new pricing plan, the task completion rate has been significantly improved, which proves the conclusion of our qualitative analysis and the validity of the optimal model

Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy

A particle swarm optimization (PSO)-based least square support vector machine (LS-SVM) method was investigated for quantitative analysis of extraction solution of Yangxinshi tablet using near infrared (NIR) spectroscopy. The usable spectral region (5400–6200 cm-1) was identified, then the first derivative spectra smoothed using a Savitzky–Golay filter were employed to establish calibration models. The PSO algorithm was applied to select the LS-SVM hyperparameters (including the regularization and kernel parameters). The calibration models of total flavonoids, puerarin, salvianolic acid B and icariin were established using the optimum hyperparameters of LS-SVM. The performance of LS-SVM models were compared with partial least squares (PLS) regression, feed-forward back-propagation network (BPANN) and support vector machine (SVM). Experimental results showed that both the calibration results and prediction accuracy of the PSO-based LS-SVM method were superior to PLS, BP-ANN and SVM. For PSO-based LS-SVM models, the determination coefficients (R2) for the calibration set were above 0.9881, and the RSEP values were controlled within 5.772%. For the validation set, the RMSEP values were close to RMSEC and less than 0.042, the RSEP values were under 8.778%, which were much lower than the PLS, BP-ANN and SVM models. The PSO-based LS-SVM algorithm employed in this study exhibited excellent calibration performance and prediction accuracy, which has definite practice significance and application value

Design of non-fragile state estimators for discrete time-delayed neural networks with parameter uncertainties

This paper is concerned with the problem of designing a non-fragile state estimator for a class of uncertain discrete-time neural networks with time-delays. The norm-bounded parameter uncertainties enter into all the system matrices, and the network output is of a general type that contains both linear and nonlinear parts. The additive variation of the estimator gain is taken into account that reflects the possible implementation error of the neuron state estimator. The aim of the addressed problem is to design a state estimator such that the estimation performance is non-fragile against the gain variations and also robust against the parameter uncertainties. Sufficient conditions are presented to guarantee the existence of the desired non-fragile state estimators by using the Lyapunov stability theory and the explicit expression of the desired estimators is given in terms of the solution to a linear matrix inequality. Finally, a numerical example is given to demonstrate the effectiveness of the proposed design approach

Biological mechanisms of glycan- and glycosaminoglycan-based nutraceuticals

A nutraceutical is defined as a standardized pharmaceutical-grade nutrient. Among hundreds of nutraceuticals, polysaccharide or glycan-based products such as those containing chondroitin sulfate glycosaminoglycan isolated from animal cartilage have been on the top nutraceutical selling list for many years. It is expected that the nutraceutical market will reach $250 billion dollars worldwide by 2018. Glycans are most abundant biopolymers on earth those are synthesized by bacteria, fungi, plants, insects, and animals. Glycans that are synthesized by animals or from all marine sources can be modified with covalently linked sulfates or containing acidic monosaccharides whereas glycans that are synthesized by terrestrial plants or fungi usually do not contain sulfates. Glycans such as starch are common sources of energy for animals, therefore they are on the nutrient-side of nutraceuticals. Undigestible polysaccharides from plants could serve as dietary fiber for humans that change the contents of the gastrointestinal tract and affect how other nutrients and chemicals are absorbed, thus dietary fibers could be called nutraceuticals. Other intra- and extracellular glycans from different sources serve as biological active components that regulate a myriad of physiological functions. The reported biological functions for such glycans are not limited to immune system regulatory, anti-coagulating, anti-tumor, anti-viral, anti-oxidant, anti-aging, and lipid-lowing activities, which make them pharmaceutical-side nutraceuticals. This review will present the full spectrum of glycan-based nutraceuticals and summarize current knowledge (published data from 1960s to current) of the structure, biological function, and mechanisms of glycans from both terrestrial and marine sources