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In this work, a broadband polarization converter in transmission mode based on tri-layered metamaterial structure is proposed to realize asymmetric transmission and linear polarization conversion s...

Horizontal well and large-scale hydraulic fracturing are the main technical means for tight gas development at present. However, tight gas reservoirs are characterized by strong heterogeneity, and different types of gas Wells have different production characteristics and pressure propagation laws. In view of the above problems, based on the physical characteristics and reservoir reconstruction evaluation of tight gas reservoir in GAS Reservoir A in Sichuan Basin, three types of reservoir hydraulic fracture models and numerical simulation models were generated by using numerical simulation method, and the production dynamic prediction and pressure wave propagation law were studied. The simulation results show that: (1) In the beginning of production, the deflated area is mainly hydraulic fracture reconstruction area, and the drainage radius of all kinds of gas Wells expands at similar speed; (2) When the drainage range is expanded in the untransformed area, the dimensionless drainage radius expansion speed of gas well is positively correlated with the reservoir physical property. The dimensionless drainage radius and drainage area of class I and II horizontal Wells are relatively faster, while that of Class III Wells is slightly slower.

Background The coronavirus disease 2019 (COVID-19) has turned into a pandemic and resulted in huge death tolls and burdens. Integrating Chinese and western medicine has played an important role in the fight against the COVID-19 pandemic. Purpose We aimed to develop a living evidence-based guideline of integrating Chinese and western medicine for COVID-19. Study design Living evidence-based guideline. Methods This living guideline was developed using internationally recognized and accepted guideline standards, dynamically monitoring the release of new clinical evidence, and quickly updating the linked living systematic review, evidence summary tables, and recommendations. Modified Delphi method was used to reach consensus for all recommendations. The certainty of the evidence, resources, and other factors were fully considered, and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach was used to rate the certainty of evidence and the strength of recommendations. Results The first version of this living guidance focuses on patients who are mild or moderate COVID-19. A multidisciplinary guideline development panel was established. Ten clinical questions were identified based on the status of evidence and a face-to-face experts' consensus. Finally, nine recommendations were reached consensus, and were formulated from systematic reviews of the benefits and harms, certainty of evidence, public accessibility, policy supports, feedback on proposed recommendations from multidisciplinary experts, and consensus meetings. Conclusion This guideline panel made nine recommendations, which covered five traditional Chinese medicine (TCM) prescription granules/decoction (MXXFJD, QFPD, XFBD, TJQW, and JWDY), three Chinese patent medicines (LHQW granules/capsule, JHQG granules, and LHQK granules), and one Chinese herbal injection (XBJ injection). Of them, two were strongly recommended (LHQW granules/capsule and QFPD decoction), and five were weakly recommended (MXXFJD decoction, XFBD decoction, JHQG granules, TJQW granules, and JWDY decoction) for the treatment of mild and moderate COVID-19; two were weakly recommended against (XBJ injection and LHQK granules) the treatment of mild and moderate COVID-19. The users of this living guideline are most likely to be clinicians, patients, governments, ministries, and health administrators.

The main purpose of visual servoing is to control the motion of a robot system based on visual information provided by one or more cameras. It is an important research topic in the robotics community. In uncalibrated visual servoing, the image Jacobian matrix estimation is of great importance to the success of visual servoing control. This paper addresses the online estimation of the total Jacobian matrix for robot visual servoing using the unscented particle filter. We first give the definition of the total Jacobian matrix and formulate the total Jacobian matrix estimation problem into Bayesian filtering framework. Then, we propose to estimate the total Jacobian matrix using the unscented particle filter. Each particle is propagated and updated using the unscented Kalman filter equations. Such an update can make full use of the image feature measurements and consequently generate more accurate estimation results. The simulation results on a 2DOF robot visual servoing platform show that the proposed method provides accurate and reliable performance in the object tracking task.

Indoor simulator and on-road instrumented vehicle are the most popular ways to analyze driving behaviors by using collected Vehicle Sensor Data (VSD). However, for a same driver, the driving perfor...

A theoretical investigation for the feasibility of laser cooling B N - anion is presented. An ab initio calculation on the three low-lying states Χ2Σ+, Α2Π and Β2Σ+ are performed at the CASSCF/MRCI + Q level. The calculated spectroscopic constants are in good agreement with the available theoretical and experimental data. Radiative properties including Franck-Condon factor, Einstein coefficients and radiative lifetimes are determined. The calculation shows that the transition B 2 Σ + ( v ' ) ↔ X 2 Σ + ( v ' ' ) has highly diagonal FCFs, especially f00 = 0.9898, and enough short radiative lifetimes. A cooling scheme by three laser beams is proposed, which requires one main pumping laser ( λ 00 = 474.67 nm) and two repumping lasers ( λ 01 = 514.64 nm, λ 12 = 514.90 nm). The population dynamics of cooling is investigated with the rate equation approach. The simulation demonstrates that the population does not remain trapped within the intermediate Α2Π state. The resultant scattered photons are about 2.5 × 10 4 , which is expected to stop B N - anion molecule in a cryogenic beam theoretically.

Aiming at the problem of enhancing the suggestion and decision-making ability of home automation system under the condition of less user intervention involved, an adaptive control system based on scene perception in smart home is proposed. Firstly, the scenario information is extracted from graphic data by reinforcement learning method, and then the adaptive decision model based on deep learning is constructed, a simulated system was tested based on its ability to predict the best interval present in day for lights and blinds in different rooms, operations of the home equipment is updated according to the user's behavior. Results shows that the proposed model is able to predict the best 15 minute interval in a day for lights and blinds in different rooms of a home without user intervention and user feedback could improve model prediction performance.

Aiming at the problems of complicated formats in graduation thesis writing, a smart graduation thesis format parsing and generation model is proposed. First, a model is built based on the features of the thesis templates analyzed and extracted. Then templates are generated automatically according to user preferences. Finally, content of the thesis is incorporated into the specified template. Experiment shows that it can automatically generate format information according to the templates without human intervention, which could save a lot of time and reduce format errors.

The near-infrared (NIR) long persistent phosphors LiGa5O8:Cr3+ have attracted considerable attention owing to the potential applications in medical imaging. A series of novel LiGa5O8:Cr3+ phosphors with different Sn4+ concentrations were successfully prepared by a high temperature solid-state reaction. The influence of codoping Sn4+ on photoluminescence, long persistent luminescence and thermoluminescence properties were systematically investigated. The results showed the persistent luminescence performance of phosphors was significantly enhanced after the incorporation of Sn4+ ions. Thermoluminescence spectra indicated that the incorporation of Sn4+ contributes to the formation of defects and increase the traps density, thus improving the persistent luminescence properties of LiGa5O8:Cr3+ phosphors.

Visual object tracking is a challenging research task in computer vision community which has been intensively studied by researchers in the past decades. Among all of the existing methods, particle filter based methods have gained special attention due to its ability to handle highly nonlinear/non-Gaussian multi-modality models. This paper proposes a robust particle filter based tracking method based on the Langevin Monte Carlo sampling. The Langevin Monte Carlo sampling method leverages the gradient of the posterior probability distribution to draw new particles. Meanwhile, an auxiliary momentum variable is introduced to ensure that the proposed sample cannot be trapped in local mode of the posterior distribution. As for the likelihood model, we introduce the locality sensitive histogram based model to handle the severe appearance variations induced by illumination change, partial occlusion or other factors. We compare the proposed method with several popular tracking methods from qualitative and quantitative perspectives. The experimental results show that the proposed method outperforms its counterparts.

A reddish orange emissive long afterglow phosphors Ca2−xSnO4:xSm3+ (x = 0.001–0.05) are prepared by solid-state reaction in air atmosphere. The synthesized phosphors are characterized and analyzed by X-ray diffraction, photoluminescence spectra, afterglow decay curves, and absorption spectra. Under excitation at 407 nm, three emission peaks locate at 565, 610 and 654 nm, respectively, which can be assigned to the 4G5/2→6HJ (J = 5/2, 7/2, 9/2) transitions of Sm3+ ion. The fluorescent intensity and the afterglow characteristic depend on the concentration of Sm3+. The optimal Sm3+ concentration is x = 0.01. The CIE 1931 chromaticity coordinates of the emission and afterglow are (x = 0.6103, y = 0.3891) and (x = 0.5668, y = 0.4325) located in the range of reddish orange light emission. The afterglow decay curves of the Ca2SnO4:Sm3+ phosphor indicate both fast and slow decay components. The striking difference of the afterglow luminescence intensities of the phosphor after irradiation under 254 and 365 nm UV is discussed in deeply with absorption spectrum.

SrSi2O2N2:Eu2+ phosphors were prepared by a high temperature solid-state reaction method from the Eu2O3–SrCO3–Si3N4–Si system. The phase characteristics and luminescence properties of the samples synthesized with various starting composition of silicon powder were investigated in detail by mean of X-ray diffraction and fluorescence spectrophotometer analysis. Single-phase SrSi2O2N2:Eu2+ was successfully obtained avoiding the formation of an unknown phase, which usually exists from Eu2O3–SrCO3–Si3N4–SiO2 system. The comparison of different composition of starting materials [Si:Si3N4 = (1.5 + x)/3:(0.5−x) (x = 0, 0.1, 0.2, 0.3, 0.4, 0.5)] indicates that different silicon powder content in starting materials plays an crucial role in phase and luminescence properties of SrSi2O2N2. The XRD diffraction intensity ratio of nitrogen-rich phase/oxygen-rich phase is increasing regularly with increasing x values in different starting composition of Si:Si3N4 = (1.5 + x)/3:(0.5−x) (x = 0, 0.1, 0.2, 0.3, 0.4, 0.5). The emission intensity of the samples is gently enhanced with the increasing Si:Si3N4. This demonstrates that the SiO originated in the reaction of Si and CO2 is superior to SiO2 for preparation of SrSi2O2N2:Eu2+.

Visual tracking is a fundamental research topic in computer vision community, which is of great importance in many application areas including augmented reality, traffic control, medical imaging and video editing. This paper presents an ordered over-relaxation Langevin Monte Carlo sampling (ORLMC) based tracking method within the Bayesian filtering framework, in which the traditional object state variable is augmented with an auxiliary momentum variable. At the proposal step, the proposal distribution is designed by simulation of the Hamiltonian dynamics. We first use the ordered over-relaxation method to draw the momentum variable which could suppress the random walk behavior in Gibbs sampling stage. Then, we leverage the gradient of the energy function of the posterior distribution to draw new samples with high acceptance ratio. The proposed tracking method could ensure that the tracker will not be trapped in local optimum of the state space. Experimental results show that the proposed tracking method successfully tracks the objects in different video sequences and outperforms several conventional methods.

Among many of the existing nonlinear filtering methods, particle filter has drawn special attention due to its ability to deal with nonlinear/non-Gaussian state estimation problems. But it suffers from degeneracy problem. In order the suppress this problem, in this paper, we introduced an iterated particle filter which is applied to the bearing only target tracking problem. The proposed method is on the base of the iterated Kalman filter which is used for building a proposal distribution for the particle filter. The iterated Kalman filter can improve the estimation accuracy by iteratively incorporating the current observations which makes it a better candidate for building proposal distribution and easy to be integrated into the particle filtering framework. Simulation results demonstrate that the iterated particle filter shows superior performance to the other methods in bearing only target tracking.

Recent researches on visual tracking have shown significant improvement in accuracy by handling the large uncertainties induced by appearance variation and abrupt motion. Most studies concentrate on random walk based Markov chain Monte Carlo(MCMC) tracking methods which have shown inefficiency in sampling from complex and high-dimensional distributions. This paper proposes an adaptive Hamiltonian Monte Carlo sampling based tracking method within the Bayesian filtering framework. In order to suppress the random walk behavior in Gibbs sampling stage, the ordered over-relaxation method is used to draw the momentum item for the joint state variable. An adaptive step-size based scheme is used to simulate the Hamiltonian dynamics in order to reduce the simulation error and improve acceptance rate of the proposed samples. Furthermore, in designing the appearnce model, we introduce the locality sensitive histogram (LSH) to deal with appearance changes induced by illumination change. The proposed tracking method is compared with several state-of-the-art trackers using different quantitative measures: success rate and abruption capture rate. Extensive experimental results have shown its superiority to several other trackers.

The red-emitting phosphors of CaBi2Ta2O9: Eu3+ and CaBi2Ta2O9: Eu3+/La3+ were prepared by the high-temperature solid-state reaction method. By using X-ray diffraction, the structural properties of powders have been analyzed. The photoluminescence excitation and emission spectra of CaBi2Ta2O9: Eu3+, and the dependence of luminescence intensity on doped Eu3+ ions concentration were investigated. The results show that CaBi2Ta2O9: Eu3+ can be efficiently excited by near-UV and blue light to realize an intense red luminescence (615 nm) corresponding to the electric dipole transition 5D0 → 7F2 of Eu3+ ions. Meantime, an enhancement of photoluminescence intensity was observed in La3+ ions doped CaBi2Ta2O9: Eu3+ phosphors. The CIE chromaticity coordinates of CaBi2Ta2O9: Eu3+ and CaBi2Ta2O9: Eu3+/La3+ are (0.6121, 0.3835) and (0.6141, 0.3821), corresponding to the hues of orange-red. The phosphors can be suggested to be a potential red-emitting phosphor for the application on white LEDs under irradiation of near-UV or blue chips.

SrSi2O2N2:Eu2+ phosphors were synthesized by a high temperature solid-state reaction method using Sr2SiO4 as the precursor. The effect of different Eu valence and partial substitution of Sr2+ by Ba2+ in Sr2SiO4 on the luminescence properties of SrSi2O2N2:Eu2+ was studied. Phase characteristics and luminescence properties of the samples were investigated in detail by mean of X-ray diffraction and fluorescence spectrophotometer analysis. The results revealed that there exist charge defects originated from the replacement of Sr2+ by Eu3+ in Sr2SiO4:Eu3+ and anion vacancies which likely occur in Sr1.95Ba0.05SiO4:Eu2+ due to partial substitutions of Sr2+ by Ba2+. The defects in the Sr2SiO4 play a role in luminescence properties of SrSi2O2N2:Eu2+. The emission intensity of the samples that use Sr2SiO4:Eu3+ and Sr1.95Ba0.05SiO4:Eu2+ as precursor was increased by 17% and 11% respectively when compared with that of using Sr2SiO4:Eu2+ as precursor(0.5 mol% Eu2+ concentration). And with the increasing Eu2+ concentration, the increasing degree of luminescence intensity of the samples declines rapidly. In additional, at 200 °C, 75–78% emission intensity of the phosphors measured at room temperature were maintained. Furthermore, partial replacement of Sr2+ by Ba2+ in samples can reduce thermal quenching.

Multi-factor models of asset pricing indicate a linear relationship between the expected return of assets while exposing to one or more risks. In this study, the sensitivity of portfolio returns to 4 selected macroeconomic factors (Market Performance, Real GDP, Inflation, Unemployment) were examined by means of multiple linear regression analyses with regard to multi-factors. Experiments show that the generalized approach of moment estimators of risk premiums lead to better results on individual assets over historical averages. Especially when the factors are weakly correlated with assets which indicates that the factors selected should be less correlated with each other.

This paper uses a previously developed spreadsheet cost model which simulates public transport modes operated on a 12-km route to analyse the total costs of different passenger demand levels. The previous cost model was a very powerful tool to estimate the social and operator costs for different public transport technologies. However, as the model is strategic, some basic assumptions were made which are relaxed in this paper. First, the speed-flow equation in the original spreadsheet model assumes that speed decreases according to the ratio of the current frequency and the lane capacity which is based on the safety headway without taking into account passenger boardings. However, this may vary in different operating environments. Therefore, the speed-flow equation is improved by moving from a linear equation to a piecewise equation that considers the features of different operating environments. Second, the model assumes that supply is sufficient to meet demand. However, when the level of demand is high for the lower-capacity public transport technologies, passengers may find the incoming vehicle full and therefore, they have to wait more than one service interval. This paper applies queuing theory to investigate the probability of having to wait longer than the expected service headways which will affect the average passenger waiting time. The extra waiting time for each passenger is calculated and applied in the spreadsheet cost model. Third, the original model assumed that demand was externally fixed (exogenous). To evaluate the differences after applying these equations, endogenous demand rather than exogenous demand will be investigated by using the elasticities for passenger waiting time and journey time.

Sequential Monte Carlo method (also named as particle filter) is now a standard framework for solving nonlinear/non-Gaussian problems, especially in computer vision fields. This paper proposes an ant colony optimization (ACO) based iterative particle filter for visual tracking. In the proposed tracking method, the basic idea of ACO is used to simulate the behavior of particle moving toward the posterior density. Such idea is incorporated into the particle filtering framework in order to overcome the well-known problem of particle impoverishment. We design an iterative proposal distribution for particle generation in order to generate better predicted sample states. The experimental results demonstrate that the proposed tracker shows better performance than the other trackers.