Factors Affecting Consultation Volume in an Online Healthcare Community: Evidence from Online Data in China

Under the background of sharing economy, online healthcare has been developing rapidly to ease the pressure of offline medical care. The interaction between doctors and patients is the key element of the sustainable development of online healthcare communities. To explore the influence of doctors\u27 online word-of-mouth, online vitality and consulting service price on consultation volume, we build a multiple regression model taking consultation volume as the dependent variable, online word-of-mouth and online vitality and consulting service price as independent variables. By analyzing the data of 37,977 doctors collected from the online healthcare community, we find that doctors\u27 online word-of-mouth and online vitality have significant positive impacts on consultation volume. Our results also show that there is an inverted U-shaped relationship between consulting service price and consultation volume, and online word-of-mouth has a greater impact on consultation volume than any other factors. Finally, we put forward suggestions for doctors in online healthcare communities

Study on the technology and properties of 3D bioprinting SF/GT/n-HA composite scaffolds

In this paper, three kinds of natural polymer materials, silk fibroin (SF), gelatin (GT), and nano-hydroxyapatite (n-HA), are mixed as 3D printing bioink to mimic protein polysaccharide and collagen fibers in natural articular cartilage. By changing the SF content, SF/GT/n-HA composite scaffolds with different ratios are prepared using 3D bioprinting technology. The microstructure and morphology, biological properties and mechanical properties of composite scaffolds are characterized. The results show that the printing precision of the bioink with 10% SF is best, and the composite scaffold with 10% SF also exhibits better mechanical properties, whose tensile elastic modulus is 10.60 ± 0.32 MPa and the compression elastic modulus is 1.22 ± 0.06 MPa. These studies are helpful to understand the interaction between SF, GT and n-HA, and provide a theoretical basis for the preparation of better silk fibroin-based composite scaffolds

Artemisinin derivative SM934, influences the activation, proliferation, differentiation and antibody-secreting capacity of β-cells in systemic lupus erythematosus mice via inhibition of TLR7/9 signaling pathway

Purpose: To study the influence of artemisinin derivative, SM934 on activation, proliferation, differentiation and antibody-secreting capacity of B cells of systemic lupus erythematosus (SLE) mice, and the underlying mechanism. Methods: Female MRL/lpr mice (n = 60) were randomly assigned to four groups of 15 mice each: SLE, 2.5 mg/kg SM934; 5 mg/kg SM934, and 10 mg/kg SM934 groups. Serum levels of interleukins 6, 10, 17 and 21 (IL-6, IL-17, IL-10 and IL-21) were determined. The secretions of immunoglobulins G and M (IgG and IgM) by B cells were determined. The population of B lymphocyte subtypes was determined flow cytometrically. The expressions of Blimp-1 and Bcl-6, Toll-like receptors 7 and 9 (TLR7 and TLR9) mRNAs were determined. Results: SLE-induced upregulation of serum IL-10, IL-6, IL-17 and IL-21 was significantly and dosedependently reduced following a 2-month treatment with SM934 (p < 0.01). Treatment with SM934 significantly and dose-dependently accentuated B cell germinal center B cell populations, but significantly and dose-dependently decreased the populations of plasma and activated B cells (p < 0.01). The splenic levels of IgG and IgM were decreased in a dose-dependent fashion after 8 weeks of treatment (p < 0.01). Artemisinin derivative SM934 decreased the expression of Blimp-1, and upregulated the expression of Bcl-6, both in a dose-dependent manner (p < 0.01). Moreover, SM934 decreased the mRNA expressions of TLR7 and TLR9 in a dose-based manner (p < 0.01). Conclusion: Artemisinin derivative SM934 mitigates LSE syndromes by suppressing the TLR-induced B-cell stimulation and plasma cell generatio

The Hotspots of Social Commerce：A Literate View

Social commerce is a new economic and business model that has been rapidly risen and developed in recent years, which has aroused widespread concern in both the business and academic community. In this paper, we adopt the information visualization technology to econometrically analyze the co-citation data of the social commerce fields retrieved by Web of Science from 2005 to 2017, then explore the time and space distribution of the social commerce, and analyze the cooperation network of institutions and periodicals for the published papers in this field. At the end of the paper, we present the frontier and hot topics of social commerce based on the analysis of high-frequency keywords, co-occurrence, and the rate of burst term. We hope that it will provide constructive reference and guidance for the following related studies in the area of social commerce

Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxes

Object detection via inaccurate bounding boxes supervision has boosted a broad interest due to the expensive high-quality annotation data or the occasional inevitability of low annotation quality (\eg tiny objects). The previous works usually utilize multiple instance learning (MIL), which highly depends on category information, to select and refine a low-quality box. Those methods suffer from object drift, group prediction and part domination problems without exploring spatial information. In this paper, we heuristically propose a \textbf{Spatial Self-Distillation based Object Detector (SSD-Det)} to mine spatial information to refine the inaccurate box in a self-distillation fashion. SSD-Det utilizes a Spatial Position Self-Distillation \textbf{(SPSD)} module to exploit spatial information and an interactive structure to combine spatial information and category information, thus constructing a high-quality proposal bag. To further improve the selection procedure, a Spatial Identity Self-Distillation \textbf{(SISD)} module is introduced in SSD-Det to obtain spatial confidence to help select the best proposals. Experiments on MS-COCO and VOC datasets with noisy box annotation verify our method's effectiveness and achieve state-of-the-art performance. The code is available at https://github.com/ucas-vg/PointTinyBenchmark/tree/SSD-Det.Comment: accepted by ICCV 202

The Viscosity Characteristics for the Mixed Refrigerant HFO-1234yf + HFC-152a

Refrigerants are improved with the development of refrigeration and air conditioning industry. Because of the long-term use of chlorine-containing halogenated hydrocarbon substances, the ozone depletion and global warming become important issues around the world. Searching for refrigerants with low GWP (Global Warming Potential) and zero ODP (Ozone Depletion Potential) is urgent. HFC-134a used to be a widely used refrigerant with zero ODP. However, its high GWP of 1300 and long atmospheric lifetime of 14 years would cause the problem of global warming. Thus, for the sake of environment, the substitution of HFC-134a is imperative. In recent years, HFO-1234yf has been regarded as one of the widely used substitutions of HFC-134a because of low GWP of 4 and similar thermophysical properties to HFC-134a. However, its COP is slightly smaller than that of HFC-134a. In order to make better use of HFO-1234yf, some HFO-1234yf + HFCs or HFO-1234yf + HCs binary mixtures were proposed as alternative refrigerants. HFC-152a with low GWP of 140 and a short atmospheric lifetime of 1.5 years was selected as component in refrigerant mixtures. The mixture HFO-1234yf + HFC-152a is a promising alternative refrigerant. Before the actual application of alternative refrigerants in the refrigeration and air conditioning systems, thermophysical properties of mixed refrigerants need to be carefully investigated. Knowledge of viscosity characteristics, as one of the major concerns in the study of the thermophysical properties of alternative refrigerants, has significant impact on heat transfer and pressure drop in the flow, and viscosity data with high accuracy are of considerable value in the calculation of heat transfer and fluid flow. Â Â Â Â Â Â Thus, in this work, the measurement of liquid viscosity of the mixture was carried out with a new type of gravitational capillary viscometer developed in our previous work. The liquid viscosity experimental system used in this paper consists of a gravitational capillary viscometer made of glass, a pressure vessel with sight glasses, a thermostatic bath system and a measurement system. The measurement system consists of a high accurate temperature measure system (the standard temperature uncertainty is less than 0.011K) and a high accurate pressure measure system (the standard pressure uncertainty is within 1.4 kPa). The expanded uncertainty of dynamic viscosity was 1.58 %. The reliability of the experimental apparatus has been validated with HFO-1234yf and the binary mixture HFC-22 + HFC-134a (0.7 + 0.3, by mole fraction) in previous work. Based on this, the gravitational capillary viscometer was firstly calibrated with HCFC-22, and then, the liquid viscosity data of the binary mixture HFO-1234yf + HFC-152a (0.81 + 0.19, by mole fraction) were given from 278.15 K to 333.15 K. Two most commonly used viscosity models based on the Andrade equation were used to correlate the experimental data of the mixture HFO-1234yf + HFC-152a (0.81 + 0.19, by mole fraction). The correlation results were discussed

Two Rapid Power Iterative DOA Estimators for UAV Emitter Using Massive/Ultra-massive Receive Array

To provide rapid direction finding (DF) for unmanned aerial vehicle (UAV) emitter in future wireless networks, a low-complexity direction of arrival (DOA) estimation architecture for massive multiple input multiple output (MIMO) receiver arrays is constructed. In this paper, we propose two strategies to address the extremely high complexity caused by eigenvalue decomposition of the received signal covariance matrix. Firstly, a rapid power-iterative rotational invariance (RPI-RI) method is proposed, which adopts the signal subspace generated by power iteration to gets the final direction estimation through rotational invariance between subarrays. RPI-RI makes a significant complexity reduction at the cost of a substantial performance loss. In order to further reduce the complexity and provide a good directional measurement result, a rapid power-iterative Polynomial rooting (RPI-PR) method is proposed, which utilizes the noise subspace combined with polynomial solution method to get the optimal direction estimation. In addition, the influence of initial vector selection on convergence in the power iteration is analyzed, especially when the initial vector is orthogonal to the incident wave. Simulation results show that the two proposed methods outperform the conventional DOA estimation methods in terms of computational complexity. In particular, the RPIPR method achieves more than two orders of magnitude lower complexity than conventional methods and achieves performance close to CRLB. Moreover, it is verified that the initial vector and the relative error have a significant impact on the performance of the computational complexity

Preparation of PVA-GO Composite Hydrogel and Effect of Ionic Coordination on Its Properties

This paper adopts a method combining hybrid self-assembly, cyclic freezing-thawing and annealing treatment to prepare polyvinyl alcohol (PVA) and graphene oxide (GO) composite hydrogel. Then, the PVA-GO composite hydrogels are re-swelled in different ionic solutions (NaCl, MgCl2, CaCl2 and AlCl3) to improve mechanical strength, toughness and wear resistance by the ionic coordination bonds. The microstructure and morphology are characterized by Fourier transforms infrared spectroscopy (FTIR), x-ray diffraction (XRD) and Scanning electron microscopy (SEM), finding that the internal structure is porous three-dimensional network. Mechanical experiments indicate that the composite hydrogel with GO content of 0.05 wt% immersed in MgCl2 solution displays the best mechanical properties overall. Its tensile strength can reach 11.10 MPa and the elastic modulus reaches 1.72 MPa, which is 175% and 85% higher than the pure PVA, respectively. Sliding friction experiments illustrate that the composite hydrogel immersed in AlCl3 solution exhibits the lowest friction coefficient, and the higher the valence state of metal cation is, the better the wear reduction effect is. We expect to enrich the development of PVA-GO hydrogels in tissue engineering through synergy of hydrogen bonds and ionic coordination bonds