Diamagnetism versus Paramagnetism in charged spin-1 Bose gases

It has been suggested that either diamagnetism or paramagnetism of Bose gases, due to the charge or spin degrees of freedom respectively, appears solely to be extraordinarily strong. We investigate magnetic properties of charged spin-1 Bose gases in external magnetic field, focusing on the competition between the diamagnetism and paramagnetism, using the Lande-factor $g$ of particles to evaluate the strength of paramagnetic effect. We propose that a gas with $g<{1/\sqrt{8}}$ exhibits diamagnetism at all temperatures, while a gas with $g>{1/2}$ always exhibits paramagnetism. Moreover, a gas with the Lande-factor in between shows a shift from paramagnetism to diamagnetism as the temperature decreases. The paramagnetic and diamagnetic contributions to the total magnetization density are also calculated in order to demonstrate some details of the competition.Comment: 6 pages, 5 figure

Magnetic properties of charged spin-1 Bose gases with ferromagnetic coupling

Magnetic properties of a charged spin-1 Bose gas with ferromagnetic interactions is investigated within mean-field theory. It is shown that a competition between paramagnetism, diamagnetism and ferromagnetism exists in this system. It is shown that diamagnetism, being concerned with spontaneous magnetization, cannot exceed ferromagnetism in very weak magnetic field. The critical value of reduced ferromagnetic coupling of paramagnetic phase to ferromagnetic phase transition $\bar I_{c}$ increases with increasing temperature. The Lande-factor $g$ is introduced to describe the strength of paramagnetic effect which comes from the spin degree of freedom. The magnetization density $\bar M$ increases monotonically with $g$ for fixed reduced ferromagnetic coupling $\bar I$ as $\bar I>\bar I_{c}$. In a weak magnetic field, ferromagnetism makes immense contribution to the magnetization density. While at a high magnetic field, the diamagnetism inclines to saturate. Evidence for condensation can be seen in the magnetization density at weak magnetic field.Comment: 6 pages, 7 figures, accepted for publication in Journal of Physics: Condensed Matte

Creation of an Early Warning Platform for Risks to the Quality and Safety of Stored Grain

Based on the fast detection of mycotoxins and heavy metals in grain by each storage enterprise of Beijing reserve grain, the real-time migration of monitoring data is realized by adopting the B/S architecture, terminal WEB browser, and using the wireless transmission function of the rapid detection equipment to collect and transmit the data through the encryption algorithm; by using P2MP point-to-multipoint network communication, the data collected by multiple devices online/offline are uploaded through the file uploading system and the relational database extraction tool to achieve synchronous integration of data from multiple sources; by using relational database MySQL, column storage database HBase and distributed file system HDFS, distributed full-text search ElasticSearch and distributed memory database Redis to achieve cloud storage; by using Storm for real-time computation, Streaming for streaming operation, Spark for memory computing, and MapReduce for batch computing to achieve rapid processing of monitoring data; by using artificial intelligence technology and MLlib/Mahout for data mining and modelling to form a spatial and temporal sequence model of Beijing's grain producing areas and credit evaluation model of grain purchasing and marketing enterprises, so as to achieve the dynamic early warning of grain quality and safety and visual expression of the data, and provide early warning judgments to facilitate real-time control by government departments and real-time response to the situation. It promotes real-time control, real-time response and traceability management of grain quality and safety by management departments, enhances healthy competition and the establishment of an integrity system in grain circulation and storage, and provides a scientific basis for government decision-making

An empirical study on impulse consumption intention of livestreaming e-commerce: The mediating effect of flow experience and the moderating effect of time pressure

Based on the Stimulus-Organism-Response (S-O-R) model, this paper studies the impulsive consumption mechanism of consumers participating in livestreaming e-commerce from the perspectives of information source characteristics and social presence and examines the mediating effect of flow experience and the moderating effect of time pressure. Based on the consumption data of 268 live shoppers, multiple regression analysis and Bootstrap method were used to test the research hypothesis. The empirical results show that the credibility, professionalism, attractiveness, and interactivity of live information sources have significant positive impacts on consumers’ flow experience and impulsive consumption intention. Furthermore, coexistence, communication and emotional presence of social presence have a significant positive impact on consumers’ flow experience and impulsive consumption intention. Flow experience plays part of the mediating role in the process of the characteristics of livestreaming information sources and social presence affecting consumers’ impulsive consumption intention, while time pressure has a positive moderating effect in the relationship between livestreaming information source characteristics and coexistence presence and flow experience. The higher the time pressure, the stronger the promotion of live information source characteristics and coexistence presence on flow experience. This study enriches the research literature on the consumption driving mechanism of livestreaming e-commerce and offers practical enlightenment and reference to improve the effectiveness of livestreaming e-commerce anchors. It is also one of the first studies to apply the theory of flow and social presence to the impulsive consumption intention of livestreaming e-commerce

A Deep Learning Framework for Hydrogen-fueled Turbulent Combustion Simulation

The high cost of high-resolution computational fluid/flame dynamics (CFD) has hindered its application in combustion related design, research and optimization. In this study, we propose a new framework for turbulent combustion simulation based on the deep learning approach. An optimized deep convolutional neural network (CNN) inspired from a U-Net architecture and inception module is designed for constructing the framework of the deep learning solver, named CFDNN. CFDNN is then trained on the simulation results of hydrogen combustion in a cavity with different inlet velocities. After training, CFDNN can not only accurately predict the flow and combustion fields within the range of the training set, but also shows an extrapolation ability for prediction outside the training set. The results from CFDNN solver show excellent consistency with the conventional CFD results in terms of both predicted spatial distributions and temporal dynamics. Meanwhile, two orders of magnitude of acceleration is achieved by using CFDNN solver compared to the conventional CFD solver. The successful development of such a deep learning-based solver opens up new possibilities of low-cost, high-accuracy simulations, fast prototyping, design optimization and real-time control of combustion systems such as gas turbines and scramjets

Antibody dependent enhancement infection of Enterovirus 71 in vitro and in vivo

BACKGROUND: Human enterovirus 71 (EV71) has emerged as a significant cause of acute encephalitis and deaths in young children. The clinical manifestations caused by EV71 varied from mild hand, foot and mouth disease to severe neurological complications and deaths, but its pathogenesis remains elusive. Antibody dependent enhancement (ADE) infection has been reported in various viruses and has been shown to contribute to disease severity. RESULTS: In this study, the presence of sub-neutralizing antibody was demonstrated to enhance EV71 infection in THP-1 cells and increase the mortality of EV71 infection in a suckling mouse model. Further, a secondary infection model was established to characterize the correlation between ADE and disease severity, and primary asymptomatic EV71 infection was shown to increase the mortality of the secondary EV71 infection in suckling mice. CONCLUSIONS: Together, these in vitro and in vivo experiments strongly supported the hypothesis of ADE infection of EV71. The present findings indicate ADE might contribute to the pathogenesis of severe EV71 infection, and raise practical issues of vaccine development and antibody-based therapy

Competition between paramagnetism and diamagnetism in charged Fermi gases

The charged Fermi gas with a small Lande-factor $g$ is expected to be diamagnetic, while that with a larger $g$ could be paramagnetic. We calculate the critical value of the $g$-factor which separates the dia- and para-magnetic regions. In the weak-field limit, $g_{c}$ has the same value both at high and low temperatures, $g_{c}=1/\sqrt{12}$. Nevertheless, $g_{c}$ increases with the temperature reducing in finite magnetic fields. We also compare the $g_{c}$ value of Fermi gases with those of Boltzmann and Bose gases, supposing the particle has three Zeeman levels $\sigma=\pm1, 0$, and find that $g_{c}$ of Bose and Fermi gases is larger and smaller than that of Boltzmann gases, respectively.Comment: 4 pages, 3 figure

Construction by artificial intelligence and immunovalidation of hypoallergenic mite allergen Der f 36 vaccine

BackgroundThe house dust mite (HDM) is widely recognized as the most prevalent allergen in allergic diseases. Allergen-specific immunotherapy (AIT) has been successfully implemented in clinical treatment for HDM. Hypoallergenic B-cell epitope-based vaccine designed by artificial intelligence (AI) represents a significant progression of recombinant hypoallergenic allergen derivatives.MethodThe three-dimensional protein structure of Der f 36 was constructed using Alphafold2. AI-based tools were employed to predict B-cell epitopes, which were subsequently verified through IgE-reaction testing. Hypoallergenic Der f 36 was then synthesized, expressed, and purified. The reduced allergenicity was assessed by enzyme-linked immunosorbent assay (ELISA), immunoblotting, and basophil activation test. T-cell response to hypoallergenic Der f 36 and Der f 36 was evaluated based on cytokine expression in the peripheral blood mononuclear cells (PBMCs) of patients. The immunogenicity was evaluated and compared through rabbit immunization with hypoallergenic Der f 36 and Der f 36, respectively. The inhibitory effect of the blocking IgG antibody on the specific IgE-binding activity and basophil activation of Der f 36 allergen was also examined.ResultsThe final selected non-allergic B-cell epitopes were 25–48, 57–67, 107–112, 142–151, and 176–184. Hypoallergenic Der f 36 showed significant reduction in IgE-binding activity. The competitive inhibition of IgE-binding to Der f 36 was investigated using the hypoallergenic Der f 36, and only 20% inhibition could be achieved, which is greatly reduced when compared with inhibition by Der f 36 (98%). The hypoallergenic Der f 36 exhibited a low basophil-stimulating ratio similar to that of the negative control, and it could induce an increasing level of IFN‐γ but not Th2 cytokines IL-5 and IL-13 in PBMCs. The vaccine-specific rabbit blocking IgG antibodies could inhibit the patients’ IgE binding and basophil stimulation activity of Derf 36.ConclusionThis study represents the first application of an AI strategy to facilitate the development of a B-cell epitope-based hypoallergenic Der f 36 vaccine, which may become a promising immunotherapy for HDM-allergic patients due to its reduced allergenicity and its high immunogenicity in inducing blocking of IgG