Electrodynamics of Magnetoelectric Media and Magnetoelectric Fields

The relationship between magnetoelectricity and electromagnetism is a subject of a strong interest and numerous discussions in microwave and optical wave physics and material sciences. The definition of the energy and momentum of the electromagnetic (EM) field in a magnetoelectric (ME) medium is not a trivial problem. The question of whether electromagnetism and magnetoelectricity can coexist without an extension of Maxwell's theory arises when the effects of EM energy propagation are studied and the group velocity of the waves in an ME medium is considered. The energy balance equation reveals unusual topological structure of fields in ME materials. Together with certain constraints on the constitutive parameters of a medium, definite constraints on the local field structure should be imposed. Analyzing the EM phenomena inside an ME material, the question “what kind of the near fields arising from a sample of such a material can be measured?” should be answered. The visualization of the ME states requires an experimental technique that is based on an effective coupling to the violation of spatial as well as temporal inversion symmetry. To observe the ME energy in a subwavelength region, it is necessary to assume the existence of first-principle near fields—the ME fields. These are non-Maxwellian near fields with specific properties of violation of spatial and temporal inversion symmetry. A particular interest to the ME fields arises in studies of metamaterials with “artificial-atoms” ME elements.     

Shape stability of a gas bubble in a soft solid

Predicting the onset of non-spherical oscillations of bubbles in soft matter is a fundamental cavitation problem with implications to sonoprocessing, polymeric materials synthesis, and biomedical ultrasound applications. The shape stability of a bubble in a Kelvin-Voigt viscoelastic medium with nonlinear elasticity, the simplest constitutive model for soft solids, is analytically investigated and compared to experiments. Using perturbation methods, we develop a model reducing the equations of motion to two sets of evolution equations: a Rayleigh-Plesset-type equation for the mean (volume-equivalent) bubble radius and an equation for the non-spherical mode amplitudes. Parametric instability is predicted by examining the natural frequency and the Mathieu equation for the non-spherical modes, which are obtained from our model. Our theoretical results show good agreement with published experiments of the shape oscillations of a bubble in a gelatin gel. We further examine the impact of viscoelasticity on the time evolution of non-spherical mode amplitudes. In particular, we find that viscosity increases the damping rate, thus suppressing the shape instability, while shear modulus increases the natural frequency, which changes the unstable mode. We also explain the contributions of rotational and irrotational fields to the viscoelastic stresses in the surroundings and at the bubble surface, as these contributions affect the damping rate and the unstable mode. Our analysis on the role of viscoelasticity is potentially useful to measure viscoelastic properties of soft materials by experimentally observing the shape oscillations of a bubble.     

Anisotropic phononic crystal structure with low-frequency bandgap and heat flux manipulation

This study presents a two-dimensional phononic crystal with heat flux manipulation and wide bandgaps of out-of-plane modes within the low-frequency range. The anisotropic matrix made of spiral-multilayered materials with different thermal conductivities, and the coating layer inserted with metal are designed for heat flux manipulation. Rubber-coated metal cylinders are periodically embedded in the anisotropic matrix to obtain the low-frequency bandgaps of out-of-plane modes. Numerical simulation is carried out to validate the heat and elastic characteristics of the spiral-multilayered anisotropic structure and reveal the effects of the laying angle and temperature on the bandgaps. Subsequently, a spiral-multilayered plate with periodic structures is studied, which shows an obvious vibration attenuation in the frequency ranges of the bandgaps and a deflected heat flux from the initial propagation direction. In the experimental investigation, the multi-phase spiral-multilayered anisotropic plate is simplified to a single-phase anisotropic plate made of aluminum. The characteristics of this type of anisotropic phononic crystal structure may pave the way for the design of a new kind of thermo-acoustic metamaterial serving in combined thermal and acoustic environments.     

Deep Hierarchical Ensemble Model for Suicide Detection on Imbalanced Social Media Data

As a serious worldwide problem, suicide often causes huge and irreversible losses to families and society. Therefore, it is necessary to detect and help individuals with suicidal ideation in time. In recent years, the prosperous development of social media has provided new perspectives on suicide detection, but related research still faces some difficulties, such as data imbalance and expression implicitness. In this paper, we propose a Deep Hierarchical Ensemble model for Suicide Detection (DHE-SD) based on a hierarchical ensemble strategy, and construct a dataset based on Sina Weibo, which contains more than 550 thousand posts from 4521 users. To verify the effectiveness of the model, we also conduct experiments on a public Weibo dataset containing 7329 users’ posts. The proposed model achieves the best performance on both the constructed dataset and the public dataset. In addition, in order to make the model applicable to a wider population, we use the proposed sentence-level mask mechanism to delete user posts with strong suicidal ideation. Experiments show that the proposed model can still effectively identify social media users with suicidal ideation even when the performance of the baseline models decrease significantly.     

Epistemic Communities under Active Inference

The spread of ideas is a fundamental concern of today’s news ecology. Understanding the dynamics of the spread of information and its co-option by interested parties is of critical importance. Research on this topic has shown that individuals tend to cluster in echo-chambers and are driven by confirmation bias. In this paper, we leverage the active inference framework to provide an in silico model of confirmation bias and its effect on echo-chamber formation. We build a model based on active inference, where agents tend to sample information in order to justify their own view of reality, which eventually leads to them to have a high degree of certainty about their own beliefs. We show that, once agents have reached a certain level of certainty about their beliefs, it becomes very difficult to get them to change their views. This system of self-confirming beliefs is upheld and reinforced by the evolving relationship between an agent’s beliefs and observations, which over time will continue to provide evidence for their ingrained ideas about the world. The epistemic communities that are consolidated by these shared beliefs, in turn, tend to produce perceptions of reality that reinforce those shared beliefs. We provide an active inference account of this community formation mechanism. We postulate that agents are driven by the epistemic value that they obtain from sampling or observing the behaviours of other agents. Inspired by digital social networks like Twitter, we build a generative model in which agents generate observable social claims or posts (e.g., ‘tweets’) while reading the socially observable claims of other agents that lend support to one of two mutually exclusive abstract topics. Agents can choose which other agent they pay attention to at each timestep, and crucially who they attend to and what they choose to read influences their beliefs about the world. Agents also assess their local network’s perspective, influencing which kinds of posts they expect to see other agents making. The model was built and simulated using the freely available Python package pymdp. The proposed active inference model can reproduce the formation of echo-chambers over social networks, and gives us insight into the cognitive processes that lead to this phenomenon.     

聚合物介质中盐离解行为的温度依赖性研究

<正> 近十年来尽管聚合物-盐复合物作为潜在的理想电解质材料已得到广泛研究,人们对于在象聚合物这样的高粘度介质中盐的离解行为仍缺乏了解。特别是关于盐离解度的温度依赖性,不同的作者对相似的体系曾得出截然相反的结论。最近,Angell和许康等相继通过不同研究途径证实了在上述介质中盐离解产生的自由离子数n随温度上升而下降,其趋势可由Arrhenius公式表示:     

部分水解聚丙烯酰胺在多孔介质中的静态吸附研究——水解度对吸附量的影响

部分水解聚丙烯酰胺在多孔介质中的静态吸附研究———水解度对吸附量的影响杨继萍李惠生黄鹏程（北京理工大学化工与材料学院北京１０００８１）（北京航空航天大学材料科学与工程系北京１０００８３）关键词部分水解聚丙烯酰胺，水解度，多孔介质，静态吸附聚合物作为...     

Mutual solubilities of hydrocarbons and water with the CPA EoS

The knowledge of hydrocarbon/water phase equilibria is important in the design and operation of equipment for petroleum transport and refining and petrochemical plants. The presence of water in a hydrocarbon mixture can affect the product quality and damage the operation equipment due to corrosion and formation of gas hydrates. Tracing the concentration of hydrocarbons in aqueous media is also important for technical purposes like preventing oil spills and for ecological concerns such as predicting the fate of these organic pollutants in the environment.     

多孔介质中气体水合物降压分解的分形理论研究

通过将水合物的分解过程看作是无固态产物层生成的气固反应过程, 结合粒径缩小的收缩核反应模型和分形理论, 建立了多孔介质中水合物降压分解的分数维动力学模型, 提出了基于水合物分解实验数据计算多孔介质分形维数的方法. 分别利用前人的甲烷水合物和CO2水合物降压分解实验数据, 对上述分数维动力学模型进行了验证. 计算结果表明, 用提出的方法所计算得到的多孔介质分形维数与前人的测定结果基本符合; 对甲烷水合物和CO2水合物的降压分解过程, 提出的分数维动力学分解模型得出了和实验结果基本一致的预测, 绝对平均误差(AAD)小于10%.     

Miceller-Mediated Phosphomolybdic Acid: Highly Effective Reusable Catalyst for Synthesis of Quinoline and Its Derivatives

A simple, efficient, and ecofriendly procedure has been developed for the synthesis of quinoline and its derivatives in a one-pot reaction of aniline with crotonaldehyde or methyl vinyl ketone using phosphomolybdic acid as solid acid catalyst in miceller media. The catalyst was easily recycled and reused.