The structural stability and polarization analysis of rhombohedral phase HfO2

A comparative theoretical study is presented for the rhombohedral R3 and R3m phase HfO2, of two possible forms in its heavily Zr-doped ferroelectric thin films found recently in experiments. Their structural stability and polarization under the in-plane compressive strain are comprehensively investigated. We discovered that there is a phase transition from R3 to R3m phase under the biaxial compressive strain. Both the direction and amplitude of their polarization can be tuned by the strain. By performing a symmetry mode analysis, we are able to understand its improper nature of the ferroelectricity. These results may help to shed light on the understanding of the hafnia ferroelectric thin films

ニコチン依存症におけるドパミンD2受容体と脂肪酸結合タンパク質３シグナルの役割

要約のみTohoku University佐々木拓哉課

Quasinormal modes and stability of higher dimensional rotating black holes under massive scalar perturbations

We consider the stability of six-dimensional singly rotating Myers-Perry black holes under massive scalar perturbations. Using Leaver's continued fraction method, we compute the quasinormal modes of the massive scalar fields. All modes found are damped under the quasinormal boundary conditions. It is also found that long-living modes called quasiresonances exist for large scalar masses as in the four-dimensional Kerr black hole case. Our numerical results provide a direct and complement evidence for the stability of six-dimensional MP black holes under massive scalar perturbation.Comment: 11 pages,9 figure

Stability of five-dimensional Myers-Perry black holes under massive scalar perturbation: bound states and quasinormal modes

The stability of five-dimensional singly rotating Myers-Perry Black Holes against massive scalar perturbations is studied. Both the quasibound states and quasinormal modes of the massive scalar field are considered. For the quasibound states, we use an analytical method to discuss the effective potential felt by the scalar field, and found that there is no potential well outside the event horizon. Thus, singly rotating Myers-Perry Black Holes are stable against the perturbation of quasibound states of massive scalar fields. Then, We use continued fraction method based on solving a seven-term recurrence relations to compute the spectra of the quasinormal modes. For different values of the black hole rotation parameter $a$, scalar mass parameter $\mu$ and angular quantum numbers, all found quasinormal modes are damped. So singly rotating Myers-Perry Black Holes are also stable against the perturbation of quasinormal modes of massive scalar fields. Besides, when the scalar mass $\mu$ becomes relatively large, the long-living quasiresonances are also found as in other rotating black hole models. Our results complement previous arguments on the stability of five-dimensional singly rotating Myers-Perry black holes against massive scalar perturbations.Comment: references adde

Comparative Study of the Amount of Re-released Hemoglobin from α-Thalassemia and Hereditary Spherocytosis Erythrocytes

Hemoglobin release test (HRT), which is established by our lab, is a new experiment to observe the re-released hemoglobin (Hb) from erythrocytes. In this study, one-dimension HRT, double dimension HRT, and isotonic and hypotonic HRT were performed to observe the re-released Hb from the blood samples of normal adult, hereditary spherocytosis (HS), and α-thalassemia. The results showed that compared with normal adult, the re-released Hb from HS blood sample was decreased significantly; however, the re-released Hb from α-thalassemia blood sample was increased significantly. The mechanism of this phenomenon was speculated to have relation with the abnormal amount of membrane-binding Hb

Biological Immune System Applications on Mobile Robot for Disabled People

To improve the service quality of service robots for the disabled, immune system is applied on robot for its advantages such as diversity, dynamic, parallel management, self-organization, and self-adaptation. According to the immune system theory, local environment condition sensed by robot is considered an antigen while robot is regarded as B-cell and possible node as antibody, respectively. Antibody-antigen affinity is employed to choose the optimal possible node to ensure the service robot can pass through the optimal path. The paper details the immune system applications on service robot and gives experimental results

A Modeling Study of the Responses of Mesosphere and Lower Thermosphere Winds to Geomagnetic Storms at Middle Latitudes

Thermosphere Ionosphere Mesosphere Electrodynamics General Circulation Model (TIMEGCM) simulations are diagnostically analyzed to investigate the causes of mesosphere and lower thermosphere (MLT) wind changes at middle latitudes during the 17 April 2002 storm. In the early phase of the storm, middle‐latitude upper thermospheric wind changes are greater and occur earlier than MLT wind changes. The horizontal wind changes cause downward vertical wind changes, which are transmitted to the MLT region. Adiabatic heating and heat advection associated with downward vertical winds cause MLT temperature increases. The pressure gradient produced by these temperature changes and the Coriolis force then drive strong equatorward meridional wind changes at night, which expand toward lower latitudes. Momentum advection is minor. As the storm evolves, the enhanced MLT temperatures produce upward vertical winds. These upward winds then lead to a decreased temperature, which alters the MLT horizontal wind pattern and causes poleward wind disturbances at higher latitudes

Deep Learning for Community Detection: Progress, Challenges and Opportunities

As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics. However, the classic methods of community detection, such as spectral clustering and statistical inference, are falling by the wayside as deep learning techniques demonstrate an increasing capacity to handle high-dimensional graph data with impressive performance. Thus, a survey of current progress in community detection through deep learning is timely. Structured into three broad research streams in this domain - deep neural networks, deep graph embedding, and graph neural networks, this article summarizes the contributions of the various frameworks, models, and algorithms in each stream along with the current challenges that remain unsolved and the future research opportunities yet to be explored.Comment: Accepted Paper in the 29th International Joint Conference on Artificial Intelligence (IJCAI 20), Survey Trac