Partitioned Method of Insect Flapping Flight for Maneuvering Analysis

This study proposed a partitioned method to analyze maneuvering of insects during flapping flight. This method decomposed the insect flapping flight into wing and body subsystems and then coupled them via boundary conditions imposed on the wing’s base using one-way coupling. In the wing subsystem, the strong coupling of the flexible wings and surrounding fluid was accurately analyzed using the finite element method to obtain the thrust forces acting on the insect’s body. The resulting thrust forces were passed from the wing subsystem to the body subsystem, and then rigid body motion was analyzed in the body subsystem. The rolling, yawing, and pitching motions were simulated using the proposed method as follows: In the rolling simulation, the difference of the stroke angle between the right and left wings caused a roll torque. In the yawing simulation, the initial feathering angle in the right wing only caused a yaw torque. In the pitching simulation, the difference between the front- and back-stroke angles in both the right and left wings caused a pitch torque. All three torques generated maneuvering motion comparable with that obtained in actual observations of insect flight. These results demonstrate that the proposed method can adequately simulate the fundamental maneuvers of insect flapping flight. In the present simulations, the maneuvering mechanisms were investigated at the governing equation level, which might be difficult using other approaches. Therefore, the proposed method will contribute to revealing the underlying insect flight mechanisms

Hierarchical Regression Discontinuity Design: Pursuing Subgroup Treatment Effects

Regression discontinuity design (RDD) is widely adopted for causal inference under intervention determined by a continuous variable. While one is interested in treatment effect heterogeneity by subgroups in many applications, RDD typically suffers from small subgroup-wise sample sizes, which makes the estimation results highly instable. To solve this issue, we introduce hierarchical RDD (HRDD), a hierarchical Bayes approach for pursuing treatment effect heterogeneity in RDD. A key feature of HRDD is to employ a pseudo-model based on a loss function to estimate subgroup-level parameters of treatment effects under RDD, and assign a hierarchical prior distribution to ``borrow strength" from other subgroups. The posterior computation can be easily done by a simple Gibbs sampling. We demonstrate the proposed HRDD through simulation and real data analysis, and show that HRDD provides much more stable point and interval estimation than separately applying the standard RDD method to each subgroup.Comment: 21 page

高性能演算サーバーによる並列流体構造連成解析

1.はじめに 2.整合圧力ポアソン方程式に基づく一体型解法 3.流体中ではばたく弾性翼の並列解析 4.おわり

A survey of T Tauri stars with AKARI toward the Taurus-Auriga region

Aims: We search new T Tauri star (TTS) candidates with the mid-infrared (MIR) part of the AKARI All-Sky Survey at 9 and 18 um wavelengths. Methods: We used the point source catalogue (PSC), obtained by the Infrared Camera (IRC) on board AKARI. We combined the 2MASS PSC and the 3rd version of the USNO CCD Astrograph Catalogue (UCAC) with the AKARI IRC-PSC, and surveyed 517 known TTSs over a 1800-square-degree part of the Taurus-Auriga region to find criteria to extract TTSs. We considered asymptotic giant branch (AGB) stars, post-AGB stars, Planetary Nebulae (PNe), and galaxies, which have similar MIR colours, to separate TTSs from these sources. Results: Of the 517 known TTSs, we detected 133 sources with AKARI. Based on the colour-colour and colour-magnitude diagrams made from the AKARI, 2MASS, and UCAC surveys, we propose the criteria to extract TTS candidates from the AKARI All-Sky data. On the basis of our criteria, we selected 176/14725 AKARI sources as TTS candidates which are located around the Taurus-Auriga region. Comparing these sources with SIMBAD, there are 148 previously identified sources including 115 Young Stellar Objects (YSOs), and 28 unidentified sources. Conclusions: Based on SIMBAD identifications, we take the TTS-identification probability using our criteria to be ~75 %. We find 28 TTS candidates, of which we expect 21 to be confirmed once follow-up observations can be obtained. Although the probability of ~75 % is not so high, it is affected by the completeness of the SIMBAD database, and we can search for TTSs over the whole sky, over all star forming regions.Comment: 12 pages, 9 figures, accepted for publication in A&