Formation of Warped Disks by Galactic Fly-by Encounters. I. Stellar Disks

Warped disks are almost ubiquitous among spiral galaxies. Here we revisit and test the `fly-by scenario' of warp formation, in which impulsive encounters between galaxies are responsible for warped disks. Based on N-body simulations, we investigate the morphological and kinematical evolution of the stellar component of disks when galaxies undergo fly-by interactions with adjacent dark matter halos. We find that the so-called `S'-shaped warps can be excited by fly-bys and sustained for even up to a few billion years, and that this scenario provides a cohesive explanation for several key observations. We show that disk warp properties are governed primarily by the following three parameters; (1) the impact parameter, i.e., the minimum distance between two halos, (2) the mass ratio between two halos, and (3) the incident angle of the fly-by perturber. The warp angle is tied up with all three parameters, yet the warp lifetime is particularly sensitive to the incident angle of the perturber. Interestingly, the modeled S-shaped warps are often non-symmetric depending on the incident angle. We speculate that the puzzling U- and L-shaped warps are geometrically superimposed S-types produced by successive fly-bys with different incident angles, including multiple interactions with a satellite on a highly elongated orbit.Comment: 16 pages, 13 figures, 3 tables. Accepted for publication in Ap

Ingestion of multiple magnets: The count does matter

AbstractIngestion of multiple magnets poses a particular risk for various intraabdominal complications in children. We herein report a case of ingestion of multiple magnets, of which 3 were spontaneously expelled, and the remaining magnets were surgically removed. Since the total amount of ingestion was unknown upon presentation and the remaining intraabdominal magnets failed to pass after 24 h, emergency surgery was performed. Two magnets sandwiched the bowel walls and formed a jejunoileal fistula. There was no peritoneal contamination. We found that not all the ingested multiple magnets attracted each other, and multiple magnets could appear as single material on a plain radiograph. Confirming the exact count of ingested magnets is important; if the count is in doubt or two or more attachments are evident, prompt surgical intervention is warranted

Impact Analysis of Transportation Modal Shift on Regional Energy Consumption and Environmental Level: Focused on Electric Automobiles

Many governments have tried to reduce CO2 emissions which are believed to be the main cause for global warming. The deployment of electric automobiles is regarded as an effective way to reduce CO2 emissions. The Korean government has planned to deploy about 200,000 electric automobiles. The policy for the deployment of electric automobiles aims at not only decreasing gasoline consumption but also increasing electricity production. However, if an electricity consuming regions is not consistent with an electricity producing region, the policy generates environmental problems between regions. Hence, this paper has established the energy multi-region input-output model to specifically analyze the impacts of the deployment of electric automobiles on regional energy consumption and CO2 emissions. Finally, the paper suggests policy directions regarding the deployment of electric automobiles

Physics-Informed Convolutional Transformer for Predicting Volatility Surface

Predicting volatility is important for asset predicting, option pricing and hedging strategies because it cannot be directly observed in the financial market. The Black-Scholes option pricing model is one of the most widely used models by market participants. Notwithstanding, the Black-Scholes model is based on heavily criticized theoretical premises, one of which is the constant volatility assumption. The dynamics of the volatility surface is difficult to estimate. In this paper, we establish a novel architecture based on physics-informed neural networks and convolutional transformers. The performance of the new architecture is directly compared to other well-known deep-learning architectures, such as standard physics-informed neural networks, convolutional long-short term memory (ConvLSTM), and self-attention ConvLSTM. Numerical evidence indicates that the proposed physics-informed convolutional transformer network achieves a superior performance than other methods.Comment: Submitted to Quantitative Financ

A Dynamic Information-Based Parking Guidance for Megacities considering Both Public and Private Parking

The constantly increasing number of cars in the megacities is causing severe parking problems. To resolve this problem, many cities adopt parking guidance system as a part of intelligent transportation system (ITS). However, the current parking guidance system stays in its infant stage since the obtainable information is limited. To enhance parking management in the megacity and to provide better parking guidance to drivers, this study introduces an intelligent parking guidance system and proposes a new methodology to operate it. The introduced system considers both public parking and private parking so that it is designed to maximize the use of spatial resources of the city. The proposed methodology is based on the dynamic information related parking in the city and suggests the best parking space to each driver. To do this, two kinds of utility functions which assess parking spaces are developed. Using the proposed methodology, different types of parking management policies are tested through the simulation. According to the experimental test, it is shown that the centrally managed parking guidance can give better results than individually preferred parking guidance. The simulation test proves that both a driver???s benefits and parking management of a city from various points of view can be improved by using the proposed methodology