The detection of gravitational waves with Advanced LIGO and Advanced Virgo has enabled novel tests of general relativity, including direct study of the polarization of gravitational waves. While general relativity allows for only two tensor gravitational-wave polarizations, general metric theories can additionally predict two vector and two scalar polarizations. The polarization of gravitational waves is encoded in the spectral shape of the stochastic gravitational-wave background, formed by the superposition of cosmological and individually unresolved astrophysical sources. Using data recorded by Advanced LIGO during its first observing run, we search for a stochastic background of generically polarized gravitational waves. We find no evidence for a background of any polarization, and place the first direct bounds on the contributions of vector and scalar polarizations to the stochastic background.

Korea has the highest air pollution among the Organization for Economic Co-operation and Development (OECD) countries. Since the recent environmental analysis (Kim et al., 2017) reported the feasibility of replacing taxis with electric vehicles (EVs) in metropolitan cities from a ‘tank-to-wheel’ emission perspective, various stakeholders have attempted to reduce air pollution by activating the EV market in Korea in accordance with international trends. Nevertheless, no holistic study has yet been published on the nationwide changes in air quality caused by EV diffusion from the perspective of ‘well-to-wheel’ emissions. This paper presents the results of estimating the stability of the electric power supply and fine particulate matter emissions in some cases, and the results of examining the structure of public discourse using big social media data.

We explore the scaling relation between the population and the public transportation system of urban buses for the two cities Daejeon and Gwangju where urban buses are the central to public transportation. These two cities are metropolitan cities in the Republic of Korea with very similar areal sizes and populations. In terms of the transportation service volume, the number of buses operating on one day and population follow a scaling law with an exponent close to 1. In terms of transportation service accessibility, the number of bus stops and population follow a scaling law with an exponent lower than 2/3. An exponent of 1 reveals that the transportation service is provided in proportion to population. An exponent lower than 2/3 reveals that the bus stops, which provide access to the transportation service, are expected to be more evenly distributed spatially.

Fluid？solid interaction has been a challenging subject due to their strong nonlinearity and multidisciplinary nature. Many of the numerical methods for solving FSI problems have struggled with non-convergence and numerical instability. In spite of comprehensive studies, it has still been a challenge to develop a method that guarantees both convergence and stability. Our discussion in this work is restricted to the interaction of viscous incompressible fluid flow and a rigid body. We take the monolithic approach by Gibou and Min (J Comput Phys 231:3245？3263, 2012) that results in an augmented Hodge projection. The projection updates not only the fluid vector field but also the solid velocities.

Robust object recognition has drawn increasing attention in the field of computer vision and machine learning with fast development in feature extraction and classification techniques, and release of public datasets, such as Caltech datasets, Pascal Visual Object Classes, and ImageNet. Recently, deep learning based object recognition systems have shown significant performance improvements in visual object recognition tasks using innovative learning methodology. However, high dimensional space searching and recognition is time consuming, so performing point and range queries in high dimension is reconsidered for object recognition.

Visual object tracking is a fundamental research area in the field of computer vision and pattern recognition because it can be utilized by various intelligent systems. However, visual object tracking faces various challenging issues because tracking is influenced by illumination change, pose change, partial occlusion and background clutter. Sparse representation-based appearance modeling and dictionary learning that optimize tracking history have been proposed as one possible solution to overcome the problems of visual object tracking. However, there are limitations in representing high dimensional deors using the standard sparse representation approach.

This study focused on understanding the termite traveling behavior. Artificial tunnels with different curvatures and base surface roughness were constructed. Each tunnel was 50 mm in length, with widths of W (W = 2, 3, or 4 mm). The distance between the two ends of the tunnel was D (D = 20, 30, 40, or 50 mm). A higher value of D means a lower curvature. The roughness, R (R = 60, 120, 240 and ∞), was generated by uniformly sanding the substrate with a sanding machine.

In this study, I constructed an agent-based model to simulate an integrative predator (wolf) -prey (rabbit) -plant (grass) relationship. When a wolf (rabbit) encountered a rabbit (grass), the wolf (rabbit) tended to move to the rabbit (grass) for hunting while the rabbit tended to escape from the wolf. These behaviors were characterized as the degrees of willingness for hunting (H w for the wolf and H r for the rabbit) and for escaping (E for the rabbit). I distributed prey refuges (with density N) in the grid space.

It has been known that some termites are responsible for tunnel excavation for foraging, while others are not involved in the excavation. The biological reason for this is that the resting termites are a backup for the termites that have used up their energy in the tunneling activity. In this study, we build an agent-based model (ABM) wherein agents (simulated termites) follow simple rules that govern their behavior. In this model, the agents are endowed with a directional vector that has been shown to exist in real termites, but they do not communicate through pheromonal or physical marking of excavation sites.

Subterranean termites form colonies containing thousands of individuals, and maintain these colonies by consuming wood and other materials containing cellulose. In this consumption process, they cause serious damage to wooden structures. Information on the population size of termites is an important factor in developing strategies aimed at controlling termites. In this study, we provide a reasonable possibility of estimating the population of an incipient nest dug by a colony that has not yet discovered any food source. We build an agentbased model to simulate termite tunnel patterns in which the behavior of simulated termites (agents) is governed by simple rules based on empirical data. The simulated termites do not communicate with each other using pheromones.