Legal Market Liberalization in South Korea: Preparations for Change

South Korea’s World Trade Organization membership requires the “Land of the Morning Calm” to liberalize its legal market. South Korea submitted its proposal for liberalization in the spring of 2003 and planned to begin opening its legal market in 2005. However, disagreements between South Korea and other World Trade Organization members over the scope of liberalization have led to a one-year negotiation period extension, pushing back the planned market opening to early 2007. The Korean Bar Association has strongly opposed liberalization, claiming that liberalization will lead to the foreign domination of South Korea’s legal market. On the other hand, most South Korean and foreign businesses, as well as foreign lawyers, have suggested that such concerns are exaggerated and that the benefits from liberalization will far outweigh its harms. Indeed, legal market liberalization will not only benefit businesses and lawyers by improving legal services quality and lowering legal costs, but it will also promote South Korea’s rise as an important financial hub in East Asia. This Comment asserts that despite the potential benefits, liberalization can only be successful if South Korea simultaneously implements proper legislative revisions, reforms enforcement and oversight mechanisms, and promotes domestic firm expansion and educational reform

Spatial-temporal reasoning applications of computational intelligence in the game of Go and computer networks

Spatial-temporal reasoning is the ability to reason with spatial images or information about space over time. In this dissertation, computational intelligence techniques are applied to computer Go and computer network applications. Among four experiments, the first three are related to the game of Go, and the last one concerns the routing problem in computer networks. The first experiment represents the first training of a modified cellular simultaneous recurrent network (CSRN) trained with cellular particle swarm optimization (PSO). Another contribution is the development of a comprehensive theoretical study of a 2x2 Go research platform with a certified 5 dan Go expert. The proposed architecture successfully trains a 2x2 game tree. The contribution of the second experiment is the development of a computational intelligence algorithm calledcollective cooperative learning (CCL). CCL learns the group size of Go stones on a Go board with zero knowledge by communicating only with the immediate neighbors. An analysis determines the lower bound of a design parameter that guarantees a solution. The contribution of the third experiment is the proposal of a unified system architecture for a Go robot. A prototype Go robot is implemented for the first time in the literature. The last experiment tackles a disruption-tolerant routing problem for a network suffering from link disruption. This experiment represents the first time that the disruption-tolerant routing problem has been formulated with a Markov Decision Process. In addition, the packet delivery rate has been improved under a range of link disruption levels via a reinforcement learning approach --Abstract, page iv

Neural regulation of cancer: from mechanobiology to inflammation.

Despite recent progress in cancer research, the exact nature of malignant transformation and its progression is still not fully understood. Particularly metastasis, which accounts for most cancer death, is a very complex process, and new treatment strategies require a more comprehensive understanding of underlying regulatory mechanisms. Recently, the sympathetic nervous system (SNS) has been implicated in cancer progression and beta-blockers have been identified as a novel strategy to limit metastasis. This review discusses evidence that SNS signaling regulates metastasis by modulating the physical characteristics of tumor cells, tumor-associated immune cells and the extracellular matrix (ECM). Altered mechanotype is an emerging hallmark of cancer cells that is linked to invasive phenotype and treatment resistance. Mechanotype also influences crosstalk between tumor cells and their environment, and may thus have a critical role in cancer progression. First, we discuss how neural signaling regulates metastasis and how SNS signaling regulates both biochemical and mechanical properties of tumor cells, immune cells and the ECM. We then review our current knowledge of the mechanobiology of cancer with a focus on metastasis. Next, we discuss links between SNS activity and tumor-associated inflammation, the mechanical properties of immune cells, and how the physical properties of the ECM regulate cancer and metastasis. Finally, we discuss the potential for clinical translation of our knowledge of cancer mechanobiology to improve diagnosis and treatment

Modified Cellular Simultaneous Recurrent Networks with Cellular Particle Swarm Optimization

A cellular simultaneous recurrent network (CSRN) [1-11] is a neural network architecture that uses conventional simultaneous recurrent networks (SRNs), or cells in a cellular structure. The cellular structure adds complexity, so the training of CSRNs is far more challenging than that of conventional SRNs. Computer Go serves as an excellent test bed for CSRNs because of its clear-cut objective. For the training data, we developed an accurate theoretical foundation and game tree for the 2x2 game board. The conventional CSRN architecture suffers from the multi-valued function problem; our modified CSRN architecture overcomes the problem by employing ternary coding of the Go board\u27s representation and a normalized input dimension reduction. We demonstrate a 2x2 game tree trained with the proposed CSRN architecture and the proposed cellular particle swarm optimization

Evaluation of automotive weatherstrip by coupled analysis of fluid-structure-noise interaction

Automotive weatherstrip plays a major role in isolating the passenger compartment from water, dust and noise, etc. Among them, the wind noise through weatherstrip is the most severe factor making the passenger uncomfortable. Weatherstrip should be in contact between the door and the body frame, and sufficient contact area is needed to minimize the wind noise through weatherstrip. But there are several factors that make it difficult to ensure sufficient contact area. First, weatherstrip rubber deteriorates as time goes by and residual stress in the rubber becomes relaxed which results in the decrease of the contact area. Second, the gap between the door and the body increases due to pressure difference at high speed. In order to predict and reduce wind noise through weatherstrip, nonlinear behaviour of rubber at high speed and he effect of rubber deformation to wind noise should both be analyzed. In the paper, rubber deformation with time is obtained by hyperelastic and viscoelastic analyses, while the gap between the door and the body frame of the vehicle going at a high speed was predicted by the coupled analysis, Fluid-Structure Interaction (FSI). And also Statistical Energy Analysis (SEA) calculates the amount of wind noise numerically caused by rubber deformation under high speed condition

Fracture characterization and estimation of fracture porosity of naturally fractured reservoirs with no matrix porosity using stochastic fractal models

Determining fracture characteristics at the laboratory scale is a major challenge. It is known that fracture characteristics are scale dependent; as such, the minimum sample size should be deduced in order to scale to reservoir dimensions. The main factor affecting mechanical and hydrological characteristics of natural fractures is aperture distribution, which is a function of scale and confining pressure, rather than roughness of one fracture surface. Scale and pressure dependencies of artificial and natural fractures were investigated in this study using an X-Ray CT Scanner. Fractal dimension, D, and amplitude parameter, A, of fracture aperture approaches a constant value with increased sampling area, similar to the behavior of fracture roughness. In addition, both parameters differ under different confining pressures for a reference sampling area. Mechanical properties of fracture-fracture deformation behavior and fracture normal stiffness were obtained from CT scan data as well. Matrix porosity is relatively easy to measure and estimate compared to fracture porosity. On the other hand, fracture porosity is highly heterogeneous and very difficult to measure and estimate. When matrix porosity of naturally fractured reservoirs (NFR) is negligible, it is very important to know fracture porosity to evaluate reservoir performance. Since fracture porosity is highly uncertain, fractal discrete fractal network (FDFN) generation codes were developed to estimate fracture porosity. To reflect scale dependent characteristics of fracture networks, fractal theories are adopted. FDFN modeling technique enables the systematic use of data obtained from image log and core analysis for estimating fracture porosity. As a result, each fracture has its own fracture aperture distribution, so that generated FDFN are similar to actual fracture systems. The results of this research will contribute to properly evaluating the fracture porosity of NFR where matrix porosity is negligible