The aim of this study was to investigate the effect of individual pain sensitivity on the results of transforaminal epidural steroid injection (TFESI) for the patients with lumbar spinal stenosis (LSS). Seventy-seven patients with LSS were included in this study. Prospectively planned evaluations were performed twice consecutively before and 2 months after TFESI. These included a detailed medical history, a physical examination, and completion of a series of questionnaires, including pain sensitivity questionnaire (PSQ), Oswestry disability index (ODI), and visual analog scale (VAS) for back and leg pain. The correlations were analyzed among variables between total PSQ/PSQ-moderate/PSQ-minor and pain and disability level measured by VAS for back/leg pain and ODI both before and 2 months after TFESI. Two months after TFESI, there were significant decreases in VAS for back/leg pain and ODI compared with those before injection. Before injection, VAS for back pain and leg pain was highly associated with the PSQ scores including total PSQ and PSQ subscores after adjustment for age, BMI, and grade of canal stenosis. However, any subscores of PSQ and total PSQ scores were not correlated with either VAS for back pain/leg pain or ODI 2 months after TFESI with adjustment made to age, BMI, gender, and grade of canal stenosis. This study highlights that individual pain sensitivity does not influence the outcomes of TFESI treatment in patients with LSS, even though pain sensitivity has a significant negative correlation with symptom severity of LSS.

Few design approaches have utilized scientific methods in dealing with aesthetic and visual factors. In an attempt to explore a scientific approach to environmental design, the relationship between visual preference and ratio variables in enclosed spaces on the Virginia Tech campus was investigated. Visual preference of enclosed spaces was measured by the Scenic Beauty Estimation (SBE) method. The SBE method was found to be reliable and valid in urban enclosed spaces. This study suggests that visual quality of an enclosed urban place can be predicted from the linear combination of three ratio variables-ground slope, height ratio, and vegetation coverage. The prediction model can be used as an heuristic or analytic tool for design practice.

In nature, to change the consciousness of those who wish to pursue something new, the road is turning function-oriented 'Walking Path' into purpose-oriented 'Walking Trails'. Though 'Walking Trails' is a long linear journey that leads people to see, to feel and to experience while walking on the trail, but considering on the landscape of trails when selecting routes is lacking. Landscapes, which are felt and perceived while walking on the trail, provide a purpose, and can be an important factor to improve visitor satisfaction. However, the study is insufficient in terms of landscape of trails. Therefore, it is the purpose of this study to find ways to help improving visitors' satisfaction in selecting routes, by analyzing the images and preferences of trails landscapes that are visually perceived, by analyzing the correlation between visitors' satisfaction and them.For this study, landscape assessment was carried out after selecting representative landscape photos of BukhansanDulegil 13 sections and landscape images adjectives for landscape assessment. Through the assessment, analyze landscape images of each section, landscape images factors affecting a wish to walk and landscape preferences, relationship between visitors' satisfaction and them.'Refreshing' image was higher on the path with many trees and less artificial elements; 'urban' image was higher on the path with artificial elements. 'A wish to walk' and 'landscape preference' was higher on the path showed 'refreshing' and 'pastoral' image with many natural elements. Factors affecting 'a wish to walk' were refreshing-unpleasant , impressive-ordinary , factors affecting 'landscape preference' were refreshing-unpleasant , comfortable-uncomfortable .In addition, landscape preference was found to have a high correlation with visitors' satisfaction.

Scene understanding is an important problem in intelligent robotics. Since visual information is uncertain due to several reasons, we need a novel method that has robustness to the uncertainty. Bayesian probabilistic approach is robust to manage the uncertainty, and powerful to model high-level contexts like the relationship between places and objects. In this paper, we propose a context-based Bayesian method with SIFT for scene understanding. At first, image pre-processing extracts features from vision information and objectsexistence information is extracted by SIFT that is rotation and scale invariant. This information is provided to Bayesian networks for robust inference in scene understanding. Experiments in complex real environments show that the proposed method is useful.

TimeML, TimeBank, and TTK (TARSQI Project) have been playing an important role in enhancement of IE, QA, and other NLP applications. TimeML is a specification language for events and temporal expressions in text. This paper presents the problems and solutions for porting TimeML to Korean as a part of the Korean TARSQI Project. We also introduce the KTTK which is an automatic markup tool of temporal and event-denoting expressions in Korean text.