Development Direction Research Of Korean Lifestyle Brands Through Analysis For Global Lifestyle Brands - Focused On The Trend Analysis

As global lifestyle brands are recognized by consumers worldwide, their business are winning a great success. Now that in Korea too, a large-family culture in which members are accustomed to concession and self-sacrifice is changing to a lifestyle of single households, people care a lot about their own properties, privacy, and personalities. As a result, many original cultures reflect that individuals ?lifestyles” are emerging. This study suggests development directions for Korean lifestyle brands to grow in line with such social trends and to compete with global lifestyle brands. Based on the survey of Koreans “major lifestyle trends, concepts and products that would represent Koreans” emotions and attract domestic consumers are examined and suggested in this study

Complex Regional Pain Syndrome of Non-hemiplegic Upper Limb in a Stroke Patient: A Case Report

Complex regional pain syndrome (CRPS) type I in stroke patients is usually known to affect the hemiplegic upper limb. We report a case of CRPS presented in an ipsilesional arm of a 72-year-old female patient after an ischemic stroke at the left middle cerebral artery territory. Clinical signs such as painful range of motion and hyperalgesia of her left upper extremity, swollen left hand, and dystonic posture were suggestive of CRPS. A three-phase bone scintigraphy showed increased uptake in all phases in the ipsilesional arm. Diffusion tensor tractography showed significantly decreased fiber numbers of the corticospinal tract and the spinothalamic tract in both unaffected and affected hemispheres. Pain and range of motion of the left arm of the patient improved after oral steroids with a starting dose of 50 mg/day

Statistical modeling of health space based on metabolic stress and oxidative stress scores

Abstract Background Health space (HS) is a statistical way of visualizing individuals health status in multi-dimensional space. In this study, we propose a novel HS in two-dimensional space based on scores of metabolic stress and of oxidative stress. Methods These scores were derived from three statistical models: logistic regression model, logistic mixed effect model, and proportional odds model. HSs were developed using Korea National Health And Nutrition Examination Survey data with 32,140 samples. To evaluate and compare the performance of the HSs, we also developed the Health Space Index (HSI) which is a quantitative performance measure based on the approximate 95% confidence ellipses of HS. Results Through simulation studies, we confirmed that HS from the proportional odds model showed highest power in discriminating health status of individual (subject). Further validation studies were conducted using two independent cohort datasets: a health examination dataset from Ewha-Boramae cohort with 862 samples and a population-based cohort from the Korea association resource project with 3,199 samples. Conclusions These validation studies using two independent datasets successfully demonstrated the usefulness of the proposed HS

Creating Livable Neighborhoods in Old Hill and Six Corners, Springfield, Massachusetts

This report documents the work of the Graduate Urban Design Studio in Springfield’s Old Hill and Six Corners neighborhoods with 12,000 inhabitants. Both neighborhoods are challenged by a very low average household income, housing foreclosures, lot vacancies, lack of substantial home ownership, high traffic volumes and poor quality of open space. The primary goal of the project was to develop a vision to renew Old Hill and Six Corners to improve livability for their residents and create new opportunities for housing, recreation, working and commercial activities. Crucial keys to attaining this goal will be creating a stronger sense of place throughout the neighborhood and improved connectivity to the existing public open spaces like the Watershops Pond and the proposed recreational trail “Arc of Recreation”. Four physical design objectives were explored: Creating a vision for livable streets - a more livable streetscape improves walkability, urban ecology, commercial activities and provides an enriching sensual experience. Creating a better physical environment for commercial and business activities – attractive streets create a new identity and support existing businesses. Creating and improving a pedestrian and bicycle oriented circulation and coherent public open space system. Exploring new housing opportunities - new sites for housing integrate the student population in the neighborhoods. We are proud that our proposals were recognized after a tornado hit Springfield in June 2011. REBUILDSPRINGFIELD targeted the tornado-impacted neighborhoods in a collaborative effort with engaged citizens and community groups. Our plans created a valuable planning and design framework to rebuild the City

AttSec: protein secondary structure prediction by capturing local patterns from attention map

Abstract Background Protein secondary structures that link simple 1D sequences to complex 3D structures can be used as good features for describing the local properties of protein, but also can serve as key features for predicting the complex 3D structures of protein. Thus, it is very important to accurately predict the secondary structure of the protein, which contains a local structural property assigned by the pattern of hydrogen bonds formed between amino acids. In this study, we accurately predict protein secondary structure by capturing the local patterns of protein. For this objective, we present a novel prediction model, AttSec, based on transformer architecture. In particular, AttSec extracts self-attention maps corresponding to pairwise features between amino acid embeddings and passes them through 2D convolution blocks to capture local patterns. In addition, instead of using additional evolutionary information, it uses protein embedding as an input, which is generated by a language model. Results For the ProteinNet DSSP8 dataset, our model showed 11.8% better performance on the entire evaluation datasets compared with other no-evolutionary-information-based models. For the NetSurfP-2.0 DSSP8 dataset, it showed 1.2% better performance on average. There was an average performance improvement of 9.0% for the ProteinNet DSSP3 dataset and an average of 0.7% for the NetSurfP-2.0 DSSP3 dataset. Conclusion We accurately predict protein secondary structure by capturing the local patterns of protein. For this objective, we present a novel prediction model, AttSec, based on transformer architecture. Although there was no dramatic accuracy improvement compared with other models, the improvement on DSSP8 was greater than that on DSSP3. This result implies that using our proposed pairwise feature could have a remarkable effect for several challenging tasks that require finely subdivided classification. Github package URL is https://github.com/youjin-DDAI/AttSec

Business Models and Performance of International Construction Companies

As the construction business environment becomes ever more competitive and intense, business models are receiving considerable attention as potential sources of sustainable survival and growth. In order to design sustainable business models in today’s global construction market, it is important to understand the business models that would make a construction company achieve higher performance in terms of profitability, growth and market competitiveness. Therefore, this study identifies the business model variables of international construction and statistically analyzes the relationship between business model variables and firm performance guiding 72 international construction companies over a six-year period from 2009 to 2014. We examine the effect of business model variables on firm performance and how different business model variables can lead to different outcomes. The results show that business models play significant roles in determining the performance of international construction companies, with financial resources being a major determinant of profitability and regional diversification a major determinant of revenue growth and market competitiveness. Each business model variable had a different effect on profitability, growth and market competitiveness. This confirms that there are ideal combinations of business model variables that can help firms achieve higher performance. These findings are expected to provide useful guidance to assist executives’ decision making when designing a business model that will enable their firm to thrive in the global marketplace

Preliminary Service Life Estimation Model for MEP Components Using Case-Based Reasoning and Genetic Algorithm

In recent decades, building maintenance has been recognized as an important issue as the number of deteriorating buildings increases around the world. In densely populated cities, building maintenance is essential for ensuring sustainable living and safety for residents. Improper maintenance can not only cause enormous maintenance costs, but also negatively affect residents and their environment. As a first step, the service life of building components needs to be estimated in advance. Mechanical, electrical, and plumbing (MEP) components especially produce many maintenance-related problems compared to other components. In this research, a model was developed that applies the genetic algorithm (GA) and case-based reasoning (CBR) methodologies to estimating the service life of MEP components. The applicability of the model was tested by comparing the outputs of 20 randomly selected test cases with those of retrieved similar cases. The experimental results demonstrated that the overall similarity scores of the retrieved cases were over 90%, and the mean absolute error rate (MAER) of 10-NN was approximately 7.48%. This research contributes to the literature for maintenance management by not only presenting an approach to estimating the service life of building components, but also by helping convert the existing maintenance paradigm from reactive to proactive measures

Two stage pattern clustering analysis in cross-over experimental design

In interventional studies, biomarkers such as metabolites, are usually measured across serial time points. And when the interest lies in comparing expression levels between different experimental conditions, summary measures such as area under curve (AUC), have been widely used. Although the summary measure based approaches have been successful in identifying novel biomarkers, they do not reveal anything about time-dependent changing patterns of biomarkers which can demonstrate the reactivity of biomarkers to various physiological conditions. To account for such patterns, all measurements across time points need to be used, and clustering analysis with the measurements can group together biomarkers having similar changing patterns. Some such popularly used clustering methods include hierarchical- and K-means clustering. While these may provide some well-clustered results, their patterns are quite dependent on input data sets, making it difficult to obtain consistent patterns across different interventional studies. In addition, it is problematic for these methods to discriminate biomarkers with weakly active patterns that need to be grouped as static, compared to those having strongly active patterns, when their patterns are highly similar. To address these issues, we propose a new clustering method for improving identification of changing patterns. Our approach is based on a two-stage process: the first is elimination of stable markers using Euclidean distances, while the second stage assigns the remaining biomarkers to predefined patterns using 1-correlation distance measure. By simulation studies, we showed that our proposed method had superior classification performances, compared to other unsupervised clustering methods. We expect that this approach can complement the existing summary measure based approaches.N

McGrath Corridor - A vision for the future in Somerville, MA

Project Goals This project seeks to create opportunities for green connections and city beautification along the McGrath Highway corridor. Key directives are: • Build greenway connections including pedestrian path and bike lanes • Increase open space • Minimize storm water flows and create on site infiltration through green infrastructure. • Improve tree canopy • Enhance economic growth and revitalization The studio’s primary goal is to plan this urban greenway project at multiple scales. The studio synthesizes information about natural features, recreation resources, and development patterns to create a green infrastructure network that addresses the unique problems and opportunities of the study area. Moreover, the studio focuses on finding innovative strategies for enhancing green space in the city, creating urban wildlife habitats, improving recreation access, and enhancing environmental quality