• Food Science of Animal Resources
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• v.43 no.2
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• pp.197-219
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• 2023

Rabbit meat has high nutritional and dietetic characteristics, but its consumption rate is comparatively lower than other meat types. The nutritional profile of rabbit meat, by comparison with beef, pork, and poultry, is attributed to relatively higher proportions of n-3 fatty acids and low amounts of intramuscular fat, cholesterol, and sodium, indicating its consumption may provide health benefits to consumers. But, the quality attributes of rabbit meat can be originated from different factors such as genetics, environment, diet, rearing system, pre-, peri-, and post-slaughter conditions, and others. Different rabbit breeds and the anatomical location of muscles may also affect the nutritional profile and physicochemical properties of rabbit meat. However, adequate information about the effect of those two factors on rabbit meat is limited. Therefore, cumulative information on nutritional composition and carcass and meat quality attributes of rabbit meat in terms of different breeds and muscle types and associated factors is more important for the production and processing of rabbits. Moreover, some studies reported that rabbit meat proteins exhibited angiotensin-converting enzyme inhibitory characteristics and antioxidant properties. The aim of this review is to elucidate the determinants of rabbit meat quality of different breeds and its influencing factors. In addition, the proven biological activities of rabbit meat are introduced to ensure consumer satisfaction.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.517-528
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• 2023

The purpose of this study is to analyze and compare UX factors of connectivity systems of domestic and overseas car brands. Using a text mining analysis, UX factors of domestic and overseas brands were compared through positive-negative sentiment index. After collecting 120,000 reviews on Hyundai Motor Group (Hyundai, Kia, Genesis) and 190,000 on Tesla, BMW, and Mercedes, pre-processing was performed. Keywords were classified into 11 UX factors in 3 dimensions of the system connection, information, and service. For domestic brands, sentiment index for 'safety' was the highest. For overseas brands, 'entertainment' was the most positive UX factor.

• Korean Journal of Construction Engineering and Management
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• v.17 no.5
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• pp.108-119
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• 2016

Since the introduction of the converted accident rate in Pre-Qualification(PQ) process in 1992, the safety evaluation system has contributed to a reduction of construction accidents and development of the safety management system in public construction projects. As the comprehensive evaluation method the government plans to introduce includes the indicators 'accident rate' and 'death rate per 10,000 workers', the influences on the safety evaluation in the bidding process would be broader in public sector construction projects. However, the current safety evaluation system is operated by different estimating standards in the bidding process. At this point, a study of improvement on the safety evaluation index is required to review its current conditions and to propose its efficient operating method. Therefore, the purpose of this study is to analyze problems of the current safety evaluation system through the questionnaire survey on the qualified workers in construction safety management and to propose an improvement plan for the problems noticed. Our results suggest the standards of the unified process for the safety evaluation index, the size of construction firms needed for accident rate estimation, and the improvement plan for unreported accidents. The proposed improvement plan enables the reasonable estimation and efficient operation of the safety evaluation index, and further, it would contribute to reducing construction accidents through the activation of voluntary safety management by construction firms.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.

• Environmental and Resource Economics Review
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• v.20 no.3
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• pp.595-628
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• 2011

This study investigates the issues and suggests reform measures in applying CV methods to the Korea Development Institute's (KDI's) Preliminary Feasibility Test (PFT) of public projects. Most public projects on culture, science and environment evaluated under the PFT system belong to the category of "nonstandard" projects whose outputs are non-marketed, and CV is currently the main tool used for their benefit estimation. A careful discussion and investigation is recommended for the selection of target population, payment vehicle, and number of payment times. Operating expert reviews, focus group interviews, and pre-tests is highly recommended to reduce the potential bias involved in the CV studies. A single or double bounded dichotomous choice format is the most popular design of questionnaire, but we identify several undissolved issues in designing and implementing the format. Some other forms of inducing WTPs may still deserve our consideration. Various specifications of the WTP function need to be tried and tested based on their stability, in particular. Employing a nonparametric approach is also recommended. Treatments of 0 or negative WTPs and protest bids are shown to be the most serious issues that affect the estimation results significantly. We review diverse measures of handling those issues and summarize their advantages and shortcomings.

• Journal of Science and Technology Studies
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• v.18 no.1
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• pp.5-62
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• 2018

Over the last 10 years, the fight against occupational diseases of semiconductor workers led by SHARPS(the Supporters for the Health And Rights of People in the Semiconductor industry, NGO) has accomplished considerable achievements, especially in the legal and institutional aspects. First, the court and the government accepted the claims that 24 injured workers respectively filed, recognizing their 10 types of diseases as occupational illness. The court not only expanded the list of work places and diseases that it recognized, but also presented more progressive logic of recognition. The most remarkable achievement among them is the case ruled by the Supreme court in July, 2017. In terms of 'worker's right to know', which is the most important factor in preventing occupational diseases, there have been significant legislative bills, court rulings and government guidelines. The revised bill of the Industrial Safety and Health Act to strengthen workers' rights to know and to introduce the pre-review system on trade secret is currently under review by the National Assembly. The court recently ruled that the government should disclose its inspection results on safety and health management at semiconductor factories. The ministry of labor has drawn up internal guidelines to more actively open its safety and health data to public. This study looks over recent developments in such rulings, bills and guidelines and then, analyzes their implications, laying the groundwork for future actions for worker health in the electronic industry.

• Economic and Environmental Geology
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• v.51 no.6
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• pp.509-520
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• 2018

Alkalinity is one of the basic variables, which determine geochemical characteristics of natural waters and participate in processes changing concentrations of various contaminants either directly or indirectly. However, not a few laboratories and researchers of Korea still use alkalinity-measurement methods not appropriate for groundwaters, and which becomes one of the major reasons for the poor ion balance errors of the geochemical analysis. This study was performed to review alkalinity-measurement methods, to discuss their advantages and disadvantages, and, thus, to help researchers and analytical specialists in analyzing alkalinity of groundwaters. The pH-titration-curve-inflection-point (PTC-IP) methods, which finds the alkalinity end point from the inflection point of the pH titration curve are revealed to be most accurate. Gran titration technique among them are likely to be most appropriate for accurate estimation of titrant volume to the end point. In contrast, other titration methods such as pH indicator method and pre-selected pH method, which are still commonly being used, are likely to cause erroneous results especially for groundwaters of low ionic strength and alkalinity.

• Herbal Formula Science
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• v.25 no.2
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• pp.135-143
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• 2017

Objectives : This trial aimed to determine if Wasong (Orostachys japonicus) extract can enhance immune system and is safe enough to be approved as a health functional food. Methods : Total 62 people, aged 45 and older, will be recruited to participate in a randomized, double-blind, placebo-controlled clinical trial. This study will compare Wasong extract and placebo. Wasong group will take 1g of Wasong extract, once a day, for 8 weeks. Placebo group will take 1g of crystalline cellulose as placebo, once a day, for 8 weeks. Outcomes will be measured at the baseline, the end of 4th week, and 8th week. Primary outcomes are the ratio of NK cells/total lymphocytes and the ratio of T-helper cells/T-suppressor cells. Secondary outcomes are total white blood cell count, the ratio of neutrophils, lymphocytes, and monocytes in total leukocytes, the ratio of total T cells, T-helper cells, T-suppressor cells, and B cells to lymphocytes, the amount of blood IgM, IgG, IgA, and cytomegalovirus (CMV) IgG, and blood metabolite target &global analysis. Results : This trial was approved by institutional review board of Pusan National University Korean Medicine Hospital (registry number: 2016006), and registered in Clinical Research information Service, one of WHO International Clinical Trials Registry Platform (registry number: PRE20161006-002). Recruitment opened in February 2017 and is supposed to be completed by August 2017. The result is expected to be published by June 2018. Conclusion : This trial will provide clinical information to determine the efficacy and safety of Wasong in enhancing immune system of middle-aged and older people.

• Resources Recycling
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• v.25 no.6
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• pp.13-22
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• 2016

This study is aimed at compare and evaluate the environmental impact of End-of-Life Vehicle(ELV) on the eco-friendly technology dismantling and recycling system, using Life Cycle Assessment (LCA) method. In this study, it was analyzed for the environmental impacts of raw materials, disassemble process, recycle parts separation and waste treatment into the process of ELV treatment by greenhouse gas and resource consumption, etc. Through this study, the indoor rail type dismantling technology were recycling rate applied on the alternate system was increased by approximately 8%. As a result, it was 3 to 88% by improving the environmental impact category. In addition, the added benefit of approximately 8 - 62% in pre-market occurred through the recycling rate, improve parts reuse rate of ELV. Through the results of this study, legal compliance, improved reuse and recycling ratio, used parts market reach, enable exports has identified the need for the effort that the dissemination and diffusion of eco-friendly technology.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.39-70
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• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.