• Title/Summary/Keyword: Public Data Platform

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Active Phytochemicals of Indian Spices Target Leading Proteins Involved in Breast Cancer: An in Silico Study

  • Ashok Kumar Krishnakumar;Jayanthi Malaiyandi;Pavatharani Muralidharan;Arvind Rehalia;Anami Ahuja;Vidhya Duraisamy;Usha Agrawal;Anjani Kumar Singh;Himanshu Narayan, Singh;Vishnu Swarup
    • Journal of the Korean Chemical Society
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    • v.68 no.3
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    • pp.151-159
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    • 2024
  • Indian spices are well known for their numerous health benefits, flavour, taste, and colour. Recent Advancements in chemical technology have led to better extraction and identification of bioactive molecules (phytochemicals) from spices. The therapeutic effects of spices against diabetes, cardiac problems, and various cancers has been well established. The present in silico study aims to investigate the binding affinity of 29 phytochemicals from 11 Indian spices with two prominent proteins, BCL3 and CXCL10 involved in invasiveness and bone metastasis of breast cancer. The three-dimensional structures of 29 phytochemicals were extracted from PubChem database. Protein Data Bank was used to retrieve the 3D structures of BCL3 and CXCL10 proteins. The drug-likeness and other properties of compounds were analysed by ADME and Lipinski rule of five (RO5). All computational simulations were carried out using Autodock 4.0 on Windows platform. The proteins were set to be rigid and compounds were kept free to rotate. In-silico study demonstrated a strong complex formation (positive binding constants and negative binding energy ΔG) between all phytochemicals and target proteins. However, piperine and sesamolin demonstrated high binding constants with BCL3 (50.681 × 103 mol-1, 137.76 × 103 mol-1) and CXCL10 (98.71 × 103 mol-1, 861.7 × 103 mol-1), respectively. The potential of these two phytochemicals as a drug candidate was highlighted by their binding energy of -6.5 kcal mol-1, -7.1 kcal mol-1 with BCL3 and -6.9 kcal mol-1, -8.2 kcal mol-1 with CXCL10, respectively coupled with their favourable drug likeliness and pharmacokinetics properties. These findings underscore the potential of piperine and sesamolin as drug candidates for inhibiting invasiveness and regulating breast cancer metastasis. However, further validation through in vitro and in vivo studies is necessary to confirm the in silico results and evaluate their clinical potential.

A Study on the Clustering Method of Row and Multiplex Housing in Seoul Using K-Means Clustering Algorithm and Hedonic Model (K-Means Clustering 알고리즘과 헤도닉 모형을 활용한 서울시 연립·다세대 군집분류 방법에 관한 연구)

  • Kwon, Soonjae;Kim, Seonghyeon;Tak, Onsik;Jeong, Hyeonhee
    • Journal of Intelligence and Information Systems
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    • v.23 no.3
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    • pp.95-118
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    • 2017
  • Recent centrally the downtown area, the transaction between the row housing and multiplex housing is activated and platform services such as Zigbang and Dabang are growing. The row housing and multiplex housing is a blind spot for real estate information. Because there is a social problem, due to the change in market size and information asymmetry due to changes in demand. Also, the 5 or 25 districts used by the Seoul Metropolitan Government or the Korean Appraisal Board(hereafter, KAB) were established within the administrative boundaries and used in existing real estate studies. This is not a district classification for real estate researches because it is zoned urban planning. Based on the existing study, this study found that the city needs to reset the Seoul Metropolitan Government's spatial structure in estimating future housing prices. So, This study attempted to classify the area without spatial heterogeneity by the reflected the property price characteristics of row housing and Multiplex housing. In other words, There has been a problem that an inefficient side has arisen due to the simple division by the existing administrative district. Therefore, this study aims to cluster Seoul as a new area for more efficient real estate analysis. This study was applied to the hedonic model based on the real transactions price data of row housing and multiplex housing. And the K-Means Clustering algorithm was used to cluster the spatial structure of Seoul. In this study, data onto real transactions price of the Seoul Row housing and Multiplex Housing from January 2014 to December 2016, and the official land value of 2016 was used and it provided by Ministry of Land, Infrastructure and Transport(hereafter, MOLIT). Data preprocessing was followed by the following processing procedures: Removal of underground transaction, Price standardization per area, Removal of Real transaction case(above 5 and below -5). In this study, we analyzed data from 132,707 cases to 126,759 data through data preprocessing. The data analysis tool used the R program. After data preprocessing, data model was constructed. Priority, the K-means Clustering was performed. In addition, a regression analysis was conducted using Hedonic model and it was conducted a cosine similarity analysis. Based on the constructed data model, we clustered on the basis of the longitude and latitude of Seoul and conducted comparative analysis of existing area. The results of this study indicated that the goodness of fit of the model was above 75 % and the variables used for the Hedonic model were significant. In other words, 5 or 25 districts that is the area of the existing administrative area are divided into 16 districts. So, this study derived a clustering method of row housing and multiplex housing in Seoul using K-Means Clustering algorithm and hedonic model by the reflected the property price characteristics. Moreover, they presented academic and practical implications and presented the limitations of this study and the direction of future research. Academic implication has clustered by reflecting the property price characteristics in order to improve the problems of the areas used in the Seoul Metropolitan Government, KAB, and Existing Real Estate Research. Another academic implications are that apartments were the main study of existing real estate research, and has proposed a method of classifying area in Seoul using public information(i.e., real-data of MOLIT) of government 3.0. Practical implication is that it can be used as a basic data for real estate related research on row housing and multiplex housing. Another practical implications are that is expected the activation of row housing and multiplex housing research and, that is expected to increase the accuracy of the model of the actual transaction. The future research direction of this study involves conducting various analyses to overcome the limitations of the threshold and indicates the need for deeper research.

A Study on the Application of Block Chain Technology on EVMS (EVMS 업무의 블록체인 기술 적용 방안 연구)

  • Kim, Il-Han;Kwon, Sun-Dong
    • Management & Information Systems Review
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    • v.39 no.2
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    • pp.39-60
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    • 2020
  • Block chain technology is one of the core elements for realizing the 4th industrial revolution, and many efforts have been made by government and companies to provide services based on block chain technology. In this study we analyzed the benefits of block chain technology for EVMS and designed EVMS block chain platform with increased data security and work efficiency for project management data, which are important assets in monitoring progress, foreseeing future events, and managing post-completion. We did the case studies on the benefits of block chain technology and then conducted the survey study on security, reliability, and efficiency of block chain technology, targeting 18 block chain experts and project developers. And then, we interviewed EVMS system operator on the compatibility between block chain technology and EVM Systems. The result of the case studies showed that block chain technology can be applied to financial, logistic, medical, and public services to simplify the insurance claim process and to improve reliability by distributing transaction data storage and applying security·encryption features. Also, our research on the characteristics and necessity of block chain technology in EVMS revealed the improvability of security, reliability, and efficiency of management and distribution of EVMS data. Finally, we designed a network model, a block structure, and a consensus algorithm model and combined them to construct a conceptual block chain model for EVM system. This study has the following contribution. First, we reviewed that the block chain technology is suitable for application in the defense sector and proposed a conceptual model. Second, the effect that can be obtained by applying block chain technology to EVMS was derived, and the possibility of improving the existing business process was derived.

Building a Korean Sentiment Lexicon Using Collective Intelligence (집단지성을 이용한 한글 감성어 사전 구축)

  • An, Jungkook;Kim, Hee-Woong
    • Journal of Intelligence and Information Systems
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    • v.21 no.2
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    • pp.49-67
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    • 2015
  • Recently, emerging the notion of big data and social media has led us to enter data's big bang. Social networking services are widely used by people around the world, and they have become a part of major communication tools for all ages. Over the last decade, as online social networking sites become increasingly popular, companies tend to focus on advanced social media analysis for their marketing strategies. In addition to social media analysis, companies are mainly concerned about propagating of negative opinions on social networking sites such as Facebook and Twitter, as well as e-commerce sites. The effect of online word of mouth (WOM) such as product rating, product review, and product recommendations is very influential, and negative opinions have significant impact on product sales. This trend has increased researchers' attention to a natural language processing, such as a sentiment analysis. A sentiment analysis, also refers to as an opinion mining, is a process of identifying the polarity of subjective information and has been applied to various research and practical fields. However, there are obstacles lies when Korean language (Hangul) is used in a natural language processing because it is an agglutinative language with rich morphology pose problems. Therefore, there is a lack of Korean natural language processing resources such as a sentiment lexicon, and this has resulted in significant limitations for researchers and practitioners who are considering sentiment analysis. Our study builds a Korean sentiment lexicon with collective intelligence, and provides API (Application Programming Interface) service to open and share a sentiment lexicon data with the public (www.openhangul.com). For the pre-processing, we have created a Korean lexicon database with over 517,178 words and classified them into sentiment and non-sentiment words. In order to classify them, we first identified stop words which often quite likely to play a negative role in sentiment analysis and excluded them from our sentiment scoring. In general, sentiment words are nouns, adjectives, verbs, adverbs as they have sentimental expressions such as positive, neutral, and negative. On the other hands, non-sentiment words are interjection, determiner, numeral, postposition, etc. as they generally have no sentimental expressions. To build a reliable sentiment lexicon, we have adopted a concept of collective intelligence as a model for crowdsourcing. In addition, a concept of folksonomy has been implemented in the process of taxonomy to help collective intelligence. In order to make up for an inherent weakness of folksonomy, we have adopted a majority rule by building a voting system. Participants, as voters were offered three voting options to choose from positivity, negativity, and neutrality, and the voting have been conducted on one of the largest social networking sites for college students in Korea. More than 35,000 votes have been made by college students in Korea, and we keep this voting system open by maintaining the project as a perpetual study. Besides, any change in the sentiment score of words can be an important observation because it enables us to keep track of temporal changes in Korean language as a natural language. Lastly, our study offers a RESTful, JSON based API service through a web platform to make easier support for users such as researchers, companies, and developers. Finally, our study makes important contributions to both research and practice. In terms of research, our Korean sentiment lexicon plays an important role as a resource for Korean natural language processing. In terms of practice, practitioners such as managers and marketers can implement sentiment analysis effectively by using Korean sentiment lexicon we built. Moreover, our study sheds new light on the value of folksonomy by combining collective intelligence, and we also expect to give a new direction and a new start to the development of Korean natural language processing.

Analysis of Educational Needs by Adult Life Cycle for Well-aging Education Program Development (웰에이징 교육 프로그램 개발을 위한 성인 생애주기별 교육 요구도 분석)

  • Ku, Jin-Hee;Lim, HyoNam;Kim, Doo-Ree;Kang, Kyung-hee;Kim, Seol-Hee;Kim, Yong-Ha;Lee, Chong-Hyung;Ahn, Sang-Yoon;Kim, Kwang-Hwan;Song, Hyeon-Dong;Hwang, Hey-Jeong;Kim, Moon-Joon;Park, A-rma;Jo, Gee-yong;Chang, Kyung-Hee;Cho, Young-Chae
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.22 no.5
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    • pp.257-269
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    • 2021
  • This study aimed to secure basic data for the development and operation of well-aging education programs by analyzing the physical, mental, and socio-economic needs of well-aging education for successful aging. The research tool was developed as a questionnaire to investigate the perception of well aging and the needs of well-aging education in terms of physical, mental, and socio-economic aspects. In February 2021, 1949 adults over the age of 19 were surveyed through an online and mobile survey by Gallup Korea. Descriptive statistics analysis, variance analysis, Borich needs analysis, and IPA analysis were conducted to analyze the needs of well-aging education. The results revealed economic power, exercise, and chronic disease management to be high in terms of the overall priority of the education needs for well-aging, and infectious disease management, independence, and social responsibility were surveyed in the order of low education needs. In terms of economic power, education needs were highest among all age groups except for the middle-age group (35-49 years old), 82.4% of all respondents, and education needs for exercise and chronic disease management were highest in the middle-age group. Therefore, it is necessary to develop well-aging education programs for each life cycle. These results are expected to be used as empirical data in establishing a platform for developing and operating educational programs for well aging.

Analysis of the Time-dependent Relation between TV Ratings and the Content of Microblogs (TV 시청률과 마이크로블로그 내용어와의 시간대별 관계 분석)

  • Choeh, Joon Yeon;Baek, Haedeuk;Choi, Jinho
    • Journal of Intelligence and Information Systems
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    • v.20 no.1
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    • pp.163-176
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    • 2014
  • Social media is becoming the platform for users to communicate their activities, status, emotions, and experiences to other people. In recent years, microblogs, such as Twitter, have gained in popularity because of its ease of use, speed, and reach. Compared to a conventional web blog, a microblog lowers users' efforts and investment for content generation by recommending shorter posts. There has been a lot research into capturing the social phenomena and analyzing the chatter of microblogs. However, measuring television ratings has been given little attention so far. Currently, the most common method to measure TV ratings uses an electronic metering device installed in a small number of sampled households. Microblogs allow users to post short messages, share daily updates, and conveniently keep in touch. In a similar way, microblog users are interacting with each other while watching television or movies, or visiting a new place. In order to measure TV ratings, some features are significant during certain hours of the day, or days of the week, whereas these same features are meaningless during other time periods. Thus, the importance of features can change during the day, and a model capturing the time sensitive relevance is required to estimate TV ratings. Therefore, modeling time-related characteristics of features should be a key when measuring the TV ratings through microblogs. We show that capturing time-dependency of features in measuring TV ratings is vitally necessary for improving their accuracy. To explore the relationship between the content of microblogs and TV ratings, we collected Twitter data using the Get Search component of the Twitter REST API from January 2013 to October 2013. There are about 300 thousand posts in our data set for the experiment. After excluding data such as adverting or promoted tweets, we selected 149 thousand tweets for analysis. The number of tweets reaches its maximum level on the broadcasting day and increases rapidly around the broadcasting time. This result is stems from the characteristics of the public channel, which broadcasts the program at the predetermined time. From our analysis, we find that count-based features such as the number of tweets or retweets have a low correlation with TV ratings. This result implies that a simple tweet rate does not reflect the satisfaction or response to the TV programs. Content-based features extracted from the content of tweets have a relatively high correlation with TV ratings. Further, some emoticons or newly coined words that are not tagged in the morpheme extraction process have a strong relationship with TV ratings. We find that there is a time-dependency in the correlation of features between the before and after broadcasting time. Since the TV program is broadcast at the predetermined time regularly, users post tweets expressing their expectation for the program or disappointment over not being able to watch the program. The highly correlated features before the broadcast are different from the features after broadcasting. This result explains that the relevance of words with TV programs can change according to the time of the tweets. Among the 336 words that fulfill the minimum requirements for candidate features, 145 words have the highest correlation before the broadcasting time, whereas 68 words reach the highest correlation after broadcasting. Interestingly, some words that express the impossibility of watching the program show a high relevance, despite containing a negative meaning. Understanding the time-dependency of features can be helpful in improving the accuracy of TV ratings measurement. This research contributes a basis to estimate the response to or satisfaction with the broadcasted programs using the time dependency of words in Twitter chatter. More research is needed to refine the methodology for predicting or measuring TV ratings.

Development and Suggestion of Integrated Collaboration System to Revitalize Community-Based National Science Museums (지역사회 기반 전국과학관 활성화를 위한 통합이용제도 개발 및 제언)

  • Park, Young Shin;Mun, Kingju;Hwang, Yohan
    • Journal of the Korean Society of Earth Science Education
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    • v.12 no.3
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    • pp.274-290
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    • 2019
  • In order for the science museum to play a role as a foundation for strengthening the national creative capability, which is the core of the advanced national development paradigm, it is important to gather the capacities of the nation's science museums and establish a platform to cooperate in a shared value system. Science museum is an independent operating system, and there is a lack of strong connections among national, public and private science museums. The existing integrated collaboration system of science museums-centered can be studied first and promoted to develop programs for the free school year according to a specific topic. The same system of science museum-inclusion which link local cultural institutions or cultural places as science culture program were also studied to do the same purpose. On the basis of problems drawn from studies of integrated collaboration systems of each participating science museum, we proposed a convergence integrated collaboration systems of science museum-centered and science museum-inclusion. To this end, data were collected from practitioners of 7 representing science museums including 5 national ones. In order to suggest improvements, we also contacted five international science museums to collect the exemplary cases. Considering the regional characteristics, science museum-inclusion integrated collaboration systems considering the cultural characteristics and the science museum-centered integrated collaboration systems for free school semesters, were developed and tried by practitioners who participated in this research. It was found that integrated collaboration system can be more activated for the community. This suggests that support from the national level or at the level of regional autonomy is essential and the connection with the curriculum is necessary for the integrated collaboration system program. Finally, professional experts such as program development or commentator can be a decisive role.

A Study on the Effects of BIM Adoption and Methods of Implementationin Landscape Architecture through an Analysis of Overseas Cases (해외사례 분석을 통한 조경분야에서의 BIM 도입효과 및 실행방법에 관한 연구)

  • Kim, Bok-Young;Son, Yong-Hoon
    • Journal of the Korean Institute of Landscape Architecture
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    • v.45 no.1
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    • pp.52-62
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    • 2017
  • Overseas landscape practices have already benefited from the awareness of BIM while landscape-related organizations are encouraging its use and the number of landscape projects using BIM is increasing. However, since BIM has not yet been introduced in the domestic field, this study investigated and analyzed overseas landscape projects and discussed the positive effects and implementation of BIM. For this purpose, landscape projects were selected to show three effects of BIM: improvement of design work efficiency, building of a platform for cooperation, and performance of topography design. These three projects were analyzed across four aspects of implementation methods: landscape information, 3D modeling, interoperability, and visualization uses of BIM. First, in terms of landscape information, a variety of building information was constructed in the form of 3D libraries or 2D CAD format from detailed landscape elements to infrastructure. Second, for 3D modeling, a landscape space including simple terrain and trees was modeled with Revit while elaborate and complex terrain was modeled with Maya, a professional 3D modeling tool. One integrated model was produced by periodically exchanging, reviewing, and finally combining each model from interdisciplinary fields. Third, interoperability of data from different fields was achieved through the unification of file formats, conversion of differing formats, or compliance with information standards. Lastly, visualized 3D models helped coordination among project partners, approval of design, and promotion through public media. Reviewing of the case studies shows that BIM functions as a process to improve work efficiency and interdisciplinary collaboration, rather than simply as a design tool. It has also verified that landscape architects could play an important role in integrated projects using BIM. Just as the introduction of BIM into the architecture, engineering and construction industries saw great benefits and opportunities, BIM should also be introduced to landscape architecture.

The Suitable Region and Site for 'Fuji' Apple Under the Projected Climate in South Korea (미래 시나리오 기후조건하에서의 사과 '후지' 품종 재배적지 탐색)

  • Kim, Soo-Ock;Chung, U-Ran;Kim, Seung-Heui;Choi, In-Myung;Yun, Jin-I.
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.11 no.4
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    • pp.162-173
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    • 2009
  • Information on the expected geographical shift of suitable zones for growing crops under future climate is a starting point of adaptation planning in agriculture and is attracting much concern from policy makers as well as researchers. Few practical schemes have been developed, however, because of the difficulty in implementing the site-selection concept at an analytical level. In this study, we suggest site-selection criteria for quality Fuji apple production and integrate geospatial data and information available in public domains (e.g., digital elevation model, digital soil maps, digital climate maps, and predictive models for agroclimate and fruit quality) to implement this concept on a GIS platform. Primary criterion for selecting sites suitable for Fuji apple production includes land cover, topography, and soil texture. When the primary criterion is satisfied, climatic conditions such as the length of frost free season, freezing risk during the overwintering period, and the late frost risk in spring are tested as the secondary criterion. Finally, the third criterion checks for fruit quality such as color and shape. Land attributes related to these factors in each criterion were implemented in ArcGIS environment as relevant raster layers for spatial analysis, and retrieval procedures were automated by writing programs compatible with ArcGIS. This scheme was applied to the A1B projected climates for South Korea in the future normal years (2011-2040, 2041-2070, and 2071-2100) as well as the current climate condition observed in 1971-2000 for selecting the sites suitable for quality Fuji apple production in each period. Results showed that this scheme can figure out the geographical shift of suitable zones at landscape scales as well as the latitudinal shift of northern limit for cultivation at national or regional scales.

Social Tagging-based Recommendation Platform for Patented Technology Transfer (특허의 기술이전 활성화를 위한 소셜 태깅기반 지적재산권 추천플랫폼)

  • Park, Yoon-Joo
    • Journal of Intelligence and Information Systems
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    • v.21 no.3
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    • pp.53-77
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    • 2015
  • Korea has witnessed an increasing number of domestic patent applications, but a majority of them are not utilized to their maximum potential but end up becoming obsolete. According to the 2012 National Congress' Inspection of Administration, about 73% of patents possessed by universities and public-funded research institutions failed to lead to creating social values, but remain latent. One of the main problem of this issue is that patent creators such as individual researcher, university, or research institution lack abilities to commercialize their patents into viable businesses with those enterprises that are in need of them. Also, for enterprises side, it is hard to find the appropriate patents by searching keywords on all such occasions. This system proposes a patent recommendation system that can identify and recommend intellectual rights appropriate to users' interested fields among a rapidly accumulating number of patent assets in a more easy and efficient manner. The proposed system extracts core contents and technology sectors from the existing pool of patents, and combines it with secondary social knowledge, which derives from tags information created by users, in order to find the best patents recommended for users. That is to say, in an early stage where there is no accumulated tag information, the recommendation is done by utilizing content characteristics, which are identified through an analysis of key words contained in such parameters as 'Title of Invention' and 'Claim' among the various patent attributes. In order to do this, the suggested system extracts only nouns from patents and assigns a weight to each noun according to the importance of it in all patents by performing TF-IDF analysis. After that, it finds patents which have similar weights with preferred patents by a user. In this paper, this similarity is called a "Domain Similarity". Next, the suggested system extract technology sector's characteristics from patent document by analyzing the international technology classification code (International Patent Classification, IPC). Every patents have more than one IPC, and each user can attach more than one tag to the patents they like. Thus, each user has a set of IPC codes included in tagged patents. The suggested system manages this IPC set to analyze technology preference of each user and find the well-fitted patents for them. In order to do this, the suggeted system calcuates a 'Technology_Similarity' between a set of IPC codes and IPC codes contained in all other patents. After that, when the tag information of multiple users are accumulated, the system expands the recommendations in consideration of other users' social tag information relating to the patent that is tagged by a concerned user. The similarity between tag information of perferred 'patents by user and other patents are called a 'Social Simialrity' in this paper. Lastly, a 'Total Similarity' are calculated by adding these three differenent similarites and patents having the highest 'Total Similarity' are recommended to each user. The suggested system are applied to a total of 1,638 korean patents obtained from the Korea Industrial Property Rights Information Service (KIPRIS) run by the Korea Intellectual Property Office. However, since this original dataset does not include tag information, we create virtual tag information and utilized this to construct the semi-virtual dataset. The proposed recommendation algorithm was implemented with JAVA, a computer programming language, and a prototype graphic user interface was also designed for this study. As the proposed system did not have dependent variables and uses virtual data, it is impossible to verify the recommendation system with a statistical method. Therefore, the study uses a scenario test method to verify the operational feasibility and recommendation effectiveness of the system. The results of this study are expected to improve the possibility of matching promising patents with the best suitable businesses. It is assumed that users' experiential knowledge can be accumulated, managed, and utilized in the As-Is patent system, which currently only manages standardized patent information.