Earticle

농업과 ICT(Information & Communication Technology)의 융합기술은 미래농업의 주요 요인으로 지목되고 있는 가운데 식량부족 및 식품 안전성 문제의 대안 책으로 주목받고 있다. 최근 들어, 국내 및 국제 농업분야에서는 농작물 생육환경에 대해 ICT기술 중 빅데이터를 이용하여, 농업분야 환경을 개선하고자 한다. 그러나 아직까지 국내 농업분야에서는 빅데이터 활용도가 낮으며, 농가별로 폐쇄적으로 운영하여 생육환경 데이터가 공유되지 않고 있다. 따라서 세계 10대 유망상업 중 하나로 꼽히는 빅데이터 활용과 더불어 개방형 지식 서비스화 할 수 있는 LOK(Linked Open Knowledge) 시스템이 필요하다. 본 연구는 국내외 생육환경과 빅데이터의 연구현황을 분석하고 국내 농업의 선진화 방안으로 농업 내의 빅데이터 구축 및 활용을 통한 농작물에 대한 LOK의 필요성을 알아보고, 제언하고자 한다.

The convergence technology of ICT(Information & Communication Technology) in agriculture is the main key of the future agricultural industry. Recently, many that by using big data it can improve crop growth-circumstance and agricultural Industry. However, the data of crop growth-circumstance has been not shared and operated separately by individual farm. Therefore, it is necessary to build the LOK(Linked Open Knowledge) system for Quality of Farming & Farm product. We research previous studies for big data and development of the corp growth-circumstance using big data system case. Also, we suggest to build LOK system for improving the domestic agricultural industry.

Knowledge and technology resources are the most crucial sources for the achievement of sustainable development in competitive advantage. Meanwhile, few empirical studies have clarified the types of knowledge and technology resources that nonprofit organizations (NPOs) use and develop. This study aims to categorize knowledge and technology resources in NPOs that both researchers and practitioners can use to develop the nonprofit sector further. A qualitative research method was used for the study. Data were collected from 31 interviews with senior and founding members of NPOs in Thailand. Analysis of qualitative data identified five critical categories of knowledge resources: human resources, organizational practices, partnership or stakeholder involvement, operational practices, and other resources. This study also illustrates both internal and external technology resources, which are used in sample organizations. The study's findings contribute to developing a body of knowledge management literature related to the knowledge and technology resources of NPOs.

Selection procedures of earth retention systems are increasingly complex and directly related to the serviceability of the retaining structure selection systems since significant changes in earth retention technology motivates the review of design, and selection processes of earth retaining structures. Collection and classification of retaining structure selection knowledge are key issues because two expert groups, geotechnical and structural engineers, are mainly involved in the retaining structure selection. The course of natural tendency of expert knowledge are investigated considering the decision factors. The decision factors for selecting retaining structures are divided into four categories: application of the structure, and spatial, behavior, and economic constraints.

Background Ecosystem services of pollinators have been gaining importance, as mentioned in the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services?thematic report on pollination in 2016. Apart from wild bees, human interventions including apiculture?can contribute to management of ecosystem services. Ecological and local knowledge of management is transmitted through various processes, varying in content and quality. Methods We analyzed the processes of knowledge transmission, beekeeping productivity, and their interactions with each other. The study was conducted in the Nagano Prefecture, Japan, where the largest quantity of bee honey is produced nationally. Beekeeping knowledge in Nagano is transmitted through different information channels, including families, friends, and books. We categorized the beekeepers based on their information channels. The relationship between productivity of beekeeping and information channels was also analyzed. Results It was identified that beekeepers who obtained their knowledge from their parents had relatively large number of bee colonies. Furthermore, we observed by interviews with them that they tended to understand the importance of ecological conditions for sustainable beekeeping. Conclusion The results implied a challenge to open the knowledge transmission channels beyond families, potentially transforming the knowledge from tacit knowledge among?limited members to an explicit manualized knowledge in an aging society.

This paper aims to determine the impact of knowledge creation and sharing on use by librarians in federal universities in Nigeria. The study adopted the survey method of the correlation type, where 518 librarians were surveyed through a close-ended questionnaire, descriptive statistics, Pearson Product Moment Correlation coefficient and multiple regression were used to test the hypotheses. Findings indicate that significant relationships exist between knowledge creation and knowledge sharing (r=0.52), knowledge sharing and knowledge use (r=0.63) and knowledge creation and knowledge use (r=0.52), respectively. Knowledge created and shared by the librarians is used for better work performance. The paper adds value to the existing body of knowledge by proposing the need to understand the importance of knowledge creation and sharing as facilitators of knowledge use by librarians. This will, in turn, enhance service delivery to library patrons, thereby improving library patronage. This paper is limited to knowledge creation and sharing and its impact on knowledge use. Types of knowledge created and channels through which knowledge is shared are outside the focus of this article.

Purpose: This study compared the walking ability of chronic stroke patients following either treadmill training with knowledge of the result (KR group) or treadmill training with knowledge of the performance (KP group). Methods: Nineteen patients with chronic stroke were recruited from a rehabilitation hospital. The patients were divided into two groups: a KR group (10 patients) and a KP group (9 patients). They received 30 minutes of neuro-developmental therapy and treadmill training 30 minutes, five times a week for three weeks. The gait parameters were measured before and after training using the Optogait system. Results: After the training periods, the KR group showed significant improvement in gait speed, cadence, step length of the unaffected limb, stance time of the affected limb, and functional gait assessment compared to the KP group (p<0.05). Conclusion: The results showed that treadmill training with KR was more effective in improving the gait speed and cadence, step length of the unaffected limb, stance time of the affected limb, and functional gait ability than the treadmill training with KP. Therefore, to improve the walking ability of stroke patients, it is necessary to consider treadmill training with KR. If it can be combined with conventional neurological physiotherapy, it would be an effective rehabilitation for stroke patients.

Purpose: The purpose of this study was to compare the effect of 'knowledge of result' and 'knowledge of performance', two types of extrinsic feedbacks, during the sit-to-stand movement in children with hemiplegic cerebral palsy. Methods: A total of ten children with hemiplegic cerebral palsy (ages 8 to 12 years) were recruited for the study. Subjects with hemiplegic cerebral palsy performed sit-to-stand movement in front of a mirror. Their performance was supervised and revised for normal movement by a pediatric physiotherapist. In the knowledge of the result, subjects performed sit-to-stand using a chair with an armrest in their mind with normal movement. In the knowledge of performance, subjects performed sit-to-stand under verbal instructions. Randomized cross over trials were used in this study. Main outcome measurements were as follows: mediolateral speed, anteroposterior speed, velocity moment, extent in mediolateral direction, extent in anteroposterior direction, and vertical distance of the center of pressure. Results: The mediolateral speed and extent of center of pressure was higher for 'knowledge of performance' in comparison with the other type of extrinsic feedbacks (p<0.05). The other parameters, including anteroposterior speed and extent, and vertical speed of the center of pressure, did not differ between the two types of extrinsic feedbacks (p>0.05). Conclusion: These findings suggested that training in sit-to-stand movement with 'knowledge of result' may result in better use of extrinsic feedback.

This study introduces novel research using Practice Context Models supported by Knowledge Networks and Percolation Theory with the aim to contribute to knowledge management in Proof-of-Concept (PoC) activities. The authors envision this proposal as a potential instrument to identify network structures based on a percolation (propagation) threshold and to analyze the importance of nodes (e.g., practitioners, practices, competencies, movements, and scenarios) during the percolation of knowledge in PoC activities. After thirty months immersed in the natural PoC habitat, acting as observers and practitioners, and supported by an ethnographic exercise and a designer-research mindset, the authors identified the production of meaning in PoC activities occurring in a hermeneutic circle characterized by the presence of several knowledge networks; thus, discovering the 'natural knowledge' in PoC as a spectrum of cognitive development spread throughout its network, as each node could produce and disseminate certain knowledge that flows and influences other nodes. Therefore, this research presents the use of Practice Context Models 'connected' to Knowledge Networks and Percolation Theory as a potential and feasible proposal to be built using the attribution of values (weights) to the nodes (e.g., practitioners, practices, competencies, movements, scenarios, and also knowledge) in the context of PoC with the aim to allow the players (e.g., PoC practitioners) to have more flexibility in building alliances with other players (new nodes); that is, focusing on those nodes with higher value (focus on quality) in collaboration networks, i.e., alliances (connections) with the aim to contribute to knowledge management in the context of PoC.

Due to the oil business, settlements in the Gulf Region developed into prosperous cities. But in the near future, oil is off. The plans of the Gulf Cooperation Council (GCC) states bank on diversified and knowledge-intensive economies. Are those development plans realistic? What is the state of the art of knowledge institutions in the GCC countries? Applying the theoretical frameworks of Knowledge City and Science Indicators research, we empirically and theoretically studied the emerging Gulf cities Kuwait City (Kuwait), Manama (Bahrain), Doha (Qatar), Abu Dhabi, Dubai, Sharjah (all UAE), and Muscat (Oman). Our methodological framework includes grounded theory, ethnographic field study, ServQual-like quantitative questionnaires and semi-standardized qualitative interviews conducted on-site with informed people, informetrics, and, finally, the use of official statistics. In particular, we describe and analyze the cities' knowledge infrastructures, their academics, and expenditure on R&D as input indicators; and publications as well as graduates as output indicators. A further crucial aspect of a knowledge society is the transition of graduates into knowledge-intensive public services and private companies.

최근 들어 다국적 기업들에 의한 전통지식과 유전자원에 대한 해적행위가 증가하고 있다. 이에 세계지식재산권기구의 국제조사기관 회의에서는 전통지식을 보호하기 위해서 전통지식문헌이 Patent Cooperation Treaty 비특허문헌에 포함되어야 한다는 것에 동의하였다. 대한민국에서는 특허청과 농촌진흥청이 전통지식보호를 위한 활동에 선도적인 역할을 수행해 오고 있다. 특허청은 Korean Journal of Traditional Knowledge을 2007년 말부터 한국전통지식포탈을 통해 서비스하고 있으며, 이는 2008년에 PCT 최소문헌으로 선정되었다. 농촌진흥청은 1997년부터 현재까지 전통 농업 지식과 향토음식에 대한 정보를 발굴하여 책자로 발간해 오고 있다. 또한 농촌진흥청이 발굴한 전통지식은 2010년부터 한국전통지식포탈에서 검색가능하다. 본 논문에서는 전통지식을 보호 및 활용하기 위한 전반적인 활동을 소개한다.

In recent years, multinational companies' pirate cases for traditional knowledge and genetic resources are growing. Meeting of International Authorities(MIA) agreed that traditional knowledge documentation should be included in the non-patent literature part of the Patent Cooperation Treaty(PCT) minimum documentation as a means of protection. In Korea, Korean Intellectual Property Office(KIPO) and Rural Development Administration(RDA) have played a leading role in traditional knowledge-related protection activities. KIPO's Korean Journal of Traditional Knowledge(KJTK) was selected as a PCT minimum documentation in 2008, and has been serviced through Korean Traditional Knowledge Portal(KTKP) since 2007. RDA has published several books which contain traditional agricultural knowledge and Korean local food information compiled from 1997 to now. Traditional knowledge of RDA is searchable in KTKP from 2010.In this paper, we introduce overview of activities for protection and utilization of traditional knowledge.

지식관리 프로세스에서 지식공유는 지식의 생성 및 분배에 많은 영향을 주고 있다. 특히, 지식기반 정보기술 환경이 트위터와 같은 소셜미디어 정보서비스 등의 라이브러리 2.0 서비스를 중심으로 지식유통이 활성화되고 있다. 지식공유의 영향 요소에 사회연결망 구조 및 특징에 대한 네트워크의 관계 요소가 조직의 지식공유 활성화를 위한 중요한 요인으로 인식되고 있다. 본 연구는 자기조직화를 통해 유기체와 같이 성장하고 있는 조직 내부의 사회연결망의 구조 및 특성을 사회연결망 분석도구와 다중회귀분석을 통해 1) 사회연결망의 연결구조와 지식공유와의 관계, 2) 구조적 공백과 중심성의 지식공유 영향도, 3) 개인적인 능력, 정보기술 및 업무 인식과 지식공유 관계를 분석하였다.

The knowledge sharing in a knowledge management process is much affecting generation and distribution of knowledge. Especially, the knowledge distribution is being revitalized with the center of social media service like twitter and library service 2.0 in the knowledge-based IT (Information Technology) environment. The present research analyzed the structure and characteristics of a social network inside an organization that is growing like an organism through self-organization through tools for SNA (Social Network Analysis) and multiple regression analysis of independent variables such as 1) a relationship between social network's structure and knowledge sharing, 2) a relationship between structural holes and knowledge sharing influence of centrality, 3) a relationship between individual ability and knowledge sharing of information technology and work recognition.

Online learning is becoming ubiquitous worldwide because of its accessibility anytime and from anywhere. However, it cannot be successfully implemented without understanding constructs that may affect its adoption. Unlike previous literature, this research extends the Unified Theory of Acceptance and Use of Technology with three well-known theories, namely compatibility, online self-efficacy, and knowledge sharing and acquisition to examine online learning adoption. A total of 264 higher education students took part in this research. Partial Least Squares-Structural Equation Modeling was used to evaluate the proposed theoretical model. The findings suggested that performance expectancy and compatibility were significant predictors of behavioral intention, whereas behavioral intention, facilitating conditions, and compatibility had a significant and direct effect on online learning's actual use. The results also showed that knowledge acquisition, knowledge sharing, and online self-efficacy were determinates of performance expectancy. Finally, online self-efficacy was a predictor of effort expectancy. The proposed model achieved a high fit and explained 47.7%, 75.1%, 76.1%, and 71.8% of the variance of effort expectancy, performance expectancy, behavioral intention, and online learning actual use, respectively. This study has many theoretical and practical implications that have been discussed for further research.

인공지능(AI) 기술이 발전함에 따라 이를 여러 분야에 적용하는 연구들이 활발하게 진행되고 있다. 그중 게임 분야에서는 게임 상황을 분석하여 전략 추천 및 승부 예측 연구가 진행되고 있다. 기존의 연구에서는 주로 수집된 게임 로그 데이터를 AI 분류 모델을 기반으로 학습하고, 이를 바탕으로 승부를 예측하거나 전략을 추천하 는 방식을 사용하고 있다. 그러나 대용량의 시계열 게임 데이터를 분류 모델을 기반으로 학습하는 기존 방식의 경 우 게임 초기 예측에서 낮은 성능을 보이고 있다. 이에 본 연구에서는 이러한 단점을 개선하기 위해 자연어 등의 시퀀스 데이터 분석에 사용되는 Transformer 모델을 개선한 LLM 모델 기반의 게임 승부 예측 시스템을 제안한 다. 또한 성능 향상을 위하여 입력된 데이터를 기반으로 전략 등의 추가 정보를 추론하여 프롬프트에 같이 활용하 는 knowledge augmentation 기법을 적용한다. 제안하는 시스템을 실시간 전략게임인 Starcraft2에 적용한 실험 을 통해 게임의 승부 예측 성능이 향상됨을 확인하였고 게임의 특징을 잘 반영되는 것을 확인하였다.

As artificial intelligence(AI) technology develops, studies that apply it to various fields are actively being conducted. Among them, in the field of games, research is being conducted to analyze game situations in order to recommend play strategies or predict game outcomes. The existing studies mainly use a method of learning from collected game log data based on AI classification models, and use this to predict match outcomes or recommend strategies. However, the existing method of learning large-scale time-series game data based on classification models shows low performance in early-game predictions. In this study, we propose a game outcome prediction system based on an improved LLM model, which enhances the Transformer model commonly used for analyzing sequence data such as natural language, to address these limitations. Additionally, to enhance performance, a knowledge augmentation technique is applied, which infers additional information such as strategies based on the input data and incorporates it into the prompt. Through experiments applying the proposed system to the real-time strategy game StarCraft2, we confirmed that the system improves the accuracy of game outcome prediction and effectively captures the key characteristics of the game.

This research delved into the dynamics of knowledge sharing within Ladoke Akintola University of Technology's library system, focusing on specific units such as Cataloguing / Classification, Acquisition, Circulation, Reference, Virtual, and Serials Units. The study explored the challenges and opportunities associated with knowledge exchange in different functional areas, providing a comprehensive understanding of knowledge sharing dynamics within the university library context. The population comprised 55 library personnel, including professional and para-professional librarians. In addition, the research employed total enumeration sampling technique due to the small population size in order to ensure minimal sampling bias and enhanced reliability of findings. Data collection relied on a structured questionnaire designed to capture information on knowledge sharing behaviors, motivations, and channels. Moreover, the questionnaire incorporated multiple response formats and clear instructions to facilitate comprehensive data collection. Descriptive statistics was used for data analysis and the results presented in tables. The findings revealed a positive inclination towards knowledge sharing among library personnel, driven by intrinsic motivations such as personal satisfaction and a sense of achievement. External incentives such as career advancement also play a role in motivating knowledge sharing. Moreover, face-to-face interaction emerges as a predominant channel for knowledge exchange, supplemented by technological platforms such as email, mobile phones, and social media.

Knowledge graphs (KGs) emerge as potential tools for information access and resource discovery in a structured format. It facilitates information retrieval, data integration, and semantic reasoning. Considering the rapid growth of literature publications, a high-relevance search is necessary for researchers and practitioners. There are numerous tools for data organization and knowledge extraction. The knowledge graph is one of them, which depicts structured information with nodes and relationships. Library and information science can help build KGs to grasp the intricate relationships between scholarly works, their authors, institutions, and topics. The knowledge graph technology produces more relevant search results, which makes it easy to explore accurately. This paper examines the construction of knowledge graphs from library and Information Science (LIS) journal articles. A systematic approach is followed to extract entities, relationships, and attributes from LIS literature. The accuracy of the constructed knowledge graph is 89.47% (Recall), 94.44% (Precision), and 91.80% (F1 Score). User satisfaction is 85% in rating their satisfaction with the search results, interface usability, and ease of exploring relationships between entities regarding Scopus and Google Scholar. This paper also discusses this research's potential areas and challenges in enhancing information organization and retrieval in the LIS domain.

Government codes are created and utilized to streamline and standardize government administrative procedures. They are generally employed in government information systems. Because they are included in open datasets of public data, users must be able to understand them. However, information that can be used to comprehend administrative code is lost during the process of releasing data in the government system, making it difficult for data consumers to grasp the code and limiting the connection or convergence of different datasets that use the same code.This study proposes a way to employ the administrative code produced by the Korean government as a standard in a public data environment on a regular basis. Because consumers of public data are barred from accessing government systems, a means of universal access to administrative code is required. An ontology model is used to represent the administrative code's data structure and meaning, and the full administrative code is built as a knowledge graph. The knowledge graph thus created is used to assess the accuracy and connection of administrative codes in public data. The method proposed in this study has the potential to increase the quality of coded information in public data as well as data connectivity.

The study investigated the contributions of digital and media literacies to knowledge sharing among school library personnel in Nigeria. The survey research design was adopted for the study while a questionnaire was used to collect data from the 190 respondents that constitute the sample size for the study. Findings from the study revealed a high level of digital and media literacy skills possessed by the school library personnel surveyed. Producing information, evaluating digital information, and finding digital were major media and digital information literacy skills that significantly contributed to knowledge sharing among the school library personnel. The study recommended that adequate attention should be given to the development of digital and media literacies of school library personnel in Nigeria to ensure effective and efficient knowledge sharing.

In a situation where there are multiple diverse datasets, it is essential to have an efficient method to provide users with the datasets they require. To address this suggestion, necessary datasets should be selected on the basis of the relationships between the datasets. In particular, in order to discover the necessary datasets for disaster resolution, we need to consider the disaster resolution stage. In this paper, in order to provide the necessary datasets for each stage of disaster resolution, we constructed a disaster type and disaster management process ontology and designed a method to determine the necessary datasets for each disaster type and disaster management process step. In addition, we introduce a method to determine relationships between datasets necessary for disaster response. We propose a method for discovering datasets based on minimal relationships such as "isA," "sameAs," and "subclassOf." To discover suitable datasets, we designed a knowledge exploration model and collected 651 disaster-related datasets for improving our method. These datasets were categorized by disaster type from the perspective of disaster management. Categorizing actual datasets into disaster types and disaster management types allows a single dataset to be classified as multiple types in both categories. We built a knowledge exploration model on the basis of disaster examples to ensure the configuration of our model.

Knowledge base means a library stored in computer system providing useful information or appropriate solutions to specific area. Knowledge base associated with autism is the complex multidimensional information set related to the disease autism for its pathogenic factor and therapy. This paper focuses on the knowledge of biological molecular information extracted from massive biomedical texts with the aid of widespread used machine learning methods. Six classes of biological molecular information (such as protein, DNA, RNA, cell line, cell component, and cell type) are concerned and the probability statistics method, conditional random fields (CRFs), is utilized to discover these knowledges in this work. The knowledge base can help biologists to etiological analysis and pharmacists to drug development, which can at least answer four questions in question-answering (QA) system, i.e., which proteins are most related to the disease autism, which DNAs play important role to the development of autism, which cell types have the correlation to autism and which cell components participate the process to autism. The work can be visited by the address http://134.175.110.97/bioinfo/index.jsp.