• Journal of The Korean Association For Science Education
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• v.4 no.2
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• pp.57-63
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• 1984

An inquiry approach in teaching science has been advocated by many science educators for the past few decades, and most elementary and secondary science curricula have incorporated it in varying degrees. It has been proven in recent studies, however, that there exists considerable discrepancy between the expectation of outcomes of the inquiry approach and the actuality. This in part implies that there is a somewhat urgent need for the systematic evaluation of the approach in teaching science. The purpose of this study is to develop a comprehensive instrument for evaluating inquiry teaching approaches embedded in science curricular materials. To develop a more valid and reliable instrument a set of empirical data was used in the developmental procedure, and most of the previous studies regarding inquiry teaching method and inquiry evaluation were consulted. The inquiry evaluation method developed in this study, called the Scientific Inquiry Evaluation Inventory (SIEI), is composed of three parts: (1) analyzing and coding each science process task of inquiry activity; (2) evaluating each inquiry activity as a whole; and (3) evaluating each science laboratory curriculum as a whole. The first part of the instrument consists of twenty science process categories and thirty subcategories grouped into four sections: (1) gathering and organizing data; (2) interpreting and analyzing data; (3) synthesizing results and evaluation; and (4) hypothesizing and designing an experiment. The science process categories are arranged according to the level of difficulty, psychological level of thinking, degree of creativity demand, and the model of the process of scientific inquiry, which is also developed in the study. The second part of the instrument contains four evaluation scales of inquiry activity: (1) competition/cooperation scale; (2) discussion scale; (3) openness scale; and (4) inquiry scope scale. And the last part consists of three methods for evaluating a science laboratory curriculum as a whole: (1) inquiry pyramid; (2) inquiry index; and (3) difficulty index. The instrument is designed to be used by teachers, science curriculum developers and science education evaluators for the purpose of diagnosing the nature and appropriateness of scientific inquiry introduced in secondary science curricular materials, especailly in laboratory work and field work.

• Safety and Health at Work
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• v.2 no.2
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• pp.105-121
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• 2011

Objectives: This paper presents the framework and protocol design for a construction industry risk management toolbox. The construction industry needs a comprehensive, systematic approach to assess and control occupational risks. These risks span several professional health and safety disciplines, emphasized by multiple international occupational research agenda projects including: falls, electrocution, noise, silica, welding fumes, and musculoskeletal disorders. Yet, the International Social Security Association says, "whereas progress has been made in safety and health, the construction industry is still a high risk sector." Methods: Small- and medium-sized enterprises (SMEs) employ about 80% of the world's construction workers. In recent years a strategy for qualitative occupational risk management, known as Control Banding (CB) has gained international attention as a simplified approach for reducing work-related risks. CB groups hazards into stratified risk 'bands', identifying commensurate controls to reduce the level of risk and promote worker health and safety. We review these qualitative solutions-based approaches and identify strengths and weaknesses toward designing a simplified CB 'toolbox' approach for use by SMEs in construction trades. Results: This toolbox design proposal includes international input on multidisciplinary approaches for performing a qualitative risk assessment determining a risk 'band' for a given project. Risk bands are used to identify the appropriate level of training to oversee construction work, leading to commensurate and appropriate control methods to perform the work safely. Conclusion: The Construction Toolbox presents a review-generated format to harness multiple solutions-based national programs and publications for controlling construction-related risks with simplified approaches across the occupational safety, health and hygiene professions.

• Asia pacific journal of information systems
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• v.20 no.1
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• pp.1-31
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• 2010

One of the prominent challenges of e-government identified in developing countries is low level of ICT literacy and skills of e-government users. For those countries at the nascent stage of e-government development, it is crucial to identify and provide e-skills needed from the demand side. However, prior research has mostly focused on the supply side of e-skills, ignoring the consumption side of e-skills. In addition, no user centric approach for e-skills identification and development for e-service consumption, with respect to the stages of e-government development, have been proposed and validated. The purpose of this article is thus to: 1) Identify skills required for e-services utilization by all participants-citizens, public, and private sector employees-involved in G2C, G2B, and G2E e-government relationships respectively; assuming they are consumers of e-government and to 2) Propose and validate an user-centric approach for e-skills identification and development based on stages of e-government utilizing the Delphi method. As a result of the study, a comprehensive list of e-skills (N = 81) was generated. We found that e-skills required for e-service consumption are not merely technical; they include a wide variety of related skills that can be applied to enhancing e-skills. Therefore, the findings can serve as a standard curriculum for training and educating both citizens and government employees in developing countries. Moreover, the findings of this research may also facilitate international organizations in indentifying and measuring citizens' readiness for e-government in terms of e-skills.

• Journal of Contemporary Eastern Asia
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• v.18 no.1
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• pp.7-29
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• 2019

This study describes an approach for comparative bibliometric analysis of scientific publications related to (i) individual or several departments comprising a university, and (ii) broader integrated subject areas using multiple disciplinary schemes. It uses a custom dataset of scientific publications (ca. 15,000 articles and reviews, published during 2009-2013, and recorded in the Web of Science Core Collections) with author affiliations to the research departments, dedicated to science, technology, engineering, mathematics, and medicine (STEMM), of a comprehensive university. The dataset was subjected, at first, to the department level and discipline level analyses using the newly available KAKEN-L3 classification (based on MEXT/JSPS Grants-in-Aid system), hierarchical clustering, correspondence analysis to decipher the major departmental and disciplinary clusters, and visualization of the department-discipline relationships using two-dimensional stacked bar diagrams. The next step involved the creation of subsets covering integrated subject areas and a comparative analysis of departmental contributions to a specific area (medical, health and life science) using several disciplinary schemes: Essential Science Indicators (ESI) 22 research fields, SCOPUS 27 subject areas, OECD Frascati 38 subordinate research fields, and KAKEN-L3 66 subject categories. To illustrate the effective use of the science mapping techniques, the same subset for medical, health and life science area was subjected to network analyses for co-occurrences of keywords, bibliographic coupling of the publication sources, and co-citation of sources in the reference lists. The science mapping approach demonstrates the ways to extract information on the prolific research themes, the most frequently used journals for publishing research findings, and the knowledge base underlying the research activities covered by the publications concerned.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.207-215
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• 2023

The ship system is complex and advanced, and the operation relationship between each element is very high. So it is necessary to approach it in terms of an overall and integrated system in addition to the traditional sequential approach of finding and removing the direct cause of the accident when analyzing the accident. In this study, it is analyzed the recent fire accidents on ships occurred the Korean terrestrial water using a STAMP methodology that is different from conventional accident analysis techniques. This analysis reviews a range of factors, including safety requirements to prevent fires in ships, inappropriate decisions and actions, situations, equipment defects, and recommendations derived from accident analysis results. Through a comprehensive approach to accident prevention using STAMP, alternative evaluations are presented at the component level within the entire system of ships, and they are systematically used for accident prevention and risk evaluation as well as simple accident analysis.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.434-442
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• 2023

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting.

• Journal of The Korean Association For Science Education
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• v.26 no.3
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• pp.342-355
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• 2006

The purpose of this study was to develop a classification scheme for laboratory instruction, which could occupy a central and distinctive role in science education. For this study, literature on laboratory instruction types were analyzed. Utilizing several of these theoretical frameworks, a Classification Scheme for Laboratory Instruction (CSLI), which clearly represents various features of laboratory instruction, was created. The developed CSLI consisted of two descriptors: one is the procedure for laboratory instruction, and the other is a way of approach. The procedure is either designed by the students or provided for them from an external source. A dichotomy also exists for the approach taken toward the activity: deductive or inductive. Validity was established for the CSLI. In addition, laboratory instruction according to CSLI was divided into four types: verification, discovery, exploratory, and investigation. Although this study demonstrated only limited features of laboratory instruction due to the absence of a field test, it serves as a model for more comprehensive studies.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.178-183
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• 2024

This study introduces an enhanced sound source localization technique, bolstered by a data-driven deep learning approach, to improve the precision and accuracy of direction of arrival estimation. Focused on refining Time Difference Of Arrival (TDOA) based sound source localization, the research hinges on accurately estimating TDOA from cross-correlation functions. Accurately estimating the TDOA still remains a limitation in this research field because the measured value from actual microphones are mixed with a lot of noise. Additionally, the digitization process of acoustic signals introduces quantization errors, associated with the sampling frequency of the measurement system, that limit the precision of TDOA estimation. A deep learning-based approach is designed to overcome these limitations in TDOA accuracy and precision. To validate the method, we conduct comprehensive evaluations using both two and three-microphone array configurations. Moreover, the feasibility and real-world applicability of the suggested method are further substantiated through experiments conducted in an anechoic chamber.

• Journal of Arbitration Studies
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• v.34 no.1
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• pp.27-50
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• 2024

This paper examines issues concerning conflicts between arbitral awards and public interests, particularly with respect to economic sanctions. Sanctions have been widely used by political entities, such as States and organizations, as means to promote public interests and to resolve cross-border disputes. In particular, economic sanctions have been increasingly more visible in recent years due to the accelerating fragmentation of the international communities, and their magnitude and range of the impacts have grown accordingly. For example, the U.S. and the EU have imposed economic sanctions on Russia and related persons in response to Russia's invasion of Ukraine. The U.S. recently re-introduced a comprehensive economic sanction on Iran. One of the notable impacts of the sanctions, particularly economic sanctions, is that on international arbitration. Sanctions are essentially built on the notion of the protection of public interests, and public interests are some of the few grounds upon which recognition and enforceability or arbitral awards may be rejected. However, jurisprudence on such conflict between sanctions and arbitral awards have not been sufficiently addressed in Korea because court case and administrative decision records on this conflict have not been sufficiently accumulated. In this regard, this paper begins with offering a survey of the concept of public interests, economic and trade sanctions, arbitral awards and their enforceability, and the relationships between them. It then examines the mechanism upon which public interests, trade and economic sanctions may lead certain arbitral awards unenforceable. Next, the paper suggests judiciaries' balanced approach toward the public interests protected by trade and economic sanctions and the predictability and fairness in the enforcement of arbitral awards. Finally, this paper concludes with the methods of the implementation of such balanced approach.

• Computers and Concrete
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• v.33 no.4
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• pp.341-348
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• 2024

Ultra-high-performance concrete (UHPC) has received remarkable attentions in civil infrastructure due to its unique mechanical characteristics and durability. UHPC gains increasingly dominant in essential structural elements, while its unique properties pose challenges for traditional inspection methods, as damage may not always manifest visibly on the surface. As such, the need for robust inspection techniques for detecting cracks in UHPC members has become imperative as traditional methods often fall short in providing comprehensive and timely evaluations. In the era of artificial intelligence, computer vision has gained considerable interest as a powerful tool to enhance infrastructure condition assessment with image and video data collected from sensors, cameras, and unmanned aerial vehicles. This paper presents a computer vision-based approach employing deep learning to detect cracks in UHPC beams, with the aim of addressing the inherent limitations of traditional inspection methods. This work leverages computer vision to discern intricate patterns and anomalies. Particularly, a convolutional neural network architecture employing transfer learning is adopted to identify the presence of cracks in the beams. The proposed approach is evaluated with image data collected from full-scale experiments conducted on UHPC beams subjected to flexural and shear loadings. The results of this study indicate the applicability of computer vision and deep learning as intelligent methods to detect major and minor cracks and recognize various damage mechanisms in UHPC members with better efficiency compared to conventional monitoring methods. Findings from this work pave the way for the development of autonomous infrastructure health monitoring and condition assessment, ensuring early detection in response to evolving structural challenges. By leveraging computer vision, this paper contributes to usher in a new era of effectiveness in autonomous crack detection, enhancing the resilience and sustainability of UHPC civil infrastructure.