Earticle

It aims to explain why data citation is important, how publishers and data repositories can do this and what use will be made of the information they provide. There are large benefits to be accrued from sharing research data such as guarantee of reproducibility and transparency. Consistent citation practice around data is essential to helping these benefits to be realized. Data citation metadata is being disseminated and used through its application programming interfaces and the Event Data application programming interface. Event Data extracts this information into a separate service, so data citations are pre-filtered from the Crossref metadata. There are two methods by which publishers can register data citation information with Crossref. The first method is to deposit data citations in the citation section of the metadata, i.e., the part containing the reference list of the article. The second method publishers can use to register data citations with Crossref is to use the relationships section of the metadata. There are a number of services already using Event Data to show information on data citation. To achieve the benefits of data citation, publishers or editors should have a data sharing and citation policy so that they share with their authors and readers.

There is a need to reduce fuel consumption in order to reduce GHG emissions from the transport sector, which accounts for large volumes. In this study, the fuel consumption rate can be compared with the method using the CAN communication data in the engine controller through the OBD II interface and the direct measurement method using the fuel flow meter. For this purpose, we measured the fuel consumption rate in the engine controller and the fuel flow meter with the chassis dynamometer, and confirmed the reliable data of the fuel flow meter. As a result, the fuel consumption rate in the engine controller and the fuel consumption rate in the fuel flow meter were directly measured. After that, the running test was carried out using the chassis dynamometer and the reliability of the fuel consumption rate using the flow meter was confirmed.

최근 개인정보 이동권을 위한 마이데이터 사업이 의료분야까지 확장하고 있다. 모든 연령대를 대상으로 의료 및 헬스케어 산업 확산을 위해선 낮은 비용으로 보급할 수 있는 IoT 기기들을 활용하여 안전하게 의료 데이터를 공유할 수 있는 기법이 필요하다. 최근 Qiu 등은 데이터와 암호키를 데이터 암호화에 사용함으로 암호화 키가 외부에 노출돼도 안전한 의료 데이터 공유기법을 제안하였다. 그러나 Qiu 등의 기법은 N개의 의료기관과 자신의 의료 데이터를 공유하기 위해선 N개의 복호화 키를 기기에 저장해야 한다는 특징을 가진다. 이에 따라 키 관리의 문제가 발생하고, 사용자 기기 측면에서는 저장공간 오버헤드와 추가적인 검색연산이 요구된다는 한계점을 가진다. 본 논문에서는 Qiu 등의 기법을 발전시켜 의료 데이터 공유 대상의 수가 증가하여도 사용자 기기 관점에서 효율적인 의료 데이터 공유 기법을 제안한다. 하나의 키로부터 나머지 키를 파생시키는 키 생성 기법을 제안함으로써 사용자 기기 측면에서 저장공간 오버헤드가 발생하지 않으며, 추가적인 검색연산이 필요 없어 저사양 기기에서 활용 가능하다. 비교 실험의 결과 제안된 기법이 Qiu 등의 기법에 비해 키 생성 시간을 포함한 암호화 시간은 0.05초가 빠르며, 에너지 소비는 4.3% 적어 시간, 에너지 측면에서도 효율적이다.

The MyData initiative for personal data portability has recently expanded into the medical field. A method for securely sharing medical data using low-cost IoT devices is essential for widespread adoption of healthcare services across all age groups. Qiu et al. proposed a secure medical data sharing technique using both data and encryption keys, ensuring safety even if the encryption key is exposed. However, their method requires storing N decryption keys for N medical institutions, leading to key management issues, storage overhead, and additional search operations on user devices. This paper presents an improved method based on Qiu et al.'s technique, maintaining efficiency from the user device perspective as the number of data sharing targets increases. We propose a key generation technique deriving multiple keys from a single key, eliminating storage overhead and additional search operations, thus suitable for low-spec devices. Experimental results show our method is 0.05 seconds faster in encryption time (including key generation) and consumes 4.3% less energy compared to Qiu et al.'s method, demonstrating improved efficiency in time and energy consumption.

4차 산업혁명의 중요한 요소인 빅데이터는 공공기관과 지자체에서 공공데이터를 적극 개방하고 있다. 공공데이 터 포털에서 모두가 편리하게 데이터를 검색하고 연관 데이터를 확인 할 수 있지만, ICT관련 분야에 있는 사람들만 공공데이터를 활용하고 있는 실정이다. 공공기관이 보유하고 있는 데이터를 시민에게 개방은 하고 있지만, 누구나 쉽 게 공공데이터를 활용하여 응용 프로그램을 개발하기에는 어려운 상황이다. 본 논문은 공공데이터 포털에서 오픈API 형식으로 제공되는 데이터가 XML과 JSON 형식이다. 우리는 본 연구에서 XML형식의 공공데이터를 GUI 인터페이 스에 연동하여 손쉽게 프로그램 개발이 가능한 부분으로 모듈화 하는 방법이다. 필요한 공공데이터를 기반으로 모바 일 프로그램을 쉽게 개발하는 방안을 제시하여, 공공데이터 활용을 활성화 하는 방안을 제안한다.

Big data, an important element of the 4th industrial revolution, is actively opening public data in public institutions and local governments. In the public data portal, everyone can conveniently search for data and check related data, but only those in ICT-related fields are using public data. Although data held by public institutions is open to citizens, it is difficult for anyone to easily utilize public data to develop applications. In this paper, data provided in open API format from public data portals has XML and JSON formats. In this study, we are a method of modularizing public data in XML format into a part that can be easily developed by linking it to a GUI interface. Based on the necessary public data, we propose a way to easily develop mobile programs and promote the use of public data.

Objectives: Although medical typologies based on indigenous biopsychological ideas have been described, their integrity has been questioned due to its theory-driven nature in categorization. Therefore, studies on the Sasang typology, a temperament-based traditional Korean medicine, are needed to examine whether it is possible to classify types of specific biopsychological profiles using data-driven analysis. Methods: Psychological measures of the Eastern Sasang Personality Questionnaire (SPQ) and Western NEO-Personality Inventory (NEO-PI) along with physical measures and Sasang types were acquired from 2,049 participants. Latent groups based on the SPQ and NEO-PI subscale scores were extracted using Latent Profile Analysis. Their psychosomatic features were then compared with those of Sasang types. Results: Three SPQ-based latent groups showed distinctive psychological and physical features consistent with those of Sasang types. However, four NEOPI-based latent groups presented only psychological features. Furthermore, SPQ-High and SPQ-Low latent groups demonstrated similar psychosomatic profiles to those of So-Yang and So-Eum Sasang types, respectively. Conclusions: This study illustrates that biopsychological profiles of Sasang types are supported by psychosomatic features of latent groups based on SPQ of Eastern psychology, signifying that the categorization of Sasang typology have acceptable validity and reliability.

본 연구는 서울 열린데이터 광장의 공공 데이터를 활용하여 의료 복지 서비스 지원 방안을 조사한다. '올해 의료비로 인한 가계 부담 정도' 데이터를 분석하여, 의료비 부담이 일반 가구에 미치는 영향을 깊이 있게 조사하였다. 이 데이터는 응답자들이 느끼는 의료비 부담의 다양한 정도(전혀 부담 안 됨, 부담 안 됨, 보통, 약간 부담됨, 매우 부담됨)를 포함하고 있으며, 이를 통해 지역별, 성별, 연령별, 소득별 의료비 부담 차이를 분석하였다. 연구 결과는 각 그룹별로 상이한 의료비 부담감을 보여주며, 이는 향후 의료 서비스 접근성 개선과 정책적 지원을 위한 근거 자료로 활용될 수 있다. 본 논문은 의료비 부담을 완화하기 위한 다양한 정책 제안을 포함하며, 공공 데이터를 활용한 의료 자원의 최적화를 목표로 한다. 이러한 분석은 보다 효율적인 의료복지 서비스 제공에 기여할 것으로 기대된다.

As the number of pet-owning travelers continues to grow, the demand for pet-friendly hospitality services has surged. This study explores how big data, particularly user-generated content in the form of Google hotel reviews, can provide insights into customer satisfaction and experience in pet-friendly five-star hotels. Focusing on the top 15 pet-friendly luxury hotels worldwide, the research applies text mining and semantic network analysis to over 5,000 online reviews. The findings reveal that guests emphasize not only functional attributes such as cleanliness, amenities, and food, but also emotional aspects like warmth, comfort, and memorable moments shared with their pets. Central keywords including "hotel" "room," service," and "stay" highlight the importance of both service quality and emotional connection in shaping guest experiences. This study demonstrates the value of big data in identifying key themes and expectations among pet-owning travelers and offers practical recommendations for hotel managers seeking to enhance their pet-friendly offerings and overall guest satisfaction.

Objectives:This paper examines an under-researched issue - the comparability between authentic and simulated data, both of which are commonly used in business and professional communication. By analysing face-to-face business meetings, this paper aims to investigate (1) whether the four meeting chairing activities are accomplished in similar or different ways in authentic and simulated meetings?, and (2) under what settings simulated data would be comparable to authentic data for business and professional communication research. Methods:The study employs a fine-grained discourse-analytic approach to examine and compare four meeting chairing/facilitation activities (i.e., meeting opening, agenda management, turn allocation, meeting closing) in two authentic and two simulated business meetings. Results:Analysis shows that simulated meetings display a considerable degree of similarities to authentic meetings in terms of communicative strategies used to open or close a meeting, to manage the meeting agenda, and to allocate speakership while differences are observed in the actual discourse used to accomplish these chairing activities. The observed differences can be attributed in terms of the participants’ orientations to the meetings’ contextual factors including the setting that a simulated meeting is embedded, the organizational roles that a meeting chair is playing, and the relationship among the meeting participants. Conclusion:Findings suggest that simulated data can be compatible for studying communicative patterns at macro-levels although it may not be suitable for researching business communication at a micro-level of interaction. We also provide implications of using simulated data for conducting research in business communication.

This study quantifies the dynamic interrelationship between the KOSPI index return and search query data derived from the Naver DataLab. The empirical estimation using a bivariate GARCH model reveals that negative contemporaneous correlations between the stock return and the search frequency prevail during the sample period. Meanwhile, the search frequency has a negative association with the one-week- ahead stock return but not vice versa. In addition to identifying dynamic correlations, the paper also aims to serve as a test bed in which the existence of profitable trading strategies based on big data is explored. Specifically, the strategy interpreting the heightened investor attention as a negative signal for future returns appears to have been superior to the benchmark strategy in terms of the expected utility over wealth. This paper also demonstrates that the big data-based option trading strategy might be able to beat the market under certain conditions. These results highlight the possibility of big data as a potential source-which has been left largely untapped-for establishing profitable trading strategies as well as developing insights on stock market dynamics.

Corpora have been considered a source of native speakers’ language use. For this reason, they are often applied to language pedagogy under the assumption that learners can benefit from experiencing authentic language use. However, it is also true that EFL learners and teachers hesitate to use complicated and massive amounts of corpus data. Besides, the complex nature of such corpus data, another crucial reason for wavering is a lack of systematic methodology to guide learners and teachers in applying corpus data to language learning and teaching. In fact, many studies have focused on the positive potential and possibilities of corpus-based approaches in terms of how they contribute to developing learners’ awareness of how language is actually used. However, few studies have presented systematic guidelines for how corpus data can be used or analysed in EFL contexts. For this reason, the purpose of the study is to present guidelines which EFL learners and teachers adopt for their language learning and teaching from a theoretical perspective. To this end, the study first discusses pedagogically useful aspects of corpus data which represent authentic language use (language normality) in terms of ‘what to learn’. To do this, three aspects (collocation, colligation, and semantic prosody) are argued. Second the study presents specific methodologies for applying corpus data in terms of ‘how to learn’. For this, the study presents two specific examples of corpus-based methodologies which are based on guidelines for EFL contexts.

This paper presents the design and implementation of an FPGA?based CAN?FD digital controller optimized for in?vehicle communication. The proposed controller is organized into modular components, including a transmitter, receiver, error handler, CRC unit, and bus monitor, to ensure accurate protocol handling and stable bit timing across both arbitration and data phases. A bus?monitor?centric synchronization method is introduced to enhance timing precision and improve robustness under high?speed data?rate switching. The controller was implemented on the AMD Zynq UltraScale+ MPSoC ZCU104 Evaluation Kit and tested with an external CAN?FD node with an MCP2562FD transceiver and an MCP2518FD-based CAN?FD shield. Experimental results demonstrate successful transmission and reception of 64?byte payloads at 4 Mbps without protocol violations, confirming that the proposed architecture is suitable for practical deployment in automotive and industrial applications.

The communications link in a space program is a crucial point for upgrading its performance by handling data between spacecraft bus and payloads, because spacecraft's missions are related to the data handling mechanism using communications ports such as a controlled area network bus (CAN Bus) and a universal asynchronous receiver and transmitter (UART). The NEXTSat-1 has a lot of communications ports for performing science and technology missions. However, the top level system requirements for the NEXTSat-1 are mass and volume limitations. Normally, the communications for units shall be conducted by using point to point link which require more mass and volume to interconnect. Thus, our approach for the novel communications link in the NEXTSat-1 program is to use CAN and serializer and deserializer low voltage differential signal (SerDesLVDS) to meet the system requirements of mass and volume. The CAN Bus and SerDesLVDS were confirmed by using already defined communications link for our missions in the NEXTSat-1 program and the analysis results were reported in this study in view of data flow and size analysis.

The objectives of this study were to determine in vitro dry matter and energy utilization ofhatchery waste products and to confirm whether in vivo energy digestibility of hatchery wasteproducts could be estimated using in vitro data. Two in vitro assays were conducted for infertileeggs, unhatched eggs, culled chicks, and a mixture(20% dried infertile eggs, 20% driedunhatched eggs, and 60% dried culled chicks). In Exp.1, in vitro dry matter disappearance(IVDMD) of hatchery waste products was determined. In Exp.2, in vitro energy disappearance(IVED) was determined using undigested residues from Exp.1. The IVDMD of infertile eggs,unhatched eggs, culled chicks, and the mixture were 81.7, 88.7, 83.9, and 85.4%, respectively. The IVED of the test ingredients were 74.4, 85.1, 77.6, and 79.8%, respectively. Both IVDMDand IVED were greater in unhatched eggs compared with infertile eggs and culled chicks(p<0.05). In vivo energy digestibility was estimated well using prediction equations for hatcherywaste products developed in the present study: In vivo energy digestibility(%) = 2.52 × IVDMD(%) ? 133.95 with r2 = 0.70 and in vivo energy digestibility(%) = 1.63 × IVED(%) ? 50.03with r2 = 0.67. In conclusion, energy utilization of unhatched eggs was the greatest among testingredients and energy utilization of hatchery waste products can be estimated using data fromin vitro procedures.

Background and Purpose This study aimed to determine the ability of deep learning using convolutional neural networks (CNNs) to diagnose transient global amnesia (TGA) based on electroencephalography (EEG) data, and to differentiate between patients with recurrent TGA events and those with a single TGA event. Methods We retrospectively enrolled newly diagnosed patients with TGA and healthy controls. All patients with TGA and the healthy controls underwent EEG. The EEG signals were converted into images using time-frequency analysis with short-time Fourier transforms. We employed two CNN models (AlexNet and VGG19) to classify the patients with TGA and the healthy controls, and for further classification of patients with recurrent TGA events and those with a single TGA event. Results We enrolled 171 patients with TGA and 68 healthy controls. The accuracy and area under the curve (AUC) of the AlexNet and VGG19 models in classifying patients with TGA and healthy controls were 70.4% and 71.8%, and 0.718 and 0.743, respectively. In addition, the accuracy and AUC of the AlexNet and VGG19 models in classifying patients with recurrent TGA events and those with a single TGA event were 71.1% and 88.4%, and 0.773 and 0.873, respectively. Conclusions We have successfully demonstrated the feasibility of deep learning in diagnosing TGA based on EEG data, and used two different CNN models to distinguish between patients with recurrent TGA events and those with a single TGA event.