The Society of Convergence Knowledge Transactions ISSN:2287-8920(Print) 융복합지식학회논문지

This study aimed to identify the factors affecting the life satisfaction of childless elderly individuals. This study utilized data from the 2023 Social Survey and conducted multiple regression analysis on 436 childless elderly individuals aged 65 and over. The factors affecting the life satisfaction of childless elderly individuals were categorizsed into sociodemographic factors, health related factors, and social factors. Sociodemographic factors include age, gender, education level, region, marital status, and income. Health related factors include activities of daily living and cognitive function. Social factors include social network, social activity participation, leisure satisfaction, and personal relationship satisfaction. The analysis results showed that, among sociodemographic factors, region and income had significant effects on the life satisfaction of childless elderly individuals. Among health related factors, activities of daily living had significant effects on life satisfaction. Among social factors, leisure satisfaction and personal relationship satisfaction had significant effects on life satisfaction. Based on these results, several ways to improve the life satisfaction of childless elderly individuals were discussed.

One of the main factors that determine the performance of a battery pack is the performance of the BMS(Battery Management System), which can be implemented by design algorithms according to battery modeling optimization and accurate voltage, current, and temperature measurements. Since battery packs are exposed to various physical environments in mobility applications such as electric vehicles, drones, and robots, battery modeling reflecting this can be said to be essential to implement high-performance BMS. This study attempted to simulate the current-voltage characteristics of the battery using the “Simple Model” method and evaluate its usefulness. To this end, a pulse-controlled battery charging and discharging system was implemented to display information on the transient phenomenon of cell voltage change during the charging and discharging process, and this was compared with the modeling results. As a result, the applied Simple Model has many parameters and variables to be considered when modeling, so it was confirmed that it is a useful evaluation tool for BMS design because it showed the results consistent with the actual cell voltage change despite the high difficulty of modeling.

Recently, as the online platform market has grown rapidly, the number of cases of dark patterns (deceptive designs) that induce consumers to make irrational choices is increasing. These dark patterns pose a risk of manipulating users’ choices online and collecting personal information without permission. In response to these threats, it is time for new measures from the perspective of personal information protection, including guaranteeing the rights of information subjects. In this study, we propose a framework that can prevent dark patterns to improve the level of personal information protection and consumer trust. We aim to raise awareness of personal information protection in society by redefining personal information dark patterns classified into 7 types and 12 detailed types and suggesting a new framework to build trust between companies and users. Through this, we expect to be able to expand research on personal information dark patterns academically and provide effective personal information protection measures by proposing a framework to prevent personal information dark patterns that can occur online in practice.

This study aims to analyze how puzzle elements from game design drive the narrative in the film “Svaha: The Sixth Finger”. The puzzle elements used for film analysis are derived from Adams’ ten categories of challenges in puzzle design, which are applied to identify puzzles within the film and examine the puzzle mechanisms in “Svaha”. The research investigates the puzzle mechanism by examining the process of item acquisition by the protagonist, the subsequent deduction and clue inference, and finally, the derivation of answers using all acquired information. The study observes how these puzzle elements drive the film’s story by mapping them onto Trottier’s three-act story structure, confirming that each puzzle factor guides the overall flow of the narrative. As a result, the study identifies six core puzzle mechanisms operating in the film: factual knowledge, memory, pattern recognition, exploration, conflict, and conceptual reasoning. The paper approaches the analysis from a gaming perspective, confirming the potential of puzzle elements as a tool for film analysis, and thereby discussing the use of game mechanisms as a design methodology.

The purpose of this study is to apply a multilevel model to examine the factors affecting the quality of life of the whole people by dividing them into personal and drone-related factors. To this end, the data of the 2022 Community Health Survey by the Korea Centers for Disease Control and Prevention and the 2022 Drone Industry Survey Report by the Aviation Safety Research Institute were combined and analyzed in a multilevel model using Stata 17.0 statistical software. The main research results are as follows. First, it was found that 1.24% of the quality of life of all citizens was the influence of drone-related factors. Second, it was confirmed that all factors input at the personal level had a significant effect on the quality of life. Third, among the factors put into the drone level, it was confirmed that only the number of drones owned by local governments had a positive significant effect on the quality of life. Based on the results of this analysis, this study suggested the necessity of intervention in social welfare considering the 4th Industrial Revolution, the universalization of drone repair and maintenance facilities, improvement of drone-related regulations, and prevention of drone damage.

This paper rigorously examines the impact of Augmented Reality (AR) on tourist experiences in cultural heritage tourism, drawing on five major theoretical frameworks: the Experience Economy, Technology Acceptance Model (TAM), Conservation of Resources (COR) theory, Experience Design, and the Stimulus-Organism-Response (SOR) model. The research methodology involved data collection through surveys, followed by applying a structural equation model to examine the differential effects of educational, recreational, aesthetic, and escapist experiences in AR on tourists' perceptions and attitudes toward use. The analysis demonstrated that AR significantly enhances tourists' understanding, satisfaction, and overall quality of experience. Specifically, AR boosts the educational and entertainment value of tourism, deepens aesthetic appreciation, and positively influences revisit intentions, heritage preservation efforts, and AR adoption intentions. This study underscores the potential of AR to revolutionize cultural heritage tourism by offering immersive, personalized experiences that strengthen the interaction between tourists and heritage sites.

This study proposes a method for automatically generating sameAs RDF triples that describe the fact that entities identified by different URIs in the Linked Open Data (LOD) cloud are essentially identical. The sameAs links have a directional nature from the source entity to the target entity, allowing the inclusion of target entity content in searches for the source entity, thereby enabling the construction of enriched search results. To generate these sameAs links, it is necessary to determine whether the source and target entities are indeed identical. To this end, this study introduces the Entropy-based Pseudo Identifier Construction and Entity Sameness Evaluation (EPIC&ESE), which incorporates the concepts of pseudo-identifier and entropy. By selecting predicate properties with high entropy to construct pseudo-identifier and evaluating the similarity levels of the connected source and target object values, we determine whether the source and target entities are sufficiently identical. Experiments have confirmed that EPIC&ESE can reliably and extensively generate a set of sameAs links.

This paper presents a reliability analysis of three different architectures (cascade, power divider, and power combiner) for the design of an 8-channel SSPA (Solid-State Power Amplifier) used in Ka-band(27~31GHz) 5G RFBL (RF Biased Lifetime) test equipment. The study compares power consumption in both ‘Under RF’ and ‘No RF’ conditions, identifying the worst-case scenario based on the highest power consumption, and calculates the corresponding channel temperatures for each architecture. The channel temperature and MTTF (Mean Time to Failure) of GaN and GaAs devices are then computed, and the failure rates are compared. The results show that the power divider architecture exhibited the highest reliability, while the cascade architecture had the lowest. This analysis provides an optimal design approach and contributes to future RFBL hardware development with improved reliability. Further performance validation under various environmental conditions is required.

This study is a descriptive research study that studied the factors affecting middle-aged women's aging anxiety. This study believes that lowering middle-aged women's aging anxiety in an aging society will be a shortcut to successful aging. In particular, we aim to provide basic data for future aging anxiety intervention through the impact of social activity participation after children's independence on aging anxiety,due to the social and cultural characteristics of middle-aged women. Using the SPSS 24 program, general characteristics and economic activity characteristics were analyzed using descriptive statistics and frequency analysis, and differences between each variable were analyzed using T-test, one way ANOVA, schffe's test, and correlation analysis. The results of these analyses show the variables that were correlated with middle-aged women's aging anxiety were education level (F=7.919, p=.001), number of children (F=5.748, p=.001), income (F=8.026, p=〈.001), occupation (t=7.202, p=〈.001), menopausal symptoms (t=2.930, p=.004), type of social activity (economic activity) (t=7.031, p=〈.001), and satisfaction with learning activities (r=.360, p=.002). Multiple regression analysis was performed with the correlated variables. The results show the most influential factor affecting middle-aged women's aging anxiety was economic activities, followed by the number of children, menopausal symptoms, and satisfaction with learning activities, income. The explanatory power was 48.8%. The results of this study were conducted in one city, and follow-up research is needed in other regions in the future. We propose intervention research to reduce aging anxiety in middle-aged women through follow-up research on nursing interventions through various educational programs.

The sight of people walking with their dogs has become an everyday scene, reflecting the rapid growth of both dog ownership and societal recognition of dogs as valued companions. Dogs are now acknowledged not merely as pets but as partners in life and as active members of society. The Dog Walker Watch exemplifies how the pet culture has evolved into a valuable public safety resource and holds potential as a key tool in realizing community-based governance in public safety. This study examines the effectiveness of the Dog Walker Watch, a voluntary crime-prevention initiative under the autonomous police system, in alleviating crime-related anxiety. To conduct an in-depth analysis of the co-production of safety outcomes, we used Giorgi’s phenomenological research method, analyzing the experiences of Dog Walker Watch participants through interviews to explore how the reduction of crime-related anxiety unfolds through emotional and cognitive elements. This research aims to establish the Dog Walker Watch as an essential community-driven safety model that contributes to crime prevention and the development of a governance framework for public safety.

With the rapid advancement of artificial intelligence (AI) technologies, AI-based tools are increasingly being adopted in educational settings. Accordingly, this study aims to examine the current status of generative AI in nursing education both domestically and internationally and to propose directions for future research. A scoping review was conducted on studies published in English and Korean without restrictions on the publication date. Literature searches were conducted in seven databases (RISS, KISS, NDSL, e-article, PubMed, Web of Science, CINAHL). Among 326 papers retrieved through search terms combining the main words of the “artificial intelligence (AI), generative AI (chatGPT, chatbot), nursing, and education”, 19 papers that met the selection criteria were analyzed. Research on nursing education using generative AI has been rapidly increasing since 2023, and exploratory research has mainly been conducted. Generative AI has been applied in practical education and interacted with learners in the form of conversation or simulation. The application of generative AI improved learners' academic performance, motivation, self-efficacy, and attitudes. While positively viewed as a class and evaluation tool, it faced criticism for lacking realism. Based on these findings, recommendations for integrating generative AI into nursing education were provided.

This study explores the implementation of a picture book creation project in the "Children's Literature Education" course, an essential course in the field of Early Childhood Education. This study integrates traditional theory- and simulation-based teaching methods with project-based learning(PjBL) to propose an instructional approach and process that allows students to take the lead in creative activities. The study also analyzes the effectiveness of this approach through content analysis of students' evaluations and reflective journals. In this case study, students were assigned a project to design digital picture books on core educational topics in early childhood education, specifically “disability awareness” and “environmental awareness,” and to apply these materials in real literature education activities. Twenty-two first-year students engaged in the project-based course over a semester, progressing through stages of preparatory learning, planning, execution, and evaluation. Results indicate that this approach enhanced creative project engagement for students with limited subject experience, strengthened instructional competencies through self-directed learning, improved collaborative learning and cohesion, and developed digital literacy and competencies. Additionally, the study suggests promoting collaborative learning and linking with co-curricular programs as strategies to alleviate project workload when implementing PjBL in major courses.

In this study, we aim to analyze the interconnectedness of stock markets in Europe, the United States, and three Asian countries (Korea, China, and Japan). The results are summarized as follows. First, the Asian markets showed a statistically insignificant lead-and-lag relationship between each other. However, the EuroStoxx and S&P 500 indices were found to lead and lag all indices in the Asian market. Second, both lag 1 and lag 2 in the stock markets of Europe and the United States were found to have a statistically significant positive impact. On the other hand, Asian stock markets were found to have a negative influence on each other. Although Northeast Asian countries are closely related geographically and historically, their financial markets have a rather close relationship with the U.S. and European markets. This is believed to show fragmentarily that the Asian market is not yet leading the world market in terms of currency public confidence or economic standing and is also dependent on cross-border trade. In the future, in-depth research on information transfer between stock markets and market efficiency needs to continue.

Digital hologram stereogram technology has proven its potential in various fields since its introduction. The task of recording a conventional large-scale outdoor physical model involved a time-consuming and complex process that required specialized tools and techniques, but this study aims to simplify this process by utilizing simple equipment including a drone and free software. In this study, two successful experiments were conducted to record large-scale contents of an outdoor environment using a drone and create hologram stereograms based on the recorded contents. Each experiment utilized drone-based photogrammetry and Gaussian splatting techniques, and the data obtained during the process contributed to creating highly realistic outdoor contents. Drone-based photogrammetry ensured high resolution and precision, and Gaussian splatting effectively expressed the surface texture and depth of the object. The experimental results show that high-quality outdoor content holograms can be produced more efficiently and economically with just a drone and basic tools. This study suggests the possibility of distributing realistic outdoor hologram contents on a large scale in various fields such as art, education, and entertainment. This approach, which can create realistic outdoor holograms using accessible tools and methods, is expected to further expand the application scope of hologram technology in the future.

In this study, ultrasound extraction was performed using 30%, 50%, and 70% ethanol concentrations to confirm the active components of persimmon leaves as a functional natural materials. The analysis of polyphenol and flavonoid contents showed that the 70% ethanol extract had the highest polyphenol content, which can be attributed to phenolic compounds. The flavonoid content exhibited a trend consistent with the polyphenol content results. When converted to IC₅₀ values, the antioxidant activity results indicated equal or higher antioxidant activity compared to the control group. In the antimicrobial activity analysis, the 70% extract showed the highest antimicrobial activity. Tyrosinase and collagenase inhibitory activities were highest in the 70% extract, although lower than the control group. In the trans-2-nonenal reduction analysis, the 70% extract showed an efficacy rate of 38.2%. The α-glucosidase inhibitory activity was 48.5% in the 30% extract, showing higher activity at lower ethanol concentrations. In the lipase inhibitory activity analysis, the 70% extract demonstrated an inhibitory activity of 79.6%, with all extracts exhibiting lipase inhibition. In the heavy metal content analysis, Sb, As, Cd, Cr, and Pb were detected below the cosmetic safety standards in all extracts.

Recent research on stock price prediction using deep learning techniques has been actively conducted, showing high prediction accuracy. However, most studies rely on point-based forecasting results utilizing sliding window techniques on training data, making long-term predictions without future data impossible. In the challenging context of predicting long-term stock prices under uncertain future conditions, this study proposes a heuristic approach aimed at improving long-term prediction accuracy. The method adjusts the trend inflection point exploration range of deep learning models based on prediction errors or manually adds/deletes trend inflection points to refine predictions. Long-term predictions require a certain amount of time after the prediction point to confirm the direction and trend of the forecasted stock price. Therefore, this study establishes criteria for identifying the points where the gap between actual and predicted stock prices widens. It then maintains the trend of predicted stock prices through iterative re-predictions, including modifications to inflection points. To achieve accurate predictions, multivariate prediction experiments were also conducted. However, it was found that overlapping long-term predictions without future data resulted in reduced effectiveness.

Since COVID-19, a significant number of companies have continued to adopt a hybrid work model that combines office-based and remote work, focusing on enhancing flexibility and efficiency. To strengthen online security, improve cost efficiency, support access to internal networks, and manage users through authentication, many companies and individuals have chosen OpenVPN. OpenVPN is an open-source Virtual Private Network (VPN) software that uses SSL/TLS-based encryption protocols to provide a secure internet connection. Many remote workers use WiFi not only at home but also in various public places such as cafes and subways, where limitations in wireless bandwidth can lead to performance degradation in OpenVPN’s encryption and tunneling processes. This performance degradation and increased latency in OpenVPN can significantly affect large data transfers, real-time video conferencing, and other essential remote work activities, ultimately reducing work efficiency. This paper aims to measure the security and optimal network connection service quality (QoS) performance of OpenVPN in a WiFi environment and suggest methods to improve the efficiency of remote work environments. The research method involved collecting and analyzing packet delay changes, packet loss and jitter, end-to-end packet delay, and download/upload response times based on unauthenticated and sniffing attack scenarios in an experimental network using a private WiFi router and OpenVPN. Tools such as Wireshark were used for this analysis. The results of quality measurements and the analysis of collected packets showed an increase in the delay parameters, an increase in packet loss from 8.1% to 9.6%, a decrease in throughput from 83.7% to 70.2%, and a reduction in bandwidth by 10-30%, indicating a decline in network quality. This was interpreted as being due to the increased time required for encryption and encapsulation processes caused by the unauthenticated attack. This study indicates that further research on simplified encryption methods and encapsulation is needed in the future.

This study aims to analyze the relationship among treatment complications and comorbidities in discharged patients with treatment complications and to explore strategies for the prevention and management of these complications. Data from the Korea National Hospital Discharge In-depth Injury Survey conducted by the Korea Disease Control and Prevention Agency from 2011 to 2020 were analyzed using the Apriori algorithm for association analysis. The results revealed a strong association among hypertension, diabetes, chronic kidney disease, dialysis status, and complications related to vascular prosthetic devices, implants, and grafts(T82) in all patients with treatment complications. In patients with treatment complications excluding those with diabetes and hypertension, significant associations were observed between chronic kidney disease and complications related to vascular prosthetic devices, implants, and grafts(T82), as well as among malignant neoplasms(C77, C16, C50, C78) and post-procedural complications(T81). The strong association between chronic kidney disease and treatment complications(lift = 4.002) highlights the critical need for targeted management of chronic kidney disease patients to prevent treatment complications. This study contributes to identifying high-risk groups and proposing data-driven prevention strategies through the analysis of the relationship between treatment complications and comorbidities.