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The development of a radial pulse simulator is pivotal for advancing wearable medical devices and enhancing pulse diagnosis methods prevalent in Oriental medicine. Such a simulator can be utilized for the calibration of wrist-worn wearable device sensors, as well as for training medical professionals in pulse diagnosis. This study introduces a novel, simple, and cost-effective pulse simulator that can generate a wide range of blood pressure waveforms. This simulator was designed and constructed as a prototype pulse simulator using two precision solenoid valves, an air chamber, a Half-CAM, a pneumatic sensor, and electronic control systems. By regulating air pressure through controlled opening and closing of the solenoid valve, the simulator can produce the desired pulse waveform. The performance of the proposed simulator was evaluated by replicating age-related radial pulses. Pulse waveforms generated by the simulator for four representative age groups (10, 50, 60, and 90 years) were compared with corresponding in vivo data. The experimental results demonstrated that the RMSE (Root Mean Square Error) estimate between the simulated in vivo pulse data and the actual in vivo pulse data was within 10% in all age groups. These findings demonstrate that fine pneumatic control by a solenoid valve allows the generation of sophisticated waveforms and validate that the proposed pulse simulator is capable of generating a diverse range of pulse waveforms.

This study proposes the design of a compact haptic actuator that can be integrated into laparoscopic scissors. In laparoscopic surgery, surgical proficiency is crucial owing to visual and spatial constraints, and a haptic feedback device with diverse force profiles can significantly contribute to skill improvement. Active actuators like AC or DC motors are too bulky for handheld devices like haptic laparoscopic scissors and suffer from instability issues that disrupt the interaction with the physical environment. To address these constraints, we designed a haptic brake based on the properties of magnetorheological (MR) fluid. The proposed haptic brake can generate a torque of up to 78.4 N·mm using the viscosity change of MR fluid under a magnetic field, with a power consumption of 1.5 W. Simulation results and theoretical calculations were used to derive the optimum design variables, enabling the implementation of a compact and efficient haptic feedback mechanism. This study is expected to contribute to enhancing the performance of laparoscopic-surgery simulators, thereby improving the realism and user experience of virtual surgical training by providing effective haptic feedback in actual laparoscopic surgical environments.

This study presents a comprehensive evaluation of various machine learning models for predicting heart failure outcomes. Leveraging a data set of clinical records, the performance of Logistic Regression, Support Vector Machine (SVM), Random Forest, Soft Voting ensemble, and XGBoost models are rigorously assessed using multiple evaluation metrics, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The analysis reveals that the XGBoost model outperforms the other techniques across all metrics, exhibiting the highest AUC score, indicating superior discriminative ability in distinguishing between patients with and without heart failure. Furthermore, the study highlights the importance of feature importance analysis provided by XGBoost, offering valuable insights into the most influential predictors of heart failure, which can inform clinical decision-making and patient management strategies. The research also underscores the significance of balancing precision and recall, as reflected by the F1-score, in medical applications to minimize the consequences of false negatives.

Currently, undergoing intense competition, it becomes a great challenge for the online community group buying (hereinafter referred to as OCGB) platforms to re-tend customers. Although researches have conducted on customers’ purchase intention in the context of OCGB, there are limited studies on factors influencing customers’ stickiness intention. This study develops a conceptual framework to clarify the factors influencing customers’ stickiness intention towards OCGB platform by integrating TPB model, Trust Transfer Theory as well as social capital theory. A questionnaire is conducted and 502 valid samples are collected to testify the proposed conceptual model. It turns out that trust in members, trust in the website and perceived behavioral control are important influencing factors of stickiness intention. Furthermore, trust in website partially mediates the association between trust in members and stickiness intention. This research improves our understanding of the mechanisms of customers’ embeddedness in the online group buying community.

The purpose of our study was to analyze the changes in positive and negative emotions when implementing a residential health tourism program that combined forest and hot spring environments for young and middleaged people. To achieve the purpose of this study, we designed a health tourism program utilizing Yeonginsan National Recreation Forest and hot spring facilities located in Asan-si, Chungcheongnam-do. The subjects of the study were 10 young and middle-aged people (20s-30s) and 22 middle-aged people (40s-50s). The forest environment health tourism program included a 1-hour walk in Yeonginsan National Recreation Forest, and the hot spring environment health tourism program included a 1-hour hot spring bath in Asan Hot Springs. Afterwards, they stayed in a glamping facility exposed to the forest environment and did camping activities. In order to investigate the changes in positive and negative emotions, the PANAS (positive affect and negative affect schedule) questionnaire was conducted before and after the application of the health tourism program. As a result, positive emotions increased by only 2-30 seconds in all groups, and negative emotions decreased. In summary, forest and hot spring health tourism programs appear to be sufficiently helpful in relieving stress and emotional stability.

Recently, brand collaboration is attracting the attention of the industry as one of the strategies for brand differentiation. It is a marketing activity in which two or more brands of the same kind or different kinds create new brands together to target consumers, and it has developed into a form of productive collaboration that combines and creates new values beyond the level of mutual complementation between individual brands, and is being attempted in many fields. The sportswear industry is also recovering as it has passed the COVID-19 pandemic and has shifted to endemics. This study is to understand consumers' perceptions of sports collaboration brands on social media and provide basic data to related industries. Social media channels are Naver and Google sites. Naver channels collected data from blogs and news sections, Google channels collected data from news and Facebook sections, and used Textom version 6.0, a big data analysis solution, for data collection. The collection period was collected from May 11, 2023, during the transition from the COVID-19 pandemic to the end of the pandemic, to June 30, 2024. The collected data are 2,667 blogs and 761 news on Naver channels. They are 222 news and 41 Facebook on Google channels. The collected data was converted into standardized data through preprocessing. TF and TF-IDF were analyzed through text mining. Sixty keywords were extracted in consideration of the frequency of keywords and the importance in the sentence. Semantic matrix and Concore analysis were performed on the extracted keywords. Big data analysis programs such as Textom and Ucinet were used for big data analysis, and NetDraw was used for visualization. As a result of text mining analysis, 'collaboration' showed the highest frequency with 4,860 times in relation to TF. Next, brand(3,835), sports(1,934), product(1,442), 'X'(1,125), global(1,116), release(1,064), and Nike(937), were shown in the highest order. In terms of TF-IDF, 'Nike' was the highest at 2153. Next, Puma(1,996), product(1,731), 'X'(1,659), collection(1,516), Golf(1,494), and release(1,476) were found to be high. As a result, 60 keywords were extracted and the centrality was analyzed through semantic matrix analysis. Finally, through CONCOR analysis, they were clustered into marketing strategy cluster, 'sports brand cluster', 'recommendation cluster', and 'Nike cluster'. For the results of these four cluster analysis, basic practical data were presented based on the main interest, perception, and context of 'sports brand collaboration' of consumers.

In the K-content industry, K-pop is a vital cultural phenomenon that extends beyond music to influence fashion, beauty, tourism, and the character industries. In particular, the idol characterization industry plays a vital role in fostering a bond between artists and fans and strengthening brand identity. For example, BTS's BT21 and New Jeans' rabbit characters are effectively expanding into a variety of products and media content. These characters appear across diverse platforms, contributing to a broader engagement with fans. Moreover, generating revenue through characters serves as a tool to deeply convey the artist's musical worldview and concept, beyond short-term profits. Idol characters have the complementary function of clearly presenting the artist's image to the public and enhancing the character's prominence. Webtoons, animations, and games featuring idol characters contribute to securing and expanding new fandoms. Interactions with these characters can boost brand loyalty and increase product sales. The study reveals that character-based marketing not only strengthens fandom and enhances brand loyalty but also effectively conveys the artist's vision. It highlights the crucial role of leveraging digital platforms to boost profitability and engage audiences. Furthermore, the study explores the long-term impact of these strategies on the K-Pop industry, showing how they facilitate global expansion, market entry, and sustained growth, thereby reinforcing K-Pop's position as a leading force in global entertainment.

In the context of art and society in the early Qing Dynasty, Chen Shu was known foremost as a morally upright woman. She was regarded as a model of virtue, being both a good wife and a caring mother, who effectively utilized her own talents and strengths. In this era, women’s worth was largely determined by standards set by men, and this also extended to artistic fields. Female artists’ expressions were often suppressed or made to conform to particular ideals, with femininity in art being described dismissively using phrases like “fat-free habits,” “womanhood,” and “popularity,” aiming to diminish any uniqueness in their style. In the case of Chen Shu, however, she transcended these restrictions. Her talent and achievements in painting were not constrained to a single genre. She made notable contributions in multiple areas, including flowers and birds, human figures, and landscapes, distinguishing her from other female artists of her time who often specialized narrowly. Unlike many of her contemporaries, Chen Shu achieved a high level of artistic accomplishment that earned her recognition not only as a skilled painter but also as an innovator. Even when evaluated through the strict lens of the traditional social norms of her time, Chen Shu stands out as a remarkable painter whose skills and contributions go beyond her gender. Her work embodies a rare depth and artistic quality, making her worthy of study and admiration by modern scholars. We can see her contributions not just as an inspiration within the context of female achievements, but as invaluable to our broader understanding of Qing Dynasty art and society as a whole..

As a significant aspect of photography, artists and photographers use it as a creative tool, yet existing image styles often remain too limited and predominantly rely on the body as a medium, lacking a systematic approach. In response, we aim to explore and organize the stylistic elements and creative processes involved in selfie photography. By examining these through an interdisciplinary lens, we identify and apply the 'participatory observation method' as a systematic approach to selfie photography creation. In this paper, we analyze the connection between participatory observation and selfie photography, investigating how this method shapes selfie imagery and its pioneering role in cultural research. Our approach positions selfie photography as a cultural research tool, serving as both a medium and a methodology that integrates observational techniques with creative expression. Through this interdisciplinary blend of observation and selfie photography, we aim to establish a more systematic methodology that can deepen the study of cultural representation and selfexpression.

We analyze the digital marketing strategies of Rare Beauty, a cosmetic brand founded by Selena Gomez in 2020, focusing on inclusivity and mental health advocacy as core pillars of its brand mission. Through an indepth review of the brand’s website design, SEO performance, social media engagement, and online review management—the key elements of a firm’s digital marketing activities—we reveal Rare Beauty’s success in authentically connecting with diverse audiences and fostering brand loyalty. Our analysis uncovers noteworthy findings: while Rare Beauty excels in creating a mission-driven aesthetic across digital platforms, there are areas for improvement, particularly in enhancing user experience by improving website readability, refining the review filtering system, and expanding social media engagement. Optimizing technical SEO could further increase discoverability. We propose these recommendations to strengthen Rare Beauty’s online presence and demonstrate how the brand’s unique approach offers valuable insights for industry professionals aiming to integrate social values into digital marketing strategies.

Prompted by the challenges posed by an ageing society, this study contemplates design orientations from the perspective of inclusive design. It explores the adaptation of digital medical interfaces for the elderly to enhance design inclusiveness, catering to the senior user group and optimizing interactive experience in the medical system. This study employs the concept of inclusive design and analyzes its characteristics through literature. It distills the elements of the digital medical interface design for the elderly from three aspects: functional purpose, interactive behavior, and emotional expression. Using user research methods such as indepth interviews and field research, it creates user personas and behavioral analysis diagrams for elderly patients with chronic diseases, organizing and categorizing their pain points. This study proposes principles for service touchpoint improvement based on inclusiveness. We optimize pain points and streamline the design process for age-friendly services, helping the elderly adapt to and integrate with digital life. By infusing inclusive design principles, we enhance the accessibility and inclusiveness of service design, elevating the service experience for the elderly. Our approach to age-friendly service design offers a valuable entry point for research focused on elderly-centered services and provides actionable strategies for developing agefriendly medical service processes.

With the continuous progress of science and technology, the film industry is rapidly developing in the direction of digitalization and intelligence, thereby the concept of "film industrialization" being also mentioned more and more frequently. The continuous development of digital special effects technology has brought brand new changes to Chinese film industry and became an indispensable part of the modern film production. This study analyzed the impact of the development of digital special effects technology on film industry and the practical experience of Chinese film industrial ization by taking the Wandering Earth series as an example. The industrialization development trend of Chinese films was elaborated in terms of standardization of postproduction visual effects process, specialization of production industry standard, and promotion of standardization of film industry. The key founding of this study is that digital special effects technology has not only changed the visual effects of films, but has also profoundly influenced the production mode and market pattern of the film industry. Therefore, we see digital effects technology as both irreplaceable and full of potential in modern film production. In our work, digital special effects play a pivotal role in advancing film industrialization. We anticipate that, as digital effects technology continues to evolve, China will make significant strides in film industrialization, providing robust support for the ongoing growth and prosperity of the market.

As Artificial Intelligence (AI) continues to permeate various sectors such as healthcare, finance, and transportation, the importance of securing AI systems against emerging threats has become increasingly critical. The proliferation of AI across these industries not only introduces opportunities for innovation but also exposes vulnerabilities that could be exploited by malicious actors. This comprehensive review delves into the current landscape of AI security, providing an in-depth analysis of the threats, challenges, and mitigation strategies associated with AI technologies. The paper discusses key threats such as adversarial attacks, data poisoning, and model inversion, all of which can severely compromise the integrity, confidentiality, and availability of AI systems. Additionally, the paper explores the challenges posed by the inherent complexity and opacity of AI models, particularly deep learning networks. The review also evaluates various mitigation strategies, including adversarial training, differential privacy, and federated learning, that have been developed to safeguard AI systems. By synthesizing recent advancements and identifying gaps in existing research, this paper aims to guide future efforts in enhancing the security of AI applications, ultimately ensuring their safe and ethical deployment in both critical and everyday environments.

This study focuses on optimizing the layout of mobile educational content using AI technology, with a particular emphasis on vertical aspect ratio design. Against the backdrop of changing educational content consumption patterns due to the increased mobile device usage and advancements in AI technology, this research analyzes the characteristics and effects of vertical aspect ratio design and explores its potential combination with AI technology. The research methodology combines John Yablonski's UX laws and the concept of human effective field of view with AI technology to analyze the impact of vertical aspect ratio design on the educational content user experience and learning effectiveness. Results show that vertical aspect ratio design effectively focuses users' attention, reduces cognitive load, and contributes to increased learning immersion. Specifically, when combined with AI technology, vertical aspect ratio design proves effective in providing personalized learning experiences, enhancing learning abilities, developing creativity, and optimizing data analysis across various domains. This study is expected to contribute to the qualitative improvement of educational content by emphasizing the importance of vertical aspect ratio design in mobile learning environments and proposing optimization methods using AI technology. Future studies are anticipated to further develop these findings, providing important guidelines for mobile educational content development and the advancement of AI educational technology.

The difference of this paper is that it analyzes the latest machine learning and deep learning tools for various tasks of program such as program search, understanding, completion, and review. In addition, the purpose of this study is to increase the understanding of various tasks of program by examining specific cases of applying various tasks of program based on tools. Recently, machine learning (ML) and deep learning (DL) technologies have contributed to automation and improvement of efficiency in various software development tasks such as program search, understanding, completion, and review. This study examines the characteristics of the latest ML and DL tools implemented for various tasks of program. Although these tools have many strengths, they still have weaknesses in generalization in various programming languages and program structures, and efficiency of computational resources. In this study, we evaluated the characteristics of these tools in a real environment.

Initializing blocks of a file is a frequently used function on computers. One of the simplest ways to initialize a file block is to open the file and initialize all data. This allows you to completely erase the data the file previously contained. In this paper, we design a system that initializes file blocks within a specific folder. When you specify a folder you want to initialize, the files in the folder are found, the file data is read, and the read data is initialized and saved in the file. In this way, all file blocks in a specific folder are initialized. The experiment was performed on the function proposed in this paper. As a result of the experiment, it was confirmed that initialization of file blocks in a specific folder worked normally.

Datasets are a foundational step in the development of any Artificial Intelligence (AI) powered solutions. In cybersecurity, especially in malware detection and mitigation, cybersecurity AI datasets focusing on malware can play a critical role in improving accuracy and efficiency of AI models. In this paper we explore several recent techniques used in construction of malware AI datasets, identify gaps and recommend practical solutions to address them. Specifically, we explore various frameworks and techniques for improving data collection, preprocessing and dataset validation. Furthermore, we explore various recent approaches applied in AI based malware detection. In a special way we examine shallow learning, deep learning, bio-inspired computing, behavior-based detection, heuristic-based approaches, and hybrid approaches. We then draw our observations and recommend specific strategies for improving the process of malware AI dataset construction as well as detection techniques. Through our research we also contribute to the ongoing much needed efforts for combating malware attacks by providing a framework for building quality malware focused cybersecurity AI datasets, there by improving the current state of the art AI-powered malware detection systems.

One of the key elements for maximizing user immersion in virtual reality (VR) is the development of intuitive and sensory interaction methods. While physical devices such as controllers in existing VR equipment are used to control the user's movement intentions, their drawback is that they cannot reflect detailed muscle strength. In this study, we designed a novel interaction method that increases user immersion by reflecting the activity of leg muscles in the VR environment, moving away from the traditional hand-centered control method. In the experiment, surface electromyography (sEMG) was used to measure the muscle activity of the gastrocnemius and tibialis anterior muscles in six participants. Within the VR program, various virtual objects were implemented that responded to the movement and strength of the lower limbs, allowing for a detailed reflection of the user's lower limb movements in the VR environment. The results showed that the interaction method using lower limb muscle activity demonstrated higher user immersion and satisfaction compared to the conventional controller-based method. Additionally, participants reported feeling as if they were using their entire body, greatly enhancing the sense of realism in the VR experience. This study presents a new interaction paradigm utilizing lower limb movements in VR technology and demonstrates its potential for application in various fields such as VR games, rehabilitation training, and sports simulation.

Currently, many industrial fields are pursuing research and development toward a hyper-connected society. However, as we become a hyper-connected society that perceives virtual reality as if it were reality, accurate classification of data to recognize objects, emotions and facial expressions must be accompanied. In other words, only when data meaning objects, emotions, and facial expressions are accurately classified will reliability of cognition and recognition be obtained not only in the physical world but also in a hyper-connected society. In addition, errors in perception and recognition of objects, emotions, and facial expressions can be reduced through big data analysis, and it will be protected from secondary incidents and damages. Therefore, in this study, we try to find out whether the classification of data is well done in the stage where AI with automatic cognition ability recognizes and recognizes objects, emotions, and facial expressions, and whether the data classified according to characteristics is a reliable classification result. In the experiment, when classifying data using a decision tree, we plan to conduct a study to find out whether the classification criteria of the data affect the classification criteria according to the degree of correlation between variables.

MongoDB, a document-based database, is suitable for distributed management environments of large-scale databases due to its high scalability, performance, and flexibility. Recently, as MongoDB has been widely used as a new database, many studies have been conducted including data modeling for MongoDB and studies on applying MongoDB to various applications. In this paper, we propose a data modeling method for implementing Seoul public transportation data with MongoDB. Seoul public transportation data is public data provided by the Korea Public Data Portal. In this study, we analyze the target data and find design patterns such as polymorphic pattern, subset pattern, computed pattern, and extended reference pattern in the data. Then, we present data modeling with these patterns. We also show examples of implementation of Seoul public transportation database in MongoDB. The proposed modeling method can improve database performance by leveraging the flexibility and scalability that are characteristics of MongoDB.

The purpose of this study was to investigate the association between smartphone use and consumption of highcaffeine drinks among adolescents. We studied with secondary data from 2022(18th) Korea Youth Risk Behavior (KYRBS). The respondents of this study were 51,850 participants. Data analysis was performed using IBM SPSS 25 ver. Descriptive statistics, chi-square analysis and complex sample logistic regression analysis. As a research result, participants reporting 3 times over of high-caffeine drink consumption showed 1.65 times higher of smartphone us(OR 1.65; 95% CI 1.220–2.243) and participants reporting 3 times and less of high-caffeine drink consumption showed 1.17 times higher smartphone use than ‘≤4 hours smartphone use. Our study results will be provided with basis information for the developing an intervention program to reduce smartphone usage time and high-caffeine drink consumption for adolescents high.

This study investigates an educational approach for enhancing foreign language writing skills in cyber university students by promoting the self-directed use of ChatGPT. Using a case study method, the research explores both the potential benefits and limitations of ChatGPT, a generative AI that can support writing tasks by providing real-time feedback, text summaries, and support for various writing forms, including essays and stories. While ChatGPT offers advantages such as reducing instructors' feedback workload and fostering improvements in students' writing, concerns arise regarding ChatGPT’s provision of inaccurate information and its potential to encourage plagiarism if students submit ChatGPT-generated content without proper revision. To address challenges, this study proposes instructional strategies for creating effective prompts that can elicit meaningful feedback from ChatGPT, alongside methods for students to integrate and reflect upon this feedback throughout each stage of the writing process. These instructional strategies are designed to enhance students' independent learning and encourage the responsible use of ChatGPT in educational settings.

Computerized cognitive training utilized to enhance cognitive function in dementia patients enables them to autonomously execute and acquire tasks while obtaining prompt and precise feedback on their performance. We are designed to highlight the efficacy and clinical relevance of computerized cognitive training programs as therapies for elderly adults with mild dementia. In accordance with the Cochrane Handbook for Systematic Reviews of Interventions, we conducted a review of pertinent literature across various databases, including the Korean Information Service System, Research Information Sharing Service, National Assembly Digital Library, DBpia, and PubMed, encompassing research from 2003 to 2023. Utilizing rigorous inclusion and exclusion criteria, we examined a final sample of 12 research. The data indicated that following computerized cognitive training interventions for senior adults with mild dementia, ADAS-Cog exhibited the most substantial effect size (g=-1.400), succeeded by MMSE (g=0.631), DRS (g=0.522), BNT (g=0.335), and GDS (g=-0.304), ranked by intervention efficacy. The findings allow us to assert that computerized cognitive training programs significantly enhance cognitive function, alleviate depressive symptoms, and improve language abilities in elderly individuals with mild dementia.

This study attempted to identify factors affecting the subjective health perception of middle-aged women. Secondary data analysis was conducted based on the first year of the 9th National Health and Nutrition Survey by the Korea Disease Control and Prevention Agency, and 969 middle-aged women aged 40 to 59 were the final analysis subjects. We set the following variables to increase the value of the paper: age, economic activity status, subjective body type perception, perceived stress, average daily sleep time, and daily sitting time. Data analysis was performed using the SPSS WIN 25.0 program, and descriptive statistics, t-test, correlation, and hierarchical multiple regression analysis were performed. Through this study, we found that subjective health perception was mainly influenced by perceived stress, subjective body shape perception, age, and daily sitting time. In order to increase the level of subjective health perception, it is necessary to provide necessary health management programs by age, and it is necessary to establish a management program that can change body shape perception into positive thinking.

South Korea has transformed from one of the world's poorest countries into one of its wealthiest. Since the Korean War, the nation has not only elevated its standard of living through technological innovations but has also become a prolific producer of globally popular cultural content. This rise in the popularity of K-culture has attracted learners from various countries to the Korean language. Located strategically between China and Japan, Korea draws numerous foreign language learners, including international students and industrial trainees from countries such as Vietnam and Uzbekistan. Pronouncing Korean accurately poses challenges due to the pronunciation habits rooted in the learners' native languages. Previous research focused on analyzing the pronunciation characteristics of Chinese or Vietnamese speakers and proposed the use of a Support Vector Machine (SVM) discriminator. This study aims to refine the parameters of the SVM's hyperplane to better distinguish pronunciation variations. It introduced research that leverages this discriminator to facilitate more precise Korean pronunciation among non-native speakers.