Present Issue

Extrachromosomal DNA (ecDNA) has appeared as a key driver of oncogene amplification, tumor heterogeneity, and therapeutic resistance, thereby reshaping the paradigm of precision oncology―particularly in metastatic and treatment-refractory cancers. However, the clinical translation of ecDNA-based discoveries into scalable therapeutic applications remains limited. This study introduces an integrated, empirically validated precision oncology platform that combines ecDNA biomarker profiling, artificial intelligence (AI)-driven multiomics modeling, and clustered regularly interspaced short palindromic repeats (CRISPR)-based functional validation. Built upon the “9 Building Blocks” business model, the platform incorporates high-resolution ecDNA detection (via whole-genome sequencing, fluorescence in situ hybridization, and Amplicon Architect), single-cell and spatial transcriptomics, and machine learning-based therapeutic response prediction. The AI-integrated multi-omics framework achieved robust predictive accuracy (area under the curve > .90) across diverse tumor types and was functionally validated through patient-derived xenograft models and CRISPR-Cas9 gene screening. Specifically, CRISPR knockdown experiments that target ecDNA-associated oncogenes (e.g., Myelocytomatosis oncogene, epidermal growth factor receptor) demonstrated high knockdown efficiency (up to 82% using quantitative polymerase chain reaction), significantly reduced cell viability (up to 45%), increased apoptosis (up to 38%), and suppressed tumor growth in vivo (up to 53% reduction in volume, p < .001), confirming their biological and therapeutic relevance. A pilot study that involved 12 patients further demonstrated concordance between liquid biopsy-derived ecDNA profiles and tissue biopsy results, whereas a structured stakeholder survey (n = 47) yielded strong consensus on the platform’s clinical relevance (4.62/5), feasibility (4.45/5), and strategic value (4.58/5). These results underscore the platform’s technical robustness, translational potential, and commercialization readiness for ecDNA-guided precision therapeutics.

keyword : Extrachromosomal DNA (ecDNA), Precision Therapeutics, AI-Driven Multi-Omics, Emotionally-Responsive Design, Commercialization Strategy, Patient-Centered Oncology

As population aging accelerates, social issues, such as emotional isolation, cognitive decline, and safety risks among older adults, are becoming more pressing, creating a growing interest in technology-based solutions. Among these, digital companion robots have attracted significant attention as personalized support tools that address the emotional, cognitive, and physical needs of older adults. Consequently, research focused on enhancing user acceptance and satisfaction through design has become increasingly important. This study evaluates the functional and aesthetic design elements of digital companion robots for older adults and proposes evaluation methods and design criteria. Over a period of approximately 3.5 years, repeated experiments and validations were conducted with older participants. Based on the evaluation of the robot’s facial expressions and the final prototype, this study outlines the key design elements and assessment strategies. The findings show that in terms of aesthetics, variable facial expressions positively influence the impression of the robot. A rounded, animal-like exterior was found to promote perceptions of cuteness and emotional familiarity, contributing to increased aesthetic satisfaction. From a functional perspective, essential features, such as calling, alarms, and video playback, received generally positive ratings. For verbal interaction, aspects, such as voice tone, consistent terminology, and vocal clarity, were identified as crucial for improving usability, especially for older users. Display-related factors―including touch interface, screen dimensions, and font size―should be adapted to the physical and cognitive characteristics of older adults. In terms of safety, structural stability with sufficient durability and protection against physical injury were identified as important considerations for daily use. Overall, this study provides elderly-centered design criteria for digital companion robots based on an integrated assessment of aesthetic appeal and functional performance.

keyword : Digital Companion Robot, Elderly-Centered Design, Aesthetic Element, Functional Element, Facial Expression, 디지털 컴패니언 로봇, 고령자 중심 디자인, 심미적 요소, 기능적 요소, 표정

This study proposes a novel hybrid brain-computer interface (BCI) system that integrates steady-state visual evoked potentials (SSVEPs) with the pupillary light reflex (PLR) to enhance the classification performance and information transfer rate (ITR) of conventional SSVEP-based BCIs. Eight participants took part in the experiment, where classification performances of SSVEP and hybrid paradigms were compared using four-class visual stimuli. Based on previous studies, stable SSVEP visual stimuli were selected. We also designed a hybrid visual stimulus combining luminance modulation patterns with frequencies to simultaneously evoke SSVEP and PLR responses. The experimental results showed that the hybrid paradigm achieved 94.79% accuracy and 24.90 bits/min―improvements of 10.21% in classification accuracy and 5.97 bits/min in ITR compared to the SSVEP paradigm (84.58% accuracy and 18.93 bits/min). Among the participants classified as BCI-illiterate, the proposed hybrid paradigm achieved a classification accuracy of 92.67%, representing a 16.33% improvement over the SSVEP paradigm (76.33%). In contrast,, no significant difference was observed between the two paradigms in the BCI-literate group. Additionally, the classification accuracy of the SSVEP paradigm gradually increased from 84.6% to 90.8% as the number of electroencephalogram channels increased from one to ten, whereas the hybrid paradigm showed no significant change. Overall, the proposed hybrid BCI has strong potential to advance the practical and widespread use of BCI technologies by enhancing the classification performance and ITR, addressing the challenges of BCI illiteracy, and improving usability. These improvements support broader industrial applications and services for disabled and nondisabled users.

keyword : Pupillary Response, Pupillary Light Reflex (PLR), Hybrid Brain-Computer Interface (BCI), Steady-State Visual Evoked Potentials (SSVEP), Visual Stimulus, Multimodal, Feature Fusion, 동공 반응, 동공 빛 반사, 하이브리드 뇌-컴퓨터 상호작용, 정상상태 시각 유발전위, 시각 자극, 다중 모달 특성 융합

This study applied network analysis to explore systematically Color, Material, and Technique (C-M-T) combinations. Based on data from the CMF HOW’s archive of the Korea Institute of Design Promotion, an integrated C-M-T network was constructed, and bipartite division with projection analysis was performed to derive structural characteristics and combination patterns. Certain applied metrics, for example, centrality, density, clustering coefficient, and modularity, showed that colors flexibly combine with diverse materials and techniques, while materials critically constrain technique selection or form repetitive combinations. Some materials exhibited high centrality, linking with many colors and techniques, but others had limited applicability. Modularity analysis identified communities sharing similar processing strategies, suggesting possible extensions to product-specific design strategies or process optimization. Interviews with experts further confirmed this analytical framework’s function as a practical reference for CMF planning and decision-making, and they highlighted potential applications in, for instance, sustainability regulations, cross-industry comparisons, and product case analyses. By quantitatively structuring CMF data, this study enables objective understanding of CMF combination tendencies and presents an analytical tool with both academic and practical significance for design practice.

keyword : CMF Design, Design Methodology, Network Analysis, Bipartite Network, CMF 디자인, 디자인 방법론, 네트워크 분석, 이분 네트워크

Although generative AI is expanding creative opportunities for non-design majors, their works tend to exhibit shortcomings in professional quality. To address this challenge, the study aimed to explore effective methods for design education that integrate generative AI. A poster competition using generative AI was conducted for non-design majors. The study identified key insights for enhancing the work quality of non-design majors by analyzing the design students’ critiques of the posters. The results indicated that non-design majors reported high levels of satisfaction (4.32/5) with the education and demonstrated increased willingness to engage in creative activities. In contrast, the analysis of the assessments of design majors highlights that fourth-year students (86.7%) expressed more negative views than first-year students (26.7%). This result implies that integrating generative AI into design education for non-majors must extend beyond simple tool utilization and focus on improvements in aesthetic sensibility, creative thinking, and professional quality of the final product.

keyword : Generative AI, Design Education, Non-design Majors, Poster Design, 생성형 AI, 디자인 교육, 비전공자, 포스터 디자인

This study quantitatively investigated the effects of the sampling frequency of eye-tracking equipment on gaze metrics and response classification performance in a saccade-based eye movement task. Thirty healthy older adults performed an interleaved prosaccade and antisaccade task. Eye gaze data were collected at a baseline frequency of 300 Hz and then downsampled to a range of 30-200 Hz for analysis. The lower sampling frequencies led to significant changes in gaze metrics. Specifically, saccade latency increased by more than 27 ms, whereas saccade amplitude decreased by approximately 2-3°. Additionally, peak velocity dropped by 90-120°/s. Fixation duration increased by as much as 397 ms, and fixation variability rose sharply by approximately three-fold in the prosaccade condition and up to 10-fold in the antisaccade condition. Conversely, the position error relative to the target remained mostly stable. The performance of gaze response classification was assessed using evaluation metrics. At sampling frequencies of ≥90 Hz, the performance remained stable, with accuracy ≥.98, precision ≥.99, F1 score ≥.99, and Cohen’s kappa ≥.95. However, at sampling frequencies of ≤60 Hz, the F1 score dropped to ≤.91, and Cohen’s kappa fell to ≤.63. These findings suggest that the sampling frequency substantially affects the accuracy and reliability of gaze analysis in eye tracking-based saccadic movement studies. A minimum sampling frequency of 90 Hz is recommended to ensure the extraction of gaze metrics and the performance of response classification.

keyword : Eye Tracking, Sampling Frequency, Interleaved Saccade Task, Gaze Analysis, Response Classification, 시선추적, 샘플링 주파수, 혼합형 신속보기 과제, 안구운동 분석, 반응 분류

This study determines whether a difference exists in the ability to detect Duchenne and non-Duchenne smiles per cultural disposition. Based on the literature, we investigated whether individuals with individualistic and collectivistic dispositions detected emotion differently, hypothesizing that individuals with collectivistic disposition would more accurately detect the difference in smiles. The participants’ cultural disposition was divided into individualism and collectivism using the cultural disposition questionnaire, and the differences in the judgment accuracy and sensitivity (d') of both smiles were examined accordingly. Notably, (1) the accuracy rate of the judgment of non-Duchenne smiles was higher for collectivistic than individualistic dispositions, and (2) those with a collectivistic disposition were more sensitive in their judgments of non-Duchenne smile. Therefore, people with collectivist dispositions are more sensitive to information crucial for detecting the difference between Duchenne and non-Duchenne smiles than are those with individualistic dispositions.

keyword : Cultural Disposition, Duchenne Smile, Non-Duchenne Smile, Judgment of Smiles, 문화성향, 진짜웃음, 거짓웃음, 웃음판단

This study examined the value of the Busan Modern and Contemporary History Museum’s media facade display entitled “Respond, Refugee Capital 1023!,” as a digital public history content that recreates local and historical memories. It also explored its sustainability as an emotionally reflective media facade content in terms of public engagement, using content design evaluation criteria. To this end, the historical and cultural implications of the content were gathered through participants’ discussions and interviews. Participants were then asked to respond to a design evaluation tool to measure its sustainability and significance. Findings indicate that the content demonstrated significant value in reviving the collective memory of Busan as a refugee capital, presenting it as a “place of memory.” Furthermore, it proved to be an effective digital public history practice that fosters a sensory understanding of historical facts, contributing to the formation of social consensus and civic identity. Furthermore, the positive responses across the evaluation criteria confirmed that the media facade content met these requirements. This suggests that the Busan Modern and Contemporary History Museum's media facade holds considerable merit not only as a case study of digital public history practice, but also as a sustainable content that resonates with the public.

keyword : Refugee Capital, Public History, Digital Contents, Busan Modern & Contemporary History Museum, Media Façade, 피란수도, 공공역사, 디지털 콘텐츠, 부산근현대역사관, 미디어 파사드

To improve the safety of take-over requests (TOR) in Level 3 autonomous driving, this study introduces a non-contact advanced driver monitoring system that avoids reactive, behavior-based approaches, in which an in-vehicle RGBW camera measures the driver’s heart rate via remote photoplethysmography (rPPG). The system then analyzes the driver’s heart rate variability to classify their real-time arousal state (drowsy, stressed, or normal) and employs a deep learning model for face detection, advanced signal processing, and a pattern recognition algorithm for state classification. This system was validated in over 105 hours of real-world driving with 28 participants, achieving 85.14% heart rate accuracy compared to an ECG and 90.81% state classification accuracy. This study is expected to enhance TOR safety by confirming that physiological monitoring provides reliable metrics to assess driver readiness.

keyword : Driver Monitoring, Drowsiness Warning, Takeover Request, Facial Recognition, Heart Rate Measurement, Heart Rate Variability(HRV), 모니터링, 졸음경고, 운전이양권, 얼굴인식, 심박수 측정, 심박변이도