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As a principal vehicle of cultural memory, traditional music plays a significant role in shaping national identity. This study employs a comparative cultural analysis method, examining representative musical practices, ritual contexts, and transmission systems in China and Korea. Primary sources, performance materials, and policy documents are comparatively analyzed to identify how ritual–music traditions, narrative–affective structures, and institutional frameworks contribute to identity formation. The results show that China constructs national identity through a model of horizontal diffusion, translating the li–yue (ritual–music) paradigm into public cultural symbols via intangible heritage policy, stage production, and general education. In contrast, Korea sustains identity through vertical deep continuity, preserving the unity of ritual–music–dance in living ceremonies such as Jongmyo jerye and reinforcing communal affect through pansori and nongak. The study demonstrates that traditional music continues to function as an active cultural resource in contemporary society, offering effective pathways for strengthening national cohesion and rearticulating cultural identity under globalization.

This study analyzed how cyber sex crime coverage in Korean media is framed according to political orientation. A total of 941 reports from conservative-leaning media (Chosun Ilbo, JoongAng Ilbo) and progressive-leaning media (Hankyoreh, Kyunghyang Shinmun) were collected from 2020 to June 2025 and subjected to LDA-based topic modeling. Analysis revealed that while both sides commonly covered victim protection, messenger-based sexual exploitation cases, and gender-based violence discourse, they showed distinct differences in perspective and emphasis. Conservative-leaning media focused on the technical characteristics of crimes like deepfakes, legal responses, and the investigation and trial processes. In contrast, progressive-leaning media centered their coverage on victim compensation, accountability, and the need for institutional reform within a socio-structural context. This suggests conservative media prioritizes technical solutions and regulation-focused approaches, while progressive media emphasizes sociocultural criticism and structural change. This study contributes to understanding how media framing of cyber sex crime coverage influences policy discourse and public opinion formation, suggesting the need for strengthened platform regulation, improved victim protection systems, and enhanced media ethics standards. Follow-up research should comprehensively examine the interaction between media coverage and public opinion through audience response analysis and comparative discourse structure studies on new media platforms.

The globalization of sports and the transformation of social values have compelled both Korea and China to reform their legal systems governing sports promotion. Korea enacted the Framework Act on Sports (스포츠기본법) in 2021 (effective 2022), while China comprehensively revised the Sports Law of the People’s Republic of China (中华人民共和国体育法) in 2022 (effective 2023). Both laws represent the highest-level legal frameworks in their respective countries and mark a significant policy shift from performance-oriented objectives toward welfare- and rights-based governance. We designed this study to compare the legislative intent, governance orientation, and rights-related implications of these two national sports laws enacted in the same period, thereby clarifying their institutional characteristics and contemporary relevance. This study analyzes the legislative backgrounds and major provisions of these two laws, highlighting their differences in governance structures, administrative efficiency, and rights protection. While Korea’s Act emphasizes the right to sport and participatory governance, China’s Law focuses on administrative clarity and implementation effectiveness. The findings suggest mutual complementarity and potential convergence in the future development of sports law and governance across East Asia.

In recent years, China's new-style tea beverage industry has witnessed rapid expansion and intensifying competition. Amid this landscape, Mixue Ice Cream & Tea has risen to prominence by leveraging a unique 'high-quality, affordable' positioning and a franchise-driven model supported by deep supply chain integration. This paper is designed to conduct a systematic competitive analysis of Mixue, identifying its core strengths and potential risks to propose strategic pathways for its sustainable development. We employ Michael Porter's Five Forces Model to assess the external competitive environment and a SWOT analysis to evaluate its internal strategic position. Our research reveals that Mixue's primary advantages lie in its highly integrated supply chain and formidable low-price strategy, which have enabled its global expansion to over 53,000 stores. However, the analysis also identifies significant challenges, including constraints from its low-cost brand image, insufficient product innovation, and management pressures from its extensive franchise network. We conclude that to ensure long-term growth, Mixue should capitalize on its strengths by deepening its international footprint and digital transformation, while simultaneously addressing its weaknesses through product diversification and a carefully managed brand upgrade.

Rapid urban redevelopment and the rise of high-rise buildings have increased the use of gang-form systems in construction. However, frequent accidents still occur due to insufficient risk management and lack of understanding of work procedures. Therefore, we designed to identify and validate key disaster risk factors in gang-form work using the Delphi technique, providing a quantitative and objective foundation for safety management. A total of 38 risk factors across five stages—material handling, on-site assembly, lifting & installation, dismantling & lifting, and finishing—were derived through literature review and expert focus group interviews. Using Delphi analysis, indices such as the Content Validity Index (CVI), Content Validity Ratio (CVR), agreement, and convergence were comprehensively evaluated, resulting in 23 validated key factors. These factors were ranked by importance using statistical measures including mean, standard deviation, and coefficient of variation. The results offer a systematic framework for prioritizing safety management, enabling practitioners to focus on high-risk activities and prevent major accidents. Furthermore, the findings can support the development of AHP-based weighting models, digital checklists, and AI-integrated inspection systems, contributing to proactive and data-driven safety management in gang-form operations.

Despite its safety and efficiency, gang-form still frequently causes serious accidents due to inadequate management during on-site application and lack of safety awareness among workers. This study aimed to improve safety by using the AHP to quantify the relative importance of each process and suggest priorities for risk factor management, using the key risk factors of gang-form work derived from previous studies. The analysis subjects were five groups: Supervisor, Contractor manage, Subcontractor manager, Field manager, Field worker. The analysis results showed that the ‘dismantling & lifting’ stage had the highest importance at 0.37, followed by ‘installation & lifting’ at 0.28 and ‘field assembly’ at 0.16. Installation/Dismantling stages were commonly evaluated as high-risk processes in all job groups, and supervisors recognized the dismantling & lifting stage as a high-risk task, evaluating it at 0.39. These results provide empirical evidence for prioritizing process-specific risk management rather than relying solely on general guidelines. The findings also highlight the importance of cooperative safety mechanisms across different occupational levels. By proposing a structured priority model for risk management, we are designed to support the development of practical safety guidelines for on-site application. Moreover, the analytical framework presented here contributes to policy-level improvements and serves as a foundation for extending safety management methods to other high-risk construction tasks.

With the continuous advancement of technology and culture, multimedia, games, and other types of digital content that integrate a variety of complex elements are being actively developed and researched. To support this trend, content development tools and engines are consistently evolving, offering enhanced features and greater flexibility. However, text authoring tools have shown relatively limited progress in comparison. This study proposes potential improvements to text authoring tools and explores their effectiveness. By conducting a focus group test with content creators, we aim to assess whether such enhancements can provide meaningful benefits in terms of usability, creativity, and overall content development experience. The research is expected to contribute to optimizing inefficient work structures and simplifying workflows by proposing automated and visualized functional improvements. Users anticipate practical benefits such as improved work productivity and reduced data search time. Ultimately, the study emphasizes the need to redefine text authoring tools as knowledge-based systems that support creative thinking and complex information processing.

The government has recently undertaken various initiatives aimed at enhancing the performance management system of the national data infrastructure. Specifically, to bolster the dataherb’s and datacenter’s roles and enhance the value of data use, the allocation of performance indicators was increased, the ratio of returning companies was incorporated, and additional best use case indicators were provided. Moreover, the operational base of the current system is progressively enlarging as efforts to establish an additional point indicator for stimulating dataherb operation and to adjust indicator weights in accordance with annual objectives gain momentum. The government’s initiatives play a significant role in establishing the policy groundwork for advancing data-driven administration and innovative applications across the entire industry. Nonetheless, the existing performance management system fails to adequately account for the unique characteristics of each dataherb. It is also limited by the absence of a multidimensional performance evaluation framework that incorporates both qualitative and quantitative measures. Accordingly, this investigation proposes a novel model for managing multidimensional indicators to enable more precise and ongoing measurement of data management performance at the national level. By creating an assessment system that incorporates multiple indicators, the proposed model seeks to ensure that dataherb performance can be analyzed and compared in a balanced manner. This study is significant for improving the objectivity and reliability of performance management regarding national data infrastructure, greatly increasing the value derived from data usage, and aiding in evidence-based assessments of policy impacts.

We are designed to analyze and compare recent YOLO object detection models (v8–v11) for military applications under unconventional enemy provocations such as sewage balloons and suicide drones. These threats have emphasized the importance of advanced object detection systems capable of accurate target recognition. Using a military dataset (rl4pcd/yolo-military-s48o9) provided by Roboflow, which contains 21,167 images across five categories—Airplane, Helicopter, Person, Tank, and Vehicle—we evaluated model performance using quantitative metrics including mAP50, Precision, Recall, and F-1 Score. The experiments revealed that YOLOv8 achieved the highest overall accuracy (mAP50 43%, Precision 55%, Recall 42%, F-1 48%), followed by YOLOv9 with moderate results (mAP50 31%, Precision 37%, Recall 38%, F-1 38%). In contrast, YOLOv10 and YOLOv11 recorded lower performance (average mAP50 16%, Precision 25%, Recall 24%, F-1 25%). These findings indicate that YOLOv8 remains the most suitable model for enemy target identification and unmanned surveillance, while the latest versions require additional optimization for military environments. The study demonstrates that model updates do not always ensure better performance without domain-specific adaptation.

We conducted a qualitative thematic analysis to systematically examine the work-related difficulties experienced by public officials responsible for social welfare services at community service centers. We collected 102 written cases from 34 team leaders and members working in three community service centers in City S. Using thematic analysis, we identified nine thematic areas encompassing challenges related to case management, organizational issues, and resident complaints. We contribute to the field by providing an in-depth understanding of the emotional, ethical, and administrative burdens faced by welfare officials. Based on these findings, we propose practical directions for improving welfare administration, including the establishment of a professional supervision system, the strengthening of organizational-level responses, and the development of policy-based infrastructure to enhance sustainable welfare service delivery.

Generative artificial intelligence (gen-AI) has rapidly emerged as an innovative tool designed to produce texts, images, and other various types of media by learning certain patterns from training data. The widespread of the tools in higher education and its integration affects pedagogical practices by instructors. Though a large number of recent studeis focus on their perspectives, there is a critical gap regarding the views of college students, the main users of gen-AI devices. We therefore undertook a systematic literature review of 11 peer-reviewed studies published between 2023 and 2025, drawn from an initial pool of 34 articles across multiple databases. Our research questions examined college learners’ perceived benefits, challenges, and institutional needs for effective and responsible gen-AI use in education. Implementing qualitative and quantitative synthesis, we found that students recognize clear academic support, personalized learning opportunities, and motivational enhancements provided by gen-AI. On the other hand, they also showed their concerns regarding misinformation, overreliance, lower critical thinking, and ethical issues. Students appeared to advocate for institutional measures including faculty training, curriculum redesign, and explicit ethical guidelines to the usage of gen-AI tools.

Epilepsy is a neurological disorder prevalent worldwide affecting individuals not just physically but also mentally and socially causing irreversible damages. It is caused when brain neurons are unable to regulate electrical signals resulting in seizures. The diagnosis of this life threatening disorder is thus critical. The current EEG test is one of the most common and effective epilepsy diagnosis medium, however it is prone to human error and time consuming. Therefore, we propose an Artificial Intelligence model that can diagnose these seizures with accuracy. Our model implements a combination of Convolutional Neural Network and Long Short-Term Memory architecture. Trained on 80% of total EEG recordings of 129 files with one or more seizures and 198 seizure events, this model used data of 23 pediatric subjects between the age of 3 -22 with 5 males, 17 females and 1 undisclosed. Data recordings with 10-20 systems of EEG electrode positions were obtained from Children’s Hospital Boston. The EEG signals obtained from the data were first segmented into fixed length windows appropriate for model input. Then they were normalized for a consistent signal amplitude. To remove background noises and artifacts, these processed recordings were filtered and then the recording segments were labeled based on the presence or absence of epileptic seizures. The data was then split into 80% training and 20% testing sets. For spatial feature extraction and capturing temporal dependencies in EEG signals CNN and LSTM models were implemented. The model was then cross validated. A confusion matrix was generated to visualize true and predicted classifications and an accuracy of 90% was achieved for currently available datasets. We are planning to include a wide range of EEG recordings with diverse age ranges and conditions to improve reliability. We are trying enhance accuracy by adding extra preprocessing steps. To conclude, this paper presents a methodological approach to analyzing brain activity via EEG dataset with the goal of epileptic detection without claiming medical accuracy or effectiveness.

Digital twins are a core technology for smart factories, but the high cost and complexity of existing industrial protocols like OPC-UA act as significant barriers to entry for resource-constrained small and medium-sized enterprises (SMEs). This study proposes a ‘modular lightweight architecture for discrete manufacturing process control’ to address these issues. It eliminates dependency on specific middleware, utilizing only the universal game engine Unity and standard TCP/IP socket communication. The proposed architecture functionally separates visualization (Unity server) and control logic (external client), enhancing system flexibility and minimizing communication overhead. To demonstrate the architecture's excellence, a virtual conveyor belt control experiment was conducted in a distributed LAN environment simulating an actual manufacturing site. The results confirmed stable response performance with an average round-trip time (RTT) of 25.5ms, proving it meets soft real-time control requirements. Notably, comparative analysis with an ideal localhost environment (average RTT of 15.8ms) quantitatively isolated the pure network overhead to approximately 9.7ms, objectively demonstrating the structural efficiency of the proposed architecture compared to middleware-based systems. This research presents a concrete methodology and engineering benchmark data enabling the construction of practical digital twins without expensive solutions.

Depression is a major mental health issue affecting over 280 million people worldwide. It is characterized not only by a depressed mood and loss of interest but also by cognitive decline, sleep disturbances, and social isolation, which significantly impair an individual's daily functioning and quality of life. In particular, pharmacotherapy has limitations, including side effects of antidepressants, the burden of long-term use, and low adherence rates. Furthermore, economic costs and the social stigma associated with mental illness act as barriers to treatment accessibility. Consequently, there is a growing demand for new forms of non-pharmacological and digital-based interventions that can complement existing medication-centered treatments. To overcome these limitations and provide a more personalized therapeutic experience, this study implemented personalized digital therapeutic content that combines MBTI personality types and music therapy elements within a Virtual Reality (VR) environment. The proposed content consists of music recommendations based on user traits and VR scenarios utilizing natural landscapes of the four seasons; it allows for progressive personalization by reflecting user interaction data. The music therapy elements utilize emotional stimuli preferred by each personality type to induce emotional stability and mood improvement, while the nature-based VR environment promotes psychological relaxation and stress reduction through high immersion. The system developed in this study operates on standalone VR hardware and implements content comprising four natural environments—spring, summer, autumn, and winter—and music tracks tailored to MBTI types within a single integrated platform. Through this, users can simultaneously experience music and visual stimuli suited to their dispositions, leading to expected positive effects such as emotional regulation, stress alleviation, and psychological stability.

In order to improve the low employment rate of people with developmental disabilities and the limitations of existing vocational education, this study designed and implemented VR-based vocational training content centered on hospital kiosk guidance jobs. Existing vocational training has structural problems such as lack of reflection of the actual work context, uniform education that does not take into account differences in individual abilities, and restrictions on repetitive practice. Therefore, VR technology was used to reinforce the learner's job transfer ability by enabling simulation similar to reality, adjustment of difficulty, immediate feedback, and repetitive learning. The content consists of the stages of start-choice-execution-feedback, and user behavior, error, and performance speed are automatically recorded and used to analyze learner proficiency. In addition, the procedural flow of the hospital kiosk was implemented in the same way as in the actual environment so that people with developmental disabilities could experience jobs and improve their manipulation ability in a safe virtual space. In addition, 12 representative scenarios that can occur in the process of performing the job were derived, and the first internal feasibility review was conducted on the compositional content. This program is highly likely to be used in special educational institutions and vocational training sites, and in future studies, educational effectiveness and practical applicability are verified through a feasibility review by external experts and small pilot studies to secure the possibility of expansion into various job environments.

This paper presented the design and implementation of SafeMediTrack (SMT Box), an AIoT-based smart cold-chain and security management system for high-risk biopharmaceuticals. From a systematic analysis of existing smart medicine lockers and real cold chain transport scenarios, we derived functional and nonfunctional requirements and reflected them in the hardware and software architecture. The SMT Box integrates temperature-humidity, shock, GPS, and lock-state sensing with a Raspberry-Pi controller, MQTT-HTTP hybrid communication, and a web based dashboard for real-time monitoring and historical analysis. All sensor data and events are stored in a database and logs, enabling traceability of who opened the box, when, where, and under which environmental conditions. Prototype implementation and scenario based tests confirmed that the system can continuously monitor transport conditions and promptly signal abnormal states. Future work will include applying AI based anomaly detection, integrating blockchain for tamper-resistant audit trails, and also conducting field trials with pharmaceutical logistics partners at industrial deployment scale.

Photovoltaic (PV) inverters are critical components that strongly affect energy yield and power quality in gridconnected PV systems, yet they are continuously exposed to fluctuating meteorological and operating conditions that often lead to latent performance degradation and anomalous events. This paper proposes an explainable anomaly detection framework for PV inverters that combines state-index-based labeling, enhanced meteorological features, and hybrid machine-learning models. First, long-term inverter, basic weather, and derived meteorological data are integrated into an hourly time series, and anomaly labels are defined using thresholds on the Inverter Efficiency Drop and Power Stability Index. The resulting dataset contains 20,303 time steps, of which 5.3% are labeled as anomalous. Second, point level anomalies are detected using an XGBoost classifier trained on 20 input features excluding the state indices, achieving 0.915 test accuracy and 0.9045 ROCAUC. Third, sequence-level anomalies over 24-hour windows are identified using an LSTM Autoencoder based on reconstruction error. Finally, feature-importance analysis and reconstruction-error distributions reveal that enhanced meteorological features, particularly Effective Solar Irradiance, play a key role in detecting inverter anomalies. The proposed framework provides both accurate and interpretable anomaly detection, supporting practical early warning and maintenance decisions for PV inverter operation.

This study presents the design and implementation of immersive VR-based hands-on content aimed at reducing the digital divide among the silver generation. The system enables older adults to repeatedly practice operating digital devices commonly encountered in daily life such as kiosks and ATMs within a virtual reality environment. The interface incorporates large buttons, high-contrast visual elements, intuitive screen layouts, and hand-gesture-based interactions to accommodate the cognitive and perceptual characteristics of older users. The content includes a guided mode that provides step-by-step instructions and an autonomous mode in which learners independently perform the required procedures. Randomized error situations and instant feedback functions are implemented to deliver a learning experience that closely reflects actual device use. Through repeated practice in a safe environment, older adults are expected to reduce usage anxiety, improve operational skills, and enhance self-efficacy. The proposed VR hands-on content has strong potential for adoption in municipalities, welfare centers, and lifelong education institutions. Future work will involve evaluating learning effectiveness through user studies and expanding the content to support other digitally vulnerable populations.

Alzheimer's disease limits daily life performance due to decreased memory, attention, and execution functions, and increases the socioeconomic burden along with aging. This study proposes a digital treatment system that integrates target cognitive training content with a low-complex physical button interface that prioritizes elderly friendliness. The proposed system is based on USB serial linkage between a controller composed of five buttons including a built-in LED and an Arduino family microcontroller-PC application, and provides immediate feedback of LEDs and sounds on button input. The content is the same three types of picture search, shopping, and archery, which target visual identification, attention, long-term memory connection, space-time sensation, and reaction speed, respectively. Each content simplifies the rules and applies an adaptive difficulty level that controls the number of stimuli, exposure time, and obstructive stimulation density, and logging the accuracy, reaction time, and content completion rate of each content with the standard schema is used for personalized training and future evaluation. The prototype moves to the right 4 buttons, is designed with selection and launch route operation mapping through the large left button, and is configured to reduce the onboarding burden and the risk of erroneous input through design consistency such as feedback rule process and common data model. The proposed system has the potential to be introduced in various environments such as nursing homes and homes in the future, and plans to verify clinical effectiveness and compliance indicators by conducting formative usability evaluation and small pilot and wireless module expansion as follow-up studies.

This study examines how written language shapes the use of a free AI-powered image-generation tool by language learners in processes of writing and meaning-making, and how such image-supported interaction affects learners’ writing performance. To evaluate the potential benefits of image utilization in language learning, DALL·E 3 was employed via the free version of ChatGPT 4.5. The participants were sixty lowproficiency Korean EFL college students enrolled in a general writing course, who were divided into two groups: Group A, comprising beginner and elementary learners (CEFR A1–A2), and Group B, made up of lower-intermediate learners (CEFR B1). The study draws on sentiment analysis of learners’ responses to image use, conducted in Python using a Hugging Face transformer model. The resulting sentiment scores were compared with learners’ Likert-scale ratings, and pre- and post-test writing performance was measured separately. The findings provide an integrated account of how images and AI support shaped both affective responses and measurable gains in EFL writing, although more for Group B than for Group A learners.

Subjective cognitive decline (SCD) is increasingly regarded as a preclinical stage of dementia with elevated risk for later cognitive impairment. Multidomain lifestyle interventions are promising for dementia prevention, but few have been tailored to community-dwelling older adults with SCD in Korea. This study examined the effects of a lifestyle-based intervention on cognitive function, instrumental activities of daily living (IADL), quality of life, and self-efficacy. Twenty older adults (≥65 years) with SCD were recruited from a dementia safety center and randomly assigned to an experimental group (n = 10) or a control group (n = 10). The experimental group completed an 8-week lifestyle-based program adapted from the My Healthy Brain model, integrating physical activity, nutrition, cognitive stimulation, social engagement, sleep hygiene, stress management. Outcomes (MoCA-K, K-IADL, WHOQOL-BREF, GSES) were analyzed using Wilcoxon signed-ranks and Mann–Whitney U tests. The experimental group showed significant pre–post improvements in all outcomes (p < .05), whereas the control group showed no significant changes. These findings suggest that a multidomain lifestyle-based intervention may enhance cognition, daily functioning, quality of life, and self-efficacy in older adults with SCD and support the feasibility of Korean community-based early intervention models.

This study examines the impact of incrementally increasing evacuation routes on evacuation safety in multistory nursing hospital under Korean standards. Using the Pathfinder evacuation simulation software, a precise model was developed for a nursing hospital. Four distinct scenarios were simulated: a baseline scenario relying solely on internal stairs (Scenario 1), followed by scenarios incorporating external ramps at both ends of the corridor on the fifth floor (Scenario 2), on both the fifth and fourth floors (Scenario 3), and finally on the fifth, fourth, and third floors (Scenario 4).Simulation results demonstrated that each additional set of ramps significantly reduced the total evacuation time. Compared to the baseline evacuation time of 293.8 seconds (Scenario 1), the times for Scenarios 2, 3, and 4 were reduced to 262 seconds, 224.8 seconds, and 181.3 seconds, respectively. The results confirm that the initial addition of ramps eliminates the top-floor bottleneck, while subsequent additions create a networked evacuation system that allows for earlier egress, thereby more effectively relieving congestion in the internal stairs.

The high-voltage transformer panel receives high voltage from KEPCO, steps it down to distribution voltage, and then distributes the power to the low-voltage panel. Due to the recent increase in heating and cooling facilities due to abnormal temperatures and the increase in heat load due to the government’s carbon reduction policy, there has been a sharp increase in electricity demand, requiring measures to prevent overheating in the high-voltage transformer panel. In this study, a three-dimensional thermal–fluid analysis of the high-voltage transformer panel under actual operating conditions was performed using COMSOL Multiphysics, and the resulting simulations were used to derive heat transfer conditions that lower the transformer temperature by relying only on natural energy. These results indicate that, when applied to enclosed electrical equipment, the proposed passive cooling strategy can suppress temperature rise using only natural energy without any external cooling devices, thereby contributing to the construction of sustainable, circular-economy-oriented energy infrastructure by reducing energy consumption, minimizing resource use, and extending equipment lifespan.

Core-contact measuring method is used as a non-invasive contact measurement technique for temperature measurement across various fields, including semiconductors, electronic devices, and medical equipment. This non-invasive contact measurement method is employed to enhance the accuracy and precision of temperature measurement compared to conventional methods. It is essential in fields requiring high-performance and high-precision temperature measurement. The non-contact system employs an IR sensor for detection and displays the measured output value on the temperature device's screen. Core-Contact Measuring System (CCMS) consists of an IR sensor that detects heat and a component that accepts the measured heat via the core method. The obtained measurement data is converted into a digital signal via an ADC and processed through a microprocessor to become usable data. The converted data is connected as a communication signal and utilized as a digitized signal. Data obtained by detecting the human body is converted into a mode capable of diagnosing diseases for use. Data for diagnosis is transmitted to medical centers and hospitals, where it is used to determine disease values and monitor the patient's vital signs. The results of the Core-Contact Measuring System (CCMS) indicate temperature variation values with depth, which were the far temperature values of the CCMS-FA-εMED with 5.80±1.20 units, which were the convenient temperature values of the CCMS-CO-εMED with 4.06±(-0.04) units, which were the flank temperature values of the CCMS-εMED with 0.91±0.07 units, which were the vicinage temperature values of the CCMS-VI-εMED with 0.18±(-0.03) units. We anticipate that the Core-contact Measuring method will serve as a core technology to enhance the accuracy and precision of temperature measurement across diverse fields such as semiconductors, healthcare, and biotechnology, and expect it to be effective for non-invasive continuous monitoring.

Triangulation Measurement Method (TCMM) technology, which uses load cells to measure an object's weight or force, continues to advance. Load cell technology is developed for high precision and sensitivity, and is used for extremely precise measurements in the development of nano and micro sensors. Systems employing Triangulation Measurement Method (TCMM) collect electrical signals from load cells to measure vertical load changes resulting from shifts in an object's center of gravity, thereby obtaining load variations over time. The minute shaking of an object reflects slight sway or balance movement as vibration. The signal amplification section in the load cell integrates the steady signal through a specific process before passing it to the microprocessor. The transmissible signal is configured for real-time data transmission and remote monitoring via a smart load cell with an embedded wireless communication module. Changes in the human body are measured as mechanical motion, generating vibrations within a frequency range of 1 to 20 Hz. The results of the Triangular Configuration Measuring Method System (TCMMS) showed motion state changes due to vibration. The wobble motion state of the TCMMS-FA-τMAX was 26.03±5.53 units from the sprinkling monochrome-dot echo, while the wobble motion state of the TCMMS-CO-τMAX was 12.13±2.19 units, the wobble motion state of the TCMMS-FL-τMAX was 4.18±0.74 units, and the wobble motion state of the TCMMS-VI-τMAX was (0.74±0.11) units. We can utilize load cells designed using this Triangular Configuration Measuring Method (TCMM) for precision medical devices, advanced industrial measuring instruments, and more. Load cells can be employed to measure forces such as tension, compression, pressure, or torque, and can serve as indicators for monitoring cardiac activity, similar to an electrocardiogram.