This study was carried out experimentally on the production and properties of silicon nanopowders using a transferred type arc plasma apparatus. To investigate the properties of silicon nanopowder, the electrode material(tungsten, graphite) and the electrode diameter(8, 13, 20, 50mm) were changed. The total pressure in chamber is 400Torr. The particle size, electrode consumption and powder evaporation rate were analyzed using the XRD, SEM and electronic scale. Silicon powders manufactured using tungsten electrodes showed smaller particle sizes than powders manufactured using graphite electrodes. Graphite electrodes have lower electrode consumption than tungsten electrodes. Therefore, graphite electrodes are considered superior to tungsten electrodes. It was shown that as the diameter of the graphite electrode increased, the amount of silicon metal evaporation and the consumption of the electrode decreased. Also, it was shown that the powder particle size became smaller as the electrode diameter increased.

This research compares and consider the regulations and policies of Korea and Indonesia for the global expansion of the domestic drone industry and the advancement of the Indonesian infrastructure business. For accurate data collection and comparative consideration, the following conclusions were drawn by conducting research with researchers at the University of Tarumanagara (UNTAR). The market size of the drones industry in Indonesia is smaller than that of the Korean market in terms of simple amounts. However, given the population and area of Indonesia, and the direction of future policies, the potential for development and potential is very high. There are regulations on licenses and flight permits, but they are not more subdivided than in Korea, and more detailed standards are needed for the safe development and expansion of the industry. In addition, it is necessary to expand the training of drone experts by referring to Korea's achievements. Therefore, if education on the acquisition and utilization of spatial information can be done together with education on flight, it will be an opportunity for the Korean drone industry to enter Indonesia and the Indonesian drone industry to grow together.

This study was conducted to propose suitable data collection and analysis methods for analyzing tourist behavior using social big data text mining. The Seonjaeryeong section of the Daegwallyeong National Forest Trail was selected as the research site, and two data collection methods (Textom and Python) and two morphological analysis methods (Mecab and Okt) were applied to generate a total of four datasets. Naver blog posts written throughout the year 2020 were collected, and analysis techniques such as word frequency, TF-IDF, N-gram, and CONCOR were applied. The results of the study indicate that collecting the entire dataset is suitable for accurately understanding tourist behavior at tourist destinations. However, depending on the research duration and objectives, focusing on the introductory part of blog posts, where the main topics are often discussed, can also be an effective approach. Regarding the characteristics of the morphological analyzers, Mecab was found to be more suitable for noun-focused morphological analysis, while Okt was more appropriate for balanced analysis across different parts of speech. The analysis of tourist behavior at Seonjaeryeong showed that the verb "to eat" ranked highly across all four analyses. Therefore, it is suggested that revitalizing the restaurant industry in the Seonjaeryeong area could contribute to increasing tourist inflows and promoting local development.

The steering of the ship is a steering gear necessary to drive the rudder to maneuver the ship in voyage. This device is a driver that rotates the tiller when installed at the rear of the ship. In existing ships, hydraulic cylinders are used. In this study, a steering gear using an integrated hydraulic unit suitable for energy reduction and electric propulsion ship was developed. In order to improve the problems of the existing hydraulic drive system, the actuator, hydraulic pump, and electric motor were directly developed to be used efficiently in small vessels. In this study, current consumption and thrust values were measured to verify the performance of the developed small ship steering system using electro-hydraulic power unit.

This study evaluates the cost efficiency and performance of traditional surveying methods versus 3D digital surveying technologies at a road construction site. GNSS-based traditional surveying provides high precision (±1.2 cm) but is less efficient for large-scale projects due to high time and cost demands (15.97 KRW/m²). In contrast, 3D digital methods, such as drone photogrammetry, drone LiDAR, and SLAM-based scanners, significantly reduce costs and time. Drone photogrammetry proved most cost-effective (2.64 KRW/m²) for large areas, while drone LiDAR excelled in complex terrains (±1.9 cm), and SLAM scanners were effective in confined spaces (±1.5 cm) despite higher costs. These findings emphasize 3D digital surveying’s advantages for large-scale projects, offering efficiency, precision, and enhanced safety in modern construction workflows.

Advances in high-performance semiconductor packaging have made surface quality in the redistribution layer increasingly important, highlighting the need for CMP. This study investigates how abrasive particle size and type affect surface roughness and material removal rate in the CMP process. Experimental results showed that colloidal silica achieved a high MRR with smaller particles, enabling a scratch-free, good surface roughness though it exhibited a generally lower removal rate overall. In contrast, alumina provided a higher MRR with larger particles but created scratches on the surface. Based on these findings, we propose a combination process using large alumina particles for initial polishing and small colloidal silica particles for final polishing. This approach demonstrates the importance of particle size and abrasive combination in achieving optimal RDL surface quality, contributing to the reliability of high-performance semiconductor packaging.

Gallium oxide is gaining attention as a next-generation wide-bandgap (WBG) semiconductor material, following silicon carbide (SiC) and gallium nitride (GaN), due to its wide bandgap of 4.4-4.9 eV. This makes gallium oxdie ideal for applications in high-voltage, high-temperature, and high-frequency devices. Additionally, gallium oxide has an economic advantage over other WBG materials due to its relatively lower manufacturing cost. As demand for gallium oxide wafers rises, ensuring stable and efficient large-scale production processes is essential. In this study, we evaluated the mechanical properties of (001) gallium oxide using nanoindentation to analyze load-displacement characteristics and scratch behavior, and we measured indentation patterns through Vickers indentation testing.. Nanoindentation experiments were conducted under various load conditions (1, 5, 10, 15, 20, and 30 mN), and the elastic and plastic deformation characteristics of gallium oxide were assessed. The scratch tests were conducted at linearly increasing loads, starting from 30 mN up to maximum loads of 1, 1.5, 2, and 2.5 N, allowing us to observe surface damage and cracking behavior. The results indicate that gallium oxide exhibits both elastic and plastic deformation at lower loads, while plastic deformation and brittleness dominate at higher loads. By considering the brittle nature and deformation characteristics of gallium oxide, this study offers essential data to improve process design, enabling more stable and efficient fabrication of gallium oxide wafers.

The issue of missing children and the elderly is a serious social problem, emphasizing the need for effective prevention and response systems. This study aims to develop an artificial intelligence-based smartphone app designed to prevent such incidents by detecting anomalies in the daily travel patterns of vulnerable populations like children and the elderly using location information. The app is developed with two modes: one for at-risk individuals and another for their guardians, allowing guardians to monitor the movement of at-risk individuals in real time. The overall workflow of the app is as follows: First, users send their location data to the system server via the Google Maps app. Next, the web server receives and processes this data, storing it in a database. Subsequently, the app uses the LSTM(Long Short-Term Memory) deep learning algorithm to learn the users' daily travel patterns. Additionally, if a user deviates beyond a set distance from their normal path, the app considers it an anomaly and immediately sends an alert to the guardian. Through the app developed in this study, it is expected to reduce the social and economic costs associated with missig children and the elderly and to prevent such incidents by real-time detection of deviations from typical daily travel patterns.

In-situ carbonation technology is a CCUS (Carbon Capture, Utilization, and Storage) mineral carbonation technology in the cement industry, where CO₂ is injected during the manufacturing process of cement-based construction materials such as mortar, ready-mix concrete (RMC), and concrete products. Through the carbonation reaction between CO₂ and Ca²⁺ ions released during the cement hydration process, CO₂ is sequestered in the form of CaCO₃. This study aimed to conduct experimental research and gather data to assess the applicability of Raman analysis techniques for real-time monitoring of changes in the flow properties that may occur during the production of ready-mix concrete (RMC) using in-situ carbonation technology. The specific research involved observing the real-time reaction extent of cement hydration products and changes in the behavior of water molecules through Raman analysis, to determine whether it is possible to monitor the flow properties of cement slurry. The experiments focused on the flow change characteristics due to the formation of hydration products over time, and the time-dependent changes in cement slurry flow with Raman analysis, considering the incorporation of NaHCO₃, with the results presented accordingly.

This study evaluates the accuracy of point cloud data generated through UAV photogrammetry and MMS LiDAR in a residential construction site in Seoul, focusing on their roles in smart construction practices. The analysis, based on eight iterations of data collection, revealed that MMS demonstrated superior accuracy, with RMS errors of 5.21cm(Dxy) and 2.77cm(Dz), compared to UAV's 6.94cm and 14cm. Statistical analysis indicated no significant difference in horizontal accuracy(Dxy) but confirmed a meaningful distinction in vertical accuracy(Dz), favoring MMS. These results highlight MMS's strength in high-precision applications, such as earthwork volume estimation, while UAV excels in flexibility and efficiency for monitoring and initial data acquisition. By examining the performance of these technologies in a high-relief construction environment, the study provides practical insights for integrating UAV and MMS data to enhance digital construction practices and improve accuracy and productivity in construction site management.

As electric motors are widely used as the main power source, the demand for reliability and efficiency is increasing. As a result, the importance of early fault diagnosis of motor failures has been emphasized. To overcome the limitations of model-based fault diagnosis, we perform our work based on data-based fault diagnosis techniques. In this paper, we introduce LSTMFormer, which improves the performance of motor fault diagnosis by combining Metaformer structure and LSTM. To verify the proposed algorithm, we utilize the electrical fault data set of synchronous motors. The experimental results show excellent performance with an average accuracy of 99.79% and an average F1-score of 1. This indicates that the model can classify most faults and shows balanced performance in various fault types. In addition, it progresses learning stably and achieves high performance with a few of learning iterations. These results verify the effectiveness of the algorithm proposed in this paper.

The process of reviewing and classifying the results of PT (Liquid Penetrant Test) is performed manually by inspectors, which is time-consuming and may lead to defect omissions. To address this issue, this study developed an automatic defect detection method in PT images of welds using a deep learning-based object detection model. The training data consisted of images taken after conducting PT on welds containing defects, and the types of weld defects were classified according to the ASME (American Society of Mechanical Engineers) Section V A-110 regulations. For effective training, the distribution of detection objects present in the data was analyzed, revealing an issue with object location imbalance where objects were concentrated in certain areas. To resolve this imbalance, the study compared an Anchor-based object detection model that deploys uniformly distributed anchors across the entire image and an Anchor-free model that generates anchors of varying sizes and positions without predefined patterns. The comparison showed that the Anchor-free model exhibited superior detection performance. As a result, the automatic weld defect detection algorithm achieved an accuracy of 92.9%.

The domestic ship repair market holds a significant position globally and is gradually growing, particularly around large shipbuilding companies. The market size is influenced by factors such as the growth of the global shipping industry, the tightening of environmental regulations, and the transition to eco-friendly vessels. The global ship repair and maintenance services market is expected to grow at an average annual rate of 4-5% from 2024 to 2032. The global ship repair and maintenance market is estimated to be approximately $15 billion (around 20 trillion KRW) in 2024, with the domestic ship repair market estimated at around 2-3 trillion KRW, accounting for about 10-15% of the global market. Focusing on high-value ship repairs is expected to ensure steady growth. In particular, domestic companies have strengths in repairing high-value ships such as LNG carriers and play a key role in the global market. This study analyzes and presents the work processes and preparation steps for repairing ship propellers, propeller shafts, propeller shaft couplings, and bow thrusters to foster the training of domestic ship repair experts and activate related industries.

This study developed a mindfulness meditation mobile application designed to promote psychological stability and enhance the health-related quality of life of elderly women living alone who have experienced bereavement. The application was created using the five stages of the ADDIE model: Analysis, Design, Development, Implementation, and Evaluation. The content focuses on themes such as ego integrity, depression, suicide prevention, and fostering positive emotions. To ensure the accuracy and relevance of the material, expert content validity evaluations were conducted. The application's effectiveness and usability were further validated through heuristic evaluations involving both experts and users. With its potential for further development, the application aims to contribute not only to the mental health management of bereaved elderly women but also to the overall mental well-being of a broader elderly population.

This study used an IoT measurement system to evaluate the vibration usability of 20 aging bridges by analyzing acquired vibration acceleration data and referencing the Reiher-Meister curve as a usability criterion. Following the initial analysis, a model was developed to calculate exceedance probabilities for each vibration usability grade, leading to several conclusions. Through the use of long-term measurement data and the Reiher-Meister curve, the usability analysis for aging bridges indicated a range of usability grades from A to F, demonstrating a straightforward method for assessing vibration usability. A probability distribution model was created to establish exceedance probabilities for each vibration grade by bridge, revealing dominant vibration characteristics specific to each bridge's structural properties. In conclusion, the study suggests that in maintaining aging bridges, those with higher exceedance probabilities in the upper grades should be prioritized for repair and reinforcement. This research highlights the value of combining long-term data with probabilistic models to support informed decision-making in bridge maintenance and management.

This study was a descriptive research study to identify the factors affecting clinical performance ability in nursing students, and was conducted on 154 nursing students. The collected data were analyzed using SPSS/WIN 25.0 for real numbers and percentages, mean and standard deviation, t-test, ANOVA, Scheffe test, Pearson's correlation, and multiple regression analysis. And clinical performance ability was found to have a significant positive correlation with self-leadership, metacognition, and emotional intelligence. The factors affecting clinical performance ability of nursing students were metacognition, emotional intelligence, and self-leadership, in that order, and the explanatory power of the model was 62.6%. Through this, we propose the development of various programs that can improve metacognition, emotional intelligence, and self-leadership to improve clinical performance ability of nursing students.

In this paper, a novel 3-axis polishing tool mechanism with compliance and force measurement capabilities is proposed. The tool employs a parallel mechanism designed for 2-axis rotational compliance, enabling adaptive polishing on complex and inclined workpiece surfaces. The mechanism was developed to address challenges in robotic polishing, such as strong vibration disturbances caused by the eccentric rotation of the polishing tool. The proposed tool was designed based on previous studies and incorporates a compliance mechanism that facilitates stable contact force maintenance during polishing, regardless of the workpiece angle or surface curvature. Joint calibration was performed to ensure accurate force measurements, and polishing experiments demonstrated that the tool effectively reduced surface roughness while maintaining stable contact forces. Experimental results confirmed a significant improvement in surface finish, showcasing the tool's capability for adaptive and precise polishing. Future work will focus on extending the tool's application to more complex workpiece geometries and conducting additional experiments under diverse conditions.

This study investigates the automation of slag removal, temperature measurement, and sampling processes in steelmaking foundries using induction furnaces, with a focus on improving operational efficiency and worker safety in high-temperature environments. Labor shortages and safety risks remain critical issues due to the manual nature of these processes. In response, a robotic system was developed to automate tasks traditionally managed by operators, utilizing adaptive control algorithms for precise slag handling and temperature monitoring. Finite Element Analysis (FEA) confirmed the system's stability and durability under extreme conditions, and thermal simulations indicated that the automated slag removal process maintains stable operating temperatures, reduces slag buildup, and optimizes molten metal quality. Testing demonstrated a reduction in manual labor intensity by over 50% and achieved slag removal efficiency exceeding 85%, leading to significant improvements in productivity and work environment. The findings underscore the potential for sustainable automation solutions in foundational industries to address labor and safety challenges effectively.

This study was conducted to examine the mediating effect of emotional clarity in the relationship between insecure adult attachment, self-compassion, and interpersonal competence in nursing students. The study collected data from 188 college students from October 1 to October 30, 2024, and analyzed the data using SPSS WIN 25.0. The collected data were analyzed through descriptive statistics, difference analysis (t-test, one-way ANOVA), correlation analysis, multiple regression analysis based on Baron & Kenny's three-step method, and bootstrapping. The results showed that insecure adult attachment and self-compassion significantly influenced interpersonal competence, and emotional clarity partially mediated the relationship between insecure adult attachment, self-compassion, and interpersonal competence. In order to enhance interpersonal competence, it is necessary to assess the degree of insecure adult attachment and self-compassion and explore methods to support them. Additionally, strategies to improve emotional clarity should be developed.

A technological innovation that is currently happening in the healthcare industry is the application of cloud computing. The implementation of cloud computing technology helps to solve most of the problems that healthcare organisations face when delivering healthcare services. It also provides an opportunity for the healthcare sector to improve the services it provides to patients. It can also enhance the easy sharing of information and reduce costs.However, there are many concerns and various security measures being raised in the healthcare industry about patient records being stored remotely. Therefore, it is important to understand the benefits of applying cloud computing in the healthcare industry and the fears associated with it. You should also be aware of both the advantages and disadvantages that come with this innovative technology. After many advances and developments, the use of cloud computing in various industries is steadily increasing. Therefore, it is the right time for the healthcare industry to explore the use of cloud computing technology to improve the healthcare sector's services to patients. The main purpose of this paper is to present various factors that healthcare organizations should consider when implementing cloud computing technology and to present a methodology for how these opportunities and challenges can be addressed through future cloud technology predictions. This also highlights the need for research into key technical issues to be considered when applying SaaS software for patient EMR electronic records, such as telemedicine. While treatment techniques that save one person's life are important, investing in cloud computing system technologies, including AI, that can protect the health of hundreds or thousands of people is even more important.

The standard deviation and standard score shown in the results of various tests are the most common way to interpret test results. In existing research, the main focus has been on surveying students' satisfaction with CBT(computer-based tests), and little research has been done on the discrimination power and distribution of CBT. This study is about the impact of CBT on standard deviation and normal distribution. CBT is highly efficient in terms of manpower, time, and cost, particularly in printing, grading, and result analysis, compared to PBT(paper-based tests), and the result analysis is processed through a program, so test takers can know the results immediately. In most tests, the larger the standard deviation, the higher the discriminatory power, and the more normal the distribution, the better the questions are. The average score of the CBT takers was 12.190 points, the standard deviation was 4.903, and the variance was 24.038, and the average score according to the test method was approximately 3.293 points higher for CBT takers. The fact that CBT scores were high indicates that, in terms of the population, students are in an environment where monitors are more familiar than paper. As a result of normal distribution analysis, it was found that CBT was theoretically and statistically unbiased, appropriate, reliable, and statistically significant.

BIPV systems are growing continuously worldwide. As PV systems grow, fire accidents also increase. Electrical factors such as arc faults are the main cause of fire accidents in PV systems. In this paper, we design an arc fault detector and analyze its characteristics to improve the safety of BIPV. The arc fault detector detects arc faults by analyzing the noise that appears when an arc fault occurs through FFT. A Loss Pass Filter and a Band Pass Filter are applied to increase the accuracy of arc detection. The arc fault detector designed in this paper is tested by performing an arc fault experiment using an arc generator capable of performing tests according to the UL1699B standard. The characteristics are analyzed, and the results are presented.

This paper proposes institutional improvements to enhance public-private partnerships (PPP) in the construction of forest roads. It revisits the historical 'Deferred Sale System', which allowed timber buyers to construct forest roads, deduct the cost from timber payments, and pay the remaining balance later. However, this system was discontinued due to outdated technology and equipment, and it currently lacks a robust legal framework for implementation. The study also examines the unique characteristics of forest roads compared to the road sector. Unlike roads, revenue from forest roads is generated by forestry operators, not end-users, requiring long-term investment strategies. The BTL (Build-Transfer-Lease) model is identified as useful for urgent projects due to its ability to ensure timely completion. However, without continuous budget increases, its long-term viability may be compromised by the financial burden on the government. In contrast, the BTO (Build-Transfer-Operate) model is more viable in areas where private investors can recover costs through thinning profits. Accurate assessments of timber value and harvesting areas are critical for its success. In conclusion, regulatory consistency and resource mobilization from stakeholders, including forest owners and private enterprises, are essential for sustainable forest road development. This study highlights the need for further discussions on effective implementation and long-term investment strategies to enhance the applicability of PPP models to the unique context of forest road construction.