Earticle

The purpose of this paper is to explore the confrontation between the digital sharing economy and the traditional economy according to the economic phenomenon of the times called inflation, and the resulting new transaction patterns. In particular, it analyzes the chasm phenomenon in the electric vehicle (EV) market and the artificial intelligence (AI) chip market, and analyzes the impact of inflation on each market. Current inflation and rising interest rates control inflation across the economy, which can have contradictory effects on the digital sharing economy and the traditional economy as it affects consumers' purchasing power. In the case of electric vehicles and AI-related markets, consumer demand is likely to slow down due to interest rate hikes and inflation. This is because it becomes difficult to purchase expensive electric vehicles. On the other hand, the AI chip market and the AI language model are likely to develop faster than the electric vehicle market due to digital transformation and increased power demand. This is because inflation plays a role in restraining the technology transition, providing time for existing internal combustion engine vehicle manufacturers to maintain their competitiveness. In conclusion, we demonstrated that while there may not be significant changes in sales and profitability of electric vehicles (Tesla) and AI companies (NVIDIA) while inflation persists, the Chasm phenomenon can affect the time gap between the digital economy and inflation..

This study addresses the issue of apple detection and segmentation, which plays a crucial role in agricultural automation systems, by employing multi-object detection using machine learning. A Support Vector Machine (SVM) model was used to accurately distinguish apples from leaves, with apple pixels classified as red and leaf pixels as green. The performance of the SVM model was evaluated using various metrics. Key evaluation metrics included IoU (Intersection over Union), Precision, Recall, and mAP (mean Average Precision). The results showed an IoU of 0.48, a Precision of 0.51, a Recall of 0.90, and an mAP of 0.48. Consequently, the SVM model exhibited a high recall rate, successfully detecting most apples, but also had a high false-positive rate due to its low precision. In the future, the need for models that can simultaneously handle real-time processing and accurate boundary recognition is emerging, which could address a critical issue in agricultural automation systems.

Depression in modern society is a prevalent mental health issue that can affect anyone, with more than 280 million people worldwide suffering from the condition. However, accessibility to treatment is often limited due to side effects of medication, high treatment costs, and social stigma. The purpose of this paper is to develop content that can be used as a personalized digital therapeutic tool for depression, aiming to overcome the limitations of traditional pharmacological treatments and address issues related to cost, social perception, and psychological burden. This study focuses on creating content for a personalized digital therapeutic solution by integrating VR-based MBTI (Myers-Briggs Type Indicator) with music. The goal is to develop content that combines virtual reality and personality-specific (MBTI) music to create a tailored digital therapeutic experience for alleviating depression. The result of the development includes visual themes inspired by four natural environments (spring, summer, autumn, and winter) and a music recommendation system based on MBTI personality types to reduce depressive symptoms and alleviate stress. The final product consists of one virtual reality system, four natural environment themes, and 16 music tracks tailored to individual MBTI preferences.

Instrumented indentation test is a useful measurement technique that can analyze the local properties of nanoscale composite materials or the surface properties of thin coated materials such as thin films that could not be measured by traditional analytical methods such as tensile tests or dynamic mechanical analysis. In this study, we would like to propose a method to obtain the optimal measurement conditions for measuring the mechanical properties of coated polymer hard coating films on rigid substrates such as displays using nanoindenter, which is the most representative testing machine of instrumented indentation test. In general, there are many factors such as indentation load and indentation time that affect the measurement result of nanoindenter, but in this study, we will discuss how to determine the indentation load, which is considered to be the most important condition. Through empirical cases, we will systematically analyze the physical properties of polymer hard coating films according to the press-fit load to derive optimal measurement conditions that guarantee accuracy and reliability.

This paper proposes an encryption-based keyboard protection method to safeguard user input from keylogging attacks. Keylogging is a hacking technique that intercepts keyboard input to steal sensitive information, and traditional detection-based defenses alone have limitations in countering the evolution of keyloggers. To address this issue, we introduce a kernel-user integrated security architecture that combines a filter driver with a security module. In the proposed method, when a user initiates keyboard input, the filter driver encrypts the input using the AES-128 ECB algorithm and transmits the encrypted data to the user-space security module. The security module then decrypts the data and relays it to the application, ensuring secure keyboard input handling. This encryption-decryption process prevents keyloggers from retrieving the original information, even if they intercept the encrypted keystrokes. Our proposed method operates automatically in the background, minimizing impact on user experience and eliminating the inconvenience of alternatives like virtual keyboards. This approach guarantees secure keyboard input without the need for detection-based keylogging defenses, thereby enhancing both user convenience and security. The study offers an effective security solution to prevent data leakage from keylogging attacks and can be applied across various application environments.

Recent advances in cellulose nanofiber (CNF) extraction methods reflect growing efforts to balance environmental sustainability and industrial feasibility. This review explores conventional chemical and mechanical methods, highlighting their limitations in scalability, energy efficiency, and environmental impact. It then examines emerging eco-friendly approaches, including hydrothermal-enzymatic treatments, microwave-assisted deep eutectic solvent (DES) pre-treatments, and supercritical CO₂ extraction, which minimize chemical usage and reduce energy demands. Additionally, enzyme-assisted mechanical grinding demonstrates significant potential for industrial applications due to its energy efficiency and high-quality output. Despite these innovations, challenges such as scalability, cost, and life cycle sustainability remain. Addressing these issues is essential for the widespread adoption of eco-friendly CNF extraction methods in various industries.

This study examines the relationship between localization strategies and market share of global food franchises using Textom. Through text mining and clustering analysis, the research highlights the impact of tailoring products and strategies to local markets. Data collected from August to October 2024 reveals a strong correlation between effective localization and market share growth, as seen in Lotteria’s success in Vietnam. The study emphasizes the importance of localized strategies for international expansion while acknowledging limitations in demographic analysis, suggesting the need for further qualitative research.

A smart home refers to a house or building equipped with advanced systems that allow users to remotely control various electronic devices. This paper proposes a novel approach utilizing an Adaptive Network-Based Fuzzy Inference System (ANFIS) to address the nonlinear challenges faced by conventional sensor-based smart home security systems. The proposed system uses multiple sensors to detect internal threats (e.g., fire, gas leaks) and external threats (e.g., theft, intrusion), analyzing sensor data to identify abnormalities. Designed with a multi-output ANFIS model, the system dynamically responds to various scenarios and provides optimal security decisions. Additionally, it features real-time transmission of security analysis results to relevant agencies or users via the internet. The findings demonstrate that ANFIS significantly enhances the efficiency and accuracy of smart home security systems compared to traditional fuzzy logic-based methods.

We conducted this study to identify the effect of character characteristics on repurchase intention in company-character collaboration products. The collection of data for the study was conducted online for three weeks from Feb.15 to Mar.7, 2024, using the convenience sampling method for companies and customers who have experience purchasing character collaboration products. At this time, factor analysis, reliability analysis, and regression analysis were used as methods of extracting factors. To summarize the analysis process, the effect of character characteristics on repurchase intention in company and character collaboration products was analyzed. In summarizing the conclusions through empirical analysis, it was found that the factors of aesthetics, familiarity, and originality, which are characteristics, affect repurchase intention. Therefore, theaesthetics, familiarity, and originality of the character must be considered in order to affect the repurchase intention of the company and character collaboration products.

In this paper, the evacuation of a hospital outpatient building with four evacuation stairways following a fire is studied. Based on the location of the fire source, five scenarios are designed: (1) the fire source location does not affect any of the four evacuation stairways; (2) the fire source is near Stairway 1; (3) the fire source is near Stairway 2; (4) the fire source is near Stairway 3; and (5) the fire source is near Stairway 4. These five scenarios are simulated using Pathfinder, and the results show that the evacuation times are 183 seconds, 268.5 seconds, 205 seconds, 208.8 seconds and 225 seconds, respectively. Compared with Scenario 1, Scenario 2 shows the largest increase in evacuation time, with an increase of 46.72%. Based on Scenario 2, three alternatives are proposed: adding a ramp in the lobby, adding one fire elevator, and adding two fire elevators. The simulation results show that, compared with Scenario 2, these alternatives reduce evacuation time by 27.75%, 22.53%, and 25.77%, respectively. Therefore, adding a ramp achieves the most significant improvement in evacuation efficiency.

University laboratories, as an important place with security risks, must always adhere to the safety of the work in the first place, firmly establish the concept of safety first, and constantly strengthen the safety management work, from the establishment of a scientific and perfect safety management system to ensure the safe and stable development of laboratory work. The key to safe production is people, because people are implementing safety management provisions and implementing safety management requirements. As the responsible person of university laboratory safety management, improving the safety quality and ability of safety management personnel is the most powerful guarantee for laboratory safety work. In this paper, SPSS software is used as a tool to study the safety quality index system, and a test system for the safety quality index system of laboratory staff is developed according to the national requirements for university laboratory laws and regulations, industry technical standards, safety management system and other aspects. According to the test results, the influencing factors are analyzed, and the countermeasures and suggestions for optimizing the safety quality of university laboratory workers are put forward.

Preschool education is an important part of China's national education system, and kindergartens, as the carrier of preschool education, mainly serve children aged 3-6. Preschool children's moving speed and body size are different from that of adults, and the design of kindergarten building evacuation is different from that of commercial buildings and office buildings. As the shape of kindergarten is an important influence factor in the evacuation process, the study of its optimal design is of great practical significance to improve the evacuation efficiency of children and reduce the loss. This paper uses simulation research methods to study "-" shaped kindergartens, 22.5 °, 45 °, 67.5 °, 90 ° "U" shaped kindergartens, and 22.5 °, 45 °, 67.5 °, and 90 ° "L" shaped kindergartens. Through the single variable principle, the impact of evacuation door width on the safety evacuation effect of the above nine types of kindergartens is simulated, and the optimal evacuation door width for kindergartens with different shapes is obtained. Through comprehensive comparison, the conclusion is drawn that U-shaped 45 ° kindergartens have the best evacuation effect.

This study systematically analyzes the evacuation safety of university lecture buildings. With the continuous improvement of educational facilities and the increase in campus population density, the efficiency and safety of evacuations in emergency situations have become critical issues for lecture buildings. Using Pathfinder software, this study models and simulates various evacuation scenarios to investigate the impact of architectural design parameters (such as stair width and emergency exit width) on evacuation times. Through three distinct evacuation scenarios, it was found that increasing stair width significantly reduces evacuation time, thereby enhancing evacuation efficiency. Although moderately widening emergency exits has a limited effect on evacuation time, it plays a positive role in alleviating congestion during evacuation processes. Moreover, a comparison between the simulation results and empirical formula calculations shows strong alignment, validating the model's practical applicability and accuracy. This research provides a scientific basis for fire evacuation design in university lecture buildings, recommending that stair and exit width optimizations be prioritized in building design and renovation. Future studies could further incorporate dynamic emergency scenarios, such as fires and earthquakes, combined with individual behavioral and psychological factors, to support the optimization of efficient evacuation strategies in complex environments.