Earticle

In this paper, we explore the advantages of digital sequencers in the processes of composition, arrangement, mixing, and mastering within broadcast media. Digital sequencers have introduced significant advancements by enhancing efficiency, creativity, and flexibility in music production. This study compares traditional analog methods with digital sequencing and evaluates the impact of automation, virtual instruments, and AI-based driven music production. As user-friendly interfaces become increasingly accessible, both amateurs and professionals are empowered to experiment freely with sophisticated production tools. Our findings suggest that digital sequencers have the potential to bridge the gap between mainstream media and independent creators, offering new possibilities for those seeking to advance broadcast music through innovative and cutting-edge technologies

This paper proposes the application method of an Adaptive Quantum-Inspired Evolutionary Algorithm (AQEA) to Vehicular Ad Hoc Networks (VANETs) for enhancing clustering and routing performance. AQEA integrates quantum-inspired principles, including quantum bits, quantum superposition, and adaptive quantum rotation gates, to effectively navigate the highly dynamic and complex environments characteristic of VANETs. By dynamically balancing exploration and exploitation, AQEA encodes cluster configurations as quantum states and adjusts them using a fitness-driven rotation operator. Comparative simulations reveal that AQEA consistently produces larger, more stable clusters and reduces both reconfiguration overhead and routing costs compared to conventional algorithms such as the Grasshopper Optimization Algorithm (GOA) and Whale Optimization Algorithm (WOA). AQEA consistently achieves larger and more stable clusters, significantly reduces cluster reconfiguration overhead, and minimizes routing costs. Statistically significant improvements were observed: a 59.5% increase in cluster size and a 29.10% reduction in stability penalty relative to WOA, and a 32.99% reduction in routing cost compared to GOA. These results confirm AQEA’s superior adaptability and robustness, positioning it as an effective solution for managing clustering and routing in dynamic VANET environments. These results validate the practical relevance and algorithmic superiority of AQEA, positioning it as a robust and adaptive solution for managing clustering and routing in dynamic VANET scenarios. Also, these results highlight AQEA’s robustness and adaptability, positioning it as an effective solution for managing clustering and routing in dynamic VANET scenarios. Future research directions include real-world validations, expanded performance evaluations, and further refinement of the algorithm's adaptive mechanisms.

This research aims to identify the types of adversarial attacks on AI image recognition models applicable to real-world security threat scenarios, evaluate their risk level and detection difficulty, and contribute to the development of robust and reliable defense measures. We identified various attack types and risk levels against deep learning-based image recognition models, and presented the characteristics and limitations of Projected Gradient Descent-based attacks. Based on this analysis, we designed experiments for different Projected Gradient Descent variants, compared their performance, and quantitatively evaluated their real-world attack probability and detection evasion. The experimental results show that the Early Stop- Projected Gradient Descent model has the highest attack performance compared to other Projected Gradient Descent-based attack models, which is a good trade-off between attack strength control and detection avoidance. We analyzed the risk by integrating attack type, medium, risk level, and detection difficulty, and proposed a unified view that enables a structural understanding of the attack-detection interaction, beyond the limitations of previous studies that are limited to studying individual techniques. This research contributes to the field of AI image recognition security by integrating attack experimentation, code improvement, risk identification, and detectability analysis.

Liquidity fragmentation remains a major obstacle to efficient cryptocurrency trading. Assets are spread across centralized exchanges (CEXs), decentralized exchanges (DEXs), and separate blockchain networks. This paper surveys key efforts to address this issue. It reviews methods such as unified order books, smart order routing, DEX aggregators, and blockchain bridge protocols. These solutions are analyzed based on how they execute trades, the scope of liquidity they cover, and their compatibility with different platforms. While many of these approaches improve local efficiency, they do not offer complete integration. To highlight this gap, we introduce a conceptual model called the meta-liquidity execution layer (MLEL). This model is not presented as a fully designed or implemented system, but rather as a research-oriented abstraction to guide future studies. The goal is to outline design principles and technical challenges, offering a foundation for future exploration rather than a prescriptive solution.

The trustworthiness of large language models (LLMs) is becoming increasingly important, but extant review studies have shown two major limitations in dynamically elucidating it over time. First, as of 2024, they have not elucidated the most recent studies on the trustworthiness of LLMs. Second, they have focused on the trustworthiness of LLMs over a limited timespan without considering how it changes over time. To overcome these limitations, this research carried out a state-of-the-art bibliometric analysis on 117 articles on the trustworthiness of LLMs based on two stages of change from a dynamic perspective. Our study revealed the following four findings. First, article publications and citations grew drastically in the first half of 2024, and the trustworthiness of LLMs was confirmed as a recent promising research area in artificial intelligence (AI). Second, business, medicine, and education were especially noteworthy research areas related to the trustworthiness of LLMs. Third, LLM governance was an important recent emergent topic. Fourth, multinational collaboration for the trustworthiness of LLMs was strengthened. We suggest the following topics for future studies on the trustworthiness of LLMs: further promoting LLM governance, employing multidisciplinary and interdisciplinary approaches, and strengthening multinational collaboration.

On-device AI environments require real-time processing but are constrained by limited computational resources. Previous studies have shown that simply replacing high-cost computational modules with low-cost alternatives does not always yield actual speed improvements on embedded hardware. Therefore, this study aims to design a YOLOv8-n–based lightweight network that can achieve real-time inference and high accuracy under stringent resource constraints. The proposed model, LGO-YOLO, applies module structures optimized for embedded computation to both the Backbone and Neck, reducing the model’s computational load and number of parameters by approximately 42% and 40%, respectively. Despite these reductions, the model achieves accuracy and precision equal to or superior to YOLOv8-n in several performance metrics—most notably, an mAP@0.5 of 99.3%. Furthermore, in an NPU environment, it records the fastest inference time (25.4 ms) among all comparison models. This work demonstrates how careful structural design can balance the limits of model lightweighting with performance requirements, indicating that the proposed network can be effectively deployed in real embedded systems or other low-power application scenarios.

In strawberry cultivation, simultaneous detection of fruit ripeness and leaf diseases is very important to maximize yield and ensure crop health. In this study, we propose an ensemble technique combining an improved YOLOv8l and a ResNet50-based Faster R-CNN model. The YOLOv8l model reduces the number of parameters by 29% by replacing the standard backbone with ConvNeXtV2, introducing the BiFPN neck structure, and integrating the GRN layer, but the accuracy is lowered. However, the accuracy is improved by integrating the model ensemble technique and the Weighted Box Fusion algorithm. The experiment is conducted using 5,000 strawberry images collected from a smart farm in Cheonan-si, South Korea from January to April 2025. The dataset has a total of nine classes, including five maturity stages (Flower, Green, White, Turning Red, and Red) and four leaf disease states (Chlorosis, Tip Dieback, wilt, and Plauge). Experimental results show that the proposed model architecture achieves 0.8623 at mAP@0.5, which improves the performance by about 8% compared to the single-model approach. In particular, the system demonstrates excellent performance in detecting visually distinct classes such as flowers, while achieving good results even for classes with subtle features such as early-stage diseases. Visual analysis confirms the robustness of the model even in complex agricultural environments with various lighting conditions and overlapping objects. Therefore, it contributes to the development of an automated monitoring system for strawberry cultivation in greenhouse environments and has significant potential for application in various smart agricultural environments.

This study investigates how social-affective features of chatbots, namely empathy and personalization, influence users’ intention to use them. Grounded in the elaboration likelihood model (ELM), empathy is conceptualized as a peripheral cue, while personalization functions as a central cue. Trust and perceived usefulness are proposed as mediators of these effects. Partial least squares structural equation modeling (PLSSEM) was applied to data collected from 289 respondents with chatbot experience. The results show that both empathy and personalization significantly impact trust and perceived usefulness but do not directly influence usage intention. Rather, their effects are fully mediated by trust and usefulness. Furthermore, a multi-group analysis reveals that users' prior chatbot experience moderates the relationships. The findings provide theoretical insights into affective persuasion mechanisms and practical implications for experience-based chatbot design.

A recently research, Speech recogntion AI model was developed to integration with system such as IoT (Internet of Things), management system and devices. so this paper indroduced to Speech sentence recognition AI guidance system. The speech recognition basic model was classifier to simple word. but as researching speech recognition topic, the speech recognition model had powerful performance what model can classifier to difficult sentence in speck. these powerful speech recognition model was provided to convinience while converging with various devices that we use in real life. In particular, smartphones, which are computers carried by modern people, are designed to control functions that can be used in the system when emergency situations and daily life by commanding by voice as well as voice recognition. Our system uses these speech recognition system and camera detection capabilities to propose an unmanned parking management system. this is expected to save labor costs and bring convenience along with strong performance by making it available 24 hours a day. the speech recognition system used in this paper introduces the speech signal processing technologies of Spectrogram, Mel spectrogram and MFCC. These speech signal processing techniques play an important role in the model as a technology for extracting information contained in speech, such as the accent, pitch and parttern of the speaker’s pronunciation. Moreover, YOLO(Your Only Look Once), which is widely used in the field of object recognition, not only provides strong object recognition, but also has accuracy while consuming less resources on the device. We expect to bring many advantages by builing a system that can integrate and manage these two model. These two systems require a framework for applications. therefore, we built applications using the QT framework and designed it to be used in devices and PC. the QT framework is a strong cross-platform framework and can be used in various operating system. Therefore, we constructed the system by fusing these two models with the application and conducted research with various function required accordingly.

This study analyzes the influence of university students’ Social Networking Service (SNS) utilization competence on career preparation behavior within a smart convergence framework. It also explores the multiple mediating roles of SNS addiction tendency, major satisfaction, class attitude, and self-directed learning. A total of 248 undergraduate students from a four-year university in G Metropolitan City participated in the study. Data were analyzed using SPSS 24.0 and Hayes’ PROCESS Macro (Model 4), with bootstrapping of 5,000 samples to test mediation effects. Results revealed that SNS utilization competence had a significant positive effect on career preparation behavior, both directly and indirectly. Class attitude and self-directed learning showed significant positive mediating effects, while SNS addiction tendency and major satisfaction demonstrated either negative or non-significant effects. These findings underscore the dual nature of SNS: it can serve as a strategic tool for learning and career exploration when supported by self-regulation and active academic engagement, but excessive or uncritical use may hinder career development. The study suggests that SNS-based career education should go beyond technical training to emphasize self-regulated learning, motivational support, and exploration strategies. Integrating SNS into active learning environments and tailored career support programs can enhance digital literacy and career readiness in a more comprehensive manner.

This paper examines the educational effectiveness and utility of remote experiential education based on the Ditoland Metaverse platform. This study evaluates how the Metaverse-based remote education process during the COVID-19 period affects students' understanding, communication, and collaboration. Pre- and post-surveys are conducted to determine satisfaction with the Metaverse remote learning experience. Overall, the post-survey responses were more positive than the pre-survey responses. Participants reported improvements in content understanding, communication with teachers and peers, engagement, satisfaction, and overall positivity. However, some participants reported challenges in peer collaboration, as well as issues related to device limitations and network instability. Survey results showed notable improvements as understanding increased from 64% to 86%, teacher-peer interaction improved by 22%, and interest and enjoyment rose by 25%. The findings suggest that experiential remote education based on the Metaverse platform during the COVID-19 period enhances understanding and engagement in education.

We developed an AI-based personalized diet recommendation system to address the increasing demand for customized health management solutions. Modern lifestyles often lead to poor eating habits and wellness issues such as fatigue, stress, and poor sleep, which require more adaptive and personalized approaches to nutrition. We designed the system to provide daily meal suggestions based on wellness indicators including sleep patterns, physical activity, stress levels, and dietary preferences. A content-based filtering algorithm was implemented to match user profiles with food nutrient data. To evaluate the system’s performance, we conducted a simulation with 10 synthetic user profiles. Each profile was assigned a wellness goal—sleep improvement, stress reduction, or energy enhancement—and received a tailored meal recommendation. The system then assessed the nutritional completeness of the meal and selectively recommended dietary supplements only when essential nutrients were missing. The results showed that the system successfully aligned recommended foods with each user's wellness goal, and recommended supplements only when essential nutrients were missing from the meal. Our approach demonstrates the practicality and adaptability of AI in preventive healthcare and personalized nutrition planning.

This study delves into an in-depth exploration of individual spatial preferences when interacting with virtual agents (VA) of varying styles. Our experimental outcomes unveil pronounced disparities in personal space (PS) selections within this contextual framework. Through a meticulous comparative analysis of viewing distances, our investigation discloses that when engaging with VA in an adorable style, female participants tend to maintain a more distant observational distance. Conversely, when faced with VA embodying a horror style, female participants exhibit a heightened inclination to approach these entities. In contrast, male participants do not manifest statistically significant alterations in their viewing preferences. This revelation not only offers a novel perspective for comprehending audience reactions to diverse animation styles but also imparts substantive insights for shaping studies in virtual reality (VR) experiences and emotional perception. Moreover, the proposed experimental framework enables accurate and replicable measurement of spatial behavior in immersive VR environments. These findings provide valuable guidance for designing emotionally adaptive virtual characters and improving user engagement across educational, therapeutic, and entertainment applications.

The purpose of this study is to identify the risks of cyber training platforms and to explore national countermeasures. The methodology of the study is to analyze the characteristics of JIOR, PCTE, and DREAM Port, which are representative cyber training systems in the United States, to identify risk management factors and solutions in the supply chain. The results of the study are to diagnose the problems of military cyber training platforms and find solutions, and then to identify the supply chain risks and issues of JIOR, PCTE, and DREAM PORT training platforms and derive solutions from them.

News discourse plays a pivotal role in representing ideological perspectives on news events. This paper analyzes news opinions on the 2023 collapse of Silicon Valley Bank in terms of political ideology by examining lexical choices, based on the assumption that political ideologies of news media influence their interpretations of economic issues and the ideological perspectives are conveyed through language. For this purpose, The New York Times and The Korean Economic Daily are selected for analysis. The headlines and leads of the news opinions are examined qualitatively, while keyword frequencies are measured as a quantitative approach. The results of the analysis clearly reveal ideological differences between the two newspapers: the former adopts a progressive stance, emphasizing accountability for the financial turmoil by criticizing the Federal Reserve for deregulation, aggressive monetary policy, and insufficient oversight of Silicon Valley Bank—issues commonly associated with progressive stances. In contrast, the latter focuses more on practical measures to mitigate the economic fallout of the crisis. These findings demonstrate that ideological differences are reflected in the newspapers’ lexical choices.

We applied grounded theory to investigate the current landscape of China’s digital game publicity in the context of rapid media convergence. Although the market scale and cultural influence of Chinese digital games are expanding, they face critical challenges such as content homogenization, fragmented communication channels, and weak user participation. Through open, axial, and selective coding of multi-source textual data, we identified core issues across communication subjects, audiences, content, media, and evaluation. We then developed a strategic model that incorporates personalized content, IP-driven narratives, target audience segmentation, diversified communication agents, multimedia integration, and effectiveness assessment. Our findings highlight a disconnection between communication efforts and user expectations, underscoring the need for systemic innovation. This study not only expands the theoretical framework of game publicity under media convergence but also provides actionable strategies to enhance brand competitiveness and cultural resonance. Our proposed model offers valuable insights for the sustainable development of China’s digital game industry in global markets.

Recently, Implicit Neural Representations (INRs) have been gaining attention as an approach where neural networks learn a continuous function that takes coordinates as input and outputs the color values at those locations. Using INRs allows for reconstructing images of any size without constraints on spatial resolution. As a result, it has emerged as a promising method for super resolution, enabling a single neural network to represent images at all resolutions. However, existing research on INR-based super-resolution still lags behind other deep learning methods in terms of performance. This is because a single neural network, which takes uniform coordinate values as input, faces challenges in representing information of varying complexity across different regions of an image. Therefore, we propose a method to improve super-resolution performance by decomposing an image into variable-sized blocks so that each block has uniform complexity, regardless of the regional variation in complexity. The INR neural network then learns the image information of each block with uniform complexity. By alleviating differences in regional complexity, the neural network is able to learn regional information more stably and accurately, enabling optimal performance even in areas with diverse levels of complexity.

This study analyzes the trends in Korean food franchise brands' expansion into Southeast Asia using Textombased text mining methods. As Korea’s domestic market saturates and global demand for Korean cuisine rises, Southeast Asia—particularly Vietnam, Thailand, and the Philippines—emerges as a key target region. Data were collected from Naver, Daum, and Google from January to December 2024, using keywords such as franchise, global expansion, and South-East Asia. Text mining and semantic network analysis revealed core strategic themes and interrelationships between localization, brand positioning, and market entry tactics. CONCOR analysis identified four main clusters: Expansion, South East Asia, Strategy, and Franchise Brand. These clusters highlight the need for tailored strategies in overseas market penetration. The findings stress the importance of adapting to local market dynamics and leveraging cultural familiarity. However, the absence of demographic detail within Textom limits interpretation. Future research should apply qualitative methods for deeper insight.

This paper uses big data to analyze domestic bakery trends after COVID-19 and to derive a marketing strategy based on them. Due to the rapidly changing trend, the bakery market is also creating bakery trends in various ways. In a rapidly changing market, the bakery market needs to develop a marketing strategy that fits the trend. To analyze bakery trends, data from 2023 to 2025 were collected on Internet platforms using Textom. After grasping the data through the data collection results, Ucinet can be used to visualize it and a marketing strategy based on the analysis results can be derived. The study found that product planning emphasizing health, marketing strategies that combine premium brand positions and emotional consumption, consumption accessibility through multiple distribution strategies, and strengthening personalized communication through sns also suggested in deriving bakery trend marketing strategies. The results through this will provide implications for reading trends in the bakery market in the future and will be a way to develop other trends.

This study was conducted to explore the direction of nursing care worker remuneration education by analyzing the mediating effect of self-efficacy and social capital on the effect of nursing care worker remuneration education quality on transfer of learning. This is meaningful in that it can look at the correct direction of the nursing care worker's remuneration education, which will be implemented for the first time in 2024. To verify this, the influence relationship between each variable was verified by setting variables such as nursing care worker maintenance education quality, self-efficacy, social capital, and learning before. In addition, selfefficacy and social capital were set as parameters to verify whether the influence relationship between education quality and transfer of learning was mediated. This was verified using structural equation statistics. As a result of the verification, first, education quality was found to have a significant positive (+) effect on transfer of learning, self-efficacy, and social capital. This means that the more positive the perception of educational quality is, the more positive the perception of transfer of learning, self-efficacy, and social capital is. Second, self-efficacy and social capital were found to partially mediate the effect of educational quality on transfer of learning, respectively. In other words, educational quality can increase transfer of learning by improving self-efficacy and social capital. Looking at the results of the analysis by sub-factors, it was suggested that these factors should be improved as a role of conservative educational institutions by verifying that continuous development of educational content and teaching methods is necessary to improve transfer of learning, and that educational content, methods, and capabilities of instructors and faculty members to improve self-efficacy and social capital are also important. Finally, it was suggested that long-term care institutions, which are nursing care workers' workplaces, also need to improve self-efficacy and social capital through periodic employee training and meetings, and monitor the effect of transfer of learning.

With the spread of COVID-19 and the implementation of non-face-to-face policy, orders for food delivery have increased. Online food delivery applications developed rapidly and attracted high attention from consumers. We have analyzed the correlation between the suitability of online food delivery services and customer engagement. The relationships between functional values, emotional values, needs-supplies fit, personal-app fit, and customer-app engagement of online food delivery applications were analyzed. We conducted among 190 women who had ever used ‘Blue Apron’ application before. We found that users are aware of the importance of service quality, self-efficacy, and self-identity. Also, consumers prefer to purchase products from companies which have the same value as themselves in these areas. At the same time, if a product meets consumers’ demands or values, consumer interest will increase, making it less likely to switch to alternative platforms. Conversely, information quality was not found to be related to these two fits. Based on our findings, theoretical and practical implications have been provided.

This paper reevaluates Pixar’s Universal Scene Description (USD) as far more than a file format, framing it as a shared data fabric for both VFX and industrial digital-twin workflows. First, we dissect USD’s layered scene graph and non-destructive referencing, showing how these mechanisms replace ad-hoc file exchanges with a single, version-safe collaboration space. Second, controlled benchmarks and blockbuster case studies confirm tangible gains in VFX pipelines: faster artist iteration, cross-department asset sharing without version clashes, and smaller storage footprints even as scene scale grows. Third, the same composition model adapts seamlessly to NVIDIA Omniverse, where engineers stream live sensor data, run GPU-accelerated simulations, and update factory- or city-scale twins in near real time— while still using familiar film-production tools. Collectively, these results position USD as a unifying layer that bridges entertainment production and industrial engineering. The study offers practitioners a cloud-ready adoption roadmap and gives researchers a reproducible benchmark for large-scale 3D data interchange, opening new avenues in graphics, HCI, and digital-twin research.

The rapid evolution of virtual reality (VR) has opened up new possibilities for training across a wide range of industries. By minimizing the need for physical space and specialized materials, VR offers a cost-effective solution to scenarios that once demanded complex, resource-heavy environments. As the technology matures, there is a growing focus on multi-platform compatibility, enabling seamless interaction across different devices. At the same time, the expansion of extended reality (XR) concepts has deepened the integration between physical sensory input and digital content, aiming to create more immersive and responsive training experiences. This study focuses on the application of both augmented reality (AR) and virtual reality (VR) in enhancing safety training protocols for personnel working on marine rescue platforms.

This study aims to empirically explore whether consumers’ perception of corporate social responsibility (CSR) in fact affects the evaluation of product, and if the willingness to participate in corporate social responsibility (CSR) activities acts as a mediator in this relationship The findings show that more favorable perceptions of corporate social responsibility (CSR) are related to more favorable product evaluations, and that the willingness to engage in corporate social responsibility (CSR) activities partially mediates this relationship. But, the type of feedback did not significantly moderate between willingness to engage and product evaluation. The study has theoretical and practical implications for shaping corporate social messaging strategy through the explanation of the dynamics between the corporate social responsibility (CSR) perception, willingness to engage, and the type of feedback.

Deaf and Hard of Hearing (DHH) children face challenges in language acquisition due to hearing loss, which is one of the most crucial factors in language development. As a result, most DHH individuals communicate using ASL gloss instead of conventional sentences used by hearing individuals. However, because they are more accustomed to ASL gloss, they often struggle with constructing and understanding standard sentences, making communication with hearing individuals difficult. To address this issue, this study proposes DHHStoryGen, an automated storybook creation system that supports the language enhancement of DHH children. DHHStoryGen is built upon a fine-tuned LLaMA model for text generation and a fine-tuned Stable Diffusion model for image generation, both trained on custom datasets specifically designed for this purpose. The text dataset is created by translating ASL gloss into natural English sentences, which is used to fine-tune LLaMA to generate storybook content. Meanwhile, the image dataset is used to fine-tune the Stable Diffusion model to generate illustrations that match with each chapter's narrative. By converting ASL gloss into structured text and providing visually engaging images, DHHStoryGen fosters a participatory language learning environment, serving as an assistant tool to enhance the literacy skills of DHH children.

We propose SQLGo, an interactive learning tool designed to help learne.rs intuitively understand the execution flow of SQL queries. SQLGo combines Common Table Expression based visualization techniques with a Large Language Model (LLM)-powered conversational interface to clearly convey intermediate operations and procedural semantics embedded in SQL.Most existing SQL learning tools provide only tabular query results, failing to adequately explain the intermediate states and execution process of a query. As a result, learners often struggle to grasp the actual execution flow underlying SQL’s declarative structure. SQLGo addresses these limitations by offering an interactive environment that supports procedural reasoning and encourages active learner engagement, thereby enabling more effective SQL learning.Through this system, learners are expected to intuitively grasp the execution flow of queries and gain a deeper and more meaningful learning experience compared to conventional tools.

This study investigates the phenomenon of food mediatization—the transformative process through which food and dining experiences are reconstituted as media content—and its implications for tourism development and regional economic revitalization. Adopting an interdisciplinary approach that integrates perspectives from the humanities and food studies, the paper explores how the convergence of gastronomy, media, and cultural representation generates novel socio-economic value. Grounded in mediatization theory and informed by comparative case analyses from Japan and Spain’s Basque Country, we propose a conceptual framework delineating three interrelated mechanisms: the mediation of culinary knowledge, the mediatized performance of food consumption, and the construction of social distinction via media-generated cultural capital. Our findings suggest that food mediatization functions as a potent driver of tourism and local economic growth, particularly when it aligns with culturally embedded narratives and the symbolic identity of place. This research advances the scholarly discourse on food communication by foregrounding the interpretive dimensions of mediatized food culture and illuminating its tangible economic effects.

Alcohol consumption significantly impacts public health in China, contributing to conditions such as hypertension, heart disease, stroke, and liver disorders. Despite the growing popularity of TikTok and other short-form video platforms, limited research has examined their role in influencing alcohol-related behaviors. Using the optimistic bias framework, this study investigates the mediating and moderating roles of content type, media engagement, optimistic bias, and self-efficacy in shaping alcohol reduction behaviors among Chinese users. Data were collected through a cross-sectional online survey (N=347) from November to December 2024. Results demonstrate that media engagement significantly moderates the effects of content type on alcohol consumption behaviors, revealing that prevention-oriented anti-drinking content effectively reduces consumption, while promotion-oriented content paradoxically increases consumption. The study also finds that optimistic bias and self-efficacy mediate these relationships, highlighting complex cognitive mechanisms. These findings provide empirical evidence to guide public health strategies leveraging shortform video platforms for responsible drinking campaigns.

we investigated the effects of inter-set rest versus static stretching on repetition performance, maximum heart rate, and blood lactate concentration during biceps curl exercise. Twelve resistance-trained male participants completed five sets at 75% of one-repetition maximum (1RM) under two different inter-set conditions: passive rest and static stretching. We observed that static stretching significantly enhanced repetition performance from the second to the fifth set, compared to passive rest. While maximum heart rate showed no statistically significant difference between the two methods, a trend toward higher heart rate was noted in the static stretching condition. Both methods resulted in elevated post-exercise blood lactate levels, with no significant difference between them. These findings suggest that static stretching between sets may serve as an effective strategy to maintain performance during resistance training by alleviating fatigue. We propose static stretching as a practical and accessible recovery technique for athletes and practitioners seeking performance improvements in training contexts.

Objectives: Effective pain management in neonates is essential to minimize distress during painful procedures. Breastfeeding, Skin-to-Skin Care (SSC), and small volumes of sweet solutions have been shown to significantly reduce pain during short-duration procedures. This review aims to synthesize evidence from systematic reviews regarding the analgesic effects of these interventions. Methods: We searched PubMed, Embase, Cochrane Library, Web of Science, CINAHL (Cumulative Index to Nursing and Allied Health Literature), and Google Scholar to identify systematic reviews published from 2010 onward. Results: A total of 12 systematic reviews were included. We found Breastfeeding, SSC, sweet solutions (glucose and sucrose solutions of at least 15%–20%) are effective in reducing pain and distress during procedures. These interventions are simple to implement, widely available, and cost-effective. Clinical guidelines consistently recommend their use before and during painful procedures. Conclusion: Although strong evidence supports the use of these interventions, their implementation in clinical practice remains inconsistent. Collaborative efforts involving healthcare providers and parents are essential to promote effective and standardized neonatal pain management, especially across diverse care settings.