Reconfiguration and miniaturization of antennas have become key attributes in modern wireless communication systems. Reconfiguration of radiation pattern can alleviate the problems encountered in modern wireless communication systems such as multi-path problems. Physical limitation of miniaturization also can be overcome by using a zeroth-order resonance (ZOR) antenna based on metamaterial. In order to achieve reconfiguration and miniaturization of antennas at the same time, we propose a new pattern reconfigurable zeroth-order resonance (ZOR) antenna that reconfigures the radiation patterns by varying the position and the number of unit cells comprising the antenna. The antenna is fabricated in an equilateral triangular shaped symmetrical structure to increase pattern variety. This structure can easily provide eight different radiation patterns (two omnidirectional and six monopole like patterns).

6G network technology represents the next generation of communications, supporting high-speed connectivity, ultra-low latency, and integration with cutting-edge technologies, such as the Internet of Things (IoT), virtual reality, and autonomous vehicles. These advancements promise to drive transformative changes in digital society. However, as technology progresses, the demand for efficient data transmission and energy management between smart devices and network equipment also intensifies. A significant challenge within 6G networks is the optimization of interactions between satellites and smart devices. This study addresses this issue by introducing a new game theory-based technique aimed at minimizing system-wide energy consumption and latency. The proposed technique reduces the processing load on smart devices and optimizes the offloading decision ratio to effectively utilize the resources of Low-Earth Orbit (LEO) satellites. Simulation results demonstrate that the proposed technique achieves a 30% reduction in energy consumption and a 40% improvement in latency compared to existing methods, thereby significantly enhancing performance.

With an increase in the relevance of next-generation integrated networking environments, the need to effectively utilize advanced networking techniques also increases. Specifically, integrating Software-Defined Networking (SDN) with Multi-access Edge Computing (MEC) is critical for enhancing network flexibility and addressing challenges such as security vulnerabilities and complex network management. SDN enhances operational flexibility by separating the control and data planes, introducing management complexities. This paper proposes a reinforcement learning-based network path optimization strategy within SDN environments to maximize performance, minimize latency, and optimize resource usage in MEC settings. The proposed Enhanced Proximal Policy Optimization (PPO)-based scheme effectively selects optimal routing paths in dynamic conditions, reducing average delay times to about 60 ms and lowering energy consumption. As the proposed method outperforms conventional schemes, it poses significant practical applications.

The paper investigates how the semiconductor and electric vehicle industries are addressing safety and security concerns in the era of autonomous driving, emphasizing the prioritization of safety over security for market competitiveness. Collaboration between these sectors is deemed essential for maintaining competitiveness and value. The research suggests solutions such as advanced autonomous driving technologies and enhanced battery safety measures, with the integration of AI chips playing a pivotal role. However, challenges persist, including the limitations of big data and potential errors in semiconductor-related issues. Legacy automotive manufacturers are transitioning towards software-driven cars, leveraging artificial intelligence to mitigate risks associated with safety and security. Conflicting safety expectations and security concerns can lead to accidents, underscoring the continuous need for safety improvements. We analyzed the expansion of electric vehicles as a means to enhance safety within a framework of converging security concerns, with AI chips being instrumental in this process. Ultimately, the paper advocates for informed safety and security decisions to drive technological advancements in electric vehicles, ensuring significant strides in safety innovation.

In this paper, we propose two 127-bit LFSR (Linear Feedback Shift Register)-based OTP (One-Time Password) generators. One is a 9-digit decimal OTP generator with thirty taps, while the other is a 12-digit OTP generator with forty taps. The 9-digit OTP generator includes only the positions of Fibonacci numbers to enhance randomness, whereas the 12-digit OTP generator includes the positions of prime numbers and odd numbers. Both proposed OTP generators are implemented on an Arduino module, and randomness evaluations indicate that the generators perform well across six criteria and are straightforward to implement with Arduino.

With the advancement of Internet technology, online sales and purchases of products have become active. Along with this, the importance of user reviews is also being highlighted. Although user reviews are actively utilized for product sales and purchases, it is difficult to quickly and easily obtain useful information due to the abundance of user reviews. Therefore, prioritizing user reviews is a necessary service for customers that requires careful consideration. Metadata, which contains important information, can be effectively used to prioritize user reviews. However, it is crucial to select and use metadata appropriately according to the purpose. Lean Startup proposes a strategy of repeatedly correcting the problems of ideas or making early transitions to continue trying different approaches. In this paper, we propose a three-step method applying the Lean Startup process to analyze ways to prioritize user reviews using metadata: Build Priority, Measure Priority, Learn Priority.

With the exponential growth of satellite data utilization, machine learning has become pivotal in enhancing innovation and cybersecurity in satellite systems. This paper investigates the role of machine learning techniques in identifying and mitigating vulnerabilities and code smells within satellite software. We explore satellite system architecture and survey applications like vulnerability analysis, source code refactoring, and security flaw detection, emphasizing feature extraction methodologies such as Abstract Syntax Trees (AST) and Control Flow Graphs (CFG). We present practical examples of feature extraction and training models using machine learning techniques like Random Forests, Support Vector Machines, and Gradient Boosting. Additionally, we review open-access satellite datasets and address prevalent code smells through systematic refactoring solutions. By integrating continuous code review and refactoring into satellite software development, this research aims to improve maintainability, scalability, and cybersecurity, providing novel insights for the advancement of satellite software development and security. The value of this paper lies in its focus on addressing the identification of vulnerabilities and resolution of code smells in satellite software. In terms of the authors' contributions, we detail methods for applying machine learning to identify potential vulnerabilities and code smells in satellite software. Furthermore, the study presents techniques for feature extraction and model training, utilizing Abstract Syntax Trees (AST) and Control Flow Graphs (CFG) to extract relevant features for machine learning training. Regarding the results, we discuss the analysis of vulnerabilities, the identification of code smells, maintenance, and security enhancement through practical examples. This underscores the significant improvement in the maintainability and scalability of satellite software through continuous code review and refactoring.

In the quest for advancing diabetes diagnosis, this study introduces a novel two-step machine learning approach that synergizes the probabilistic predictions of Logistic Regression with the classification prowess of Random Forest. Diabetes, a pervasive chronic disease impacting millions globally, necessitates precise and early detection to mitigate long-term complications. Traditional diagnostic methods, while effective, often entail invasive testing and may not fully leverage the patterns hidden in patient data. Addressing this gap, our research harnesses the predictive capability of Logistic Regression to estimate the likelihood of diabetes presence, followed by employing Random Forest to classify individuals into diabetic, pre-diabetic or nondiabetic categories based on the computed probabilities. This methodology not only capitalizes on the strengths of both algorithms-Logistic Regression's proficiency in estimating nuanced probabilities and Random Forest's robustness in classification-but also introduces a refined mechanism to enhance diagnostic accuracy. Through the application of this model to a comprehensive diabetes dataset, we demonstrate a marked improvement in diagnostic precision, as evidenced by superior performance metrics when compared to other machine learning approaches. Our findings underscore the potential of integrating diverse machine learning models to improve clinical decision-making processes, offering a promising avenue for the early and accurate diagnosis of diabetes and potentially other complex diseases.

AI technology is a central focus of the 4th Industrial Revolution. However, compared to some existing non-artificial intelligence technologies, new AI adversarial attacks have become possible in learning data management, input data management, and other areas. These attacks, which exploit weaknesses in AI encryption technology, are not only emerging as social issues but are also expected to have a significant negative impact on existing IT and convergence industries. This paper examines various cases of AI adversarial attacks developed recently, categorizes them into five groups, and provides a foundational document for developing security guidelines to verify their safety. The findings of this study confirm AI adversarial attacks that can be applied to various types of cryptographic modules (such as hardware cryptographic modules, software cryptographic modules, firmware cryptographic modules, hybrid software cryptographic modules, hybrid firmware cryptographic modules, etc.) incorporating AI technology. The aim is to offer a foundational document for the development of standardized protocols, believed to play a crucial role in rejuvenating the information security industry in the future.

This paper focuses on improving accuracy in constrained computing settings by employing the ReLU (Rectified Linear Unit) activation function. The research conducted involves modifying parameters of the ReLU function and comparing performance in terms of accuracy and computational time. This paper specifically focuses on optimizing ReLU in the context of a Multilayer Perceptron (MLP) by determining the ideal values for features such as the dimensions of the linear layers and the learning rate (Ir). In order to optimize performance, the paper experiments with adjusting parameters like the size dimensions of linear layers and Ir values to induce the best performance outcomes. The experimental results show that using ReLU alone yielded the highest accuracy of 96.7% when the dimension sizes were 30 - 10 and the Ir value was 1. When combining ReLU with the Adam optimizer, the optimal model configuration had dimension sizes of 60 - 40 - 10, and an Ir value of 0.001, which resulted in the highest accuracy of 97.07%.

The main problem with existing edge detection techniques is that they have many limitations in detecting edges for complex and diverse images that exist in the real world. This is because only edges of a defined shape are discovered based on an accurate definition of the edge. One of the methods to solve this problem is the cost minimization method. In the cost minimization method, cost elements and cost functions are defined and used. The cost function calculates the cost for the candidate edge model generated according to the candidate edge generation strategy, and if the cost is found to be satisfactory, the candidate edge model becomes the edge for the image. In this study, we proposed an enhanced candidate edge generation strategy to discover edges for more diverse types of images in order to improve the shortcoming of the cost minimization method, which is that it only discovers edges of a defined type. As a result, improved edge detection results were confirmed.

This study examines the factors influencing loyalty to cultural arts events using data from the Survey Report on National Culture and Arts Activity in 2022 and 2023. The dependent variable is a binary variable representing the intention to revisit in the future, which serves as a proxy for loyalty. Given that the dependent variable is binary, the logit model specification is employed to estimate the average marginal effects. The estimation results indicate that audience satisfaction exerts the strongest influence on loyalty in both years. It can be observed that participation in cultural arts events is the second most important variable in determining loyalty. This suggests that the government should support the expansion of the scope of these activities and the diversification of programs in order to facilitate greater participation in a wider range of cultural arts activities.

A nonlinear numerical analysis of orientation and velocity fields of the full Ericksen-Leslie theory for a nematic liquid crystal under shear flow is given. We obtained for the first time the three-dimensional orientation and two component velocity profiles evolutions for both in- and out-of-shear plane orientation anchorings. Complex evolution routes to steady state were found even for shear aligning nematic. As the Ericksen number increases monotonic evolution of velocity and orientation shifts towards multi-region nucleating director rotation growth with complex secondary flow generations. We found that contrary to the in-shear-plane anchorings like homeotropic or parallel anchorings, binormal anchoring gives rise to substantial non-planar three-dimensional orientation with nonzero secondary flow.

This study examines factors influencing nonusers' resistance to the adoption of the metaverse, focusing on propagation mechanisms. It elucidates the role of innovation resistance within the metaverse adoption process. We applied the Innovation Resistance Model in the context of the metaverse and considers three major groups of factors influencing resistance to the metaverse: innovation characteristics (perceived usefulness, compatibility, perceived risk, and complexity), consumer characteristics (personal innovativeness), and propagation mechanisms (mass media, online media, and personal communication). An online survey of college students who do not use the metaverse revealed that perceived usefulness, compatibility, personal innovativeness, and online media were negative predictors of resistance to the metaverse. Conversely, perceived risk, mass media, and personal communication were positive predictors of resistance to the metaverse. Furthermore, innovation resistance was found to play a mediating role in the metaverse adoption process. Drawing upon the findings, we suggested marketing strategies to decrease resistance to the metaverse.

We look at Web 3.0 business model canvas (BMC) of metaverse gaming platform, The Sandbox (TS). As results, the decentralized, blockchain-based platform, TS benefits its creators and players by providing true ownership, tradability of decentralized assets, and interoperability. First, in terms of the governance and ownership, The SAND functions a governance token allowing holders to participate in decision and SAND owners can vote themselves or delegate voting rights to other players of their choice. Second, in terms of decentralized assets and activities, TS offers three products as assets like Vox Edit as a 3D tool for voxel ASSETS, Marketplace as NFT market, and Game Maker as a visual scripting toolbox. The ASSETS made in Vox Edit, sold on the Marketplace, can be also utilized with Game Maker. Third, in terms of the network technology, in-game items are no longer be confined to a narrow ecosystem. The ASSETS on the InterPlanetary File System (IPFS) are not changed without the owner's permission. LAND and SAND are supported on Polygon, so that users interact with their tokens in a single place. Last, in terms of the token economics, users can acquire in-game assets, upload these assets to the marketplace, use for paying transaction fees, and use these as governance token for supporting the foundation.

The Stable diffsuion AI tool is popular among designers because of its flexible and powerful image generation capabilities. However, due to the diversity of its AI models, it needs to spend a lot of time testing different AI models in the face of different design plans, so choosing a suitable general AI model has become a big problem at present. In this paper, by comparing the AI images generated by two different Stable diffsuion models, the advantages and disadvantages of each model are analyzed from the aspects of the matching degree of the AI image and the prompt, the color composition and light composition of the image, and the general AI model that the generated AI image has an aesthetic sense is analyzed, and the designer does not need to take cumbersome steps. A satisfactory AI image can be obtained. The results show that Playground V2.5 model can be used as a general AI model, which has both aesthetic and design sense in various style design requirements. As a result, content designers can focus more on creative content development, and expect more groundbreaking technologies to merge generative AI with content design.

Chang'an (Chinese: 长安三万里), also known as 30,000 Miles from Chang'an, is a 2023 Chinese 3D animated historical drama film directed by Xie Junwei and Zou Jing.This thesis aims to explore the visual expression of traditional culture in the 3D animated film Chang'an as an example to reveal the reasons for the success of this type of film. The study analyses in detail the design of the character models and costumes, as well as the use of the traditional landscape painting technique of 'white space' in the composition of the screen from the visual aspect. Through the analysis of character design and screen composition, the thesis concludes that the success of Chang'an lies in its elaborate visual design and clever use of traditional culture, which makes it a 3D animation film with both artistic and commercial values. Finally, the thesis concludes that the production of a successful 3D animation film needs to combine the visual elements of 3D animation with traditional culture in order to win audience recognition and achieve commercial success.

The emergence of programs like Midjourney, particularly known for its text-to-image capability, has significantly impacted design and creative industries. Midjourney continually updates its database and algorithms to enhance user experience, with a focus on character consistency. This paper's examination of the latest V6 version of Midjourney reveals notable advancements in its characteristics and design principles, especially in the realm of character generation. By comparing V6 with its predecessors, this study underscores the significant strides made in ensuring consistent character portrayal across different plots and timelines.Such improvements in AI-driven character consistency are pivotal for storytelling. They ensure coherent and reliable character representation, which is essential for narrative clarity, emotional resonance, and overall effectiveness. This coherence supports a more immersive and engaging storytelling experience, fostering deeper audience connection and enhancing creative expression.The findings of this study encourage further exploration of Midjourney's capabilities for artistic innovation. By leveraging its advanced character consistency, creators can push the boundaries of storytelling, leading to new and exciting developments in the fusion of technology and art.

This article embarks on an exploration of how the burgeoning landscape of AI technology is reshaping and augmenting creative expression within the realm of 3D animation. As AI continues to evolve and mature, its integration into the process of 3D animation creation has become an increasingly focal point of investigation and analysis. The article undertakes a comprehensive examination of the myriad applications of AI within the domain of 3D animation, shedding light on its multifaceted contributions to various aspects of the creative process. Furthermore, it delves into the transformative impact that AI technology has on enhancing creative expression within 3D animation, particularly through increased productivity, personalized content creation, and the expansion of creative boundaries. By automating repetitive and time-consuming tasks inherent in traditional production methods, AI liberates artists and animators to unleash their creative ingenuity and push the boundaries of their craft. Through empirical research and case studies, the article elucidates how AI serves as a catalyst for innovation, fostering a conducive environment for the exploration of novel techniques and artistic styles.

The purpose of this study is to investigate the relationship between organizational culture (innovation culture, relationship culture, hierarchy culture, task culture) as perceived by organizational members and its impact on organizational justice and job performance. This contributes to providing additional data for the revitalization and development of the organizational system for efficient management and operation measures included in the organization's sustainable management. To this end, the hypothesis established through the traditional process of quantitative research was tested as follows. First, organizational culture showed a positive effect on organizational justice. Second, organizational culture had a positive (+) effect on job performance. Third, organizational justice was significantly analyzed in terms of job performance. In other words, the importance of systematic re-establishment and continuous implementation of organizational culture (innovation culture, relationship culture, hierarchy culture, task culture) and organizational justice consistent with organizational characteristics was emphasized in order to improve job performance, which is the result of organizational competitiveness. In addition, it is the aspect of drawing practical implications for strategic human resource management and human resource development to systematically improve it.

Recently, there has been an increasing trend in the role of social media in tourism marketing. We analyze changes in tourism marketing trends using tourism marketing keywords through social media networks. The aim is to understand marketing trends based on the analyzed data and effectively create, maintain, and manage customers, as well as efficiently supply tourism products. Data was collected using web data from platforms such as Naver, Google, and Daum through TexTom. The data collection period was set for one year, from December 1, 2022, to December 1, 2023. The collected data, after undergoing refinement, was analyzed as keyword networks based on frequency analysis results. Network visualization and CONCOR analysis were conducted using the Ucinet program. The top words in frequency were 'tourists,' 'promotion,' 'travel,' and 'research.' Clusters were categorized into four: tourism field, tourism products, marketing, and motivation for visits. Through this, it was confirmed that tourism marketing is being conducted in various tourism sectors such as MICE, medical tourism, and conventions. Utilizing digital marketing via online platforms, tourism products are promoted to tourists, and unique tourism products are developed to increase city branding and tourism demand through integrated tourism content. We identify trends in tourism marketing, providing tourists with a positive image and contributing to the activation of local tourism.

With the globalization of the film industry, ethnic minority films have been developed and studied by many scholars for their special ethnic representation. The film "Tharlo" directed by Pema Tseden carefully explores the identity anxiety of a Tibetan shepherd. Through the connection and separation between the protagonist and traditional culture, it shows a complexity of modern ethnic identity for minority people. This study explores what kind of cinematic techniques and symbolic elements the director uses to shape ordinary characters, build a narrative space, and show ethnic representation. This paper puts forward a theoretical framework combining cinematic quantitative methods with qualitative narrative and semiotic analysis, aiming to deepen our understanding of cinematic techniques and ethnic representation, and provides a new perspective and profound insights for discussing the complexity faced by ethnic minorities in contemporary films. This study finds that Tseden's "Tharlo" successfully portrays the complex transformation of Tibetan cultural identity in the context of globalization and modernization through cinematic techniques such as fixed camera positions, long take and black-and-white cinematography, combined with the use of symbolic elements like mirrors, lambs and identity cards.

For precise cardiac diagnostics and treatment, we introduce a novel method for patient-specific mapping between myocardial and coronary anatomy, leveraging local variations in myocardial thickness. This complex system integrates and automates multiple sophisticated components, including left ventricle segmentation, myocardium segmentation, long-axis estimation, coronary artery tracking, and advanced geodesic Voronoi distance mapping. It meticulously accounts for variations in myocardial thickness and precisely delineates the boundaries between coronary territories according to the conventional 17-segment myocardial model. Each phase of the system provides a step-by-step approach to automate coronary artery mapping onto the myocardium. This innovative method promises to transform cardiac imaging by offering highly precise, automated, and patient-specific analyses, potentially enhancing the accuracy of diagnoses and the effectiveness of therapeutic interventions for various cardiac conditions.

In this paper, we investigate the comparative performance of two leading object detection architectures, YOLOv7 and Detection Transformer (DETR), across varying levels of data augmentation using CycleGAN. Our experiments focus on chest scan images within the context of biomedical informatics, specifically targeting the detection of abnormalities. The study reveals that YOLOv7 consistently outperforms DETR across all levels of augmented data, maintaining better performance even with 75% augmented data. Additionally, YOLOv7 demonstrates significantly faster convergence, requiring approximately 30 epochs compared to DETR's 300 epochs. These findings underscore the superiority of YOLOv7 for object detection tasks, especially in scenarios with limited data and when rapid convergence is essential. Our results provide valuable insights for researchers and practitioners in the field of computer vision, highlighting the effectiveness of YOLOv7 and the importance of data augmentation in improving model performance and efficiency.

This study uses a large language model (LLM) to identify Aristotle's rhetorical principles (ethos, pathos, and logos) in COVID-19 information on Naver Knowledge-iN, South Korea's leading question-and-answer community. The research analyzed the differences of these rhetorical elements in the most upvoted answers with random answers. A total of 193 answer pairs were randomly selected, with 135 pairs for training and 58 for testing. These answers were then coded in line with the rhetorical principles to refine GPT 3.5-based models. The models achieved F1 scores of .88 (ethos), .81 (pathos), and .69 (logos). Subsequent analysis of 128 new answer pairs revealed that logos, particularly factual information and logical reasoning, was more frequently used in the most upvoted answers than the random answers, whereas there were no differences in ethos and pathos between the answer groups. The results suggest that health information consumers value information including logos while ethos and pathos were not associated with consumers' preference for health information. By utilizing an LLM for the analysis of persuasive content, which has been typically conducted manually with much labor and time, this study not only demonstrates the feasibility of using an LLM for latent content but also contributes to expanding the horizon in the field of AI text extraction.

This study explores the relationship between childhood abuse experiences and subsequent mental disorders in adults, with a particular focus on the moderating role of spiritual well-being. Using self-reported data from 210 graduate students in the Daejeon and Chungcheong regions, the findings demonstrate that spiritual well-being significantly moderate how childhood abuse impacts adult mental health. Specifically, individuals with lower levels of spiritual well-being experience a greater exacerbation of metnal disorders related to past abuse, while those with higher levels show a buffering effect. These results suggest that enhancing spiritual well-being could be a vital component of therapeutic interventions aimed at preventing mental disorders in adults who have experienced childhood abuse. We highlight the potential benefits of incorporating spiritual well-being into mental health strategies and call for additional research to substantiate these findings across broader populations. This unique contribution underscores the importance of considering spiritual factors int the therpeutic process, offering a new and valuable perspective in the field of mental health research.

The purpose of this study was to investigate the effect of 8 weeks of forestry exercise on the quality of life and physical self-concept of breast cancer survivors. The subjects of this study were eight breast cancer survivors 6 months after mastectomy. The forest combined exercise program consisted of aerobic exercise through forest walking and resistance exercise using elastic bands. The forest combined exercise was conducted twice for 8 weeks. Forest trekking consisted of a 2km walking speed and resistance exercise consisted of three levels of sets and intensity. The format was divided into gradual increases. The exercise time was 40 to 60 minutes for forest trekking, 20 to 30 minutes for descent, and 40 to 60 minutes for resistance exercise, for a total of 120 to 130 minutes per day. Breast cancer survivors' quality of life was measured using a questionnaire, and changes in quality of life were measured using a t-test (α=.05). Physical self-concept was assessed through in-depth interviews. There was no statistically significant difference in quality of life before and after 8 weeks of combined forestry exercise, but there was a slight tendency to increase in the area of physical well-being. Physical self-concept showed positive changes in motivation, physical strength improvement, health promotion, physical competence, and self-confidence through the forest composite exercise. Therefore, the forest composite exercise is believed to have a positive effect on the physical self-concept of breast cancer survivors.

A smart factory is defined as a cutting-edge, intelligent factory that integrates all production processes from product planning to sales with information and communication technology. Through these factories, each company produces customized products with minimal cost and time. The smart factory promotion project in Korea has produced positive results even in difficult environments such as the COVID-19 situation. Through the transition to a smart manufacturing production system, the competitiveness of small and medium-sized businesses has been greatly strengthened, including increased productivity and reduced costs. This study was based on surveyed data conducted by organizations related to smart factory promotion in 2020. Significant contents and major characteristics that emerged from the surveyed data were inferred and described. Since the meaningful contents reflect the reality of the company, more efficient promotion of smart factories will be possible in the future.

The performance of informatization differs depending on its utilization, investment and construction methods. This study analyzed the key factors affecting the performance of informatization based on a public survey on the information system operation of small and medium-sized companies in Korea. Through structural equation modeling and one-way ANOVA, the study identified the pathways leading to performance. As a result of the analysis, it was confirmed that higher levels of top management support are associated with increased information system use and performance. Similarly, higher levels of information system use are correlated with better performance of information systems. This research is significant as it investigates and reveals how top management support and information system usage work in the cases of SMEs in South Korea, which is a leading country in manufacturing. The findings of this study will provide valuable insights for SMEs, whether they have already developed an information system or plan to do so, in their efforts to enhance corporate competitiveness.

As the main driver of economic growth and employment, the agricultural sector plays an important role in Vietnam's economy. However, in recent years, the sector has faced new challenges and also presented new investment opportunities to stimulate agricultural growth. Many Vietnamese agricultural producers currently lack the modern technology and decision support tools needed to maintain and improve productivity in a rapidly changing environment. Other stakeholders in the agricultural value chain, such as input suppliers, distributors, and consumers, also face significant challenges, including disrupted value chains, transportation costs. The cost of transporting goods across the supply chain continues to increase and information exchange remains fragmented. A potential solution to address these challenges is the application of digital transformation in agricultural supply chains. Farmers and other value chain participants can improve the production of their goods and procedures by utilizing new and cutting-edge technologies that are integrated into a unified system as part of the digital transformation of agricultural supply chains. In this study, we evaluate the current status of digital transformation in the supply chain of the agriculture industry by finding and examining pertinent publications from key agencies as well as prior research. From there, in the framework of the digital economy, this study suggests a digital transformation roadmap for the agricultural supply chain.

E-learning systems have proliferated in recent years, particularly in the wake of the global COVID-19 pandemic. For kids, there isn't a specific online learning platform available, though. To do this, new conceptual models of training and learning software that are adapted to the abilities and preferences of end users must be created. Young pupils: those in kindergarten, preschool, and elementary school are unique subjects with little research history. From the standpoint of software technology, young students who have never had access to a computer system are regarded as specific users with high expectations for the functionality and interface of the software, social network connectivity, and instantaneous Internet communication. In this study, we suggested creating an electronic learning management system that is web-based and appropriate for primary school pupils. User-centered design is the fundamental technique that was applied in the development of the system that we are proposing. Test findings have demonstrated that students who are using the digital environment for the first time are studying more effectively thanks to the online learning management system.

Smart factory is a remarkable development from traditional manufacturing systems to data-based smart manufacturing systems that can connect and process data continuously, collected from machines, production equipment to production and business processes, capable of supporting workers in making decisions or performing work automatically. Smart factory is the key and center of the fourth industrial revolution, combining improvements in traditional manufacturing activities with digital technology to help factories achieve greater efficiency, contributing to increased revenue and reduce operating costs for businesses. Besides, the importance of smart factories is to make production more quality, efficient, competitive and sustainable. Businesses in Vietnam are in the process of learning and applying smart factory models. However, the number of businesses applying the pine factory model is still limited due to many barriers and difficulties. Therefore, in this paper we conduct a survey to assess the needs and current situation of businesses in applying smart factories and propose some specific solutions to develop and promote application of smart factory model in Vietnamese businesses.

Currently, the overall tendency for green and sustainable economic development is creating a circular economy. In actuality, agricultural output is currently benefiting greatly from the growth of the circular economy. The creation of a circular economy helps address resource scarcity, save the environment, combat climate change, and increase economic efficiency. Vietnam's economy can grow quickly and sustainably by shifting to a circular economy production model. Comparing prior growth techniques to the digital age and implementing circular economic development connected with high technology will be a fantastic opportunity to boost growth efficiency. In actuality, Vietnam currently has a large number of agricultural circular economy models. These are models: Creating and using gas from waste and wastewater in livestock and farming; model combining cultivation, livestock, and aquaculture; agro-forestry model; garden-forest model; Circular model using agricultural by-products as a catalyst or creating other valuable products; model of moderation, linked to reducing the use of growth hormones, veterinary medications, pesticides, and artificial fertilizers in agriculture and animal husbandry. Unfortunately, there have been few studies and applications of the aforementioned models, which has made it difficult to build the agricultural sector sustainably. In this paper, we outline the current situation and propose solutions to develop a circular economy model in agriculture in Vietnam for sustainable development.