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As the Internet of Things (IoT) continues to permeate every facet of modern life, the imperative to secure this vast and dynamic frontier becomes increasingly paramount. This presents a comprehensive exploration of the challenges and opportunities inherent in safeguarding the interconnected web of IoT devices. The research critically examines the limitations of current cybersecurity measures through an extensive review of diverse topics, including IoT network performance, smart grid security, and the escalating cyber threats against critical infrastructures. A meticulous analysis of research findings underscores the need for enhanced infrastructure and ongoing research to fortify the cybersecurity mechanisms surrounding IoT objects. We underline the imperative of relentless research efforts to parry the advancing threats and leverage the promise of nascent technologies. Our findings affirm the pivotal influence of robust cybersecurity measures in crafting a resiliently connected ecosystem. The paper underscores the importance of ongoing research to address evolving threats and harness the potential of emerging technologies, reaffirming the central role of cybersecurity in shaping a secure interconnected world. In conclusion, the study emphasizes the dynamic and ever-evolving nature of cybersecurity on the IoT frontier. It unveils a complex landscape of challenges, ranging from network performance intricacies to the security concerns of critical infrastructures.

Recently, audio systems are changing the configuration of conventional sound reinforcement (SR) systems and public address (PA) systems by using audio over IP (AoIP), a technology that can transmit and receive audio signals based on internet protocol (IP). With the advancement of IP technology, AoIP technologies are leading the audio market and various technologies are being released. In particular, audio networks and control hierarchy over peer-to-peer (Anchor) technology based on AoIP is a system that transmits and receives audio signals over a wide bandwidth without an audio mixer, creating a novel paradigm for existing audio system configurations. Anchor technology forms an audio system by connecting audio sources and output equipment with On-site audio center (OAC), a device that can transmit and receive IP. Anchor's receiving OAC is capable of receiving and mixing audio signals transmitted from different IPs, making it possible to configure a novel audio system by replacing the conventional audio mixer. However, Anchor technology does not have the ability to provide audio effects to input devices such as microphones and instruments in the audio system configuration. Due to this, when individual control of each audio source is required, there is a problem of not being able to control the input signal, and it is impossible to individually affect a specific input signal. In this paper, we implemented a tone control module that can individually control the tone of the audio source of the input device using the audio processor core in the audio system based on Anchor technology, tone control for audio sources is possible through a tone control module connected to the transmitting OAC. As a result of the study, we confirmed that OAC receives the signal from the audio source, adjusts the tone and outputs it on the tone control module. Based on this, it was possible to solve problems that occurred in Anchor technology through transmitting OAC and tone control modules. In the future, we hope that the audio system configuration using Anchor technology will become established as the standard for audio equipment.

Image compression-decompression methods have become increasingly crucial in modern times, facilitating the transfer of high-quality images while minimizing file size and internet traffic. Historically, early image compression relied on rudimentary codecs, aiming to compress and decompress data with minimal loss of image quality. Recently, a novel compression framework leveraging colorization techniques has emerged. These methods, originally developed for infusing grayscale images with color, have found application in image compression, leading to colorization-based coding. Within this framework, the encoder plays a crucial role in automatically extracting representative pixels—referred to as color seeds—and transmitting them to the decoder. The decoder, utilizing colorization methods, reconstructs color information for the remaining pixels based on the transmitted data. In this paper, we propose a novel approach to image compression, wherein we decompose the compression task into grayscale image compression and colorization tasks. Unlike conventional colorization-based coding, our method focuses on the colorization process rather than the extraction of color seeds. Moreover, we employ the Denoising Diffusion Null-Space Model (DDNM) for colorization, ensuring high-quality color restoration and contributing to superior compression rates. Experimental results demonstrate that our method achieves higher-quality decompressed images compared to standard JPEG and JPEG2000 compression schemes, particularly in high compression rate scenarios.

Today, news avoidance has become an inevitable trend, particularly exacerbated since the outbreak of the COVID-19 pandemic in 2020. To delve deeper into the shifting tendencies of news consumers towards news avoidance and unveil the motivations behind this avoidance, this study recruited 500 Chinese news consumers aged between 20 and 60 years old, employing survey questionnaires as the research method. Through an indepth examination of their news consumption behavior at different stages of the COVID-19 pandemic, we discovered that individuals' risk perceptions and efficacy beliefs significantly influence their patterns of news consumption. Furthermore, we identified negative emotions, information overload, and media distrust as the primary reasons for news avoidance among Chinese news consumers during the COVID-19 crisis. These findings Not only provide crucial insights into understanding the dynamics of news consumption behavior but also offer valuable reference points for the news industry to better fulfill its role and value during crises in the future.

In early 2020, as the COVID-19 pandemic hit the world, widespread changes occurred throughout society. COVID-19 also brought changes in consumers' consumption behaviors and preferences. This study aims to find out how the current status of the tourism industry and the coffee industry has changed since the end of COVID-19 by conducting big data analysis focusing on the search frequency of Naver, Google, and the following, which are representative social networks in Korea. Designating "Coffee Industry + Tourism Industry" as the representative keyword, January 1, 2020 to December 31, 2020, the time of each COVID-19 outbreak, was set before the COVID-19 type, and January 1, 2023 to December 31, 2023 was set after the end of COVID-19. Based on the analyzed search binder big data analysis within the period, we would like to find out how the current status of the tourism industry and the coffee industry has changed since the end of COVID- 19. Finaly, the coffee and tourism industries are on the path of recovery and growth. In particular, the rise in coffee consumption, the recovery of the number of tourists, the emphasis on local tourism, and the strengthening of links with global markets are prominent.

Body temperature plays an important role in medicine, some diseases are characterized by changes in human body temperature. Monitoring body temperature also allows doctors to monitor the effectiveness of medical treatments. Accurate body temperature measurement is key to detecting fevers, especially fevers related to infection with the SARS-CoV-2 virus that caused the recent Covid-19 pandemic in the world. The solution of measuring body temperature using a thermal camera is fast but has a high cost and is not suitable for some organizations with difficult economic conditions today. Use a medical thermometer to measure body temperature directly for a slow rate, making it easier to spread disease from person to person. In this paper, we propose a completely automatic body temperature measurement system that can adjust the height according to the person taking the measurement, has a measurement logging system and is monitored via the internet. Experimental results show that the proposed method has successfully created a fully automatic human body measurement system. Furthermore, this research also helps the school's scientists and students gain more knowledge and experience to apply Internet of Things technology in real life.

Current straw mushroom farming in countries with large rice growing areas has great development potential, and was once considered a way to generate additional income and reduce poverty in rural areas. However, currently most people still grow mushrooms using traditional processes, leading to low productivity and unguaranteed output quality. Currently, due to climate change and unusual weather changes, people tend to switch to growing straw mushrooms indoors. In the process of growing straw mushrooms indoors, the design of an automatic control and monitoring system is very important to ensure the growing process is carried out effectively and achieves high yields. In this paper, we propose a system that can automatically control and monitor the humidity and temperature of the indoor straw mushroom growing process and other parameters that can be monitored through a network system using Internet of Things. The control algorithm automatically adjusts the grow house equipment based on feedback from sensors to maintain an optimal environment for growing straw mushrooms. Experimental results show that the straw mushroom growing system with automatically controlled and monitored environmental parameters helps improve efficiency, reduce costs and increase the sustainability of the current straw mushroom growing industry..

This thesis explores the embodiment of a performer and a character/role specifically by examining what the term character is associated with and implies in a sense of the performer’s bodily training through which what happens to their body. First of all, this research begins to investigate the relationship between a performer and a character centred on the performer’s bodily experience through training and/or studio work. From a perspective of a performer, the concept and practical approach of a character itself essentially includes and signifies all the given circumstance of a specific play which has to be acknowledged then inhabited through the performer’s body. That is, the internal structure of the text parallels with articulating and developing the spine of a specific character which take place as the substance leads the performer’s body to an organic action and/or that of way corresponding to what the character needs and wants to obtain through a series of moment on stage. Here, we argue that the purposeful action as a process and result of applying/inhabiting the substance enhances the performer’s body as the whole being participates in the given environment within which his/her body can also work or function by means of the integrated oneness. Second, in a manner of the most fundamental level, both the ethic of acting and the central task of a performer remind us the significance of allowing therefore experiencing subtle bodily movement, namely, responses to stimulus from in/outside of his/her body either visible or invisible on the one hand. At the same time, such a journey of self-discovery empowers the performer to explore new potential possibilities on the other. Finally, as the research finding suggests that these practical insights are necessarily need to be acknowledged as a point of the departure through which the quality of a performer’s body is also cultivated by means of the changeable wholeness in order to being on stage.

Research on the visual narrative impact of Unreal Engine's Lumen lighting system in games aims to delve into how Lumen's lighting technology plays a crucial role in game design and gameplay experience, thereby enhancing the visual storytelling of games. Lumen, Unreal Engine's dynamic global illumination solution, calculates lighting and shadows in real-time during gameplay, creating a more realistic and immersive environment. Analysis indicates that Lumen technology not only provides visually realistic and dynamic lighting effects but also significantly enriches the expressiveness and immersion of the game narrative through its changes in light and shadow.

Advancements in generative AI are reshaping graphic and 3D content design landscapes, where AI not only enriches graphic design but extends its reach to 3D content creation. Though 3D texture mapping through AI is advancing, AI-generated 3D modeling technology in this realm remains nascent. This paper presents AI 2D image-driven 3D modeling techniques, assessing their viability in 3D content design by scrutinizing various algorithms. Initially, four OBJ model-exporting AI algorithms are screened, and two are further evaluated. Results indicate that while AI-generated 3D models may not be directly usable, they effectively capture reference object structures, offering substantial time savings and enhanced design efficiency through manual refinements. This endeavor pioneers new avenues for 3D content creators, anticipating a dynamic fusion of AI and 3D design.

The rapid development of artificial intelligence technology in recent years is evident, from the emergence of ChatGPT to innovations like Midjourney, Stable Diffution, and the upcoming SORA text-to-video technology by OPENai. Animation capture technology, driven by the AI technology trend, is undergoing significant advancements, accelerating the progress of the animation industry. Through an analysis of the current application of DEEPMOTION, this paper explores the development direction of AI motion capture technology, analyzes issues such as errors in multi-person object motion capture, and examines the vast prospects. With the continuous advancement of AI technology, the ability to recognize and track complex movements and expressions faster and more accurately, reduce human errors, enhance processing speed and efficiency. This advancement lowers technological barriers and accelerates the fusion of virtual and real worlds.

We introduce a novel outdoor linear LED luminaire enhanced with Gore-Tex filters, designed to overcome the challenges of moisture and thermal management in harsh environments. This luminaire integrates expanded polytetrafluoroethylene (ePTFE)—commercially known as Gore-Tex—achieving superior waterproofing and dustproof qualities while maintaining breathability to prevent internal condensation. The design process, from conceptualization through prototyping and testing, is detailed, highlighting the luminaire's improved durability and stability under varying conditions. Experimental results demonstrate that our design significantly extends the operational lifespan and reliability of outdoor LED lighting systems by mitigating thermal and moisture-related degradation. This study not only advances ePTFE's application in lighting technologies but also offers a scalable model for enhancing the performance of LED luminaires in outdoor settings.

We address the problematic issue of blinking in residential LED lighting systems—a phenomenon that has recently become a significant concern due to voltage sags caused by high-power household appliances. To combat this, we developed a two-stage LED converter with integrated anti-blinking circuitry, specifically designed for T5 LED indirect lighting fixtures. The first stage employs a Power Factor Correction (PFC) boost circuit to enhance voltage stability by aligning the voltage and current phases, thereby minimizing power losses. The second stage, a meticulously engineered DC-DC buck converter, ensures stable lighting despite electrical fluctuations. Rigorous testing has confirmed our converter's efficacy in maintaining consistent light output without blinking, thereby substantially improving user comfort and adhering to strict standards for harmonic distortion and electromagnetic compatibility. Our breakthrough provides a robust solution to a pressing issue, marking a significant advancement in LED lighting technology.

We Design The problem of underdeveloped areas is emerging as a social problem. Industrialization drove the population to the cities, creating underdeveloped areas. Underdeveloped areas are causing social problems such as population decline and aging. It is necessary to study the continuous tourism development of underdeveloped areas through development and improvement projects. Using social media big data to investigate keywords in underdeveloped areas and see the connection between keywords. The purpose of this study was to conduct core research divided by type and to investigate the keywords of tourism in underdeveloped areas through concor analysis of underdeveloped areas. As a result of the study, keywords were connected for each type of redevelopment, regional development, regional economy, and underdeveloped areas. Through this, the keywords for sustainable tourism in underdeveloped areas were identified. It is hoped that this study will develop sustainable tourism for the keywords of underdeveloped areas.

The volume of genomic data is constantly increasing in various modern industries and research fields. This growth presents new challenges and opportunities in terms of the quantity and diversity of genetic data. In this paper, we propose a distributed cloud system for integrating and managing large-scale gene databases. By introducing a distributed data storage and processing system based on the Hadoop Distributed File System (HDFS), various formats and sizes of genomic data can be efficiently integrated. Furthermore, by leveraging Spark on YARN, efficient management of distributed cloud computing tasks and optimal resource allocation are achieved. This establishes a foundation for the rapid processing and analysis of large-scale genomic data. Additionally, by utilizing BigQuery ML, machine learning models are developed to support genetic search and prediction, enabling researchers to more effectively utilize data. It is expected that this will contribute to driving innovative advancements in genetic research and applications.

Recent studies have shown that inverse design using deep learning has the potential to rapidly generate the optimal design that satisfies the target performance without the need for iterative optimization processes. Unlike traditional methods, deep learning allows the network to rapidly generate a large number of solution candidates for the same objective after a single training, and enables the generation of diverse designs tailored to the objectives of inverse design. These inverse design techniques are expected to significantly enhance the efficiency and innovation of design processes in various fields such as aerospace, biology, medical, and engineering. We analyzes inverse design models that are mainly utilized in the nano and chemical fields, and proposes inverse design models based on supervised and unsupervised learning that can be applied to the engineering system. It is expected to present the possibility of effectively applying inverse design methodologies to the design optimization problem in the field of engineering according to each specific objective.

In recent years, public attention to health and wellness issues has increased. The integration of smart fitness hardware and innovative technologies have made the development of smart fitness a trend. The number of fitness applications in the market has surged, and demand for an optimal experience is increasingly high. This study selects Sweatcoin, Home Workout, Six Pack in 30 Days, and Fitness Coach & Diet as research subjects from the top ten global mobile health and fitness apps in 2022 based on download rankings. The research is based on eight gamification elements: motivation, challenge, achievement, relationships, sharing, reward, level, and competition, identified through preliminary studies. We distributed a total of 166 questionnaires to users and collected 163 valid responses for data analysis. The Kano Model was used to study the desires of fitness enthusiasts using fitness apps. To reduce the limitations of the research results, the Better-Worse Method was employed for satisfaction index analysis. Based on the final analysis, we propose suggestions for improvement for the four fitness apps to better meet user needs and create a more attractive and efficient application experience.

For examining Web3.0 video streaming (VS) platforms in terms of the decentralized technology, tokenization and decentralized autonomous organization (DAO), we look at four platforms like DLive, DTube, Livepeer, and Theta Network (Theta). As a result, DLive which firstly partnered with Medianova for CDN and with Theta for peer to peer (P2P) network and migrates to Tron blockchain (BC), receives no commission from what creators earn, gives rewards to viewers by measuring engagement, and incentivizes participation by allowing 20% of donation & fees for funding development, 5% to BitTorrent Token (BTT) stakeholders (among these 5%, 20% to partners, 80% to other BTT stakeholders). DTube on its own lower-layer BC, Avalon, offers InterPlanetary File System (IPFS), gives 90% of the created value to creators or curators, and try to empower the community. Livepeer on Ethereum BC offers decentralized CDN, P2P, gives Livepeer Token (LPT) as incentive for network participants, and delegators can stake their LPT to orchestrators doing good. Theta on its native BC pulls streams from peering caching nodes, creates P2P network, gives Theta utility token, TFUEL for caching or relay nodes contributors, and allows Theta governance token, THETA as staking token. We contribute to the categorization of Web3.0 VS platforms: DLive and DTube reduce the risk of platform censorship, promote the diverse content, and allow the community to lead to more user-friendly environments. On the other hand, Livepeer and Theta provide new methods to stream content, but they have some differences. Whereas Livepeer focuses on the transcoding layer, Theta concentrates both on the video application layer and content delivery layer. It means, Theta tries to deliver value to all participants by enhancing network quality, reducing CDN cost, and rewarding users in utility tokens for the storage and bandwidth they provide.

As the size of big data models grows, distributed training is emerging as an essential element for largescale machine learning tasks. In this paper, we propose ParamHub for distributed data training. During the training process, this agent utilizes the provided data to adjust various conditions of the model's parameters, such as the model structure, learning algorithm, hyperparameters, and bias, aiming to minimize the error between the model's predictions and the actual values. Furthermore, it operates autonomously, collecting and updating data in a distributed environment, thereby reducing the burden of load balancing that occurs in a centralized system. And Through communication between agents, resource management and learning processes can be coordinated, enabling efficient management of distributed data and resources. This approach enhances the scalability and stability of distributed machine learning systems while providing flexibility to be applied in various learning environments.

This study aimed to investigate the effects of water depth and speed on the activation of lower muscles during squat exercises, utilizing electromyography(EMG). It involved ten male participants in there. Participants performed 30 squats over a minute at a speed of 60bpm and maximum speed squats until exhaustion within a minute. The Integrated electromyography(iEMG) readings for the rectus femoris showed statistically significant differences due to water depth and speed, with a significant interaction effect between depth and speed during squat exercises. The iEMG readings for the biceps femoris also showed statistically significant differences, with a significant interaction effect between depth and speed during squat exercises. The iEMG readings for the gastrocnemius showed statistically significant differences according to water depth and speed. However, the interaction effect of water depth and speed during squat exercises did not show a statistically significant difference. In contrast, the iEMG readings for the tibialis anterior demonstrated statistically significant differences, with a statistically significant interaction effect during squats. These findings suggest that water depth and speed positively influence the activation patterns of lower muscles. Therefore, appropriately tailored aquatic exercises based on water depth for individuals with musculoskeletal discomfort, including the elderly or those with physical impairments, can effectively reduce physical strain and enhance balance, as well as physical and perceptual aspects. It is concluded that such exercises could provide a safer and more effective method of exercise compared to ground-based alternatives.

Franchise are now a red ocean in Food industry and they need to find other options to appeal for their product, the uprising content, food tech. The franchises are working on R&D to help franchisees with the operations. Through this paper, we analyze the franchise interest on food tech and to help find the necessity of development for franchisees who are in needs with hand, not of human, but of technology. Using Textom, a big data analysis tool, "franchise" and “food tech” were selected as keywords, and search frequency information of Naver and Daum was collected for a year from 01 January, 2023 to 31 December, 2023, and data preprocessing was conducted based on this. For the suitability of the study and more accurate data, data not related to "food tech" was removed through the refining process, and similar keywords were grouped into the same keyword to perform analysis. As a result of the word refining process, a total of 10,049 words were derived, and among them, the top 50 keywords with the highest relevance and search frequency were selected and applied to this study. The top 50 keywords derived through word purification were subjected to TF-IDF analysis, visualization analysis using Ucinet6 and NetDraw programs, network analysis between keywords, and cluster analysis between each keyword through Concor analysis. By using big data analysis, it was found out that franchise do have interest on food tech. “technology”, “franchise”, “robots” showed many interests and keyword “R&D” showed that franchise are keen on developing food tech to seize competitiveness in Franchise Industry.

Both message appeals and individual characteristics can influence the effectiveness of sustainable product promotions. Applying the agency-communion orientation to the advertising message research context, this study examined the interplay between message appeals and agency-communication orientations in impacting purchase intentions. The findings from a 2 (message appeal: self-interest vs. public-interest message) × 2 (motivational orientation: agency vs. communion) experiment revealed a communion-over-agency effect on consumer purchasing decisions for public-interest message appeals. In the self-interest message condition, we found no statistically significant difference in impact between agency and communion on purchase intentions. In short, we contribute to advertising effectiveness research by showing that agency-communion orientations moderate the effect of message appeals. We also explain the practical implications of these findings for effective sustainable communication in advertisements based on individuals’ motivational orientations.

As companies specialize, they use more modern applications, but they still rely on legacy systems and data access is limited by data silos. In this paper, we propose the Fabricator system, a design system for metadata based on Data Fabric that plays a key role in the data orchestration layer consisting of three layers: Sources Engine, Workload Builder, and Data Fabric Ingestion, thereby achieving meaningful integration of data and information. Provides useful insights to users through conversion. This allows businesses to efficiently access and utilize data, overcoming the limitations of legacy systems.

The convergence of deep learning and smart factory is drawing a lot of attentions from not only industrial but also academic circles. The objective of this article is to quantitatively review on deep learning and smart factory from 2010 to 2023. This research analyzed the 138 articles, extracted from the Core Collection of Web of Science, in terms of four dimensions such as the main trend in article publications, the main trend in article citations, the distribution of article publications by research area, and the keywords representing the main contents of published articles. The quantitative review results reveal the following four points: First, the article publications drastically grew from 2019 to 2022 in its annual trend. Second, the article citations have rapidly grown since 2018. Third, Engineering, Computer Science, and Telecommunications are the top 3 research areas composing the 138 articles. Fourth, it is the top 10 keywords such as ‘deep’, ‘learning’, ‘smart’, ‘detection’, factory’, ‘data’, ‘system’, ‘manufacturing’, ‘neural’, and ‘network’ that represent the main contents of the 138 articles published from 2010 to 2023 in deep learning and smart factory. These findings revealed by this quantitative review will be significantly useful for deepening and widening relevant future research on deep learning and smart factory.

Recent years have seen a notable increase in fire incidents in university cafeterias, yet the social attention to these occurrences remains limited. Despite quick responses to these incidents preventing loss of life, the need for large-scale evacuation in such high foot traffic areas can cause significant disruptions, economic losses, and panic among students. The potential for stampedes and unpredictable damage during inadequate evacuations underscores the importance of fire safety and evacuation research in these settings. Previous studies have explored evacuation models in various university environments, emphasizing the influence of environmental conditions, personal characteristics, and behavioral patterns on evacuation efficiency. However, research specifically focusing on university cafeterias is scarce. This paper addresses this gap by employing Pathfinder software to analyze fire spread and evacuation safety in a university cafeteria. Pathfinder, an advanced emergency evacuation assessment system, offers realistic 3D simulations, crucial for intuitive and scientific evacuation analysis. The studied cafeteria, encompassing three floors and various functional areas, often exceeds a capacity of 1500 people, primarily students, during peak times. The study includes constructing a model of the cafeteria in Pathfinder and analyzing evacuation scenarios under different fire outbreak conditions on each floor. The paper sets standard safe evacuation criteria (ASET > RSET) and formulates three distinct evacuation scenarios, considering different fire outbreak locations and initial evacuation times on each floor. The simulation results reveal the impact of the fire's location and the evacuation preparation time on the overall evacuation process, highlighting that fires on higher floors or longer evacuation preparation times tend to reduce overall evacuation time.In conclusion, the study emphasizes a multifaceted approach to improve evacuation safety and efficiency in educational settings. Recommendations include expanding staircase widths, optimizing evacuation routes, conducting regular drills, strengthening command during evacuations, and upgrading emergency facilities. The use of information and communication technology for managing emergencies is also suggested. These measures collectively form a comprehensive framework for ensuring safety in educational institutions during fire emergencies.

According to the actual situation of a university, the emergency evacuation simulation is modeled based on the physical sign, evacuation speed and personnel ratio using the pathfinder software.The experimental construction export utilization rate is compared with the preliminary simulation scenario. The simulation results show that the utilization rate of evacuation stairs and evacuation exits is significantly improved.The optimized solution can provide the most effective evacuation passage, and the research results can provide the basis for the rational planning and management of evacuation passage in university experiment building.

Chinese high-rise hotels are large in size, densely populated, and have a lot of combustibles. Once a fire occurs, the fire and smoke spread rapidly, and once a fire accident occurs, it is easy to cause a large number of deaths. Fires have a greater impact on special populations such as elderly and children who move slowly. At present, research mainly focuses on the impact of high-rise building structures on evacuation consequences, but there is very little research on the safety evacuation consequences of elderly people and children in high-rise hotels. This paper focuses on the elderly and children living in high-rise hotels in China. We studied three scenarios in which the elderly and children were placed on high floors, middle floors, and low floors. For the above three scenarios, use pathfinder software for simulation, According to the simulation results, when the elderly and children are mainly concentrated in the lower floors (2nd and 3rd floors), the evacuation time is the shortest, 147 seconds. The evacuation time for the elderly and children on the middle floor (6th and 7th floors) is the longest, at 191.5 seconds. Compared to being placed on high floors, safely staying on low floors for all ages reduces evacuation time by 44.5 seconds and improves evacuation efficiency by 23.24%. The final safety evacuation plan is that in daily safety management, hotels should arrange elderly and children occupants on lower floors as much as possible to reduce the total evacuation time and improve personnel evacuation efficiency. This has great guiding significance in the safety management of high-rise hotels.

In recent years, the frequent occurrence of fire accidents in university libraries has posed significant threats to the safety of students' lives and property, alongside negative social impacts. Accurately analyzing the factors affecting evacuation during library fires and proposing optimized measures for safe evacuation is thus crucial. This paper utilizes a specific university library as a case study, simulating fire evacuation scenarios using the Pathfinder software, to assess and validate evacuation strategies and propose relevant optimizations. Pathfinder, developed by Thunderhead Engineering in the United States, is an intuitive and straightforward personnel emergency evacuation assessment system, offering advanced visualization interfaces and 3D animation effects. This study aims to construct evacuation models and perform simulation analysis for the selected university library using Pathfinder. The library's structural layout, people flow characteristics, and the nature of fire and smoke spread are considered in the analysis. Additionally, evacuation scenarios involving different fire outbreak locations and the status of emergency exits are examined. The findings underscore the importance of effective evacuation in fire situations, highlighting how environmental conditions, individual characteristics, and behavioral patterns significantly influence evacuation efficiency. Through these investigations, the study enhances understanding and optimization of evacuation strategies in fire scenarios, thereby improving safety and efficiency. The research not only provides concrete and practical guidelines for building design, management, and emergency response planning in libraries but also offers valuable insights for the design and management of effective evacuation systems in buildings, crucial for ensuring occupant safety and minimizing loss of life in potential hazard situations

In this paper, we compare and analyze the evacuation time required at a general hospital using an evacuation simulation program and propose an optimized procedure to improve safety. The paper analyzed the evacuation time of all occupants, including patients who cannot evacuate by themselves. The following four cases were analyzed in order: the width of evacuation stairs is 120cm, 130cm, 140cm, and 150cm. The results of the evacuation simulation showed that the total evacuation time is 1998s, 1796s, 1651s, and 1161s, respectively. For every 10cm increase in the width of the evacuation stairs, the evacuation time decreases by 202s, 145s, and 91s in sequence. The evacuation time decreases as the width of the evacuation stairs increases. However, the rate of reduction in evacuation time decreases. Therefore, simply increasing the width of evacuation stairs cannot significantly improve evacuation efficiency, and it is necessary to choose an appropriate width of evacuation stairs. In addition, all four cases simulations display that after 600 seconds, the evacuees are concentrated in two evacuation stairs, while there are very few evacuees in the other stairs. To solve this problem, it is necessary to disperse the movement route and consider multiple avoidance methods.

The purpose is to evaluate evacuation safety by simulating the toxic effects of hydrogen fluoride leaks in areas surrounding national industrial complexes and to suggest alternatives for areas that do not satisfy evacuation safety. For human casualties caused by hydrogen fluoride leakage accidents, Available Safe Egress Time (ASET) is calculated by the toxic effects quantified with the Areal Locations of Hazardous Atmospheres (ALOHA), an off-site consequence assessment program. The Required Safe Egress Time (RSET) is calculated through Pathfinder, an evacuation simulation program. Evacuation safety is assessed by comparing ASET and RSET. The ALOHA program was used to evaluate the time to reach AEGL-2 concentration in 12 scenarios. The Pathfinder program was used to assess the total evacuation time of the high school among specific fire-fighting objects. Of the 12 accident scenarios, ASET was larger than RSET in the worst-case scenarios 1 and 9. For the remaining 10 accident scenarios, the ASET is smaller than the RSET, so we found that evacuation safety is not guaranteed, and countermeasures are required. Since evacuation safety is not satisfactory, we proposed to set up an evacuation area equipped with positive pressure equipment and air respirators inside specific fire-fighting objects such as the high school.