International Journal of Internet, Broadcasting and Communication
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v.16
no.2
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pp.209-217
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2024
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.
At 0843 UTC 30 May 2021, a commercial aircraft encountered severe turbulence at z = 11.5 km associated with the rapid development of Mesoscale Convective System (MCS) in the Gyeonggi Bay of Korea. To investigate the generation mechanisms of Near-Cloud Turbulence (NCT) near the MCS, Weather Research and Forecasting model was used to reproduce key features at multiple-scales with four nested domains (the finest ∆x = 0.2 km) and 112 hybrid vertical layers. Simulated subgrid-scale turbulent kinetic energy (SGS TKE) was located in three different regions of the MCS. First, the simulated NCT with non-zero SGS TKE at z = 11.5 km at 0835 UTC was collocated with the reported NCT. Cloud-induced flow deformation and entrainment process on the downstream of the overshooting top triggered convective instability and subsequent SGS TKE. Second, at z = 16.5 km at 0820 UTC, the localized SGS TKE was found 4 km above the overshooting cloud top. It was attributed to breaking down of vertically propagating convectively-induced gravity wave at background critical level. Lastly, SGS TKE was simulated at z = 11.5 km at 0930 UTC during the dissipating stage of MCS. Upper-level anticyclonic outflow of MCS intensified the environmental westerlies, developing strong vertical wind shear on the northeastern quadrant of the dissipating MCS. Three different generation mechanisms suggest the avoidance guidance for the possible NCT events near the entire period of the MCS in the heavy air traffic area around Incheon International Airport in Korea.
KSII Transactions on Internet and Information Systems (TIIS)
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v.18
no.6
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pp.1500-1517
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2024
With the rapid development of the tourism economy, large-scale construction of tourist attractions to achieve resource utilization and ensure the healthy development of the tourism industry has become a hot topic. However, there are still issues with resource utilization and coordinated management in the economic development of the tourism industry, which in turn affects the coordinated development of the tourism industry economy. Therefore, this study utilizes self-organization theory to explore the structure, organizational mechanism, conditional driving force of evolution, and evolutionary operation mechanism of the tourism system, analyze the current tourism situation in Hunan Province and related regions, and construct a self-organization evolution model of the tourism system. The result shows that the cumulative variance contribution rate of tourism areas in Hunan Province is 78.8%, with Zhangjiajie having the highest industrial management factors and tourism resource levels in the province, with 1.6 and 3.2 respectively. Hunan Province has abundant tourism resources but overall uneven development, with a comprehensive score of -1.03. Therefore, it is necessary to leverage the coordination advantages of various departments and industries to promote sustainable and healthy development of tourism areas. The self-organizing evolution of the tourism system not only discovers the current problems of the tourism industry, but also provides theoretical support and mechanism suggestions for the tourism system.
The prediction of VIV amplitude is essential for the design and fatigue life estimation of steel tubes in tubular transmission towers. Limited to costly and time-consuming traditional experimental and computational fluid dynamics (CFD) methods, a machine learning (ML)-based method is proposed to efficiently predict the VIV amplitude of steel tubes in transmission towers. Firstly, by introducing the first-order mode shape to the two-dimensional CFD method, a simplified response analysis method (SRAM) is presented to calculate the VIV amplitude of steel tubes in transmission towers, which enables to build a dataset for training ML models. Then, by taking mass ratio M*, damping ratio ξ, and reduced velocity U* as the input variables, a Kriging-based prediction method (KPM) is further proposed to estimate the VIV amplitude of steel tubes in transmission towers by combining the SRAM with the Kriging-based ML model. Finally, the feasibility and effectiveness of the proposed methods are demonstrated by using three full-scale steel tubes with C-shaped, Cross-shaped, and Flange-plate joints, respectively. The results show that the SRAM can reasonably calculate the VIV amplitude, in which the relative errors of VIV maximum amplitude in three examples are less than 6%. Meanwhile, the KPM can well predict the VIV amplitude of steel tubes in transmission towers within the studied range of M*, ξ and U*. Particularly, the KPM presents an excellent capability in estimating the VIV maximum amplitude by using the reduced damping parameter SG.
Purpose - This study applies the traditional Structure-Conduct-Performance (SCP) model from industrial organization theory to investigate the relationship between market structure and performance in China's banking industry. Design/methodology/approach - For analysis, financial data from the People's Bank of China's "China Financial Stability Report" and financial reports of 6 state-owned banks and 11 joint-stock banks for the period 2010 to 2021 were collected to create a balanced panel dataset. The study employs panel fixed-effects regression analysis to assess the impact of changes in market structure and ownership structure on performance variables including return on asset, profitability, costs, and non-performing loan ratios. Findings - Empirical findings highlight significant differences in the effects of market structure between state-owned and joint-stock banks. Notably, increased market competition positively correlates with higher profits for state-owned banks and with lower costs for joint-stock banks. Research implications or Originality - State-owned banks demonstrate larger scale and stability, yet they struggle to respond effectively to market shifts. Conversely, joint-stock banks face challenges in raising profitability against competitive pressures. Additionally, the study emphasizes the importance for Chinese banks to strengthen risk management due to the increase of non-performing loans with competition. The results provide insights into reform policies for Chinese banks regarding the involvement of private sector in the context of market liberalization process in China.
In Korea, as part of the Green New Deal project toward a carbon-neutral society, it is necessary to build a climate-resilient urban environment to green the city, space, and living infrastructure. To this end, SWMM-ING was improved and the model was modified to analyze the carbon reduction effect. In addition, I plan to select target watersheds where urbanization is rapidly progressing and evaluate runoff, non-point pollution, and carbon reduction effects to conduct cost estimation and optimal design review for domestic rainwater circulation green infrastructure. In this study, green infrastructure facilities were selected using SWMM-ING. Various scenarios were presented considering the surface area and annual cost of each green infrastructure facility, and The results show that the scenario derived through the APL2 method was selected as the optimal scenario. In this optimal scenario, a total facility area of 190,517.5 m2 was applied to 7 out of 30 subwatersheds to achieve the target reduction. The target reduction amount was calculated a 23.50 % reduction in runoff and a 26.99 % reduction in pollutant load. Additionally, the annual carbon absorption was analyzed and found to be 385,521 kg/year. I aim to achieve additional carbon reduction effects by achieving the goal of reducing runoff and non-point pollution sources and analyzing annual carbon absorption. Moreover, considering the scale-up of these interventions across the basin, it is believed that an objective assessment of economic viability can be conducted.
Kamel Ghobadi;AhmadAli Eslami;Asiyeh Pirzadeh;Seyed Mohammad Mazloomi;Fatemeh Hosseini
Safety and Health at Work
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v.14
no.4
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pp.358-367
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2023
Background: This study aimed to develop and assess the psychometric features of the Physical Activity at Workplace Questionnaire (PAWPQ) based on the Social Cognitive Theory (SCT) to evaluate employees' physical activity (PA) behaviors at the workplace. Methods: This psychometric cross-sectional study was conducted on 455 employees working in one of the gas refineries in Iran. The participants were selected using the proportional stratified sampling method in 2019. The data collection tools were a demographic information questionnaire, the short form of the International Physical Activity Questionnaire (IPAQ), and a questionnaire developed based on the SCT, whose psychometric features were confirmed in terms of validity and reliability. Data were analyzed using SPSS22 and AMOS20 software. Results: The first version of PAWPQ-SCT had 74 items. After evaluating content and face validity, nine items were removed. The results of the content validity index (0.98), content validity ratio (0.86), and impact score (3.62) were acceptable for the whole instrument. In exploratory factor analysis, after removing seven items-58-item final version of the scale-six factors could explain 73.54% of the total variance. The results of structural equation modeling showed the acceptable fit of the model into the data (RMSEA = 0.052, CFI = 0.917, NFI = 0.878, TLI = 0.905, IFI = 0.917, CMIN/DF = 2.818). Cronbach's alpha coefficient and Intraclass Correlation were 0.90 and 0.86, respectively. Conclusion: This study confirmed that the psychometric features of the 58-item final version of PAWPQSCT constructs were acceptable in a sample of Iranian employees. This questionnaire can be used as a valid and reliable tool to evaluate Iranian employees' PA behaviors and develop effective educational interventions for workers and managers.
Ittaka Aldini;Adhistya E. Permanasari;Risanuri Hidayat;Andri Ramdhan
Ocean Systems Engineering
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v.14
no.1
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pp.85-99
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2024
Ocean surface currents have an essential role in the Earth's climate system and significantly impact the marine ecosystem, weather patterns, and human activities. However, predicting ocean surface currents remains challenging due to the complexity and variability of the oceanic processes involved. This review article provides an overview of the current research status, challenges, and opportunities in the prediction of ocean surface currents. We discuss the various observational and modelling approaches used to study ocean surface currents, including satellite remote sensing, in situ measurements, and numerical models. We also highlight the major challenges facing the prediction of ocean surface currents, such as data assimilation, model-observation integration, and the representation of sub-grid scale processes. In this article, we suggest that future research should focus on developing advanced modeling techniques, such as machine learning, and the integration of multiple observational platforms to improve the accuracy and skill of ocean surface current predictions. We also emphasize the need to address the limitations of observing instruments, such as delays in receiving data, versioning errors, missing data, and undocumented data processing techniques. Improving data availability and quality will be essential for enhancing the accuracy of predictions. The future research should focus on developing methods for effective bias correction, a series of data preprocessing procedures, and utilizing combined models and xAI models to incorporate data from various sources. Advancements in predicting ocean surface currents will benefit various applications such as maritime operations, climate studies, and ecosystem management.
Hyun Seok Lee;Ji Hyung Nam;Dong Jun Oh;Yeo Rae Moon;Yun Jeong Lim
The Korean journal of internal medicine
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v.39
no.2
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pp.261-271
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2024
Background/Aims: Mucoprotective agents, such as eupatilin, are often prescribed to prevent gastrointestinal (GI) bleeding in addition to an acid suppressant despite the absence of a large-scale study. We evaluated the additional effect of eupatilin on the prevention of GI bleeding in both the upper and lower GI tract in concomitant aspirin and acid suppressant users using the nationwide database of national claims data from the Korean National Health Insurance Service (NHIS). Methods: An aspirin cohort was constructed using the NHIS claims data from 2013 to 2020. Patients who manifested with hematemesis, melena, or hematochezia were considered to have GI bleeding. A Cox proportional hazards regression model was used to determine the risk factors for GI bleeding associated with the concomitant use of GI drugs and other covariates among aspirin users. Results: Overall, a total of 432,208 aspirin users were included. The concurrent use of an acid suppressant and eupatilin (hazard ratio [HR] = 0.85, p = 0.016, vs. acid suppressant only) was a statistically significant preventive factor for GI bleeding. Moreover, a more than 3-month duration (HR = 0.88, p = 0.030) of acid suppressant and eupatilin prescription (vs. acid suppressant only) was a statistically significant preventive factor for GI bleeding. Conclusions: Eupatilin administration for ≥ 3 months showed additional preventive effect on GI bleeding in concomitant aspirin and acid suppressant users. Thus, cotreatment with eupatilin with a duration of 3 months or longer is recommended for reducing GI bleeding among aspirin plus acid suppressant users.
Seung-Bin Woo;Chang-Young Lee;Chang-Hyun Kim;Min-Yong Kwon;Young San Ko;Jong-Ha Lee;Jin-Chul Heo;Sae Min Kwon
Journal of Cerebrovascular and Endovascular Neurosurgery
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v.25
no.1
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pp.19-27
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2023
Objective: The purpose of this study was to determine the efficacy of a 3D-printed aneurysm simulation model (3DPM) in educating patients and improving physicians' comprehension and performance. Methods: This prospective study involved 40 patients who were diagnosed with unruptured intracranial aneurysms (UIAs) and scheduled for surgical clipping or endovascular coiling and randomly divided into two groups (the 3DPM group and the non-3DPM group). The 3DPM was used in preoperative consultation with patients and intraoperatively referenced by surgeons. The patients, 7 neurosurgical residents, and 10 surgeons completed questionnaires (5-point Likert scale) to determine the usefulness of the 3DPM. Results: Patients in the 3DPM group had significantly higher scores in terms of their understanding of the disease (mean 4.85 vs. 3.95, p<0.001) and the treatment plan (mean 4.85 vs. 4.20, p=0.005) and reported higher satisfaction during consultation (5.0 vs. 4.60, p=0.036) than patients in the non-3DPM group. During patient consultation, 3DPMs were most useful in improving doctor-patient communication (mean 4.57, range 4-5). During clipping surgery, the models were most useful in assessing adjacent arteries (mean 4.9, range 4-5); during endovascular coiling, they were especially helpful in microcatheter shaping (mean 4.7, range 4-5). Conclusions: In general, 3DPMs are beneficial in educating patients and improving the physician's performance in terms of surgical clipping and endovascular coiling of UIAs.
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