• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.22 no.3
• /
• pp.1-19
• /
• 2023

Bikes have recently emerged as an alternative to carbon neutrality. To understand the demand for public bikes, we endeavored to estimate travel frequency of public bike by considering the intermediate stops. Using the GPS trajectory data of 'Ttareungyi', a public bike service in Seoul, we identified a stay point and estimated travel frequency reflecting population, land use, and physical characteristics. Application of map matching and a stay point detection algorithm revealed that stay point appeared in about 12.1% of the total trips. Compared to a trip without stay point, the trip with stay point has a longer average travel distance and travel time and a higher occurrence rate during off-peak hours. According to visualization analysis, the stay points are mainly found in parks, leisure facilities, and business facilities. To consider the stay point, the unit of analysis was set as a hexagonal grid rather than the existing rental station base. Travel frequency considering the stay point were analyzed using the Zero-Inflated Negative Binomial (ZINB) model. Results of our analysis revealed that the travel frequency were higher in bike infrastructure where the safety of bike users was secured, such as 'Bikepath' and 'Bike and pedestrian path'. Also, public bikes play a role as first & last mile means of access to public transportation. The measure of travel frequency was also observed to increase in life and employment centers. Considering the results of this analysis, securing safety facilities and space for users should be given priority when planning any additional expansion of bike infrastructure. Moreover, there is a necessity to establish a plan to supply bike infrastructure facilities linked to public transportation, especially the subway.

• Health Policy and Management
• /
• v.32 no.4
• /
• pp.347-355
• /
• 2022

Background: Since November 2019, long-term care hospitals have been able to provide patients with discharging programs to support the elderly in the community. This study aimed to identify both patient- and hospital-level factors that affect successful community discharge from long-term care hospitals. Methods: A multilevel logistic regression model was performed using hospitals as a clustering unit. The dependent variable was whether a patient stayed in the community for at least 30 days after discharge from a long-term care hospital. As for the patient-level independent variables, an agreement between a patient and the family about discharge, length of hospital stay, patient category, and residence at discharge were included. The number of beds and the ratio of long-stay patients were selected for the hospital-level factors. The sample size was 1,428 patients enrolled in the discharging program from November 2019 to December 2020. Results: The number of patients who were discharged to the community and stayed at least for 30 days was 532 (37.3%). The intraclass correlation coefficient was 22.9%, indicating that hospital-level factors had a significant impact on successful community discharge. The odds ratio (OR) of successful community discharge increased by 1.842 times when the patients and their families agreed on discharge. The ORs also increased by 3.020 or 2.681 times, respectively when the patients planned to discharge to their own house or their child's house compared to those who didn't have a plan for residence at discharge. The ORs increased by 1.922 or 2.250 times when the hospitals were owned by corporate or private property compared to publicly owned hospitals. The ORs decreased by 0.602 or 0.520 times when the hospital was sized over 400 beds or located in small and medium-sized cities compared to less than 200 bedded hospitals or located in metropolitan cities. Conclusion: The results of the study showed that the patients' and their family's willingness for discharge had a great impact on successful community discharge and the hospital-level factors played a significant role in it. Therefore, it is important to acknowledge and support long-term care hospitals to involve active in the patient discharge planning process.

• Tuberculosis and Respiratory Diseases
• /
• v.86 no.2
• /
• pp.133-141
• /
• 2023

Background: The present study evaluated the association between participation in a rehabilitation program during a hospital stay and 1-year survival of patients requiring at least 21 days of mechanical ventilation (prolonged mechanical ventilation [PMV]) with various respiratory diseases as their main diagnoses that led to mechanical ventilation. Methods: Retrospective data of 105 patients (71.4% male, mean age 70.1±11.3 years) who received PMV in the past 5 years were analyzed. Rehabilitation included physiotherapy, physical rehabilitation, and dysphagia treatment program that was individually provided by physiatrists. Results: The main diagnosis leading to mechanical ventilation was pneumonia (n=101, 96.2%) and the 1-year survival rate was 33.3% (n=35). One-year survivors had lower Acute Physiology and Chronic Health Evaluation (APACHE) II score (20.2±5.8 vs. 24.2±7.5, p=0.006) and Sequential Organ Failure Assessment score (6.7±5.6 vs. 8.5±2.7, p=0.001) on the day of intubation than non-survivors. More survivors participated in a rehabilitation program during their hospital stays (88.6% vs. 57.1%, p=0.001). The rehabilitation program was an independent factor for 1-year survival based on the Cox proportional hazard model (hazard ratio, 3.513; 95% confidence interval, 1.785 to 6.930; p<0.001) in patients with APACHE II scores ≤23 (a cutoff value based on Youden's index). Conclusion: Our study showed that participation in a rehabilitation program during hospital stay was associated with an improvement of 1-year survival of PMV patients who had less severe illness on the day of intubation.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.5B
• /
• pp.529-537
• /
• 2006

This paper investigates the impacts of the drag-related weighting coefficients on mean velocity and turbulence structures. The transport equations for the Reynolds stress of vegetated open-channel flows are derived by using the temporal- and horizontal-averaging scheme. It is found that the total Reynolds stress of vegetated open channel flows consists of the Reynolds stress due to temporally fluctuating velocities and the Reynolds stress due to spatially fluctuating velocities. The drag-related weighting coefficient $C_{fk}$ for the total Reynolds stress component is found to be unit, while the coefficient for the Reynolds stress due to temporally fluctuating velocities can be negligible. This is the reason why very small weighting coefficients in previous studies yield very good agreements with measured data. In other words, the Reynolds stress due to spatially fluctuating velocities remains still unknown, especially due to the large number of measuring locations. Through a developed Reynolds stress model, vegetated open-channel flows are simulated and compared with measured data from the literature. Comparisons reveal that the computed mean flow and Reynolds stress structures are hardly affected by the drag-related weighting coefficients. However, the computed turbulence intensity profiles are significant different with the drag-related weighting coefficients. A budget analysis of the transport equations for the Reynolds stress component is carried to investigate why turbulence intensity is affected by the drag-related weighting coefficients.

• Earthquakes and Structures
• /
• v.23 no.4
• /
• pp.373-384
• /
• 2022

To improve the seismic performance of suspended ceiling structures, various vibration-damping devices have been developed. However, the devices made of metals have a limit in that they cause large deformation and seriously damages the exterior of the suspended ceiling structure from the wall. As a results, their strengthening effect of the suspended ceiling structure was minimal. Thus, this study employed a spring and vibration-proof rubber effectively controlled vibrations without increasing horizontal seismic loads on the ceiling to enhance the seismic resistance of suspended ceiling structures. The objective of the study is to examine the dynamic properties of a seismic damping-isolation unit (SDI) with various details developed. The developed SDI was composed of a spring, embossed rubbers, and prestressed bolts, which were the main factors enhancing the damping effect. The shaking table tests were performed on eight SDI specimens produced with the number of layers of embossed rubber (n_s), presence or absence of a spring, prestressed force magnitude introduced in bolts (f_ps), and mass weight (W_m) as the main parameters. To identify the enhancement effect of the SDI, the dynamic properties of the control specimen with a conventional hanger bolt were compared to those of the SDI specimens. The SDI specimens were effective in reducing the maximum acceleration (A_{c max}), acceleration amplification factor (α_p), relative displacement (δ_R), and increasing the damping ratio (ξ) when compared to the control specimen. The A_{c max}, α_p, and δ_R of the SDI specimens with two rubbers, spring, and f_ps of 0.1f_by, where f_by is the yielding strength of the screw bolt were 57.8%, 58.0%, and 61.9% lower than those of the conventional hanger bolt specimens, respectively, resulting in the highest ξ (=0.127). In addition, the α_p of the SDI specimens was 50.8% lower than those specified in ASCE 7 and FEMA 356. Consequently, to accurately estimate the α_p of the SDI specimens, a simple model was proposed based on the functions of f_ps, stiffness constant of the spring (K), W_m, and n_s.

• Korean Journal of Construction Engineering and Management
• /
• v.24 no.6
• /
• pp.74-80
• /
• 2023

This study examines the characteristics of architectural review opinions as unstructured data, focusing on the most challenging risk for developers of small and medium-sized apartment projects in response to the increasing number of single-person households in Korea. Using multidimensional analysis methods, the study analyzes the review opinions of 25 projects in B City. Correspondence analysis and MDS (Multidimensional Scale) analysis show that, consistent with prior research, the keywords related to 'structure' and 'planning' dominate architectural review opinions in B City. While the MDS model's stress is very poor at 34.4%, correspondence analysis reveals that this is due to the characteristics of unstructured data in architectural reviews. In addition, the non-structured data analyzed in this study, such as architectural review opinions, exhibited a probability distribution with low kurtosis and high skewness, as they involved various combinations and occurrences of data depending on the discretion of the review committee members and the specific formats of different local governments. This often led to the emergence of keywords that differed significantly from commonly mentioned terms. Although the study has some limitations, it provides a foundation for future detailed analysis by identifying the characteristics of architectural review opinions as unstructured data.

• Ecology and Resilient Infrastructure
• /
• v.10 no.4
• /
• pp.161-170
• /
• 2023

We attempted to estimate the carbon accumulation of Hibiscus hamabo and Paliurus ramosissimus, semimangroves native to Jeju Island, by remote sensing and to build an artificial intelligence model that predicts its spatial variation with climatic factors. The aboveground carbon accumulation of semi-mangroves was estimated from the aboveground biomass density (AGBD) provided by the Global Ecosystem Dynamics Investigation (GEDI) lidar upscaled using the normalized difference vegetation index (NDVI) extracted from Sentinel-2 images. In Jeju Island, carbon accumulation per unit area was 16.6 t C/ha for H. hamabo and 21.1 t C/ha for P. ramosissimus. Total carbon accumulation of semi-mangroves was estimated at 11.5 t C on the entire coast of Jeju Island. Random forest analysis was applied to predict carbon accumulation in semi-mangroves according to environmental factors. The deviation of aboveground biomass compared to the distribution area of semi-mangrove forests in Jeju Island was calculated to analyze spatial variation of biomass. The main environmental factors affecting this deviation were the precipitation of the wettest month, the maximum temperature of the warmest month, isothermality, and the mean temperature of the wettest quarter. The carbon accumulation of semi-mangroves predicted by random forest analysis in Jeju Island showed spatial variation in the range of 12.0 t C/ha - 27.6 t C/ha. The remote sensing estimation method and the artificial intelligence prediction method of carbon accumulation in this study can be used as basic data and techniques needed for the conservation and creation of mangroves as carbon sink on the Korean Peninsula.

• Journal of Soil and Groundwater Environment
• /
• v.28 no.6
• /
• pp.71-89
• /
• 2023

A groundwater potential map (GPM) was built for the Nakdonggang River Basin based on ten variables, including hydrogeologic unit, fault-line density, depth to groundwater, distance to surface water, lineament density, slope, stream drainage density, soil drainage, land cover, and annual rainfall. To integrate the thematic layers for GPM, the criteria were first weighted using the Analytic Hierarchical Process (AHP) and then overlaid using the Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) model. Finally, the groundwater potential was categorized into five classes (very high (VH), high (H), moderate (M), low (L), very low (VL)) and verified by examining the specific capacity of individual wells on each class. The wells in the area categorized as VH showed the highest median specific capacity (5.2 m³/day/m), while the wells with specific capacity < 1.39 m³/day/m were distributed in the areas categorized as L or VL. The accuracy of GPM generated in the work looked acceptable, although the specific capacity data were not enough to verify GPM in the studied large watershed. To create GPMs for the determination of high-yield well locations, the resolution and reliability of thematic maps should be improved. Criterion values for groundwater potential should be established when machine learning or statistical models are used in the GPM evaluation process.

• Information Systems Review
• /
• v.25 no.4
• /
• pp.89-112
• /
• 2023

In March 2020, as it was declared a COVID-19 pandemic, various quarantine measures were taken. Accordingly, many changes have occurred in the tourism and hospitality industries. In particular, quarantine guidelines, such as the introduction of non-face-to-face services and social distancing, were implemented in the restaurant industry. For decades, research on restaurant attributes has emphasized the importance of three attributes: atmosphere, service quality, and food quality. Nevertheless, to the best of our knowledge, research on restaurant attributes considering the COVID-19 situation is insufficient. To respond to this call, this study attempted an exploratory approach to classify new restaurant attributes based on understanding environmental changes. This study considered 31,115 online reviews registered in Naverplace as an analysis unit, with 475 general restaurants located in Euljiro, Seoul. Further, we attempted to classify restaurant attributes by clustering words within online reviews through TF-IDF and LDA topic modeling techniques. As a result of the analysis, the factors of "prevention of infectious diseases" were derived as new attributes of restaurants in the context of COVID-19 situations, along with the atmosphere, service quality, and food quality. This study is of academic significance by expanding the literature of existing restaurant attributes in that it categorized the three attributes presented by existing restaurant attributes and further presented new attributes. Moreover, the analysis results have led to the formulation of practical recommendations, considering both the operational aspects of restaurants and policy implications.

• Korean Journal of Radiology
• /
• v.22 no.6
• /
• pp.994-1004
• /
• 2021

Objective: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. Materials and Methods: All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients' needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. Results: While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79-0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77-0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85-0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66-0.88). Conclusion: Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.