• Geomechanics and Engineering
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• v.23 no.4
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• pp.327-338
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• 2020

The role of precise prediction of subsurface fluids and discrimination among them cannot be ignored in reservoir characterization and petroleum prospecting. A suitable rock physics model should be build for the extraction of valuable information form seismic data. The main intent of current work is to present a rock physics model to analyze the characteristics of seismic wave propagating through a cracked porous rock saturated by a three phase fluid. Furthermore, the influence on wave characteristics due to variation in saturation of water, oil and gas were also analyzed for oil and water as wet cases. With this approach the objective to explore wave attenuation and dispersion due to wave induce fluid flow (WIFF) at seismic and sub-seismic frequencies can be precisely achieved. We accomplished our proposed approach by using BISQ equations and by applying appropriate boundary conditions to incorporate heterogeneity due to saturation of three immiscible fluids forming a layered system. To authenticate the proposed methodology, we compared our results with White's mesoscopic theory and with the results obtained by using Biot's poroelastic relations. The outcomes reveals that, at low frequencies seismic wave characteristics are in good agreement with White's mesoscopic theory, however a slight increase in attenuation at seismic frequencies is because of the squirt flow. Moreover, our work crop up as a practical tool for the development of rock physical theories with the intention to identify and estimate properties of different fluids from seismic data.

• Wind and Structures
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• v.36 no.4
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• pp.277-292
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• 2023

Low-rise structures are generally immersed within the roughness layer of the atmospheric boundary layer flows and represent the largest class of the structures for which wind loads for design are being obtained from the wind standards codes of distinct nations. For low-rise buildings, wind loads are one of the decisive loads when designing a roof. For the case of cylindrical roof structures, the information related to wind pressure coefficient is limited to a single span only. In contrast, for multi-span roofs, the information is not available. In this research, the numerical simulation has been done using ANSYS CFX to determine wind pressure distribution on the roof of low-rise cylindrical structures arranged in rectangular plan with variable spacing in accordance with building width (B=0.2 m) i.e., zero, 0.5B, B, 1.5B and 2B subjected to different wind incidence angles varying from 0° to 90° having the interval of 15°. The wind pressure (P) and pressure coefficients (C_pe) are varying with respect to wind incidence angle and variable spacing. The results of present numerical investigation or wind induced pressure are presented in the form of pressure contours generated by Ansys CFD Post for isolated as well as variable spacing model of cylindrical roofs. It was noted that the effect of wind shielding was reducing on the roofs by increasing spacing between the buildings. The variation pf Coefficient of wind pressure (C_pe) for all the roofs have been presented individually in the form of graphs with respect to angle of attacks of wind (AoA) and variable spacing. The critical outcomes of the present study will be so much beneficial to structural design engineers during the analysis and designing of low-rise buildings with cylindrical roofs in an isolated as well as group formation.

• Journal of Korean Critical Care Nursing
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• v.17 no.1
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• pp.30-43
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• 2024

Purpose : Post-intensive care syndrome (PICS) is characterized by a constellation of mental health, physical, and cognitive impairments, and is recognized as a long-term sequela among survivors of intensive care units (ICUs). The objective of this study was to explore the impact of intensive care experience (ICE) on the development of PICS in individuals surviving critical care. Methods : This secondary analysis utilized data derived from a prospective, multicenter cohort study of ICU survivors. The cohort comprised 143 survivors who were enrolled between July and August 2019. The original study's participants completed the Korean version of the ICE questionnaire (K-ICEQ) within one week following discharge from the ICU. Of these, 82 individuals completed the PICS questionnaire (PICSQ) three months subsequent to discharge from hospital. The influence of ICE on the manifestation of PICS was examined through Partial Least Squares-Structural Equation Modeling (PLS-SEM). Result : The R² values of the final model ranged from 0.35 to 0.51, while the Q² values were all greater than 0, indicating adequacy for prediction of PICS. Notable pathways in the relationship between the four ICE dimensions and the three PICS domains included significant associations from 'ICE-awareness of surroundings' to 'PICS-cognitive', from 'ICE-recall of experience' to 'PICS-cognitive', and from 'ICE-frightening experiences' to 'PICS-mental health'. Analysis found no significant moderating effects of age or disease severity on these relationships. Additionally, gender differences were identified in the significant pathways within the model. Conclusion : Adverse ICU experiences may detrimentally impact the cognitive and mental health domains of PICS following discharge. In order to improve long-term outcomes of individuals who survive critical care, it is imperative to develop nursing interventions aimed at enhancing the ICU experience for patients.

• Toxicological Research
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• v.21 no.1
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• pp.1-14
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• 2005

Transgenic mouse models have been introduced and accepted by regulatory bodies as an alternative to carcinogenicity assay models to predict and evaluate chemical carcinogens. The recent research outcomes in transgenic mouse models have made progressive advances in the understanding of chemical carcinogenesis and the evaluation of potential human carcinogens. However, these models still remain to be insufficient assay systems although the insufficiencies have been recognised and are being resolved. Based on up to date information from literature, this review article intends to understand currently accepted transgenic mouse models, issues arising from study design, interpretation of the study, results of validation project and their cancer prediction rate, and further perspectives of cancer assay models from the regulatory view point.

• Journal of Korean Neurosurgical Society
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• v.60 no.6
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• pp.730-737
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• 2017

Objective : Postoperative hydrocephalus is a common complication following craniectomy in patients with traumatic brain injury, and affects patients' long-term outcomes. This study aimed to verify the risk factors associated with the development of hydrocephalus after craniectomy in patients with acute traumatic subdural hemorrhage (tSDH). Methods : Patients with acute traumatic SDH who had received a craniectomy between December 2005 and January 2016 were retrospectively assessed by reviewing the coexistence of other types of hemorrahges, measurable variables on computed tomography (CT) scans, and the development of hydrocephalus during the follow-up period. Results : Data from a total of 63 patients who underwent unilateral craniectomy were analyzed. Postoperative hydrocephalus was identified in 34 patients (54%) via brain CT scans. Preoperative intraventricular hemorrhage (IVH) was associated with the development of hydrocephalus. Furthermore, the thickness of SDH (p=0.006) and the extent of midline shift before craniectomy (p=0.001) were significantly larger in patients with postoperative hydrocephalus. Indeed, multivariate analyses showed that the thickness of SDH (p=0.019), the extent of midline shift (p<0.001) and the coexistence of IVH (p=0.012) were significant risk factors for the development of postoperative hydrocephalus. However, the distance from the midline to the craniectomy margin was not an associated risk factor for postoperative hydrocephalus. Conclusion : In patients with acute traumatic SDH with coexisting IVH, a large amount of SDH, and a larger midline shift, close follow-up is necessary for the early prediction of postoperative hydrocephalus. Furthermore, craniectomy margin need not be limited in acute traumatic SDH patients for the reason of postoperative hydrocephalus.

• Journal of Gastric Cancer
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• v.20 no.4
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• pp.385-394
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• 2020

Purpose: Patients with gastric cancer who receive neoadjuvant therapy are staged before treatment (cStage) and after treatment (ypStage). We aimed to compare the prognostic reliability of cStage and ypStage, alone and in combination. Materials and Methods: Data for all patients who received neoadjuvant therapy followed by surgery for gastric adenocarcinoma from 2004 to 2015 were extracted from the National Cancer Database. Kaplan-Meier (KM)curves were used to model overall survival based on cStage alone, ypStage alone, cStage stratified by ypStage, and ypStage stratified by cStage. P-values were generated to summarize the differences in KM curves. The discriminatory power of survival prediction was examined using Harrell's C-statistics. Results: We included 8,977 patients in the analysis. As expected, increasing cStage and ypStage were associated with worse survival. The discriminatory prognostic power provided by cStage was poor (C-statistic 0.548), while that provided by ypStage was moderate (C-statistic 0.634). Within each cStage, the addition of ypStage information significantly altered the prognosis (P<0.0001 within cStages I-IV). However, for each ypStage, the addition of cStage information generally did not alter the prognosis (P=0.2874, 0.027, 0.061, 0.049, and 0.007 within ypStages 0-IV, respectively). The discriminatory prognostic power provided by the combination of cStage and ypStage was similar to that of ypStage alone (C-statistic 0.636 vs. 0.634). Conclusions: The cStage is unreliable for prognosis, and ypStage is moderately reliable. Combining cStage and ypStage does not improve the discriminatory prognostic power provided by ypStage alone. A ypStage-based prognosis is minimally affected by the initial cStage.

• Journal of Korean Neurosurgical Society
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• v.66 no.6
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• pp.632-641
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• 2023

Central nervous system tumors are identified as tumors of the brain and spinal cord. The associated morbidity and mortality of cerebrospinal tumors are disproportionately high compared to other malignancies. While minimally invasive techniques have initiated a revolution in neurosurgery, artificial intelligence (AI) is expediting it. Our study aims to analyze AI's role in the neurosurgical management of cerebrospinal tumors. We conducted a scoping review using the Arksey and O'Malley framework. Upon screening, data extraction and analysis were focused on exploring all potential implications of AI, classification of these implications in the management of cerebrospinal tumors. AI has enhanced the precision of diagnosis of these tumors, enables surgeons to excise the tumor margins completely, thereby reducing the risk of recurrence, and helps to make a more accurate prediction of the patient's prognosis than the conventional methods. AI also offers real-time training to neurosurgeons using virtual and 3D simulation, thereby increasing their confidence and skills during procedures. In addition, robotics is integrated into neurosurgery and identified to increase patient outcomes by making surgery less invasive. AI, including machine learning, is rigorously considered for its applications in the neurosurgical management of cerebrospinal tumors. This field requires further research focused on areas clinically essential in improving the outcome that is also economically feasible for clinical use. The authors suggest that data analysts and neurosurgeons collaborate to explore the full potential of AI.

• The Journal of Engineering Geology
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• v.34 no.1
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• pp.107-115
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• 2024

This study assesses forest fire susceptibility in Gangwon-do, South Korea, which hosts the largest forested area in the nation and constitutes ~21% of the country's forested land. With 81% of its terrain forested, Gangwon-do is particularly susceptible to wildfires, as evidenced by the fact that seven out of the ten most extensive wildfires in Korea have occurred in this region, with significant ecological and economic implications. Here, we analyze 480 historical wildfire occurrences in Gangwon-do between 2003 and 2019 using 17 predictor variables of wildfire occurrence. We utilized three machine learning algorithms—random forest, logistic regression, and support vector machine—to construct wildfire susceptibility prediction models and identify the best-performing model for Gangwon-do. Forest and soil map data were integrated as important indicators of wildfire susceptibility and enhanced the precision of the three models in identifying areas at high risk of wildfires. Of the three models examined, the random forest model showed the best predictive performance, with an area-under-the-curve value of 0.936. The findings of this study, especially the maps generated by the models, are expected to offer important guidance to local governments in formulating effective management and conservation strategies. These strategies aim to ensure the sustainable preservation of forest resources and to enhance the well-being of communities situated in areas adjacent to forests. Furthermore, the outcomes of this study are anticipated to contribute to the safeguarding of forest resources and biodiversity and to the development of comprehensive plans for forest resource protection, biodiversity conservation, and environmental management.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.6
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• pp.2805-2810
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• 2016

Background: Differentiated thyroid cancer is the most common endocrine malignancy with a generally good prognosis. Knowing long-term outcomes of each patient helps management planning. The study was conducted to develop and validate a clinical prognostic score for predicting disease remission in patients with differentiated thyroid cancer based on patient, tumor and treatment factors. Materials and Methods: A retrospective cohort study of 1,217 differentiated thyroid cancer patients from two tertiary-care hospitals in the Northeast of Thailand was performed. Associations between potential clinical prognostic factors and remission were tested by Cox proportional-hazards analysis in 852 patients (development cohort). The prediction score was created by summation of score points weighted from regression coefficients of independent prognostic factors. Risks of disease remission were estimated and the derived score was then validated in the remaining 365 patients (validation cohort). Results: During the median follow-up time of 58 months, 648 (76.1%) patients in the development cohort had disease remission. Five independent prognostic factors were identified with corresponding score points: duration from thyroid surgery to $^{131}I$ treatment (0.721), distant metastasis at initial diagnosis (0.801), postoperative serum thyroglobulin level (0.535), anti-thyroglobulin antibodies positivity (0.546), and adequacy of serum TSH suppression (0.293). The total risk score for each patient was calculated and three categories of remission probability were proposed: ${\leq}1.628$ points (low risk, 83% remission), 1.629-1.816 points (intermediate risk, 87% remission), and ${\geq}1.817$ points (high risk, 93% remission). The concordance (C-index) was 0.761 (95% CI 0.754-0.767). Conclusions: The clinical prognostic scoring model developed to quantify the probability of disease remission can serve as a useful tool in personalized decision making regarding treatment in differentiated thyroid cancer patients.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.2
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• pp.1-13
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• 2015

The Coupled Model Intercomparison Project Phase 5 (CMIP5), coordinated by the World Climate Research Programme in support of the Intergovernmental Panel on Climate Change (IPCC) AR5, is the most recent, provides projections of future climate change using various global climate models under four major greenhouse gas emission scenarios. There is a wide selection of climate models available to provide projections of future climate change. These provide for a wide range of possible outcomes when trying to inform managers about possible climate changes. Hence, future agrometeorological indicators estimation will be much impacted by which global climate model and climate change scenarios are used. Decision makers are increasingly expected to use climate information, but the uncertainties associated with global climate models pose substantial hurdles for agricultural resources planning. Although it is the most reasonable that quantifying of the future uncertainty using climate change scenarios, preliminary analysis using reasonable factors for selecting a subset for decision making are needed. In order to narrow the projections to a handful of models that could be used in a climate change impact study, we could provide effective information for selecting climate model and scenarios for climate change impact assessment using maximum/minimum temperature, precipitation, reference evapotranspiration, and moisture index of nine Representative Concentration Pathways (RCP) scenarios.