• Tunnel and Underground Space
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• v.28 no.6
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• pp.651-669
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• 2018

This study suggests the prediction model to estimate the specific energy of a pick cutter using a gene expression programming (GEP) and particle swarm optimization (PSO). Estimating the performance of mechanical excavators is of crucial importance in early design stage of tunnelling projects, and the specific energy (SE) based approach serves as a standard performance prediction procedure that is applicable to all excavation machines. The purpose of this research, is to investigate the relationship between UCS and BTS, penetration depth, cut spacing, and SE. A total of 46 full-scale linear cutting test results using pick cutters and different values of depth of cut and cut spacing on various rock types was collected from the previous study for the analysis. The Mean Squared Error (MSE) associated with the conventional Multiple Linear Regression (MLR) method is more than two times larger than the MSE generated by GEP-PSO algorithm. The $R^2$ value associated with the GEP-PSO algorithm, is about 0.13 higher than the $R^2$ associated with MLR.

• Korean Journal of Clinical Pharmacy
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• v.31 no.2
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• pp.115-124
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• 2021

Background: With an increase in the number of breast cancer survivors, greater importance is attached to health-related quality of life, particularly pain and symptom control. This study aimed to identify the factors that are associated with pain in cancer patients based on the patterns of prescribing opioid, non-opioid, and adjuvant analgesics. Methods: This analysis included new patients who had developed breast cancer between 2003 and 2012. The degree of pain was analyzed based on the socio-demographic (age, income quintile, number of hospitalizations, and duration of disease), indicator (Body Mass Index; BMI, Charlson Comorbidity Index; CCI, Cumulative Analgesic Consumption Score; CACS), operation (mastectomy, lymph node dissection), and therapy (chemotherapy, radiation therapy), as well as complication-related variable (lymphedema). Results: As for the patterns of prescribing analgesics by stages, non-opioid and opioid analgesics constituted 30.7 and 69.3%, respectively. The mean value and variance of CACS were 5.596 and 12.567, respectively. The factors that significantly affected the degree of pain were age (≥50; IRR: 1.848, 95% CI 1.564-2.184, p=0.000), income quintile (IRR: 0.964, 95% CI 0.938-0.991, p=0.008), BMI (≥ 25; IRR: 1.479, 95% CI 1.222-1.795, p=0.000), CCI (≥ 4; IRR: 1.649, 95% CI 1.344-2.036, p=0.000), and lymphedema (yes; IRR: 1.267, 95% CI 1.006-1.610, p=0.047). Conclusions: It is necessary to develop systematic and comprehensive pain control measures to improve the quality of life for breast cancer survivors, especially for those who are 50 years or older, lie in the lower-income quintile, have BMI of ≥25 and CCI score ≥ 4, or have lymphedema.

• Journal of the Semiconductor & Display Technology
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• v.20 no.2
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• pp.92-97
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• 2021

The importance of processes in cryogenic environments is increasing in a way to address problems such as critical dimension (CD) narrow and bottlenecks in micro-processing. Accordingly, in this paper, we proceed with the design and analysis of Electrostatic Chuck(ESC) and Coolant in cryogenic environments, and present optimal model conditions to provide the temperature distribution analysis of ESC in these environments and the appropriate optimal design. The wafer temperature uniformity was selected as the reference model that the operating conditions of the refrigerant of the liquid nitrogen in the doubled aluminum path were excellent. Design of simulation (DOS) was carried out based on the wheel settings within the selected reference model and the classification of three mass flow and diameter case, respectively. The comparison between factors with p-value less than 0.05 indicates that the optimal design point is when five turns of coolant have a flow rate of 0.3 kg/s and a diameter of 12 mm. ANOVA determines the interactions between the above factor, indicating that mass flow is the most significant among the parameters of interests. In variable selection procedure, Case 2 was also determined to be superior through the two-Sample T-Test of the mean and variance values by dividing five coolant wheels into two (Case 1 : 2+3, Case 2: 3+2). Finally, heat transfer analysis processes such as final difference method (FDM) and heat transfer were also performed to demonstrate the feasibility and adequacy of the analysis process.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Journal of Preventive Medicine and Public Health
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• v.57 no.1
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• pp.55-64
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• 2024

Objectives: This study investigated the prevalence and characteristics of comorbid conditions in patients exposed to ionizing radiation and those who were involved in the Soviet-Afghan war. Methods: This study analyzed the frequency and spectrum of morbidity and comorbidity in patients over a long-term period (30-35 years) following exposure to ionizing radiation at the Semipalatinsk nuclear test site or the Chornobyl nuclear power plant, and among participants of the Soviet-Afghan war. A cohort study, both prospective and retrospective, was conducted on 675 patients who underwent comprehensive examinations. Results: Numerical data were analyzed using the Statistica 6 program. The results are presented as the mean±standard deviation, median, and interquartile range (25-75th percentiles). The statistical significance of between-group differences was assessed using the Student t-test and Pearson chi-square test. A p-value of less than 0.05 was considered statistically significant. We found a high prevalence of cardiovascular diseases, including hypertension (55.0%) and cardiac ischemia (32.9%); these rates exceeded the average for this age group in the general population. Conclusions: The cumulative impact of causal occupational, environmental, and ultra-high stress factors in the combat zone in participants of the Soviet-Afghan war, along with common conventional factors, contributed to the formation of a specific comorbidity structure. This necessitates a rational approach to identifying early predictors of cardiovascular events and central nervous system disorders, as well as pathognomonic clinical symptoms in this patient cohort. It also underscores the importance of selecting suitable methods and strategies for implementing treatment and prevention measures.

• Korean Journal of Environment and Ecology
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• v.36 no.6
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• pp.627-638
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• 2022

This study was conducted at a village fishing farm on 4 peaks on the main island of Jeju Island and 2 peaks on an inhabited island to compare the distribution characteristics of seaweeds along the coast of Jeju Island from May to December 2018. A total of 101 species of seaweeds were surveyed, including 13 species (12.9%) of green algae, 24 species (23.8%) of Phaeophyta, and 64 species (63.4%) of Rhodophyta. The largest number of seaweeds appeared in May and the fewest in October, showing typical features of a temperate sea area. The number of seaweed species that appeared was 66 and 65 species at the water depths of 5 m and 8 m, respectively, and the largest was 74 species at 12 m. The number of seaweeds that appeared by area was the largest at 66 species on Udo Island, an eastern island near Jeju Island, and the lowest at 27 species in Pyoseon-ri, an eastern part of Jeju Island. The important values of emerging species were high in the order of, Ecklonia cavaand Corallina crassissima at 21.1% and 20.3%, respectively, Corallina aberransat 9.2%, Amphora ephedraeaat 6.2%, and Sargassum macrocarpumat 4.4%. Among seaweeds, an average of 11.2 species of coralline algae appeared, and the mean importance value was 32.6% in the sear area. The lowest importance value was 14.7% on Udo Island, and the highest was 41.0% in Pyoseon-ri. The mean ecological evaluation index (EEI) of seaweed colonies ranged from 2.1 to 10. It was the lowest at the water depth of 12 m in Pyoseon-ri in May and June and was 7.3 or higher in other areas, indicating good condition. This study rated the standardized ecological grade I for the water depth of 12 m on Udo Island and grade II for the water depths of 5 m and 8 m in Sagye-ri and on Chujado Island. Grade III was the water depth of 5 m and 12 m in Pyoseon-ri and Guideok 2-ri and the water depth of 5 m and 8 m in Pyeongdae-ri, and grade IV was the water depth of 8 m in Guideok 2-ri.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.6
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• pp.39-47
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• 2010

The coastal area is the place where two conflicting values provide opportunities for recreation, tourism and conservation of the natural environment. Most of these coastal areas have been disregarded in terms of tourism because of the high value placed on natural resources. Management strategy has usually been first on the priority list of natural. resources. However, due to the significant number of visitors, the Important-Satisfaction Analysis(ISA) was applied as a mean of complement since the 1970s. This study analyzed the difference between visitor satisfaction and the importance of the main management strategy targeting visitors to Sun-cheon Bay Ecological Park on weekdays and weekends. Results show that Sun-cheon Bay Ecological Park and the city government have paid a lot of attention and invested a lot of money, but efficiency of publicity didn't come up to their afford. Therefore, we should prepare information facilities, public education facilities and human power. Also needed are visitors' temporal-spacial control to set specific programs and a guide for information education control. It means visitors' company forms change depends on weekday and weekend. In addition, a breeding space for birds should be built for observation, education and exhibition to help meet visitor expectations. Visitors' positive satisfaction might be provided in establishing strategy as a very important measure in limited area. In conclusion, this study might be provided as preliminary data when the management strategy and related guidelines are established through the management priority of coastal regions where importance and satisfaction conflict.

• Journal of Environmental Science International
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• v.19 no.4
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• pp.415-425
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• 2010

The purpose of the present study was to analyze the vegetation structure and distributional characteristics of Abies koreana forests in Mt. Halla, and to provide basicdata for an ecological study on Abies koreana in Mt. Halla. The results of the analysis showed that the mean importance percentage(M.I.P,) of Abies koreana in the Youngsil and Bangaeoreum and the Azalea field were 28.3%, 23.6%, and 46.4%, respectively. The ratios of DBH (diameter at breast height) to height were similar in all region, except in the Azalea field, where Abies koreana of various ages, both young and old, were found. The species diversity (H) of the upper and lower layers in the Youngsil and Bangaeoreum and in the Azalea field were 0.625 and 0.810, 0.731 and 0.848, and 0.342 and 0.757, respectively. A total of 52 community were distributed at locations higher than 1,300m above sea level. The proportions of each community in the whole Abies koreana forest were 56.5%(Azalea field), 11.0% (Youngsil trail at 1,550-1,650 m above sea level), and 8.1%(Janggumok and Kundurewat region). The total area of the Abies koreana forest was calculated to be 795.3ha by combining all the areas of each community. An Abies koreana forest with the largest area was found at locations 1,500-1,600 m above sea level, taking up 38.8% of the total Abies koreana forest area. For the slopes of the distributional area of Abies koreana, 46.1%(highest proportion) of the total area was $10\sim25^{\circ}$, and for the azimuth of the distributional area, 17.4%(the highest proportion) of the total area was $0-45^{\circ}$. The vegetation structure showed large differences between areas. It was found, however, that the distribution was mostly in the areas with a relatively gentle slope. It is suggested that research be done to forecast the possible changes in the differences in the vegetation structures between different areas caused by climate changes. In addition, there is a need to monitor the Abies koreana and alpine plants in the subalpine zones of Mt. Halla, which are sensitive to climate change, to obtain the basic data that are necessary for the protection and maintenance of the ecosystem.

• Korean Journal of Environment and Ecology
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• v.27 no.4
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• pp.462-472
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• 2013

This study was carried out to provide a basic data for preservation of Pinus desiflora forest as cultural landscape forest by analyzing characteristics of plant community of P. desiflora forest in Geumjeongsan(mountatin) in Busan city. In order to analyze plant community of P. densiflora in Geumjeongsan, we set up 10 study plots inside and 8 plots outside of Geumjeongsansung(mountain fortress, hereinafter 'Sansung')(unit area: $400m^2$), a total of 18 plots. TWINSPAN analysis divided these 18 study plots into 6 communities which are Querqus serrata-P. desiflora community, P. desiflora community, P. desiflora-Q. serrata community, P. thunbergii-P. densiflora community, P. densiflora-P. thubergii-Q. acutissima community, and P. densiflora-Platycarya strobilacea community. Importance Percentage (I.P.) of each area and DBH class distribution of main species showed that P. densiflora community would succeed to Q. serrata community or C. tschonoskii community. Analysis on tree age found out that communities in the Sansung were 32~37 years old and those outside the Sansung were 44~57 years old. Shannon's species diversity index ranged from 0.4826 to 1.2499. Regarding correlation between species, P. densiflora had negative correlation with Styrax japonica. Based on abovementioned result we expected ecological succession from P. densiflora community to Q. serrata community inside of the Sansung. Outside the Sansung, succession from P. densiflora-P. thunbergii community to C. tschonoskii-Q. serrata community was expected. In order to manage P. densiflora forest as cultural landscape forest, Q. spp in the understory and shrub layer and deciduous broad-leaved arboreal trees should be managed. Tree crown management of deciduous broad-leaved trees in competition with P. desiflora, is also required.