• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.05a
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• pp.228-229
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• 2018

In the construction industry, we have set goals such as reduction of greenhouse gas emissions and reduction of energy use. In particular, reduction of CO₂ emissions in the concrete manufacturing process, reduction of industrial waste and industrial wastes into concrete The zero-emission level of reuse as a resource is under review. On the other hand, the cost of stone is expensive due to small quantity production of domestic stone production in order, it is difficult to carry and construct with heavy material, and it takes long time to construct. In order to solve the shortage of supply and demand of natural stone, various kinds of stone powder, artificial stone made by putting stone texture on the surface of mortar or concrete, fiber reinforced plate, tiles and the like are increasingly used. In this study, the artificial stone using slag and recycled aggregate instead of natural stone was fabricated and the strength characteristics were evaluated for its applicability and feasibility.

• Journal of Korea Trade
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• v.24 no.8
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• pp.1-18
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• 2020

Purpose - This paper examines how differently output and input tariffs affect domestic employment across industrial characteristics of comparative advantage such as labor quality and capital intensity. Design/methodology - The paper focuses on 453 Korean industries from 2007 to 2014 because Korea is a typical example of a natural resource-scarce open economy and experienced the transition of the export pattern from labor intensity to technology intensity during this period. Findings - The results show that input tariff reduction stimulated total employment, focusing on the early 2010s, while the effects of output tariff reduction were statistically insignificant in general. However, the stimulation effects of output tariff reduction on employment were found in comparative advantage industries with greater labor quality and capital intensity. As for input tariff reduction, its stimulation effects on employment were more prominent in comparative disadvantage industries with lower labor quality and capital intensity. Originality/value - These results provide significant implications for natural resource-scarce open economies which are experiencing the transition of the export pattern from labor intensity to technology intensity and the unequal distribution of income after trade liberalization: imported intermediate inputs has become increasing important, leading to trade effects on employment and alleviation of income inequality.

• Resources Recycling
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• v.30 no.3
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• pp.47-54
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• 2021

Palladium present in colloidal-type plated spent catalyst solution that is used in electroless plating process has not been recovered but discharged as wastewater so far. Recyclig of paladium in colloidal-type plated spent catalyst solution is achieved with this study. This study presents the estimation of resource consumption and GHG emissions during the recycling and disposal of palladium in the plated spent catalyst solution using life cycle assessment. The reduction of resources and GHG are also estimated. Based on the palladium amount of 1 kg during disposal, the GHG emission amount was estimated to be 9.67E+03 kgCO₂eq., and the amount of resource consumption was 3.94E+01 kgSb-eq. However, GHG emission was 1.96E+03 kgCO₂eq., and the amount of resource consumption was 1.54E+01 kgSb-eq. during recycling. Considering the major substances affecting GHG emissions and amount of resource consumption, CO₂ was found to significantly affect GHG emissions, accounting for 91.42% in disposal and 98.37% in recycling. The major substance affecting the amount of resource consumption was hard coal, which accounted for 40.63% in disposal and 60.73% in recycling. Upon recycling 1 kg palladium, 8,967.17 kgCO₂eq. of greenhouse gas emission was reduced, while the resource consumption was reduced to 10.10 kg Sb-eq. In addition, the direct palladium resource reduction rate due to palladium recycling was 50%.

• Korea Trade Review
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• v.47 no.4
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• pp.125-135
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• 2022

This study is to analyze the economic effects of tariff reduction using computable general equilibrium(CGE) model. We set up the social accounting matrix for five-base equilibrium year. Our main findings are as follows. First, the impact of tariff reduction on GDP was different from time to time. It meas that the differentiated economics structure was affected by tariff reduction. As our economic grew up, the impact of tariff reduction was measured much higher. Second, until 1995 the impact of tariff reduction on total export and import was increased, then while 1995 the increase was dropped. This is because we reduced the tariff by the WTO negotiations. Third, the tariff reduction affected the price of imported goods, so it contributed to substitute effects between domestic and imported goods. According to these results, we found out the importance of the linkage between the tariff reduction and economic structure.

• Environmental and Resource Economics Review
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• v.30 no.2
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• pp.169-205
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• 2021

This study quantitatively analyzes the impact of greenhouse gas (GHG) emissions reduction policies on the global economy. To this end, we develop a multi-national and multi-industry static computational general equilibrium model that includes three components-GHG emissions from production, disutility due to GHG emissions, and governments' GHG emissions reduction policies. Then we calibrate the model with the relevant data and solve for the equlibrium using the most recent methodology (exact hat algebra). We find that the strengthening of unilateral GHG emissions reduction policies for each country reduces carbon emissions from domestic producers, but does not necessarily reduce global carbon emissions as production is relocated to other countries. On the other hand, we can reduce GHG emissions when all major countries simultaneously implement the strengthened reduction policies proposed by the OECD (2016). Our results imply that aligned reduction efforts of major countries are necessary to reduce global GHG emissions.

• Journal of Animal Science and Technology
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• v.60 no.2
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• pp.3.1-3.9
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• 2018

Background: Poultry breeding has increased by 306% in Korea, inevitably increasing the production of manure which may contribute to environmental pollution. The nutrients (NP) in the manure are essential for crop cultivation and soil fertility when applied as compost. Excess nutrients from manure can be accumulated on the land and can lead to eutrophication. Therefore, a nutrient load on the finite land should be calculated. Methods: This study calculates the nutrient production from Korean poultry by investigating 11 broiler and 16 laying hen farms. The broiler manure was composted using deep litter composting while for layer deep litter composting, drying, and simple static pile were in practice. The effect of weight reduction and storing period during composting was checked. Three weight reduction cases of compost were constructed to calculate nutrient loading coefficients (NLCs) using data from; i) farm investigation, ii) theoretical P changes (${\Delta}P=0$), and iii) dry basis. Results: During farm investigation of broiler and layer with deep litter composting, there was a 68 and 21% N loss whereas 77 and 33% P loss was found, respectively. In case of layer composting, a loss of 10-56% N and a 52% P loss was observed. Drying manure increased the P concentrations therefore NLCs calculated using dry basis that showed quite higher reductions (67% N; 53% P). Nutrient loss from farm investigation was much higher than reported by Korean Ministry of Environment (ME). Conclusions: Nutrients in manure are decreased when undergo storing or composting process due to microbial action, drying, and leaching. The nutrient load applied to soil is less than the fresh manure, hence the livestock manure management and conservation of environment would be facilitated.

• Bulletin of the Korean Chemical Society
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• v.35 no.10
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• pp.2995-3000
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• 2014

A sensitive concentration method utilising modified gas-purge microsyringe extraction (GP-MSE) was developed. Concentration (reduction in volume) to a microlitre volume was achieved. PAHs were utilised as semivolatile analytes to optimise the various parameters that affect the concentration efficiency. The injection rate and temperature were the key factors that affected the concentration efficiency. An efficient concentration (75.0-96.1%) of PAHs was obtained under the optimised conditions. The method exhibited good reproducibility (RSD values that ranged from 1.5 to 9.0%). The GP-MSE concentration method enhances the volume reduction (concentration factor), leading to a low method detection limit ($0.5-15ngL^{-1}$). Furthermore, this method offers the advantage of small-volume sampling, enabling even the detection of diurnal hourly changes in the concentration of PAHs in ambient air. Utilising this method in combination with GC-MS, the diurnal hourly flux of PAHs from the gas phase of ambient air was measured. Indeed, the proposed technique is a simple, fast, low-cost and environmentally friendly.

• Journal of Oriental Neuropsychiatry
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• v.35 no.2
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• pp.177-189
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• 2024

Objectives: This study aimed to evaluate the impact of Resource Mindfulness on participants' emotional state, focusing on anxiety and stress reduction and to explore the importance of psychological resources in this process. The effectiveness of Resource Mindfulness was investigated through both quantitative and qualitative analyses, examining its influence on subjective distress, mindfulness, core emotions, and identification of personal resources. Methods: This study involved 27 participants, including Korean medicine students, psychological counselors, clinical psychologists, and Korean medicine specialists, who attended a 2-night, 3-day M&L psychological support education program from July 2 to July 4, 2022. Participants were informed of the study's purpose and procedures. They provided written consent. Quantitative measures included Subjective Units of Disturbance Scale (SUDS), Five Facet Mindfulness Questionnaire (FFMQ), and Core Seven-Emotions Inventory Short Form (CSEI-s). Qualitative analysis was conducted using the "Drawing the Rooms of the Mind" technique. Pre- and post-program assessments were conducted to compare changes in subjective distress, mindfulness, and core emotions. Data were analyzed using paired t-tests and qualitative content analysis methods. Results: Significant reductions in subjective distress and improvements in mindfulness components were observed. Core emotions showed significant decreases in negative emotions. Qualitative analysis revealed increased tranquility, relief, and confidence, with resourceful places often being nature-related. Conclusions: Resource Mindfulness effectively reduced anxiety and stress and enhanced self-awareness and self-efficacy. It is useful for managing various mental health issues. Further research is needed to generalize these findings.

• Journal of the Korean Home Economics Association
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• v.34 no.5
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• pp.183-196
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• 1996

The purposes of this study were to review the effects of textiles production on the environment, and to investigate the environmental consciousness and environmental preservation behavior of textile producers. This study was conducted by reference analysis and empirical research. To develope theoretical framework of dimensions of environmental behavior, references concerned were analyzed. And for empirical study, researcher developed a questionnaire based on the free writing by producers and references. The questionnaire included problems about environmental consciousness, environmental behavior, demographic variables, and environmental variables. 135 questionnaires were used for final data analysis. ANOVA and factor analysis were used. The results were as follows: First, the level of global environmental problem consciousness was relatively high. The conscious level of water pollution caused by the waste water from textile mills was the highest, and that of desertation of mountain caused by timber cutting was the lowest. The effects of textile dyers and finishers on the environment were the highest, and that of designers were the lowest. Second, the results of reference analysis showed that the dimensions of textile producers environmental behavior were resource and energy saving, solid waste reduction, and green product production. And the results of empirical study were resource and energy saving, resource reuse or recycling, solid waste reduction, and green product production, and total variances was 62.3%. The practice was the lowest. Third, global environment problem consciousness, environment problem consciousness caused by the textile life-cycle concerned, and clothing seperate-collection or not at residing place were effective on environmental behavior, and 52.45% of environmental behavior was explained with above variables.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.319-325
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• 2023

In this study, we proposed a method for hyperparameter optimization in the building and training of a deep learning model designed to process point cloud data collected by a millimeter-wave radar system. The primary aim of this study is to facilitate the deployment of a baseline model in resource-constrained IoT devices. We evaluated a RadHAR baseline deep learning model trained on a public dataset composed of point clouds representing five distinct human activities. Additionally, we introduced a coarse-to-fine hyperparameter optimization procedure, showing substantial potential to enhance model efficiency without compromising predictive performance. Experimental results show the feasibility of significantly reducing model size without adversely impacting performance. Specifically, the optimized model demonstrated a 3.3% improvement in classification accuracy despite a 16.8% reduction in number of parameters compared th the baseline model. In conclusion, this research offers valuable insights for the development of deep learning models for resource-constrained IoT devices, underscoring the potential of hyperparameter optimization and model size reduction strategies. This work contributes to enhancing the practicality and usability of deep learning models in real-world environments, where high levels of accuracy and efficiency in data processing and classification tasks are required.