• Korean Journal of Environmental Agriculture
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• v.31 no.1
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• pp.37-44
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• 2012

BACKGROUND: Many edible mushrooms are known to accumulate high levels of heavy metals. This research was focused on health risk assessment to investigate the mushrooms in Korea, arsenic (As), cadmium (Cd), lead (Pb) and mercury (Hg) contaminations in edible mushrooms in cultivated areas were investigated, and health risk was assessed through dietary intake of mushrooms. METHODS AND RESULTS: The heavy metals in mushrooms were analyzed by ICP/MS after acid digestion. Probabilistic health risk were estimated by Monte-Carlo simulation techniques. The average contents of As, Cd, Pb, and Hg were $0.035{\pm}0.042$ mg/kg, $0.017{\pm}0.020$ mg/kg, $0.043{\pm}0.013$ mg/kg, and $0.004{\pm}0.004$ mg/kg, respectively. The results showed that contents of Cd and Pb did not exceed maximum residual levels established by European Uion regulation (Cd 0.20 mg/kg and Pb 0.30 mg/kg). For health risk assessment, estimated intakes in all age populations did not exceed the provisional tolerable daily intake of As and Hg, provisional tolerable monthly intake of Cd, provisional tolerable weekly intake of Pb. The Hazard Index (HI) were ranged from $0.03{\times}10^{-4}{\sim}0.01{\times}10^{-3}$ for As, $0.02{\times}10^{-3}{\sim}0.81{\times}10^{-3}$ for Cd, $0.06{\times}10^{-3}{\sim}0.38{\times}10^{-3}$ for Pb, and $0.08{\times}10^{-4}{\sim}0.14{\times}10^{-3}$ for Hg at general population. CONCLUSION: The HI from the ratio analysis between daily exposure and safety level values was less than 1.0. This results demonstrated that human exposure to heavy metals through dietary intake of mushrooms might not cause adverse effect.

• Safety and Health at Work
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• v.8 no.3
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• pp.267-275
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• 2017

Background: The mining industry is known worldwide for its highly risky and hazardous working environment. Technological advancement in ore extraction techniques for proliferation of production levels has caused further concern for safety in this industry. Research so far in the area of safety has revealed that the majority of incidents in hazardous industry take place because of human error, the control of which would enhance safety levels in working sites to a considerable extent. Methods: The present work focuses upon the analysis of human factors such as unsafe acts, preconditions for unsafe acts, unsafe leadership, and organizational influences. A modified human factor analysis and classification system (HFACS) was adopted and an accident predictive fuzzy reasoning approach (FRA)-based system was developed to predict the likelihood of accidents for manganese mines in India, using analysis of factors such as age, experience of worker, shift of work, etc. Results: The outcome of the analysis indicated that skill-based errors are most critical and require immediate attention for mitigation. The FRA-based accident prediction system developed gives an outcome as an indicative risk score associated with the identified accident-prone situation, based upon which a suitable plan for mitigation can be developed. Conclusion: Unsafe acts of the worker are the most critical human factors identified to be controlled on priority basis. A significant association of factors (namely age, experience of the worker, and shift of work) with unsafe acts performed by the operator is identified based upon which the FRA-based accident prediction model is proposed.

• Korean Journal of Adult Nursing
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• v.28 no.1
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• pp.13-29
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• 2016

Purpose: This study was performed to evaluate the effects of non-pharmacological interventions on sleep disturbance amongst adults aged 55 and above. Methods: PubMed, Cochrane Library, EMBASE, CINAHL and several Korean databases were searched. The main search strategy combined terms including non-pharmacological interventions and presence of insomnia. Non-pharmacological interventions included cognitive behavioral therapy, auricular acupuncture, aromatherapy, and emotional freedom techniques. Methodological quality was assessed using Cochrane's Risk of Bias for randomized studies and Risk of Bias Assessment tool for non randomized studies. Data were analyzed by the RevMan 5.3 program of Cochrane Library. Results: Sixteen clinical trials met the inclusion criteria with a total of 962 participants. Non-pharmacological interventions was conducted for a mean of 5.5 weeks, 7.7 sessions, and an average of 70 minutes per session. The effects of non-pharmacological interventions on sleep quality (ES=-1.18), sleep efficiency (ES=-1.14), sleep onset latency (ES=-0.88), awakening time after sleep onset (ES=-0.87), and sleep belief (ES=-0.71) were significant, and their effect sizes were ranged from moderate to large. However, the effects on total sleep time and insomnia severity were not significant. Conclusion: The findings of the current study suggest that non-pharmacological interventions have a positive impact on attitudes and beliefs about sleep, sleep quality, sleep duration, and sleep efficiency. Therefore, the findings of the study provide an evidence to incorporate various non-pharmacological interventions into nursing practice to improve both sleep quality and quantity in patients with insomnia.

• Journal of Korean Society of Water and Wastewater
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• v.25 no.2
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• pp.201-212
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• 2011

Nanotechnology is the applied science which develops new materials and systems sized within 1 to 100 nanometer, and improves their physical, chemical, and biological characteristics by manipulating on an atomic and molecular scale. This nanotechnology has been applied to wide spectrum of industries resulting in production of various nanoparticles. It is expected that more nanoparticles will be generated and enter to natural water bodies, imposing great threat to potable water resources. However their toxicity and treatment options have not been throughly investigated, despite the significant growth of nanotechnology-based industries. The objective of this study is to provide fundamental information for the management of nanoparticles in water supply systems through extensive literature survey. More specifically, two types of nanoparticles are selected to be a potential problem for drinking water treatment. They are carbon nanoparticles such as carbon nanotube and fullerene, and metal nanoparticles including silver, gold, silica and titanium oxide. In this study, basic characteristics and toxicity of these nanoparticles were first investigated systematically. Their monitoring techniques and treatment efficiencies in conventional water treatment plants were also studied to examine our capability to mitigate the risk associated with nanoparticles. This study suggests that the technologies monitoring nanopartilces need to be greatly improved in water supply systems, and more advanced water treatment processes should be adopted for better control of these nanoparticles.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 1997.11a
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• pp.392-403
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• 1997

There is a strong international move towards performance based fire regulations for buildings with New Zealand and Australia at the forefront of research in this fold. The reform of regulations is thought to offer more innovation and flexibility in building design and greater cost effectiveness in construction. An important part of the research in this area is related to the development of agreed approaches to fire safety design, such as the Fire Code Reform Centre's "Fire Engineering Guidelines" or New Zealand's "Fire Engineering Design Guide". Such design process documents have incorporated or referenced much of the latest research in areas such as: tenability criteria fire compartment models egress models risk assessment. Use of such design guidelines or equivalents in major projects in countries such as Hong Kong and Australia have highlighted where fro engineering can offer real benefits to building designers and ultimately building owners and operators. However, there is still much research to be done and use of a systematic, logical design approach clearly identifies where design data or modelling techniques are still urgently required. Such areas are: fire growth rates and peak heat release rates for non-residential occupancies pre-movement times related to egress experimental validation and limits of applicability of CFD and other compartment Ire models probability/reliability data on fire protection systems for risk based analysis. Examples from case studies will be shown where lack of such research and poor judgement can lead to inferior design solutions or where unnecessarily conservative designs can lead to cost excesses. In summary, the link between Ire engineering designers and the research community is very important to highlight areas of fire research that will have the most benefit to the building and construction industry.nstruction industry.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.7
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• pp.907-915
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• 2018

With the increase of metal usage in various industries, metal pollution and ecological toxicity in the environmental system have become a significant concern. A geo-statistical index has been widely used to determine contamination level with normalization through a background value. In this study, geo-statistical indexes such as an enrichment factor, accumulation index, and potential ecological risk index were used to assess metal pollution in soil at locations associated with shipbuilding manufacturing industries. Metal contamination, especially of Cu and Pb, was observed in some samples located closer to manufacturing sites. Enrichment factor and accumulation (IGEO) values were indicative of concerning levels of soil contamination in specific samples, and the soil contamination could be induced by anthropogenic sources. In further study, after more detailed sampling for soil and potential pollution sources, high interpretation techniques such as Pb isotope analysis and X-ray analysis will be needed to investigate source identification.

• Journal of the Korean GEO-environmental Society
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• v.20 no.3
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• pp.5-14
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• 2019

This study attempted to study the soil erosion dynamic in the Bagmati Basin of Nepal. In this study, an inclusive methodology that combines Revised Universal Soil Loss Equation (RUSLE) and GIS techniques was adopted to determine the distribution of soil loss in the study basin. As well, this study attempts to study the intensity of soil erosion in the seven different land use patterns in the Bagmati Basin. Soil loss is an associated phenomenon of hydrologic cycle and this dynamic phenomenon possesses threats to sustainability of basin hydrology, agriculture system, hydraulic structures in operation and overall ecosystem in a long run. Soil conservation works, and various planning and design of watersheds works demands quantification of soil loss. The results of the study in Bagmati Basin shows the total annual soil loss in the basin is 22.93 million tons with an average rate of 75.83T/ha/yr. The computed soil loss risk was divided into five classes from tolerable to severe and the spatial pattern was mapped for easy interpretation. Also, evaluation of soil loss in different land use categories shows barren area has highest rate of soil loss followed by agriculture area. This is a preliminary work and provides erosion risk scenario in the basin. The study can be further used for strategic planning of land use and hydrologic conservation works in a basin.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.17-25
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• 2023

The alarming global prevalence of Type 2 Diabetes Mellitus (T2DM) has catalyzed an urgent need for robust, early diagnostic methodologies. This study unveils a pioneering approach to predicting T2DM, employing the Extreme Gradient Boosting (XGBoost) algorithm, renowned for its predictive accuracy and computational efficiency. The investigation harnesses a meticulously curated dataset of 4303 samples, extracted from a comprehensive Chinese research study, scrupulously aligned with the World Health Organization's indicators and standards. The dataset encapsulates a multifaceted spectrum of clinical, demographic, and lifestyle attributes. Through an intricate process of hyperparameter optimization, the XGBoost model exhibited an unparalleled best score, elucidating a distinctive combination of parameters such as a learning rate of 0.1, max depth of 3, 150 estimators, and specific colsample strategies. The model's validation accuracy of 0.957, coupled with a sensitivity of 0.9898 and specificity of 0.8897, underlines its robustness in classifying T2DM. A detailed analysis of the confusion matrix further substantiated the model's diagnostic prowess, with an F1-score of 0.9308, illustrating its balanced performance in true positive and negative classifications. The precision and recall metrics provided nuanced insights into the model's ability to minimize false predictions, thereby enhancing its clinical applicability. The research findings not only underline the remarkable efficacy of XGBoost in T2DM prediction but also contribute to the burgeoning field of machine learning applications in personalized healthcare. By elucidating a novel paradigm that accentuates the synergistic integration of multifaceted clinical parameters, this study fosters a promising avenue for precise early detection, risk stratification, and patient-centric intervention in diabetes care. The research serves as a beacon, inspiring further exploration and innovation in leveraging advanced analytical techniques for transformative impacts on predictive diagnostics and chronic disease management.

• Journal of the Korean Geotechnical Society
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• v.39 no.6
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• pp.5-12
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• 2023

Proactive assessment of landslide susceptibility is necessary for minimizing casualties. This study proposes a methodology for classifying the landslide safety factor using a classification algorithm based on machine learning techniques. The high-risk area model is adopted to perform the classification and eight geotechnical parameters are adopted as inputs. Four classification algorithms-namely decision tree, k-nearest neighbor, logistic regression, and random forest-are employed for comparing classification accuracy for the safety factors ranging between 1.2 and 2.0. Notably, a high accuracy is demonstrated in the safety factor range of 1.2~1.7, but a relatively low accuracy is obtained in the range of 1.8~2.0. To overcome this issue, the synthetic minority over-sampling technique (SMOTE) is adopted to generate additional data. The application of SMOTE improves the average accuracy by ~250% in the safety factor range of 1.8~2.0. The results demonstrate that SMOTE algorithm improves the accuracy of classification algorithms when applied to geotechnical data.

• Journal of the Korean Institute of Gas
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• v.15 no.6
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• pp.8-13
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• 2011

All over the nation, a lot of industrial complex utilize gas as their energy source. Possibilities are fire, explosion, and leakage could happen any time in these large complexes. To prevent these tragic accidents and to minimize the damage when the accident occurs, the development of diagnostic technology for these facilities is imperative. The safety check is conducted on an individual and partial basis, currently. Accordingly, the accumulation and improvement of the safety management technology is necessary in order to make all the different checking techniques and management systems compatible, since checking processes, result interpretation techniques, and subsequent prognoses are not the same. The program provides damage scenarios from gas leakage. The output enables policy makers to predict the degree of infliction. Through this program, engineers are able to design an effective gas safety program to operate and maintain ubiquitous gas facilities.