• Journal of the Korean Institute of Gas
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• v.10 no.1 s.30
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• pp.48-55
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• 2006

In order to establish the comprehensively, quantitatively predictable program to the fire and explosion accidents in the urban gas system, and to set up domestic criteria of societal risk, the collected urban gas accident data have been deeply analyzed. The Poisson probability distribution functions with t=5 for the database of the gas accidents in recent 11 year shows that 'careless work-explosion-pipeline' item has the lowest frequency, whereas 'joint loosening & erosion-release-pipeline' item has the highest frequency. And thus the proper counteractions must be carried out. The further works requires setting up successive database on the fire and explosion accidents systematically to obtain reliable analyses.

• Journal of Information Management
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• v.23 no.3
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• pp.45-73
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• 1992

STN International is operated in North America by CAS, a division of the American Chemical Society;by FIZ Karlsruhe in Eruope ; and by JICST in Japan. All three are not-for-profit scientific organizations. This paper describes Messenger software that is designed for fast and efficient information retrieval, the advanced front-end STN Express software that saves time and effort, and databases in STN.

• Proceedings of the Korean Society of Life Science Conference
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• 2005.04a
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• pp.152-152
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• 2005

• Journal of the Society of Disaster Information
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• v.4 no.2
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• pp.124-136
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• 2008

The systematic information management of chemical accidents has been required as a tool for the policy making, system improvement and release of information concerning accident prevention. However, there is not yet a systematic chemical accidents tracking system in Korea, which make confusion among the related government agencies and the parties to accidents that the related statistics are different from each others. In this study, We developed the Chemical Accident Tracking System (CATS) using chemical accident classification which was made up of 12 upper classes, 70 middle classes, 272 lower classes. The CATS is mainly consist list up module, reporting module, searching and statistic module, etc. The CATS is expected to be applied to the information tracking and database system for chemical accidents and improve its manageability.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.10
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• pp.1082-1089
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• 2006

This study describes a methodology to develop a environmental and geographical information system for managing a national scale which is supports the environmental fate modeling. This system was developed via the integration of environmental and geographical information database(DB) and the DB usage software. For the sound environmental management, the DB were constructed by extracting the geometrical figures-such as points, polylines, and/or polygons-based on the geographical information previously developed in Korea. Then it was connected with the environmental information. Based on the Visual Basic Complier, the software can be a useful tool for visualizing the DB, for searching the environmental & geographical information, and supporting the environmental fate modeling.

• Journal of the Korea Safety Management & Science
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• v.12 no.3
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• pp.27-35
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• 2010

In this study, we have developed the database of safety and health information for pesticide active ingredients used in Korea. There were 1,283 pesticide items among which 296 were found to be out of use in current. A total of 349 pesticide ingredients were being used in Korea. The database consists of 32 types of information including chemical characteristics, acute toxicity, chronic toxicity (carcinogenic and reproductive toxicity), specific symptoms by exposure route and first aid. When pesticide ingredients were assessed in terms of key properties such as color, odor, acute toxicity, carcinogenic and reproductive toxicity, they were white, colorless and odorless, in general. When ingredients were classified by category of acute toxicity, 'Non-hazardous' represented 29%, followed by 'Slightly hazardous' at 16%, 'Moderately hazardous' at 14%, 'Highly hazardous' at 5%, and 'Extremely hazardous' at 2%. 85 out of 349, or 24% of ingredients were found to be possibly carcinogenic to human. This database is expected to provide an easy access for farmers, agriculture supervisors, researchers and consumers, and it can ultimately be used as basic data on farmer's safety and health.

• Earthquakes and Structures
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• v.11 no.2
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• pp.327-346
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• 2016

The 2012 Emilia (Italy) earthquakes struck a highly industrialized area including several thousands of industrial prefabricated buildings. Due to the lack of specific design and detailing for earthquake resistance, precast reinforced concrete (RC) buildings suffered from severe damages and even partial or total collapses in many cases. The present study reports a data inventory of damages from field survey on prefabricated buildings. The damage database concerns more than 1400 buildings (about 30% of the total precast building stock in the struck region). Making use of the available shakemaps of the two mainshocks, damage distributions were related with distance from the nearest epicentre and corresponding Pseudo-Spectral Acceleration for a period of 1 second (PSA at 1 s) or Peak Ground Acceleration (PGA). It was found that about 90% of the severely damaged to collapsed buildings included into the database stay within 16 km from the epicentre and experienced a PSA larger than 0.12 g. Moreover, 90% of slightly to moderately damaged buildings are located at less than 25 km from the epicentre and were affected by a PSA larger than 0.06 g. Nevertheless, the undamaged buildings examined are almost uniformly distributed over the struck region and 10% of them suffered a PSA not lower than 0.19g. The damage distributions in terms of the maximum experienced PGA show a sudden increase for $PGA{\geq}0.28g$. In this PGA interval, 442 buildings were collected in the database; 55% of them suffered severe damages up to collapse, 32% reported slight to moderate damages, whereas the remaining 13% resulted undamaged.

• Journal of Environmental Health Sciences
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• v.47 no.5
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• pp.462-471
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• 2021

Background: The number of synthesized chemicals has rapidly increased over the past decade. For many chemicals, there is a lack of information on toxicity. With the current movement toward reducing animal testing, the use of toxicity big data and deep learning could be a promising tool to screen potential toxicants. Objectives: This study identified potential chemicals related to reproductive and estrogen receptor (ER)-mediated toxicities for 1135 cleaning products and 886 laundry products. Methods: We listed chemicals contained in cleaning and laundry products from a publicly available database. Then, chemicals that potentially exhibited reproductive and ER-mediated toxicities were identified using the European Union Classification, Labeling and Packaging classification and ToxCast database, respectively. For chemicals absent from the ToxCast database, ER activity was predicted using deep learning models. Results: Among the 783 listed chemicals, there were 53 with potential reproductive toxicity and 310 with potential ER-mediated toxicity. Among the 473 chemicals not tested with ToxCast assays, deep learning models indicated that 42 chemicals exhibited ER-mediated toxicity. A total of 13 chemicals were identified as causing reproductive toxicity by reacting with the ER. Conclusions: We demonstrated a screening method to identify potential chemicals related to reproductive and ER-mediated toxicities utilizing chemical toxicity big data and deep learning. Integrating toxicity data from in vivo, in vitro, and deep learning models may contribute to screening chemicals in consumer products.

• Journal of the Korean Institute of Gas
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• v.2 no.2
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• pp.48-54
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• 1998

In this study a computerized prototype system was developed with Safety Management Information System(SMIS version 1.0) as a main system and database as subsystems to handle information. Safety management information consists of management aspects and technical elements, but SMIS consists of 4 modules of technical elements to interrelate safety technologies closely. SMIS enables gas industries to manage process safety information effectively and to evaluate hazards. The results from SMIS can be used to the operation manual and the emergency plan. Data base consists of 3 modules of accident data, material data, and equipment data to support SMIS. Also, the case study results proved the usefulness of SMIS for searching and accumulating process safety data. Especially, MIS which has the database suggests a formal structure for scattered raw safety data in gas industries and brings reduction of man power and time.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1141-1141
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• 2001

The aim of the study was to evaluate the performance of 3 calibration methods, modified partial least squares (MPLS), local PLS (LOCAL) and artificial neural network (ANN) on the prediction of chemical composition of forages, using a large NIR database. The study used forage samples (n=25,977) from Australia, Europe (Belgium, Germany, Italy and Sweden) and North America (Canada and U.S.A) with information relative to moisture, crude protein and neutral detergent fibre content. The spectra of the samples were collected with 10 different Foss NIR Systems instruments, which were either standardized or not standardized to one master instrument. The spectra were trimmed to a wavelength range between 1100 and 2498 nm. Two data sets, one standardized (IVAL) and the other not standardized (SVAL) were used as independent validation sets, but 10% of both sets were omitted and kept for later expansion of the calibration database. The remaining samples were combined into one database (n=21,696), which was split into 75% calibration (CALBASE) and 25% validation (VALBASE). The chemical components in the 3 validation data sets were predicted with each model derived from CALBASE using the calibration database before and after it was expanded with 10% of the samples from IVAL and SVAL data sets. Calibration performance was evaluated using standard error of prediction corrected for bias (SEP(C)), bias, slope and R2. None of the models appeared to be consistently better across all validation sets. VALBASE was predicted well by all models, with smaller SEP(C) and bias values than for IVAL and SVAL. This was not surprising as VALBASE was selected from the calibration database and it had a sample population similar to CALBASE, whereas IVAL and SVAL were completely independent validation sets. In most cases, Local and ANN models, but not modified PLS, showed considerable improvement in the prediction of IVAL and SVAL after the calibration database had been expanded with the 10% samples of IVAL and SVAL reserved for calibration expansion. The effects of sample processing, instrument standardization and differences in reference procedure were partially confounded in the validation sets, so it was not possible to determine which factors were most important. Further work on the development of large databases must address the problems of standardization of instruments, harmonization and standardization of laboratory procedures and even more importantly, the definition of the database population.