The production processes of the petrochemical industry expose workers to high potential hazards. Our previous study showed that hazard recognition was closely related to worker safety and health training activities. The purpose of this study was to establish and validate a safety and health training model. It is expected that the training model will help workers to recognize hazards, thereby lowering their operating risks. The training model, which included a complete training course and follow-up scoring using a questionnaire, was applied to three groups of subjects for comparison. Group A had joined our study previously and took the training course again at this time. Group B had also joined our previous study but did not take this training course. Group C was new to our study and took this training course for the first time. Groups A and C (who took the training course) had higher cognition and attitude scores than group B (who did not take the training course). The training course was a significant factor that positively influenced both cognition and attitude scores among managers and workers. The training course was more significant for managers while the duration of education was more significant for workers.
In this study, the authors attempted to determine factors associated with earthquake deaths in the great Chi-Chi Earthquake that occurred on September 21, 1999, in Taiwan. An isoseismal map was used to identify life-threatening hazards. The vertical peak ground acceleration of ground motion intensity was deemed the most appropriate index for the evaluation of building collapse and mortality. Mortality increased with the increase in earthquake intensity, and building collapse, approaching the epicenter. The greatest number of collapsed buildings and human deaths occurred between the Chelungpu Fault and the Shuantun Fault. Individuals 65 yr of age and older were the most vulnerable to the impact. The authors' findings suggest that improvements in earthquake-resistant building design and construction, as well as improved medical rescue for the elderly, could reduce the level of exposure to earthquake hazards.
Some data mining (DM) methods, or software tools, require normalized data, others rely on categorized data, and some can accommodate multiple data scales. Each DM technique has a specific background theory; therefore, different results are expected when applying multiple methods. The purpose of this study is to find the data format appropriate for each DM classification technique for wider applications, and efficiently to obtain trustworthy results. Considering the nature of medical data, categorical variables are sometimes useful for making decisions and can make it easier to extrapolate knowledge. In this study, three mathematical data categorization methods (Fusinter, minimum description length principle [MDLPC] and Chi-merge) were applied to accommodate five data mining classification techniques (statistics discriminant analysis, supervised classification with Neural Networks, Decision trees, Genetic supervised clustering and Bayesian classification [probability neural networks; PNN]) using a heart disease database with four types of data (continuous data, binary data, nominal data, and ordinal data). Compared with original or normalized data, data categorized by the MDLPC categorization method was found to perform better in most of the DM classification techniques used in this study. Categorical data is good for most DM classification techniques (e.g. classification of disease and non-disease groups) and is relatively easy to use for extracting medical knowledge.
BackgroundAn adaptive-network-based fuzzy inference system (ANFIS) was compared with an artificial neural network (ANN) in terms of accuracy in predicting the combined effects of temperature (10.5 to 24.5°C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions.MethodsThe ANFIS and ANN models were compared in terms of six statistical indices calculated by comparing their prediction results with actual data: mean absolute percentage error (MAPE), root mean square error (RMSE), standard error of prediction percentage (SEP), bias factor (Bf), accuracy factor (Af), and absolute fraction of variance (R
2). Graphical plots were also used for model comparison.ConclusionsThe learning-based systems obtained encouraging prediction results. Sensitivity analyses of the four environmental factors showed that temperature and, to a lesser extent, NaCl had the most influence on accuracy in predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. The observed effectiveness of ANFIS for modeling microbial kinetic parameters confirms its potential use as a supplemental tool in predictive mycology. Comparisons between growth rates predicted by ANFIS and actual experimental data also confirmed the high accuracy of the Gaussian membership function in ANFIS. Comparisons of the six statistical indices under both aerobic and anaerobic conditions also showed that the ANFIS model was better than all ANN models in predicting the four kinetic parameters. Therefore, the ANFIS model is a valuable tool for quickly predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions.
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