Search results for: Hsuan Teh Hu

Authors: Chiao-Cheng Huang, Pei-Te Chiueh, Ya-Hsuan Liou

Abstract:

Chromium-contaminated wastewater from electroplating industrial activity has been a long-standing environmental issue, as it can degrade surface water quality and is harmful to soil ecosystems. The traditional method of treating chromium-contaminated wastewater has been to use chemical coagulation processes. However, this method consumes large amounts of chemicals such as sulfuric acid, sodium hydroxide, and sodium bicarbonate in order to remove chromium. However, a series of new methods for treating chromium-containing wastewater have been developed. This study aimed to compare the environmental impact of four different lab scale chromium removal processes: 1.) chemical coagulation process (the most common and traditional method), in which sodium metabisulfite was used as reductant, 2.) electrochemical process using two steel sheets as electrodes, 3.) reduction by iron-copper bimetallic powder, and 4.) photocatalysis process by TiO2. Each process was run in the lab, and was able to achieve 100% removal of chromium in solution. Then a Life Cycle Assessment (LCA) study was conducted based on the experimental data obtained from four different case studies to identify the environmentally preferable alternative to treat chromium wastewater. The model used for calculating the environmental impact was TRACi, and the system scope includes the production phase and use phase of chemicals and electricity consumed by the chromium removal processes, as well as the final disposal of chromium containing sludge. The functional unit chosen in this study was the removal of 1 mg of chromium. Solution volume of each case study was adjusted to 1 L in advance and the chemicals and energy consumed were proportionally adjusted. The emissions and resources consumed were identified and characterized into 15 categories of midpoint impacts. The impact assessment results show that the human ecotoxicity category accounts for 55 % of environmental impact in Case 1, which can be attributed to the sulfuric acid used for pH adjustment. In Case 2, production of steel sheet electrodes is an energy-intensive process, thus contributed to 20 % of environmental impact. In Case 3, sodium bicarbonate is used as an anti-corrosion additive, which results mainly in 1.02E-05 Comparative Toxicity Unit (CTU) in the human toxicity category and 0.54E-05 (CTU) in acidification of air. In Case 4, electricity consumption for power supply of UV lamp gives 5.25E-05 (CTU) in human toxicity category, 1.15E-05 (kg Neq) in eutrophication. In conclusion, Case 3 and Case 4 have higher environmental impacts than Case 1 and Case 2, which can be attributed mostly to higher energy and chemical consumption, leading to high impacts in the global warming and ecotoxicity categories.

Keywords: chromium, lab scale, life cycle assessment, wastewater

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Authors: Wei-Chih Huang, Pei-Lung Chung, Ching-Heng Lin, Hsuan-Chia Yang, Der-Ming Liou

Abstract:

Background: In-Hospital Cardiac Arrest (IHCA) threaten life of the inpatients, cause serious effect to patient safety, quality of inpatients care and hospital service. Health providers must identify the signs of IHCA early to avoid the occurrence of IHCA. This study will consider the potential association between early signs of IHCA and the essence of patient care provided by nurses and other professionals before an IHCA occurs. The aim of this study is to identify significant associations between nursing interventions and abnormal early evaluation results of IHCA that can assist health care providers in monitoring inpatients at risk of IHCA to increase opportunities of IHCA early detection and prevention. Materials and Methods: This study used one of the data mining techniques called association rules mining to compute associations between nursing interventions and abnormal early evaluation results of IHCA. The nursing interventions and abnormal early evaluation results of IHCA were considered to be co-occurring if nursing interventions were provided within 24 hours of last being observed in abnormal early evaluation results of IHCA. The rule based methods were utilized 23.6 million electronic medical records (EMR) from a medical center in Taipei, Taiwan. This dataset includes 733 concepts of nursing interventions that coded by clinical care classification (CCC) codes and 13 early evaluation results of IHCA with binary codes. The values of interestingness and lift were computed as Q values to measure the co-occurrence and associations’ strength between all in-hospital patient care measures and abnormal early evaluation results of IHCA. The associations were evaluated by comparing the results of Q values and verified by medical experts. Results and Conclusions: The results show that there are 4195 pairs of associations between nursing interventions and abnormal early evaluation results of IHCA with their Q values. The indication of positive association is 203 pairs with Q values greater than 5. Inpatients with high blood sugar level (hyperglycemia) have positive association with having heart rate lower than 50 beats per minute or higher than 120 beats per minute, Q value is 6.636. Inpatients with temporary pacemaker (TPM) have significant association with high risk of IHCA, Q value is 47.403. There is significant positive correlation between inpatients with hypovolemia and happened abnormal heart rhythms (arrhythmias), Q value is 127.49. The results of this study can help to prevent IHCA from occurring by making health care providers early recognition of inpatients at risk of IHCA, assist with monitoring patients for providing quality of care to patients, improve IHCA surveillance and quality of in-hospital care.

Keywords: in-hospital cardiac arrest, patient safety, nursing intervention, association rule mining

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Authors: Pei-Jyun Hsieh, Cheng-Hsuan Li, Bor-Chen Kuo

Abstract:

For hyperspectral image classification, feature reduction is an important pre-processing for avoiding the Hughes phenomena due to the difficulty for collecting training samples. Hence, lots of researches developed feature selection methods such as F-score, HSIC (Hilbert-Schmidt Independence Criterion), and etc., to improve hyperspectral image classification. However, most of them only consider the class separability in the original space, i.e., a linear class separability. In this study, we proposed a nonlinear class separability measure based on kernel trick for selecting an appropriate feature subset. The proposed nonlinear class separability was formed by a generalized RBF kernel with different bandwidths with respect to different features. Moreover, it considered the within-class separability and the between-class separability. A genetic algorithm was applied to tune these bandwidths such that the smallest with-class separability and the largest between-class separability simultaneously. This indicates the corresponding feature space is more suitable for classification. In addition, the corresponding nonlinear classification boundary can separate classes very well. These optimal bandwidths also show the importance of bands for hyperspectral image classification. The reciprocals of these bandwidths can be viewed as weights of bands. The smaller bandwidth, the larger weight of the band, and the more importance for classification. Hence, the descending order of the reciprocals of the bands gives an order for selecting the appropriate feature subsets. In the experiments, three hyperspectral image data sets, the Indian Pine Site data set, the PAVIA data set, and the Salinas A data set, were used to demonstrate the selected feature subsets by the proposed nonlinear feature selection method are more appropriate for hyperspectral image classification. Only ten percent of samples were randomly selected to form the training dataset. All non-background samples were used to form the testing dataset. The support vector machine was applied to classify these testing samples based on selected feature subsets. According to the experiments on the Indian Pine Site data set with 220 bands, the highest accuracies by applying the proposed method, F-score, and HSIC are 0.8795, 0.8795, and 0.87404, respectively. However, the proposed method selects 158 features. F-score and HSIC select 168 features and 217 features, respectively. Moreover, the classification accuracies increase dramatically only using first few features. The classification accuracies with respect to feature subsets of 10 features, 20 features, 50 features, and 110 features are 0.69587, 0.7348, 0.79217, and 0.84164, respectively. Furthermore, only using half selected features (110 features) of the proposed method, the corresponding classification accuracy (0.84168) is approximate to the highest classification accuracy, 0.8795. For other two hyperspectral image data sets, the PAVIA data set and Salinas A data set, we can obtain the similar results. These results illustrate our proposed method can efficiently find feature subsets to improve hyperspectral image classification. One can apply the proposed method to determine the suitable feature subset first according to specific purposes. Then researchers can only use the corresponding sensors to obtain the hyperspectral image and classify the samples. This can not only improve the classification performance but also reduce the cost for obtaining hyperspectral images.

Keywords: hyperspectral image classification, nonlinear feature selection, kernel trick, support vector machine

Procedia PDF Downloads 240