This study evaluated the applicability of stabilization of arsenic (As)-contaminated soil with iron (Fe) oxides at the former Janghang smelter site. Three Fe oxides (magnetite, goethite, and hematite) were tested as stabilizing agents to one soil sample collected from the study site. Amendment of 5% of magnetite, goethite, or hematite for one week showed the 64, 58, and 36% of reduction of the SBRC (Solubility/Bioavailability Research Consortium)-extractable (bioaccessible) As, respectively. Duration of stabilization more than one week did not show an additional reduction in SBRC-extractable As. Amendment of 5% of magnetite, which showed the highest As stabilization efficiency, was applied to 24 soil samples collected from the same site for one week, and 72% of reduction in the bioaccessible As was observed. The potential carcinogenic human health risk at the study site caused by As was 1.7×10 −5, which could be reduced to 8.1×10−6 by the amendment of 5% magnetite for one week.

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Several algorithms have been developed to classify arrhythmia which rely on specific ECG(Electrocardiogram) database. Nevertheless personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. But it is difficult to detect the P and T wave signal because of person's individual difference. Therefore it is necessary to classify the pattern by analyzing personalized ECG signal and extracting minimal feature. Thus, QRS pattern Analysis of personalized ECG Signal by Q, R, S peak variability is presented in this paper. For this purpose, we detected R wave through the preprocessing method and extract eight feature by amplitude and phase variability. Also, we classified nine pattern in realtime through peak and morphology variability. PVC, PAC, Normal, LBBB, RBBB, Paced beat arrhythmia is evaluated by using 43 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 93.72% in QRS pattern detection classification.

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