The influence of specimen geometry and notch on the hydrogen embrittlement of an SA372 steel for pressure vessels was investigated in this study. A slow strain-rate tensile (SSRT) test after the electrochemical hydrogen charging method was conducted on four types of tensile specimens with different directions, shapes (plate, round), and notches. The plate-type specimen showed a significant decrease in hydrogen embrittlement resistance owing to its large surface-to-volume ratio, compared to the round-type specimen. It is well established that most of the hydrogen distributes over the specimen surface when it is electrochemically charged. For the round-type specimens, the notched specimen showed increased hydrogen susceptibility compared with the unnotched one. A notch causes stress concentration and thus generates lots of dislocations in the locally deformed regions during the SSRT test. The solute hydrogen weakens the interactions between these dislocations by promoting the shielding effect of stress fields, which is called hydrogen-enhanced localized plasticity mechanisms. These results provide crucial insights into the relationship between specimen geometry and hydrogen embrittlement resistance.

The effect of Cr and Mo contents on the hydrogen embrittlement of tempered martensitic steels was investigated in this study. After the steels with different Cr and Mo contents were austenitized at 820 °C for 90 min, they were tempered at 630 °C for 120 min. The steels were composed of fully tempered martensite with a lath-type microstructure, but the characteristics of the carbides were dependent on the Cr and Mo contents. As the Cr and Mo contents increased, the volume fraction of film-like cementite and prior austenite grain size decreased. After hydrogen was introduced into tensile specimens by electrochemical charging, a slow strain-rate test (SSRT) was conducted to investigate hydrogen embrittlement behavior. The SSRT results revealed that the steel with lower Cr or lower Mo content showed relatively poor hydrogen embrittlement resistance. The hydrogen embrittlement resistance of the tempered martensitic steels increased with increasing Mo content, because the reduction in the film-like cementite and prior austenite grain size plays an important role in improving hydrogen embrittlement resistance. The results indicate that controlling the Cr and Mo contents is essential to achieving a tempered martensitic steel with a combination of high strength and excellent hydrogen embrittlement resistance.

This study aimed to test a convolutional neural network (CNN) in two different settings of training and testing data. Panoramic radiographs were selected from 1170 female dental patients (mean age 49.19 ± 21.91 yr). The cortical bone of the mandible inferior border was evaluated for osteoporosis or normal condition on the panoramic radiographs. Among them, 586 patients (mean age 27.46 ± 6.73 yr) had normal condition, and osteoporosis was interpreted on 584 patients (mean age 71.00 ± 7.64 yr). Among them, one data set of 569 normal patients (mean age 26.61 ± 4.60 yr) and 502 osteoporosis patients (mean age 72.37 ± 7.10 yr) was used for training CNN, and the other data set of 17 normal patients (mean age 55.94 ± 4.0 yr) and 82 osteoporosis patients (mean age 62.60 ± 5.00 yr) for testing CNN in the first experiment, while the latter was used for training CNN and the former for testing CNN in the second experiment. The error rate was 15.15% in the first experiment and 5.14% in the second experiment. This study suggests that age-matched training data make more accurate testing results.

The purpose of this study is to evaluate and to predict of eutrophication in lakes by using Vollenweider- OECD model and total phosphorus concentration and inflow rate which were measurded in 1993～2001. The results of study were as follows. The annual total phosphorus loading from the watershed was calculated to be 55～195tP/yr at lake Soyang, 221～466tP/yr at lake Taechong, 123～278tP/yr at lake Andong, 57～109tP/yr at lake Seomjin. These are discharged, for the most parts, from population and fishfarm facility. TP loading on the surface area at lake Soyang was 3.01gP/㎡/yr, 2.82gP/㎡/yr, 2.84gP/㎡/yr, 3.03gP/㎡/yr, 2.34gP/㎡/yr, 1.78gP/㎡/yr, 0.91gP/㎡/yr, 0.89gP/㎡/yr, 0.86gP/㎡/yr, lake Taechong was 6.71gP/㎡/yr, 7.25gP/㎡/yr, 7.24gP/㎡/yr, 6.53gP/㎡/yr, 6.50gP/㎡/yr, 7.06gP/㎡/yr, 7.04gP/㎡/yr, 4.05gP/㎡/yr, 3.44gP/㎡/yr and TP loading on the surface area of lake Andong, lake Soemjin were 5.39gP/㎡/yr, 4.47gP/㎡/yr, 4.56gP/㎡/yr, 4.45gP/㎡/yr, 3.33gP/㎡/yr, 2.38gP/㎡/yr, 2.53gP/㎡/yr, 2.46gP/㎡/yr, 2.54gP/㎡/yr, 4.09gP/㎡/yr, 4.10gP/㎡/yr, 3.98gP/㎡/yr, 3.73gP/㎡/yr, 2.80gP/㎡/yr, 3.46gP/㎡/yr, 3.22gP/㎡/yr, 2.19gP/㎡/yr, 2.13gP/㎡/yr respectively. The tropic states of four lakes can be assessed as eutrophy because phosphorus loading exceeds the critical phosphorus loading by Vollenweider-OECD model.

This study was performed to improve water demand estimation and analize correlation between generation of domestic sewage and domestic water use. To improve the prediction of water demand estimation, new water demand equation was developed. The results is as follows. InQt = β0 + β₁InPt + β₂InYt + β₃InHt + εt By using the statistical analysis of the "generation of domestic sewage" and "domestic water use", the regression equation between them is formed. The result is as follows. ＿ Generation of domestic sewage = 0.8487 × Domestic water use + 684.57 (R² = 0.972)

The purpose of this study is to evaluate and to predict of eutrophication in lakes by using Vollenweider-OECD model and total phosphorus concentration and inflow rate which were measurded in 1993-1996. The results of study was as follows. The annual total phosphorus loading from the watershed was calculated to be 181∼195tP/yr at lake Soyang, 591∼680tP/yr at lake Chungju, 420∼466tP/yr at lake Taechong, 229∼278tP/yr at lake Andong, 103∼106tP/yr at lake Hapchon, 57∼59tP/yr at lake Imha, 194∼244tP/yr at lake Namgang, 83∼86tP/yr at lake Chuam, 99∼109tP/yr at lake Somjin. These are discharged, for the most parts, from population and fishfarm facility. TP loading on the surface area at lake Soyang was 3.01gP/㎡/yr, 2.82gP/㎡/yr, 2.84gP/㎡/yr. 3.03gP/㎡/yr, at lake Chungju 7.9lgP/㎡/yr, 6.87gP/㎡/yr, 7.38gP/㎡/yr, 7.18gP/㎡/yr, at lake Taechong 6.7lgP/㎡/yr, 7.25gP/㎡/yr, 7.24gP/㎡/yr, 6.53gP/㎡/yr and TP loading on the surface area of Nakdong river basin, that is, lake Andong, Imha, Hapchon and Namgang were 5.39gP/㎡/yr, 4.47gP/㎡/yr, 4.56gP/㎡/yr, 4.45gP/㎡/yr and 2.20gP/㎡/yr, 2.23gP/㎡/yr, 2.24gP/㎡/yr, 2.17gP/㎡/yr and 4.50gP/㎡/ yr, 4.50gP/㎡/yr, 4.54gP/㎡/yr, 4.43gP/㎡/yr and 8.25gP/㎡/yr, 8.48gP/㎡/yr, 8.48gP/㎡/yr, 10. 39gP/㎡/yr respectively. Also those of lake Chuam was 2.51gP/㎡/yr, 2.61gP/㎡/yr, 2.52gP/㎡/yr, 2.54gP/㎡/yr and TP loading on the surface area at lake Somjin was analysed 4.09gP/㎡ /yr, 4.10gP/㎡/yr, 3.98gP/㎡/yr, 3.73gP/㎡/yr. The tropic states of nine lakes can be assessed as eutrophy because phosphorus loading exceeds the critical phosphorus loading by Vollenweider-OECD model.

This study was performed to analyze the variation characteristics of water qulity, correlation analysis of water quality data at each site and among the items of water quality data. Water quality for analysis was monthly values of water temperature, pH, dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solid, T-N and T-P checked in Daecheong Lake from January to December, 1995. It was analyzed variation of monthly water qulity was well from February to April, water temperature and COD seemed to have high correlationships at all sites. Regression equation is COD = 0.07 Water temperature + 1.23 (R^2 = 0.7616) . Results of the correlation analysis of water quality data showed that DO had higt correlationships between site 1 and site 2, BOD did site 1 and 3, COD did site 1 and 2, SS did site 5 and 6, T-N did 2 and 3, T-P did site 4 and 6. Regression equations for estimate of water quality data are as follows. DO_1 = 4.46 + 0.59 DO_2 (R^2 = 0.8868), BOD_1 = 0.52 + 0.63 BOD_3 (R^2 = 0.6390) COD_2 = 0.44 + 0.71 COD_1 (R^2 = 0.9183), SS_6 = 0.89 + 0.70 SS_5 (R^2 = 0.9155) TN_3 = 0.151 + 0.886 TN_2 (R^2 = 0.9415), TP_4 = 0.004 + 0.758 TP_6 (R^2 = 0.9669)