• Asian-Australasian Journal of Animal Sciences
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• v.26 no.2
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• pp.233-240
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• 2013

The objective of this study was to investigate the effects of L-carnitine on growth performance, organ weight, biochemical parameters of blood, heart and liver, and ascites susceptibility of broilers at different ages reared under a low-temperature environment. A total of 420 1-d-old male Ross 308 broilers were randomly assigned to two dietary treatments with fifteen replicates of fourteen broilers each. Treatment diets consisted of L-carnitine supplementation at levels of 0 and 100 mg/kg. At 11-d of age, low temperature stress was used to increase ascites susceptibility. Blood, heart and liver samples were collected at different ages for analysis of boichemical parameters. The results showed that, there was no significant difference in growth performance with L-carnitine supplementation, but the mortality due to ascites was significantly decreased. Dietary L-carnitine supplementation significantly reduced heart index (HI) and ascites heart index (AHI) on d 21, lung index (LUI) on d 35 and liver index (LI) on d 42. The broilers fed diets containing L-carnitine had significantly lower red blood cell counts (RBC), hemoglobin (HGB) concentration and hematocrit (HCT) on d 42. Dietary L-carnitine supplementation significantly reduced malondialdehyde (MDA) content of heart tissue on d 21 and 35, and significantly increased total superoxide dismutase (T-SOD) and Glutathione peroxidase (GSH-Px) activity of the heart on d 21 and 42. L-carnitine supplementation significantly reduced serum triglyceride (TG) content on d 28 and 35 and serum glucose (GLU) on d 35 and 42, and significantly increased serum total protein (TP) and globulin (GLO) content on d 42. L-carnitine supplementation significantly enhanced liver succinodehydrogenase (SDH), malic dehydrogenase (MDH) and $Na^+$-$K^+$-ATPase activity on d 28, and tended to reduce the lactic acid (LD) level of liver on d 35 (p = 0.06). L-carnitine supplementation significantly reduced serum uric acid (UA) content on d 28, 35 and 42. Based on the current results, it can be concluded that dietary L-carnitine supplementation reduced organ index, red blood cell counts and hematocrit, enhanced antioxidative capacity of the heart, enhanced liver enzymes activity involved in tricarboxylic acid cycle, and reduced serum glucose and triglyceride. Therefore, it is suggested that L-carnitine can potentially reduce susceptibility and mortality due to ascites.

• Korean Journal of Metals and Materials
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• v.50 no.9
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• pp.669-675
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• 2012

Hot tearing was the most significant casting defect when the castability evaluation of the Al-Zn-Mg-Cu alloy system was conducted. It was related to the solidification range of the alloy. Therefore, the hot tear susceptibility of the AA7075 alloy, whose solidification range is the widest, was evaluated. The hot tear susceptibility was evaluated by using a mold for a hot tearing test designed to create the condition for the occurrence of hot tear in 8 steps. According to the tearing location and shape, a hot tear susceptibility index (HTS) score was measured. The solidification range of each alloy and hot tear susceptibility was compared and thereafter the microstructure of a near tear defect was observed. As a result, the HTS of the AA7075 alloy was found to be 67. Also, the HTS in relation to a change in Zn, Mg, Cu composition showed a difference of about 6-11% compared to the AA7075 alloy.

• Economic and Environmental Geology
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• v.37 no.2
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• pp.207-223
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• 2004

The purpose of this study is to reveal spatial relationships between landslide and geospatial data set, to map the landslide susceptibility using the relationship and to verify the landslide susceptibility using the landslide occurrence data in Boun area in 1998. Landslide locations were detected from aerial photography and field survey, and then topography, soil, forest, and land cover data set were constructed as a spatial database using GIS. Various spatial parameters were used as the landslide occurrence factors. They are slope, aspect, curvature and type of topography, texture, material, drainage and effective thickness of soil. type, age, diameter and density of wood, lithology, distance from lineament and land cover. To calculate the relationship between landslides and geospatial database, Bayesian probability methods, weight of evidence. were applied and the contrast value that is >$W^{+}$->$W^{-}$ were calculated. The landslide susceptibility index was calculated by summation of the contrast value and the landslide susceptibility maps were generated using the index. The landslide susceptibility map can be used to reduce associated hazards, and to plan land cover and construction.

• Journal of The Geomorphological Association of Korea
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• v.27 no.1
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• pp.61-89
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• 2020

The objective of this study is to characterize landslide susceptibility depending on various geo-environmental variables as well as to compare the Frequency Ratio (FR) and Evidential Belief Function (EBF) methods for landslide susceptibility analysis of rainfall-induced landslides. In 2013, a total of 259 landslides occurred in Chuncheon, Gangwon Province, South Korea, due to heavy rainfall events with a total cumulative rainfall of 296~721mm in 106~231 hours duration. Landslides data were mapped with better accuracy using the geographic information system (ArcGIS 10.6 version) based on the historic landslide records in Chuncheon from the National Disaster Management System (NDMS), the 2013 landslide investigation report, orthographic images, and aerial photographs. Then the landslides were randomly split into a testing dataset (70%; 181 landslides) and validation dataset (30%; 78 landslides). First, geo-environmental variables were analyzed by using FR and EBF functions for the full data. The most significant factors related to landslides were altitude (100~200m), slope (15~25°), concave plan curvature, high SPI, young timber age, loose timber density, small timber diameter, artificial forests, coniferous forests, soil depth (50~100cm), very well-drained area, sandy loam soil and so on. Second, the landslide susceptibility index was calculated by using selected geo-environmental variables. The model fit and prediction performance were evaluated using the Receiver Operating Characteristic (ROC) curve and the Area Under Curve (AUC) methods. The AUC values of both model fit and prediction performance were 80.5% and 76.3% for FR and 76.6% and 74.9% for EBF respectively. However, the landslide susceptibility index, with classes of 'very high' and 'high', was detected by 73.1% of landslides in the EBF model rather than the FR model (66.7%). Therefore, the EBF can be a promising method for spatial prediction of landslide occurrence, while the FR is still a powerful method for the landslide susceptibility mapping.

• Applied Chemistry for Engineering
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• v.5 no.3
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• pp.537-546
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• 1994

The physical characteristics of polymer modified asphalt depend on many parameters, such as, the polymer nature, polymer content and the asphalt properties. The objective of this study is to investigate the temperature susceptibility of polymer modified asphalt. The asphalts employed in this study were two different grades : a soft(200/300) grade and a hard(85/100) grade. And chlorinated polyethylene of two different characteristics were used : plastomer(Tyrin 2552) and elastomer(Tyrin CM0730). Temperature susceptibility of asphalt is a fundamental feature for characterizing asphalt and modified asphalt. It can be quantified by the penetration index(PI) and pen-vis number(PVN). These indices were obtained from the measurements of penetration and viscosity of the asphalt samples. For both of asphalts, the addition of the polymers increases the value of PI and PVN. Plastomer modified asphalt shows higher value of PI and PVN than elastomer modified asphalt. Soft grade shows more temperature susceptibility than hard grade at elevated temperatures.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.306-309
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• 2004

The susceptibility of landslides and the effect of landslide-related factors at Penang in Malaysia using the Geographic Information System (GIS) and remote sensing data have been evaluated. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from Landsat TM (Thermatic Mapper) satellite images; and the vegetation index value from SPOT HRV (High Resolution Visible) satellite images. Landslide hazardous areas were analysed and mapped using the landslide-occurrence factors employing the probability-frequency ratio method. To assess the effect of these factors, each factor was excluded from the analysis, and its effect verified using the landslide location data. As a result, land 'cover had relatively positive effects, and lithology had relatively negative effects on the landslide susceptibility maps in the study area. In addition, the landslide susceptibility maps using the all factors showed the relatively good results.

• Journal of The Geomorphological Association of Korea
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• v.26 no.3
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• pp.53-67
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• 2019

The purpose of this study is to identify the quality of landslide susceptibility in a landslide-prone area (Jinbu-myeon, Gangwon-do, South Korea) by spatial prediction modeling approach and compare the results obtained. For this goal, a landslide inventory map was prepared mainly based on past historical information and aerial photographs analysis (Daum Map, 2008), as well as some field observation. Altogether, 550 landslides were counted at the whole study area. Among them, 182 landslides are debris flow and each group of landslides was constructed in the inventory map separately. Then, the landslide inventory was randomly selected through Excel; 50% landslide was used for model analysis and the remaining 50% was used for validation purpose. Total 12 contributing factors, such as slope, aspect, curvature, topographic wetness index (TWI), elevation, forest type, forest timber diameter, forest crown density, geology, landuse, soil depth, and soil drainage were used in the analysis. Moreover, to find out the co-relation between landslide causative factors and incidents landslide, pixels were divided into several classes and frequency ratio for individual class was extracted. Eventually, six landslide susceptibility maps were constructed using the Bayesian Predictive Discriminant (BPD), Empirical Likelihood Ratio (ELR), and Linear Regression Method (LRM) models based on different category dada. Finally, in the cross validation process, landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract success rate curve. The result showed that Bayesian, likelihood and linear models were of 85.52%, 85.23%, and 83.49% accuracy respectively for total data. Subsequently, in the category of debris flow landslide, results are little better compare with total data and its contained 86.33%, 85.53% and 84.17% accuracy. It means all three models were reasonable methods for landslide susceptibility analysis. The models have proved to produce reliable predictions for regional spatial planning or land-use planning.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.19-27
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• 2017

This research was aimed to analyze landslide susceptibility and compare the prediction accuracy using ensemble frequency ratio (FR) and logistic regression at the Inje area, Korea. The landslide locations were identified with the before and after aerial photographs of landslide occurrence that were randomly selected for training (70%) and validation (30%). The total twelve landslide-related factors were elevation, slope, aspect, distance to drainage, topographic wetness index, stream power index, soil texture, soil sickness, timber age, timber diameter, timber density, and timber type. The spatial relationship between landslide occurrence and landslide-related factors was analyzed using FR and ensemble model. The produced LSI maps were validated and compared using relative operating characteristics (ROC) curve. The prediction accuracy of produced ensemble LSI map was about 2% higher than FR LSI map. The LSI map produced in this research could be used to establish land use planning and mitigate the damages caused by disaster.

• The Plant Pathology Journal
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• v.36 no.5
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• pp.398-405
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• 2020

Nutrient manipulation is a promising strategy for controlling plant diseases in sustainable agriculture. Although many studies have investigated the relationships between certain elements and plant diseases, few have comprehensively explored how differing mineral nutrition levels might affect plant-fungal pathogen interactions, namely plant susceptibility and resistance. Here, we systematically explored the effects of the seven mineral elements that plants require in the greatest amounts for normal development on the susceptibility of soybean plants (Glycine max) to Fusarium oxysporum infection in controlled greenhouse conditions. Nitrogen (N) negligibly affected plant susceptibility to infection in the range 4 to 24 mM for both tested soybean cultivars. At relatively high concentrations, phosphorus (P) increased plant susceptibility to infection, which led to severely reduced shoot and root dry weights. Potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), and iron (Fe) induced plant resistance to infection as their concentrations were increased. For K and Ca, moderate concentrations had a positive effect on plant resistance to the pathogen, whereas relatively high doses of either element adversely affected plant growth and promoted disease symptoms. Further experiments were conducted, assessing disease suppression by selected combinations of macro-elements and Fe at screened concentrations, i.e., K (9 mM) plus Fe (0.2 mM), and S (4 mM) plus Fe (0.2 mM). The disease index was significantly reduced by the combination of K plus Fe. In conclusion, this systematic investigation of soybean plant responses to F. oxysporum infection provides a solid basis for future environmentally-friendly choices for application in soybean disease control programs.