Prediction of Ground Water Content Using Hyperspectral Information through Laboratory Test

With the technological advances led by the fourth industrial revolution, automation has been implemented in road earthworks and paving in the road construction sector. For preparation of construction works, achieving an optimal degree of compaction of the subgrade soil is one of the key factors required for automation of construction and digitalization of quality control. The degree of compaction is greatly affected by water content in geotechnical aspects, and measurement of water content is a necessary process in construction sites. However, conventional methods of water content measurement have limitations and drawbacks and have low efficiency considering the recent trend of construction automation and digitalization of quality control. Therefore, in this study, hyperspectral remote sensing was applied for efficient large-scale measurement of water content over a wide area. To this end, first, through laboratory tests, soil water content was normalized with spectral information. A spectrum was derived with a varying water content using standard sand, and reflectance was obtained for specific ranges of wavelength. Finally, we obtained the relationship between the reflectance and the water content by comparing with various fitting models. At this time, the ranges of wavelength to be used in the equation were specified and presented in an exponential model

Prediction of Ground Water Content Using Hyperspectral Information through Laboratory Test

With the technological advances led by the fourth industrial revolution, automation has been implemented in road earthworks and paving in the road construction sector. For preparation of construction works, achieving an optimal degree of compaction of the subgrade soil is one of the key factors required for automation of construction and digitalization of quality control. The degree of compaction is greatly affected by water content in geotechnical aspects, and measurement of water content is a necessary process in construction sites. However, conventional methods of water content measurement have limitations and drawbacks and have low efficiency considering the recent trend of construction automation and digitalization of quality control. Therefore, in this study, hyperspectral remote sensing was applied for efficient large-scale measurement of water content over a wide area. To this end, first, through laboratory tests, soil water content was normalized with spectral information. A spectrum was derived with a varying water content using standard sand, and reflectance was obtained for specific ranges of wavelength. Finally, we obtained the relationship between the reflectance and the water content by comparing with various fitting models. At this time, the ranges of wavelength to be used in the equation were specified and presented in an exponential model

Proposal of Construction Method of Smart Liner to Block and Detect Spreading of Soil Contaminants by Oil Spill

Soil is an important factor for public health, and when a soil contaminant occurs by oil spill, it has a great impact on the ecosystem, including humans. Accordingly, the area is blocked using a vertical barrier, and various remediation methods are being applied when an oil spill occurs. This study intends to use a smart liner to prevent and detect the spreading of soil contaminants in a situation in which oil spill detection is important. However, the smart liner is in the form of a fiber, so it is impossible to construct it in a general method. Therefore, the roll spreading and inserting method (RSIM) is proposed for smart liner construction. RSIM is a method of installing a supporting pile after excavating the ground and connecting the smart liner vertically to the ground surface. This method is the first method proposed in this study, and the design and concept have not been established. In this study, a conceptual design was established to apply RSIM in the actual field, and a scale model experiment was performed to prove it. As a result of the scale model experiment, the applicability of RSIM was confirmed. Finally, numerical analysis using Abaqus/CAE was performed to carry out the detailed design of RSIM (installation conditions such as dimensions). Analysis parameters were embedded depth, thickness, diameter, and material properties of a supporting pile according to the ground type. As a result of the analysis, it was confirmed that the results of RSIM analysis were interacting with all parameters according to the ground conditions. Therefore, it was confirmed that the actual design should be based on ground investigation and economic conditions, not standardized regulations

Iridium-Catalyzed Single-Step <i>N</i>‑Substituted Lactam Synthesis from Lactones and Amines

Catalytic lactam synthesis was achieved directly from lactones and amines using an Ir catalyst. Three sequential transformationsaminolysis of lactone, <i>N</i>-alkylation of amine with hydroxyamide, and intramolecular transamidation of aminoamideafforded the corresponding <i>N</i>-substituted lactams

Spectrum Index for Estimating Ground Water Content Using Hyperspectral Information

Quality control considerably affects road stability and operability and is directly linked to the underlying ground compaction. The degree of compaction is largely determined by water content, which is typically measured at the actual construction site. However, conventional methods for measuring water content do not capture entire construction sites efficiently. Therefore, this study aimed to apply remote sensing of hyperspectral information to efficiently measure the groundwater content of large areas. A water content prediction equation was developed through an indoor experiment. The experimental samples comprised 0–40% (10% increase) of fine contents added to standard sand. As high water content is not required in road construction, 0–15% (1% increase) of water content was added. The test results were normalized, the internal and external environments were controlled for precise results, and a wavelength–reflection curve was derived for each test case. Data variability analyses were performed, and the appropriate wavelength for water content reflection, as well as reflectance, was determined and converted into a spectrum index. Finally, various fitting models were applied to the corresponding spectrum index for water content prediction. Reliable results were obtained with the reflectance corresponding to a wavelength of 720 nm applied as the spectrum index

Effects of Powder Carrier on the Morphology and Compressive Strength of Iron Foams: Water vs Camphene

With its well-known popularity in structural applications, considerable attention has recently been paid to iron (Fe) and its oxides for its promising functional applications such as biodegradable implants, water-splitting electrodes, and the anode of lithium-ion batteries. For these applications, iron and its oxides can be even further utilized in the form of porous structures. In order to control the pore size, shape, and amount, we synthesized Fe foams using suspensions of micrometric Fe2O3 powder reduced to Fe via freeze casting in water or liquid camphene as a solvent through sublimation of either ice or camphene under 5 pct H2/Ar gas and sintering. We then compared them and found that the resulting Fe foam using water as a solvent (p = 71.7 pct) showed aligned lamellar macropores replicating ice dendrite colonies, while Fe foam using camphene as a solvent (p = 68.0 pct) exhibited interconnected equiaxed macropores replicating camphene dendrites. For all directions with respect to the loading axis, the compressive behavior of the water-based Fe foam with a directional elongated wall pore structure was anisotropic (11.6 ± 0.9 MPa vs 7.8 ± 0.8 MPa), whereas that of the camphene-based Fe foam with a random round pore structure was nearly isotropic (12.0 ± 1.1 MPa vs 11.6 ± 0.4 MPa). © 2018 The Minerals, Metals & Materials Society and ASM Internation

Simultaneous determination of nine UV filters and four preservatives in suncare products by high-performance liquid chromatography

HPLC method for quantitative determination of four preservatives and nine UV filters worldwide authorized in commercial suncare product was developed and validated, and then 101 samples of commercial suncare products were analyzed for the UV filters and preservatives using the proposed method. The mobile phase was acetonitrile-water containing 0.5% acetic acid using a gradient elution at a flow rate of 0.9 mL/min and UV measurements were carried out at 320 nm for UV filters and 254 nm for preservatives. The correlation coefficients of each calibration curves were mostly higher than 0.999. The percent relative standard deviations (%RSD)ranged from 0.97% to 6.1% for five sample aliquots. The recoveries from the spiked solutions were 98–102%. 2-ethylhexylp-methoxycinnamate (EHMC) was detected in 96 of 101 commercial suncare products and the concentration was in the range of 3.08–8.16% and 18 samples were found to exceed the 7.5% which has been defined as the maximum allowed concentration in Korea. Methyl paraben was detected in 81 of 101 samples and the next-most often detected preservatives were propyl paraben (25), ethyl paraben (18), and butyl paraben (4). Three samples of 101 suncare products exceeded the maximum allowed concentration (i.e., 0.58– 0.79%). The proposed HPLC method allows efficient and simultaneous analysis of preservatives and UV filters suitable for quality control assays of commercial suncare products

Prediction of gestational diabetes mellitus in Asian women using machine learning algorithms

Abstract This study developed a machine learning algorithm to predict gestational diabetes mellitus (GDM) using retrospective data from 34,387 pregnancies in multi-centers of South Korea. Variables were collected at baseline, E0 (until 10 weeks’ gestation), E1 (11–13 weeks’ gestation) and M1 (14–24 weeks’ gestation). The data set was randomly divided into training and test sets (7:3 ratio) to compare the performances of light gradient boosting machine (LGBM) and extreme gradient boosting (XGBoost) algorithms, with a full set of variables (original). A prediction model with the whole cohort achieved area under the receiver operating characteristics curve (AUC) and area under the precision-recall curve (AUPR) values of 0.711 and 0.246 at baseline, 0.720 and 0.256 at E0, 0.721 and 0.262 at E1, and 0.804 and 0.442 at M1, respectively. Then comparison of three models with different variable sets were performed: [a] variables from clinical guidelines; [b] selected variables from Shapley additive explanations (SHAP) values; and [c] Boruta algorithms. Based on model [c] with the least variables and similar or better performance than the other models, simple questionnaires were developed. The combined use of maternal factors and laboratory data could effectively predict individual risk of GDM using a machine learning model