• Molecules and Cells
• /
• v.45 no.7
• /
• pp.502-511
• /
• 2022

Bacterial volatile compounds (BVCs) exert beneficial effects on plant protection both directly and indirectly. Although BVCs have been detected in vitro, their detection in situ remains challenging. The purpose of this study was to investigate the possibility of BVCs detection under in situ condition and estimate the potentials of in situ BVC to plants at below detection limit. We developed a method for detecting BVCs released by the soil bacteria Bacillus velezensis strain GB03 and Streptomyces griseus strain S4-7 in situ using solid-phase microextraction coupled with gas chromatography-mass spectrometry (SPME-GC-MS). Additionally, we evaluated the BVC detection limit in the rhizosphere and induction of systemic immune response in tomato plants grown in the greenhouse. Two signature BVCs, 2-nonanone and caryolan-1-ol, of GB03 and S4-7 respectively were successfully detected using the soil-vial system. However, these BVCs could not be detected in the rhizosphere pretreated with strains GB03 and S4-7. The detection limit of 2-nonanone in the tomato rhizosphere was 1 µM. Unexpectedly, drench application of 2-nonanone at 10 nM concentration, which is below its detection limit, protected tomato seedlings against Pseudomonas syringae pv. tomato. Our finding highlights that BVCs, including 2-nonanone, released by a soil bacterium are functional even when present at a concentration below the detection limit of SPME-GC-MS.

• Atmosphere
• /
• v.31 no.5
• /
• pp.637-656
• /
• 2021

The United States has been known as the world's major producer of crops such as wheat, corn, and soybeans. Therefore, using meteorological long-term forecast data to project reliable crop yields in the United States is important for planning domestic food policies. The current study is part of an effort to improve the seasonal predictability of regional-scale precipitation across the United States for estimating crop production in the country. For the purpose, a dynamic downscaling method using Weather Research and Forecasting (WRF) model is utilized. The WRF simulation covers the crop-growing period (March to October) during 2000-2020. The initial and lateral boundary conditions of WRF are derived from the Pusan National University Coupled General Circulation Model (PNU CGCM), a participant model of Asia-Pacific Economic Cooperation Climate Center (APCC) Long-Term Multi-Model Ensemble Prediction System. For bias correction of downscaled daily precipitation, empirical quantile mapping (EQM) is applied. The downscaled data set without and with correction are called WRF_UC and WRF_C, respectively. In terms of mean precipitation, the EQM effectively reduces the wet biases over most of the United States and improves the spatial correlation coefficient with observation. The daily precipitation of WRF_C shows the better performance in terms of frequency and extreme precipitation intensity compared to WRF_UC. In addition, WRF_C shows a more reasonable performance in predicting drought frequency according to intensity than WRF_UC.

• Nuclear Engineering and Technology
• /
• v.52 no.12
• /
• pp.2789-2802
• /
• 2020

Small Modular Reactors (SMRs) are attracting wide attention due to their outstanding performance, extensive studies have been carried out for lead-based fast reactors (LFRs) that cooled with Lead or Lead-bismuth (LBE), and small modular natural circulation LFR is one of the promising candidates for SMRs and LFRs development. One of the challenges for the design small modular natural circulation LFR is to master the natural circulation thermal-hydraulic performance in the reactor primary circuit, while the natural circulation characteristics is a coupled thermal-hydraulic problem of the core thermal power, the primary loop layout and the operating state of secondary cooling system etc. Thus, accurate predicting the natural circulation LFRs thermal-hydraulic features are highly required for conducting reactor operating condition evaluate and Thermal hydraulic design optimization. In this study, a thermal-hydraulic analysis code is developed for small modular natural circulation LFRs, which is based on several mathematical models for natural circulation originally. A small modular natural circulation LBE cooled fast reactor named URANUS developed by Korea is chosen to assess the code's capability. Comparisons are performed to demonstrate the accuracy of the code by the calculation results of MARS, and the key thermal-hydraulic parameters agree fairly well with the MARS ones. As a typical application case, steady-state analyses were conducted to have an assessment of thermal-hydraulic behavior under nominal condition, and several parameters affecting natural circulation were evaluated. What's more, two characteristics parameters that used to analyze natural circulation LFRs natural circulation capacity were established. The analyses show that the core thermal power, thermal center difference and flow resistance is the main factors affecting the reactor natural circulation. Improving the core thermal power, increasing the thermal center difference and decreasing the flow resistance can significantly increase the reactor mass flow rate. Characteristics parameters can be used to quickly evaluate the natural circulation capacity of natural circulation LFR under normal operating conditions.

• Geomechanics and Engineering
• /
• v.27 no.5
• /
• pp.433-445
• /
• 2021

Artificial ground freezing (AGF) is a commonly used geotechnical support technique that can be applied in any soil type and has low environmental impact. Experimental and numerical investigations have been conducted to optimize AGF for application in diverse scenarios. Precise simulation of groundwater flow is crucial to improving the reliability these investigations' results. Previous experimental research has mostly considered horizontal seepage flow, which does not allow accurate calculation of the groundwater flow velocity due to spatial variation of the piezometric head. This study adopted vertical seepage flow-which can maintain a constant cross-sectional area-to eliminate the limitations of using horizontal seepage flow. The closure time is a measure of the time taken for an impermeable layer to begin to form, this being the time for a frozen soil-ice wall to start forming adjacent to the freeze pipes; this is of great importance to applied AGF. This study reports verification of the reliability of our experimental apparatus and measurement system using only water, because temperature data could be measured while freezing was observed visually. Subsequent experimental AFG tests with saturated sandy soil were also performed. From the experimental results, a method of estimating closure time is proposed using the inflection point in the thermal conductivity difference between pore water and pore ice. It is expected that this estimation method will be highly applicable in the field. A further parametric study assessed factors influencing the closure time using a two-dimensional coupled thermo-hydraulic numerical analysis model that can simulate the AGF of saturated sandy soil considering groundwater flow. It shows that the closure time is affected by factors such as hydraulic gradient, unfrozen permeability, particle thermal conductivity, and freezing temperature. Among these factors, changes in the unfrozen permeability and particle thermal conductivity have less effect on the formation of frozen soil-ice walls when the freezing temperature is sufficiently low.

• Journal of the Korean Society of Systems Engineering
• /
• v.18 no.2
• /
• pp.75-93
• /
• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.64 no.5
• /
• pp.9-16
• /
• 2022

Syngas, also known as synthesis gas, synthetic gas, or producer gas, is a combustible gas mixture generated when organic material (biomass) is heated in a gasifier with a limited airflow at a high temperature and elevated pressure. The present research was aimed at modifying the existing LPG engine generator for fully operated syngas. During this study, the designed gasifier-powered woodchip biomass was used for syngas production to generate power. A 6.0 kW LPG engine generator was modified and tested for operation on syngas. In the experiments, syngas and LPG fuels were tested as test fuels. For syngas production, 3 kg of dry woodchips were fed and burnt into the designed downdraft gasifier. The gasifier was connected to a blower coupled with a slider to help the air supply and control the ignition. The convection cooling system was connected to the syngas flow pipe for cooling the hot produce gas and filtering the impurities. For engine modification, a customized T-shaped flexible air/fuel mixture control device was designed for adjusting the correct stoichiometric air-fuel ratio ranging between 1:1.1 and 1.3 to match the combustion needs of the engine. The composition of produced syngas was analyzed using a gas analyzer and its composition was; 13~15 %, 10.2~13 %, 4.1~4.5 %, and 11.9~14.6 % for CO, H₂, CH₄, and CO₂ respectively with a heating value range of 4.12~5.01 MJ/Nm³. The maximum peak power output generated from syngas and LPG was recorded using a clamp-on power meter and found to be 3,689 watts and 5,001 watts, respectively. The results found from the experiment show that the LPG engine generator operated on syngas can be adopted with a de-ration rate of 73.78 % compared to its regular operating fuel.

• Journal of Animal Science and Technology
• /
• v.65 no.2
• /
• pp.401-411
• /
• 2023

Many studies have been conducted to improve technology for semen cryopreservation in pigs. However, computer-assisted analysis of sperm motility and morphology is insufficient to predict the molecular function of frozen-thawed semen. More accurate expression patterns of boar sperm proteins may be derived using the isobaric tags for relative and absolute quantification (iTRAQ) technique. In this study, the iTRAQ-labeling system was coupled with liquid chromatography tandem-mass spectrometry (LC-MS/MS) analysis to identify differentially expressed CM10-fractionated proteins between fresh and frozen-thawed boar semen. A total of 76 protein types were identified to be differentially expressed, among which 9 and 67 proteins showed higher and lower expression in frozen-thawed than in fresh sperm samples, respectively. The classified functions of these proteins included oxidative phosphorylation, mitochondrial inner membrane and matrix, and pyruvate metabolic processes, which are involved in adenosine triphosphate (ATP) synthesis; and sperm flagellum and motile cilium, which are involved in sperm tail structure. These results suggest a possible network of biomarkers associated with survival after the cryopreservation of Duroc boar semen.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 1996.06c
• /
• pp.314-324
• /
• 1996

A high speed NMR quality evaluation sensor was designed , constructed and tested . The device consists of an NMR spectrometer coupled to a conveyor system. The conveyor was run at speeds ranging from 0 to 250 mm/s. Spectral of avocado fruits and one-dimensional magnetic resonance images of pickled olives were acquired while the samples were moving on a conveyor belt mounted through a 20Tesla NMR magnet with a 20 mm diameter surface coil and a 150 mm diameter imaging coil respectively. Fro a magnetic resonance spectrum analysis, motion through variations in the magnetic field tends to narrow spectral line width just like using sample rotation in high resolution NMR to narrow spectral line width. Spectrum analysis was used to detect the dry weight of avocado fruits using the ratio oil and water resonance peaks. Good correlations maximum r=0.970@ 50 mm/s and minimum r=0.894@250mm/s ) between oil and water resonance peak ratio and dry weight of avocados were observed at speeds ra ging from0 to 250mm/s. For the application of magnetic resonance imaging (MRI) method, the projections were used to distinguish between pitted and non-pitted olives . Effect of fruit position in the coil was tested and coil degree effects were noticed when projects were generated under dynamic conditions. Various belt speeds (up to 250mm/s) were tested and detection results were compared to static measurements. Higher classification errors were occurred at dynamic conditions compared to errors while olives were at rest.

• Structural Engineering and Mechanics
• /
• v.81 no.2
• /
• pp.219-230
• /
• 2022

Due to the large number of railway bridges along China's high-speed railway (HSR) lines, which cover a wide area with many lines crossing the seismic zone, the possibility of a HSR train running over a bridge when an earthquake occurs is relatively high. Since the safety performance of the train will be threatened, it is necessary to study the safety of trains running over HSR bridges during earthquakes. However, ground motion (GM) is highly random and selecting the appropriate ground-motion intensity measures (IMs) for train running safety analysis is not trivial. To deal this problem, a model of a coupled train-bridge system under seismic excitation was established and 104 GM samples were selected to evaluate the correlation between 16 different IMs and train running safety over HSR bridges during earthquakes. The results show that spectral velocity (SvT₁) and displacement (SdT₁) at the fundamental period of the structure have good correlation with train running safety for medium-and long-period HSR bridges, and velocity spectrum intensity (VSI) and Housner intensity (HI) have good correlation for a wide range of structural periods. Overall, VSI and HI are the optimal IMs for safety analysis of trains running over HSR bridges during earthquakes. Finally, based on VSI and HI, the IM thresholds of an HSR bridge at different speed were analyzed.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.344-352
• /
• 2022

Accurate indoor localization of construction workers and mobile assets is essential in safety management. Existing positioning methods based on GPS, wireless, vision, or sensor based RTLS are erroneous or expensive in large-scale indoor environments. Tightly coupled sensor fusion mitigates these limitations. This research paper proposes a state-of-the-art positioning methodology, addressing the existing limitations, by integrating Stereo Visual Inertial Odometry (SVIO) with fiducial landmarks called AprilTags. SVIO determines the relative position of the moving assets or workers from the initial starting point. This relative position is transformed to an absolute position when AprilTag placed at various entry points is decoded. The proposed solution is tested on the NVIDIA ISAAC SIM virtual environment, where the trajectory of the indoor moving forklift is estimated. The results show accurate localization of the moving asset within any indoor or underground environment. The system can be utilized in various use cases to increase productivity and improve safety at construction sites, contributing towards 1) indoor monitoring of man machinery coactivity for collision avoidance and 2) precise real-time knowledge of who is doing what and where.