• Advances in concrete construction
• /
• v.8 no.2
• /
• pp.77-90
• /
• 2019

This paper aims to investigate vibration frequency decrease (vibration period elongation) of reinforced concrete (RC) structure with unreinforced infill wall and reinforced infill wall exposed to progressively increased artificial earthquake load on shaking table. For this purpose, two shaking table experiments were selected as a case study. Shaking table experiments were carried on 1:1 scaled prototype one bay one storey RC structure with infill walls. The purpose of this shaking table experiment sequence is to assess local behavior and progressive collapse mechanism. Frequency decrease and eigen-vector evolution are directly related to in-plane and out-of-plane bearing capacities of infill wall enclosure with reinforced concrete frame. Firstly, frequency decrease-damage relationship was evaluated on the base of experiment results. Then, frequency decrease and stiffness degradation were evaluated with applied Peak Ground Acceleration (PGA) by considering strength deterioration. Lastly, eigenvector evolution-local damage and eigenvector evolution-frequency decrease relationship was investigated. Five modes were considered while evaluating damage and frequency decrease of the tested specimens. The relationship between frequency decrease, stiffness degradation and damage level were presented while comparing with Unreinforced Brick Infill (URB) and Reinforced Infill wall with Bed Joint Reinforcement (BJR) on the base of natural vibration frequency.

• Smart Structures and Systems
• /
• v.16 no.1
• /
• pp.47-65
• /
• 2015

A novel finite element (FE) model updating method based on multi-resolution analysis (MRA) is proposed. The true stiffness of the FE model is considered as the superposition of two pieces of stiffness information of different resolutions: the pre-defined stiffness information and updating stiffness information. While the resolution of former is solely decided by the meshing density of the FE model, the resolution of latter is decided by the limited information obtained from the experiment. The latter resolution is considerably lower than the former. Second generation wavelet is adopted to describe the updating stiffness information in the framework of MRA. This updating stiffness in MRA is realized at low level of resolution, therefore, needs less number of updating parameters. The efficiency of the optimization process is thus enhanced. The proposed method is suitable for the identification of multiple irregular cracks and performs well in capturing the global features of the structural damage. After the global features are identified, a refinement process proposed in the paper can be carried out to improve the performance of the MRA of the updating information. The effectiveness of the method is verified by numerical simulations of a box girder and the experiment of a three-span continues pre-stressed concrete bridge. It is shown that the proposed method corresponds well to the global features of the structural damage and is stable against the perturbation of modal parameters and small variations of the damage.

• Structural Engineering and Mechanics
• /
• v.46 no.4
• /
• pp.459-486
• /
• 2013

A novel damage classification method based on wavelet packet transform and statistical analysis is developed in this study for structural health monitoring. The response signal of a structure under an impact load is normalized and then decomposed into wavelet packet components. Energies of these wavelet packet components are then calculated to obtain the energy distribution. Statistical similarity comparison based on an F-test is used to classify the structure from changes in the wavelet packet energy distribution. A statistical indicator is developed to describe the damage extent of the structure. This approach is applied to the test results from simply supported reinforced concrete beams in the laboratory. Cases with single and two damages are created from static loading, and accelerations of the structure from under impact loads are analyzed. Results show that the method can be used with no reference baseline measurement and model for the damage monitoring and assessment of the structure with alarms at a specified significance level.

• Molecules and Cells
• /
• v.42 no.7
• /
• pp.546-556
• /
• 2019

Protein phosphatase 4 (PP4) is a crucial protein complex that plays an important role in DNA damage response (DDR), including DNA repair, cell cycle arrest and apoptosis. Despite the significance of PP4, the mechanism by which PP4 is regulated remains to be elucidated. Here, we identified a novel PP4 inhibitor, protein phosphatase 4 inhibitory protein (PP4IP) and elucidated its cellular functions. PP4IP-knockout cells were generated using the CRISPR/Cas9 system, and the phosphorylation status of PP4 substrates (H2AX, KAP1, and RPA2) was analyzed. Then we investigated that how PP4IP affects the cellular functions of PP4 by immunoprecipitation, immunofluorescence, and DNA double-strand break (DSB) repair assays. PP4IP interacts with PP4 complex, which is affected by DNA damage and cell cycle progression and decreases the dephosphorylational activity of PP4. Both overexpression and depletion of PP4IP impairs DSB repairs and sensitizes cells to genotoxic stress, suggesting timely inhibition of PP4 to be indispensable for cells in responding to DNA damage. Our results identify a novel inhibitor of PP4 that inhibits PP4-mediated cellular functions and establish the physiological importance of this regulation. In addition, PP4IP might be developed as potential therapeutic reagents for targeting tumors particularly with high level of PP4C expression.

• Structural Engineering and Mechanics
• /
• v.70 no.3
• /
• pp.339-350
• /
• 2019

This research focuses on finite element model updating and damage assessment of structures at element level based on global nondestructive test results. For this purpose, an optimization system is generated to minimize the structural dynamic parameters discrepancies between numerical and experimental models. Objective functions are selected based on the square of Euclidean norm error of vibration frequencies and modal assurance criterion of mode shapes. In order to update the finite element model and detect local damages within the structural members, modern optimization techniques is implemented according to the evolutionary algorithms to meet the global optimized solution. Using a simulated numerical example, application of genetic algorithm (GA), particle swarm (PSO) and artificial bee colony (ABC) algorithms are investigated in FE model updating and damage detection problems to consider their accuracy and convergence characteristics. Then, a hybrid multi stage optimization method is presented merging advantages of PSO and ABC methods in finding damage location and extent. The efficiency of the methods have been examined using two simulated numerical examples, a laboratory dynamic test and a high-rise building field ambient vibration test results. The implemented evolutionary updating methods show successful results in accuracy and speed considering the incomplete and noisy experimental measured data.

• The Plant Pathology Journal
• /
• v.39 no.5
• /
• pp.449-465
• /
• 2023

Plants are challenged by various pathogens throughout their lives, such as bacteria, viruses, fungi, and insects; consequently, they have evolved several defense mechanisms. In addition, plants have developed localized and systematic immune responses due to biotic and abiotic stress exposure. Animals are known to activate DNA damage responses (DDRs) and DNA damage sensor immune signals in response to stress, and the process is well studied in animal systems. However, the links between stress perception and immune response through DDRs remain largely unknown in plants. To determine whether DDRs induce plant resistance to pathogens, Arabidopsis plants were treated with bleomycin, a DNA damage-inducing agent, and the replication levels of viral pathogens and growth of bacterial pathogens were determined. We observed that DDR-mediated resistance was specifically activated against viral pathogens, including turnip crinkle virus (TCV). DDR increased the expression level of pathogenesis-related (PR) genes and the total salicylic acid (SA) content and promoted mitogen-activated protein kinase signaling cascades, including the WRKY signaling pathway in Arabidopsis. Transcriptome analysis further revealed that defense-and SA-related genes were upregulated by DDR. The atm-2atr-2 double mutants were susceptible to TCV, indicating that the main DDR signaling pathway sensors play an important role in plant immune responses. In conclusion, DDRs activated basal immune responses to viral pathogens.

• Korean Journal of Agricultural Science
• /
• v.50 no.3
• /
• pp.539-547
• /
• 2023

This study evaluated the impact of rain shelters made of plastic film on spring frost damage and fruit quality in Asian pears ('Niitaka') over two years. In 2021, during the coldest spring days (between 0:00 am and 7:00 am), temperatures dropped to -1.20 - 0.43℃ at 120 cm and -1.33 - 0.57℃ at 200 cm above ground level in the control. Conversely, the rain shelter treatment maintained higher temperatures, -0.40 - 0.87℃ at 120 cm and -0.43 - 0.77℃ at 200 cm. Flower damage was significantly lower in the rain-sheltered group, with incidences of 1.3 and 6.9% at 120 and 200 cm, respectively, compared with 18.1 and 22.6% in the control group. Visual observations verified the prevention of frost adhesion on flower organs in the sheltered group, compared with noticeable pistil death and petal browning in the control group. In 2022, when temperatures remained above 0℃, fruitlet stalk length was 5 - 6 mm longer in the sheltered group. The cumulative impact of rain shelters was evident in the improved fruit quality over the two years. This study suggests resilient cultivation strategies in the face of climate change to reduce frost damage, increase productivity, improve fruit quality, and potentially increase incomes of the farmers.

• Proceedings of the KSR Conference
• /
• 2011.10a
• /
• pp.281-286
• /
• 2011

Station structures, one of important infrastructures, which have been being operated since the 1970s, are especially vulnerable to even the medium-level earthquake and they could be damaged by long-term internal or external vibrations such as ambient vibrations. Recently, much attention has been paid to real-time monitoring of the fatal defect or long-term deterioration of civil infrastructures to ensure their safety and adequate performance throughout their life span. In this study, a structural health monitoring methodology using acceleration responses is proposed to evaluate the health-state of the station structures and to detect initial damage-stage. A damage index is developed using the acceleration data and it is applied to outlier analysis, one of unsupervised learning based pattern recognition methods. A threshold value for the outlier analysis is determined based on confidence level of the probabilistic distribution of the acceleration data. The probabilistic distribution is selected according to the feature of the collected data.

• Journal of The Korean Digital Architecture Interior Association
• /
• v.5 no.2
• /
• pp.36-42
• /
• 2005

The interest about noise and vibration which occurs in buildings is increasing by a living level elevation recently. Particularly, the vibration which occurs from the elevator cage of the building is possibility of damage to the users of the building continuously. So, the purpose of this study is a placeThe interest about noise and vibration which occurs in buildings is increasing by a living level elevation recently. Particularly, the noise and vibration which occurs from the elevator of the building is possibility of damage to the users of the building continuously. So, the objective of this research is to investigate the noise and vibration of the elevator and propose the method which reduces of it gained thorough this study.

• Journal of the Korean Society for Precision Engineering
• /
• v.14 no.12
• /
• pp.48-55
• /
• 1997

A useful method to detect tool breakage suing neural network of cutting force signal is porposed and implemented in a basic cutting process. Cutting signal is gathered by tool dynamometer and normalized as a preprocessing. The cutting force signal level is continually monitored and compared with the predefined level. The neural network has been trained normalized sample data of the normal operation and cata-strophic tool failure using backpropagation learning process. The develop[ed system is verified to be very effective in real-time usage with minor modification in conventional cutting processes.