Damage Characteristics Research Articles

Long-range PCR as a tool for evaluating mitochondrial DNA damage: Principles, benefits, and limitations of the technique.

Mitochondrial DNA (mtDNA) is often more susceptible to damage compared to nuclear DNA. This is due to its localization in the mitochondrial matrix, where a large portion of reactive oxygen species are produced. Mitochondria do not have histones and mtDNA is only slightly protected by histone-like proteins and is believed to have less efficient repair mechanisms. In this review, we discuss the long-range PCR method, which allows for the effective detection of mtDNA damage. The method is based on the assumption that various types of DNA lesions can interfere the progress of DNA polymerase, resulting in reduced amplification efficiency. It can be used to estimate the number of additional (above background) lesions in mtDNA. The review outlines the evolution of the methodology, its variations, applications in a wide range of model organisms, the advantages of the method and its limitations, as well as ways to overcome these limitations. Over the past two decades, the use of long-range PCR has allowed the study of mtDNA repair mechanisms, the characteristics of mitochondrial genome damage in various neurodegenerative diseases, aging, ischemic and oncological processes, as well as in anticancer therapy. The assessment of mtDNA damage has also been proposed for use in environmental biomonitoring. This review provides a critical evaluation of the various variations of this method, summarizes the accumulated data, and discusses the role of mtDNA damage in different organs at the organismal level.

Numerical Simulation of the Damage Characteristics of Carbon fiber-reinforced Aluminum Laminates under Hypervelocity Impact Based on the FE-SPH Adaptive Method

Numerical Simulation of the Damage Characteristics of Carbon fiber-reinforced Aluminum Laminates under Hypervelocity Impact Based on the FE-SPH Adaptive Method

Deep guided wave convolution neural network committee-based multi-path fusion diagnosis method for fatigue corner crack

Accurate diagnosis of crack size is a critical task for guided wave (GW)-based structural health monitoring (SHM). However, fatigue cracks would have complex morphology due to complex structural geometries and loading conditions, in which multiple dimension characteristics, like crack length, depth, and angle are involved. It is challenging to quantitatively evaluate these characteristics with GW signals from a single excitation-sensing path. This paper proposes a novel deep guided wave convolution neural network (CNN) committee-based multi-path GW fusion diagnosis method, aiming at quantitative evaluation of dimension characteristics of the complex fatigue damage. GW signals from multiple excitation-sensing paths are synthesized as a high-dimension input image to enhance the effects of the fatigue crack. Besides, the deep GW-CNN committee is developed for damage quantification, in which each GW-CNN is trained with a portion of the training dataset to reduce the impact of small sample size. The proposed method is validated on fatigue tests of landing gear beam specimens under variable amplitude loading, which is designed referring to the critical region of a real aircraft and its fatigue crack presents as a corner crack. The leave-one-out validation results show the effectiveness of the proposed method, especially improvements in the diagnosis of small cracks.

Damage Evolution Characteristics of Steel-Fiber-Reinforced Cellular Concrete Based on Acoustic Emission

In order to investigate the steel fiber parameters on the damage characteristics and crack evolution of cellular concrete materials, uniaxial compression–acoustic emission combined tests were carried out on steel-fiber-reinforced cellular concrete (SFRCC) with different steel fiber contents (0%, 0.5%, 1%, 1.5%, and 2%) and different porosities (10% and 20%). The material damage evolution characteristics were analyzed by acoustic emission parameters and IB values, and the crack types were identified using Gaussian mixture clustering method (GMM) pairs. The results show the following: the inclusion of steel fibers increased the compressive strength of cellular concrete by 19.8~46.3% at 10% porosity, and by 37.1~102.2% at 20% porosity; the addition of steel fibers significantly increased the density and intensity of the acoustic emission signals; the decreasing tendency of the IB value at the peak stress slowed down with the increase in the amount of steel fibers, and the steel fibers could effectively inhibit the crack development; crack classification results show that the proportion of shear cracks in all stages of cellular concrete increased significantly after the addition of steel fibers.

Research on health monitoring of concrete structure based on G-S-G

An improved concrete structure health monitoring method based on G-S-G is proposed, which fully combines an optimized Gray-Level Co-occurrence Matrix (GLCM) with an improved Self-Organizing Map (SOM) neural network to achieve accurate and real-time concrete structure health monitoring. First of all, in order to obtain a dynamic image of the crack damage region of interest (ROI) with clear contrast and obvious target, the image acquisition system and image optimization method are used to process the damaged image. Moreover, in order to realize the accurate location of crack damage, crack damage identification research based on GLCM-SOM effectively eliminates the interference of honeycomb and pothole damage on crack damage. In order to obtain the indicators for monitoring the health status of the structure, the damage characteristics and probability distribution characteristics of the concrete structure in the gray level co-occurrence matrix are combined to extract the probability range index (PRI). On the basis of extracting the crack damage index, in order to verify the reliability of the sensitive feature index, starting from the two dimensions of damage texture feature and data expansion, through reverse research of the damage model, the damage index of accurately locating the crack damage was selected. It follows that the final sensitive indicators: entropy (ENT) and PRI can be used for structural health monitoring because of their strong damage characterization ability and sensitivity to damage characteristics. This research shows that is helpful to realize the high precision intelligent concrete structure health monitoring of modern concrete structure crack damage.

Characterization of feeding damage by tea mosquito bug, Helopeltis theivora Waterhouse (Hemiptera: Miridae) on Hainan Dayezhong tea cultivar.

The tea mosquito bug, Helopeltis theivora Waterhouse (Hemiptera: Miridae), is a devastating piercing-sucking pest in tropical tea plantations. The Hainan Dayezhong (HNDYZ) is a large-leaf tea cultivar widely cultivated around the Hainan tea region in South China. However, information regarding the feeding damage of H. theivora on the HNDYZ tea plant remains scarce. Here, we first describe the morphology of H. theivora in Hainan tea region. Subsequently, we investigate the feeding biology of H. theivora on HNDYZ tea shoots under laboratory conditions. Additionally, we survey the infestations of H. theivora in a small-leaf Jinxuan tea plantation and three large-leaf HNDYZ tea plantations under varying shaded conditions. The results indicated that the morphological features of eggs, nymphs, and adults of H. theivora in the Hainan tea region were similar to those of the same species reported in other tropical tea regions. Nymphs and adults of H. theivora primarily fed on tender leaves and produced a subcircular spot within 2 to 4 minutes. This feeding spot would gradually turn dark brown within 24 hours. Furthermore, the adjacent scattered spots would connect after 48 hours, resulting in a necrotic patch on the leaves by 72 hours. The peak feeding time for H. theivora occurred at night, specifically from 7:00 PM to 1:00 AM. The most preferred feeding site was at the second leaf position, accounting for 70.94 ± 3.68% of daily feeding spots. During the feeding peak, adults H. theivora produced more feeding spots than nymphs, with females and 5th-instar nymphs creating the largest feeding areas among all life stages. Field investigations showed that damage caused by H. theivora on the large-leaf HNDYZ tea cultivar was significantly greater than that on the small-leaf Jinxuan tea cultivar. More serious infestations of H. theivora were observed in the high-shade HNDYZ tea plantation compared to the medium-shade and no-shade HNDYZ tea plantations. This suggests that the different tea cultivars and shade conditions in tea plantations may influence the population of H. theivora in the field. These findings provide new insights for further research related to the feeding strategy of H. theivora on the HNDYZ tea cultivar.

The Anchorage Performance and Mechanism of Prefabricated Concrete Shear Walls with Closed-Loop Rebar

To thoroughly investigate the anchorage performance of a novel prefabricated concrete shear wall system assembled by anchoring closed-loop rebar, rebar pull-out tests were conducted. The effects of different rebar distribution forms, closed-loop rebar anchoring heights, and dowel rebar diameters on anchorage performance were considered. Strain measurements at key points were taken, and the failure modes and peak loads of shear walls with various closed-loop rebar assemblies were obtained. The results indicated that the rebars in all specimens fractured, with peak loads ranging from 90 kN to 100 kN, satisfying the anchorage requirements of the rebar. This demonstrates that even when the anchorage length of the rebar is less than specified, the method of assembling by anchoring closed-loop rebar can still provide good anchorage performance. Moreover, steel bars and concrete have different damage and failure characteristics under different load levels. This research also indicates that specimens with uniformly distributed closed-loop rebar exhibit superior anchorage performance compared to those with adjacent distribution. Furthermore, increasing the overlapping height of the closed-loop rebar contributed to enhancing the safety margin of the anchorage, while the diameter of the dowel rebar (similar to stirrups) had a relatively minor effect on the anchorage performance. These findings provide a scientific basis for the design and construction of prefabricated concrete shear walls with closed-loop rebar.

Characterization of damage in REBCO tapes for joint-less 2G-HTS magnets during mechanical slitting

Characterization of damage in REBCO tapes for joint-less 2G-HTS magnets during mechanical slitting

Low-velocity impact behaviors of TFMLs in hydrothermal and thermal-cycling environments

Thermoplastic fiber metal laminates (TFMLs) have garnered significant attention due to their excellent impact resistance. However, the influence of practical environmental factors on the impact resistance of TFMLs remains inadequately explored. This study endeavor to investigate the impact behaviors and damage tolerance of TFMLs, employing a novel thermoplastic resin, Elium®188, under two simulated marine environments: a thermal-cycling condition (involving 90 days of exposure to 70℃ for 6 h followed by 18 h at room temperature daily) and a hydrothermal environment (consisting of 90 days of immersion in distilled water at 70℃). The impact response and damage characteristics were analyzed through low-velocity impact (LVI) and compression-after-impact (CAI) tests. The findings indicated that under thermal-cycling, the LVI resistance decreased as a result of resin aging, but the resin hardened during aging led to a 48 % increase in CAI strength over the 90-day period. In the hydrothermal environment, the absorbed energy decreased by 5.6 % and CAI strength decreased by 13.7 % after 90 days, primarily due to resin hydrolysis and erosion of the TFMLs’ internal structure by water molecules. This research offers valuable insights into the impact resistance and damage tolerance of TFMLs, serving as a vital reference for practical applications in marine settings.

Investigation on structure stability and damage mechanism of cemented paste backfill under the coupling effect of water-static load

Cemented paste backfill (CPB) is easy to withstand the coupling effect of the mining operation equipment's crushing, water immersion, forming all kinds of intrinsic or extrinsic defects affecting its load-bearing capacity. In this paper, the initial immersion age and immersion time were used as variables, the damage and uniaxial compression characteristics of CPB under the coupling effect of water-static load were explored. Results show that the damage of water-immersed CPB under static load are mainly affected by water lubrication and pore water pressure and it improves the plastic deformation of CPB and weakens the energy storage capacity. When the initial immersion age was 3d, the effect is more significant. Water immersion increases the rate of damage with strain before peak strain and decreases the rate of damage with strain after peak strain. The strength of CPB varies from 0.3MPa to 0.8MPa at the same initial immersion age. The damage constitutive model of CPB under water-static load coupling is established, and the damage mechanism is revealed. Compared with the immersion time, reducing the initial immersion age is the key factor to improve the structure stability of CPB.

High-Frequency Active Interrogation for Structural Damage Identification Enabled by Intelligent Hierarchical Search

A common issue in structural damage identification is the measurement information being limited while the baseline structural model is complex, which renders the inverse analysis underdetermined. Leveraging that the damage in its early stage usually affects only small area(s), a multiobjective optimization can be formulated by minimizing the difference between model prediction and physical measurement and concurrently enforcing the sparsity in damage locations. The challenges, however, lie in handling the high-dimensional parametric space with multimodal objective functions as well as the computational cost brought by incorporating the l0 norm minimization. In this research, we synthesize a hierarchical framework that searches potential damage locations first and then delves into accurate characterization of damage. In particular, to address the high-dimensional and multimodal challenges intensified by high-frequency measurement toward high-precision identification, we adopt the Group Lasso technique for sparsity induction and use the technique for order preference by similarity to ideal solution to realize optimization phase transition, aiming at improving the computational efficiency as well as the accuracy. The validation of our approach through a piezoelectric admittance sensing testbed underscores its potential for structural health monitoring practices by providing a robust, accurate, and computationally efficient approach for early-stage damage identification and assessment.

Mechanical properties and damage characteristics of modified polyurethane concrete under uniaxial and cyclic compression

Mechanical properties and damage characteristics of modified polyurethane concrete under uniaxial and cyclic compression

Fatigue damage characteristics and reliability analysis of asphalt mixtures using damage mechanics

Fatigue damage characteristics and reliability analysis of asphalt mixtures using damage mechanics

Dynamic damage characteristics and control mechanism of rocks anchored by constant resistance and energy absorption material

Dynamic damage characteristics and control mechanism of rocks anchored by constant resistance and energy absorption material

Defect And Damage Characterization Of Additively Manufactured Titanium Alloy Ti-5553 Using Traditional Computed Tomography Volume Segmentation And Machine Learning Algorithms

The mechanical response of a component is affected by defects, such as porosity, arising from the laser powder bed fusion (LPBF) fabrication process. Thus, it is important to develop accurate and efficient inspection methods for identifying porosity. In this work, porosity identified in an X-ray computed tomography (XCT) volume of a Ti-5553 coupon was compared to pores identified in a serial sectioned volume that represented the ground truth. The porosity of the XCT scan was identified using contrast-based, ISO-based, and machine learning (ML) methods for segmentation. Large inherent porosity was easy to identify, but the ISO thresholding still struggled due to the intensity gradient resulting from both the beam hardening in XCT and the uneven lighting of the serial sectioning panels. The results show that ML-based methods were better suited for identifying small pores and reducing the amount of false positives. Additionally, high strain-rate impact testing was done on some of the XCT samples as well as post-mortem XCT inspection, and the same suite of segmentation and quantification tools were used to identify the large spallation cavities. The comparison of porosity pre- and post-mortem provides insight on the influence of the LPBF porosity on the formation of spall cavities.

A damage characterization method for thin-walled butt welded joints with slant fracture in 6005A-T6 aluminum alloy

A damage characterization method for thin-walled butt welded joints with slant fracture in 6005A-T6 aluminum alloy

Disruption of BCAA degradation is a critical characteristic of diabetic cardiomyopathy revealed by integrated transcriptome and metabolome analysis.

In this study, we integrated transcriptomic and metabolomic analyses to achieve a comprehensive understanding of the underlying mechanisms of diabetic cardiomyopathy (DCM) in a diabetic rat model. Functional and molecular characterizations revealed significant cardiac injury, dysfunction, and ventricular remodeling in DCM. A thorough analysis of global changes in genes and metabolites showed that amino acid metabolism, especially the breakdown of branched-chain amino acids (BCAAs) such as valine, leucine, and isoleucine, is highly dysregulated. Furthermore, the study identified the transcription factor Gata3 as a predicted negative regulator of the gene encoding the key enzyme for BCAA degradation. These findings suggest that the disruption of BCAA degradation is a critical characteristic of diabetic myocardial damage and indicate a potential role for Gata3 in the dysregulation of BCAA metabolism in the context of DCM.

Features of placental damage in patients with perinatal losses during premature birth and acute COVID-19

Features of placental damage in patients with perinatal losses during premature birth and acute COVID-19

Fatigue Experiment and Failure Mechanism Analysis of Aircraft Titanium Alloy Wing-Body Connection Joint.

Taking the titanium alloy wing-body connection joint at the rear beam of a certain type of aircraft as the research object, this study analyzed the failure mechanism and verified the structural safety of the wing-body connection joint under actual flight loads. Firstly, this study verified the validity of the loading system and the measuring system in the test system through the pre-test, and the repeatability of the test was analyzed for error to ensure the accuracy of the experimental data. Then, the test piece was subjected to 400,000 random load tests of flight takeoffs and landings, 100,000 Class A load tests, and ground-air-ground load tests, and the test piece fractured under the ground-air-ground load tests. Lastly, the mechanism analysis and structural safety verification of the fatigue fracture of the joints were carried out by using a stereo microscope and scanning electron microscope. The results show that fretting fatigue is the main driving force for crack initiation, and the crack shows significant fatigue damage characteristics in the stable growth stage and follows Paris' law. Entering the final fracture region, the joint mainly experienced ductile fracture, with typical plastic deformation features such as dimples and tear ridges before fracture. The fatigue crack growth behavior of the joint was quantitatively analyzed using Paris' law, and the calculated crack growth period life was 207,374 loadings. This result proves that the crack initiation life accounts for 95.19% of the full life cycle, which is much higher than the design requirement of 400,000 landings and takeoffs, indicating that the structural design of this test piece is on the conservative side and meets the requirements of aircraft operational safety. This research is of great significance in improving the safety and reliability of aircraft structures.

An efficient method for identifying surface damage in hydraulic concrete buildings

Traditional hydraulic structures rely on manual visual inspection for apparent integrity, which is not only time-consuming and labour-intensive but also inefficient. The efficacy of deep learning models is frequently constrained by the size of available data, resulting in limited scalability and flexibility. Furthermore, the paucity of data diversity leads to a singular function of the model that cannot provide comprehensive decision support for improving maintenance measures. This paper proposes an efficacious methodology for identifying diverse apparent damages in hydraulic structures to address the limitations of existing technologies. The advanced features of apparent damage in hydraulic structures were elucidated by fine-tuning the top-level parameters of the lightweight pre-trained model, thereby mitigating the data dependency issue inherent in the model. Ensemble learning algorithms are employed to classify high-dimensional samples to enhance the accuracy and stability of the classification. However, ensemble learning algorithms are subject to time consuming issues when applied to high-dimensional datasets. To this end, we propose a robust discriminative feature selection model to identify the most salient features, thereby enhancing the performance of apparent damage recognition in hydraulic structures while concurrently reducing the inference time. The results demonstrated that the accuracies of this method in identifying crack, fracture, hole and normal structures were 87.65%, 87.82%, 96.99%, and 95.25%, respectively. This methodology exhibits significant applicability and practical value for the intelligent inspection of hydraulic structures.