Dataset Information

Dataset for surface and internal damage after impact on CFRP laminates.

ABSTRACT: Various foreign objects can collide with CFRP structures, such as CFRP aircraft. Once something impacts with CFRP laminates, both surface damage and internal damage can occur. Even if the external damage is such invisible as called barely visible impact damage, there are matrix cracks or delamination that are the main cause of compressive strength reduction, so it is difficult to find the relationship between external and internal damage on CFRP laminates. This dataset is prepared for predicting impact information only from surface damage profiles using Machine Learning (Hasebe et al., 2022). It includes three data, surface damage image (png), surface depth contour image(png), and internal damage image after ultrasound C-scanning (jpg) after low-velocity impact testing under various impact conditions. The data are helpful for researchers and engineers who deal with the impact behavior of CFRP or data science.

SUBMITTER: Hasebe S

PROVIDER: S-EPMC9294053 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Dataset for surface and internal damage after impact on CFRP laminates.

Hasebe Saki S Higuchi Ryo R Yokozeki Tomohiro T Takeda Shin-Ichi SI

Data in brief 20220710

Various foreign objects can collide with CFRP structures, such as CFRP aircraft. Once something impacts with CFRP laminates, both surface damage and internal damage can occur. Even if the external damage is such invisible as called barely visible impact damage, there are matrix cracks or delamination that are the main cause of compressive strength reduction, so it is difficult to find the relationship between external and internal damage on CFRP laminates. This dataset is prepared for predicting ...[more]

PMID: 35864875

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Project description:BackgroundTicks are obligate blood feeders transmitting major pathogens worldwide. Over the past few years, considerable research efforts have focused on the diversity, distribution and impact of gut and intracellular bacterial symbionts on tick development and tick-borne pathogen transmission. The study of this internal microbiome requires the use of a sterilization method to remove external (i.e. cuticular) microbes present on the tick's surface and to avoid any further contamination. Several sterilization methods exist, including ethanol- or bleach-based treatments that are both effective in killing microbes but with different potential effects on DNA denaturation.MethodsWe examined how these different sterilization methods impact the measure of internal microbial diversity hosted by the Cayenne tick Amblyomma cajennense (sensu stricto). Bacterial barcoding investigations based on 16S rRNA gene sequences were conducted on two batches of 50 individuals each: Ticks of the first batch were sterilized with bleach diluted at 1% and the second batch with 70% ethanol. Tick external microbiome was also determined from cuticle smearing and water samples used for tick washing.ResultsBacterial barcoding investigations showed major differences between ethanol- and bleach-treated specimens. Both methods led to the detection of major intracellular bacteria associated with A. cajennense (s.s.) but ethanol-treated ticks always harbored a higher bacterial diversity than bleach-treated ticks. Further examinations of tick gut and tick external microbiome revealed that ethanol-based surface sterilization method is inefficient to eliminate the DNA of external bacteria.ConclusionsWe herein provide evidence that studies investigating the internal microbiome of ticks should consider bleach as the gold standard to efficiently remove cuticular bacterial DNA. Indeed, this method does not impact the internal bacterial diversity hosted by ticks and is thus a better method than the ethanol-based one for studying the internal microbiome.

Dataset Information

Dataset for surface and internal damage after impact on CFRP laminates.

Publications

Dataset for surface and internal damage after impact on CFRP laminates.

Similar Datasets

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