RESUMEN
In response to the growing inspection demand exerted by process automation in component manufacturing, non-destructive testing (NDT) continues to explore automated approaches that utilize deep-learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep-learning models. This study investigated the influence of two image-quality parameters, namely signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), on the performance of a U-net deep-learning semantic segmentation model. Input images were acquired with varying combinations of exposure factors, such as kilovoltage, milli-ampere, and exposure time, which altered the resultant radiographic image quality. The data were sorted into five different datasets according to their measured SNR and CNR values. The deep-learning model was trained five distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection-over-union (IoU) metric of 0.9594 on test data of the same category but dropped to 0.5875 when tested on lower CNR test data. The result of this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters in order to enhance the performance of deep-learning segmentation models for NDT digital X-ray radiography applications.
RESUMEN
Advanced materials such as continuous carbon fiber-reinforced thermoplastic (CFRP) laminates are commonly used in many industries, mainly because of their strength, stiffness to weight ratio, toughness, weldability, and repairability. Structural components working in harsh environments such as satellites are permanently exposed to some sort of damage during their lifetimes. To detect and characterize these damages, non-destructive testing and evaluation techniques are essential tools, especially for composite materials. In this study, artificial intelligence was applied in combination with infrared thermography to detected and segment impact damage on curved laminates that were previously submitted to a severe thermal stress cycles and subsequent ballistic impacts. Segmentation was performed on both mid-wave and long-wave infrared sequences obtained simultaneously during pulsed thermography experiments by means of a deep neural network. A deep neural network was trained for each wavelength. Both networks generated satisfactory results. The model trained with mid-wave images achieved an F1-score of 92.74% and the model trained with long-wave images achieved an F1-score of 87.39%.
RESUMEN
Dietary fatty acids (FAs) determine the quality of dairy products. The trans-10, cis-12 conjugated linoleic acid (t10c12-CLA) is commonly considered an FA factor leading to milk fat depression syndrome (MFDs) in dairy cow. However, its effect on dairy cow performance and involvement in milk fat metabolism have been insufficiently explored. This study administered 136.17 g/day of rumen-protected CLA (RP-CLA) to dairy cows and found a diminution in milk fat percentage and a trend of increasing milk protein percentage on day 21 postpartum. Lactose content, milk yield, and net energy for lactation were unaffected. In the cell experiments, Oil Red O staining showed a notable increase in lipid droplets. Gene and protein expression analysis showed that 300 µM t10c12-CLA upregulated the expression of CD36, DGAT2, and ADRP, while downregulating the expression of ACACA, FASN, SREBP1, FABP3, FATP3, ACSL4, LPIN1, DGAT1, BTN1A1, XDH, SNAP23, and VAMP4. This provides a possible mechanistic pathway for the contradictory phenomenon of t10c12-CLA reducing milk fat while increasing lipid droplets. Overall, t10c12-CLA, as a long-chain fatty acid, can promote lipid droplet synthesis but may reduce milk fat by inhibiting lipid droplet fusion and secretion, FAs de novo synthesis, and triglyceride biosynthesis.
RESUMEN
Milk fat synthesis in cows mainly includes the synthesis of short- and medium-chain fatty acids, the uptake, transport, and activation of long-chain fatty acids (LCFAs), the synthesis of triglycerides, and the synthesis of the genes, transcription factors, and signaling pathways involved. Although the various stages of milk fat synthesis have been outlined in previous research, only partial processes have been revealed. CLA consists of an aggregation of positional and geometric isomers of linoleic fatty acid, and the accumulated evidence suggests that the two isomers of the active forms of CLA (cis-9, trans-11 conjugated linoleic acid and trans-10, cis-12 conjugated linoleic acid, abbreviated as c9, t11-CLA and t10, c12-CLA) can reduce the fat content in milk by regulating lipogenesis, fatty acid (FA) uptake, oxidation, and fat synthesis. However, the mechanism through which CLA inhibits milk fat synthesis is unique, with most studies focusing only on the effects of CLA on one of the genes, transcription factors, or signaling pathways involved. In this study, we summarized the structure and function of classic genes and pathways (mTOR, SREBP, AMPK, and PPARG) and new genes or pathways (THRSP, METTL3, ELOVL, and LPIN1) involved in each stage of milk fat synthesis and demonstrated the interactions between genes and pathways. We also examined the effects of other substances (melanin, nicotinic acid, SA, etc.). Furthermore, we evaluated the influence of ß-sitosterol, sodium butyrate, Met arginine, and Camellia oleifera Abel on milk fat synthesis to improve the mechanism of milk fat synthesis in cows and provide a mechanistic reference for the use of CLA in inhibiting milk fat biosynthesis.