Comprehensive heart-cut multidimensional gas chromatography (CH/C MDGC) without a cryogenic trapping device was developed with an established approach for calculation of first and second dimensional retention indices (I and I) for improved compound identification. A first dimensional (D) DB-1MS column (60 m) and a second dimensional (D) DB-WAX column (60 m) were applied with a Deans switch (DS) using a constant H/C window of 0.2 min and a periodic multiple heartcut strategy comprising 225H/C throughout the CH/C. I was calculated based on comparison of the middle of the heartcut time with the alkane retention times on the D column. A multi-location peak parking approach using sixteen sets of automated injections of alkane references was also established with the least square curve fitting method for construction of the alkane isovolatility curves which were applied for I calculation. The untargeted compound analysis of a perfume sample was then performed according to comparison with the libraries of mass spectra, I and I. The CH/C MDGC system with a 25 h analysis time showed a peak capacity (n) of 9198 and 128 separated peaks with 71 compounds successfully identified according to MS, I and I library match under the established error approximation criteria. Furthermore, relationship between the analysis time and number of separated peaks was proposed based on the set of 84 identifiable compounds. With the compensation of lower separation performance and greater I errors, the analysis time could be reduced by applying a 2.5 min H/C window with a total analysis time of 2 h and n of 1134.
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December 2024
College of Information Engineering, SuQian University, SuQian, 223800, China.
The safety and reliability of rotating machinery hinge significantly on the proper functioning of rolling bearings. In the last few years, there have been significant advances in the algorithms for intelligent fault diagnosis of bearings. However, the vibration signals collected by machines are inevitably affected by irrelevant noise because of the complex working environments of bearings.
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December 2024
Shandong Agricultural University, Taian, 271018, China.
Acoustic emission information can describe the damage degree of rock samples in the process of failure. However, as a discrete non-stationary signal, acoustic emission information is difficult to be effectively processed by conventional methods, while wavelet analysis is an effective method for non-stationary signal processing. Therefore, acoustic emission signal is deeply studied by using wavelet analysis method.
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December 2024
Department of Psychiatry and Behavioral Sciences and Weill Center for Neurosciences, University of California, San Francisco, CA, 94107, USA.
Telomere attrition is a hallmark of biological aging, contributing to cellular replicative senescence. However, few studies have examined the determinants of telomere attrition in vivo in humans. Mitochondrial Health Index (MHI), a composite marker integrating mitochondrial energy-transformation capacity and content, may be one important mediator of telomere attrition, as it could impact telomerase activity, a direct regulator of telomere maintenance.
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December 2024
Department of Computer Engineering, Marwadi University, Rajkot, 360003, India.
The contributed absorber design in graphene addition with the displacement of three materials for resonator design in Aluminum (Al), the middle substrate position with Titanium nitride (TiN), and the ground layer deposition by Iron (Fe) respectively. For the absorption validation highlight, the best four absorption wavelengths (µm) of 0.29, 0.
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December 2024
Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh.
Prediction and discovery of new materials with desired properties are at the forefront of quantum science and technology research. A major bottleneck in this field is the computational resources and time complexity related to finding new materials from ab initio calculations. In this work, an effective and robust deep learning-based model is proposed by incorporating persistent homology with graph neural network which offers an accuracy of and an F1 score of in classifying topological versus non-topological materials, outperforming the other state-of-the-art classifier models.
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