2,616 research outputs found
Generating Giant and Tunable Nonlinearity in a Macroscopic Mechanical Resonator from Chemical Bonding Force
Nonlinearity in macroscopic mechanical system plays a crucial role in a wide
variety of applications, including signal transduction and processing,
synchronization, and building logical devices. However, it is difficult to
generate nonlinearity due to the fact that macroscopic mechanical systems
follow the Hooke's law and response linearly to external force, unless strong
drive is used. Here we propose and experimentally realize a record-high
nonlinear response in macroscopic mechanical system by exploring the
anharmonicity in deforming a single chemical bond. We then demonstrate the
tunability of nonlinear response by precisely controlling the chemical bonding
interaction, and realize a cubic elastic constant of \mathversion{bold}, many orders of magnitude larger in strength
than reported previously. This enables us to observe vibrational bistate
transitions of the resonator driven by the weak Brownian thermal noise at 6~K.
This method can be flexibly applied to a variety of mechanical systems to
improve nonlinear responses, and can be used, with further improvements, to
explore macroscopic quantum mechanics
Nutritional Composition and Modern Pharmacological Research Progress of Coicis Semen
Coicis semen has the effects of invigorating the spleen, invigorating the lungs and removing dampness, clearing heat, expelling pus, removing stagnation and stopping diarrhea. The main nutrients of Coicis semen are fatty acids, esters, polysaccharides, flavonoids, glycoproteins and other components. The results of modern pharmacological studies have shown that Coicis semen has multiple pharmacological effects such as anti-tumor, improving immunity, lowering blood sugar, anti-inflammatory and analgesic, and regulating blood lipid metabolism. By consulting relevant literature in recent years, this paper reviewed the extraction process of Coicis semen nutritional components, including fatty acids, lipids, polysaccharides, flavonoids, Coicis semen oil. Modern pharmacological effects such as anti-tumor, improving immunity, lowering blood glucose and regulating blood lipid metabolism were also included. The development directions of Coicis semen for hypoglycemic, anti-inflammatory, analgesic, osteoporosis and other related functional foods were summarized. This review could provide reference for further development and application of Coicis semen
Intelligent diagnostic scheme for lung cancer screening with Raman spectra data by tensor network machine learning
Artificial intelligence (AI) has brought tremendous impacts on biomedical
sciences from academic researches to clinical applications, such as in
biomarkers' detection and diagnosis, optimization of treatment, and
identification of new therapeutic targets in drug discovery. However, the
contemporary AI technologies, particularly deep machine learning (ML), severely
suffer from non-interpretability, which might uncontrollably lead to incorrect
predictions. Interpretability is particularly crucial to ML for clinical
diagnosis as the consumers must gain necessary sense of security and trust from
firm grounds or convincing interpretations. In this work, we propose a
tensor-network (TN)-ML method to reliably predict lung cancer patients and
their stages via screening Raman spectra data of Volatile organic compounds
(VOCs) in exhaled breath, which are generally suitable as biomarkers and are
considered to be an ideal way for non-invasive lung cancer screening. The
prediction of TN-ML is based on the mutual distances of the breath samples
mapped to the quantum Hilbert space. Thanks to the quantum probabilistic
interpretation, the certainty of the predictions can be quantitatively
characterized. The accuracy of the samples with high certainty is almost
100. The incorrectly-classified samples exhibit obviously lower certainty,
and thus can be decipherably identified as anomalies, which will be handled by
human experts to guarantee high reliability. Our work sheds light on shifting
the ``AI for biomedical sciences'' from the conventional non-interpretable ML
schemes to the interpretable human-ML interactive approaches, for the purpose
of high accuracy and reliability.Comment: 10 pages, 7 figure
Robust Adaptive Fuzzy Output Tracking Control for a Class of Twin-Roll Strip Casting Systems
This paper is concerned with the adaptive fuzzy control problem for a class of twin-roll strip casting systems. By using fuzzy logic systems (FLSs) to approximate the compounded nonlinear functions, a novel robust output tracking controller with adaptation laws is designed based on the high gain observer. First, the nonlinear dynamic equations for the roll gap and the molten steel level are constructed, respectively. Then, the mean value theorem is employed to transform the nonaffine nonlinear systems to the corresponding affine nonlinear systems. Moreover, it is also proved that all the closed-loop signals are bounded and the systems output tracking errors can converge to the desired neighborhoods of the origin via the Lyapunov stability analysis. Finally, simulation results, based on semiexperimental system dynamic model and parameters, are worked out to show the effectiveness of the proposed adaptive fuzzy design method
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Development of China’s first space-borne aerosol-cloud high-spectral-resolution lidar: retrieval algorithm and airborne demonstration
Aerosols and clouds greatly affect the Earth’s radiation budget and global climate. Light detection and ranging (lidar) has been recognized as a promising active remote sensing technique for the vertical observations of aerosols and clouds. China launched its first space-borne aerosol-cloud high-spectral-resolution lidar (ACHSRL) on April 16, 2022, which is capable for high accuracy profiling of aerosols and clouds around the globe. This study presents a retrieval algorithm for aerosol and cloud optical properties from ACHSRL which were compared with the end-to-end Monte-Carlo simulations and validated with the data from an airborne flight with the ACHSRL prototype (A2P) instrument. Using imaging denoising, threshold discrimination, and iterative reconstruction methods, this algorithm was developed for calibration, feature detection, and extinction coefficient (EC) retrievals. The simulation results show that 95.4% of the backscatter coefficient (BSC) have an error less than 12% while 95.4% of EC have an error less than 24%. Cirrus and marine and urban aerosols were identified based on the airborne measurements over different surface types. Then, comparisons were made with U.S. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) profiles, Moderate-resolution Imaging Spectroradiometer (MODIS), and the ground-based sun photometers. High correlations (R > 0.79) were found between BSC (EC) profiles of A2P and CALIOP over forest and town cover, while the correlation coefficients are 0.57 for BSC and 0.58 for EC over ocean cover; the aerosol optical depth retrievals have correlation coefficient of 0.71 with MODIS data and show spatial variations consistent with those from the sun photometers. The algorithm developed for ACHSRL in this study can be directly employed for future space-borne high-spectral-resolution lidar (HSRL) and its data products will also supplement CALIOP data coverage for global observations of aerosol and cloud properties
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