49,093 research outputs found
Two-wavelength lidar inversion algorithm for determination of aerosol extinction-to-backscatter ratio and its application to CALIPSO lidar measurements,
No full textA modified two-wavelength lidar inversion algorithm is proposed to aid in the retrieval of aerosol extinction-to-backscatter ratios (lidar ratio) as well as backscatter coefficients and extinction color ratios from simultaneous two-wavelength elastic backscatter lidar measurements. To demonstrate the feasibility of the algorithm, both the Raman method and the two-wavelength method have been applied to the ground-based measurements at 355 and 532. nm; moreover, it has been applied to the data acquired by the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar, and to the simultaneous ground-based lidar measurements carried out at Napoli (southern Italy, 40.838 °N, 14.183 °E, 118. m above sea level). Three cases of Saharan dust transport towards Europe have been considered. From the comparison, it can be found that the values of lidar ratio and backscatter coefficient retrieved by the modified two-wavelength algorithm are in good agreement with those obtained by the Raman method. Moreover the retrieved mean values of the lidar ratios and color ratios are in agreement with those reported by other authors
A 2 h periodic variation in the low-mass X-ray binary Ser X-1
No full textSpectroscopy of the low-mass X-ray binary Ser X-1 using the Gran Telescopio Canarias have revealed a ?2 h periodic variability that is present in the three strongest emission lines. We tentatively interpret this variability as due to orbital motion, making it the first indication of the orbital period of Ser X-1. Together with the fact that the emission lines are remarkably narrow, but still resolved, we show that a main-sequence K dwarf together with a canonical 1.4 M? neutron star gives a good description of the system. In this scenario, the most likely place for the emission lines to arise is the accretion disc, instead of a localized region in the binary (such as the irradiated surface or the stream-impact point), and their narrowness is due instead to the low inclination (?10°) of Ser X-1
Chinese Herb Patrinia Herba (Bai Jiang Cao) for Acute Respiratory Tract Infections: A Systematic Review of Clinical Studies
No full textIntroduction: acute respiratory tract infections (ARTIs) are a group of common diseases with a high incidence that cause tremendous pressure on the healthcare system globally. Patrinia Herba (Bai Jiang Cao) has a long history of use in China. This systematic review aimed to evaluate the clinical effectiveness and safety of Patrinia Herba for ARTIs.Methods: we searched the Chinese and English databases from their inception to March 2022 for clinical studies that tested single-herb preparations or formulae containing Patrinia Herba for ARTIs. We used the Cochrane Risk of Bias 2 and non-randomised studies as intervention tools for quality assessment. The review protocol was registered with PROSPERO (CRD42021260330).Results: six trials (2 107 participants) were identified. Two trials were pooled. The results showed that, in adults and children with influenza, single Patrinia Herba granules or injections improved the participants’ feelings of aversion to cold, general malaise, nasal obstruction, runny nose, sore throat, cough, headache, and dizziness after a two-day treatment. For adults and children with pneumonia, Patrinia Herba compound preparations plus antibiotics appeared better than antibiotics alone in relieving fever, cough, shortness of breath, and chest pain. The overall methodological quality of the included randomised controlled trials was rated as unclear, low, or moderate for controlled clinical trials. No severe adverse events were reported.Discussion: low- or moderate-quality evidence demonstrates that single herb or compound preparations of Patrinia Herba may be effective for ARTIs in terms of symptom remission. Further high-quality studies are needed to confirm their potential for treating ARTIs.</p
Electrophysiological and behavioural responses of Thrips hawaiiensis (Thysanoptera: Thripidae) to the Floral Volatiles of the Horticultural Plant Magnolia grandiflora (Magnoliales: Magnoliaceae)
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AI3SD Video: The Application of Machine Learning in Molecular Spectroscopy Study
No full textOptical-spectroscopy provides powerful toolkits to decipher molecular structures and their configuration evolutions. However, the theoretical analysis of spectroscopic signals and connecting them with structural detail is a challenging task. Moreover, the intrinsic complexity of spectroscopic signals of molecular systems makes it difficult to correlate spectral characteristics with the underlying molecular structure and dynamics. Herein, we have developed data-driven machine learning (ML) protocols that can predict infrared (IR), ultraviolet/visible (UV/Vis) and Raman spectra of molecule systems with 3 to 5 orders of magnitude reduced computation cost compared to direct quantum chemistry calculations. A convolutional neural network (CNN) model was trained and tested on a dataset consisting 87993 spectra computed from protein peptide segments with α-helical, β-sheet, and other typical secondary structures. The secondary structure classification accuracy reached near 100% and over 98.7% on spectra sets of new segments extracted from the same and homologous proteins, respectively. Importantly, we demonstrate the ML protocol to realize cost-effective relations between spectra, structure, and chemical properties, i.e. spectra determination/prediction from structural information, and configuration or chemical properties determination/recognition from spectroscopic signals.1. S. Ye, K. Zhong, J.X. Zhang, W. Hu, J. Hirst, G.Z. Zhang, S. Mukamel, J. Jiang*, A Machine Learning Protocol for Predicting Protein Infrared Spectra, J. Am. Chem. Soc. 142 (2020) 19071-19077.2. X.J. Wang, S. Ye, W. Hu, E. Sharman, R. Liu, Y. Liu, Y. Luo, J. Jiang*, Electric Dipole Descriptor for Machine Learning Prediction of Catalyst Surface-Molecular Adsorbate Interactions, J. Am. Chem. Soc. 142 (2020) 7737-7743.3. S. Ye, W. Hu, X. Li, J.X. Zhang, K. Zhong, G.Z. Zhang, Y. Luo, S. Mukamel*, J. Jiang*, A Neural Network Protocol for Electronic excitations of N-Methylacetamide, Proc Natl Acad Sci USA. 116 (2019) 11612-11617.4. W. Hu, S. Ye, Y.J Zhang, T.D. Li, G.Z. Zhang, Y. Luo, S. Mukamel, J. Jiang*, Machine Learning Protocol for Surface-Enhanced Raman Spectroscopy, J. Phys. Chem. Lett. 10 (2019) 6026-6031
Relations between x-ray timing features and spectral parameters of galactic black hole x-ray binaries
No full textWe present a study of correlations between spectral and timing parameters for a sample of black hole X-ray binary candidates. Data are taken from GX
339-4, H 1743-322, and XTE J1650-500, as the Rossi X-ray Timing Explorer
(RXTE) observed complete outbursts of these sources. In our study we investigate outbursts that happened before the end of 2009 to make use of the high-energy coverage of the HEXTE detector and select observations that
show a certain type of quasi-periodic oscillations (type-C QPOs). The spectral parameters are derived using the empirical convolution model simpl to model the Comptonized component of the emission together with a disc blackbody for the emission of the accretion disc. Additional spectral features, namely a reflection component, a high-energy cut-off, and excess emission at 6.4 keV, are taken into account. Our investigations confirm the known positive
correlation between photon index and centroid frequency of the QPOs and reveal an anti-correlation between the fraction of up-scattered photons and the QPO frequency. We show that both correlations behave as expected in the “sombrero”
geometry. Furthermore, we find that during outburst decay the correlation between photon index and QPO frequency follow a general track, independent of individual outbursts
Joint Distribution-Based Test Selection for Fault Detection and Isolation under Multiple Faults Condition
No full textEffective and comprehensive fault information can reduce the maintenance cost of a system. The selection of a proper test is important to obtain accurate information for fault detection and isolation (FDI) once a fault occurs. In real systems, the FDI task is made difficult by the correlations in the physical behaviors of the components and the possible coexistence of multiple faults. In this article, a new type of test selection model based on deep joint distribution is proposed, which takes into account the dependence and ambiguity groups. The optimal test can, then, be selected by an improved discrete binary particle swarm optimization (IBPSO) algorithm. The strengths of the proposed optimal test selection strategy are: 1) it can greatly improve the accuracy of test selection; 2) it considers the influence of multiple faults; 3) the dependence problem of the test outcomes can be addressed; and 4) it is computationally efficient and capable of selecting optimal test points meeting the requirements of real-time FDI. The application to a negative feedback circuit demonstrates the performance of the proposed method
Machine learning-based algorithm for SAR wave parameters retrieval during a tropical cyclone
No full textThe major objective of our research is to retrieve wave parameters from synthetic aperture radar (SAR) images during a tropical cyclone (TC) based on a machine learning method. In this study, more than 2000 Sentinel-1 (S-1) images obtained in interferometric-wide (IW) and extra wide (EW) mode are collected during 200 tropical cyclones (TCs), which are collocated with hindcasted waves by a third-generation numeric model, namely WAVEWATCH-III (WW3). It is found that wave parameters, i.e., SWH, MWP and MWL, are correlated with several SAR-measured image variables. Based on these findings, a machine learning method, namely eXtreme Gradient Boosting (XGBoost), is developed through the training dataset using 1600 images. The trained algorithm is tested over 400 images and the retrievals are compared with WW3 simulations. The statistical analysis shows that the root mean squared error (RMSE) and scatter index (SI) of significant wave height (SWH) are 0.19 m and 0.06 respectively. The RMSE and SI of mean wave period (MWP) are 0.19 s and 0.03 respectively. The RMSE of the mean wave length (MWL) is 3.77 m and the SI is 0.04. Comparisons between inverted SWH by XGBoost methods and the altimeter measurements presents a 0.59 m RMSE of SWH with and 0.19 SI. This result is improved comparing to the results (i.e., a 1.44 m RMSE of SWH with a 0.45 SI) achieved by a previous algorithm. Collectively, it is considered that machine learning is a valuable method to extract wave parameters from dual-polarization SAR images
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