Taxonomic analysis of asteroids with artificial neural networks

We study the surface composition of asteroids with visible and/or infrared spectroscopy. For example, asteroid taxonomy is based on the spectral features or multiple color indices in visible and near-infrared wavelengths. The composition of asteroids gives key information to understand their origin and evolution. However, we lack compositional information for faint asteroids due to limits of ground-based observational instruments. In the near future, the Chinese Space Survey telescope (CSST) will provide multiple colors and spectroscopic data for asteroids of apparent magnitude brighter than 25 mag and 23 mag, respectively. For the aim of analysis of the CSST spectroscopic data, we applied an algorithm using artificial neural networks (ANNs) to establish a preliminary classification model for asteroid taxonomy according to the design of the survey module of CSST. Using the SMASS II spectra and the Bus-Binzel taxonomy system, our ANN classification tool composed of 5 individual ANNs is constructed, and the accuracy of this classification system is higher than 92 %. As the first application of our ANN tool, 64 spectra of 42 asteroids obtained in 2006 and 2007 by us with the 2.16-m telescope in the Xinglong station (Observatory Code 327) of National Astronomical Observatory of China are analyzed. The predicted labels of these spectra using our ANN tool are found to be reasonable when compared to their known taxonomic labels. Considering the accuracy and stability, our ANN tool can be applied to analyse the CSST asteroid spectra in the future.Comment: 10 pages,8 figures,accepted by AJ for publicatio

The Attenuation of Moutan Cortex on Oxidative Stress for Renal Injury in AGEs-Induced Mesangial Cell Dysfunction and Streptozotocin-Induced Diabetic Nephropathy Rats

Oxidative stress (OS) has been regarded as one of the major pathogeneses of diabetic nephropathy (DN) through damaging kidney which is associated with renal cells dysfunction. The aim of this study was to investigate whether Moutan Cortex (MC) could protect kidney function against oxidative stress in vitro or in vivo. The compounds in MC extract were analyzed by HPLC-ESI-MS. High-glucose-fat diet and STZ (30 mg kg−1) were used to induce DN rats model, while 200 μg mL−1 AGEs were for HBZY-1 mesangial cell damage. The treatment with MC could significantly increase the activity of SOD, glutathione peroxidase (GSH-PX), and catalase (CAT). However, lipid peroxidation malondialdehyde (MDA) was reduced markedly in vitro or in vivo. Furthermore, MC decreased markedly the levels of blood glucose, serum creatinine, and urine protein in DN rats. Immunohistochemical assay showed that MC downregulated significantly transforming growth factor beta 2 (TGF-β2) protein expression in renal tissue. Our data provided evidence to support this fact that MC attenuated OS in AGEs-induced mesangial cell dysfunction and also in high-glucose-fat diet and STZ-induced DN rats

Load prediction with an improved feature selection method for building energy management of an office park

Load prediction plays a significant role in building energy management. An accurate HVAC load prediction model highly depends on the feature selection and the quality of training data. In previous work on load prediction, the input features are majorly manually selected by expertise, which is relatively subjective and lacks theoretical supports. Using the real building operational data collected from an office park located in Hangzhou, this paper developed a short-term cooling load prediction model, in which the input features are selected based on an analysis on the heat transfer process. Combined with qualitative analysis of the real data, several features such as outdoor air enthalpy and indoor black-bulb temperatures from different orientations are introduced into the model. The proposed model was then applied to the HVAC control system of the office park. Compared to the load prediction model with commonly used features, the proposed model reduced CRVMSE by 21% and MAPE by 30% during the operation period of the system. Furthermore, the impacts of training dataset size and prediction time range on model’s accuracy and training time were discussed

Refined system parameters and TTV study of transiting exoplanetary system HAT-P-20

This work is supported by National Natural Science Foundation of China through grants No. U1531121, No. 10873031 and No. 11473066.We report new photometric observations of the transiting exoplanetary system HAT-P-20, obtained using CCD cameras at Yunnan Observatories and Ho Koon Nature Education cum Astronomical Centre, China, from 2010 to 2013, and Observatori Ca l'Ou, Sant Marti Sesgueioles, Spain, from 2013 to 2015. The observed data are corrected for systematic errors according to the coarse de-correlation and SYSREM algorithms, so as to enhance the signal of the transit events. In order to consistently model the star spots and transits of this exoplanetary system, we develop a highly efficient tool STMT based on the analytic models of Mandel & Agol and Montalto et al. The physical parameters of HAT-P-20 are refined by homogeneously analyzing our new data, the radial velocity data, and the earlier photometric data in the literature with the Markov chain Monte Carlo technique. New radii and masses of both host star and planet are larger than those in the discovery paper due to the discrepancy of the radius among K-dwarfs between predicted values by standard stellar models and empirical calibration from observations. Through the analysis of all available mid-transit times calculated with the normal model and spotted model, we conclude that the periodic transit timing variations in these transit events revealed by employing the normal model are probably induced by spot crossing events. From the analysis of the distribution of occulted spots by HAT-P-20b, we constrain the misaligned architecture between the planetary orbit and the spin of the host star.Publisher PDFPeer reviewe

The Coupling and Competition of Crystallization and Phase Separation, Correlating Thermodynamics and Kinetics In OPV Morphology and Performances

The active layer morphology transition of organic photovoltaics under non-equilibrium conditions are of vital importance in determining the device power conversion efficiency and stability; however, a general and unified picture on this issue has not been well addressed. Using combined in situ and ex situ morphology characterizations, morphological parameters relating to kinetics and thermodynamics of morphology evolution are extracted and studied in model systems under thermal annealing. The coupling and competition of crystallization and demixing are found to be critical in morphology evolution, phase purification and interfacial orientation. A unified model summarizing different phase diagrams and all possible kinetic routes is proposed. The current observations address the fundamental issues underlying the formation of the complex multi-length scale morphology in bulk heterojunction blends and provide useful morphology optimization guidelines for processing devices with higher efficiency and stability

10 Gbps wavelength division multiplexing using UV-A, UV-B and UV-C micro-LEDs

Deep ultraviolet (DUV) optical wireless communications have seen increased interest in recent years due to the unique properties of light in this spectral region. However, the reported DUV data rates remain significantly lower than comparable demonstrations at visible wavelengths due to lower modulation bandwidths and/or output power of the sources. Here, we present a wavelength division multiplexing demonstration using three UV microlight-emitting diodes emitting at nominal peak wavelengths of 285, 317, and 375 nm, respectively, each with an emitting area of approximately 1369 μm 2 (equivalent to circular device pixels of diameter ∼40 μm). Using orthogonal frequency division multiplexing, data rates of 4.17, 3.02, and 3.13 Gbps were achieved from the 285, 317, and 375 nm devices, respectively, for a combined data rate of 10.32 Gbps transmitted over a distance of 0.5 m

The value of enhanced CT scanning for predicting lymph node metastasis along the right recurrent laryngeal nerve in esophageal squamous cell carcinoma

Background: The right recurrent laryngeal nerve (RRLN) is the region most prone to lymph node metastasis in esophageal squamous cell carcinoma (ESCC). Nodal involvement may be underestimated by traditional imaging prediction criteria, such as a short axis diameter of 10 mm. The purpose of this study was to determine a more accurate imaging criterion to guide clinical treatment strategy selection. Methods: The clinical data of 307 patients with thoracic ESCC who underwent surgery at Shanghai Chest Hospital between January 2018 and December 2018 were retrospectively analyzed. Utilizing 1-mm layer thickness enhanced computed tomography (CT), the RRLN lymph node short diameter (LNSD) size was measured. Univariate and multivariate analyses were performed to determine the risk factors for lymph node metastasis along the RRLN. Results: In our study, RRLN lymph node metastasis occurred in 60 (19.5%) patients and general lymph node metastasis occurred in 150 (48.9%) patients. Of the resected lymph nodes along the RRLN, 14.5% (121/832) were positive. Multivariate analysis identified LNSD [odds ratio (OR), 1.236] as an independent risk factor for RRLN lymph node metastasis. In CT evaluation, a short diameter of 6.5 mm in the RRLN lymph nodes is a critical predictor of metastasis at this site (sensitivity =50%, specificity =83.4%) and a larger short diameter was associated with a higher risk of metastasis (P<0.001). Conclusions: A 6.5 mm cutoff in LNSD can be applied to clinically predict lymph node metastasis in the RRLN region for patients with ESCC