28,317 research outputs found
Preparation of (R)-2-chloro-1-(m-chlorophenyl)ethanol by Lipozyme TL IM-catalyzed second resolution
No full text(R)-2-Chloro-1-(m-chlorophenyl)ethanol, a precursor of (R)-3-chlorostyrene oxide which is the key chiral intermediate for the preparation of several beta 3-adrenergic receptor agonists was prepared in 40% yield and 99% ee by the Lipozyme TL IM-catalyzed second resolution of the corresponding racemate in the presence of vinyl acetate. (C) 2012 Shi Wen Xia. Published by Elsevier B.V. on behalf of Chinese Chemical Society. All rights reserved
Overview of Challa, V. R., Prasad, M. G., Shi, Y., and Fisher, F. T.’s 2008 Paper on a Vibration Energy Harvesting Device with Bidirectional Resonance Frequency Tunability
No full textOverview of Challa, V. R., Prasad, M. G., Shi, Y., and Fisher, F. T.’s 2008 Paper on a Vibration Energy Harvesting Device with Bidirectional Resonance Frequency Tunability
Periodic fibre devices for advanced applications in all-optical systems
No full textThe main objective of this work is to investigate advanced applications of fibre gratings with the combination of nonlinear fibre optical effects, including the stimulated Raman scattering (SRS), Kerr effects, four-wave mixing (FWM) and second-harmonic generation. A Raman distributed-feedback (R-DFB) fibre laser formed in a passive optical fibre by using Raman gain is considered as the most promising route to generate a single-frequency and narrow-linewidth laser source at any wavelength given a proper pump source.In this thesis, the R-DFB fibre laser has been intensively studied both numerically and experimentally. Simulation results of centre pi phase-shifted R-DFB fibre lasers show that the longer length of the DFB grating, the higher Raman gain coefficient and the lower background loss of the host fibre are always beneficial for achieving low threshold R-DFB fibre lasers. 30 cm long centre pi phase-shifted R-DFB fibre lasers have been respectively demonstrated in two types of commercially available Ge/Si fibres of PS980 and UHNA4. Both un-polarised and linearly polarised CW Yb-doped fibre lasers at ~1.06 µm were used as the pump sources. The R-DFB fibre lasers are single-frequency operation at around 1.11 µm and have 3 dB linewidth less than 2.5 kHz; lasing thresholds down to sub-watt power levels; total output powers up to ~2 W; and total conversion efficiencies against incident pump power around 13%. Ultra-wide range (>110 nm) wavelength conversion by using FWM in these 30 cm-long R-DFB fibre lasers have been observed and up to ~-25 dB FWM conversion efficiency has been obtained.The nonlinearities and photosensitivity of several high-index non-silica glasses and fibres are also studied in order to incorporate fibre Bragg gratings (FBGs) with the highly nonlinear fibres to form R-DFB fibre lasers with lower thresholds. In particular, the Raman gain coefficient of a house-made tellurite glass fibre has been found to be ~35 times higher than the silica fibre and a SRS-assisted supercontinuum from ~1.1-1.7 µm has been observed in the fibre with a length of ~1.35 m by pumping at ~1.06 µm in the normal dispersion region of the fibre.Preliminary investigations into concatenating periodic poled silica fibres (PPSFs) to improve the frequency-doubling conversion efficiency are also presented
Expression profiles of micro RNA in proliferating and differentiating 32D murine myeloid cells
No full text32D cells are murine myeloid cells that grow indefinitely in Interleukin-3 (IL-3). In these cells, the type 1 insulin-like growth factor (IGF-I) and granulocytic-colony stimulating factor (G-CSF) induce differentiation to granulocytes. 32D cells do not express insulin receptor substrate-1 (IRS-1) or IRS-2, docking proteins of the IGF-I receptor. Ectopic expression of IRS-1 in these cells inhibits differentiation, the cells become IL-3 independent and IGF-1 dependent and can form tumors in mice. 32D and 32D-derived cells offer a good model in which to study the expression profiles of Micro Rna (miR) related to sustained proliferation or differentiation. We present here the data obtained with miR micro-arrays and identify the miR that are regulated by IGF-1 or G-CSF and are associated with either differentiation or indefinite cell proliferation of 32D murine myeloid cells
A radical approach to handwritten Chinese character recognition using active handwriting models
No full textCorrelated hyperfine interactions in amorphous Cr72-xFexC17Si8Al3 alloys
No full textPT: J; CR: BAHADUR D, 1987, J MATER SCI, V22, P2477 DINI K, 1986, J MATER SCI, V21, P1037 DUNLAP RA, 1982, CAN J PHYS, V60, P909 DUNLAP RA, 1985, J MATER SCI LETT, V4, P773 EIBSCHUTZ M, 1983, PHYS REV B, V28, P425 INGALLS R, 1978, MOSSBAUER ISOMER SHI, P361 LECAER G, 1979, J PHYS E SCI INSTRUM, V12, P1083 OLIVIER M, 1982, J APPL PHYS, V53, P7696 POLLARD RJ, 1984, PHYS REV B, V29, P4864 YU BL, 1984, J APPL PHYS, V55, P1748; NR: 10; TC: 10; J9: J PHYS-F-METAL PHYS; PG: 8; GA: R0716Source type: Electronic(1
Y(4143) is probably a molecular partner of Y(3930)
No full textAfter discussing the various possible interpretations of the Y(4143) signal observed by the CDF collaboration in the J/Sigma phi mode, we tend to conclude that Y(4143) is probably a D(s)(*)D(s)(*) molecular state with J(PC)=0(++) or 2(++) while Y(3930) is its D(*)D(*) molecular partner as predicted in our previous work [X. Liu, Z. G. Luo, Y. R. Liu, and Shi-Lin Zhu, Eur. Phys. J. C 61, 411 (2009)]. Both the hidden-charm and open-charm two-body decays occur through the rescattering of the vector components within the molecular states while the three- and four-body open-charm decay modes are forbidden kinematically. Hence, their widths are narrow naturally. CDF, BABAR and Belle collaborations may have discovered heavy molecular states already. We urge experimentalists to measure their quantum numbers and explore their radiative decay modes in the future.Astronomy & AstrophysicsPhysics, Particles & FieldsSCI(E)50ARTICLE1null8
Dolichosciara multisetosa Shi & Huang, sp. nov.
No full textDolichosciara multisetosa Shi & Huang, sp. nov. (Figs. 8, 10 G, 11) Specimens examined. Holotype, male. CHINA. ZHEJIANG province, Linan, Qingliangfeng, Shunxiwu, Malaise trap, 25.VI. 2012 [SM01457]. Paratypes, FUJIAN. 1 male, Wuyishan, Xingcunzhen, Tongmucun, Guadun, sweepnet, 26.IV. 2012, Kai Shi [SM01341]; 1 male, Mt. Longqishan, Kengwei, sweep-net, 15.VIII. 2012, Kai Shi [SM01555]. GUANGXI. 1 male, Nanning, Mt. Damingshan, sweep-net, 23.V. 2011, Ting-Ting Zhang [SM01319]. TAIWAN. 1 male, Xuanlan, Fushan Botanical Garden, 635 m, sweep-net, 13.VI. 2010, Ding Yang [SM01314]. ZHEJIANG. Linan, Mt. Qingliangfeng: 16 males, Shunxiwu, Malaise trap, 25.VI. 2012 [SM01353, SM01360, SM01374–01375, SM01378, SM01382, SM01385, SM01406, SM01414, SM01440, SM01442, SM01445, SM01448– 1449, SM01452, SM01480]; 1 male, Shunxiwu, Malaise trap, 15.V. 2012 [SM01539]; 4 males, Longtangshan, Malaise trap, 15.V. 2012 [SM01465–01466, SM01496, SM01502]; 3 males, Qianqingtang, Malaise trap, 15.V. 2012 [SM01484, SM01553]; 1 male, Qianqingtang, Malaise trap, 13.V. 2012 [SM01546]. 2 males, Anji, Mt. Longwangshan, sweep-net, 31.III. 2012, Kai Shi [SM01330, SM01338]. 1 male, Pan’an, Mt. Dapanshan, Tengyungong, Malaise trap, 15.VII. 2012, Su-Jiong Zhang [SM01429]. 1 male, Linan, Mt. Tianmushan, Malaise trap, 6.VII. 2012 [SM01424]. 1 male, Lishui, Mt. Fengyangshan, Datianping, Cangku, Malaise trap, 18.VI. 2008, Sheng-Long Liu [SM00253]. Description (Male). Color. Head dark brown; antenna, palpus, thorax, abdomen and hypopygium brown; legs yellowish-brown; wing fumose. Head (Fig. 8 C, D). Eye bridge with 3 rows of facets. Prefrons with 13–30 setae. Clypeus with 0–2 setae. Palpus three-segmented, basal segment with 4–5 setae; 2 nd segment with 9–13 setae; 3 rd segment with 8–12 setae. Length/width of 4 th flagellomere: 2.50–3.65. Thorax. Anterior pronotum with 4–5 setae, episternum 1 with 3–8 setae. Wings (Fig. 10 G). Wing length 2.67–3.41 mm, width/length: 0.34–0.38. c/w: 0.41– 0.53. R 1 /R: 0.57–0.82. Y/X: 1.01–2.10. Y with 0–2 setae, stM with 2–5 setae, M and Cu with numerous setae. Legs. Fore tibia with a comb of 6–9 setae (Fig. 8 E). Length of spur/width of foretibia 1.54–2.25. Length of femur/ length of metatarsus: foreleg 0.65 –1.00. Length of metatarsus/length of tibia: foreleg 0.60–0.82, hind leg 0.50– 0.64. Length of hind tibia/length of thorax 2.03–2.73. Fore tibia with 1 dorsal, 7–13 ventral, 3–15 prolateral and 4– 8 retrolateral spinose setae. Tarsal claws with large teeth. Hypopygium (Fig. 8 A, B). Gonocoxite longer than gonostylus. Ventral setosity of gonocoxite sparse, 2–3 setae at the apicoventral corner greatly elongated, setosity dense on intercoxal area of hypopygium. Gonostylus slightly curved, eventually narrowed towards apex; setosity dense at apex, with 3–5 slightly curved subapical megasetae that partly not visible in ventral view. Tegmen slightly wider than long. Sternite 10 with 2–3 setae on each half. N = 16 for all measurements. Distribution. China (Fujian, Guangxi, Taiwan, Zhejiang, Fig. 11). Remarks. This species is unique by a rather broad gonostylus with 3–5 slightly curved subapical megasetae, arising from the dorsal side of the gonostylus and only partly visible in the ventral view, and by a densely setose intercoxal lobe of the hypopygium. Etymology. This species is named after its dense setosity on the intercoxal area of the hypopygium, from the Latin adjective multisetosus, meaning many setae.Published as part of Wu, Hong, Shi, Kai, Huang, Junhao & Zhang, Sujiong, 2013, Review of the genus Dolichosciara Tuomikoski (Diptera, Sciaridae) from China, pp. 343-364 in Zootaxa 3745 (3) on pages 356-359, DOI: 10.11646/zootaxa.3745.3.3, http://zenodo.org/record/22285
Machine learning-driven credit risk: a systemic review
No full textCredit risk assessment is at the core of modern economies. Traditionally, it is measured by statistical methods and manual auditing. Recent advances in financial artificial intelligence stemmed from a new wave of machine learning (ML)-driven credit risk models that gained tremendous attention from both industry and academia. In this paper, we systematically review a series of major research contributions (76 papers) over the past eight years using statistical, machine learning and deep learning techniques to address the problems of credit risk. Specifically, we propose a novel classification methodology for ML-driven credit risk algorithms and their performance ranking using public datasets. We further discuss the challenges including data imbalance, dataset inconsistency, model transparency, and inadequate utilization of deep learning models. The results of our review show that: 1) most deep learning models outperform classic machine learning and statistical algorithms in credit risk estimation, and 2) ensemble methods provide higher accuracy compared with single models. Finally, we present summary tables in terms of datasets and proposed models
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