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Probabilistic characteristics of damping in buildings
Fang, J.Q.,Li, Q.S.,Jeary, A.P.,Liu, D.K.,Wong, C.K. Techno-Press 1999 Wind and Structures, An International Journal (WAS Vol.2 No.2
This paper describes probabilistic characteristics of damping in a tall building based on the results of full-scale measurement. It is found, through statistical analysis of the damping data, that the probability density function(PDF) of damping at the high amplitude plateau can be well represented by Normal distribution (Gaussian distribution). A stochastic damping model is proposed to estimate amplitude-dependent damping for practical application.
Optical and acoustic metamaterials: superlens, negative refractive index and invisibility cloak
Wong, Zi Jing,Wang, Yuan,O’Brien, Kevin,Rho, Junsuk,Yin, Xiaobo,Zhang, Shuang,Fang, Nicholas,Yen, Ta-Jen,Zhang, Xiang IOP 2017 Journal of optics Vol.19 No.8
<P>Metamaterials are artificially engineered materials that exhibit novel properties beyond natural materials. By carefully designing the subwavelength unit cell structures, unique effective properties that do not exist in nature can be attained. Our metamaterial research aims to develop new subwavelength structures with unique physics and experimentally demonstrate unprecedented properties. Here we review our research efforts in optical and acoustic metamaterials in the past 15 years which may lead to exciting applications in communications, sensing and imaging.</P>
Using neural networks to model and predict amplitude dependent damping in buildings
Li, Q.S.,Liu, D.K.,Fang, J.Q.,Jeary, A.P.,Wong, C.K. Techno-Press 1999 Wind and Structures, An International Journal (WAS Vol.2 No.1
In this paper, artificial neural networks, a new kind of intelligent method, are employed to model and predict amplitude dependent damping in buildings based on our full-scale measurements of buildings. The modelling method and procedure using neural networks to model the damping are studied. Comparative analysis of different neural network models of damping, which includes multi-layer perception network (MLP), recurrent neural network, and general regression neural network (GRNN), is performed and discussed in detail. The performances of the models are evaluated and discussed by tests and predictions including self-test, "one-lag" prediction and "multi-lag" prediction of the damping values at high amplitude levels. The established models of damping are used to predict the damping in the following three ways : (1) the model is established by part of the data measured from one building and is used to predict the another part of damping values which are always difficult to obtain from field measurements : the values at the high amplitude level. (2) The model is established by the damping data measured from one building and is used to predict the variation curve of damping for another building. And (3) the model is established by the data measured from more than one buildings and is used to predict the variation curve of damping for another building. The prediction results are discussed.
MODULATING CH 3 NH 3 PbI 3 PEROVSKITE CRYSTALLIZATION BEHAVIOR THROUGH PRECURSOR CONCENTRATION
KUNWU FU,SUBODH MHAISALKAR,SWEE SIEN LIM,PABLO P. BOIX,NRIPAN MATHEWS,YANAN FANG,LYDIA H. WONG,TZE CHIEN SUM 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2014 NANO Vol.9 No.5
Perovskite-based photovoltaic devices have recently achieved impressively high e±ciencies be-yond 15% and gained great interest. We show here the formation of perovskite cluster overlayerstructures which consist of individual perovskite grains on top of mesoporous TiO 2 ¯lms, coex-isting with the randomly distributed nanocrystals within the ¯lms. Perovskite solution concen-tration was found to play an important role in modulating the perovskite crystallization andcluster overlayer formation process. Absorbance increase in visible wavelength range and shift ofphotoluminescence (PL) responses of perovskite ¯lms due to the e®ect of precursor concentrationchange were observed and investigated in detail. The crystallographic analysis of theCH 3 NH 3 PbI 3 ¯lms shows a gradual decrease of the perovskite lattice parameters and shrinkage ofunit volume as precursor solution concentration increases, which is correlated to the changes ofoptical properties. Finally, perovskite-based solar cell device performance was enhanced at higherprecursor concentration.
( Andrew Li ),( Hiang Ping Chan ),( Phyllis X.L. Gan ),( Mei Fong Liew ),( W.S. Fred Wong ),( Hui-Fang Lim ) 대한내과학회 2021 The Korean Journal of Internal Medicine Vol.36 No.6
Approximately 25% to 40% of patients with chronic obstructive pulmonary disease (COPD) have the eosinophilic endotype. It is important to identify this group accurately because they are more symptomatic and are at increased risk for exacerbations and accelerated decline in forced expiratory volume in the 1st second. Importantly, this endotype is a marker of treatment responsiveness to inhaled corticosteroid (ICS), resulting in decreased mortality risk. In this review, we highlight differences in the biology of eosinophils in COPD compared to asthma and the different definitions of the COPD eosinophilic endotype based on sputum and blood eosinophil count (BEC) with the corresponding limitations. Although BEC is useful as a biomarker for eosinophilic COPD endotype, optimal BEC cut- offs can be combined with clinical characteristics to improve its sensitivity and specificity. A targeted approach comprising airway eosinophilia and appropriate clinical and physiological features may improve identification of subgroups of patients who would benefit from biologic therapy or early use of ICS for disease modification.