An Evaluation Of Postgraduate School-Based Teacher Education Program In Malaysia

PHD 2011 The primary purpose of this study was to evaluate the strengths and weaknesses of the postgraduate SBTE program as an alternative route in training teachers. The post graduate SBTE was conducted by using modules through self-directed learning and tutorials, with the collaboration of the school mentor teachers. Tujuan utama kajian ini adalah untuk menilai kekuatan dan kelemahan program LPBS lepas ijazah sebagai satu kaedah alternatif dalam melatih guru. Program LPBS lepas ijazah dilaksanakan dengan menggunakan modul-modul melalui pembelajaran arah kendiri dan tutorial, dengan kolaborasi guru pembimbing di sekolah

NMR Observable-Based Structure Refinement of DAP12-NKG2C Activating Immunoreceptor Complex in Explicit Membranes

This is the published version. Copyright 2012 by Elsevier.NMR observables, such as NOE-based distance measurements, are increasingly being used to characterize membrane protein structures. However, challenges in membrane protein NMR studies often yield a relatively small number of such restraints that can create ambiguities in defining critical side chain-side chain interactions. In the recent solution NMR structure of the DAP12-NKG2C immunoreceptor transmembrane helix complex, five functionally required interfacial residues (two Asps and two Thrs in the DAP12 dimer and one Lys in NKG2C) display a surprising arrangement in which one Asp side chain faces the membrane hydrophobic core. To explore whether these side-chain interactions are energetically optimal, we used the published distance restraints for molecular dynamics simulations in explicit micelles and bilayers. The structures refined by this protocol are globally similar to the published structure, but the side chains of those five residues form a stable network of salt bridges and hydrogen bonds, leaving the Asp side chain shielded from the hydrophobic core, which is also consistent with available experimental observations. Moreover, the simulations show similar short-range interactions between the transmembrane complex and lipid/detergent molecules in micelles and bilayers, respectively. This study illustrates the efficacy of NMR membrane protein structure refinements in explicit membrane systems

NMR Observable-Based Structure Refinement of DAP12-NKG2C Activating Immunoreceptor Complex in Explicit Membranes

This is the published version. Copyright 2012 by Elsevier.NMR observables, such as NOE-based distance measurements, are increasingly being used to characterize membrane protein structures. However, challenges in membrane protein NMR studies often yield a relatively small number of such restraints that can create ambiguities in defining critical side chain-side chain interactions. In the recent solution NMR structure of the DAP12-NKG2C immunoreceptor transmembrane helix complex, five functionally required interfacial residues (two Asps and two Thrs in the DAP12 dimer and one Lys in NKG2C) display a surprising arrangement in which one Asp side chain faces the membrane hydrophobic core. To explore whether these side-chain interactions are energetically optimal, we used the published distance restraints for molecular dynamics simulations in explicit micelles and bilayers. The structures refined by this protocol are globally similar to the published structure, but the side chains of those five residues form a stable network of salt bridges and hydrogen bonds, leaving the Asp side chain shielded from the hydrophobic core, which is also consistent with available experimental observations. Moreover, the simulations show similar short-range interactions between the transmembrane complex and lipid/detergent molecules in micelles and bilayers, respectively. This study illustrates the efficacy of NMR membrane protein structure refinements in explicit membrane systems

Destination image in travel magazines: A textual and pictorial analysis of Hong Kong and Macau

Based on the analyses of texts and pictures in the top six outbound travel magazines in Mainland China, this article presents an evaluation of the destination images of Hong Kong and Macau as portrayed in 88 travel articles over a three-year period. The results showed that the projected destination images of Hong Kong and Macau were dominated by attributes related to culture, history, and art and leisure and recreation. Hong Kong was often described by image attributes such as places and attractions, shopping, cuisine and food, hotels, and the creative industries. For Macau, history and heritage, places and attractions, gambling, cuisine and food, and hotels were the most often reported. During the study period, Hong Kong and Macau witnessed several significant changes in the image attributes featured in both texts and pictures. These changes were partly influenced by news and events over the period. In this article, implications for destination marketing organizations and directions for future research were suggested

Acrylic resin reinforced with high performance polyethylene fiber

Abstract no. 1269published_or_final_versio

Homological algebra for osp(1/2n)

We discuss several topics of homological algebra for the Lie superalgebra osp(1|2n). First we focus on Bott-Kostant cohomology, which yields classical results although the cohomology is not given by the kernel of the Kostant quabla operator. Based on this cohomology we can derive strong Bernstein-Gelfand-Gelfand resolutions for finite dimensional osp(1|2n)-modules. Then we state the Bott-Borel-Weil theorem which follows immediately from the Bott-Kostant cohomology by using the Peter-Weyl theorem for osp(1|2n). Finally we calculate the projective dimension of irreducible and Verma modules in the category O

The psychological impact of a dying child on Chinese family members

Author name used in this publication: Alice Cheng LaiAuthor name used in this publication: Thomas Wong2003-2004 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Assessing uncertainty and heterogeneity in machine learning-based spatiotemporal ozone prediction in Beijing-Tianjin- Hebei region in China

Accurate prediction of spatiotemporal ozone concentration is of great significance to effectively establish advanced early warning systems and regulate air pollution control. However, the comprehensive assessment of uncertainty and heterogeneity in spatiotemporal ozone prediction remains unknown. Here, we systematically analyze the hourly and daily spatiotemporal predictive performances using convolutional long short term memory (ConvLSTM) and deep convolutional generative adversarial network (DCGAN) models over the Beijing-Tianjin-Hebei region in China from 2013 to 2018. In extensive scenarios, our results show that the machine learning-based (ML-based) models achieve better spatiotemporal ozone concentration prediction performance with multiple meteorological conditions. A further comparison to the air pollution model-Nested Air Quality Prediction Modelling System (NAQPMS) and monitoring observations, the ConvLSTM model demonstrates the practical feasibility of identifying high ozone concentration distribution and capturing spatiotemporal ozone variation patterns at a high spatial resolution (here 15 km × 15 km)

Ensemble Kalman filter for GAN-ConvLSTM based long lead-time forecasting

Data-driven machine learning techniques have been increasingly utilized for accelerating nonlinear dynamic system prediction. However, machine learning-based models for long lead-time forecasts remain a significant challenge due to the accumulation of uncertainty along the time dimension in online deployment. To tackle this issue, the ensemble Kalman filter (EnKF) has been introduced to machine learning-based long-term forecast models to reduce the uncertainty of long lead-time forecasts of chaotic dynamic systems. Both the deep convolutional generative adversarial network (DCGAN) and convolutional long short term memory (ConvLSTM) are used for learning the complex nonlinear relationships between the past and future states of dynamic systems. Using an iterative Multi-Input Multi-Output (MIMO) algorithm, the two-hybrid forecast models (DCGAN-EnKF and ConvLSTM-EnKF) are able to yield long lead-time forecasts of dynamic states. The performance of the hybrid models has been demonstrated by one-level and two-level Lorenz 96 models. Our results show that the use of EnKF in ConvLSTM and DCGAN models successfully corrects online model errors and significantly improves the real-time forecasting of dynamic systems for a long lead-time

A real-time flow forecasting with deep convolutional generative adversarial network: Application to flooding event in Denmark

Real-time flood forecasting is crucial for supporting emergency responses to inundation-prone regions. Due to uncertainties in the future (e.g., meteorological conditions and model parameter inputs), it is challenging to make accurate forecasts of spatiotemporal floods. In this paper, a real-time predictive deep convolutional generative adversarial network (DCGAN) is developed for flooding forecasting. The proposed methodology consists of a two-stage process: (1) dynamic flow learning and (2) real-time forecasting. In dynamic flow learning, the deep convolutional neural networks are trained to capture the underlying flow patterns of spatiotemporal flow fields. In real-time forecasting, the DCGAN adopts a cascade predictive procedure. The last one-time step-ahead forecast from the DCGAN can act as a new input for the next time step-ahead forecast, which forms a long lead-time forecast in a recursive way. The model capability is assessed using a 100-year return period extreme flood event occurred in Greve, Denmark. The results indicate that the predictive fluid flows from the DCGAN and the high fidelity model are in a good agreement (the correlation coefficien