Dynamic wavelet neural network model for forecasting returns of SHFE copper futures price

Session C8: P13Appropriate forecasting of commodity futures price returns is of crucial importance to achieve hedging effectiveness against the returns volatility risk. This paper presents a nonparametric dynamic recurrent wavelet neural network model for forecasting returns of Shanghai Futures Exchange (SHFE) copper futures price. The proposed model employs a wavelet basis function as the activation function for hidden-layer neurons of the neural network. The aim of this arrangement is to incorporate the fractal properties discovered in futures price return series. In the wavelet transform domain, fractal self-similarity information of the returns series over a certain time scale can be extracted. Input variables are analyzed and selected to facilitate effective forecasting. Statistical indices such as normal mean square error (NMSE) are adopted to evaluate forecasting performance of the proposed model. The forecasted result shows that dynamic wavelet neural network has good prediction properties compared with traditional linear statistical model such as ARIMA and other neural network forecasting models.published_or_final_versionThe 7th International Conference on Digital Enterprise Technology (DET 2011), Athens, Greece, 28-30 September 2011. In Proceedings of the 7th DET, 2011, p. 109-11

A new automatic detection approach for hepatocellular carcinoma using ¹¹C-acetate positron emission tomography

Author name used in this publication: Dagan FengCentre for Multimedia Signal Processing, Department of Electronic and Information EngineeringRefereed conference paper2003-2004 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Advantages of video trigger in problem-based learning

Conference Theme: Quality Accreditation and Standard in Medical Educationpublished_or_final_versio

Effects of stitching on delamination of satin weave carbon-epoxy laminates under mode I, mode II and mixed-mode I/II loadings

The objective of the present study is to characterize the effect of modified chain stitching on the delamination growth under mixed-mode I/II loading conditions. Delamination toughness under mode I is experimentally determined, for unstitched and stitched laminates, by using untabbed and tabbed double cantilever beam (TDCB) tests. The effect of the reinforcing tabs on mode I toughness is investigated. Stitching improves the energy release rate (ERR) up to 4 times in mode I. Mode II delamination toughness is evaluated in end-notched flexure (ENF) tests. Different geometries of stitched specimens are tested. Crack propagation occurs without any failure of stitching yarns. The final crack length attains the mid-span or it stops before and the specimen breaks in bending. The ERR is initially low and gradually increases with crack length to very high values. The mixedmode delamination behaviour is investigated using a mixed-mode bending (MMB) test. For unstitched specimens, a simple mixed-mode criterion is identified. For stitched specimens, stitching yarns do not break during 25% of mode I ratio tests and the ERR increase is relatively small compared to unstitched values. For 70% and 50% of mode I ratios, failures of yarns are observed during crack propagation and tests are able to capture correctly the effect of the stitching: it clearly improves the ERR for these two mixed modes, as much as threefold

Advantages of video trigger in problem-based learning

Background: Traditionally, paper cases are used as 'triggers' to stimulate learning in problem-based learning (PBL). However, video may be a better medium because it preserves the original language, encourages the active extraction of information, avoids depersonalization of patients and allows direct observation of clinical consultations. In short, it exposes the students to the complexity of actual clinical problems. Aim: The study aims to find out whether students and facilitators who are accustomed to paper cases would prefer video triggers or paper cases and the reasons for their preference. Method: After students and facilitators had completed a video PBL tutorial, their responses were measured by a structured questionnaire using a modified Likert scale. Results: A total of 257 students (92) and 26 facilitators (100) responded. The majority of students and facilitators considered that using video triggers could enhance the students' observational powers and clinical reasoning, help them to integrate different information and better understand the cases and motivate them to learn. They found PBL using video triggers more interesting and preferred it to PBL using paper cases. Conclusion: Video triggers are preferred by both students and facilitators over paper cases in PBL. © 2010 Informa UK Ltd All rights reserved.postprin

Improved model identification for non-linear systems using a random subsampling and multifold modelling (RSMM) approach

In non-linear system identification, the available observed data are conventionally partitioned into two parts: the training data that are used for model identification and the test data that are used for model performance testing. This sort of 'hold-out' or 'split-sample' data partitioning method is convenient and the associated model identification procedure is in general easy to implement. The resultant model obtained from such a once-partitioned single training dataset, however, may occasionally lack robustness and generalisation to represent future unseen data, because the performance of the identified model may be highly dependent on how the data partition is made. To overcome the drawback of the hold-out data partitioning method, this study presents a new random subsampling and multifold modelling (RSMM) approach to produce less biased or preferably unbiased models. The basic idea and the associated procedure are as follows. First, generate K training datasets (and also K validation datasets), using a K-fold random subsampling method. Secondly, detect significant model terms and identify a common model structure that fits all the K datasets using a new proposed common model selection approach, called the multiple orthogonal search algorithm. Finally, estimate and refine the model parameters for the identified common-structured model using a multifold parameter estimation method. The proposed method can produce robust models with better generalisation performance

Growth Factors Regulate Expression of Mineral Associated Genes in Cementoblasts

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141778/1/jper1591.pd

Rapid quantification of semen hepatitis B virus DNA by real-time polymerase chain reaction

Aim: To examine the sensitivity and accuracy of real-time polymerase chain reaction (PCR) for the quantification of hepatitis B virus (HBV) DNA in semen. Methods: Hepatitis B viral DNA was isolated from HBV carriers' semen and sera using phenol extraction method and QIAamp DNA blood mini kit (Qiagen, Germany). HBV DNA was detected by conventional PCR and quantified by TaqMan technology-based real-time PCR (quantitative polymerase chain reaction (qPCR)). The detection threshold was 200 copies of HBV DNA for conventional PCR and 10 copies of HBV DNA for real time PCR per reaction. Results: Both methods of phenol extraction and QIAamp DNA blood mini kit were suitable for isolating HBV DNA from semen. The value of the detection thresholds was 500 copies of HBV DNA per mL in the semen. The viral loads were 7.5×10 7 and 1.67×10 7 copies of HBV DNA per mL in two HBV infected patients' sera, while 2.14×10 5 and 3.02×10 5 copies of HBV DNA per mL in the semen. Conclusion: Real-time PCR is a more sensitive and accurate method to detect and quantify HBV DNA in the semen. © 2005 The WJG Press and Elsevier Inc. All rights reserved.published_or_final_versio

Are autistic traits measured equivalently in individuals with and without an Autism Spectrum Disorder?:An invariance analysis of the Autism Spectrum Quotient Short Form

It is common to administer measures of autistic traits to those without autism spectrum disorders (ASDs) with, for example, the aim of understanding autistic personality characteristics in non-autistic individuals. Little research has examined the extent to which measures of autistic traits actually measure the same traits in the same way across those with and without an ASD. We addressed this question using a multi-group confirmatory factor invariance analysis of the Autism Quotient Short Form (AQ-S: Hoekstra et al. in J Autism Dev Disord 41(5):589-596, 2011) across those with (n = 148) and without (n = 168) ASD. Metric variance (equality of factor loadings), but not scalar invariance (equality of thresholds), held suggesting that the AQ-S measures the same latent traits in both groups, but with a bias in the manner in which trait levels are estimated. We, therefore, argue that the AQ-S can be used to investigate possible causes and consequences of autistic traits in both groups separately, but caution is due when combining or comparing levels of autistic traits across the two group