Open Access. Powered by Scholars. Published by Universities.®
Social and Behavioral Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 19 of 19
Full-Text Articles in Social and Behavioral Sciences
On The Accuracy Of Dose Prediction Near Metal Fixation Devices For Spine Sbrt, Zhangkai J. Cheng, Regina M. Bromley, Bradley M. Oborn, Martin Carolan, Jeremy T Booth
On The Accuracy Of Dose Prediction Near Metal Fixation Devices For Spine Sbrt, Zhangkai J. Cheng, Regina M. Bromley, Bradley M. Oborn, Martin Carolan, Jeremy T Booth
Faculty of Engineering and Information Sciences - Papers: Part A
The metallic fixations used in surgical procedures to support the spine mechanically usually consist of high-density materials. Radiation therapy to palliate spinal cord compression can include prophylactic inclusion of potential tumor around the site of such fixation devices. Determination of the correct density and shape of the spine fixation device has a direct effect on the dose calculation of the radiation field. Even with the application of modern computed tomography (CT), under- or overestimation of dose, both immediately next to the device and in the surrounding tissues, can occur due to inaccuracies in the dose prediction algorithm. In this study, …
Rolling Force Prediction In Heavy Plate Rolling Based On Uniform Differential Neural Network, Fei Zhang, Yuntao Zhao, Jian Shao
Rolling Force Prediction In Heavy Plate Rolling Based On Uniform Differential Neural Network, Fei Zhang, Yuntao Zhao, Jian Shao
Faculty of Engineering and Information Sciences - Papers: Part A
Accurate prediction of the rolling force is critical to assuring the quality of the final product in steel manufacturing. Exit thickness of plate for each pass is calculated from roll gap, mill spring, and predicted roll force. Ideal pass scheduling is dependent on a precise prediction of the roll force in each pass. This paper will introduce a concept that allows obtaining the material model parameters directly from the rolling process on an industrial scale by the uniform differential neural network. On the basis of the characteristics that the uniform distribution can fully characterize the solution space and enhance the …
Interaction Prediction Between Groundwater And Quarry Extension Using Discrete Choice Models And Artificial Neural Networks, Johan Barthelemy, Timoteo Carletti, Louise Collier, Vincent Hallet, Marie Moriame, Annick Sartenear
Interaction Prediction Between Groundwater And Quarry Extension Using Discrete Choice Models And Artificial Neural Networks, Johan Barthelemy, Timoteo Carletti, Louise Collier, Vincent Hallet, Marie Moriame, Annick Sartenear
Faculty of Engineering and Information Sciences - Papers: Part A
Groundwater and rock are intensively exploited in the world. When a quarry is deepened the water table of the exploited geological formation might be reached. A dewatering system is therefore installed so that the quarry activities can continue, possibly impacting the nearby water catchments. In order to recommend an adequate feasibility study before deepening a quarry, we propose two interaction indices between extractive activity and groundwater resources based on hazard and vulnerability parameters used in the assessment of natural hazards. The levels of each index (low, medium, high, very high) correspond to the potential impact of the quarry on the …
A Local Field Correlated And Monte Carlo Based Shallow Neural Network Model For Non-Linear Time Series Prediction, Qingguo Zhou, Huaming Chen, Hong Zhao, Gaofeng Zhang, Jianming Yong, Jun Shen
A Local Field Correlated And Monte Carlo Based Shallow Neural Network Model For Non-Linear Time Series Prediction, Qingguo Zhou, Huaming Chen, Hong Zhao, Gaofeng Zhang, Jianming Yong, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part A
Water resource problems currently are much more important in proper planning especially for arid regions, such as Gansu in China. For agricultural and industrial activities, prediction of groundwater status is critical. As a main branch of neural network, shallow artificial neural network models have been deployed in prediction areas such as groundwater and rainfall since late 1980s. In this paper, artificial neural network (ANN) model within a newly proposed algorithm has been developed for groundwater status forecasting. Having considered previous algorithms for ANN model in time series forecast, this new Monte Carlo based algorithm demonstrated a good result. The experiments …
Dce-Mri For Pre-Treatment Prediction And Post-Treatment Assessment Of Treatment Response In Sites Of Squamous Cell Carcinoma In The Head And Neck, Ann D. King, Steven Kwok Keung Chow, Kwok-Hung Yu, Frankie Kwok Fai Mo, David K. W. Yeung, Jing Yuan, Benjamin King Hong Law, Kunwar S. Bhatia, Alexander C. Vlantis, Anil T. Ahuja
Dce-Mri For Pre-Treatment Prediction And Post-Treatment Assessment Of Treatment Response In Sites Of Squamous Cell Carcinoma In The Head And Neck, Ann D. King, Steven Kwok Keung Chow, Kwok-Hung Yu, Frankie Kwok Fai Mo, David K. W. Yeung, Jing Yuan, Benjamin King Hong Law, Kunwar S. Bhatia, Alexander C. Vlantis, Anil T. Ahuja
Faculty of Engineering and Information Sciences - Papers: Part A
Background and Purpose It is important to identify patients with head and neck squamous cell carcinoma (SCC) who fail to respond to chemoradiotherapy so that they can undergo post-treatment salvage surgery while the disease is still operable. This study aimed to determine the diagnostic performance of dynamic contrast enhanced (DCE)-MRI using a pharmacokinetic model for pre-treatment predictive imaging, as well as post-treatment diagnosis, of residual SCC at primary and nodal sites in the head and neck. Material and Methods Forty-nine patients with 83 SCC sites (primary and/or nodal) underwent pre-treatment DCEMRI, and 43 patients underwent post-treatment DCE-MRI, of which 33 …
95% Prediction Regions: Multivariate Uncertainty Quantification For Retrieved Atmospheric States, Noel A. Cressie, Sandy Burden
95% Prediction Regions: Multivariate Uncertainty Quantification For Retrieved Atmospheric States, Noel A. Cressie, Sandy Burden
Faculty of Engineering and Information Sciences - Papers: Part A
Poster presentation
A New Model For The Prediction Of Earthquake Ground-Motion Duration In Iran, Saman Yaghmaei-Sabegh, Zhila Shoghian, M Neaz Sheikh
A New Model For The Prediction Of Earthquake Ground-Motion Duration In Iran, Saman Yaghmaei-Sabegh, Zhila Shoghian, M Neaz Sheikh
Faculty of Engineering and Information Sciences - Papers: Part A
The paper proposes a new empirical model to estimate earthquake ground-motion duration, which significantly influences the damage potential of an earthquake. The paper is concerned with significant duration parameters that are defined as the time intervals between which specified values of Arias intensity are reached. In the proposed model, significant duration parameters have been expressed as a function of moment magnitude, closest site-source distance, and site condition. The predictive model has been developed based on a database of earthquake ground-motion records in Iran, containing 286 records up to the year 2007, and a random-effect regression procedure. The result of the …
The Prediction Of Turbulence Intensities In Unsteady Flow, Ishraq Alfadhli, Shu-Qing Yang, Muttucumaru Sivakumar
The Prediction Of Turbulence Intensities In Unsteady Flow, Ishraq Alfadhli, Shu-Qing Yang, Muttucumaru Sivakumar
Faculty of Engineering and Information Sciences - Papers: Part A
This study investigates the distribution of turbulence intensities in unsteady non-uniform flows. Yang & Chow's (2008) work was extended to express this distribution based on the relationship between Reynolds shear stress and turbulence intensities in unsteady flow. It was found a self-similarity relationship between Reynolds shear stress and turbulence intensities in unsteady flow. This relationship has been developed as empirical equations based on experimental data available in the literature. By applying the self-similarity relationship, good agreements between the measured and predicted turbulence intensities have been achieved.
Performance And Prediction Of Marine Clay Treated With Vacuum And Surcharge Consolidation At Port Of Brisbane, Buddhima Indraratna, A S. Balasubramaniam, Harry Poulos, Cholachat Rujikiatkamjorn, Jayantha Ameratunga
Performance And Prediction Of Marine Clay Treated With Vacuum And Surcharge Consolidation At Port Of Brisbane, Buddhima Indraratna, A S. Balasubramaniam, Harry Poulos, Cholachat Rujikiatkamjorn, Jayantha Ameratunga
Faculty of Engineering and Information Sciences - Papers: Part A
During the past decade, the application of vacuum preloading for stabilising soft coastal clay and other low-lying estuarine soils has become popular in Australia. The cost-effectiveness isa major factor in most projects in view of the significantly reduced time for achieving high degree of consolidation. Due to an increase in trade activities at the Port of Brisbane, new facilities on Fisherman Islands at the mouth of the Brisbane River will be constructed on the new outer area (235 ha) adjacent to the existing port facilities via land reclamation. A vacuum assited surcharge load and conventional surcharge scheme in conjunction with …
Prediction Of Long-Term Urban Stormwater Loads At Single Sites, Daniel May, Muttucumaru Sivakumar
Prediction Of Long-Term Urban Stormwater Loads At Single Sites, Daniel May, Muttucumaru Sivakumar
Faculty of Engineering and Information Sciences - Papers: Part A
Long-term total phosphorus loads from 17 urban catchments in the USA were predicted using five different measures of central tendency defining site mean concentration (arithmetic mean, geometric mean, median, harmonic mean and flow-weighted mean). Overall, the flow-weighted mean concentration produced the most accurate predictions of long-term loads. The geometric mean produced the second most accurate predictions. Along with the median and harmonic mean, the geometric mean predicted long-term load relatively well at most catchments exhibiting negative correlations between event mean concentration and total event runoff depth. However, they significantly underestimated long-term load at catchments exhibiting a positive correlation between these …
Prediction Of Earthquake-Induced Liquefaction For Level And Gently Sloped Ground, G Chiaro, J Koseki
Prediction Of Earthquake-Induced Liquefaction For Level And Gently Sloped Ground, G Chiaro, J Koseki
Faculty of Engineering and Information Sciences - Papers: Part A
This paper presents a simplified procedure for predicting earthquake-induced level and sloped ground failure, namely liquefaction and shear failure. It consists of a framework where cyclic stress ratio (CSR), static stress ratio (SSR) and undrained shear strength (USS) are formulated considering simple shear conditions, which simulate field stress during earthquakes more realistically.
Thermal, Microstructural And Mechanical Coupling Analysis Model For Flatness Change Prediction During Run-Out Table Cooling In Hot Strip Rolling, Xiao-Dong Wang, Fei Li, Zheng-Yi Jiang
Thermal, Microstructural And Mechanical Coupling Analysis Model For Flatness Change Prediction During Run-Out Table Cooling In Hot Strip Rolling, Xiao-Dong Wang, Fei Li, Zheng-Yi Jiang
Faculty of Engineering and Information Sciences - Papers: Part A
Non-uniformity of temperature distribution across strip width direction is the ultimate reason why the flatness defect occurs on the strip after cooling process although the strip is flat at the exit of finishing mill. One thermal, microstructural and mechanical coupling analysis model for predicting flatness change of steel strip during the run-out table cooling process was established using ABAQUS finite element software. K Esaka phase transformation kinetics model was employed to calculate the phase transformation, and coupled with temperature calculation using the user subroutine program HETVAL. Elasto-plasticity constitutive equations of steel material, in which conventional elastic and plastic strains, thermal …
Prediction Model Of A Joint Analysis Of Beef Growth And Carcass Quality Traits, H R. Mirzaei, Arunas P. Verbyla, Wayne S. Pitchford
Prediction Model Of A Joint Analysis Of Beef Growth And Carcass Quality Traits, H R. Mirzaei, Arunas P. Verbyla, Wayne S. Pitchford
Faculty of Engineering and Information Sciences - Papers: Part A
A joint growth-carcass analysis was conducted to develop equations for predicting carcass quality traits associated with variation in growth path of crossbred cattle. During a four-year period (1994-1997) of the Australian "Southern Crossbreeding Project", mature Hereford cows (r = 581) were mated to 97 sires of Jersey, Wagyu, Angus, Hereford, South Devon, Limousin, and Belgian Blue breeds, resulting in 1141 calves. Data included body weight measurements of steers and heifers from birth until slaughter and four carcass quality traits: hot standard carcass weight, rump fat depth, rib eye muscle area, and intramuscular fat content. The model provides nine outputs: median …
Hierarchical Anatomical Brain Networks For Mci Prediction By Partial Least Square Analysis, Luping Zhou, Yaping Wang, Yang Li, Pew-Thian Yap, Dinggang Shen
Hierarchical Anatomical Brain Networks For Mci Prediction By Partial Least Square Analysis, Luping Zhou, Yaping Wang, Yang Li, Pew-Thian Yap, Dinggang Shen
Faculty of Engineering and Information Sciences - Papers: Part A
Owning to its clinical accessibility, T1-weighted MRI has been extensively studied for the prediction of mild cognitive impairment (MCI) and Alzheimer's disease (AD). The tissue volumes of GM, WM and CSF are the most commonly used measures for MCI and AD prediction. We note that disease-induced structural changes may not happen at isolated spots, but in several inter-related regions. Therefore, in this paper we propose to directly extract the inter-region connectivity based features for MCI prediction. This involves constructing a brain network for each subject, with each node representing an ROI and each edge representing regional interactions. This network is …
Hierarchical Anatomical Brain Networks For Mci Prediction: Revisiting Volumetric Measures, Luping Zhou, Yaping Wang, Yang Li, Pew-Thian Yap, Dinggang Shen, Alzheimers Disease Neuroimaging Initiative (Adni)
Hierarchical Anatomical Brain Networks For Mci Prediction: Revisiting Volumetric Measures, Luping Zhou, Yaping Wang, Yang Li, Pew-Thian Yap, Dinggang Shen, Alzheimers Disease Neuroimaging Initiative (Adni)
Faculty of Engineering and Information Sciences - Papers: Part A
Owning to its clinical accessibility, T1-weighted MRI (Magnetic Resonance Imaging) has been extensively studied in the past decades for prediction of Alzheimer's disease (AD) and mild cognitive impairment (MCI). The volumes of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) are the most commonly used measurements, resulting in many successful applications. It has been widely observed that disease-induced structural changes may not occur at isolated spots, but in several inter-related regions. Therefore, for better characterization of brain pathology, we propose in this paper a means to extract inter-regional correlation based features from local volumetric measurements. Specifically, our approach …
Discovering Prediction Model For Environmental Distribution Maps, Ke Zhang, Huidong Jin, Nianjun Liu, Rob Lesslie, Lei Wang, Zhouyu Fu, Terry Caelli
Discovering Prediction Model For Environmental Distribution Maps, Ke Zhang, Huidong Jin, Nianjun Liu, Rob Lesslie, Lei Wang, Zhouyu Fu, Terry Caelli
Faculty of Engineering and Information Sciences - Papers: Part A
Currently environmental distribution maps, such as for soil fertility, rainfall and foliage, are widely used in the natural resource management and policy making. One typical example is to predict the grazing capacity in particular geographical regions. This paper uses a discovering approach to choose a prediction model for real-world environmental data. The approach consists of two steps: (1) model selection which determines the type of prediction model, such as linear or non-linear; (2) model optimisation which aims at using less environmental data for prediction but without any loss on accuracy. The latter step is achieved by automatically selecting non-redundant features …
Contact Element Method With Two Relative Coordinates And Its Application To Prediction Of Strip Profile Of A Sendzimir Mill, Hai-Liang Yu, Xiang-Hua Liu, Gyoo-Taek Lee
Contact Element Method With Two Relative Coordinates And Its Application To Prediction Of Strip Profile Of A Sendzimir Mill, Hai-Liang Yu, Xiang-Hua Liu, Gyoo-Taek Lee
Faculty of Engineering and Information Sciences - Papers: Part A
A new numerical method, Contact Element Method with Two Relative Coordinates, has been developed. The main features of this method are that element meshing depends on the contact length between rolls or between the work roll and the strip, and that each element has two relative coordinates based on two separate contact objects. With this method, a program code “Setup Models for Sendzimir Mill” (SM4SM) has been developed for prediction of the strip profile of a 20-high Sendzimir mill with double AS-U-Roll systems. The strip profiles are predicted under various rolling conditions, such as the positions of AS-U racks, the …
Spatial Prediction For Massive Datasets, Noel A. Cressie, Gardar Johannesson
Spatial Prediction For Massive Datasets, Noel A. Cressie, Gardar Johannesson
Faculty of Engineering and Information Sciences - Papers: Part A
Remotely sensed spatio-temporal datasets on the order of megabytes to terrabytes are becoming more common. For example, polar-orbiting satellites observe Earth from space, monitoring the Earth's atmospheric, oceanic, and terrestrial processes, and generate massive amounts of environmental data. The current generation of satellites, such as the National Aeronautic and Space Administration's (NASA) Earth Observing System (EOS) Terra and Aqua satellites, generate about 1.5 terrabytes of data per day. In the USA, there are remote-sensing projects in preparation that will dwarf even these datasets. NASA, the National Oceanic and Atmospheric Administration (NOAA), and the Department of Defense (DoD) have created the …
Mtd Method For Better Prediction Of Sea Surface Temperature, Velappa Ganapathy, K Meena, Kashem M. Muttaqi
Mtd Method For Better Prediction Of Sea Surface Temperature, Velappa Ganapathy, K Meena, Kashem M. Muttaqi
Faculty of Engineering and Information Sciences - Papers: Part A
A class of incremental learning procedures known as the Modified Temporal Difference (MTD) method is introduced in this paper for fixed-step prediction problems which uses the functional features of Multilayer Perceptron. The method is applied for weekly prediction of the Sea Surface Temperature (SST) from oceanographic data. Temporal Difference (TD) methods suggest how each output of a temporal sequence must be changed, whereas a back-propagation algorithm decides which part(s) of a network to change in order to influence its output and reduce the overall error. In other words, TD methods and back-propagation address temporal credit and structural credit assignment issues, …