A pre-treatment verification tool for IMRT based on EPID measurements and a GEANT4 back projection application

The aim of this work is to develop software that is capable of back projecting primary fluence images obtained from EPID measurements through phantom and patient geometries in order to calculate 3D dose distributions. In the first instance, we aim to develop a tool for pretreatment verification in IMRT. In our approach, a Geant4 application is used to back project primary fluence values from each EPID pixel towards the source. Each beam is considered to be polyenergetic, with a spectrum obtained from Monte Carlo calculations for the LINAC in question. At each step of the ray tracing process, the energy differential fluence is corrected for attenuation and beam divergence. Subsequently, the TERMA is calculated and accumulated to an energy differential 3D TERMA distribution. This distribution is then convolved with monoenergetic point spread kernels, thus generating energy differential 3D dose distributions. The resulting dose distributions are accumulated to yield the total dose distribution, which can then be used for pre-treatment verification of IMRT plans. Preliminary results were obtained for a test EPID image comprised of 100 9 100 pixels of unity fluence. Back projection of this field into a 30 cm9 30 cm 9 30 cm water phantom was performed, with TERMA distributions obtained in approximately 10 min (running on a single core of a 3 GHz processor). Point spread kernels for monoenergetic photons in water were calculated using a separate Geant4 application. Following convolution and summation, the resulting 3D dose distribution produced familiar build-up and penumbral features. In order to validate the dose model we will use EPID images recorded without any attenuating material in the beam for a number of MLC defined square fields. The dose distributions in water will be calculated and compared to TPS predictions.

Coordinated probabilistic optimal decision-making model for multi-area ATC with risk control

In the decision-making of multi-area ATC (Available Transfer Capacity) in electricity market environment, the existing resources of transmission network should be optimally dispatched and coordinately employed on the premise that the secure system operation is maintained and risk associated is controllable. The non-sequential Monte Carlo simulation is used to determine the ATC probability density distribution of specified areas under the influence of several uncertainty factors, based on which, a coordinated probabilistic optimal decision-making model with the maximal risk benefit as its objective is developed for multi-area ATC. The NSGA-II is applied to calculate the ATC of each area, which considers the risk cost caused by relevant uncertainty factors and the synchronous coordination among areas. The essential characteristics of the developed model and the employed algorithm are illustrated by the example of IEEE 118-bus test system. Simulative result shows that, the risk of multi-area ATC decision-making is influenced by the uncertainties in power system operation and the relative importance degrees of different areas.

A Bayesian approach to the determination of ignition delay

A novel in-cylinder pressure method for determining ignition delay has been proposed and demonstrated. This method proposes a new Bayesian statistical model to resolve the start of combustion, defined as being the point at which the band-pass in-cylinder pressure deviates from background noise and the combustion resonance begins. Further, it is demonstrated that this method is still accurate in situations where there is noise present. The start of combustion can be resolved for each cycle without the need for ad hoc methods such as cycle averaging. Therefore, this method allows for analysis of consecutive cycles and inter-cycle variability studies. Ignition delay obtained by this method and by the net rate of heat release have been shown to give good agreement. However, the use of combustion resonance to determine the start of combustion is preferable over the net rate of heat release method because it does not rely on knowledge of heat losses and will still function accurately in the presence of noise. Results for a six-cylinder turbo-charged common-rail diesel engine run with neat diesel fuel at full, three quarters and half load have been presented. Under these conditions the ignition delay was shown to increase as the load was decreased with a significant increase in ignition delay at half load, when compared with three quarter and full loads.

Dosimetric effects of a high-density spinal implant

In this study, a treatment plan for a spinal lesion, with all beams transmitted though a titanium vertebral reconstruction implant, was used to investigate the potential effect of a high-density implant on a three-dimensional dose distribution for a radiotherapy treatment. The BEAMnrc/DOSXYZnrc and MCDTK Monte Carlo codes were used to simulate the treatment using both a simplified, recltilinear model and a detailed model incorporating the full complexity of the patient anatomy and treatment plan. The resulting Monte Carlo dose distributions showed that the commercial treatment planning system failed to accurately predict both the depletion of dose downstream of the implant and the increase in scattered dose adjacent to the implant. Overall, the dosimetric effect of the implant was underestimated by the commercial treatment planning system and overestimated by the simplified Monte Carlo model. The value of performing detailed Monte Carlo calculations, using the full patient and treatment geometry, was demonstrated.

The benefit of modeling jumps in realized volatility for risk prediction : evidence from Chinese mainland stocks

Recent literature has focused on realized volatility models to predict financial risk. This paper studies the benefit of explicitly modeling jumps in this class of models for value at risk (VaR) prediction. Several popular realized volatility models are compared in terms of their VaR forecasting performances through a Monte Carlo study and an analysis based on empirical data of eight Chinese stocks. The results suggest that careful modeling of jumps in realized volatility models can largely improve VaR prediction, especially for emerging markets where jumps play a stronger role than those in developed markets.

Effects of free flow state highway traffic parameters on the performance of amplify and forward cooperative multi-hop vehicular networks

In this paper we analyse the effects of highway traffic flow parameters like vehicle arrival rate and density on the performance of Amplify and Forward (AF) cooperative vehicular networks along a multi-lane highway under free flow state. We derive analytical expressions for connectivity performance and verify them with Monte-Carlo simulations. When AF cooperative relaying is employed together with Maximum Ratio Combining (MRC) at the receivers the average route error rate shows 10-20 fold improvement compared to direct communication. A 4-8 fold increase in maximum number of traversable hops can also be observed at different vehicle densities when AF cooperative communication is used to strengthen communication routes. However the theorical upper bound of maximum number of hops promises higher performance gains.

Pseudo-marginal algorithms with multiple CPUs

The emergence of pseudo-marginal algorithms has led to improved computational efficiency for dealing with complex Bayesian models with latent variables. Here an unbiased estimator of the likelihood replaces the true likelihood in order to produce a Bayesian algorithm that remains on the marginal space of the model parameter (with latent variables integrated out), with a target distribution that is still the correct posterior distribution. Very efficient proposal distributions can be developed on the marginal space relative to the joint space of model parameter and latent variables. Thus psuedo-marginal algorithms tend to have substantially better mixing properties. However, for pseudo-marginal approaches to perform well, the likelihood has to be estimated rather precisely. This can be difficult to achieve in complex applications. In this paper we propose to take advantage of multiple central processing units (CPUs), that are readily available on most standard desktop computers. Here the likelihood is estimated independently on the multiple CPUs, with the ultimate estimate of the likelihood being the average of the estimates obtained from the multiple CPUs. The estimate remains unbiased, but the variability is reduced. We compare and contrast two different technologies that allow the implementation of this idea, both of which require a negligible amount of extra programming effort. The superior performance of this idea over the standard approach is demonstrated on simulated data from a stochastic volatility model.

Spin-polarization and ferromagnetism of graphitic carbon nitride materials

Polymeric graphitic carbon nitride materials have attracted increasing attention in recent years owning to their potential applications in energy conversion, environment protection, and so on. Here, from first-principles calculations, we report the electronic structure modification of graphitic carbon nitride (g-C3N4) in response to carbon doping. We showed that each dopant atom can induce a local magnetic moment of 1.0 μB in non-magnetic g-C3N4. At the doping concentration of 1/14, the local magnetic moments of the most stable doping configuration which has the dopant atom at the center of heptazine unit prefer to align in a parallel way leading to long-range ferromagnetic (FM) ordering. When the joint N atom is replaced by C atom, the system favors an antiferromagnetic (AFM) ordering at unstrained state, but can be tuned to ferromagnetism (FM) by applying biaxial tensile strain. More interestingly, the FM state of the strained system is half-metallic with abundant states at the Fermi level in one spin channel and a band gap of 1.82 eV in another spin channel. The Curie temperature (Tc) was also evaluated using a mean-field theory and Monte Carlo simulations within the Ising model. Such tunable electron spin-polarization and ferromagnetism are quite promising for the applications of graphitic carbon nitride in spintronics.

A multi-objective optimization approach for coordinating available transfer capability with risk control

In the electricity market environment, coordination of system reliability and economics of a power system is of great significance in determining the available transfer capability (ATC). In addition, the risks associated with uncertainties should be properly addressed in the ATC determination process for risk-benefit maximization. Against this background, it is necessary that the ATC be optimally allocated and utilized within relative security constraints. First of all, the non-sequential Monte Carlo stimulation is employed to derive the probability density distribution of ATC of designated areas incorporating uncertainty factors. Second, on the basis of that, a multi-objective optimization model is formulated to determine the multi-area ATC so as to maximize the risk-benefits. Then, the solution to the developed model is achieved by the fast non-dominated sorting (NSGA-II) algorithm, which could decrease the risk caused by uncertainties while coordinating the ATCs of different areas. Finally, the IEEE 118-bus test system is served for demonstrating the essential features of the developed model and employed algorithm.

In-cylinder pressure and inter-cycle variability analysis for a compression ignition engine : Bayesian approaches

This thesis introduced Bayesian statistics as an analysis technique to isolate resonant frequency information in in-cylinder pressure signals taken from internal combustion engines. Applications of these techniques are relevant to engine design (performance and noise), energy conservation (fuel consumption) and alternative fuel evaluation. The use of Bayesian statistics, over traditional techniques, allowed for a more in-depth investigation into previously difficult to isolate engine parameters on a cycle-by-cycle basis. Specifically, these techniques facilitated the determination of the start of pre-mixed and diffusion combustion and for the in-cylinder temperature profile to be resolved on individual consecutive engine cycles. Dr Bodisco further showed the utility of the Bayesian analysis techniques by applying them to in-cylinder pressure signals taken from a compression ignition engine run with fumigated ethanol.

Estimating the robustness of long-haul train plans

In Australia, and elsewhere, the movement of trains on long-haul rail networks is usually planned in advance. Typically, a train plan is developed to confirm that the required train movements and track maintenance activities can occur. The plan specifies when track segments will be occupied by particular trains and maintenance activities. On the day of operation, a train controller monitors and controls the movement of trains and maintenance crews, and updates the train plan in response to unplanned disruptions. It can be difficult to predict how good a plan will be in practice. The main performance indicator for a train service should be reliability - the proportion of trains running the service that complete at or before the scheduled time. We define the robustness of a planned train service to be the expected reliability. The robustness of individual train services and for a train plan as a whole can be estimated by simulating the train plan many times with random, but realistic, perturbations to train departure times and segment durations, and then analysing the distributions of arrival times. This process can also be used to set arrival times that will achieve a desired level of robustness for each train service.

Polymorphisms of the SIPA1 gene and sporadic breast cancer susceptibility

Background The novel breast cancer metastasis modulator gene signal-induced proliferation-associated 1 (Sipa1) underlies the breast cancer metastasis efficiency modifier locus Mtes 1 and has been shown to influence mammary tumour metastatic efficiency in the mouse, with an ectopically expressing Sipa1 cell line developing 1.5 to 2 fold more surface pulmonary metastases. Sipa1 encodes a mitogen-inducible GTPase activating (GAP) protein for members of the Ras-related proteins; participates in cell adhesion and modulates mitogen-induced cell cycle progression. Germline SIPA1 SNPs showed association with positive lymph node metastasis and hormonal receptor status in a Caucasian cohort. We hypothesized that SIPA1 may also be correlated to breast carcinoma incidence as well as prognosis. Therefore, this study investigated the potential relationship of SIPA1 and human breast cancer incidence by a germline SNP genotype frequency association study in a case-control Caucasian cohort in Queensland, Australia. Methods The SNPs genotyped in this study were identified in a previous study and the genotyping assays were carried out using TaqMan SNP Genotyping Assays. The data were analysed with chi-square method and the Monte Carlo style CLUMP analysis program. Results Results indicated significance with SIPA1 SNP rs3741378; the CC genotype was more frequently observed in the breast cancer group compared to the disease-free control group, indicating the variant C allele was associated with increased breast cancer incidence. Conclusion This observation indicates SNP rs3741378 as a novel potential sporadic breast cancer predisposition SNP. While it showed association with hormonal receptor status in breast cancer group in a previous pilot study, this exonic missense SNP (Ser (S) to Phe (F)) changes a hydrophilic residue (S) to a hydrophobic residue (F) and may significantly alter the protein functions of SIPA1 in breast tumourgenesis. SIPA1 SNPs rs931127 (5' near gene), and rs746429 (synonymous (Ala (A) to Ala (A)), did not show significant associations with breast cancer incidence, yet were associated with lymph node metastasis in the previous study. This suggests that SIPA1 may be involved in different stages of breast carcinogenesis and since this study replicates a previous study of the associated SNP, it implicates variants of the SIPA1 gene as playing a potential role in breast cancer.

A flexible Bayesian model for describing temporal variability of N2O emissions from an Australian pasture

Soil-based emissions of nitrous oxide (N2O), a well-known greenhouse gas, have been associated with changes in soil water-filled pore space (WFPS) and soil temperature in many previous studies. However, it is acknowledged that the environment-N2O relationship is complex and still relatively poorly unknown. In this article, we employed a Bayesian model selection approach (Reversible jump Markov chain Monte Carlo) to develop a data-informed model of the relationship between daily N2O emissions and daily WFPS and soil temperature measurements between March 2007 and February 2009 from a soil under pasture in Queensland, Australia, taking seasonal factors and time-lagged effects into account. The model indicates a very strong relationship between a hybrid seasonal structure and daily N2O emission, with the latter substantially increased in summer. Given the other variables in the model, daily soil WFPS, lagged by a week, had a negative influence on daily N2O; there was evidence of a nonlinear positive relationship between daily soil WFPS and daily N2O emission; and daily soil temperature tended to have a linear positive relationship with daily N2O emission when daily soil temperature was above a threshold of approximately 19°C. We suggest that this flexible Bayesian modeling approach could facilitate greater understanding of the shape of the covariate-N2O flux relation and detection of effect thresholds in the natural temporal variation of environmental variables on N2O emission.

Investigation of a neuronal nitric oxide synthase gene (NOS1) polymorphism in a multiple sclerosis population

Multiple Sclerosis (MS) is a chronic neurological disease characterized by demyelination associated with infiltrating white blood cells in the central nervous system (CNS). Nitric oxide synthases (NOS) are a family of enzymes that control the production of nitric oxide. It is possible that neuronal NOS could be involved in MS pathophysiology and hence the nNOS gene is a potential candidate for involvement in disease susceptibility. The aim of this study was to determine whether allelic variation at the nNOS gene locus is associated with MS in an Australian cohort. DNA samples obtained from a Caucasian Australian population affected with MS and an unaffected control population, matched for gender, age and ethnicity, were genotyped for a microsatellite polymorphism in the promoter region of the nNOS gene. Allele frequencies were compared using chi-squared based statistical analyses with significance tested by Monte Carlo simulation. Allelic analysis of MS cases and controls produced a chi-squared value of 5.63 with simulated P = 0.96 (OR(max) = 1.41, 95% CI: 0.926-2.15). Similarly, a Mann-Whitney U analysis gave a non-significant P-value of 0.377 for allele distribution. No differences in allele frequencies were observed for gender or clinical course subtype (P > 0.05). Statistical analysis indicated that there is no association of this nNOS variant and MS and hence the gene does not appear to play a genetically significant role in disease susceptibility.

A linkage and cross-sectional study of hypertension and obesity using a poly(A) Alu-repeat polymorphism at the glucagon receptor gene locus (17q25)

1. Previous glucagon receptor gene (GCGR) studies have shown a Gly40Ser mutation to be more prevalent in essential hypertension and to affect glucagon binding affinity to its receptor. An Alu-repeat poly(A) polymorphism colocalized to GCGR was used in the present study to test for association and linkage in hypertension as well as association in obesity development. 2. Using a cross-sectional approach, 85 hypertensives and 95 normotensives were genotyped using polymerase chain reaction primers flanking the Alu-repeat. Both hypertensive and normotensive populations were subdivided into lean and obese categories based on body mass index (BMI) to determine involvement of this variant in obesity. For the linkage study, 89 Australian Caucasian hypertension affected sibships (174 sibpairs) were genotyped and the results were analysed using GENE-HUNTER, Mapmaker Sibs, ERPA and SPLINK (all freely available from http://linlkage.rockefeller. edu/soft/list.html). 3. Cross-sectional results for both hypertension and obesity were analysed using Chi-squared and Monte Carlo analyses. Results did not show an association of this variant with either hypertension (χ2 = 6.9, P = 0.14; Monte Carlo χ2 = 7.0, P = 0.11; n = 5000) or obesity (χ2 = 3.3, P = 0.35; Monte Carlo χ2 = 3.26, P = 0.34; n = 5000). In addition, results from the linkage study using hypertensive sib-pairs did not indicate linkage of the poly(A) repent with hypertension. Hence, results did not indicate a role far the Alu-repeat in either hypertension or obesity. However, as the heterozygosity of this poly(A) repeat is low (35%), a larger number of hypertensive sib-pairs may be required to draw definitive conclusions.