Tatjana Jevremovic

Rapid MCNP simulation of DNA double strand break (DSB) relative biological effectiveness (RBE) for photons, neutrons, and light ions.

To account for particle interactions in the extracellular (physical) environment, information from the cell-level Monte Carlo damage simulation (MCDS) for DNA double strand break (DSB) induction has been integrated into the general purpose Monte Carlo N-particle (MCNP) radiation transport code system. The effort to integrate these models is motivated by the need for a computationally efficient model to accurately predict particle relative biological effectiveness (RBE) in cell cultures and in vivo. To illustrate the approach and highlight the impact of the larger scale physical environment (e.g. establishing charged particle equilibrium), we examined the RBE for DSB induction (RBEDSB) of x-rays, (137)Cs γ-rays, neutrons and light ions relative to γ-rays from (60)Co in monolayer cell cultures at various depths in water. Under normoxic conditions, we found that (137)Cs γ-rays are about 1.7% more effective at creating DSB than γ-rays from (60)Co (RBEDSB  =  1.017) whereas 60-250 kV x-rays are 1.1 to 1.25 times more efficient at creating DSB than (60)Co. Under anoxic conditions, kV x-rays may have an RBEDSB up to 1.51 times as large as (60)Co γ-rays. Fission neutrons passing through monolayer cell cultures have an RBEDSB that ranges from 2.6 to 3.0 in normoxic cells, but may be as large as 9.93 for anoxic cells. For proton pencil beams, Monte Carlo simulations suggest an RBEDSB of about 1.2 at the tip of the Bragg peak and up to 1.6 a few mm beyond the Bragg peak. Bragg peak RBEDSB increases with decreasing oxygen concentration, which may create opportunities to apply proton dose painting to help address tumor hypoxia. Modeling of the particle RBE for DSB induction across multiple physical and biological scales has the potential to aid in the interpretation of laboratory experiments and provide useful information to advance the safety and effectiveness of hadron therapy in the treatment of cancer.

Core Design of a Direct-Cycle, Supercritical-Water-Cooled Fast Breeder Reactor

The conceptual design of a direct-cycle fast breeder reactor (FBR) core cooled by supercritical water is carried out as a step toward a low-cost FBR plant. The supercritical water does not exhibit change of phase. The turbines are directly driven by the core outlet coolant. In comparison with a boiling water reactor (BWR), the recirculation systems, steam separators, and dryers are eliminated. The reactor system is much simpler than the conventional steam-cooled FBRs, which adopted Loeffler boilers and complicated coolant loops for generating steam and separating it from water. Negative complete and partial coolant void reactivity are provided without much deterioration in the breeding performances by inserting thin zirconium-hydride layers between the seeds and blankets in a radially heterogeneous core. The net electric power is 1245 MW (electric). The estimated compound system doubling time is 25 yr. The discharge burnup is 77.7 GWd/t, and the refueling period is 15 months with a 73% load factor. The thermal efficiency is high (41.5%), an improvement of 24% relative to a BWRs. The pressure vessel is not thick at 30.3 cm.

Intelligent recognition of signature patterns in NRF spectra

Abstract Nuclear resonance fluorescence (NRF) has been considered as a promising method for cargo inspection. Almost all isotopes existing in nature yield a unique NRF spectral signature. NRF signals obtained during cargo inspection are aggregates of various signatures from materials hidden inside. The challenge is to identify individual signatures embedded in this signature aggregation. Background noise and spectra overlap to further complicate the NRF signal analysis. This paper addresses these concerns through an intelligent methodology recognizing signature spectra and, subsequently, identifying cargo materials. The methodology relies on fuzzy logic for pattern identification and evaluation of the weighted options involved in decision making. The intelligent methodology is presented using different simulated NRF signal scenarios. The results obtained demonstrate that the algorithm is highly accurate in most spectra carrying a signal-to-noise ratio (SNR) >20 db. Misses and false alarms were observed for isotopes with only one NRF peak (lead) with SNR <35 db. Extensive parameter testing under different scenarios indicated the existence of parameter couples that maximize the accuracy even for SNR values <20 db. In all cases the algorithm execution time was <0.1 s and was significantly faster than that of the maximum likelihood algorithm.

A Multisignal detection of hazard ousma terials for homeland security

The detection of hazardous materials has been identified as one of the most urgent needs of homeland security, especially in scanning cargo containers at United States ports. To date, special nuclear materials have been detected using neutron or gamma interrogation, and recently the nuclear resonance fluorescence has been suggested. We show a new paradigm in detecting the materials of interest by a method that combines four signals (radiography/computer tomography, acoustic, muon scattering, and nuclear resonance fluorescence) in cargos. The intelligent decision making software system is developed to support the following scenario: initially, radiography or the computer tomography scan is constructed to possibly mark the region(s) of interest. The acoustic interrogation is utilized in synergy to obtain information regarding the ultrasonic velocity of the cargo interior. The superposition of the computer tomography and acoustic images narrows down the region(s) of interest, and the intelligent system guides the detection to the next stage: no threat and finish, or proceed to the next interrogation. If the choice is the latter, knowing that high Z materials yield large scattering angle for muons, the muon scattering spectrum is used to detect the existence of such materials in the cargo. Additionally, the nuclear resonance fluorescence scan yields a spectrum that can be likened to the fingerprint of a material. The proposed algorithm is tested for detection of special nuclear materials in a comprehensive scenario.

iMASS: Computational NRF Spectra Signal from Geant4

A primary objective of the intelligent model-assisted sensing system (iMASS) project is to combine computer simulated nuclear resonance fluorescence (NRF) with real-time Monte Carlo calculations to be used in the detection of nuclear materials hidden in cargo containers. This paper introduces the preliminary results related to the Monte Carlo simulation of NRF spectra signal and the importance of reconfigurable computational technique to accelerate Monte Carlo iMASS computational sequences. The selected Monte Carlo platform Geant4 was analyzed for the NRF implementation, modeling of a 3.2MV bremsstrahlung spectrum and the radiography images of cargo container.

iMASS: Evolved NRF Simulations for More Accurate Detection of Nuclear Threats

The intelligent Model-Assisted Sensing System (iMASS) combines computer simulated nuclear resonance fluorescence (NRF), real-time Monte Carlo analysis, and signal data processing for the detection of nuclear materials hidden in cargo containers. There are on average nine million cargo containers entering the US every year. Fast and robust scanning for nuclear materials and explosives is of the outmost importance for a country’s security and safety. This paper presents the Geant4 platform development for direct application in nuclear material detections, including: an improved module to simulate the bremsstrahlung spectra, a model for NRF simulations addressing the Compton continuum background that would be found during an actual scan, and the improvements in output data interpretation. The dynamic flexibility of Geant4 and the ability to be sped up with field programmable gate array (FPGA) and parallelization is also described.Copyright

Radiation binary targeted therapy for HER-2 positive breast cancers: assumptions, theoretical assessment and future directions

A novel radiation targeted therapy is investigated for HER-2 positive breast cancers. The proposed concept combines two known approaches, but never used together for the treatment of advanced, relapsed or metastasized HER-2 positive breast cancers. The proposed radiation binary targeted concept is based on the anti HER-2 monoclonal antibodies (MABs) that would be used as vehicles to transport the nontoxic agent to cancer cells. The anti HER-2 MABs have been successful in targeting HER-2 positive breast cancers with high affinity. The proposed concept would utilize a neutral nontoxic boron-10 predicting that anti HER-2 MABs would assure its selective delivery to cancer cells. MABs against HER-2 have been a widely researched strategy in the clinical setting. The most promising antibody is Trastuzumab (Herceptin). Targeting HER-2 with the MAB Trastuzumab has been proven to be a successful strategy in inducing tumour regression and improving patient survival. Unfortunately, these tumours become resistant and afflicted women succumb to breast cancer. In the proposed concept, when the tumour region is loaded with boron-10 it is irradiated with neutrons (treatment used for head and neck cancers, melanoma and glioblastoma for over 40 years in Japan and Europe). The irradiation process takes less than an hour producing minimal side effects. This paper summarizes our recent theoretical assessments of radiation binary targeted therapy for HER-2 positive breast cancers on: the effective drug delivery mechanism, the numerical model to evaluate the targeted radiation delivery and the survey study to find the neutron facility in the world that might be capable of producing the radiation effect as needed. A novel method of drug delivery utilizing Trastuzumab is described, followed by the description of a computational Monte Carlo based breast model used to determine radiation dose distributions. The total flux and neutron energy spectra of five currently available neutron irradiation treatment facilities are examined for this application. The tumour boron concentrations and tumour to healthy tissue concentration ratios required to deliver 50 Gy-Eq to the tumour without exceeding 18 Gy-Eq in the skin are determined, as well as the associated therapeutic ratios. Discussion is provided to address the future research direction for assessing the feasibility of the proposed concept.

Computational assessment of improved cell-kill by gadolinium-supplemented boron neutron capture therapy

Potential improvement in neutron capture therapy (NCT) by utilizing both 157Gd and 10B is assessed considering two parameters calculated in transport models in MCNP4B, the dose to quiescent cells and the therapeutic ratio. Improved sterilization of quiescent or more generally non-uptaking cells is demonstrated with the addition of 157Gd to conventional 10B loading. The improved dose delivery to non-uptaking cells from concurrent administration of 157Gd and 10B is weighed against a second index, degradation in the therapeutic ratio resulting from the longer interaction lengths of the 157Gd capture products. Optimal concentrations of 157Gd are determined considering varying assumptions for boron uptake levels and selectivity. By analysing the dosimetry results of varying 157Gd concentrations applied concurrently with BPA-delivered boron in NCT, this work seeks to determine a balance between the high tumour-specific dose provided by BPA and the high dose to quiescent cells provided by potential gadolinium agents. Depending upon the assumptions for drug specificity, tumour size and fraction of quiescent cells, NCT with low levels of 157Gd (125 microg g(-1)) supplementing 10B loadings was shown to be superior to treatments applying 10B alone.

Anticipatory monitoring and control of complex energy systems using a fuzzy based fusion of support vector regressors

This paper places itself in the realm of anticipatory systems and envisions monitoring and control methods being capable of making predictions over system critical parameters. Anticipatory systems allow intelligent control of complex systems by predicting their future state. In the current work, an intelligent model aimed at implementing anticipatory monitoring and control in energy industry is presented and tested. More particularly, a set of support vector regressors (SVRs) are trained using both historical and observed data. The trained SVRs are used to predict the future value of the system based on current operational system parameter. The predicted values are then inputted to a fuzzy logic based module where the values are fused to obtain a single value, i.e., final system output prediction. The methodology is tested on real turbine degradation datasets. The outcome of the approach presented in this paper highlights the superiority over single support vector regressors. In addition, it is shown that appropriate selection of fuzzy sets and fuzzy rules plays an important role in improving system performance.

PEAKSEEK: A Statistical Processing Algorithm for Radiation Spectrum Peak Identification

An accurate analysis of radiation data is essential in many nuclear related applications. In the intelligent Model Assisted Sensing System (iMASS) development, nuclear resonance florescence (NRF) spectra of radioactive isotopes are used for detection of nuclear material in cargo containers at US ports. The NRF spectrum of a particular radioactive isotope has a unique signature at unique energy levels. This paper presents a statistical processing algorithm, Peakseek, developed to identify radiation peaks in NRF spectra with a certain degree of confidence. The algorithm tracks the changes in the count rate (theta) of the NRF spectrum and identifies the point of abrupt change in the count rate, i.e., energy level. Identification of abrupt changes in the count rate is performed on the basis of a generalized likelihood ratio statistical test.