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At least 1,171 records · Page 65

Correcting lateral response artifacts from flatbed scanners for radiochromic film dosimetry

Purpose: A known factor affecting the accuracy of radiochromic film dosimetry is the lateral response artifact (LRA) induced by nonuniform response of a flatbed scanner in the direction perpendicular to the scan direction. This work reports a practical solution to eliminate such artifacts for all forms of dose QA. Methods: EBT3 films from a single production lot (02181401) cut into rectangular 4 × 5 cm{sup 2} pieces, with the long dimension parallel to the long dimension of the original 20.3 × 25.4 cm{sup 2} sheets, were exposed at a depth of 5 cm on a Varian Trilogy at the center of a 20 × 20 cm{sup 2} open field at seven doses between 50 and 1600 cGy using 6 MV photons. These films together with an unexposed film from the same production lot were lined one next to the other on an Epson 10000XL or 11000XL scanner in portrait orientation with their long dimension parallel to the scan direction. Scanned images were then obtained with the line of films positioned at seven discrete lateral locations perpendicular to the scan direction. The process was repeated in landscape orientation and on three other Epson scanners. Data were also collected for three additional production lots of EBT3 film (11051302, 03031401, and 03171403). From measurements at the various lateral positions, the scanner response was determined as a function of the lateral position of the scanned film. For a given color channel X, the response at any lateral position L is related to the response at the center, C, of the scanner by Response(C, D, X) = A{sub L,X} + B{sub L,X}⋅Response(L, D, X), where D is dose and the coefficients A{sub L,X} and B{sub L,X} are determined from the film measurements at the center of the scanner and six other discrete lateral positions. The values at intermediate lateral positions were obtained by linear interpolation. The coefficients were determined for the red, green, and blue color channels, preserving the ability to apply triple-channel dosimetry once corrections were applied to compensate for the lateral position response artifact. To validate this method, corrections were applied to several films that were exposed to 15 × 15cm{sup 2} open fields and large IMRT and VMAT fields and scanned at the extreme edges of the scan window in addition to the central location. Calibration and response data were used to generate dose maps and perform gamma analysis using single- or triple-channel dosimetry with FilmQAPro 2014 software. Results: The authors’ study found that calibration curves at the different lateral positions could be correlated by a simple two-point rescaling using the response for unexposed film as well as the response of film exposed at high doses between 800 and 1600 cGy. The coefficients A{sub L,X} and B{sub L,X} for each color channel X were found to be independent of dose at each lateral location L. This made it possible to apply the relationship Response(C, D, X) = A{sub L,X} + B{sub L,X}⋅Response(L, D, X), to the raw film responses, permitting correction of the response values at any lateral position to an equivalent response, as if that part of the film was located at the center of the scanner. This correction method was validated for several films exposed to open as well as large IMRT and VMAT fields. Conclusions: The work reported elaborates on the process using the correction procedures to eliminate the lateral response artifact and demonstrates improvements in the accuracy of radiochromic film dosimetry for the radiation therapy quality assurance applications.

07 ISOTOPE AND RADIATION SOURCES↗

SU-E-J-216: A Sequence Independent Approach for Quantification of MR Image Deformations From Brachytherapy Applicators

Purpose: MRI is increasingly used as a single imaging modality for brachytherapy treatment planning. The presence of a brachytherapy applicator may cause distortions in the images, especially at higher field strengths. Our aim is to develop a procedure to quantify these distortions theoretically for any MR-sequence and to verify the estimated deformations for clinical sequences. Methods: Image distortions due to perturbation of the B0-field are proportional to the ratio of the induced frequency shift and the read-out bandwidth of the applied sequence. By reconstructing a frequency-shift map from the phase data from a multi-echo sequence, distortions can be calculated for any MR-sequence. Verification of this method for estimating distortions was performed by acquiring images with opposing read-out directions and consequently opposing distortions. The applicator shift can be determined by rigidly matching these images. Clinically, T2W-TSE-images are used for this purpose. For pre-clinical tests, EPI-sequences with narrow read-out bandwidth (19.5–47.5Hz), consequently large distortions, were added to the set of clinical MRsequences. To quantify deformations of the Utrecht Interstitial CT/MR applicator (Elekta Brachytherapy) on a Philips Ingenia 3T MRI, pre-clinical tests were performed in a phantom with the applicator in water, followed by clinical validation. Results: Deformations observed in the narrow bandwidth EPI-images were well predicted using the frequency-shift, the latter giving an overestimation up to 30%/up to 1 voxel. For clinically applied MR-sequences distortions were well below the voxel size. In patient setup distortions determined from the frequency-shift map were at sub-voxel level (<0.7mm). Using T2W-images larger distortions were found (1–2mm). This discrepancy was caused by patient movement between/during acquisition of the T2W-images with opposing read-out directions. Conclusion: Phantom experiments demonstrated the feasibility of a clinical procedure for quantification of MR-image distortions for any MR-sequence. In a clinical set-up the distortions from a Utrecht interstitial CT/MR applicator are sub-voxel level. This work was partially funded by Elekta Brachytherpy.

60 APPLIED LIFE SCIENCES↗

Sim-Situ: A Framework for the Faithful Simulation of in situ Processing

The amount of data generated by numerical simulations in various scientific domains led to a fundamental redesign of how the analysis and visualization of simulation outputs are performed. The throughput and capacity of storage subsystems have not evolved as fast as the computing power in extreme-scale supercomputers, making the classical post-hoc approach highly inefficient. In situ processing has then emerged as a solution in which simulation and data analysis/visualization are intertwined for better performance and greater interactivity.Determining the best allocation, i.e., how many resources to allocate to simulation and analysis respectively, mapping, i.e., where and at which frequency to run the analysis/visualization, and data transfer mode is a complex task whose performance assessment is crucial to the efficient execution of in situ processing. However, such a performance evaluation of different strategies usually relies either on directly running them on the targeted execution environments, which can rapidly become extremely time- and resource-consuming, or on resorting to simplified models of the components of an in situ application, which can lack of realism. In both cases, the validity of the performance evaluation is limited.In this paper, we present Sim-Situ, a simulation-based framework for the faithful performance evaluation of in situ processing strategies. We designed Sim-Situ to reflect the typical features of in situ processing systems. Thanks to its modular design, Sim-situ has the necessary flexibility to easily and faithfully evaluate the behavior and performance of various allocation, mapping, and data transfer strategies. We illustrate the simulation capabilities of Sim-Situ on a Molecular Dynamics use case. We study the impact of different strategies on performance and show how users can leverage Sim-Situ to determine interesting tradeoffs when adding analysis/visualization components to their application.

Honoré, Valentin↗

Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti’s Theorem for Markov Chains

Conformal prediction is a widely used method to quantify uncertainty in settings where the data is independent and identically distributed (IID), or more generally, exchangeable. Conformal prediction takes in a pre-trained classifier and a calibration dataset as inputs, and returns a function which maps feature vectors to subsets of classes. The output of the returned function for a new feature vector is guaranteed to contain the true class with a pre-specified confidence. Despite its success and usefulness in IID settings, extending conformal prediction to non-exchangeable (e.g., Markovian) data in a manner that provably preserves all desirable theoretical properties has largely remained an open problem. As a solution, we extend conformal prediction to the setting of a Hidden Markov Model (HMM) with unknown parameters. The key idea behind the proposed method is to partition the non-exchangeable Markovian data from the HMM into exchangeable blocks by exploiting the de Finetti’s Theorem for Markov Chains discovered by Diaconis and Freedman (1980). The permutations of the exchangeable blocks are then viewed as randomizations of the observed Markovian data from the HMM. The proposed method provably retains all desirable theoretical guarantees offered by the classical conformal prediction framework and is general enough to be useful in many sequential prediction problems.

Nettasinghe, Don Buddhika Wijayantha↗

Machine Learning Approaches to Increasing Value of Spaceflight Omics Databases

The number of spaceflight bioscience mission opportunities is too small to allow all relevant biological and environmental parameters to be experimentally identified. Simulated spaceflight experiments in ground-based facilities (GBFs), such as clinostats, are each suitable only for particular investigations -- a rotating-wall vessel may be 'simulated microgravity' for cell differentiation (hours), but not DNA repair (seconds) -- and introduce confounding stimuli, such as motor vibration and fluid shear effects. This uncertainty over which biological mechanisms respond to a given form of simulated space radiation or gravity, as well as its side effects, limits our ability to baseline spaceflight data and validate mission science. Machine learning techniques autonomously identify relevant and interdependent factors in a data set given the set of desired metrics to be evaluated: to automatically identify related studies, compare data from related studies, or determine linkages between types of data in the same study. System-of-systems (SoS) machine learning models have the ability to deal with both sparse and heterogeneous data, such as that provided by the small and diverse number of space biosciences flight missions; however, they require appropriate user-defined metrics for any given data set. Although machine learning in bioinformatics is rapidly expanding, the need to combine spaceflight/GBF mission parameters with omics data is unique. This work characterizes the basic requirements for implementing the SoS approach through the System Map (SM) technique, a composite of a dynamic Bayesian network and Gaussian mixture model, in real-world repositories such as the GeneLab Data System and Life Sciences Data Archive. The three primary steps are metadata management for experimental description using open-source ontologies, defining similarity and consistency metrics, and generating testing and validation data sets. Such approaches to spaceflight and GBF omics data may soon enable unique insight into which measured phenomena correlate to biological mechanisms that are truly affected by spaceflight conditions; which are most likely to be confounded by other variables; and which are insufficiently characterized, significantly increasing existing and future science return from ISS and spaceflight missions.

Gentry, Diana↗

Bias‐Dependent Quantum Efficiency Reveals Recombination Pathways in Thin Film Solar Cells

Identifying where recombination predominantly occurs—whether at the front interface, back interface, or throughout the bulk—is crucial for optimizing CdSeTe solar cells and many other photovoltaic device architectures. Here, a simple and effective diagnostic is demonstrated: measuring external quantum efficiency (QE) under varying forward biases. The drift–diffusion simulations reveal that each recombination pathway leaves a distinct bias-induced signature in the normalized QE: a progressive drop at long wavelengths for back-limited devices, a short-wavelength decline for front-limited devices, and a relatively uniform decrease across all wavelengths for bulk-limited devices. These predictions are validated with experiments on As-doped and Cu-doped CdSeTe devices, with and without passivation layers or different front buffers. In each case, the observed bias-dependent QE spectral changes align with the simulated recombination map. Because this method uses standard QE instrumentation, it offers a broadly accessible and cost-efficient means to diagnose recombination pathways—extending beyond CdSeTe to other thin-film and emerging solar cell technologies. By pinpointing recombination bottlenecks, researchers and manufacturers can strategically refine doping profiles, passivation schemes, and interface designs to further improve device performance.

CdTe↗

Subdiffraction Imaging of Carrier Dynamics in Halide Perovskite Semiconductors: Effects of Passivation, Morphology, and Ion Motion

In this article, we spatially resolve photocarrier dynamics in halide perovskites using time-resolved electrostatic force microscopy (trEFM) to map surface potential equilibration during photoexcitation. We present a unified interpretation of trEFM, which measures the evolution of the surface potential in response to photoexcitation. We show that trEFM measurements correlate with surface recombination velocity and carrier lifetimes, validated with time-resolved photoluminescence imaging. We further validate the interpretation of trEFM through wavelength- and intensity-dependent measurements and with drift-diffusion simulations. We compare several passivation agents, including (3-aminopropyl)trimethoxysilane (APTMS), [3-(2-aminoethylamino)propyl]trimethoxysilane (AEAPTMS), and phenethylammonium iodide (PEAI). The results reveal heterogeneity in surface potential equilibration times that correlates with perovskite film morphology and nanoscale variations in recombination dynamics following surface passivation. Not only do our results highlight the potential for further improvement of passivation strategies, but also the necessity of high spatial and temporal resolution methods, like trEFM, to evaluate next-generation semiconductors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Quantifying the unknown impact of segmentation uncertainty on image-based simulations

Abstract Image-based simulation, the use of 3D images to calculate physical quantities, relies on image segmentation for geometry creation. However, this process introduces image segmentation uncertainty because different segmentation tools (both manual and machine-learning-based) will each produce a unique and valid segmentation. First, we demonstrate that these variations propagate into the physics simulations, compromising the resulting physics quantities. Second, we propose a general framework for rapidly quantifying segmentation uncertainty. Through the creation and sampling of segmentation uncertainty probability maps, we systematically and objectively create uncertainty distributions of the physics quantities. We show that physics quantity uncertainty distributions can follow a Normal distribution, but, in more complicated physics simulations, the resulting uncertainty distribution can be surprisingly nontrivial. We establish that bounding segmentation uncertainty can fail in these nontrivial situations. While our work does not eliminate segmentation uncertainty, it improves simulation credibility by making visible the previously unrecognized segmentation uncertainty plaguing image-based simulation.

42 ENGINEERING↗

Radio-frequency sheath voltages and slow wave electric field spatial structure

We investigate theoretically how sheath radio-frequency (RF) oscillations relate to the spatial structure of the RF parallel electric field emitted by Ion Cyclotron (IC) wave launchers, using a simple model of Slow Wave (SW) evanescence coupled with Direct Current (DC) plasma biasing via sheath boundary conditions in a plasma-filled 2-dimensional (parallel, radial) rectangle. Within a “wide sheaths” asymptotic regime, valid for large-amplitude near RF fields, our model becomes partly linear: the sheath oscillating voltage at open field line boundaries is a linear combination of elementary contributions by every source point of the radiated RF field map. These individual contributions are all the more intense as the SW emission point is toroidally nearer to the sheath walls. A limit formula is given for a source infinitely close to the sheaths. The decay of sheath RF voltages with the sheath/source parallel distance is quantified as a function of two characteristic SW evanescence lengths. Decay lengths are smaller than antenna parallel extensions. The sheath RF voltages at an IC antenna side limiter are therefore mainly sensitive to SW emission near this limiter, as recent observations suggest. Toroidal proximity effects could also explain why sheath oscillations persist with antisymmetric strap toroidal phasing, despite the parallel anti-symmetry of the radiated field map. They could also justify current attempts at reducing the RF fields induced near antenna boxes to attenuate sheath oscillations in their vicinity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Mapping Strain-rate Dependent Dislocation-Defect Interactions by Atomistic Simulations

Probing the mechanisms of defect-defect interactions at strain rates lower than 106 s-1 is an unresolved challenge to date to molecular dynamics (MD) techniques. Here we propose a novel atomistic approach based on transition state theory and the concept of a strain-dependent effective activation barrier that is capable of simulating the kinetics of dislocation-defect interactions at virtually any strain rate, exemplified within 10-7 to 107 s-1. We apply this approach to the problem of an edge dislocation colliding with a cluster of self-interstitial atoms (SIA) under shear deformation. Using an activation-relaxation algorithm (1), we uncover a unique strain-rate dependent trigger mechanism that allows the SIA cluster to be absorbed during the process leading to dislocation climb. Guided by this finding, we determine the activation barrier of the trigger mechanism as a function of shear strain, and use that in a coarse-graining rate equation formulation for constructing a mechanism map in the phase space of strain-rate and temperature. Our predictions of a crossover from a defect recovery at the low strain rate regime to defect absorption behavior in the high strain-rate regime are validated against our own independent, direct MD simulations at 105 to 107 s-1. Implications of the present approach for probing molecular-level mechanisms in strain-rate regimes previously considered inaccessible to atomistic simulations are discussed.

activation mecanism↗

Galaxy clustering in harmonic space from the dark energy survey year 1 data: compatibility with real-space results

ABSTRACT We perform an analysis in harmonic space of the Dark Energy Survey Year 1 Data (DES-Y1) galaxy clustering photometric data using products obtained for the real-space analysis. We test our pipeline with a suite of lognormal simulations, which are used to validate scale cuts in harmonic space as well as to provide a covariance matrix that takes into account the DES-Y1 mask. We then apply this pipeline to DES-Y1 data taking into account survey property maps derived for the real-space analysis. We compare with real-space DES-Y1 results obtained from a similar pipeline. We show that the harmonic space analysis we develop yields results that are compatible with the real-space analysis for the bias parameters. This verification paves the way to performing a harmonic space analysis for the upcoming DES-Y3 data.

79 ASTRONOMY AND ASTROPHYSICS↗

Dark Energy Survey Year 3 results: redshift calibration of the MagLim lens sample from the combination of SOMPZ and clustering and its impact on cosmology

ABSTRACT We present an alternative calibration of the MagLim lens sample redshift distributions from the Dark Energy Survey (DES) first 3 yr of data (Y3). The new calibration is based on a combination of a self-organizing-map-based scheme and clustering redshifts to estimate redshift distributions and inherent uncertainties, which is expected to be more accurate than the original DES Y3 redshift calibration of the lens sample. We describe in detail the methodology, and validate it on simulations and discuss the main effects dominating our error budget. The new calibration is in fair agreement with the fiducial DES Y3 n(z) calibration, with only mild differences (<3σ) in the means and widths of the distributions. We study the impact of this new calibration on cosmological constraints, analysing DES Y3 galaxy clustering and galaxy–galaxy lensing measurements, assuming a Lambda cold dark matter cosmology. We obtain Ωm = 0.30 ± 0.04, σ8 = 0.81 ± 0.07, and S8 = 0.81 ± 0.04, which implies a ∼0.4σ shift in the Ω − S8 plane compared to the fiducial DES Y3 results, highlighting the importance of the redshift calibration of the lens sample in multiprobe cosmological analyses.

79 ASTRONOMY AND ASTROPHYSICS↗

Parametrized Hamiltonian simulation using quantum optimal control

Analog quantum simulation offers a hardware-specific approach to studying quantum dynamics, but mapping a model Hamiltonian onto the available device parameters requires matching the hardware dynamics. In this work, we introduce a paradigm for quantum Hamiltonian simulation that leverages digital decomposition techniques and optimal control to perform analog simulation. We validate this approach by constructing the optimal analog controls for a superconducting transmon device to emulate the dynamics of an extended Bose-Hubbard model. We demonstrate the role of control time, digital error, and pulse complexity, and we explore the accuracy and robustness of these controls. We conclude by discussing the opportunity for implementing this paradigm in near-term quantum devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

GraphCH: A Deep Framework for Assessing Cyber-Human Aspects in Insider Threat Detection

Insider threat is one of the most damaging cyber attacks that could cause the loss of intellectual property and enterprise data security breaches. Action sequence data such as host logs are used to investigate such threats and develop anomaly-based AI detectors. However, insider threat actions are similar to legitimate user activities, causing AI detectors to fail and suffer from high false alarm rates. Therefore, user cyber activity logs are inadequate to fully unfold insider threats. In this study, we adopt human psychological principles of risk-taking and impulsiveness along with host data to assess the influence and usefulness of human behavioral aspects in insider threat detection. Here, we hypothesize that individuals' impulsive and risk-taking behavior correlates with cyberspace activities. To validate our hypothesis, we conducted an IRB-approved study recruiting 35 participants who work in a large U.S. university and collected their cyber and psychological data for 90 days. Host and human-behavioral data analysis and mapping indicate that impulsive and risk-taking users trigger more system errors causing (un)intentional insider threats and are susceptible to attackers' social engineering and cognitive hacking. Utilizing cyber-human aspects, we introduce a Cyber-Human Graph Neural Network (GNN) based framework GraphCH to identify abnormal user behaviors and detect insider threats.

97 MATHEMATICS AND COMPUTING↗

High‐quality genome‐scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities

Summary Genome‐scale reconstructions of metabolism are computational species‐specific knowledge bases able to compute systemic metabolic properties. We present a comprehensive and validated reconstruction of the biotechnologically relevant bacterium Pseudomonas putida KT2440 that greatly expands computable predictions of its metabolic states. The reconstruction represents a significant reactome expansion over available reconstructed bacterial metabolic networks. Specifically, i JN1462 (i) incorporates several hundred additional genes and associated reactions resulting in new predictive capabilities, including new nutrients supporting growth; (ii) was validated by in vivo growth screens that included previously untested carbon (48) and nitrogen (41) sources; (iii) yielded gene essentiality predictions showing large accuracy when compared with a knock‐out library and Bar‐seq data; and (iv) allowed mapping of its network to 82 P . putida sequenced strains revealing functional core that reflect the large metabolic versatility of this species, including aromatic compounds derived from lignin. Thus, this study provides a thoroughly updated metabolic reconstruction and new computable phenotypes for P . putida , which can be leveraged as a first step toward understanding the pan metabolic capabilities of Pseudomonas .

Microbiology↗

Reevaluation of the Coding Potential and Proteomic Analysis of the BAC Derived Rhesus Cytomegalovirus Strain 68-1

Cytomegaloviruses are highly host restricted resulting in co-speciation with their hosts. As a natural pathogen of rhesus macaques (RM), Rhesus Cytomegalovirus (RhCMV) has therefore emerged as a highly relevant experimental model for pathogenesis and vaccine development due to its close evolutionary relationship to human CMV (HCMV). To date, most in vivo experiments performed with RhCMV employed strain 68-1 cloned as bacterial artificial chromosome (BAC). However, the complete genome sequence of the 68-1 BAC has not been determined. Furthermore, the gene content of the RhCMV genome is unknown and previous open reading frame (ORF) predictions relied solely on uninterrupted ORFs with an arbitrary cutoff of 300bp. To obtain a more precise picture of the actual proteins encoded by the most commonly used molecular clone of RhCMV we re-evaluated the RhCMV 68-1 BAC-genome by whole genome shotgun sequencing and determined the protein content of the resulting RhCMV virions by proteomics. By additionally comparing the RhCMV genome to that of several closely related Old World Monkey (OWM) CMVs we were able to filter out many unlikely ORFs and obtain a simplified map of the RhCMV genome. This comparative genomics analysis eliminated many genes previously characterized as RhCMV-specific while consolidating a high conservation of ORFs among OWM-CMVs and between RhCMV and HCMV. Moreover, virion proteomics independently validated the revised ORF predictions since only proteins encoded by predicted ORFs could be detected. Taken together these data suggest a much higher conservation of genome and virion structure between CMVs of humans, apes and OWMs than previously assumed. Remarkably, BAC-derived RhCMV is able to establish and maintain persistent infection despite the lack of multiple genes homologous to HCMV genes involved in tissue tropism.

Environmental Molecular Sciences Laboratory↗

Nonparametric Method for Genomics-Based Prediction of Performance of Quantitative Traits Involving Epistasis in Plant Breeding

Genomic selection (GS) procedures have proven useful in estimating breeding value and predicting phenotype with genome-wide molecular marker information. However, issues of high dimensionality, multicollinearity, and the inability to deal effectively with epistasis can jeopardize accuracy and predictive ability. We, therefore, propose a new nonparametric method, pRKHS, which combines the features of supervised principal component analysis (SPCA) and reproducing kernel Hilbert spaces (RKHS) regression, with versions for traits with no/low epistasis, pRKHS-NE, to high epistasis, pRKHS-E. Instead of assigning a specific relationship to represent the underlying epistasis, the method maps genotype to phenotype in a nonparametric way, thus requiring fewer genetic assumptions. SPCA decreases the number of markers needed for prediction by filtering out low-signal markers with the optimal marker set determined by cross-validation. Principal components are computed from reduced marker matrix (called supervised principal components, SPC) and included in the smoothing spline ANOVA model as independent variables to fit the data. The new method was evaluated in comparison with current popular methods for practicing GS, specifically RR-BLUP, BayesA, BayesB, as well as a newer method by Crossa et al., RKHS-M, using both simulated and real data. Results demonstrate that pRKHS generally delivers greater predictive ability, particularly when epistasis impacts trait expression. Beyond prediction, the new method also facilitates inferences about the extent to which epistasis influences trait expression.

54 ENVIRONMENTAL SCIENCES↗

Emerging Technologies and Techniques for Wide Area Radiological Survey and Remediation

Technologies to survey and decontaminate wide-area contamination and process the subsequent radioactive waste have been developed and implemented following the Chernobyl nuclear power plant release and the breach of a radiological source resulting in contamination in Goiania, Brazil. These civilian examples of radioactive material releases provided some of the first examples of urban radiological remediation. Many emerging technologies have recently been developed and demonstrated in Japan following the release of radioactive cesium isotopes (Cs-134 and Cs-137) from the Fukushima Dai-ichi nuclear power plant in 2011. Information on technologies reported by several Japanese government agencies, such as the Japan Atomic Energy Agency (JAEA), the Ministry of the Environment (MOE) and the National Institute for Environmental Science (NIES), together with academic institutions and industry are summarized and compared to recently developed, deployed and available technologies in the United States. The technologies and techniques presented in this report may be deployed in response to a wide area contamination event in the United States. In some cases, additional research and testing is needed to adequately validate the technology effectiveness over wide areas. Survey techniques can be deployed on the ground or from the air, allowing a range of coverage rates and sensitivities. Survey technologies also include those useful in measuring decontamination progress and mapping contamination. Decontamination technologies and techniques range from non-destructive (e.g., high pressure washing) and minimally destructive (plowing), to fully destructive (surface removal or demolition). Waste minimization techniques can greatly impact the long-term environmental consequences and cost following remediation efforts. Recommendations on technical improvements to address technology gaps are presented together with observations on remediation in Japan.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗