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DOE OSTI2013

Review of critical factors for SEA implementation

The implementation process involved in translating Strategic Environmental Assessment (SEA) intention into action is vital to an effective SEA. Many factors influence implementation and thus the effectiveness of an SEA. Empirical studies have identified and documented some factors influencing the implementation of an SEA. This research is fragmented, however, and it is still not clear what are the most critical factors of effective SEA performance, and how these relate to different stages of the implementation process or other contextual circumstances. The paper takes its point of departure in implementation theory. Firstly, we introduce implementation theory, and then use it in practice to establish a more comprehensive model related to the stages in the implementation process. Secondly, we identify the critical factors in order to see how they are related to the different stages of SEA or are more general in character. Finally we map the different critical factors and how they influence the overall results of an SEA. Based on a literature review, we present a comprehensive picture of the critical factors and where they are found in the process. We conclude that most of the critical factors identified are of a more general character influencing the SEA process as such, while only one out of four of these factors relates to the specific stages of the SEA. Based on this mapping we can sketch a picture of the totality of critical factors. In this study 266 notions of critical factors were identified. Seen at the level of notions of critical factors, only 24% of these relate to specific stages while for 76% the critical factors are of a more general nature. These critical factors interact in complex ways and appear in different combinations in different stages of the implementation process so tracing the cause and effect is difficult. The pervasiveness of contextual and general factors also clearly suggests that there is no single way to put SEA into practice. The paper identifies some of the critical factors for effective SEA implementation, but further research is still needed to conclude which factors are more critical than others, just as the contingencies on which they depend are not easy to unravel. - Highlights: Black-Right-Pointing-Pointer The research on critical factors influencing SEA implementation is fragmented. Black-Right-Pointing-Pointer The critical factors are used to discuss 'hot-spots' in the implementation process. Black-Right-Pointing-Pointer Critical factors are just as broad as the concept of effectiveness. Black-Right-Pointing-Pointer Both stage and general factors are relevant in explaining the effectiveness of SEA.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Landslide hazard mapping with selected dominant factors: A study case of Penang Island, Malaysia

Landslide is one of the destructive natural geohazards in Malaysia. In addition to rainfall as triggering factos for landslide in Malaysia, topographical and geological factors play important role in the landslide susceptibility analysis. Conventional topographic factors such as elevation, slope angle, slope aspect, plan curvature and profile curvature have been considered as landslide causative factors in many research works. However, other topographic factors such as diagonal length, surface area, surface roughness and rugosity have not been considered, especially for the research work in landslide hazard analysis in Malaysia. This paper presents landslide hazard mapping using Frequency Ratio (FR) and the study area is Penang Island of Malaysia. Frequency ratio approach is a variant of probabilistic method that is based on the observed relationships between the distribution of landslides and each landslide-causative factor. Landslide hazard map of Penang Island is produced by considering twenty-two (22) landslide causative factors. Among these twenty-two (22) factors, fourteen (14) factors are topographic factors. They are elevation, slope gradient, slope aspect, plan curvature, profile curvature, general curvature, tangential curvature, longitudinal curvature, cross section curvature, total curvature, diagonal length, surface area, surface roughness and rugosity. These topographic factors are extracted from the digital elevation model of Penang Island. The other eight (8) non-topographic factors considered are land cover, vegetation cover, distance from road, distance from stream, distance from fault line, geology, soil texture and rainfall precipitation. After considering all twenty-two factors for landslide hazard mapping, the analysis is repeated with fourteen dominant factors which are selected from the twenty-two factors. Landslide hazard map was segregated into four categories of risks, i.e. Highly hazardous area, Hazardous area, Moderately hazardous area and Not hazardous area. The maps was assessed using ROC (Rate of Curve) based on the area under the curve method (AUC). The result indicates an increase of accuracy from 77.76% (with all 22 factors) to 79.00% (with 14 dominant factors) in the prediction of landslide occurrence.

58 GEOSCIENCES↗

DOE OSTI2014

TU-F-BRE-05: Experimental Determination of K Factor in Small Field Dosimetry

Purpose: Small-field dosimetry is challenging due to charged-particle disequilibrium, source occlusion and more importantly finite size of detectors. IAEA/AAPM has published approach to convert detector readings to dose by k factor. Manufacturers have been trying to provide various types of micro-detectors that could be used in small fields. However k factors depends on detector perturbations and are derived using Monte Carlo simulation. PTW has introduced a microDiamond for small-field dosimetry. An experimental approach is presented to derive the k factor for this detector. Methods: PTW microDiamond is a small volume detector with 1.1 mm radius and 1.0 micron thick synthetic diamond. Output factors were measured from 1×1cm2 to 12×12 cm2 on a Varian machine at various depths using various micro-detectors with published k factors. Dose is calculated as reading * K. Assuming k factor is accurate, output factor should be identical with every micro-detectors. Hence published k values (Francescon et al Med Phys 35, 504-513,2008) were used to covert readings and then output factors were computed. Based on the converged curve from other detectors, k factor for microDiamond was computed versus field size. Results: Traditional output factors as ratio of readings normalized to 10×10 cm2 differ significantly for micro-detectors for fields smaller than 3×3 cm2 which are now being used extensively. When readings are converted to dose, the output factor is independent of detector. Based on this method, k factor for microDiamond was estimated to be nearly constant 0.993±0.007 over varied field sizes. Conclusion: Our method provides a unique opportunity to determine the k factor for any unknown detector. It is shown that even though k factor depends on machine type due to focal spot, however for fields ≥1×1 cm2 this method provides accurate evaluation of k factor. Additionally microDiamond could be used with assumption that k factor is nearly unity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2011

Measured and Monte Carlo calculated k{sub Q} factors: Accuracy and comparison

Purpose: The journal Medical Physics recently published two papers that determine beam quality conversion factors, k{sub Q}, for large sets of ion chambers. In the first paper [McEwen Med. Phys. 37, 2179-2193 (2010)], k{sub Q} was determined experimentally, while the second paper [Muir and Rogers Med. Phys. 37, 5939-5950 (2010)] provides k{sub Q} factors calculated using Monte Carlo simulations. This work investigates a variety of additional consistency checks to verify the accuracy of the k{sub Q} factors determined in each publication and a comparison of the two data sets. Uncertainty introduced in calculated k{sub Q} factors by possible variation of W/e with beam energy is investigated further. Methods: The validity of the experimental set of k{sub Q} factors relies on the accuracy of the NE2571 reference chamber measurements to which k{sub Q} factors for all other ion chambers are correlated. The stability of NE2571 absorbed dose to water calibration coefficients is determined and comparison to other experimental k{sub Q} factors is analyzed. Reliability of Monte Carlo calculated k{sub Q} factors is assessed through comparison to other publications that provide Monte Carlo calculations of k{sub Q} as well as an analysis of the sleeve effect, the effect of cavity length and self-consistencies between graphite-walled Farmer-chambers. Comparison between the two data sets is given in terms of the percent difference between the k{sub Q} factors presented in both publications. Results: Monitoring of the absorbed dose calibration coefficients for the NE2571 chambers over a period of more than 15 yrs exhibit consistency at a level better than 0.1%. Agreement of the NE2571 k{sub Q} factors with a quadratic fit to all other experimental data from standards labs for the same chamber is observed within 0.3%. Monte Carlo calculated k{sub Q} factors are in good agreement with most other Monte Carlo calculated k{sub Q} factors. Expected results are observed for the sleeve effect and the effect of cavity length on k{sub Q}. The mean percent differences between experimental and Monte Carlo calculated k{sub Q} factors are -0.08, -0.07, and -0.23% for the Elekta 6, 10, and 25 MV nominal beam energies, respectively. An upper limit on the variation of W/e in photon beams from cobalt-60 to 25 MV is determined as 0.4% with 95% confidence. The combined uncertainty on Monte Carlo calculated k{sub Q} factors is reassessed and amounts to between 0.40 and 0.49% depending on the wall material of the chamber. Conclusions: Excellent agreement (mean percent difference of only 0.13% for the entire data set) between experimental and calculated k{sub Q} factors is observed. For some chambers, k{sub Q} is measured for only one chamber of each type--the level of agreement observed in this study would suggest that for those chambers the measured k{sub Q} values are generally representative of the chamber type.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2019

Extraction and Parametrization of Isobaric Trinucleon Elastic Cross Sections and Form Factors

By mining data from Jefferson Lab Hall A experiment E08-014 a new 3 He elastic cross section was extracted from a large quasielastic background. This measurement was taken with an initial beam energy of 3.356 GeV and an angle of 20.51°. The cross section was found to be 1.345 × 10 -6 µb/sr ± 0.086 × 10 -6 µb/sr at Q 2 = 34.19 fm -2 . This new data point falls approximately halfway between the first and second diffractive minima of the 3 He form factors. When combined with recent high Q 2 3 He elastic cross section measurements from Jefferson Lab and MIT-Bates this new data point improves our knowledge of the cross section and form factors at large momentum transfers. The new high Q 2 data motivate a reanalysis of the 3 He elastic cross section world data and provide an improved understanding of the magnetic form factor in particular. For this analysis the elastic cross section world data for 3 He, and its mirror nuclei 3 H, were collected. The world data spans a time frame from 1965 to 2016. The dataset contains electron energy ranges from tens of MeV to above 12 GeV for measurements performed at many different facilities. The world data were then fit using a sum of Gaussians parametrization which allowed for the extraction of both targets’ magnetic and electric form factors which were then used to calculate charge densities and radii. The new charge and magnetic form factors for 3 H and the charge form factor for 3 He are in good agreement with the 1994 fits from Amroun et al . However, the addition of the new high Q 2 data has caused the 3 He magnetic form factor’s first diffractive minimum to shift up in Q 2 by 1-3 fm -2 while also decreasing the magnitude of the magnetic form factor above Q2 ≈ 20 fm -2 . The first diffractive minima for 3H are located at Q2 ≈ 13 fm -2 and Q 2 ≈ 23-24 fm -2 for the charge and magnetic form factors respectively. The first diffractive minima for 3 He are located at Q 2 ≈ 11 fm -2 and Q 2 ≈ 17-20 fm -2 for the charge and magnetic form factors respectively. The charge radius for 3 He was found to be 1.90 fm ± 0.00144 fm in reasonable agreement with past measurements, and the charge radius for 3 H was found to be 2.02 fm ± 0.0133 fm which is much larger than past measurements. However, each of these charge radii has an additional uncertainty that must be applied to them due to allowing all parameters to float freely during the sum of Gaussians fitting procedure. This additional uncertainty should be small for 3 He, but it is likely quite significant for 3 H and would help bring this charge radius closer to agreement with past measurements that made different fitting choices. Unfortunately, this analysis was unable to quantify this additional uncertainty. The new form factor fits were compared to modern theoretical predictions from the 2016 paper of Marcucci et al . The ‘conventional’ theoretical approach applied in this paper modelled two and three-body nucleon interactions with relativistic corrections and was reasonably successful at predicting the charge form factors of 3 H and 3 He. χEFT predictions were also often successful. However, while the ‘conventional’ approach still performed best, theory failed to accurately predict the magnetic form factors of either 3 H or 3 He. The first diffractive minimum of the new 3 He magnetic form factor fits actually moved further away from theory. This disagreement between theory and experiment provides motivation for new asymmetry measurements using polarized 3 He and a polarized electron beam. When the beam is scanned in Q 2 on the target the sign of the asymmetry will flip at the form factor minima pinning down their true location.

Barcus, Scott↗

DOE OSTI2018

Multi-factor authentication using quantum communication

Multi-factor authentication using quantum communication ("QC") includes stages for enrollment and identification. For example, a user enrolls for multi-factor authentication that uses QC with a trusted authority. The trusted authority transmits device factor information associated with a user device (such as a hash function) and user factor information associated with the user (such as an encrypted version of a user password). The user device receives and stores the device factor information and user factor information. For multi-factor authentication that uses QC, the user device retrieves its stored device factor information and user factor information, then transmits the user factor information to the trusted authority, which also retrieves its stored device factor information. The user device and trusted authority use the device factor information and user factor information (more specifically, information such as a user password that is the basis of the user factor information) in multi-factor authentication that uses QC.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2014

Output correction factors for nine small field detectors in 6 MV radiation therapy photon beams: A PENELOPE Monte Carlo study

Purpose: To determine detector-specific output correction factors,k{sub Q} {sub c{sub l{sub i{sub n}}}} {sub ,Q} {sub m{sub s{sub r}}} {sup f{sub {sup {sub c}{sub l}{sub i}{sub n}{sub {sup ,f{sub {sup {sub m}{sub s}{sub r}{sub ,}}}}}}}} in 6 MV small photon beams for air and liquid ionization chambers, silicon diodes, and diamond detectors from two manufacturers. Methods: Field output factors, defined according to the international formalism published byAlfonso et al. [Med. Phys. 35, 5179–5186 (2008)], relate the dosimetry of small photon beams to that of the machine-specific reference field; they include a correction to measured ratios of detector readings, conventionally used as output factors in broad beams. Output correction factors were calculated with the PENELOPE Monte Carlo (MC) system with a statistical uncertainty (type-A) of 0.15% or lower. The geometries of the detectors were coded using blueprints provided by the manufacturers, and phase-space files for field sizes between 0.5 × 0.5 cm{sup 2} and 10 × 10 cm{sup 2} from a Varian Clinac iX 6 MV linac used as sources. The output correction factors were determined scoring the absorbed dose within a detector and to a small water volume in the absence of the detector, both at a depth of 10 cm, for each small field and for the reference beam of 10 × 10 cm{sup 2}. Results: The Monte Carlo calculated output correction factors for the liquid ionization chamber and the diamond detector were within about ±1% of unity even for the smallest field sizes. Corrections were found to be significant for small air ionization chambers due to their cavity dimensions, as expected. The correction factors for silicon diodes varied with the detector type (shielded or unshielded), confirming the findings by other authors; different corrections for the detectors from the two manufacturers were obtained. The differences in the calculated factors for the various detectors were analyzed thoroughly and whenever possible the results were compared to published data, often calculated for different accelerators and using the EGSnrc MC system. The differences were used to estimate a type-B uncertainty for the correction factors. Together with the type-A uncertainty from the Monte Carlo calculations, an estimation of the combined standard uncertainty was made, assigned to the mean correction factors from various estimates. Conclusions: The present work provides a consistent and specific set of data for the output correction factors of a broad set of detectors in a Varian Clinac iX 6 MV accelerator and contributes to improving the understanding of the physics of small photon beams. The correction factors cannot in general be neglected for any detector and, as expected, their magnitude increases with decreasing field size. Due to the reduced number of clinical accelerator types currently available, it is suggested that detector output correction factors be given specifically for linac models and field sizes, rather than for a beam quality specifier that necessarily varies with the accelerator type and field size due to the different electron spot dimensions and photon collimation systems used by each accelerator model.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2019

Distributed-memory lattice H-matrix factorization

We parallelize the LU factorization of a hierarchical low-rank matrix (H-matrix) on a distributed-memory computer. This is much more difficult than the H-matrix-vector multiplication due to the dataflow of the factorization, and it is much harder than the parallelization of a dense matrix factorization due to the irregular hierarchical block structure of the matrix. Block low-rank (BLR) format gets rid of the hierarchy and simplifies the parallelization, often increasing concurrency. However, this comes at a price of losing the near-linear complexity of the H-matrix factorization. In this work, we propose to factorize the matrix using a “lattice H-matrix” format that generalizes the BLR format by storing each of the blocks (both diagonals and off-diagonals) in the H-matrix format. These blocks stored in the H-matrix format are referred to as lattices. Thus, this lattice format aims to combine the parallel scalability of BLR factorization with the near-linear complexity of H-matrix factorization. We first compare factorization performances using the H-matrix, BLR, and lattice H-matrix formats under various conditions on a shared-memory computer. Our performance results show that the lattice format has storage and computational complexities similar to those of the H-matrix format, and hence a much lower cost of factorization than BLR. In conclusion, we then compare the BLR and lattice H-matrix factorization on distributed-memory computers. Our performance results demonstrate that compared with BLR, the lattice format with the lower cost of factorization may lead to faster factorization on the distributed-memory computer.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

On the perturbation effect and LET dependence of beam quality correction factors in carbon ion beams

Background: In a recent study, we reported beam quality correction factors, f Q , in carbon ion beams using Monte Carlo (MC) methods for a cylindrical and a parallel-plate ionization chamber (IC). A non-negligible perturbation effect was observed; however, the magnitude of the perturbation correction due to the specific IC subcomponents was not included. Furthermore, the stopping power data presented in the International Commission on Radiation Units and Measurements (ICRU) report 73 were used, whereas the latest stopping power data have been reported in the ICRU report 90. Purpose: The aim of this study was to extend our previous work by computing f Q correction factors using the ICRU 90 stopping power data and by reporting IC-specific perturbation correction factors. Possible energy or linear energy transfer (LET) dependence of the f Q correction factor was investigated by simulating both pristine beams and spread-out Bragg peaks (SOBPs). Methods: The TOol for PArticle Simulation (TOPAS)/GEANT4 MC code was used in this study. A 30 × 30 × 50 cm 3 water phantom was simulated with a uniform 10 × 10 cm 2 parallel beam incident on the surface. A Farmer-type cylindrical IC (Exradin A12) and two parallel-plate ICs (Exradin P11 and A11) were simulated in TOPAS using the manufacturer-provided geometrical drawings. The f Q correction factor was calculated in pristine carbon ion beams in the 150–450 MeV/u energy range at 2 cm depth and in the middle of the flat region of four SOBPs. The k Q correction factor was calculated by simulating the f Qo correction factor in a 60 Co beam at 5 cm depth. The perturbation correction factors due to the presence of the individual IC subcomponents, such as the displacement effect in the air cavity, collecting electrode, chamber wall, and chamber stem, were calculated at 2 cm depth for monoenergetic beams only. Additionally, the mean dose-averaged and track-averaged LET was calculated at the depths at which the f Q was calculated. Results: The ICRU 90 f Q correction factors were reported. The p dis correction factor was found to be significant for the cylindrical IC with magnitudes up to 1.70%. The individual perturbation corrections for the parallel-plate ICs were <1.0% except for the A11 p cel correction at the lowest energy. The f Q correction for the P11 IC exhibited an energy dependence of >1.00% and displayed differences up to 0.87% between pristine beams and SOBPs. Conversely, the f Q for A11 and A12 displayed a minimal energy dependence of <0.50%. The energy dependence was found to manifest in the LET dependence for the P11 IC. A statistically significant LET dependence was found only for the P11 IC in pristine beams only with a magnitude of <1.10%. Conclusions: The perturbation and k Q correction factor should be calculated for the specific IC to be used in carbon ion beam reference dosimetry as a function of beam quality.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2019

Structural basis of ECF-σ-factor-dependent transcription initiation

Extracytoplasmic (ECF) σ factors, the largest class of alternative σ factors, are related to primary σ factors, but have simpler structures, comprising only two of six conserved functional modules in primary σ factors: region 2 (σR2) and region 4 (σR4). Here, we report crystal structures of transcription initiation complexes containing Mycobacterium tuberculosis RNA polymerase (RNAP), M. tuberculosis ECF σ factor σL, and promoter DNA. The structures show that σR2 and σR4 of the ECF σ factor occupy the same sites on RNAP as in primary σ factors, show that the connector between σR2 and σR4 of the ECF σ factor–although shorter and unrelated in sequence–follows the same path through RNAP as in primary σ factors, and show that the ECF σ factor uses the same strategy to bind and unwind promoter DNA as primary σ factors. The results define protein-protein and protein-DNA interactions involved in ECF-σ-factor-dependent transcription initiation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-C-201-06: Small Field Correction Factors for the MicroDiamond Detector in the Gamma Knife-Model C Derived Using Monte Carlo Methods

Purpose: To determine small field correction factors for PTW’s microDiamond detector in Elekta’s Gamma Knife Model-C unit. These factors allow the microDiamond to be used in QA measurements of output factors in the Gamma Knife Model-C; additionally, the results also contribute to the discussion on the water equivalence of the relatively-new microDiamond detector and its overall effectiveness in small field applications. Methods: The small field correction factors were calculated as k correction factors according to the Alfonso formalism. An MC model of the Gamma Knife and microDiamond was built with the EGSnrc code system, using BEAMnrc and DOSRZnrc user codes. Validation of the model was accomplished by simulating field output factors and measurement ratios for an available ABS plastic phantom and then comparing simulated results to film measurements, detector measurements, and treatment planning system (TPS) data. Once validated, the final k factors were determined by applying the model to a more waterlike solid water phantom. Results: During validation, all MC methods agreed with experiment within the stated uncertainties: MC determined field output factors agreed within 0.6% of the TPS and 1.4% of film; and MC simulated measurement ratios matched physically measured ratios within 1%. The final k correction factors for the PTW microDiamond in the solid water phantom approached unity to within 0.4%±1.7% for all the helmet sizes except the 4 mm; the 4 mm helmet size over-responded by 3.2%±1.7%, resulting in a k factor of 0.969. Conclusion: Similar to what has been found in the Gamma Knife Perfexion, the PTW microDiamond requires little to no corrections except for the smallest 4 mm field. The over-response can be corrected via the Alfonso formalism using the correction factors determined in this work. Using the MC calculated correction factors, the PTW microDiamond detector is an effective dosimeter in all available helmet sizes. The authors would like to thank PTW (Friedberg, Germany) for providing the PTW microDiamond detector for this research.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2016

SU-F-T-23: Correspondence Factor Correction Coefficient for Commissioning of Leipzig and Valencia Applicators with the Standard Imaging IVB 1000

Purpose: To determine the correction factor of the correspondence factor for the Standard Imaging IVB 1000 well chamber for commissioning of Elekta’s Leipzig and Valencia skin applicators. Methods: The Leipzig and Valencia applicators are designed to treat small skin lesions by collimating irradiation to the treatment area. Published output factors are used to calculate dose rates for clinical treatments. To validate onsite applicators, a correspondence factor (CFrev) is measured and compared to published values. The published CFrev is based on well chamber model SI HDR 1000 Plus. The CFrev is determined by correlating raw values of the source calibration setup (Rcal,raw) and values taken when each applicator is mounted on the same well chamber with an adapter (Rapp,raw). The CFrev is calculated by using the equation CFrev =Rapp,raw/Rcal,raw. The CFrev was measured for each applicator in both the SI HDR 1000 Plus and the SI IVB 1000. A correction factor, CFIVB for the SI IVB 1000 was determined by finding the ratio of CFrev (SI IVB 1000) and CFrev (SI HDR 1000 Plus). Results: The average correction factors at dwell position 1121 were found to be 1.073, 1.039, 1.209, 1.091, and 1.058 for the Valencia V2, Valencia V3, Leipzig H1, Leipzig H2, and Leipzig H3 respectively. There were no significant variations in the correction factor for dwell positions 1119 through 1121. Conclusion: By using the appropriate correction factor, the correspondence factors for the Leipzig and Valencia surface applicators can be validated with the Standard Imaging IVB 1000. This allows users to correlate their measurements with the Standard Imaging IVB 1000 to the published data. The correction factor is included in the equation for the CFrev as follows: CFrev= Rapp,raw/(CFIVB*Rcal,raw). Each individual applicator has its own correction factor, so care must be taken that the appropriate factor is used.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Assessment and Use of State-Specific Factors in the Implementation of Safeguards

The use of safeguards-relevant features and characteristics of a State in the implementation of safeguards has been a long-standing practice of the International Atomic Energy Agency (IAEA). These ‘State-specific factors’ can affect the development of State-level safeguards approaches and the planning, conduct and evaluation of safeguards activities. The IAEA has identified six State-specific factors for systematic consideration and use in the application of the State-level concept. These factors are: (i) the type of safeguards agreement in force for the State and the nature of the safeguards conclusion drawn by the IAEA; (ii) the nuclear fuel cycle and related technical capabilities of the State; (iii) the technical capabilities of the State or regional system of accounting for and control of nuclear material; (iv) the ability of the IAEA to implement certain safeguards measures in the State; (v) the nature and scope of cooperation between the State and the IAEA in the implementation of safeguards; and (vi) the IAEA’s experience in implementing safeguards in the State. State-specific factors are not to be used for rating or grading a State’s safeguards credentials but to identify issues that could affect the effectiveness or efficiency of safeguards implementation for the State. IAEA reports and presentations on the State-level concept have described these factors and have given examples of information that could be considered in assessing each factor. This paper expands upon the IAEA examples and makes recommendations as to how State-specific factors could be assessed by the IAEA and how these factors might affect safeguards implementation. Although quantitative metrics were considered for assessing the State-specific factors, it was determined that the general State characteristics comprising the factors cannot be meaningfully quantified; however, performance indicators can be developed for some elements comprising some factors. The paper thus discusses ‘considerations’ that the IAEA should assess with respect to the State-specific factors (iii), (v), and (vi) and how those considerations could affect safeguards implementation.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Nonlinear control of the minimum safety factor in tokamaks by optimal allocation of spatially moving electron cyclotron current drive

The minimum value of the safety factor profile is related to the magnetohydrodynamic (MHD) stability of the plasma confined in a tokamak. Therefore, active control of the minimum safety factor may mitigate MHD instabilities that can degrade or even terminate plasma confinement. Typically, in most tokamak scenarios, the minimum safety factor evolves spatially with time, i.e., the location at which the safety factor achieves the minimum value changes with time. In addition to the inherent nonlinearities in the minimum safety factor evolution, its spatial variation makes the control design challenging. In particular, complexity in control design may arise from the need for time-dependent nonlinear models that account for spatial variation of the minimum safety factor. Furthermore, the minimum safety factor may drift to locations where the actuator authority is low. The problem of minimum safety factor control with target location tracking and moving electron cyclotron current drive (ECCD) is addressed in this work. A nonlinear time-dependent model that incorporates the spatial variation of the minimum safety factor is presented. A nonlinear controller based on optimal feedback linearization is developed to track a target minimum safety factor. The proposed controller treats the ECCD position as a controllable variable. In other words, the controller prescribes the ECCD position (in addition to the non-inductive powers) in real time based on an optimal criterion that is defined a priori. This work also presents the steps necessary to integrate the minimum safety factor controller with a total energy controller to achieve multiple control objectives simultaneously. In conclusion, the proposed integrated control algorithm is tested using nonlinear simulations in the Control Oriented Transport SIMulator (COTSIM) for a DIII-D tokamak scenario.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

SU-E-T-35: A General Fill Factor Definition Serving to Characterise the MLC Misalignment Detection Capabilities of Two-Dimensional Detector Arrays

Purpose: To present a general definition of the fill factor realistically characterizing the “field coverage”, i.e. the MLC misalignment detection capabilities of a detector array. Methods: According to Gago-Arias et al.{sup 1} the fill factor of a 2D array is defined as the ratio of the area enclosed by the FWHM of the fluence response function KM(x) of a single detector and its cell area defined by the detector spacing. More generally - accounting also for the possible overlap between FWHM’s of neighboured detectors - the fill factor is here defined as that fraction of the sum of the detector cell areas in which a defined MLC misalignment is detectable when the induced percentage signal changes exceed a detection threshold d. Ideally the generalized fill factor may reach 100 %. With user code EGS-chamber and a 2 MeV photon slit beam 0.25 mm wide, both types of the fill factor were calculated for an array with total cell area 100 cm{sup 2} for chamber widths 1–9 mm, using =1mm, d=5%. Results: For single chamber width 5 mm, fill factors were 0.49 (FWHM) and 0.61 (generalized). For chamber width 2 mm the FWHM fill factor was 0.13 whereas the generalized fill factor was 0.32. For chamber widths above 7 mm, the FWHM fill factor exceeds unity, and the general fill factor is exactly 1.00. Conclusions: An updated fill factor definition is introduced which, as a generalization of the FWHM-based definition, more closely estimates the performance of small array chambers and gives a realistic value in the case of overlapping sensitive areas of neighboured chambers. References:{sup 1}A. Gago-Arias, L. Brualla-Gonzalez, D.M. Gonzalez-Castano, F. Gomez, M.S. Garcia, V.L. Vega, J.M. Sueiro, J. Pardo-Montero, “Evaluation of chamber response function influence on IMRT verification using 2D commercial detector arrays,” Phys. Med. Biol. 57, 2005–2020 (2012)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Use of 1–4 interaction scaling factors to control the conformational equilibrium between α-helix and β-strand

Highlights: • 1–4 interaction scaling factors are used to adjust conformational energy. • This article reports the effects of these factors on protein conformations. • Reducing these factors changes a helix to a strand in molecular dynamics simulation. • Increasing these factors causes the reverse conformational change. • These factors control the conformational equilibrium between helix and strand. - Abstract: 1–4 interaction scaling factors are used in AMBER forcefields to reduce the exaggeration of short-range repulsion caused by the 6–12 Lennard-Jones potential and a nonpolarizable charge model and to obtain better agreements of small-molecule conformational energies with experimental data. However, the effects of these scaling factors on protein secondary structure conformations have not been investigated until now. This article reports the finding that the 1–4 interactions among the protein backbone atoms separated by three consecutive covalent bonds are more repulsive in the α-helix conformation than in two β-strand conformations. Therefore, the 1–4 interaction scaling factors of protein backbone torsions ϕ and ψ control the conformational equilibrium between α-helix and β-strand. Molecular dynamics simulations confirm that reducing the ϕ and ψ scaling factors readily converts the α-helix conformation of AcO-(AAQAA){sub 3}-NH{sub 2} to a β-strand conformation, and the reverse occurs when these scaling factors are increased. These results suggest that the ϕ and ψ scaling factors can be used to generate the α-helix or β-strand conformation in situ and to control the propensities of a forcefield for adopting secondary structure elements.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Update of Emission Factors of Greenhouse Gases and Criteria Air Pollutants, and Generation Efficiencies of the U.S. Electricity Generation Sector

The last decade has seen a steady evolution of the electricity generation sector. Fuels used for electricity generation have shifted from coal to cleaner energy sources such as natural gas and renewables including solar, wind, and other renewable sources. The share of U.S. electricity generated from coal decreased from 45% in 2010 to 24% in 2019, and is expected to decrease further to 13% by 2050. The conversion efficiency of electricity generation has also increased gradually for fuels such as natural gas due as less-efficient old generators are retired and more-efficient generators replace them. These changes in the electricity generation industry are likely to cause changes in the emissions from power generation units. Emission factors of greenhouse gases (GHG) including CO 2 , CH 4 , and N 2 O, and criteria air pollutants (CAPs) including CO, NO x , PM 10 , PM 2.5 , and SO x , from power plants are important parameters for estimating life-cycle emissions associated with vehicle electrification, energy systems, and the production of materials and chemicals. The electricity generation technologies and associated emission factors in the Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (GREET) model need to be updated to reflect recent developments in the electricity generation sector. The most recent update of the electricity generation emission factors in GREET adopted a mixed method. The emission factors of CH 4 , N 2 O, NO x , and SOx were estimated using a “topdown” approach by dividing the total emissions by the total net electricity generation, because emission data of these pollutants are readily available in the Emissions & Generation Resource Integrated Database (eGRID). For other CAPs such as CO, VOC, PM 10 , and PM 2.5 , emission data were not reported in eGRID. A “bottom-up” method was used to estimate the emission factors for these pollutants by considering generic uncontrolled emission factors and the pollutant removal efficiencies of emission control technologies adopted in the electricity generation sector. However, the uncontrolled emission factors and the emission removal efficiencies of various emission control technologies considered in the 2012 study came from the legacy AP-42 emission factors, and may not reflect the actual emission performances of the electricity generation sector of today. To leverage new data that recently became available, especially emission data measured from continuous emission monitoring systems (CEMS), we developed a new “top-down” approach to estimate efficiencies and GHG and CAP emission factors for electricity generation from combustion of individual fuel types by individual combustion technologies on the basis of power-generation data from U.S. Energy Information Administration’s (EIA’s) form EIA-923, and plant emission data from Environmental Protection Agency’s (EPA’s) Clean Air Markets Division (CAMD) dataset and National Emissions Inventory (NEI) dataset. Detailed discussion of the method and data used in this study can be found in Section 2.1. With this topdown approach, we aim to improve the estimates of energy efficiencies and emission factors for power plants using a more consistent methodology, and to update the emission factors, generation efficiencies, and generation technologies mixes in GREET to reflect recent technology advancements in the electricity generation sector.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2021

TracKlinic: Diagnosis of Challenge Factors in Visual Tracking

Generic visual object tracking is difficult due to many challenge factors (e.g., occlusion, blur, etc.). Each of these factors may cause serious problems for a tracker, and when they work together can make things even more complicated. Despite a great amount of efforts devoted to understanding the behavior of trackers, reliable and quantifiable ways for studying the per factor tracking behavior remain barely available. Addressing this issue, in this paper we contribute to the community a tracking diagnosis toolkit, TracKlinic, for diagnosis of challenge factors of tracking algorithms. TracKlinic consists of two novel components focusing on the data and analysis aspects, respectively. For the data component, we carefully prepare a set of 2,390 annotated videos, each involving one and only one major challenge factor. When analyzing an algorithm for a specific challenge factor, such one-factor-per-sequence rule greatly inhibits the disturbance from other factors and consequently leads to more faithful analysis. For the analysis component, given the tracking results on all sequences, it investigates the behavior of the tracker under each individual factor and generates the report automatically. With TracKlinic, a thorough study is conducted on ten state-of-the-art trackers on nine challenge factors (including two compound ones). The results suggest that, heavy shape variation and occlusion are the two most challenging factors faced by most trackers. Besides, out-of-view, though does not happen frequently, is often fatal. By sharing TracKlinic1, we expect to make it much easier for diagnosing tracking algorithms, and to thus facilitate developing better ones.

97 MATHEMATICS AND COMPUTING↗