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A Search for the Standard Model Higgs Boson in CDF II Data

This dissertation presents a search for the standard model Higgs boson in the associated production process p $\bar{p}$ → ZH → e + e - b$\bar{b}$. Data amounting to an integrated luminosity of 7.5 fb -1 at √s = 1.96 TeV collected at the Collider Detector at Fermilab (CDF) at the Tevatron are analyzed. Two objectives are pursued in the methods applied: maximize acceptance, and distinguish the signal from background. The first aim is met by applying a neural-network-based electron identi cation and considering multiple electron triggers in an effort to improve Z acceptance. In an attempt to maximize the Higgs acceptance, three b quark identification schemes are used allowing for varying event conditions. The latter goal is met by employing more multivariate techniques. First, the dijet mass resolution is improved by a neural network. Then, both single variables and boosted decision tree outputs are fed into a segmented final discriminant simultaneously isolating the signal-like events from the Z with additional jets background and the kinematically di erent tt background. Good agreement is seen with the null hypothesis and upper production cross section ( ZH) times branching ratio (BR(H →b $\bar{b}$)) limits are set for 11 mass hypotheses between 100 and 150 GeV/c 2 at the 95% confidence level. For a Higgs boson mass of 115 GeV/c 2 , this channel sets an observed (expected) upper limit of 3.9 (5.8) times the standard model value of ZH BR(H → b $\bar{b}$). The inclusion of this channel within the combined CDF and Tevatron limits is discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Study of Single Top Quark Production Using Bayesian Neural Networks With D0 Detector at the Tevatron

Top quark, the heaviest and most intriguing among the six known quarks, can be created via two independent production mechanisms in {\pp} collisions. The primary mode, strong {\ttbar} pair production from a $gtt$ vertex, was used by the {\d0} and CDF collaborations to establish the existence of the top quark in March 1995. The second mode is the electroweak production of a single top quark or antiquark, which has been observed recently in March 2009. Since single top quarks are produced at hadron colliders through a $Wtb$ vertex, thereby provide a direct probe of the nature of $Wtb$ coupling and of the Cabibbo-Kobayashi-Maskawa matrix element, $V_{tb}$. So this mechanism provides a sensitive probe for several, standard model and beyond standard model, parameters such as anomalous $Wtb$ couplings. In this thesis, we measure the cross section of the electroweak produced top quark in three different production modes, $s+t$, $s$ and $t$-channels using a technique based on the Bayesian neural networks. This technique is applied for analysis of the 5.4 $fb^{-1}$ of data collected by the {\d0} detector. From a comparison of the Bayesian neural networks discriminants between data and the signal-background model using Bayesian statistics, the cross sections of the top quark produced through the electroweak mechanism have been measured as: \[\sigma(p\bar{p}→tb+X,tqb+X) = 3.11^{+0.77}_{-0.71}\;\rm pb\] \[\sigma(p\bar{p}→tb+X) = 0.72^{+0.44}_{-0.43}\;\rm pb\] \[\sigma(p\bar{p}→tqb+X) = 2.92^{+0.87}_{-0.73}\;\rm pb\] % The $s+t$-channel has a gaussian significance of $4.7\sigma$, the $s$-channel $0.9\sigma$ and the $t$-channel~$4.7\sigma$. The results are consistent with the standard model predictions within one standard deviation. By combining these results with the results for two other analyses (using different MVA techniques) improved results \[\sigma(p\bar{p}→tb+X,tqb+X) = 3.43^{+0.73}_{-0.74}\;\rm pb\] \[\sigma(p\bar{p}→tb+X) = 0.68^{+0.38}_{-0.35}\;\rm pb\] \[\sigma(p\bar{p}→tqb+X) = 2.86^{+0.69}_{-0.63}\;\rm pb\] % were obtained with a significance of $5.4\sigma$, $1.8\sigma$ and $5.0\sigma$ respectively for $s+t$, $s$ and $t$-channels. Using this measured cross section and constraining $0 \leq |V_{tb}|^2 \leq 1$, the lower limit has been calculated to be $|V_{tb}| > 0.79$ with 95\% confidence level (C.L.). Another measurement of $tqb$ production cross section is done using the same dataset and discriminant but without any assumption on the $tb$ production rate. From this measurement, we obtain a cross section of $2.90^{+0.59}_{-0.59}$~pb for $t$-channel and corresponding significance of $5.5\sigma$. In addition to the above mentioned work, a search is made for the anomalous $Wtb$ couplings in single top quark production. Within the Standard Model, the $Wtb$ vertex is purely left-handed, and its amplitude is given by the $V_{tb}$, related to weak interaction between a top and a $b$-quark. In a more general way, additional anomalous couplings such as right-handed vectorial couplings and left and right-handed tensorial couplings can also be considered. An analysis based on the Bayesian neural networks method is used to separate the signal from expected backgrounds. We find no evidence for anomalous couplings and set 95\% C.L. limits on these couplings as $|V_{tb} \cdot f_{L_T}|^2<0.06$, $|V_{tb} \cdot f_{R_V}|^2<0.93$ and $|V_{tb} \cdot f_{R_T}|^2<0.13$. This result represents the most stringent direct constraints on anomalous $Wtb$ interactions. This work has been done in collaboration with \d0 experiment but the analyses and results presented in this thesis are my contribution.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Seismic Ambient Noise Analyses Reveal Changing Temperature and Water Signals to 10s of Meters Depth in the Critical Zone

The critical zone sustains terrestrial life, but we have few tools to explore it efficiently beyond the first few meters of the subsurface. Using analyses of high-frequency ambient seismic noise from densely spaced seismometers deployed in the forested Shale Hills subcatchment of the Susquehanna Shale Hills Critical Zone Observatory (SSHCZO), we show that temporal changes in seismic velocities at depths from ~1 m to tens of m can be detected. Furthermore, these changes are driven by variations at the land surface. The Moving-Window Cross-Spectral (MWCS) method was employed to measure seismic-velocity changes in coda waves at hourly resolution in 10 different frequency bands. We observed a diurnal signal, a seasonal signal, and a meteorological-event-based signal. These signals were compared to time-series measurements of precipitation, well water levels, soil moisture, soil temperature, air temperature, latent heat flux, and air pressure in the heavily instrumented catchment. Most of the velocity changes can be explained by variations in temperature that result in thermoelastic strains that propagate to depth. But some double minima in seismic velocity time-series observed after large rain events were attributed in part to the effects of water infiltration. These results show that high-frequency ambient noise data may in some locations be used to detect changes in the critical zone from ~1 to ~100 m or greater depth with hourly resolution. But interpretation of such data requires multiple environmental data sets to deconvolve the complex interrelationships among thermoelastic and hydrological effects in the subsurface critical zone.

54 ENVIRONMENTAL SCIENCES↗

SU-E-J-09: Image Quality Comparison and Dose Quantification for 2.5 MV

Purpose: To compare the image quality of the 2.5MV imaging beam (2.5X-IMB) to that of a 6MV beam and to quantify the imaging dose of a 2.5X-IMB for constancy as specified by AAPM TG-142 Methods: The image quality of the 2.5X-IMB was compared to the 6MV imaging beam using the SNC ImagePro MV-QA phantom and the Varian supplied Las Vegas phantom (LVP). High resolution (1280×1280×16, 2 frames at 1.5MU/frame) and low resolution (640×640×16, 2 frames at 0.75MU/frame) images were compared for each phantom. MV-QA phantom images were evaluated quantitatively, and the LVP images were evaluated qualitatively. The imaging dose for 2.5X-IMB was quantified using the procedure outlined in TG51. PTWCC13-31013 chambers were used to measure a percent depth dose (PDD) curve for the 2.5X-IMB. All the factors described in TG51 were calculated using the 2.5X-IMB and a PTW30013 farmer chamber. Results: A comparison between 2.5X-IMB and 6MV image quality was performed both visually and with DoseLab software. The optimal window and level were set for each image of the LVP by the user. Visual inspection showed greater contrast resolution with the 2.5MV beam, but no significant difference with the change in imaging resolution. DoseLab reported similar spatial resolutions between the two energies, but the contrast-to-noise ratio (CNR) was greater for 2.5MV. The PDDx(10cm) for a 10x10cm2 field was measured to be 51.5%. Although this PDD value is off the scale of Figure 4 in TG51, the trend of the curve corresponding to the PTW31003 (equivalent) chamber led to an approximate kQ value of 1.00. Conclusion: When compared to 6MV imaging, 2.5X-IMB results in a better CNR. At low resolution, the DoseLab results for the two energies are comparable, but visual analysis favors the 2.5X-IMB images. Imaging dose was quantified for the 2.5X-IMB after following the TG51 methodology with appropriate approximations.

60 APPLIED LIFE SCIENCES↗

Alternatives Analysis for Selecting ET #3 Site

Engineered trenches (ETs) are considered to be a cost-effective method for disposing Low Level Waste (LLW). Based on waste forecasts from waste generators, the last engineered trench in operation (ET No.2) is anticipated to close in FY14, requiring development of a new ET. Solid Waste requested that SRNL develop an assessment report that reviews four disposal options for this new ET (ET No.3) and determine which option would provide the 'best' Performance Assessment (PA) disposal limits for LLW (Appendix A). Those four options (see option footprint locations in Figure 1-1) are: (1) Disposal at grade on TRU Pads 7-13 where soil would be mounded over waste packages; (2) Excavation at a slightly modified SLIT No.13 location - near the Used Equipment Storage Area; (3) Excavation at a modified SLIT No.12 location - near the 643-26E Naval Reactor Component Disposal Area; and (4) Excavation east of TRU Pad No.26 that replaces northeast portions of four slit trench (ST) disposal units in the eastern set of STs. The assessment consisted of both quantitative and qualitative analyses. The quantitative analysis captured key aspects that were readily quantifiable and had predictable impacts on limits and doses. A simplified modeling strategy stemming from current Special Analysis (SA) practices was employed. Both inventory capacity for a specific nuclide (a quasi-inventory limit) and overall performance for specified inventory mixtures (doses resulting from historical inventories) were considered. The qualitative analysis evaluated other key aspects based on engineering judgment in the form of pros and cons.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Light-Front Holography, Light-Front Wavefunctions, and Novel QCD Phenomena

Light-Front Holography is one of the most remarkable features of the AdS/CFT correspondence. In spite of its present limitations it provides important physical insights into the nonperturbative regime of QCD and its transition to the perturbative domain. This novel framework allows hadronic amplitudes in a higher dimensional anti-de Sitter (AdS) space to be mapped to frame-independent light-front wavefunctions of hadrons in physical space-time. The model leads to an effective confining light-front QCD Hamiltonian and a single-variable light-front Schroedinger equation which determines the eigenspectrum and the light-front wavefunctions of hadrons for general spin and orbital angular momentum. The coordinate z in AdS space is uniquely identified with a Lorentz-invariant coordinate {zeta} which measures the separation of the constituents within a hadron at equal light-front time and determines the off-shell dynamics of the bound-state wavefunctions, and thus the fall-off as a function of the invariant mass of the constituents. The soft-wall holographic model modified by a positive-sign dilaton metric, leads to a remarkable one-parameter description of nonperturbative hadron dynamics - a semi-classical frame-independent first approximation to the spectra and light-front wavefunctions of meson and baryons. The model predicts a Regge spectrum of linear trajectories with the same slope in the leading orbital angular momentum L of hadrons and the radial quantum number n. The hadron eigensolutions projected on the free Fock basis provides the complete set of valence and non-valence light-front Fock state wavefunctions {Psi}{sub n/H} (x{sub i}, k{sub {perpendicular}i}, {lambda}{sub i}) which describe the hadron's momentum and spin distributions needed to compute the direct measures of hadron structure at the quark and gluon level, such as elastic and transition form factors, distribution amplitudes, structure functions, generalized parton distributions and transverse momentum distributions. The effective confining potential also creates quark-antiquark pairs from the amplitude q {yields} q{bar q}q. Thus in holographic QCD higher Fock states can have any number of extra q{bar q} pairs. We discuss the relevance of higher Fock-states for describing the detailed structure of space and time-like form factors. The AdS/QCD model can be systematically improved by using its complete orthonormal solutions to diagonalize the full QCD light-front Hamiltonian or by applying the Lippmann-Schwinger method in order to systematically include the QCD interaction terms. A new perspective on quark and gluon condensates is also obtained.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Track Seeding and Labelling with Embedded-space Graph Neural Networks

To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event as a graph that connects track measurements (detector hits corresponding to nodes) with candidate line segments between the hits (corresponding to edges). Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables. Previously, message-passing GNNs have shown success in predicting doublet likelihood, and we here report updates on the state-of-the-art architectures for this task. In addition, the Exa.TrkX project has investigated innovations in both graph construction, and embedded representations, in an effort to achieve fully learned end-to-end track finding. Hence, we present a suite of extensions to the original model, with encouraging results for hitgraph classification. In addition, we explore increased performance by constructing graphs from learned representations which contain non-linear metric structure, allowing for efficient clustering and neighborhood queries of data points. We demonstrate how this framework fits in with both traditional clustering pipelines, and GNN approaches. The embedded graphs feed into high-accuracy doublet and triplet classifiers, or can be used as an end-to-end track classifier by clustering in an embedded space. A set of post-processing methods improve performance with knowledge of the detector physics. Finally, we present numerical results on the TrackML particle tracking challenge dataset, where our framework shows favorable results in both seeding and track finding.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Radiological data consolidation and 3D modeling of a building - 15473

A portion of a building at an industrial facility that conducted monazite sands-processing operations in the 1950's under contract to the Atomic Energy Commission (AEC) became contaminated. Subsequently the U.S. Department of Energy designated the facility for remedial action under the Formerly Used Sites Remedial Action Program (FUSRAP). Remedial investigation, remedial actions and post-remediation surveys have been conducted in portions of the building. Radiological data for the interior of the building were compiled, evaluated and presented in a three dimensional (3-D) model (the model) created by AMEC Foster Wheeler in order to identify areas that meet the Record of Decision (ROD) cleanup criteria, and identity areas that may require remedial action or may require additional investigation due to data gaps. The data from different phases of investigation and remediation cover different portions of the building and in some cases overlap. In addition, the data were collected over a period of more than ten years, for different objectives, and thus the instruments and methods for establishing efficiency were not necessarily consistent. The data consolidation process included determining the relative quality of data points to include in the model and the development of rationale associated with assigning a quality level (i.e. ranking) to each data point. The rationale for ranking data quality included, as the primary determining factors, chronological order of the data (most recent was ranked as more reliable), certainty of the sample locations within the building, and analytical data quality. A 3-D model of the building was created using a laser point cloud scan which was then converted into a 3-D design model. Sample locations were then entered into the model and linked with the analytical database to allow display of the radiological results in the model. The visual model allows both strategic and tactical decisions to be made, depending on the level of detail examined. The evaluation process allowed maximum use of the historical data set, rather than requiring older data to be disregarded, and provided at a minimum a qualitative sense of data quality/confidence in the data. The (authors)process also highlights the importance of collecting high quality data from the very beginning with a consistent locational coordinate system, and the need for a good visualization model in order to assess contaminant locations during remedial action or post-remediation surveys.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

BATPAC--VERSION 5.0

Argonne National Laboratory has worked on electrochemical energy storage for several decades. The focus on lithium-ion chemistries started in the early 1990s, developing new materials, synthesis methods, and performance characterizations. Sponsored by the U.S. Department of Energy, Energy Efficiency and Renewable Energy, Vehicle Technologies Office (DOE-EERE-VTO), Argonne has led with many advances. The experimental activities were complemented with multi-scale modeling that ranged from the atomic to the system (manufacturing processes and automobiles) level. The Battery Performance and Cost (BatPaC) model is a calculation method based on Microsoft¿ Office Excel spreadsheets that have been developed at Argonne for estimating the performance and manufacturing cost of lithium-ion batteries for electric-drive vehicles including hybrid-electrics (HEV), plug-in hybrids (PHEV) and pure electrics. The effort is being funded by the Vehicle Technology Office (VTO), which is part of the Energy Efficiency and Renewable Energy (EERE) office of the U.S. Department of Energy (USDOE). BatPaC was first developed in 2007, was subsequently peer-reviewed, and it has served Argonne researchers and the greater battery community in studying the impact of material properties on performance at the pack level. With further developments, the model now allows the design of cells and battery packs for automotive applications, to meet performance requirements (power, energy, recharge time), and estimates the cost of manufacturing the designed batteries. Since the cost depends on the materials he design, and the manufacturing process, this bottom-up model/tool enables the user to study their effects. Designed or the lithium-ion cell and battery researcher, BatPaC helps answer many questions by being 1.Transparent in the assumptions made and the method of calculation 2.Capable of designing a battery specifically for the requirements of an application 3.Constrained by the physical limitations that govern battery performance 4. A bottom-up calculation approach to account for every cost factors. BatPaC predicts the impact of promising materials (and their properties) on the performance metrics relevant for the different applications. Researchers can use the specific capacities and the half-cell voltages of a particular set of electrode materials to calculate the mass and volume of a cell to develop a model incorporating the properties of all the other materials in the cell and the design of the cell enclosure. These calculations not only reveal the impact of an improved material but also enable researchers to calculate the material properties that would be needed to meet the performance criteria of a full battery pack. With this information, researchers can provide he battery industry with realistic expectations that will help it more successfully advance novel battery technologies an Applications.

AHMED, SHABBIR↗

Risk Importance Ranking of Fire Data Parameters to Enhance Fire PRA Model Realism

Fire is historically and analytically a significant contributor to nuclear power plant risk. The level of fire risk and the methods, tools and data for modeling this risk is highly debated by experts. One area of debate is the input data used in fire modeling and how to deal with this data’s high uncertainty. This report outlines initial work performed for determining the key parameters causing this uncertainty and how it propagates into nuclear power plant models. This research paves the way for the development of methods to reduce fire data uncertainty used in modeling. The Nuclear Regulatory Commission has mandated that nuclear power plants perform fire risk modeling. However, there are several issues with the current risk modeling implementation that affect the results. Approved modeling methods can be overly conservative and often do not match plant experience. Also, the data used in the modeling can have high uncertainties and is influenced by expert judgement. To evaluate input data uncertainty, researchers performed an initial review of several fire experiments done at Sandia National Laboratories. Uncertainties for fire data can come from many sources, such as experiment design constraints, environmental conditions, or other plant-specific aspects. There are many different significant and insignificant parameters driving the uncertainty. Additionally, the uncertainty of the different input data used in the fire modeling could have a significant or insignificant effect on the entire plant risk. A four-step methodology was developed to perform Integrated Probabilistic Risk Assessment Importance Ranking. A demonstration case using these steps was set up and three of the four steps were completed in fiscal year (FY) 2019 and the fourth step done FY 2020. These steps are: 1. The qualitative analysis of potential sources was conducted with the following items identified for the demonstration. • Maximum heat release rate • Time to maximum heat release rate • Duration of max heat release rate • Time to decay • Thermal conductivity of concrete • Specific heat of concrete • Density of concrete • Cable jacket thickness 2. A quantitative characterization of dominant sources of uncertainty was performed. A list of distributions and determined values of the dominant sources is shown in Appendix A. 3. A quantitative screening of the potential sources of uncertainty using Morris Elementary Effects Analysis was completed. An experimental model using the physics-based fire modeling tool Fire Dynamics Simulator was developed and coupled with the Risk Analysis Virtual Environment. The Morris analysis identified at least two parameters that can be eliminated as significant contributors (specific heat of concrete and cable jacket thickness). 4. Global importance measure (Global IM) analysis to generate a comprehensive ranking based on their influence on the plant risk. In this research, a moment-independent Global IM is used since it can address (a) uncertainty in the input parameters of the fire model, (b) uncertainty in the risk outputs, and (c) non-linearity and interactions among input parameters in the fire model, more accurately than the correlation-based and variance-based global methods. The observations from the research showed that, depending on the initial and boundary conditions of the fire scenarios, fire-induced damage could have a very small probability and could be dominated by the tail of the uncertainty distribution; hence, the accuracy of the correlation-based and variance-based methods is questionable. The moment-independent Global IM analysis in this research provides a better understanding of how experimental uncertainty data affects industry’s plant models and where improvements in that data will have the largest benefit for improving fire modeling accuracy in causing core damage. Among the five unscreened parameters obtained from the Morris EE analysis, the Global IM analysis results for the case study indicated that max heat release rate and fire location are the most important parameters. The report also outlines benefits of using a unified computational platform that integrates the underlying simulations (e.g., a fire progression model), quantitative screening (using the Morris EE method), and the Global IM analysis. A unified platform can (i) facilitate the ranking of input parameters considering multiple key fire scenarios simultaneously, rather than considering one scenario at a time, (ii) contribute to more explicit and accurate treatment of dependencies at multiple levels of Fire PRA, (iii) facilitate the sampling-based uncertainty quantification for Fire PRA, and (iv) help generating both “industry-wide” and "plant-specific" ranking of uncertainty sources in Fire PRA. Future research should be done to include additional parameters such as detection/suppression or cable fire spread. Adding a PRA software such as SAPHIRE to the RAVEN platform would help with plant model integration and improve treatment of fire-induced dependency. The I-PRA risk importance ranking methodology offered in this report can provide valuable information for efficiently (a) enhancing the realism of Fire PRA for existing plants and (b) supporting the development of Dynamic Fire PRA for advanced reactors and new plants.

97 MATHEMATICS AND COMPUTING↗

Concept of an upright wearable positron emission tomography imager in humans

Abstract Background Positron Emission Tomography ( PET ) is traditionally used to image patients in restrictive positions, with few devices allowing for upright, brain‐dedicated imaging. Our team has explored the concept of wearable PET imagers which could provide functional brain imaging of freely moving subjects. To test feasibility and determine future considerations for development, we built a rudimentary proof‐of‐concept prototype (Helmet_ PET ) and conducted tests in phantoms and four human volunteers. Methods Twelve Silicon Photomultiplier‐based detectors were assembled in a ring with exterior weight support and an interior mechanism that could be adjustably fitted to the head. We conducted brain phantom tests as well as scanned four patients scheduled for diagnostic F 18‐ FDG PET / CT imaging. For human subjects the imager was angled such that field of view included basal ganglia and visual cortex to test for typical resting‐state pattern. Imaging in two subjects was performed ~4 hr after PET / CT imaging to simulate lower injected F 18‐ FDG dose by taking advantage of the natural radioactive decay of the tracer (F 18 half‐life of 110 min), with an estimated imaging dosage of 25% of the standard. Results We found that imaging with a simple lightweight ring of detectors was feasible using a fraction of the standard radioligand dose. Activity levels in the human participants were quantitatively similar to standard PET in a set of anatomical ROI s. Typical resting‐state brain pattern activation was demonstrated even in a 1 min scan of active head rotation. Conclusion To our knowledge, this is the first demonstration of imaging a human subject with a novel wearable PET imager that moves with robust head movements. We discuss potential research and clinical applications that will drive the design of a fully functional device. Designs will need to consider trade‐offs between a low weight device with high mobility and a heavier device with greater sensitivity and larger field of view.

60 APPLIED LIFE SCIENCES↗

PHANOTATE: a novel approach to gene identification in phage genomes

Motivation: Currently there are no tools specifically designed for annotating genes in phages. Several tools are available that have been adapted to run on phage genomes, but due to their underlying design, they are unable to capture the full complexity of phage genomes. Phages have adapted their genomes to be extremely compact, having adjacent genes that overlap and genes completely inside of other longer genes. This non-delineated genome structure makes it difficult for gene prediction using the currently available gene annotators. Here we present PHANOTATE, a novel method for gene calling specifically designed for phage genomes. Although the compact nature of genes in phages is a problem for current gene annotators, we exploit this property by treating a phage genome as a network of paths: where open reading frames are favorable, and overlaps and gaps are less favorable, but still possible. We represent this network of connections as a weighted graph, and use dynamic programing to find the optimal path. Results: We compare PHANOTATE to other gene callers by annotating a set of 2133 complete phage genomes from GenBank, using PHANOTATE and the three most popular gene callers. We found that the four programs agree on 82% of the total predicted genes, with PHANOTATE predicting more genes than the other three. We searched for these extra genes in both GenBank’s non-redundant protein database and all of the metagenomes in the sequence read archive, and found that they are present at levels that suggest that these are functional protein-coding genes. Availability and implementation: https://github.com/deprekate/PHANOTATE. Supplementary information: Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗

Testing of Strategies for the Acceleration of the Cost Optimization

The general problem addressed in the Nuclear-Renewable Hybrid Energy System (N-R HES) project is finding the optimum economical dispatch (ED) and capacity planning solutions for the hybrid energy systems. In the present test-problem configuration, the N-R HES unit is composed of three electrical power-generating components, i.e. the Balance of Plant (BOP), the Secondary Energy Source (SES), and the Energy Storage (ES). In addition, there is an Industrial Process (IP), which is devoted to hydrogen generation. At this preliminary stage, the goal is to find the power outputs of each one of the N-R HES unit components (BOP, SES, ES) and the IP hydrogen production level that maximizes the unit profit by simultaneously satisfying individual component operational constraints. The optimization problem is meant to be solved in the Risk Analysis Virtual Environment (RAVEN) framework. The dynamic response of the N-R HES unit components is simulated by using dedicated object-oriented models written in the Modelica modeling language. Though this code coupling provides for very accurate predictions, the ensuing optimization problem is characterized by a very large number of solution variables. To ease the computational burden and to improve the path to a converged solution, a method to better estimate the initial guess for the optimization problem solution was developed. The proposed approach led to the definition of a suitable Monte Carlo-based optimization algorithm (called the preconditioner), which provides an initial guess for the optimal N-R HES power dispatch and the optimal installed capacity for each one of the unit components. The preconditioner samples a set of stochastic power scenarios for each one of the N-R HES unit components, and then for each of them the corresponding value of a suitably defined cost function is evaluated. After having simulated a sufficient number of power histories, the configuration which ensures the highest profit is selected as the optimal one. The component physical dynamics are represented through suitable ramp constraints, which considerably simplify the numerical solving. In order to test the capabilities of the proposed approach, in the present report, the dispatch problem only is tackled, i.e. a reference unit configuration is assumed, and each one of the N-R HES unit components is assumed to have a fixed installed capacity. As for the next steps, the main improvement will concern the operation strategy of the ES facility. In particular, in order to describe a more realistic battery commitment strategy, the ES operation will be regulated according to the electricity price forecasts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

MONTI: A Multi-Omics Non-negative Tensor Decomposition Framework for Gene-Level Integrative Analysis

Multi-omics data is frequently measured to enrich the comprehension of biological mechanisms underlying certain phenotypes. However, due to the complex relations and high dimension of multi-omics data, it is difficult to associate omics features to certain biological traits of interest. For example, the clinically valuable breast cancer subtypes are well-defined at the molecular level, but are poorly classified using gene expression data. Here, we propose a multi-omics analysis method called MONTI (Multi-Omics Non-negative Tensor decomposition for Integrative analysis), which goal is to select multi-omics features that are able to represent trait specific characteristics. Here, we demonstrate the strength of multi-omics integrated analysis in terms of cancer subtyping. The multi-omics data are first integrated in a biologically meaningful manner to form a three dimensional tensor, which is then decomposed using a non-negative tensor decomposition method. From the result, MONTI selects highly informative subtype specific multi-omics features. MONTI was applied to three case studies of 597 breast cancer, 314 colon cancer, and 305 stomach cancer cohorts. For all the case studies, we found that the subtype classification accuracy significantly improved when utilizing all available multi-omics data. MONTI was able to detect subtype specific gene sets that showed to be strongly regulated by certain omics, from which correlation between omics types could be inferred. Furthermore, various clinical attributes of nine cancer types were analyzed using MONTI, which showed that some clinical attributes could be well explained using multi-omics data. We demonstrated that integrating multi-omics data in a gene centric manner improves detecting cancer subtype specific features and other clinical features, which may be used to further understand the molecular characteristics of interest. The software and data used in this study are available at: https://github.com/inukj/MONTI.

59 BASIC BIOLOGICAL SCIENCES↗

Assessing High-energy, Laser-driven, X-ray Sources as a Technique to Analyse ILW Containers - 18242

The principle concern regarding intermediate level waste (ILW) containers is the internal volumetric expansion due to the formation of corrosion products. To tackle this (via non-destructive testing) the development of a new technique to identify problematic containers was initiated by radiographic imaging and neutron assay of small scale, simulated, nuclear waste packages. This was achieved using a high-energy laser pulse to produce a bright burst of high-energy Bremsstrahlung radiation for imaging and a pulse of thermal energy neutrons for isotopic quantification. The small scale, simulated, nuclear waste packages (typically a quarter of a 28 mm natural uranium penny encased in grout) were produced with corrosion from oxygen and hydrogen rich environments to simulate the potential effects of long term storage. These samples were characterised using electron and ion beam techniques prior to encapsulation to establish the extent of corrosion and sample morphology. The small-scale samples were analysed using high-resolution x-ray tomography (XRT). These 3D pictures identified the corrosion state of the material and the effect on the surrounding grout matrix. These data sets were used as a baseline to compare with the 2D images obtained from the high-energy radiography to ascertain the imaging quality and potential capability of a laser driven inspection system. The study established the non-destructive imaging capability of a high-energy laser driven x-ray source for identifying fine features such as corrosion products on uranium samples encased in grout and cracks, and for penetrative imaging through large thicknesses of grout. In addition, generation of laser-driven neutrons of thermal energy required for active interrogation of uranium isotopes is demonstrated experimentally and simulations of the neutron inspection method are presented, thus paving the way for criticality testing of ILW containers. This current study is single pulse exposure acquisition, but the data can be used to predict the potential capability of a diode-pumped laser-driven system operating at, or above, 10 Hz by utilising, for example, the Dipole laser technology recently delivered by the STFC Central Laser Facility. Such a system will enable 3D tomography of nuclear waste containers and also greatly reduced signal to noise ratio via multiple acquisition and image stacking. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

A simple method for simulating wind profiles in the boundary layer of tropical cyclones

A method to simulate characteristics of wind speed in the boundary layer of tropical cyclones in an idealized manner is developed and evaluated. The method can be used in a single-column modelling set-up with a planetary boundary-layer parametrization, or within large-eddy simulations (LES). The key step is to include terms in the horizontal velocity equations representing advection and centrifugal acceleration in tropical cyclones that occurs on scales larger than the domain size. Compared to other recently developed methods, which require two input parameters (a reference wind speed, and radius from the centre of a tropical cyclone) this new method also requires a third input parameter: the radial gradient of reference wind speed. With the new method, simulated wind profiles are similar to composite profiles from dropsonde observations; in contrast, a classic Ekman-type method tends to overpredict inflow-layer depth and magnitude, and two recently developed methods for tropical cyclone environments tend to overpredict near-surface wind speed. When used in LES, the new technique produces vertical profiles of total turbulent stress and estimated eddy viscosity that are similar to values determined from low-level aircraft flights in tropical cyclones. Lastly, temporal spectra from LES produce an inertial subrange for frequencies ≳0.1 Hz, but only when the horizontal grid spacing ≲20 m.

17 WIND ENERGY↗

Block Design with Common Reference Samples Enables Robust Large-Scale Label-Free Quantitative Proteome Profiling

Label-free quantitative proteomics has become an increasingly popular tool for profiling global protein abundances. However, one major weakness is the potential performance drift of the LC-MS platform over time, which in turn limits its utility for analyzing large-scale sample sets. To address this, in this work we introduce an experimental and data analysis scheme based on a block-design with common controls within each block for enabling LC-MS-based large-scale label-free quantification. In this scheme, a large number of samples (e.g., >100 samples) are analyzed in smaller, and more manageable blocks, minimizing instrument drift and variability within a block. Furthermore, each designated block also contains common controls (or reference) samples for normalization within and across blocks. We demonstrated the effectiveness of this method by profiling the proteome response of human macrophage THP-1 cells to 11 engineered nanomaterials (ENMs) at two different doses. A total of 116 samples were analyzed in six blocks, yielding an average coverage of 4500 proteins per sample. The data revealed consistent quantification of proteins across all six blocks, as shown by highly stable quantification of house-keeping proteins in all samples and high levels of quantification correlation among samples from different blocks. The data also demonstrated that label-free quantification is robust and accurate enough to quantify even very subtle abundance changes as well as large fold-changes without potential ratio compression as often encountered with isobaric labeling. Our streamlined workflow is easy to implement and can be readily adapted to other large cohort studies for reproducible label-free proteome quantification.

59 BASIC BIOLOGICAL SCIENCES↗

Enhanced Boundary Layer Height Detection Using Ceilometer, Surface Meteorology, and Radiation Products With a Random Forest Ensemble Method

This study develops and evaluates a Random Forest (RF) model for estimating planetary boundary layer height (PBLH) using 9 years of data from the Atmospheric Radiation Measurement Southern Great Plains (ARM SGP) user facility, with potential application in the NOAA Surface Radiation (SURFRAD) Network. The model integrates ceilometer, surface meteorology, and radiation measurements, and is trained using thermodynamic PBLH estimates derived from radiosondes. This approach aims to bridge gaps between aerosol-based and thermodynamic-based PBLH estimates. The RF model outperformed traditional methods during daytime and better captured transition periods, demonstrating improved accuracy and robustness. At ARM SGP, it showed a substantial reduction in both bias and RMSE, with a bias near zero (−4.9 m) compared with traditional Haar Wavelet (HW) (70.9 m) and Vaisala BL-View software (124.1 m), and an RMSE of 303.2 m, lower than both BL-View (566.9 m) and HW (404.6 m). During daytime hours, RF consistently outperformed both alternatives, maintaining lower bias and RMSE across all periods. At a second evaluation site, RF achieved the lowest overall RMSE (323.7 m), similar to HW (326.4 m) and significantly better than BL-View (738.3 m). However, all models showed reduced accuracy under stable nighttime conditions, limiting the reliability of PBLH estimates. Key predictors for the model included the lifting condensation level height (LCLH), aerosol gradients, and month for seasonal variability. The study underscores the potential of integrating machine learning with multiple data sets such as surface energy and thermodynamic data to advance PBLH estimation.

boundary layer height↗

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