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At least 199 records · Page 11

Optimal experimental design: Formulations and computations

Questions of ‘how best to acquire data’ are essential to modelling and prediction in the natural and social sciences, engineering applications, and beyond. Optimal experimental design (OED) formalizes these questions and creates computational methods to answer them. This article presents a systematic survey of modern OED, from its foundations in classical design theory to current research involving OED for complex models. We begin by reviewing criteria used to formulate an OED problem and thus to encode the goal of performing an experiment. We emphasize the flexibility of the Bayesian and decision-theoretic approach, which encompasses information-based criteria that are well-suited to nonlinear and non-Gaussian statistical models. We then discuss methods for estimating or bounding the values of these design criteria; this endeavour can be quite challenging due to strong nonlinearities, high parameter dimension, large per-sample costs, or settings where the model is implicit. A complementary set of computational issues involves optimization methods used to find a design; we discuss such methods in the discrete (combinatorial) setting of observation selection and in settings where an exact design can be continuously parametrized. Finally we present emerging methods for sequential OED that build non-myopic design policies, rather than explicit designs; these methods naturally adapt to the outcomes of past experiments in proposing new experiments, while seeking coordination among all experiments to be performed. Throughout, we highlight important open questions and challenges.

97 MATHEMATICS AND COMPUTING↗

Orthogonal Degron System for Controlled Protein Degradation in Cyanobacteria

Synechococcus elongatus PCC 7942 is a model cyanobacterium for study of the circadian clock, photosynthesis, and bioproduction of chemicals, yet nearly 40% of its gene identities and functions remain unknown, in part due to limitations of the existing genetic toolkit. While classical techniques for the study of genes (e.g., deletion or mutagenesis) can yield valuable information about the absence of a gene and its associated protein, there are limits to these approaches, particularly in the study of essential genes. In this research, we developed a tool for inducible degradation of target proteins in S. elongatus by adapting a method using degron tags from the Mesoplasma florum transfer-messenger RNA (tmRNA) system. We observed that M. florum lon protease can rapidly degrade exogenous and native proteins tagged with the cognate sequence within hours of induction. We used this system to inducibly degrade the essential cell division factor, FtsZ, as well as shell protein components of the carboxysome. Our results have implications for carboxysome biogenesis and the rate of carboxysome turnover during cell growth. Lon protease control of proteins offers an alternative approach for the study of essential proteins and protein dynamics in cyanobacteria.

59 BASIC BIOLOGICAL SCIENCES↗

Multicolor multifocal 3D microscopy using in-situ optimization of a spatial light modulator

Abstract Multifocal microscopy enables high-speed three-dimensional (3D) volume imaging by using a multifocal grating in the emission path. This grating is typically designed to afford a uniform illumination of multifocal subimages for a single emission wavelength. Using the same grating for multicolor imaging results in non-uniform subimage intensities in emission wavelengths for which the grating is not designed. This has restricted multifocal microscopy applications for samples having multicolored fluorophores. In this paper, we present a multicolor multifocal microscope implementation which uses a Spatial Light Modulator (SLM) as a single multifocal grating to realize near-uniform multifocal subimage intensities across multiple wavelength emission bands. Using real-time control of an in-situ-optimized SLM implemented as a multifocal grating, we demonstrate multicolor multifocal 3D imaging over three emission bands by imaging multicolored particles as well as Escherichia coli ( E. coli ) interacting with human liver cancer cells, at $$\sim 2.5$$ ∼ 2.5 multicolor 3D volumes per second acquisition speed. Our multicolor multifocal method is adaptable across SLM hardware, emission wavelength band locations and number of emission bands, making it particularly suited for researchers investigating fast processes occurring across a volume where multiple species are involved.

47 OTHER INSTRUMENTATION↗

A formally certified end-to-end implementation of Shor’s factorization algorithm

Quantum computing technology may soon deliver revolutionary improvements in algorithmic performance, but it is useful only if computed answers are correct. While hardware-level decoherence errors have garnered significant attention, a less recognized obstacle to correctness is that of human programming errors—“bugs.” Techniques familiar to most programmers from the classical domain for avoiding, discovering, and diagnosing bugs do not easily transfer, at scale, to the quantum domain because of its unique characteristics. To address this problem, we have been working to adapt formal methods to quantum programming. With such methods, a programmer writes a mathematical specification alongside the program and semiautomatically proves the program correct with respect to it. The proof’s validity is automatically confirmed—certified—by a “proof assistant.” Formal methods have successfully yielded high-assurance classical software artifacts, and the underlying technology has produced certified proofs of major mathematical theorems. As a demonstration of the feasibility of applying formal methods to quantum programming, we present a formally certified end-to-end implementation of Shor’s prime factorization algorithm, developed as part of a framework for applying the certified approach to general applications. By leveraging our framework, one can significantly reduce the effects of human errors and obtain a high-assurance implementation of large-scale quantum applications in a principled way.

Science & Technology - Other Topics↗

Dynamic PRA-Based Estimation of PWR Coping Time Using a Surrogate Model for Accident Tolerant Fuel

In this study, we propose an interpolation-based response surface surrogate methodology to manage a large number of scenarios in dynamic probabilistic risk assessment. It adopts the shape Dynamic Time Warping algorithm to cluster the interpolation neighborhood from time series sample data. The interpolation method was adapted from Taylor Kriging to allow a reduced-order model of the Taylor series. In order to demonstrate its applicability to complex issues in risk assessment for nuclear engineering, an example risk response surface to estimate emergency core cooling system (ECCS) criteria for triplex silicon carbide (SiC) accident-tolerant fuel was constructed. The response surface was exploited to estimate the cumulative failure probability of the fuel cladding structure due to the uncertainties in operator actions and safety systems. The functional failures were assessed based on a combination of individual layer failures computed by coupling Risk Analysis Virtual Environment software with a pressurized water reactor 1000-MW(electric) RELAP5 model and the in-house fuel performance assessment module. Results showed that SiC cladding failure probability spiked less than 1 min after a large-break loss-of- coolant accident whenever the current ECCS criteria for Zircaloy-4 (Zr-4) cladding was used. However, it still provides an increased safety margin of three orders of magnitude compared to Zr-4. This positive margin could be utilized to relax active ECCS requirements by allowing deviations of up to 450 s in its actuation time. The proposed surrogate methodology generated a response surface of SiC cladding failure probability reasonably well, with a significant savings of computation time. This methodology is expected to be useful in the analysis of system response with complex uncertainty sources.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

The evolution of analytical techniques for multiplex analysis of protein biomarkers

Introduction: The landscape of biomarker development has evolved with advanced analytical technologies, particularly affinity- and mass spectrometry-based techniques. These advancements have deepened our understanding of disease mechanisms, enabling the development of precise diagnostic tools and personalized medicine. Protein biomarkers, which play pivotal roles in biological processes, have become invaluable in diagnosing and monitoring diseases, aided by their presence in various biological samples and the availability of established detection methods. Areas covered: This review covers the role of protein biomarkers in clinical practice, the development and dimensionality of protein biomarkers, advancements in detection technologies, a comparison of these technologies, and future directions in biomarker discovery and disease mechanism elucidation. Expert opinion: Advances in biomarker technologies have the potential to transform diagnostics and personalized treatment but face challenges such as high costs and technical complexity. Enhancing reproducibility and integrating multi-omics approaches may offer better insights. In conclusion, the field should evolve toward high-throughput, automated methods, continuously adapting research, and clinical practices.

59 BASIC BIOLOGICAL SCIENCES↗

Nonlinear saturation of ballooning modes in stellarators

Ballooning mode saturation is investigated in realistic stellarator configurations using the flux tube approach of Ham et al (2018 Plasma Phys. Control. Fusion 60 075017), Ham et al (2016 Phys. Rev. Lett. 116 235001). The method is adapted to account for the lack of exact force balance in stellarator equilibrium solvers that assume existence of nested flux surfaces. A variational approach for calculating flux tube energy is developed to overcome this force error problem in stellarator numerical equilibria. Saturated (equilibrium) flux tube states that cross 10%–20% of the plasma minor radius are shown to exist for linearly ballooning unstable profiles. It is shown that several features of the displaced flux tube structure in a full nonlinear MHD simulation of Wendelstein 7X are reproduced by our model. Saturated states are found in a compact stellarator equilibrium close but below the marginal ballooning linear instability, i.e. the unperturbed equilibrium is metastable. This suggests that edge-localized-mode-like explosive MHD behavior may be possible in stellarators.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

How different power plant types contribute to electric grid reliability, resilience, and vulnerability: a comparative analytical framework

Abstract This work explores the dependability tradeoffs provided by the most common types of central power plants in the United States. Historically, the electricity sector has lacked consensus on how reliability , resilience , and vulnerability differ and how those metrics change depending on the power plant fleet composition. We propose distinct definitions for these metrics and an analytical framework to evaluate power plant fleet dependability. Using data analysis and literature review, we identify fifteen dependability attributes across which we rank eleven power plant types relative to natural gas combined-cycle (NGCC) plants. We use NGCC as the benchmark because it is common to many locations and is of relatively recent vintage. The framework shows that each power plant type has unique dependability benefits and drawbacks. We provide examples of how researchers may use the framework to evaluate grid dependability qualitatively under different scenarios. We find that assuming all attributes that contribute to grid dependability are equally important and additive, electric grid dependability is best supported when power plant fleets include a mixture of power generation technologies. Then, we discuss scenario characteristics that could alter the prioritization and relationships of attributes. We also find that if current capacity installation trends continue to favor low- and zero-carbon power plants, US power grids may benefit from increased resilience and reduced vulnerability at the cost of decreased reliability. We conclude by recommending methods for adapting the framework and quantifying relationships between attributes in individual scenarios.

Ramirez-Meyers, K. (ORCID:0000000291216952)↗

Perturbative QCD contribution to transverse single spin asymmetries in the Drell-Yan process and SIDIS

In a previous publication [Benić , ], we have computed the perturbative QCD contribution to transverse single spin asymmetries (SSAs) in semi-inclusive deep inelastic scattering (SIDIS) involving the g T ( x ) distribution. In this paper, we first present a more efficient derivation of the asymmetries which is applicable to both transverse and longitudinal SSAs and correct some inconsistencies in our previous calculation. We then adapt the method to compute transverse SSAs in the Drell-Yan process proportional to g T ( x ) and its gluonic counterpart and discuss the crossing symmetry between the results for SIDIS and the Drell-Yan process. Finally, we present numerical results for various asymmetries measurable at the EIC, RHIC, COMPASS, and Fermilab (SpinQuest), including also part of the genuine twist-three corrections to g T ( x ) from a recent global analysis. We find that the asymmetries can reach percent-level magnitude, if the kinematics predominantly probes the large- x (valence) region of the polarized proton, but remain at subpercent levels otherwise. Published by the American Physical Society 2024

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Constraining physical models at gigabar pressures

High-energy-density (HED) experiments in convergent geometry are able to test physical models at pressures beyond hundreds of millions of atmospheres. The measurements from these experiments are generally highly integrated and require unique analysis techniques to procure quantitative infor mation. This work describes a methodology to constrain the physics in convergent HED experiments by adapting the methods common to many other fields of physics. As an example, a mechanical model of an imploding shell is constrained by data from a thin-shelled direct-drive exploding-pusher experiment on the OMEGA Laser System using Bayesian inference, resulting in the reconstruction of the shell dynamics and energy transfer during the implosion. The model is tested by analyzing synthetic data from a 1-D hydrodynamics code and is sampled using a Markov chain Monte Carlo to generate the posterior distributions of the model parameters. As a result, the goal of this work is to demonstrate a general methodology that can be used to draw conclusions from a wide variety of HED experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Fermionic Partial Tomography via Classical Shadows

Here we propose a tomographic protocol for estimating any k-body reduced density matrix (k-RDM) of an n-mode fermionic state, a ubiquitous step in near-term quantum algorithms for simulating many-body physics, chemistry, and materials. Our approach extends the framework of classical shadows, a randomized approach to learning a collection of quantum-state properties, to the fermionic setting. Our sampling protocol uses randomized measurement settings generated by a discrete group of fermionic Gaussian unitaries, implementable with linear-depth circuits. We prove that estimating all k-RDM elements to additive precision ϵ requires on the order of ($^{n}_{k}$)k 3/2 log(n)/ϵ 2 repeated state preparations, which is optimal up to the logarithmic factor. Furthermore, numerical calculations show that our protocol offers a substantial improvement in constant overheads for k ≥ 2, as compared to prior deterministic strategies. We also adapt our method to particle-number symmetry, wherein the additional circuit depth may be halved at the cost of roughly 2–5 times more repetitions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

An Adaptive Reference Scheme to Extend the Functional Range of Optical Backscatter Reflectometry in Extreme Environments

Optical frequency domain reflectometry (OFDR) measurements are performed by recording the interference pattern of light backscattered by density fluctuations or Bragg gratings along the length of an optical fiber. Changes in local temperature or strain in the fiber cause shifts in the backscattered light spectrum, which can be calibrated to the applied temperature or strain. Comparing the backscattered optical spectra from each reflection site with a reference (unstrained) spectrum allows for quantification of local spectral shifts. While mature OFDR-based technologies can provide high-precision spatially distributed measurements over kilometer lengths, the post-processing approaches used to recover spectral shifts from raw optical intensity data can limit the use of OFDR in harsh environments. This paper presents a novel approach to post-processing OFDR data which extends the usability of optical fibers exposed to harsh environments. This approach is the first to use the spectral shift quality, calculated for two OFDR measurements, to identify an appropriate reference scan from which the spectral shift can be determined. Here, three experimental data sets are used to test the algorithm: (1) an optical fiber heated to >950°C, (2) an aluminum-embedded optical fiber under strain from differential thermal expansion, and (3) an optical fiber exposed to a total neutron flux of 2×10 13 n/cm 2 /s over 40 hours in a nuclear test reactor. Results show that using a metric of quality to select an appropriate reference measurement extends the functional range of OFDR strain and temperature sensors. Furthermore, this algorithm can be applied to existing OFDR data.

47 OTHER INSTRUMENTATION↗

Zero-Order Reaction Kinetics v3.0

Zero-RK is a software package that simulates chemically reacting systems using sparse, preconditioned, adaptive matrix methods to achieve orders-of-magnitude reduction in simulation time while maintaining accurate results.

Mcnenly, MatthewJ.↗

Zero-Order Reaction Kinetics

Zero-RK is a software package that simulates chemically reacting systems using sparse, preconditioned, adaptive matrix methods to achieve orders-of-magnitude reduction in simulation time while maintaining accurate results

Lapointe, Simon↗

Zero-Order Reaction Kinetics v. 3.4.hip

Zero-RK is a software package that simulates chemically reacting systems using sparse, preconditioned, adaptive matrix methods to achieve orders-of-magnitude reduction in simulation time while maintaining accurate results.

Mcnenly, MatthewJ↗

Zero-Order Reaction Kinetics v. 3.6

Zero-RK is a software package that simulates chemically reacting systems using sparse, preconditioned, adaptive matrix methods to achieve orders-of-magnitude reduction in simulation time while maintaining accurate results.

McNenly, MatthewJ [Lawrence Livermore National Lab↗

Establishing Pb-203 production from electrodeposited Tl targets at Brookhaven National Laboratory

Background: Promising developments in Pb-212 radiopharmaceutical therapies have increased demand for Pb-203 diagnostic agents. Building on previous work from various isotope production facilities, this study optimized Pb-203 production from electrodeposited Tl targets at Brookhaven National Laboratory (BNL). The additional supply of Pb-203 may help meet growing preclinical and clinical demands. Results: Two Tl targets were irradiated at the Brookhaven Linac Isotope Producer facility with 30 ± 1 MeV protons, measured using previously published cross section data. Distribution coefficients for Pb Resin in acetate media were investigated for both Na + and K + cations, where potassium acetate was ~ 4 times more effective at stripping Pb from the Pb Resin. The Tl electrodeposition was optimized to deposit 350 mg of Tl (~ 60 mg/cm 2 ) on Au backing in under 6 h. The proposed separation process was completed in < 1.5 h and achieved > 98% and 92 ± 3% recovery of Tl and Pb, respectively, with an overall Tl-Pb separation factor of 6 × 10 5 . The experimentally measured half-life of Pb-203 was 52.4 ± 0.7 h, agreeing with 51.93 ± 0.02 h reported by the National Nuclear Data Center. The radioisotopic purity of the Pb fraction at 24 h post end of bombardment (EOB) from a 24 h irradiation was 66% Pb-203, 28% Pb-201, and 6% Pb-200. Following chemical separation, the Pb-203 produced in this work (21 MBq Pb-203 EOB) achieved apparent molar activities of 10 ± 5 and 0.9 ± 0.5 GBq/µmol for [ 203 Pb]Pb-DOTAM and [ 203 Pb]Pb-DO3A, respectively, decay corrected to EOB. Data derived from this work suggests BNL can produce > 10’s GBq Pb-203 with > 99% radiochemical and radioisotopic purity from Tl-205 for worldwide distribution. Conclusions: The production and separation of Pb-203 from natural Tl target material was successfully demonstrated at BNL. Existing methods were adapted and optimized for the facilities at BNL. Results from this work will guide future large-scale Pb-203 production opportunities at BNL for clinical applications.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

Using Generative AI to implement the discrepancy checker for a Nearly Autonomous Management and Control System for Advanced Reactors

Developments related to generative artificial intelligence (AI) have brought a major breakthrough in AI. These developments are rapidly accelerating developments in different science and engineering applications. Nearly Autonomous Management and Control (NAMAC) system provides recommendations to the operator for maintaining the safety and performance of the reactor. The discrepancy checker (DC) is an important component of the NAMAC) system, whose goal is to determine if the plant is moving towards the expected system state after the control actions are injected. In this work, we explore generative AI methods, particularly, a generative pretrained transformer (GPT) for implementing the DC function in NAMAC. The GPT-based DC aims to alert the operator in situations outside NAMAC’s scope and act as a chatbot the operator can use to retrieve relevant information. This study involves two versions of GPT developed by OpenAI: GPT-3.5 and GPT-4. These GPTs are trained on huge amounts of undisclosed general domain datasets. We explored two methods to adapt GPTs for DC implementation in NAMAC: fine-tuning and retrieval augmented generation. A small knowledge base (information file) that encompasses rules for DC implementation and some general information related to NAMAC has been created to support DC implementation using GPT. In this work, the GPT-based DC implementations have been tested for their reasoning abilities, comprehension, information retrieval, and extraction abilities. It should be noted that this paper only presents a preliminary study to test the feasibility of DC implementation using generative AI technology. Given the potential risks and severe consequences associated with nuclear reactor applications, combined with the black-box nature of AI, extensive offline and online testing and reliability analyses of GPT-based DCs are needed for further developing such capabilities.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗