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At least 487 records · Page 27

CRCNS US-France Research Proposal: Collaborative Research: Encoding reward expectation in Drosophilia

The fruit fly Drosophila melanogaster has been a valuable model for investigating the genetic and neural bases that underlie learning and memory. Early and most current studies use basic behavior conditioning protocols to study learning in controlled laboratory settings. More recently, the ability to transgenically manipulate many of the brain neurons in the fruit fly with exquisite specificity, and the recent knowledge of the synaptic ‘connectome’ of the fruit fly brain, makes these animals almost unique as a comprehensive model for studies of learning, memory and motivated behavior. In fact, the connectome has revealed many types of new connections that had until now been overlooked. Within this context, the thesis of this proposal is that studies of learning and memory will be greatly enhanced by using more sophisticated means for evaluating memory representations, such as have been developed in vertebrates, and combining those studies with information from the connectome guided by computational modelling. We propose to push beyond the boundaries of existing conditioning protocols for fruit flies to investigate more complex memory representations. In particular, we will investigate the function of reinforcement pathways in relation to the absence of expected reinforcement. More specifically, we propose a series of experiments designed to investigate the memory representations in fruit flies when an expected consequence of a Conditioned Stimulus (CS) fails to occur. Although studies have evaluated how this failure can establish extinction memory for the CS, our studies will go beyond studying extinction. Specifically, we predict that in Drosophila when a CS is associated with a failed expectation of an appetitive food reinforcement it will acquire aversive value, and vice versa for a failed expectation of an aversive reinforcer. We combine these studies with manipulations of reinforcement pathways in the CNS inspired from the connectome, iteratively knitted in with established computational models. Intellectual Merit: The concept of reinforcement expectation and incentive contrast have been influential in the development of studies of associative learning in mammals. These questions are particularly challenging to answer in vertebrates because they require exquisite cellular, temporal, and genetic specificity of experimental manipulations. The recent development of work with identified neurons and their connectomes makes the larval and adult fly brains ripe as models for pushing our understanding of neural bases for these higher- order conditioning phenomena. Broader Impacts: Public health: These analyses and the conceptual framework of prediction error processing underlying them have a profound impact on our understanding of reinforcement-related behavior in humans, including monetary rewards and the mnemonic consequences of traumatic experiences, and for pathologies of the dopamine reinforcement system. Educational: This project will provide interdisciplinary training for postdoctoral researchers, Ph.D. and undergraduate students. The PIs will act as co-supervisors or mentors of students working in the different labs via face-to-face and internet-based technologies. We will also work with ASU’s award-winning Ask- A-Biologist program. This is an online science program designed to enrich the learning experiences of students of all ages and to provide classroom material for use by K-12 teachers. We will develop an extension of a game developed under a prior NSF award, and the new game will include modules to teach K-12 students about how insects learn. We will also integrate into the AAB site a program developed by a collaborator (B Gerber) at the Leibniz Institut für Neurobiologie, Magdeburg, and now in use in schools in Germany, to teach K-12 students how to train animals using the fruit fly larval learning paradigm. Underrepresented groups: All PIs will work with their university offices of Academic Diversity and Equal Opportunity for reaching underrepresented students.

59 BASIC BIOLOGICAL SCIENCES↗

New approaches to Bayesian uncertainty quantification for Nuclear Science (Final Technical Report)

Inverse problems play a central role in experimentation and theory/data comparisons for many areas of modern Nuclear Physics (NP) and High-Energy Physics (HEP). Bayes’s Theorem is a powerful tool for solving Inverse Problems, providing conceptually transparent and unbiased constraints on theoretical parameters and their uncertainties (“Bayesian Inference”) and enabling the quantification of agreement or tension between models and data. However, analyses based on Bayesian Inference are often challenging for NP and HEP applications, either because of the large number of parameters in the problem, the high computational cost, or both. We propose a multi-institutional collaboration to develop and deploy novel Bayesian analysis tools that advance the scientific scope of a broad range of current and future NP experiments. This project brings together NP domain scientists working on several high-profile NP projects for which new, high-performance Bayesian Uncertainty Quantification (“Bayesian UQ”) methods are essential to carry out the science, and data scientists who are developing state-of-the-art methods applicable to these problems. The NP projects in this proposal comprise measurements of the mass and fundamental nature of the neutrino; study of the Quark-Gluon Plasma that filled the early universe; and mapping of natural and anthropogenic radiation environments. While these NP projects have very different scientific goals, with datasets and analysis approaches that differ significantly, they share common requirements for improving computationally intensive Bayesian analyses using advanced Machine Learning algorithms and will benefit strongly from a coherent effort to develop general solutions. This proposal brings together these projects and forefront ML-based data science algorithms to develop such general solutions. The methods developed in this project will also be more widely applicable, thereby advancing science in the larger Nuclear Physics portfolio.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Particle–Continuum Coupling and its Scaling Regimes: Theory and Applications

Abstract This work is motivated by the goal of designing simulation software for technical devices that, at their functional core, rely on atomistic‐scale processes embedded in a larger‐scale fluid environment. The core of the problem is the conceptual and technical approach for coupling particle and continuum representations of a fluid. The state of the art for key aspects including physical modeling, mathematical formalization, computational implementation, and applications, is discussed and organized in a consistent picture across the relevant physical regimes.

Delle Site, Luigi↗

Ground and excited state gradients with end-to-end differentiable semiempirical quantum chemistry

Accurate and efficient gradients of molecular energy with respect to nuclear degrees of freedom are essential for geometry optimization and molecular dynamics, including simulations that go beyond the Born–Oppenheimer regime. A common approach involves deriving analytical formulas for new electronic structure methods, which is often conceptually difficult and requires tedious coding. Here, we implement analytical, semi-numerical, and automatic differentiation (AD)-based gradient pathways for semiempirical Hamiltonian models in the PYSEQM software package, leveraging both graphics processing unit (GPU) and central processing unit (CPU) architectures. We further extend these capabilities to excited states calculated using the configuration interaction singles and time-dependent Hartree–Fock ansätze. We benchmark wall time, peak memory usage, and accuracy across three molecular families of varying chemical complexity, including systems of up to a thousand atoms. For ground-state simulations, analytical and AD gradients achieve near-identical GPU runtimes, while semi-numerical gradients are slower on GPU but remain competitive on CPU. For excited states, both analytical and custom AD approaches using implicit differentiation show similar performance and low memory requirements, whereas gradients with full AD are memory-limited. AD gradients match analytical ones in accuracy across all tested systems, aided by a quaternion-based diatomic frame rotation for two-center quantities that ensures smooth energy surfaces. Overall, automatic differentiation emerges as a practical alternative to analytical gradients in semiempirical quantum chemistry, offering high accuracy while allowing seamless integration in AI-driven workflows and popular packages, such as PyTorch and JAX. Our results provide actionable guidance for selecting optimal gradient strategies in large-scale ground- and excited-state molecular dynamics simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Advanced Gas Turbine and sCO 2 Combined Cycle Power System (Final Report)

The team of Southwest Research Institute (SwRI), Hanwha Power Systems Americas (HPSA), and Solar Turbines Incorporated (Solar) has worked to develop a modular, highly efficient combined-cycle power system consisting of a supercritical carbon dioxide (sCO 2 )-based waste-heat recovery system (WHRS) coupled to a Solar Titan 130 gas turbine (GT). During Phase 1 of the project, the project team used advanced thermodynamic cycle and techno-economic modeling to find an optimal sCO 2 WHRS bottoming cycle and machinery configuration for a Solar Titan 130. In addition to this, a conceptual design of the machinery and overall system layout was created and technology gaps were identified.

30 DIRECT ENERGY CONVERSION↗

DME-Propane Ignition Delay Time Measurements at Mixing Controlled Compression Ignition Engine-Relevant Conditions

The blend of dimethyl ether (DME, CH 3 OCH 3 ) and propane (C 3 H 8 ) is a potentially renewable fuel mixture that has the potential to replace diesel in compression ignition engines. The combination can potentially reduce particulate and greenhouse gas emissions compared to a conventional diesel engine operating under similar conditions. However, detailed conceptual and simulation studies must be conducted before adopting a new fuel on a compression ignition engine. For these simulations, accurate chemical kinetic models are necessary. However, the validity of chemical kinetic mechanisms in the literature is unknown for mixing controlled compression ignition (MCCI) engine operating conditions. Hence, in this work, we studied the ignition of dimethyl ether (DME) and propane blends in a shock tube at MCCI engine conditions. Ignition delay time (IDT) data was collected behind the reflected shock for DME-propane mixtures for heavy-duty compression ignition (CI) engine parameters. Undiluted experiments spanning temperatures of 700 to 1100 K and pressures of 55 to 84 bar for various blends (100% CH 3 OCH 3 / 0% C 3 H 8, 100% C 3 H 8 / 0% CH 3 OCH 3 , 60% CH 3 OCH 3 / 40% C 3 H 8 ) of DME and propane were combusted in synthetic air (21% O 2 / 79% N 2 ). Some experiments were conducted at higher pressures (90-120 bar) to understand model performance at these conditions. Comparisons of IDT were made with the predictions of recent chemical kinetic mechanisms for DME-propane mixture, including the Aramco3.0, NUIG, and Dames et al. mechanisms. All mechanisms overpredicted IDT compared to experimental values. Sensitivity analysis was conducted with Dames et al. model, and critical reactions sensitive to IDT of DME-propane mixture near 100 bar are outlined.

Mohammed, Zuhayr Pasha↗

Whole-plant phenotypic engineering: moving beyond ratios for multi-objective optimization of nutrient use efficiency

Nutrient use efficiency (NUE) is typically measured as the ratio of yield to soil nutrient availability but ignores contributions of underlying plant traits. Relevant plant traits can be grouped as root acquisition efficiency, shoot radiation use efficiency, and plant metabolic efficiency. The intentional integration of these traits will lead to synergistic improvements of NUE. Recent progress in trait-focused research includes phenotyping root nutrient uptake rates and respiration, engineering reduced photorespiration, and identification of nutrient assimilation pathways. Traits need to be conceptualized in agricultural systems contexts to improve synchrony of plant demand and soil supply of nutrients, including consideration of crop mixtures. Use of simulation modeling and multi-objective optimization will allow accelerating NUE gains beyond selection for a single ratio.

59 BASIC BIOLOGICAL SCIENCES↗

Global root traits (GRooT) database

Motivation: Trait data are fundamental to the quantitative description of plant form and function. Although root traits capture key dimensions related to plant responses to changing environmental conditions and effects on ecosystem processes, they have rarely been included in large-scale comparative studies and global models. For instance, root traits remain absent from nearly all studies that define the global spectrum of plant form and function. Thus, to overcome conceptual and methodological roadblocks preventing a widespread integration of root trait data into large-scale analyses we created the Global Root Trait (GRooT) Database. GRooT provides readyto- use data by combining the expertise of root ecologists with data mobilization and curation. Specifically, we (a) determined a set of core root traits relevant to the description of plant form and function based on an assessment by experts, (b) maximized species coverage through data standardization within and among traits, and (c) implemented data quality checks. Main types of variables contained: GRooT contains 114,222 trait records on 38 continuous root traits. Spatial location and grain: Global coverage with data from arid, continental, polar, temperate and tropical biomes. Data on root traits were derived from experimental studies and field studies. Time period and grain: Data were recorded between 1911 and 2019. Major taxa and level of measurement: GRooT includes root trait data for which taxonomic information is available. Trait records vary in their taxonomic resolution, with subspecies or varieties being the highest and genera the lowest taxonomic resolution available. It contains information for 184 subspecies or varieties, 6,214 species, 1,967 genera and 254 families. Owing to variation in data sources, trait records in the database include both individual observations and mean values. Software format: GRooT includes two csv files. A GitHub repository contains the csv files and a script in R to query the database.

59 BASIC BIOLOGICAL SCIENCES↗

SHOCK TUBE IGNITION STUDIES OF RENEWABLE DIESEL FUELS FOR MEDIUM AND HEAVY-DUTY TRANSPORTATION

Currently extensive research on alternative fuels is being conducted due to their increasing demand to reduce greenhouse emissions. One renewable fuel studied in this work is dimethyl ether (DME) blended with propane(C3H8) as a potential mixture for heavy-duty engines used in semi-trucks. The blend has the potential to drastically reduce particulate and greenhouse gas emissions compared to a conventional diesel engine operating under similar conditions. To develop the use of mixture, one must conduct detailed conceptual and simulation studies before progressing to detail studies in CFD, engine modifications, and live testing. For simulations, accurate high-fidelity chemical kinetic models are necessary. However, the validity of the chemical kinetic mechanism for operating conditions of a heavy-duty mixing-controlled compression (MCCI) engine was widely unknown until recent work presented here and published. In this work, we studied the ignition of DME and propane blends in a shock tube under MCCI engine conditions. Ignition delay time (IDT) gathered behind the reflected shock for DME-propane mixtures for heavy-duty compression ignition (CI) engine parameters. Testing was conducted for undiluted varieties spanning from temperatures of 700 to 1100 K at pressures ranging from 55 to 84 bar for various blends (100% CH3OCH3, 100% C3H8, 60% CH3OCH3/ 40% C3H8) of DME and propane were combusted in synthetic air (21% O2/ 79% N2). Several experiments were conducted at higher pressures (90-120 bar) to improve the model performance and accuracy. The ignition delay times (IDTs) were compared to recent mechanisms, including Aramco3.0, NUIG, and Dames et al. A common trend among the mechanisms was overpredicted experimental IDTs. Further studies were conducted by a sensitivity analysis using the Dames et al. model, and critical reactions sensitive to IDTs of DME-propane mixture near 60 bar are outlined. Chemical analysis was conducted on the NTC region to explain chemical kinetics which is critical for developing MCCI heavy duty engines.

Mohammed, Zuhayr Pasha↗

Multi-Physics Simulations of Molten Chloride Fast Reactor using Nek5000 and PROTEUS-NODAL

This report documents the FY20 work to demonstrate a specific multi-physics capability within NEAMS tools to model molten chloride fast reactors (MCFR). Specifically, the CFD code Nek5000 and the neutronics code PROTEUS-NODAL were coupled off-line to calculate the delayed neutron precursor distribution within the flowing fuel salts in an MCFR core. An MCFR benchmark problem and a 3D test problem based on a conceptual MSR design were successfully solved through the coupled simulation of Nek5000 and PROTEUS-NODAL. In the coupled simulations for these test problems, Nek5000 provided high-quality flow field information, and PROTEUS-NODAL performed the neutron diffusion calculation along with the precursor drift model. The impact of precursor drift on the core status was also investigated. Additionally, the precursor drift model implemented in PROTEUS-NODAL was verified by making use of the reference precursor distribution generated from Nek5000. Results of these test problems suggest that the multi-physics capability of Nek5000 and PROTEUS-NODAL was properly developed for tracking delayed neutron precursor drift.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Development of an ERT‐Based Framework for Bentonite Buffers Monitoring From Laboratory Tests: 1. Characterizing Thermal–Hydrological–Mechanical Processes

Abstract Bentonite clay is widely used in engineered barrier systems for the permanent disposal of high‐level radioactive waste due to its low permeability, high swelling capacity, and thermal stability. However, the complex thermal‐hydrological‐mechanical (THM) processes induced by heating from decaying radioactive waste and hydration from surrounding rock can lead to heterogeneous changes that are difficult to measure and predict. This study develops an Electrical Resistivity Tomography (ERT)‐based framework for monitoring THM processes, progressing from sample‐scale to bench‐scale tests, to inform field‐scale applications. Sample‐scale tests analyzed small bentonite samples under controlled variations in water content, temperature, and porosity to establish fundamental resistivity relationships. Bench‐scale tests involved larger bentonite columns subjected to heating (up to 200°C) and hydration under controlled pressure, simulating repository conditions. ERT measurements, complemented by X‐ray CT imaging, temperature monitoring, and tracing sensors, revealed coupled THM processes, such as hydration‐induced compression, swelling, and thermal gradients, leading to complex resistivity patterns. The results demonstrate the potential of ERT for capturing THM‐induced resistivity changes, though challenges remain in upscaling and quantitative analysis. This study evaluates laboratory test capabilities and proposes future improvements for understanding THM‐induced resistivity responses. A conceptual framework for ERT implementation in field‐scale monitoring is presented, synthesizing findings from both scales and exploring how ERT data can inform long‐term modeling and reduce prediction uncertainties. Overall, this ERT‐based framework offers a robust method for monitoring bentonite buffers, aiding in early issue detection and supporting the safe long‐term disposal of radioactive waste in geological repositories, while highlighting the need for future development. Plain Language Summary Bentonite clay is crucial in engineered barrier systems (EBS) for containing high‐level radioactive waste due to its ability to absorb water, swell, seal and remain stable under high temperatures. When bentonite absorbs water and heats up from radioactive decay, it experiences complex changes in its physical and mechanical properties. Understanding these changes is important for ensuring the long‐term safety and effectiveness of EBS. This study used Electrical Resistivity Tomography (ERT), a non‐invasive method that measures electrical conductivity to monitor these changes during laboratory experiments. The ERT data revealed significant variations in resistivity corresponding to changes in water content, temperature, and density, providing detailed spatial and temporal insights into the behavior of bentonite. These findings enhance our ability to predict the long‐term performance of bentonite barriers, ensuring the safe containment of radioactive waste. By improving our understanding of bentonite's behavior, this research supports the development of more reliable and effective barrier systems for radioactive waste disposal, protecting the environment and public health. Key Points ERT monitoring was employed to capture resistivity changes in bentonite during controlled heating and hydration experiments, providing insights into THM processes ERT data reveal significant resistivity changes correlated with water content, temperature, and mechanical effects, enhancing the understanding of THM dynamics in bentonite This study explores the potential of the framework for application in field‐scale EBS monitoring, emphasizing the need for integrating additional geophysical methods for comprehensive subsurface imaging

Chen, Hang↗

When do molecular polaritons behave like optical filters?

This review outlines several linear optical effects featured by molecular polaritons arising in the collective strong light–matter coupling regime. Under weak laser irradiation and when the single-molecule light–matter coupling can be neglected (often in the limit when the number of molecules per photon mode is large), we show that the excited-state molecular dynamics under collective strong coupling can be exactly replicated without the cavity using a shaped (or “filtered”) laser, whose field amplitude is enhanced by the cavity quality factor, shining on the bare molecules. As a consequence, the absorption within a cavity can be understood as the overlap between the polariton transmission and the bare molecular absorption, suggesting that polaritons act in part as optical filters. This framework demystifies and provides a straightforward explanation for a large class of experiments and theoretical models in molecular polaritonics, highlighting that the same effects can be achieved without the cavity with shaped laser pulses. With a few modifications, this simple conceptual picture can also be adapted to understand the incoherent nonlinear response of polaritonic systems. This review establishes a clear distinction between polaritonic phenomena that can be fully explained through classical linear optics and those that require a quantum electrodynamics approach. It also highlights the need to differentiate between effects that necessitate polaritons (i.e., hybrid light–matter states) and those that can occur in the weak coupling regime. Here, we further discuss that certain quantum optical effects like fluorescence can be partially described as optical filtering, whereas some others like cavity-induced Raman scattering go beyond this. Further exploration in these areas is needed to uncover novel polaritonic phenomena beyond optical filtering.

Schwennicke, Kai [University of California, San Di↗

Turbulence suppression at extreme plasma densities on DIII-D and EAST

Recent high-poloidal-beta (high-βP) experiments on DIII-D and EAST have made coordinated breakthroughs for high confinement quality at high density near the Greenwald limit. Density gradient amplification of turbulence suppression at high βP can explain both of these achievements. Experiments on DIII-D have achieved Greenwald fraction (fGr = line-averaged density/Greenwald density) above 1 simultaneously with normalized energy confinement (H98y2) around 1.5, as required in fusion reactor designs but never before verified in tokamak experiments with the divertor configuration. A synergy between increased H98y2 and fGr is observed with strong gas puffing, due to the build-up of an internal transport barrier at large radius in the temperature and density channels. Transport simulations reveal that the favorable trend of reduced turbulent energy transport at higher density is only expected when increasing the density gradient at high local safety factor and high β, thus at high βP to ensure strong α-stabilization. These conditions are crucial to many conceptual designs for steady-state reactors. New experiments on EAST have nearly doubled the ion temperature at fGr ∼ 0.9, consistent with predict-first modeling results based on the same physics revealed from the DIII-D analysis. All previous EAST long-pulse H-modes have Ti ≪ Te near plasma axis. Transport modeling indicates that the profiles are limited by ion-temperature-gradient modes at mid-radius. The modeling also suggested potential solutions, including reducing magnetic shear, enhancing density gradients, and higher impurity concentration. Following this guidance, EAST experiments directly show a strong enhancement of Ti achieved with a combination of a second plasma current ramp-up, a density gradient increase, and a Zeff perturbation by a short pulse (100 ms) of impurity injection, as predicted by the earlier modeling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Utilizing Sensitivity and Correlation Coefficients from MCNP and Whisper to Guide Microreactor Experiment Design

When designing experiments for full-scale reactor systems, MCNP®* and Whisper can be used to create neutronic models and compare the similarity of two nuclear systems via correlation coefficients for κ eff , effective multiplication factor. This thesis applies this framework to a conceptual heat-pipe, yttrium-hydride moderated microreactor system and experiments. The framework is intended as a supplement to other neutronics/thermal/multiphysics analyses and provides a concrete method to measure the neutronic similarity of two systems. By analyzing the shared nuclear data uncertainty, as well as sensitivity to nuclear data over all neutron energies, highly informative experiments can be designed to aid in the development of microreactor and other advanced reactor technologies and systems.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Preliminary Thermal-Hydraulic Analysis of the UTA–2 Subcritical Linear Accelerator–Driven System

The National Nuclear Security Administration’s mission includes establishing a reliable supply of 99 Mo without highly enriched uranium. Oak Ridge National Laboratory (ORNL) supports this objective through collaborative research and development with industrial partners. Niowave Inc., a current partner, is currently designing a subcritical linear accelerator-driven system (ADS) and preparing for the US Nuclear Regulatory Commission’s licensing process. Niowave’s system has the potential to efficiently supply medical radioisotopes. The technology includes a superconducting electron accelerator and a pile of both natural and low-enriched uranium (LEU) targets. The process fissions uranium and many valuable isotopes can then be extracted from the targets. Niowave is currently iterating through conceptual and detailed design processes for several system sizes. This report discusses UTA–2, which is at the demonstration stage. UTA–2 will validate numerical modeling results with experimental measurements before progressing to the detailed design of UTA–3, the commercial-sized ADS. The thermal-hydraulic behavior of the UTA–2 core design was numerically investigated using STAR-CCM+, as described in this report. STAR-CCM+, a state-of-the-art computational fluid dynamics (CFD) software that was commercially developed by Siemens, has an extensive user base and a set of validation studies. It is also compliant with the American Society of Mechanical Engineers’ Nuclear Quality Assurance 1 standard. Three cases are investigated in this report. Case 1 quantified the temperature field within the UTA–2 assembly and water tank using only conduction as the method of thermal energy transport. This simplified approach was overly conservative and yielded wetted cladding temperatures above the coolant saturation temperature. In this case, the maximum temperature of the wetted cladding surface of the highest power rod exceeded the saturation temperature of water by 63.8°C. Because of the overly conservative approach taken in Case 1 and its negative subcooled margin, buoyancy-driven natural circulation flow physics were implemented in Case 2. Adding coolant motion significantly distributed the thermal energy of the system through convective heat transfer. This relatively small amount of convective heat transfer significantly reduced system temperatures and increased the subcooled margin from –63.8 to 61.3°C. This margin confirmed that no boiling was expected during normal operation of UTA–2 at 230 W. Case 3 had no additional physics models but considered an overpower event during which the power of each LEU and natural uranium rod was at its respective peak values. This resulted in a study with the total assembly power equal to 176% of the nominal power of 230 W considered in Cases 1 and 2. The resulting natural circulation flows were slightly enhanced. The subcooled margin decreased slightly to 46.3°C, which is still a significant margin to local boiling of the water within the tank. This margin confirmed that no boiling was expected during an abnormal operation of UTA–2 at 406 W.

07 ISOTOPE AND RADIATION SOURCES↗

Addressing Human and Organizational Factors in Nuclear Industry Modernization: A Sociotechnically Based Strategic Framework

The modernization of nuclear power plants will require an advanced concept of operations, involving an integrated set of tightly coupled systems in which all stakeholders act in a coordinated manner. For this modernization effort to be enabled, we developed a human and organizational factors approach based on a broad sociotechnical framework. Starting from core human factors principles, we conducted a literature review of the methods and approaches relevant to the modernization problem. These included not only core disciplines such as cognitive systems engineering, systems theoretic accident modeling and processes, human systems integration, resilience engineering, and macroergonomics but also related topics of safety culture and organizational change. From this literature, we developed a conceptual framework centered around the work system with its four interacting components: people, technology, process, and governance. In an effective work system, these four components are jointly optimized according to three systems criteria: efficiency, effectiveness, and safety. System failure may result from excessive emphasis on any one criterion. The actual work of attaining joint optimization in a given work system can be accomplished by utilizing three high-level functions: knowledge elicitation, knowledge representation, and cross-functional integration. Finally, we illustrated the utility of this approach by applying it to practical problems and case studies.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

The development of the NNBAR experiment

The NNBAR experiment for the European Spallation Source will search for free neutrons converting to antineutrons with a sensitivity improvement of three orders of magnitude compared to the last such search. This paper describes progress towards a conceptual design report for NNBAR. The design of a moderator, neutron reflector, beamline, shielding and annihilation detector is reported. The simulations used form part of a model which will be used for optimisation of the experiment design and quantification of its sensitivity.

47 OTHER INSTRUMENTATION↗

Leveraging Prior Concept Learning Improves Generalization From Few Examples in Computational Models of Human Object Recognition

Humans quickly and accurately learn new visual concepts from sparse data, sometimes just a single example. The impressive performance of artificial neural networks which hierarchically pool afferents across scales and positions suggests that the hierarchical organization of the human visual system is critical to its accuracy. These approaches, however, require magnitudes of order more examples than human learners. We used a benchmark deep learning model to show that the hierarchy can also be leveraged to vastly improve the speed of learning. We specifically show how previously learned but broadly tuned conceptual representations can be used to learn visual concepts from as few as two positive examples; reusing visual representations from earlier in the visual hierarchy, as in prior approaches, requires significantly more examples to perform comparably. These results suggest techniques for learning even more efficiently and provide a biologically plausible way to learn new visual concepts from few examples.

Rule, Joshua S.↗