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Proceedings of the Rexx Symposium for Developers and Users, May 1-3, 1995
No abstract prepared.
A Unitary Anesthetic Binding Site at High Resolution
Propofol is the most widely used injectable general anesthetic. Its targets include ligand-gated ion channels such as the GABA{sub A} receptor, but such receptor-channel complexes remain challenging to study at atomic resolution. Until structural biology methods advance to the point of being able to deal with systems such as the GABA{sub A} receptor, it will be necessary to use more tractable surrogates to probe the molecular details of anesthetic recognition. We have previously shown that recognition of inhalational general anesthetics by the model protein apoferritin closely mirrors recognition by more complex and clinically relevant protein targets; here we show that apoferritin also binds propofol and related GABAergic anesthetics, and that the same binding site mediates recognition of both inhalational and injectable anesthetics. Apoferritin binding affinities for a series of propofol analogs were found to be strongly correlated with the ability to potentiate GABA responses at GABA{sub A} receptors, validating this model system for injectable anesthetics. High resolution x-ray crystal structures reveal that, despite the presence of hydrogen bond donors and acceptors, anesthetic recognition is mediated largely by van der Waals forces and the hydrophobic effect. Molecular dynamics simulations indicate that the ligands undergo considerable fluctuations about their equilibrium positions. Finally, apoferritin displays both structural and dynamic responses to anesthetic binding, which may mimic changes elicited by anesthetics in physiologic targets like ion channels.
Nodeworks Introduction
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Nodeworks MFiX Interface Tutorial
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When Does a Bit Matter? Techniques for Verifying the Correctness of Assembly Languages and Floating-Point Programs.
Abstract not provided.
NWGraph: A Library of Generic Graph Algorithms and Data Structures in C++20
The C++ Standard Library is a valuable collection of generic algorithms and data structures that improves the usability and reliability of C++ software. Graph algorithms and data structures are notably absent from the standard library, and previous attempts to fill this gap have not gained widespread adoption. In this paper we show that the richness of graph algorithms and data structures can in fact be captured by straightforward composition of existing C++ mechanisms. Generic programming is algorithm-oriented. Accordingly, we apply a systematic approach to analyzing a broad set of graph algorithms, “lift” unnecessary constraints from them, and organize the resulting set of minimal common type requirements, i.e., concepts, for defining their interfaces. By using the newly available ranges and concepts in C++20, the type requirements for generic graph algorithms can be succinctly expressed. The generic algorithms and data structures resulting from our analysis are realized in NWGraph, in a modern, composable, and extensible C++ library.
DDGui, a new and fast way to analyse DRAGON and DONJON code results
With the largely increased performance of computer, the results from DRAGON and DONJON have increase in size and complexity. The scroll, copy and paste technique to get the result is not appropriate anymore. Many in-house script, software, macro have been developed to make the data gathering easier. However, the limit of these solutions is their specificity and the difficulty to export them from one place to another. A general tool usable and accessible by everyone was needed. The first bricks for a very fast and intuitive way to analyse the DRAGON and DONJON results have been put together in the graphic user interface DDGUI. Based on the extensive ROOT C++ package, the possible features are numerous. For this first version of the software, we have programmed the fundamental tools which may be the more useful on an everyday basis: view the data structures content, draw the geometry and draw the flux or power from a DONJON computation. The tests show how amazingly fast the user can get the information needed for a general overview or more precise analyses. Several other features will be implemented in the near feature. (authors)
Distributions of the Mean for Lognormally Distributed Data - 19427
In environmental sciences, a distribution of the mean is often needed for right-skewed data. For example, risk assessments are often based on distributions of the mean to obtain upper confidence limits (UCLs) for analyte concentrations, and, in probabilistic performance assessment (PPA) models, parameters are given distributions rather than deterministic values, and they are usually given distributions of their mean rather than the population distributions to account for the large spatial and temporal domains to which they are applied. How this distribution of the mean is developed depends on the characteristics of the parameter's full distribution and how the parameter relates to its use. In this presentation, I will discuss the implications of a right skew in distribution development. Parameters may vary over orders of magnitude and/or may be right-skewed. The material property of saturated hydraulic conductivity, Ks, is a prime example of such a parameter. Right-skewed parameters are often assumed to follow lognormal distributions. If X follows a lognormal distribution, then log(X) follows a normal distribution. For some right-skewed parameters, practitioners go back and forth between the two forms of the data depending on their needs. For example, it may be preferred to use the untransformed version of X in a model or equation, but it is easier to visualize the parameter values on a logarithmic scale. However, distribution development for the average value of the parameter will differ depending on whether X or log(X) is modeled. This difference is due to Jensen's Inequality, a theorem which states that the expected value of a concave function of a variable is less than or equal to the same function of the expected value of the variable; a logarithm is a concave function. In other words, the mean of log(X) is always less than or equal to the log of the mean of X and therefore the mean estimated from a distribution based on log(X) is less than or equal to the mean estimated from a distribution based on X. If a parameter is naturally considered on a log scale by subject matter experts, then distributions developed for the mean of log(X) may be more consistent with the use of the parameters in the literature than distributions developed for X. The statistical decisions of distribution development will be discussed within the context of right-skewed, lognormal data. These issues include method-of-moments versus maximum likelihood estimation of the distribution parameters and the use of bootstrapping to aid in the visualization of a distribution of the mean. The details surrounding Jensen's Inequality will be explained and visualized within the context of distribution development of a right-skewed parameter such as saturated hydraulic conductivity. (authors)
Design and implementation of HuREX analysis supporting interface - 171
Human error is recognized as one of the main causes to affect to the socio-technical system including nuclear power plant. Therefore, reducing human error through human reliability analysis (HRA) is a critical issue. Moreover, sufficient and reliable human performance data collection is a prerequisite for ensuring the safety of nuclear power plants. Korea Atomic Energy Research Institute developed the HuREX (Human Reliability data Extraction) framework to collect and analyze the responses of human operators from simulator training records of nuclear power plant. Thus, the development and computer software interfaces of the database are essential to efficiently support human reliability analysis. In this study, OPERA database was developed in accordance with the following process. First, the essential data required for the HRA analysis (e.g., plant data, procedures, system and equipment info, operating crews, results of simulator experiments, and so forth) was stored in the database. In addition, standardized procedures and related items were stored in the database to support HRA quantification. A data structure was developed to store crew responses conducted by operators to cope with an emergency situation during simulator experiments. In addition, various interfaces that escalate efficiency of the HuREX applications were developed. The HuREX analysis support interface was developed with HuREX Video Analyzer for observation of simulator collection data (e.g., audio-visual records, plant parameters, operator's action logs) and HuREX Data Analyzer for analysis of operator's conducted task. By using the OPERA database and HuREX analysis support interface developed through this research, maximizing the efficiency of human reliability analysis based on simulator training data. (authors)
Characterizing Fluence as a Function of Areal Density for 1 and 14 MeV Neutrons Transported Through Air
Computing neutron dosimetry involves transporting neutrons [n] from a source to a sink, determining the neutron heating within the sink and multiplying the neutron heating by a quality factor to compute the dose equivalent. The space and energy dependent neutron fluence is a measure of the amount of neutrons crossing a particular surface area, about a particular energy and is given in units of [n/cm{sup 2}]. Microscopic cross sections are given in units of area and vary by isotope and reaction type as a function of energy. The product of the atom density of a particular atom in a specie and the microscopic cross section of a particular reaction results in the macroscopic cross section of a particular reaction, given in units of per length [1/cm] as a function of energy. Summing the macroscopic cross sections of all isotopes, and all collisions resulting in heating, within a volume, results in a total collision heating macroscopic cross section for that volume. The product of the energy and spatial dependent collision heating macroscopic cross section and the energy and spatial dependent fluence, within an energy group, results in a collision heating reaction density for that particular reaction in that energy group. Weighting each energy group's collision heating reactions, with the corresponding energy dependent particle damage per collision heating event (usually biological damage), and then integrating over energy and volume results in the delivered dose. Therefore understanding dose delivery involves examining the spatial and energy (and time) dependent fluence behavior. Because of the coupled energy and space dependence of the heating weighting and macroscopic cross sections, the fluence is not so easily separable in energy and space; however, the energy integrated fluence can be used as a first order approximation of the relative magnitude of reactions as a function of space. Here we examine the first part in the dosimetry calculation process, transporting neutrons from source to sink, as applied to neutron transport in air, examining characteristics of the fluence. Presented here is analysis regarding the nature of the fluence peak between 10 g/cm{sup 2} and 100 g/cm{sup 2} for a typical air transport calculation. Due to the high concentration of nitrogen and oxygen isotopes of these elements, these isotopes tend to dominate the transport. From the analysis presented here, the fluence peak is the result of scatter back and forth across the peak regime, and (n,2n) has a minimal (<4%) contribution. Further evidence of minimal (n,2n) impact in the 10-100 g/cm{sup 2} span includes the 1 MeV fluence peak being so much larger than the 14 MeV peak; where 1 MeV is not above the (n,2n) threshold. Also presented here is the usefulness of using tally tagging with cell flagging to further dissect the nature of the fluence. These technologies allow us to understand the originating volume that makes a contribution to a scoring regime. (authors)
Large Eddy Simulations for a Helical Coil Steam Generator
The desire for scalable power plants in remote locations and specialized applications has brought forth the small modular reactor (SMR). Now, smaller light water reactors are seen as viable means for meeting global energy requirements. Generally, straight or U-shape tubes are used to exchange heat from the primary to secondary coolant; however this is not ideal when size is a criterion. Helical coil steam generators (HCSGs) are advantageous for SMRs due to their enhanced heat transfer in compact spaces. Still, there is the concern of safety as steam generator tubes could rupture due to vibrations and fatigue on the material. Understanding flow characteristics and quantifying pressures for these tubes are important components for the design of safety and efficiency in these steam generators. The current study uses the spectral element computational fluid dynamic (CFD) code, Nek5000, to develop large eddy simulations (LES) for validation against experiments that will be conducted at Texas A and M University. Time-averaged pressure data will be compared with pressure tap readings, and velocity profile with particle image velocimetry (PIV) measurements. The presented preliminary results investigate the differences between 5.-, 7.-, and 9.-order polynomial simulations. (authors)
A Theoretical Investigation of the Chemical Behaviour of Irradiated U-Mo/Mg Fuel
A significant international effort has been put forth to replace Highly Enriched Uranium (HEU) fuel used in research and test reactors with Low Enriched Uranium (LEU) fuel to reduce proliferation concerns. Furthermore, some fuel processing issues have been encountered with some LEU candidate fuel designs. In order to address both of the foregoing issues, new fuel designs must be developed. One of these designs has U-Mo particles dispersed in a Mg matrix (U-Mo/Mg) that is cladded in an Al-alloy. While characterization analyses and thermophysical measurements have been reported for fresh U-Mo/Mg fuel, the effects of irradiation are not yet well known nor are they understood. Given that earlier international campaigns that investigated U-Mo/Al as a candidate fuel design resulted in consistent failures due to the formation of an interaction layer between the U-Mo fuel particles and Al meat, it seems prudent to theorize and predict any phases that may form as a result of irradiation of U-Mo/Mg fuel. The formation of these phases and any interaction that they may have with the cladding may potentially affect the reliability of this fuel design. This short paper provides a preliminary theoretical framework for the prediction of minor phases that may appear in irradiated U-Mo/Mg research reactor fuel, which should be confirmed via postirradiation examination at a later time.
High-Fidelity Modeling of a Type-5 Wind Turbine Gearbox
Type-5 wind turbines are characterized by their use of a hydraulic torque converter and permanent synchronous generator. This combination promotes steady and grid-ready energy without the use of a power converter. Thus, researchers were prompted to study the potential impact on grid reliability, stability, and resilience using a Real Time Digital Simulator (RTDS) model of the type-5 turbine, including a high-fidelity model of its gear box.
Cybersecurity Considerations for State Procurement of Solar Assets
This tool provides an overview of existing procurement programs and example language that prioritizes cybersecurity language and references that can readily be applied to state solar cybersecurity practices . The tool also identifies sample language for procurement agreements, contracts, and grants which can serve as lower entry points for state officials interested in pursuing this subject matter more immediately. As this approach may be novel for many states, the information therein should be considered as a model only . The programs and example language included are not exhaustive and should be evaluated in accordance with state processes and policies .
A tool and a methodology to use macros for abstracting variations in code for different computational demands
Scientific software used on high-performance computing platforms is in a phase of transformation because of the combined increase in the heterogeneity and complexity of models and hardware platforms. Having separate implementations for different platforms can easily lead to combinatorial explosions; therefore, the computational science community has been looking for mechanisms to express code through abstractions that can be specialized for different platforms. Most existing approaches use template meta-programming in C++, and are, therefore language specific. Here, we have developed a tool that uses customized expansion of macros to mimic some of C++ behavior in other languages. It enables unification of any code variants that may be necessary to run efficiently on different target architectures and different computational environments through use of macros with multiple alternative definitions and ability to arbitrate on definition selection for expansion. Combined with two other tools, a custom runtime, and a user specified recipe translator, our custom macroprocessor becomes a part of an overall performance portability solution that does not depend on any specific programming language. We also use macros as code-shorthand that lets code snippets become building blocks that allow variations in control flow to explore performance options. We demonstrate use of macros in Flash-X, a multiphysics multicomponent code with many Fortran legacy components derived from an earlier community code FLASH.
ON THE EFFECTIVENESS OF LLMS IN UNIT TEST GENERATION FOR STRUCTURED TEXT PROGRAMS
The reliability of industrial automation systems heavily depends on the correctness of Programmable Logic Controller (PLC) programs, which are often written in Structured Text (ST). While Large Language Models (LLMs) have shown promise in automating test generation for mainstream programming languages, their effectiveness for the syntactically strict ST language remains underexplored. This thesis presents a systematic empirical evaluation of three state-of-the-art LLMs—GPT-4o, Gemini 2.5 Pro, and Claude Sonnet 4.5—for generating ST unit tests. We examine three prompting strategies: Natural Language (NL), Code Language (CL), and Chain-of-Thought (CoT), across a curated set of 11 ST function blocks. The quality of the generated tests is assessed using Compilation Success Rate (CSR), Statement Coverage (SC), and Branch Coverage (BC). In the zero-shot setting, Claude Sonnet 4.5 achieves the highest CSR, while Gemini 2.5 Pro consistently delivers the best statement and branch coverage, particularly under CL prompts. By incorporating a one-shot CL prompt, all models exhibit substantial improvements—most notably GPT-4o, whose CSR increases from 45.45% to 90.91%, with substantial gains in both SC and BC. To further contextualize these findings, we compare GPT-4o’s one-shot results with PLCAutoTester, a state-ofthe- art ST unit test generation tool, on an additional benchmark dataset. While LLMgenerated tests approach competitive coverage levels, PLCAutoTester maintains significantly higher and more stable coverage across programs. This study provides the first comprehensive benchmark of modern LLMs for ST unit testing, highlighting their strengths, limitations, and improvements through one-shot prompting, and positioning their performance relative to specialized automated testing tools in industrial automation.
Chemical classification program synthesis using generative artificial intelligence
Accurately classifying chemical structures is essential for cheminformatics and bioinformatics, including tasks such as identifying bioactive compounds of interest, screening molecules for toxicity to humans, finding non-organic compounds with desirable material properties, or organizing large chemical libraries for drug discovery or environmental monitoring. However, manual classification is labor-intensive and difficult to scale to large chemical databases. Existing automated approaches either rely on manually constructed classification rules, or are deep learning methods that lack explainability. This work presents an approach that uses generative artificial intelligence to automatically write chemical classifier programs for classes in the Chemical Entities of Biological Interest (ChEBI) database. These programs can be used for efficient deterministic run-time classification of SMILES structures, with natural language explanations. The programs themselves constitute an explainable computable ontological model of chemical class nomenclature, which we call the ChEBI Chemical Class Program Ontology (C3PO). We validated our approach against the ChEBI database, and compared our results against deep learning models and a naive SMARTS pattern based classifier. C3PO outperforms the naive classifier, but does not reach the performance of state of the art deep learning methods. However, C3PO has a number of strengths that complement deep learning methods, including explainability and reduced data dependence. C3PO can be used alongside deep learning classifiers to provide an explanation of the classification, where both methods agree. The programs can be used as part of the ontology development process, and iteratively refined by expert human curators.