Predictive Modeling Methodology Research Articles

Correlating Fire Incidents with Meteorological Variables in Dry Temperate Forest

Forest fires pose a significant ecological threat, particularly in the Diamer District, Gilgit-Baltistan, Pakistan, where climatic factors combined with human activities have resulted in severe fire incidents. The present study sought to investigate the correlation between the incidence of forest fires and critical meteorological elements, including temperature, humidity, precipitation, and wind speed, over a period of 25 years, from 1998 to 2023. We analyzed 169 recorded fire events, collectively burning approximately 109,400 hectares of forest land. Employing sophisticated machine learning algorithms, Random Forest (RF), and Gradient Boosting Machine (GBM) revealed that temperature and relative humidity during the critical fire season, which spans May through July, are key factors influencing fire activity. Conversely, wind speed was found to have a negligible impact. The RF model demonstrated superior predictive accuracy compared to the GBM model, achieving an RMSE of 5803.69 and accounting for 49.47% of the variance in the burned area. This study presents a novel methodology for predictive fire risk modeling under climate change scenarios in the region, offering significant insights into fire management strategies. Our results underscore the necessity for real-time early warning systems and adaptive management strategies to mitigate the frequency and intensity of escalating forest fires driven by climate change.

Review of Fourth-Order Predictive Modeling and Illustrative Application to a Nuclear Reactor Benchmark. I. Typical High-Order Sensitivity and Uncertainty Analysis

This work (in two parts) will review the recently developed predictive modeling methodology called “4th-BERRU-PM” and its applicability to nuclear energy systems as exemplified by an illustrative application to the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. The acronym 4th-BERRU-PM designates the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” methodology, which uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters, while yielding best-estimate results with reduced uncertainties for the first fourth-order moments (mean values, covariance, skewness, and kurtosis) of the optimally predicted posterior distribution of model results and calibrated model parameters. The 4th-BERRU-PM methodology encompasses the scopes of high-order sensitivity analysis (SA), uncertainty quantification (UQ), data assimilation (DA) and model calibration (MC), as will be illustrated in this work by means of the above-mentioned OECD/NEA reactor physics benchmark. This benchmark is modeled using the neutron transport Boltzmann equation involving 21,976 imprecisely known parameters, the solution of which is representative of “large-scale computations”. The model result (“response”) of interest is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. Part 1 of this work illustrates the impact of high-order sensitivities, in conjunction with parameter standard deviations of various magnitudes, on the determination of the expected value and variance of the computed response in terms of the first four moments of the distribution of the uncertain model parameters. Part 2 of this work will illustrate the capabilities of the 4th-BERRU-PM methodology for combining computational and experimental information, up to and including forth-order sensitivities and distributional moments, for producing best-estimate values for the predicted responses and model parameters while reducing their accompanying uncertainties.

An inferential spatiotemporal approach for knowledge synthesis to identify trends in public health research

BackgroundDecisions follow patterns that are introduced by human perception. Research and development (R&D) are influenced by these patterns. Furthermore, R&D publications can represent repetitive attempts to solve similar, or the same problems. Literature reviews serve as an important tool for identifying these trends, but they are time consuming. The time commitment of a literature review can be reduced by using a sample of research. This will allow an infinite population of research to be generalized. Additionally, spatiotemporal analysis is most appropriate for fields that follow time and geographic trends, such as public health. Also, using research locations to perform this analysis potentially captures the social return of R&D, as knowledge gained. As a result, an inferential spatiotemporal methodological framework is introduced to quickly identify research trends using public health research. This was applied to a childhood Pb exposure case study. MethodsA body of more than 1000 childhood elevated blood lead (Pb) level (EBLL) research articles were used to extract publication years, research locations, and subtopics. These publications were grouped into research locations (i.e., U.S. states where research was conducted; not publication location) and averaged over years published (i.e., 29 years). Binary indicator variables were derived using the subtopics extracted and the periods identified in time trend analyses. Explanatory variables were used to conduct hypothesis testing. Significant variables were used to generalize the population of the annual average EBLL articles written per state. ResultsThe range of the annual average of EBLL research articles by state was 0–1.7 articles, with a mean of 0.3 articles. Thirty-eight explanatory variables suggested a significant effect on research article production. These included temporal, sociodemographic, education, structure age, environmental, and economic variables. The strongest effect on research production for U.S. states came from the number of structures built before 1950. A predictive model was selected to generalize the population of articles using time-periods 1990-95, environmental subtopic, and structures built before 1950. The locations with the most research production for this topic were California and New York. The locations with the least research production for this topic were Alaska, Hawaii, Nevada, Wyoming, North Dakota, South Dakota, Mississippi, Delaware, and New Hampshire. ConclusionIf the trend for R&D is to make fast decisions, more human bias will be introduced into the decision-making process. Analytical tools that enable researchers to identify trends and ask more questions about their field will mitigate these biases. This hypothesis testing and predictive modeling methodology provide researchers and other decision makers with analytical tools they can use to quickly identify research trends and narrow their field of research. Additionally, this analysis potentially captures the impact of discovered ideas, as a social return spillover, for this topic.

AC motor impedance predictive modeling methodology taking into account windings variability

This paper presents a unified predictive modeling for Common-Mode (CM) and Differential-Mode (DM) impedance estimation of a Permanent Magnet Synchronous Motor (PMSM) with random wounds used in aeronautic applications. This methodology combines 2D Finite Element modeling and generated lumped parameter circuits in a Spice environment. It is then used to determine the consequences of design choices and evaluate the importance of controlling the winding process in PMSM manufacturing. By doing so and by changing parts of the PMSM design, the overall high-frequency response of the system with regard to input parameters can help in satisfying ElectroMagnetic Compatibility (EMC) high-frequency constraints (between 1 kHz and 10 MHz).This paper presents evidence demonstrating the importance of design parameters such as the number of wires in parallel used by turns and the overall placement of the conductor not only with regard to the slot but to other wires within the slot

Integrating artificial neural networks and response surface methodology for predictive modeling and mechanistic insights into the detoxification of hazardous MB and CV dyes using Saccharum officinarum L. biomass

The presence of dye pollutants in industrial wastewater poses significant environmental and health risks, necessitating effective treatment methods. The optimal adsorption treatment of methylene blue (MB) and crystal violet (CV) dye-simulated wastewater utilising Saccharum officinarum L presents a key challenge in the selection of appropriate modelling approaches. While RSM and ANN models are frequently used, there is a noticeable knowledge gap when it comes to evaluating their relative strengths and weaknesses in this context. The study compared the predictive abilities of response surface methodology (RSM) and artificial neural network (ANN) for the adsorption treatment of MB and CV dye-simulated wastewater using Saccharum officinarum L. The process experimental variables were modelled and predicted using a three-layer artificial neural network trained using the Levenberg-Marquard backpropagation algorithm and 30 central composite designs (CCD). The adsorption study used a specific mechanism, which led to noteworthy maximum removals of 98.3% and 98.2% for dyes (MB and CV), respectively. The RSM model achieved an impressive R2 of 0.9417, while the ANN model achieved 0.9236 in MB. Adsorption is commonly used to remove colour from many different materials. Saccharum officinarum L., a byproduct of sugarcane processing, has shown potential as an efficient and ecological adsorbent in this environment. The purpose of this study is to evaluate sugarcane bagasse's potential as an adsorbent for the removal of dyes MB and CV from industrial wastewater, providing a long-term strategy for reducing dye pollution. Due to its beneficial economic and environmental characteristics, the Saccharum officinarum L. adsorbent has prompted research into sustainable resources with low pollutant indices.

Demonstrative Application to an OECD/NEA Reactor Physics Benchmark of the 2nd-BERRU-PM Method—II: Nominal Computations Apparently Inconsistent with Measurements

This work presents illustrative applications of the 2nd-BERRU-PM (second-order best-estimate results with reduced uncertainties predictive modeling) methodology to the leakage response of a polyethylene-reflected plutonium OECD/NEA reactor physics benchmark, which is modeled using the neutron transport Boltzmann equation. The 2nd-BERRU-PM methodology simultaneously calibrates responses and parameters while simultaneously reducing the predicted standard deviation values of these quantities. The situations analyzed in this work pertain to the values of measured responses that appear to be inconsistent with the computed response values, in that the standard deviation values of the measured responses do not initially overlap with the standard deviation values of the computed responses. It is shown that the inconsistency diminishes as higher-order sensitivities are progressively included, thus illustrating their significant impact. In all cases, the 2nd-BERRU-PM methodology yields predicted best-estimate standard deviation values that are smaller than both the computed and the experimentally measured values of the standard deviation for the model response under consideration.

Demonstrative Application to an OECD/NEA Reactor Physics Benchmark of the 2nd-BERRU-PM Method—I: Nominal Computations Consistent with Measurements

This work illustrates the applications of the 2nd-BERRU-PM methodology to polyethylene-reflected plutonium (PERP) OECD/NEA reactor physics benchmark. Using concepts from information theory and thermodynamics, the 2nd-BERRU-PM (2nd-order best-estimate results with reduced uncertainties predictive modeling) methodology is constructed in the most inclusive “joint-phase-space of parameters, computed and measured responses”. Consequently, the 2nd-BERRU-PM methodology simultaneously calibrates responses and parameters, while simultaneously reducing the predicted standard deviations in these quantities. This uncertainty reduction is illustrated for the PERP benchmark, which is modeled using the neutron transport Boltzmann equation, the solution of which is representative of “large-scale computations”. The situations analyzed in this work pertain to values of the measured responses which are consistent with the computed response values, in that the standard deviations of the measured responses overlap with the standard deviations of the computed responses. The situations that can arise when the measured values appear to be inconsistent with the computed values will be analyzed in the accompanying Part II.

Perspective on Predictive Modeling: Current Status, New High-Order Methodology and Outlook for Energy Systems

This work presents a perspective on deterministic predictive modeling methodologies, which aim at extracting best-estimate values for model responses and parameters along with reduced predicted uncertainties for these best-estimate values. The two oldest such methodologies are the data-adjustment method, which stems from the nuclear energy field, and the data-assimilation method, which is implemented in the geophysical sciences. Both of these methodologies attempt to minimize, in the least-square sense, a user-defined functional that represents the discrepancies between computed and measured model responses. These two methodologies were briefly reviewed and shown to be inconsistent even to first-order in the sensitivities of the response to the model parameters. In contrast to these methodologies, it was shown that the “maximum entropy”-based predictive modeling methodology (called BERRU-PM) that was developed by the author not only dispenses with the subjective “user-chosen functional to be minimized” but is also inherently amenable to high-order formulations. This inherent potential was illustrated by presenting a novel, higher-order, MaxEnt-based predictive modeling methodology, labelled BERRU-PM-2+, which is complete and exact to second-order sensitivities and moments of both the a priori and posterior distributions of responses and parameters, while explicitly including third- and fourth-order sensitivities and correlations, thus indicating the mechanism for incorporating information of orders higher than second in predictive modeling. The presentation of this new predictive modeling methodology also aims at motivating a widespread application of predictive modeling principles and methodologies in the energy sciences for obtaining best-estimate results with reduced uncertainties.

Multiscale EMC Modeling, Simulation, and Validation of a Synchronous Step-Down DC-DC Converter

The proliferation of power electronics in automotive and industrial applications raises compliance challenges in meeting electromagnetic compatibility (EMC) regulatory standards. In this work, we develop a robust multiscale system-level modeling and simulation methodology for predicting CISPR 25 conducted emission (CE) and radiated emission (RE). The method is based on a novel two-stage process. In the first stage, the IC model is generated either by non-linear time-domain simulation using a device-level physics model or oscilloscope measurements if a prototype is available. In the second stage, the IC model waveforms are used in a simulation environment comprising 3D full-wave frequency domain analysis and specially prepared macro-models for the laboratory equipment. Silicon validation of CISPR 25 EMC measurements on a “low-EMI,” high-performance DCDC automotive/industrial synchronous step-down converter is presented to validate the integrity of the predictive modeling methodology. Good correlations between modeling and EMC-certified testing laboratory emission measurements are achieved (i.e., within +/- 3dBuV for CE and +/- 6dBuV for RE). As a result, the predictive EMC modeling methodology can be implemented, early in the design cycle, to ensure first-pass EMC-compliant design.

Second-Order MaxEnt Predictive Modelling Methodology. III: Illustrative Application to a Reactor Physics Benchmark

This work illustrates the innovative results obtained by applying the recently developed the 2nd-order predictive modeling methodology called “2nd- BERRU-PM”, where the acronym BERRU denotes “best-estimate results with reduced uncertainties” and “PM” denotes “predictive modeling.” The physical system selected for this illustrative application is a polyethylene-reflected plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. This benchmark is modeled using the neutron transport Boltzmann equation (involving 21,976 uncertain parameters), the solution of which is representative of “large-scale computations.” The results obtained in this work confirm the fact that the 2nd-BERRU-PM methodology predicts best-estimate results that fall in between the corresponding computed and measured values, while reducing the predicted standard deviations of the predicted results to values smaller than either the experimentally measured or the computed values of the respective standard deviations. The obtained results also indicate that 2nd-order response sensitivities must always be included to quantify the need for including (or not) the 3rd- and/or 4th-order sensitivities. When the parameters are known with high precision, the contributions of the higher-order sensitivities diminish with increasing order, so that the inclusion of the 1st- and 2nd-order sensitivities may suffice for obtaining accurate predicted best- estimate response values and best-estimate standard deviations. On the other hand, when the parameters’ standard deviations are sufficiently large to approach (or be outside of) the radius of convergence of the multivariate Taylor-series which represents the response in the phase-space of model parameters, the contributions stemming from the 3rd- and even 4th-order sensitivities are necessary to ensure consistency between the computed and measured response. In such cases, the use of only the 1st-order sensitivities erroneously indicates that the computed results are inconsistent with the respective measured response. Ongoing research aims at extending the 2nd-BERRU-PM methodology to fourth-order, thus enabling the computation of third-order response correlations (skewness) and fourth-order response correlations (kurtosis).

Query-adaptive training data recommendation for cross-building predictive modeling

Predictive modeling in buildings is a key task for the optimal management of building energy. Relevant building operational data are a prerequisite for such task, notably when deep learning is used. However, building operational data are not always available, such is the case in newly built, newly renovated, or even not yet built buildings. To address this problem, we propose a deep similarity learning approach to recommend relevant training data to a target building solely by using a minimal contextual description on it. Contextual descriptions are modeled as user queries. We further propose to ensemble most used machine learning algorithms in the context of predictive modeling. This contributes to the genericity of the proposed methodology. Experimental evaluations show that our methodology offers a generic methodology for cross-building predictive modeling and achieves good generalization performance.

Numerical Simulation of Bird Strike on Jet Engine Considering Bird Ingestion Requirements

To mitigate the consequences of bird strikes, compliance with bird ingestion requirements must be substantiated by corresponding physical tests relying on a jet engine. However, current simulation models capturing soft-impact damage on the engine are mostly limited to part of the front of the engine, such as fan blades. In this paper, considering relationships and interactions among engine components under normal flight operations, the numerical model of a jet engine subjected to bird strike was developed to evaluate impact damage and the effect of fan rotor unbalancing on the jet engine. First, substitute bird models were established using the smoothed particle hydrodynamics method, and then validated with experimental and theoretical results. Second, to characterize the maximum impact damage, critical ingestion parameter was derived from simulations of the prescribed bird targeted at fan blades at the required bird ingestion conditions. Finally, considering single-bird-ingestion requirements, numerical analysis of a jet engine during impact was investigated. It is of great significance for engine manufacturers to incorporate a predictive modeling methodology of a turbine engine under bird strikes into crashworthiness of engine designs.

СПЕЦИАЛЬНОЕ МОДЕЛЬНО-АЛГОРИТМИЧЕСКОЕ И ПРОГРАММНОЕ ОБЕСПЕЧЕНИЕ ПРОАКТИВНОГО УПРАВЛЕНИЯ ГРУППОВЫМ ПОВЕДЕНИЕМ РОБОТОТЕХНИЧЕСКИХ СРЕДСТВ

The paper describes the proactive control of robots group behavior using Behavior-Based Systemmodels, where the intellect is formed by a physical entities behavior. The observed complex is anarray of distributed agents functioning in real time under disturbances. John Boyd’s OODA loopmodel is used to describe the control system work cycle of such network object. The input data of thecontrol task are a planning horizon, a group action scenario, an array of agents and their possibleelementary operations, a set of scenarios using restrictions and a quality indicator of the controlproblem solution. The output data is the distribution plan of agents in space and time to realize thescenario under restrictions. The developed technology predicts the environmental disturbances. Thecomplex predictive modeling methodology for a self-organized robots group control is used withlogical-dynamic models. One of the key advantages of developed combined models, methods, algorithmsand software is the possibility to coordinate analytical and simulation control models of complexdynamic objects and their logical-algebraic analogs and models based on intelligent informationtechnology. This coordination is on the conceptual, model-algorithmic, information and softwaredetailing levels. The special language for modeling, planning, proactive monitoring and controltask description is also developed. This language can be used for dialog interaction, calculationplanning and data mining too. The proposed method main advantage is the non-isolated, but integratedsolving of robotics configuration (reconfiguration) modeling, planning and management withthe structural dynamics proactive control common problem solution.

Optimization of erosion wears of Al–Mg–Si–Cu–SiC composite produced by the PM method

Abstract Metal matrix composites are expanding their range every day due to their various industrial applications in manufacturing sectors, to attain high performance and favorable characteristics such as light weight, more excellent corrosion as well as wear resistance, high specific strength and high temperature-resistance than conventional materials. This study deals with analysis on erosion wear characteristic and corrosion behavior of newly-engineered aluminum metal–matrix composite (Al–0.5Si–0.5Mg–2.5Cu–5SiC) developed by powder metallurgy method. Solid particle erosion test was conducted on the newly developed AMMC product and the execution of design of experiments through Taguchi and statistical techniques demonstrates the feasibility of investigating the erosion characterization and behaviors of the composites. Sixteen set of experimental trials were performed by considering three process parameters (impact angle, stand-off distance, and impact velocity) associated with four levels each. Experimental results in accordance of Taguchi’s orthogonal array design of experiments are analyzed by employing analysis of variance (ANOVA), response surface methodology (RSM) and desirability function approach for analysis, predictive modeling and optimization of erosion rate, respectively. Thereafter, an observation on eroded surface morphology is performed under the influence of impact velocity by employing scanning electron microscope (SEM) to entrench the process. Result shows that, the impact velocity followed by impact angle have significant contribution (80.42 and 8.71%, respectively) in improvement of erosion rate. The methodology proposed in this study collects the experimental results and builds a mathematical model in the domain of interest and optimized the process model. Under the highest desirability (1), desirability-function approach of RSM presented the optimal manufacturing conditions at impact velocity of 18 m/s, stand-off distance of 26 mm and impact angle of 67° with estimated erosion rate of 65.155 mg/kg. The experimental data generated for Al–0.5Si–0.5Mg–2.5Cu–5SiC AMMC will be useful for the industry.

A gray-box modeling methodology for runtime prediction of Apache Spark jobs

Apache Spark jobs are often characterized by processing huge data sets and, therefore, require runtimes in the range of minutes to hours. Thus, being able to predict the runtime of such jobs would be useful not only to know when the job will finish, but also for scheduling purposes, to estimate monetary costs for cloud deployment, or to determine an appropriate cluster configuration, such as the number of nodes. However, predicting Spark job runtimes is much more challenging than for standard database queries: cluster configuration and parameters have a significant performance impact and jobs usually contain a lot of user-defined code making it difficult to estimate cardinalities and execution costs. In this paper, we present a gray-box modeling methodology for runtime prediction of Apache Spark jobs. Our approach comprises two steps: first, a white-box model for predicting the cardinalities of the input RDDs of each operator is built based on prior knowledge about the behavior and application parameters such as applied filters data, number of iterations, etc. In the second step, a black-box model for each task constructed by monitoring runtime metrics while varying allocated resources and input RDD cardinalities is used. We further show how to use this gray-box approach not only for predicting the runtime of a given job, but also as part of a decision model for reusing intermediate cached results of Spark jobs. Our methodology is validated with experimental evaluation showing a highly accurate prediction of the actual job runtime and a performance improvement if intermediate results can be reused.

Analysis, predictive modelling and multi-response optimization in electrical discharge machining of Al-22%SiC metal matrix composite for minimization of surface roughness and hole overcut

Due to the widespread engineering applications of metal matrix composites especially in automotive, aerospace, military, and electricity industries; the achievement of desired shape and contour of the machined end product with intricate geometry and dimensions that are very challenging task. This experimental investigation deals with electrical discharge machining of newly engineered metal matrix composite of aluminum reinforced with 22 wt.% of silicon carbide particles (Al-22%SiC MMC) using a brass electrode to analyze the machined part quality concerning surface roughness and overcut. Forty-six sets of experimental trials are conducted by considering five machining parameters (discharge current, gap voltage, pulse-on-time, pulse-off-time and flushing pressure) based on Box-Behnken's design of experiments (BBDOEs). This article demonstrates the methodology for predictive modeling and multi-response optimization of machining accuracy and surface quality to enhance the hole quality in Al-SiC based MMC, employing response surface methodology (RSM) and desirability function approach (DFA). Finally, a novel approach has been proposed for economic analysis which estimated the total machining cost per part of rupees 211.08 during EDM of Al-SiC MMC under optimum machining conditions. Thereafter, under the influence of discharge current several observations are performed on machined surface morphology and hole characteristics by scanning electron microscope to establish the process. The result shows that discharge current has the significant contribution (38.16% for Ra, 37.12% in case of OC) in degradation of surface finish as well as the dimensional deviation of hole diameter, especially overcut. The machining data generated for the Al-SiC MMC will be useful for the industry.

Reprint of “Nuclear thermal-hydraulics applications illustrating the key roles of adjoint-computed sensitivities for overcoming the curse of dimensionality in sensitivity analysis, uncertainty quantification and predictive modeling”

This work is dedicated to the memory of Prof. Bal Raj Sehgal who, while a Program Manager at the Electric Power Institute (EPRI) in Palo Alto, USA, had foresightedly funded during 1979–1984 the author’s pioneering work on conceiving the adjoint sensitivity analysis methodology for computing first-order sensitivities of responses of nonlinear systems to imprecisely known system parameters, and applying this methodology to a variety of ground-breaking investigations in nuclear reactor physics, thermal-hydraulics, dynamics, and safety. In the spirit of this dedication, the present original work highlights the application of the first-order adjoint sensitivity analysis methodology (1st-ASAM) to the generic thermal-hydraulics model that underlies the well-known reactor analysis codes COBRA/TRAC, RELAP5/MOD3.2, RELAP5/MOD3.3, and MARS, thus deriving the corresponding adjoint sensitivity model needed for the exact and most efficient computation of model response sensitivities to thermal-hydraulics parameters. This work also presents the fundamental role of the 1st-ASAM as the first step in the quest to overcome the curse of dimensionality in sensitivity analysis, uncertainty quantification and predictive modelling by presenting the concepts of a novel predictive modeling methodology that uses the maximum entropy principle in conjunction with saddle-point techniques to eliminate the widespread current use of arbitrarily defined “functionals to be minimized,” thus significantly extending the currently used data assimilations procedures. Since this novel predictive modeling methodology provides best-estimate results with reduced uncertainties for either forward or inverse problems, it has been called the BERRU-PM methodology. This work also indicates the next steps, starting with the complete second-order predictive modeling methodology, currently undertaken by the author in the quest to develop practical high-order procedures that overcome in practice the “curse of dimensionality” in sensitivity analysis, uncertainty quantification, and predictive modelling, thereby enabling the future computation of non-Gaussian features of otherwise intractable distributions of results predicted by large-scale computational model, while using experimental information to reduce the uncertainties in the predicted results and implicitly calibrated model parameters.

The CASUALIDAD Method for Uncertainty Evaluation of Best-Estimate System Thermal-Hydraulic Calculations

Predictive Modeling Methodology constitutes an innovative approach to perform uncertainty analysis (UA) that reduces the subjective and user-defined ways to manage experimental data and derive uncertainty of input parameters that characterize the Propagation of Input Uncertainties (PIU) and/or Propagation of Output Accuracies (POA) methods. The Code with the capability of Adjoint Sensitivity and Uncertainty AnaLysis by Internal Data ADjustment and assimilation (CASUALIDAD) method can be developed as a fully deterministic method based on advanced mathematical tools to internally perform in the thermal-hydraulic system code the sensitivity analysis (SA) and the UA. The method is based upon powerful mathematical tools to perform the SA and upon the Data Adjustment and Assimilation methodology by which experimental observations are combined with code predictions and their respective errors through the application of the Bayes theorem and of the Principle of Maximum Likelihood to provide an improved estimate of the system state and of the associated uncertainty considering all input parameters that affect any prediction. The methodology has been structured in two main steps. The first step generates the database of improved estimations (IEs) starting from the available set of experimental data and related qualified calculations. The second step deals with the use of the selected (from the obtained database) set of IEs for the uncertainty evaluation of the predicted nuclear power plant transient scenario. The proposed methodology clearly interrelates in a consistent and robust framework the code validation issue with the evaluation of the uncertainty of code responses passing through the quantification of input uncertainty parameters of code models, thus constituting a step forward with respect to the subjectivity of the current methods based on PIU and/or POA.

1106-P: Predictive Modeling Suggests Subgroup Effects for Severe Hypoglycemia in T2DM Patients Treated with Gla-300 vs. IDeg: The LIGHTNING Study

The LIGHTNING study predictive modeling of U.S. health record data previously predicted similar severe hypoglycemia rates with insulin glargine 300 U/mL (Gla-300) and insulin degludec 100 U/mL (IDeg) in T2DM, using methodology including hypothesis-free generation of subgroups automatically derived from all available data using machine learning algorithms. This exploratory analysis examines those data-driven subgroups in which predicted severe hypoglycemia rates were lower with Gla-300 vs. IDeg. The dataset used in LIGHTNING included 831,456 patients with T2DM using basal insulin (BI), and 198,198 patient-treatments (periods when patients used a BI) were eligible for hypoglycemia rate prediction. We present 10 subgroups showing the greatest absolute difference in predicted severe hypoglycemia rate in favor of Gla-300 vs. IDeg (Table). These subgroups frequently reflected cardiovascular (CV) disease, while other subgroups included acute kidney disease, thyroid disease, and neuromuscular block. The novelty of the predictive modeling methodology used in LIGHTNING means that these data-driven subgroup findings are hypotheses-generating. Therefore, LIGHTNING indicates that further investigation into differences in severe hypoglycemia risk between Gla-300 and IDeg in possible high-risk subgroups (such as those with CV or renal disease) may be worthwhile. Disclosure I. Hramiak: Advisory Panel; Self; AstraZeneca, Eli Lilly and Company, GlaxoSmithKline plc., Insulet Corporation, Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk Inc., Sanofi. Research Support; Self; AstraZeneca, Eli Lilly and Company, GlaxoSmithKline plc., Novo Nordisk A/S, Sanofi. Speaker's Bureau; Self; Merck & Co., Inc. M. Haluzik: None. J. Westerbacka: Employee; Self; Sanofi. Stock/Shareholder; Self; Sanofi. Z. Bosnyak: Employee; Self; Sanofi. Stock/Shareholder; Self; Sanofi. B. Eliasson: Advisory Panel; Self; Amgen Inc., AstraZeneca, Merck Sharp & Dohme Corp. Speaker's Bureau; Self; Boehringer Ingelheim International GmbH. Other Relationship; Self; Eli Lilly and Company, Novo Nordisk A/S, Sanofi. L. Blonde: Consultant; Self; Gilead Sciences, Inc., Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk Inc., Sanofi US. Research Support; Self; Janssen Pharmaceuticals, Inc., Lexicon Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk Inc., Sanofi US. Speaker's Bureau; Self; Janssen Pharmaceuticals, Inc., Novo Nordisk Inc., Sanofi US. T.S. Bailey: Advisory Panel; Self; Abbott. Consultant; Self; Capillary Biomedical, Inc., Eli Lilly and Company, Medtronic, Novo Nordisk Inc., Sanofi. Research Support; Self; Abbott, Ascensia Diabetes Care, Becton, Dickinson and Company, Boehringer Ingelheim Pharmaceuticals, Inc., Calibra Medical, Capillary Biomedical, Inc., Companion Medical, Dance Biopharm Holdings Inc., Dexcom, Inc., Diasome Pharmaceuticals, Inc., Eli Lilly and Company, GlySens Incorporated, Kowa Pharmaceutical Europe Co. Ltd., Lexicon Pharmaceuticals, Inc., Medtronic, Novo Nordisk Inc., POPS! Diabetes Care, POPS! Diabetes Care, Sanofi, Senseonics, vTv Therapeutics, Xeris Pharmaceuticals, Inc., Zealand Pharma A/S. Speaker's Bureau; Self; Abbott, MannKind Corporation, Medtronic, Novo Nordisk Inc., Sanofi US, Senseonics. Funding Sanofi

Nuclear thermal-hydraulics applications illustrating the key roles of adjoint-computed sensitivities for overcoming the curse of dimensionality in sensitivity analysis, uncertainty quantification and predictive modeling

This work is dedicated to the memory of Prof. Bal Raj Sehgal who, while a Program Manager at the Electric Power Institute (EPRI) in Palo Alto, USA, had foresightedly funded during 1979–1984 the author’s pioneering work on conceiving the adjoint sensitivity analysis methodology for computing first-order sensitivities of responses of nonlinear systems to imprecisely known system parameters, and applying this methodology to a variety of ground-breaking investigations in nuclear reactor physics, thermal-hydraulics, dynamics, and safety. In the spirit of this dedication, the present original work highlights the application of the first-order adjoint sensitivity analysis methodology (1st-ASAM) to the generic thermal-hydraulics model that underlies the well-known reactor analysis codes COBRA/TRAC, RELAP5/MOD3.2, RELAP5/MOD3.3, and MARS, thus deriving the corresponding adjoint sensitivity model needed for the exact and most efficient computation of model response sensitivities to thermal-hydraulics parameters. This work also presents the fundamental role of the 1st-ASAM as the first step in the quest to overcome the curse of dimensionality in sensitivity analysis, uncertainty quantification and predictive modelling by presenting the concepts of a novel predictive modeling methodology that uses the maximum entropy principle in conjunction with saddle-point techniques to eliminate the widespread current use of arbitrarily defined “functionals to be minimized,” thus significantly extending the currently used data assimilations procedures. Since this novel predictive modeling methodology provides best-estimate results with reduced uncertainties for either forward or inverse problems, it has been called the BERRU-PM methodology. This work also indicates the next steps, starting with the complete second-order predictive modeling methodology, currently undertaken by the author in the quest to develop practical high-order procedures that overcome in practice the “curse of dimensionality” in sensitivity analysis, uncertainty quantification, and predictive modelling, thereby enabling the future computation of non-Gaussian features of otherwise intractable distributions of results predicted by large-scale computational model, while using experimental information to reduce the uncertainties in the predicted results and implicitly calibrated model parameters.