New method explains complex fuel compound classifications.
problem Understanding complex quantitative structure-activity relationship models.
method Locally Interpretable Machine-Agnostic Explanations (LIME) applied to 2-D chemical structures.
result Replicates chemical intuition, allowing direct acceptance/rejection of decisions.
Framework optimizes PV-battery investment timing to maximize value.
problem Optimizing investments in residential PV-battery systems under uncertain market conditions.
method Real options valuation framework with multi-stage compound options, incorporating Monte Carlo simulation.
result Optimal timing of PV-battery investment increases overall value.
Machine learning monitors fueling infrastructure reliability.
problem Ensuring reliability of alternative fueling infrastructure for ZEV adoption.
method Unsupervised temporal clustering algorithm and survey analysis.
result Infrastructure performance and reliability determined for hydrogen stations in California.
Study shows climate change can cause a 'run on fossil fuels' affecting prices and production.
problem Impact of climate change expectations on fossil fuel markets and prices.
method Dynamic, general equilibrium model of climate-change-linked transition risk.
result Climate change expectations can lead to either increased or decreased fossil fuel prices, depending on economic responses.
Study builds ML models to predict fuel properties accurately.
problem Accurate prediction of liquid fuel properties over a wide range of conditions.
method Used Gaussian Processes and probabilistic conditional generative learning to train ML models on fuel density data.
result ML models can predict fuel properties accurately across various pressure and temperature conditions.
AI speeds up hydrogen fuel cell stack development time.
problem Reducing time to market for hydrogen fuel cell stacks.
method Generative Adversarial Networks for data augmentation.
result Reduced testing time from 1000 hours to 3 hours.
Airlines optimize fuel loading with better flight time predictions.
problem Flight time uncertainties and their impact on fuel consumption.
method Developed a spatial weighted recurrent neural network model.
result The model provides more accurate flight time predictions, reducing fuel consumption.
Real-time fuel leakage detection framework MOCPD improves accuracy.
problem Early detection of fuel leakage to prevent hazards and losses.
method Memory-based Online Change Point Detection (MOCPD) framework.
result MOCPD outperforms baseline methods in detection accuracy.
New indicator detects financial strain through smart meter data.
problem Fuel poverty in households, affecting millions.
method Smart meters and machine learning for behavior measurement.
result Early detection of financial strain in households.
Study on AI-driven modeling for high burnup accident-tolerant fuels in SMRs.
problem Design and optimization of high burnup accident-tolerant fuels for SMRs.
method Artificial intelligence and multi-scale modeling (neutronics, thermal hydraulics, fuel performance).
result Demonstrated the effectiveness of AI in modeling and optimizing SMR fuels.
Paper develops robust neural network sensors for fuel injection quantities.
problem Adversarial noise increases error in standard neural network models for fuel injection measurements.
method Apply provable robust network learning and verification methods to fuel injection measurements.
result Provable robust model reduces mean relative error to 16.5% under sensor noise.
Custom loss functions improve accuracy of wildfire rate of spread forecasts.
problem Improving accuracy of wildfire rate of spread forecasts.
method Examined custom loss functions in machine learning models of fuel moisture content.
result Custom loss functions improved accuracy of ROS forecasts by a small amount.
This paper describes a pattern recognition approach aiming to estimate fuel cell duration time from electrochemical impedance spectroscopy measurements. It consists in first extracting features from both real and imaginary parts of the impedance spectrum. A parametric model is considered in the case of the real part, w…
This research assesses uncertainty quantification and sensitivity analysis for DTs in nuclear fuel performance.
problem Understanding the reliability and performance of advanced nuclear fuels using DTs.
method Introduces ML-based uncertainty quantification and sensitivity analysis methods applied to BISON fuel performance code.
result Demonstrates the effectiveness of DTs in multi-criteria decision-making for nuclear fuel performance.
A method for quickly determining deployment schedules that meet a given fuel cycle demand is presented here. This algorithm is fast enough to perform in situ within low-fidelity fuel cycle simulators. It uses Gaussian process regression models to predict the production curve as a function of time and the number of depl…
We introduce a new and highly tractable structural model for spot and derivative prices in electricity markets. Using a stochastic model of the bid stack, we translate the demand for power and the prices of generating fuels into electricity spot prices. The stack structure allows for a range of generator efficiencies p…
Estimates reliability of nuclear fuel using advanced modeling techniques.
problem Determining the reliability of TRISO-coated particle fuel, which has small failure probabilities and expensive computational models.
method Coupled active learning, multifidelity modeling, and subset simulation.
result Multifidelity modeling strategies consistently reduce the number of high-fidelity model calls.
We introduce two Python frameworks to train neural networks on large datasets: Blocks and Fuel. Blocks is based on Theano, a linear algebra compiler with CUDA-support. It facilitates the training of complex neural network models by providing parametrized Theano operations, attaching metadata to Theano's symbolic comput…
Divestment from fossil fuels can accelerate climate policy, study finds.
problem Achieving Paris climate agreement requires reducing fossil fuel reserves.
method Stochastic agent-based model of financial market and investors' beliefs.
result Small share of socially responsible investors can initiate decarbonization.
In April 2009, we introduced a model representing the evolution of motor fuel price (a subcategory of the consumer price index of transportation) relative to the overall CPI as a linear function of time. Under our framework, all price deviations from the linear trend are transient and the price must promptly return to …
New model predicts electricity spot and forward prices in coupled markets.
problem Predicting electricity prices in markets with limited interconnection.
method Structural approach to model link between prices, consumption, and production.
result Explicit formulas for forward prices and transmission rights values.
Deep learning detects corrosion in nuclear fuel canisters.
problem Ensuring safety and integrity of used nuclear fuel dry storage canisters.
method Residual neural networks (ResNets) for real-time corrosion detection of canister images.
result Deep learning approach accurately detects corrosion and classifies canisters as corroded or intact.
Conversion of corn to ethanol in the US since 2005 has been a major cause of global food price increases during that time and has been shown to be ineffective in achieving US energy independence and reducing environmental impact. We make three key statements to enhance understanding and communication about ethanol prod…
Study estimates Medallion's compounded return before fees at 31.8%.
problem Incorrectly using yearly returns for compounding leads to overestimation of fund performance.
method Used fund sizes and trading profits to estimate compounded return; used manager's wealth as proxy for Simons.
result Annualized compounded return of Medallion before fees is likely under 35%
We solve a class of control problems with fuel constraint by means of the log-Laplace transforms of J-functionals of Dawson-Watanabe superprocesses. This solution is related to the superprocess solution of quasilinear parabolic PDEs with singular terminal condition. For the probabilistic verification proof, we develo…
Characterizes measures preserving compound mixed renewal process properties.
problem Preserving compound mixed renewal process properties under different probability measures.
method Characterization of progressively equivalent probability measures.
result Any compound mixed renewal process can be converted into a compound mixed Poisson process through a change of measures.
This chapter is an attempt to present a mathematical theory of compound fractional Poisson processes. The chapter begins with the characterization of a well-known Lévy process: The compound Poisson process. The semi-Markov extension of the compound Poisson process naturally leads to the compound fractional Poisson proc…
Bayesian method improves hit identification in compound screening.
problem Identifying candidate hits from thousands to millions of compounds.
method Bayesian nonparametric modeling for cross-plate correlation and statistical strength.
result Significant improvements in hit identification sensitivity and specificity.
New risk model based on compound Hawkes process for insurance claims.
problem Modeling the arrival of insurance claims for risk assessment.
method Introducing a new risk model based on general compound Hawkes process (GCHP) and proving LLN and FCLT.
result Similar results for RMGCHP applied to RMCPP, including net profit condition, premium principle, and ruin time.
Study prices compound and extendible options using mixed fractional Brownian motion with jumps.
problem Pricing compound and extendible options under mixed fractional Brownian motion with jumps.
method Analytic formula derived under risk-neutral measure, applied to extendible options, discussed special cases, provided numerical results.
result An analytic formula for pricing compound options derived.
Generates natural product-like compounds using GPT models.
problem Challenges in generating and evaluating natural product-like compounds.
method Trained GPT-based chemical language models on natural product dataset.
result Generated compounds have similar distribution to natural products.
Develops an efficient method for compound option valuation.
problem Valuation of compound options with numerical quadrature.
method Analytic Fourier cosine (COS) method for closed-form expressions.
result Improved computational efficiency with high accuracy.
Analyzes compound interest with constant payments and interest rate.
problem Examines the properties of compound interest balance and payment functions.
method Analyzes the outstanding balance and payment functions for constant payments and interest rate.
result The outstanding balance function is not generally concave in the interest rate.
Semi-supervised learning improves QSAR model predictions for novel compounds.
problem Improving model predictions for compounds not in the training set and adjusting for selection bias.
method Semi-supervised learning framework to estimate model quality and adjust for selection bias.
result Predictions for novel compounds are improved by accounting for compound similarity and selection bias.
Normalized compound random measures are flexible nonparametric priors for related distributions. We consider building general nonparametric regression models using normalized compound random measure mixture models. Posterior inference is made using a novel pseudo-marginal Metropolis-Hastings sampler for normalized comp…
Paper proposes a framework to predict therapeutic properties of compounds.
problem Predict therapeutic properties of compounds with heterogeneous data.
method Domain-adversarial multi-task framework using adversarial learning.
result Framework improves performance over competitive baselines.
We consider a semilinear parabolic degenerated Hamilton-Jacobi-Bellman (HJB) equation with singularity which is related to a stochastic control problem with fuel constraint. The fuel constraint translates into a singular initial condition for the HJB equation. We first propose a transformation based on a change of vari…
Compound Finance optimizes risk metrics for V3 protocol using Chainrisk simulations.
problem Optimizing systemic risks in Compound V3 protocol.
method Millions of Chainrisk simulations to evaluate VaR and LaR, providing parameter adjustments.
result Optimization framework enhances protocol stability.
New method calculates tail probabilities of compound heavy-tailed distributions.
problem Computing tail probabilities of compound distributions with heavy tails.
method Contour integration method to represent tail probability as a rapidly convergent integral.
result Viable alternative to Monte Carlo and FFT methods for high percentile levels.
STMT predicts compounds in unknown areas with trend reflection.
problem Predicting properties of unknown compounds in the extrapolation area.
method Stochastic Threshold Model Trees (STMT) that reflects data trends.
result STMT improves prediction accuracy for one compound in the extrapolation area.
New Riemannian geometry for Compound Gaussian distributions applied to efficient change detection.
problem Change detection in multivariate image times series.
method Developed a recursive approach based on Riemannian optimization.
result Optimal performance achieved with computational efficiency.
ChemGrapher uses deep learning to automatically convert chemical compound images into accurate graphs.
problem Automatically converting chemical compound images into accurate graphs with correct bond multiplicity and stereochemical information.
method Developed a deep neural network model for optical compound recognition, including segmentation and classification models.
result Significant error reductions in bond multiplicity and stereochemical information compared to existing tools.
Novel model predicts anticancer compound sensitivity with high accuracy and interpretability.
problem Predicting anticancer compound sensitivity with high accuracy and interpretability.
method Multimodal attention-based convolutional encoder using SMILES, gene expression profiles, and protein-protein interaction networks.
result The model significantly outperforms baseline models and demonstrates high interpretability.
This paper has been withdrawn by the author due to some inaccurate descriptions in the section of INTRODUCTION and CONCLUSIONS.
Paper quantifies uncertainties in EIS spectra of SOFCs, proposing VB method for online monitoring.
problem Distortions in EIS spectra due to disturbances, drifts, and sensor noise.
method Proposes variational Bayes (VB) method for quantifying spectral uncertainty in EIS of SOFCs.
result VB method provides approximate distributions of ECM parameters with low computational load.
The study improves compound selection in in silico screening by focusing on model's ability to predict desirable outcomes.
problem Improving compound selection in in silico screening to reduce errors and enhance generalization.
method Extending learning theory, the study analyzes the impact of selection policies on generalization and proposes a method to mitigate challenges.
result Generalization can be enhanced by considering a model's ability to predict the fraction of desired outcomes in a batch.
Proof-of-Stake networks with EIP-1559 exhibit stable token prices and secure network security.
problem Token valuation and institutional capital in Proof-of-Stake networks under EIP-1559
method Open-economy macroeconomic equilibrium model
result Stable steady-state equilibrium price for ETH
China's rapid economic growth resulted in serious air pollution, which caused substantial losses to economic development and residents' health. In particular, the road transport sector has been blamed to be one of the major emitters. During the past decades, fluctuation in the international oil prices has imposed signi…