Paper improves gas species identification in complex mixtures using neural networks.
problem Identifying gas species in multi-gas mixtures with high accuracy.
method Multi-label neural networks with optimal thresholding for IR spectroscopy.
result Optimal thresholding improves classification performance over conventional methods.
Support Vector Machines predict gas-liquid flow patterns with 97% accuracy.
problem Predicting gas-liquid flow patterns in multiphase flow systems.
method Support Vector Machine (SVM) applied to a dataset of two-phase flow patterns.
result Achieved 97% correct classification of flow patterns.
Linking GANs to binary classification through divergences.
problem Training GANs and understanding their divergence properties.
method Revisiting the discriminator's role in computing f-divergences.
result Alternative training perspective for f-GANs by designing discriminator loss.
GA optimizes EEG feature selection for BCI systems, improving classification accuracy.
problem Finding optimal EEG features for accurate classification in BCI systems.
method Genetic Algorithm (GA) for feature selection and classifier optimization.
result Katz fractal feature with LDA yields highest fitness value.
Machine learning detects underwater gas leaks.
problem Early detection of gas leaks in underwater reservoirs.
method Machine learning and Passive Acoustic Monitoring (PAM).
result Classification algorithms achieve good performance in detecting leaks.
Let E1,…,Ek and E be natural vector bundles defined over the category $\Cal Mf_m^+$ of smooth oriented m--dimensional manifolds and orientation preserving local diffeomorphisms, with m≥2. Let M be an object of $\Cal Mf_m^+$ which is connected. We give a complete classification of all separately con…
This study introduces a new GAS blending ensemble model for Bitcoin price prediction.
problem Predicting Bitcoin price fluctuations in the cryptocurrency market.
method Integrates advanced ensemble learning methods, feature selection algorithms, and sentiment analysis.
result The GAS model demonstrates excellent performance in daily Bitcoin trend prediction.
R package GAS for analyzing time series with GAS models.
problem Analyzing time-varying parameters in nonlinear models.
method Using the score function to drive time variation in parameters.
result Estimation of time-varying conditional densities of financial assets.
GAS models improve VaR prediction in finance.
problem Improving Value-at-Risk (VaR) prediction in finance.
method Use of GAS models in R for VaR prediction.
result GAS models enhance VaR forecasting performance.
For a given real generic curve $\ga: S^1\to \Bbb {RP}^n$ let $D_\ga$ denote the ruled hypersurface in RPn consisting of all osculating subspaces to $\ga$ of codimension 2. A curve $\ga: S^1\to \Bbb {RP}^n$ is called convex if the total number of its intersection points (counted with multiplicities) with any h…
A method uses ITD and XGBoost for precise power transformer fault diagnosis.
problem Fault diagnosis of power transformers using DGA data.
method Ranking DGA parameters by skewness, extracting ITD features, and using an XGBoost classifier.
result The method achieves over 95% accuracy in classification.
Modified deep LSTM model with EnKF improves gas flow rate predictions in mature gas wells.
problem Predicting gas production from mature gas wells with high accuracy and robustness.
method Modified deep LSTM model for flow rate prediction and EnKF for updating predictions.
result EnKF updated model leads to better flow predictions with lower Jeffreys' J-divergences.
In this note we consider the problem of local classification of Bonnet pairs of surfaces in 3-dimensional Minkowski space. We use split quaternions in a way similar to the use of quaternions in, arXiv:dg-ga/9610006, for the solution in Euclidean 3-Space.
This paper analyzes Ethereum's gas fees and their derivatives, providing a comprehensive model.
problem Understanding and predicting gas fees on the Ethereum blockchain.
method Analyzed Ethereum's gas fee structure and used a fractional Ornstein-Uhlenbeck process to model gas prices.
result A model for pricing and trading gas fee derivatives to hedge against volatility.
Geodesic X-ray transform proves injective for smooth one-forms on gas giant manifolds.
problem Injectivity of geodesic X-ray transform for one-forms on specific manifolds.
method Pestov identity and asymptotic analysis of short geodesics.
result Geodesic X-ray transform is solenoidally injective for smooth one-forms on gas giant manifolds.
Study on-chain peak shaving to reduce Ethereum transaction costs.
problem Reducing transaction costs in blockchain networks, especially during congested periods.
method Analyzing transaction-level data from multiple firms across various industries to understand scheduling responses and cost management strategies.
result Firms' scheduling responses to congestion vary, leading to different fee savings and residual costs.
Deep learning improves combustor anomaly detection in gas turbines.
problem Improving anomaly detection performance in gas turbine combustors.
method Hierarchically learned features from exhaust gas temperature sensor measurements using deep learning.
result Deep learning-based anomaly detection significantly improved combustor anomaly detection performance.
Study optimizes natural gas power plant valuation using Levy copulas and regime-switching models.
problem Optimizing the valuation and operation of natural gas-fired power plants under market fluctuations.
method Stochastic control problem, Levy regime-switching model, skewed Levy copulas, HJB equation, finite difference method.
result Numerical method provides optimal operating strategies and plant values based on market prices and conditions.
Study the Hessian geometry of an ideal gas in a centrifuge.
problem Understanding the Hessian geometry of an ideal gas in a centrifuge.
method Investigate the Hessian geometry associated with an ideal gas in a spherical centrifuge, using the action of the Euclidean rotation group.
result The Hessian geometry of a spherical rigid body is isometric to a hyperbolic space in the high angular velocity limit.
Introduces GA-P/E, a growth-adjusted stock valuation measure.
problem Evaluating stock value and predicting future returns.
method Computes a payback period adjusted for earnings growth, using a sorted portfolio methodology.
result Low GA-P/E stocks outperform high GA-P/E stocks in absolute and risk-adjusted returns.
Paper develops a machine olfaction method using sensor graphs and scattering networks.
problem Classifying gas type and origin location from sensor data.
method Constructs a learning architecture using redundant wavelet decomposition and scattering networks on sensor multiresolution graphs.
result Demonstrates superior performance compared to classical methods.
Improved GAS models using trees and forests for better forecasts.
problem Improving forecasts from GAS models to avoid curse of dimensionality.
method Localized parameters using decision trees and random forests.
result Significantly outperform baseline GAS model in empirical analyses.
Improved gas demand forecasting using ensemble methods.
problem Short-term prediction of gas demand components.
method Nine base forecasters (Ridge Regression, GP, NN, ANN, Torus, LASSO, Elastic Net, RF, SVR) and four ensemble predictors (simple, weighted, subset, SVR aggregation) were evaluated.
result Ensemble predictors outperformed individual base forecasters and TSO predictions.
This study compares GNNs and GA-MLPs, finding GA-MLPs can distinguish graphs but not count walks.
problem Comparing expressive power and graph isomorphism testing capabilities of GNNs and GA-MLPs.
method GA-MLPs augment node features with multi-hop operators and apply MLPs node-wise; GNNs are compared as a baseline.
result GA-MLPs can distinguish almost all non-isomorphic graphs but cannot count attributed walks, unlike GNNs.
Paper proposes GAS-ALD model for financial risk prediction.
problem Skewed distribution of financial return data.
method Generalized autoregressive score (GAS) framework with asymmetric Laplace distribution (ALD).
result GAS-ALD model predicts VaR and ES more accurately than traditional models.
Optimizes routing in decentralized exchanges with gas fees.
problem Routing in decentralized exchanges with fixed gas fees.
method General optimization framework with mixed-integer model, incorporating gas fees.
result Explicit Karush-Kuhn-Tucker system linking prices, fees, and activation.
GAS-Norm improves deep learning time series forecasting in non-stationary settings.
problem Deep learning models struggle with non-stationary time series data.
method Combines GAS model for adaptive normalization with deep neural networks.
result Improves deep learning performance in 21 out of 25 settings.
Study the geometry of gas giant planets to infer their internal structure.
problem Determine the interior structure of gas giant planets using boundary data.
method Geometric analysis of Riemannian manifolds with conformal blow-up at the boundary.
result The interior structure of a gas giant is uniquely determined by different types of boundary data.
We calculate the free energy of Coulomb gas systems on Riemann surfaces.
problem Analyzing the free energy of Coulomb gas systems on Riemann surfaces.
method Using bosonization formula and analytic torsion, we derive the asymptotic expansion of the partition function.
result We prove the geometric version of the Zabrodin-Wiegmann conjecture in the determinantal case.
Modeling gas fee competition in decentralized exchanges to optimize arbitrage profits.
problem Gas fees and transaction ordering in decentralized exchanges create arbitrage opportunities.
method Developed a first equilibrium model of gas fee competition between two arbitrageurs under three transaction reversion settings.
result Mixed equilibria exist, and their characteristics depend on inventory risk and transaction settings.
The study finds a long-term relationship between Dubai crude oil and US natural gas prices.
problem Examining the relationship between Dubai crude oil and US natural gas prices.
method Used unit root and cointegration tests, ARDL cointegration technique, and Toda-Yamamoto causality test.
result There is a long-run relationship with unidirectional causality from Dubai crude oil to US natural gas.
New method detects rock type changes in real-time during drilling.
problem Detecting rock type changes in time during directional drilling.
method Combines machine learning and change detection procedures.
result Significant reduction in change detection delay and false positives.
The paper compares machine learning models for forecasting residential gas demand, highlighting the impact of temperature forecasts.
problem Forecasting residential gas demand for optimal energy planning.
method Implemented and compared five models: Ridge Regression, GP, k-Nearest Neighbour, ANN, and Torus Model.
result ANN is the best model in terms of RMSE, while GP is the best in terms of MAE.
EU plans to integrate gas markets, increasing consumer welfare and reducing supplier power.
problem Integrate EU gas markets to increase consumer welfare and security.
method Spatial partial equilibrium model to analyze changes in consumption, prices, and welfare.
result Consumer surplus increases by 15.9%, prices decrease slightly, and SW converges at Western European levels.
We consider the ideal-gas models of trading markets, where each agent is identified with a gas molecule and each trading as an elastic or money-conserving (two-body) collision. Unlike in the ideal gas, we introduce saving propensity λ of agents, such that each agent saves a fraction λ of its money and trades with t…
We consider the ideal-gas models of trading markets, where each agent is identified with a gas molecule and each trading as an elastic or money-conserving (two-body) collision. Unlike in the ideal gas, we introduce saving propensity λ of agents, such that each agent saves a fraction λ of its money and trades with t…
Model forecasts natural gas consumption with Fourier series and feedback.
problem Forecast natural gas consumption for risk minimization.
method Modulated Fourier series with temperature deviations, day-ahead feedback.
result Model outperforms time series methods for long-term projections.
Blockchain scaling reduces gas fees, allowing more frequent liquidity updates and concentration.
problem Adverse selection risk and high gas fees on decentralized exchanges.
method Instrumental variables analysis using blockchain scaling solutions (Arbitrum, Polygon) as instruments.
result Higher repositioning intensity and precision lead to greater liquidity concentration, benefiting small trades.
Paper introduces a new volatility model for natural gas markets and discusses swing option pricing.
problem Modeling price and storage dynamics in natural gas markets with path-dependent volatility.
method Developed a novel stochastic path-dependent volatility model and used deep learning for swing option pricing.
result Proposed a deep learning method for numerical approximations of swing option pricing.
How does dynamic price information flow among Northern European electricity spot prices and prices of major electricity generation fuel sources? We use time series models combined with new advances in causal inference to answer these questions. Applying our methods to weekly Nordic and German electricity prices, and oi…
Paper uses neural networks to predict NOx emissions from gas turbines.
problem Predicting NOx emissions from degrading gas turbines.
method Applied neural network algorithm to model NOx emissions from nine process variables.
result Neural network model optimizes process variables for minimal NOx emissions.
Deep learning optimizes gas storage operations.
problem Optimizing underground natural gas storage operations.
method Reinforcement learning techniques applied to high-dimensional forward markets with constraints.
result Performance of deep learning method superior to least-squares Monte-Carlo approach.
Introduces generalized almost statistical convergence and its properties.
problem Developing a new convergence concept for sequences.
method Introducing generalized almost statistical convergence and proving its properties.
result Existence of a GAS convergent sequence that is neither statistical nor almost convergent.
GA-Net selectively attends to parts of a sequence for text classification.
problem Inefficient global attention mechanisms on long sequences.
method Gated Attention Network (GA-Net) using an auxiliary network to dynamically select and attend to important parts of the sequence.
result GA-Net achieves better performance with less computation and interpretability.
Deep RL strategy improves natural gas trading performance.
problem Improving natural gas trading performance using Deep RL.
method Domain-adapted Deep RL for natural gas futures trading.
result Deep RL strategy outperforms benchmarks and reduces transaction costs.
Study examines value relevance of oil and gas reserve disclosures in London Stock Exchange.
problem Uncertainty in oil and gas reserves poses accounting challenges for investors.
method Empirical analysis using archival data and multifactor framework.
result Changes in reserves and their components are associated with share returns, but insignificantly due to oil price and longitudinal effects. Quality of disclosures positively impacts share returns.
In this article we show that some of the recent results of Marino, Moore, and Peradze (math.DG/9812042, hep-th/9812055) -- in particular their conjecture that all closed, smooth four-manifolds with b_2^+ > 1 (and Seiberg-Witten simple type) are of `superconformal simple type' -- can be understood using a simple mathema…
We have numerically simulated the ideal-gas models of trading markets, where each agent is identified with a gas molecule and each trading as an elastic or money-conserving two-body collision. Unlike in the ideal gas, we introduce (quenched) saving propensity of the agents, distributed widely between the agents ($0 \le…