Results 91 to 100 of about 4,489 (189)

Application of Even Span Greenhouse Solar Dryer (ESGSD) for drying Persian shallot; Kinetic analysis, machine learning modeling and quality evaluation

Case Studies in Thermal Engineering
This paper presents an experimental analysis of the Even Span Greenhouse Solar Dryer (ESGSD) for drying Persian shallot. The results are compared with three different oven drying methods at three temperatures including: 70 °C, 60 °C and 50 °C ...
Morteza Taki, Mohammad Noshad, Mohammad Mehdi Jasemi, Fatemeh Isvandi   +3 more
doaj   +1 more source

Machine Learning Prediction of Aggregation‐Induced Emission Energies for Organic Luminogens and Metal Complexes Using Physicochemical Descriptors

ChemPhotoChem, Volume 10, Issue 8, August 2026.
We report a machine learning (ML) approach for predicting the emission energies of aggregation‐induced emission (AIE) luminogens directly from their molecular structures. Physicochemical descriptors combined with Gaussian process regression (GPR) revealed the key factors governing emission energy, including donor–acceptor (D–A) features, π‐conjugation ...
Kohsuke Matsumoto, Aoi Nakamura, Yui Ieki, Yuto Ueda, Osamu Tsutsumi   +4 more
wiley   +1 more source

Enhancing Rock Strength Prediction and Features Selection by Coupling Well Log Data and Deep Learning Approaches in Reservoir Geomechanics

International Journal for Numerical and Analytical Methods in Geomechanics, Volume 50, Issue 11, Page 4724-4738, 10 August 2026.
ABSTRACT Accurate rock strength parameters play a vital role in petroleum and mining operations for sustainable drilling activities, and wellbore stability analysis. This study investigates the applicability of deep learning techniques for assessing data‐driven models, and to conduct parametric sensitivity examination for feature attributes ranking to ...
Mohammad Islam Miah, Md. Shakil Rahaman, Travis Wiens   +2 more
wiley   +1 more source

Tunable Surface Chemistry of Molten Salt Derived MXenes for Data‐Driven Electrochemical Materials Discovery

Small, Volume 22, Issue 46, 18 August 2026.
The surface chemistries of MXenes have been largely broadened by Lewis acid melts through expanded compositional diversity and functionalization routes. Such complexity makes data‐driven approaches particularly suitable for accelerated functionality discovery. This review examines surface chemistry from synthetic history to structure‐activity relations,
Yingrui Wang, Simin Li, Tao Yang, Xinmei Hou, Bo Shen   +4 more
wiley   +1 more source

Estimation and validation of solubility of recombinant protein in E. coli strains via various advanced machine learning models

Scientific Reports
This study presents a comprehensive approach to predicting solubility of recombinant protein in four E. coli samples by employing machine learning techniques and optimization algorithms.
Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah   +2 more
doaj   +1 more source

Bathymetry Prediction With SWOT Gravity Anomaly Using Machine Learning Methods: Paper 1–Model Development

Journal of Geophysical Research: Solid Earth, Volume 131, Issue 8, August 2026.
Abstract Only one quarter of the global ocean floor has been directly surveyed; the remaining three quarters are inferred from satellite altimeter‐derived gravity data using techniques developed in the 1990s. These classical methods correlate gravity anomalies with known depths and extrapolate bathymetry in unsounded regions.
David Sandwell, Benjamin J. Phrampus, Farshad Salajegheh, Bjarke Nilsson, Biao Lu, Yao Yu, Hugh Harper, Ole B. Andersen, Walter Smith, Paul Elmore, Jonathan Kirby, James Beale, Jamie Roberts, Luis Altamirano   +13 more
wiley   +1 more source

Development of machine vision-based system for Iron ore grade prediction using gaussian process regression (GPR)

, 2016
India is one of the major iron ore producing country and requires quality monitoring of iron ore. An attempt has made to develop a vision-based system for continuous iron ore grade prediction during transportation of ores through conveyors. A Gaussian process regression (GPR) algorithm was used to develop the model.
Ashok Kumar Patel, Amit Kumar Gorai, Snehamoy Chatterjee   +2 more
openaire   +1 more source

Advancements in evaporation prediction: introducing the Gated Recurrent Unit–Multi-Kernel Extreme Learning Machine (MKELM)–Gaussian Process Regression (GPR) model

Environmental Sciences Europe
Abstract Predicting evaporation is an essential topic in water resources management. It is critical to plan irrigation schedules, optimize hydropower production, and accurately calculate the overall water balance. Thus, researchers have developed many prediction models for predicting evaporation. Despite the development of these models, there are still
Sharareh Pourebrahim, Mohammad Ehteram, Mehrdad Hadipour, Ozgur Kisi, Ahmed El-Shafie, Ali Najah Ahmed, Jit Ern Chen   +6 more
openaire   +2 more sources

Error Quantification of Gaussian Process Regression for Extracting Eulerian Velocity Fields from Ocean Drifters

Journal of Marine Science and Engineering
Drifter observations can provide high-resolution surface velocity data (Lagrangian data), commonly used to reconstruct Eulerian velocity fields. Gaussian Process Regression (GPR), a machine learning method based on Gaussian probability distributions, has
Junfei Xia, Mohamed Iskandarani, Rafael C. Gonçalves, Tamay Özgökmen   +3 more
doaj   +1 more source

Machine Learning-Driven Prediction of CO2 Solubility in Brine: A Hybrid Grey Wolf Optimizer (GWO)-Assisted Gaussian Process Regression (GPR) Approach

Energies
The solubility of CO2 in brine systems is critical for both carbon storage and enhanced oil recovery (EOR) applications. In this study, Gaussian Process Regression (GPR) with eight different kernels was optimized using the Grey Wolf Optimizer (GWO) algorithm to model this important phase behavior.
Seyed Hossein Hashemi, Farshid Torabi, Paitoon Tontiwachwuthikul   +2 more
openaire   +1 more source