Results 61 to 70 of about 7,493 (200)

An intelligent lithology identification method for sandstone and mudstone strata and its applications: A case study of the Jurassic strata in the Lunnan area, Xinjiang, China

Meitian dizhi yu kantan
ObjectiveLithology identification lays the foundation for fine-scale reservoir evaluation. However, traditional identification methods generally utilize the interactive relationships between only 2‒3 logging parameters, suffering from low utilization ...
Ming CAI, Qingwen ZHOU, Cong YANG, Feng CHEN, Dong WU, Wang LIN, Chengguang ZHANG, Yuanjun ZHANG, Yuxin MIAO   +8 more
doaj   +1 more source

Analysis of Ruddlesden‐Popper and Dion‐Jacobson 2D Lead Halide Perovskites Through Integrated Experimental and Computational Analysis

Battery Energy, Volume 4, Issue 2, March 2025.
Optimized ML framework for predicting RP and Dj phases in perovskite solar cells. ABSTRACT Two‐dimensional (2D) lead halide perovskites (LHPs) have captured a range of interest for the advancement of state‐of‐the‐art optoelectronic devices, highly efficient solar cells, next‐generation energy harvesting technologies owing to their hydrophobic nature ...
Basir Akbar, Kil To Chong, Hilal Tayara
wiley   +1 more source

Machine Learning Paradigm for Advanced Battery Electrolyte Development

Carbon Energy, EarlyView.
Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su, Mengfan Pei, Keyu Chen, Xigao Jian, Fangyuan Hu   +4 more
wiley   +1 more source

Hybrid Simulation–Machine Learning Surrogates for Coordinate‐Based Solar and Wind Energy Yield Assessment in Iraq: A Streamlit Decision‐Support Tool

Energy Science &Engineering, EarlyView.
This study integrates climatic simulations with machine learning to predict solar and wind energy across Iraq. Results show Random Forest excels for solar (R2 = 0.98) and neural networks for wind (R2 = 0.97), enabling a practical web tool for renewable energy planning. ABSTRACT Driven by the global shift away from fossil fuels, solar and wind resources
Bassam Musheer Kareem, Abdolsalam Ebrahimpour, Jafar Ghafouri, Roghayyeh Motallebzadeh   +3 more
wiley   +1 more source

Predicting the Strength of Eccentrically Compressed Short Circular Concrete Filled Steel Tube Columns

Structural Mechanics of Engineering Constructions and Buildings
The process of predicting the load-bearing capacity of eccentrically compressed circular concrete filled steel tube (CFST) columns using machine learning algorithms is investigated.
Tatiana N. Kondratieva, Anton S. Chepurnenko, Batyr M. Yazyev   +2 more
doaj   +1 more source

Strategies for Identification and Mitigation of Sanguinarine in Mustard Oil Adulterated by Argemone—A Comprehensive Review

Food Chemistry International, EarlyView.
Sanguinarine, a toxic alkaloid present in argemone, can lead to epidemic dropsy or chronic diseases through DNA intercalation and immune system suppression. Regulatory efforts face challenges due to economic motivations for adulteration as well as technical, social, and infrastructure barriers.
Gururaj Pejavara Narayana, Subhangi Panigrahi, Suresh Kumar Kalakandan, Hema Vincent, Ashish Rawson   +4 more
wiley   +1 more source

Artificial intelligence–driven decoupling structure–activity relationship for lithium‐ion batteries

InfoScience, EarlyView.
Artificial intelligence can efferently accelerate the high‐throughput screening of battery materials, the analysis of multiphase mechanisms, and the precise prediction of capacity and cycle life. This review systematically summarizes the applications of machine learning (ML) in decoupling the complex structure‐activity relationships of lithium‐ion ...
Tao Wang, Yuhang Meng, Tianhao Chen, Xiangfen Jiang, Yoshio Bando, Jinjue Zeng, Xuebin Wang   +6 more
wiley   +1 more source

Detection and Comparative Evaluation of Noise Perturbations in Simulated Dynamical Systems and ECG Signals Using Complexity-Based Features

Machine Learning and Knowledge Extraction
Noise contamination is a common challenge in the analysis of time series data, where stochastic perturbations can obscure deterministic dynamics and complicate the interpretation of signals from chaotic and physiological systems.
Kevin Mallinger, Sebastian Raubitzek, Sebastian Schrittwieser, Edgar Weippl   +3 more
doaj   +1 more source

Machine Learning‐Based Estimation of Reference Evapotranspiration and Crop Coefficients for Wheat Under Diverse Climatic Conditions

Irrigation and Drainage, EarlyView.
ABSTRACT Accurate estimation of reference evapotranspiration (ET0) and crop coefficients (Kc) is critical for irrigation planning, particularly in data‐limited regions where agriculture dominates freshwater consumption. Although machine learning (ML) methods have been widely applied to ET0 and Kc estimation, most studies address these parameters ...
Ilker Angin, Hasan Er, Mete Yaganoglu, Muhammed Sungur Demir, Abdullah Muratoglu   +4 more
wiley   +1 more source

Building Energy Consumption Prediction Using CatBoost With Hybrid Random Search and Bayesian Optimization

IEEE Access
Residential buildings are major contributors to global energy consumption, with cooling and heating loads representing a substantial portion of this demand.
Kadir Ileri
doaj   +1 more source