Results 131 to 140 of about 176 (168)

Explainable hybrid stacking ensemble method for hard rock pillar stability prediction and engineering applications

Deep Underground Science and Engineering, EarlyView.
This research proposes an interpretable hybrid stacking ensemble framework, optimized by the Sparrow Search Algorithm, to enhance hard rock pillar stability prediction. By integrating six machine learning models—k‐nearest neighbors, support vector machines, random forests, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, and Light Gradient ...
Ning Wang, Guoyan Zhao, Meng Wang, Taraka Pattela   +3 more
wiley   +1 more source

C-parameter version of robust bounded one-class support vector classification. [PDF]

Sci Rep
Ye J, Yang Z, Hu Y, Zhang Z.
europepmc   +1 more source

‘biomod2' – extending presence–absence species distribution models to multiple data types

Ecography, EarlyView.
The R package ‘biomod2' is one of the most widely used and versatile tools for species distribution modelling (SDM), enabling ecologists to calibrate, evaluate, and project species–environment relationships across space and time using multiple modelling algorithms and ensemble forecasting.
Maya Guéguen, Hélène Blancheteau, Rémi Lemaire‐Patin, Wilfried Thuiller   +3 more
wiley   +1 more source

Cryo-EM structures of the CDK11-cyclin L-SAP30BP complex reveal mechanisms of CDK11 regulation. [PDF]

Nat Commun
McGeoch AJS, Cushing VI, Roumeliotis TI, Cronin NB, Hearnshaw SJ, Choudhary JS, Alfieri C, Greber BJ.   +7 more
europepmc   +1 more source

Generative Models in Inorganic Crystals Discovery and Inverse Design

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
Generative inverse‐design samples from the vast inorganic crystal design space by starting from target properties such as band gap, stability, and ion transport. This Review examines the representations, generative models, and validation workflows needed to translate candidate structures into stable, potentially synthesizable materials for applications
Tao Li, Xiaolin Liu, Fei Wang, Yan Xiong, Yu Chen, Yan Zhao   +5 more
wiley   +1 more source

Pathological hippocampal–cortical hypersynchronization disrupts memory in temporal lobe epilepsy: A multimodal intracranial electroencephalographic–functional magnetic resonance imaging study

Epilepsia, EarlyView.
Abstract Objective Memory impairment is a common comorbidity in temporal lobe epilepsy (TLE) and is thought to arise from hippocampal dysfunction and disrupted interactions within a distributed memory network involving medial temporal, frontal, and parietal cortical regions.
Ruxue Gong, Xiaolong Peng, Rebecca W. Roth, Haris Rashid, William Yun, Daniel L. Drane, Leonardo Bonilha, Lisa McTeague, Ezequiel Gleichgerrcht   +8 more
wiley   +1 more source

Spatial and temporal pattern of structure-function coupling of human brain connectome with development. [PDF]

Elife
Feng G, Wang Y, Huang W, Chen H, Cheng J, Shu N.   +5 more
europepmc   +1 more source

Bridging computational and clinical strategies for presurgical identification of epileptogenic networks

Epilepsia Open, EarlyView.
Abstract Objective About one third of epilepsy patients are drug‐resistant. Resective epilepsy surgery remains a key treatment option but depends critically on accurate identification of the seizure onset zone (SOZ), which is still guided mainly by subjective visual inspection of electrophysiological signals.
Tena Dubcek, Debora Ledergerber, Kristina Koenig, Adham Elshahabi, Rafael Polania, Lukas Imbach   +5 more
wiley   +1 more source

Natural Induction: Spontaneous Adaptive Organisation without Natural Selection. [PDF]

Entropy (Basel)
Buckley CL, Lewens T, Levin M, Millidge B, Tschantz A, Watson RA.   +5 more
europepmc   +1 more source

Learning Rocking Dynamics From Sparse Shake‐Table Data With Interpretable Physics‐Informed Neural Networks

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT We present a hybrid interpretable Physics‐Informed Neural Network Long‐Short Term Memory (Hybrid PINN LSTM) framework for predicting the seismic response of rocking blocks. Existing analytical models rely on uncertain idealizations, while purely data‐driven and machine‐learning approaches lack physical consistency and interpretability.
Shirley Shen, Christian Málaga‐Chuquitaype   +1 more
wiley   +1 more source