Results 201 to 210 of about 24,371,794 (246)

Combining MRI dual-sequence radiomics and clinical parameters predicts post-hypertriglyceridemic acute pancreatitis diabetes mellitus. [PDF]

Front Endocrinol (Lausanne)
Li Y, Wan X, Wang Y, Li H, Tang C, Zeng W, Huang X.   +6 more
europepmc   +1 more source

Strain Tuning the Occupation of Candidate Topological Weyl States in W‐Doped MoTe2

Advanced Science, Volume 13, Issue 50, 7 September 2026.
The present study investigates strain‐induced modifications of the electronic structure in the Weyl semimetal Td${\rm T}_d$‐Mo0.91W0.09Te2${\mathrm{Mo}}_{0.91}{\mathrm{W}}_{0.09}{\mathrm{Te}}_{2}$ using hard X‐ray angle‐resolved photoemission spectroscopy.
Amon Lanz, Olena Tkach, Yaryna Lytvynenko, Florian Diekmann, Harshit Agarwal, Sofia Michaela Souliou, Mehdi Frachet, Michael Merz, Sergii V. Chernov, Andrei Gloskovskii, Volkmar Koller, Dmytro Kutnyakhov, Moritz Hoesch, Christoph Schlueter, Philipp Rüssmann, Yuriy Mokrousov, Matthieu Le Tacon, Kai Rossnagel, Jure Demsar, Gerd Schönhense, Hans‐Joachim Elmers, Olena Fedchenko   +21 more
wiley   +1 more source

Robust Representation Learning for Clean Feature Discovery in Incomplete Multi‐View Clustering

Advanced Intelligent Systems, Volume 8, Issue 9, September 2026.
Robust feature discovery in incomplete multi‐view clustering is achieved by coupling RPCA‐based clean representation recovery with neural‐network‐assisted graph learning. The resulting RIMVC framework constructs cleaner and more discriminative graph‐structured representations from incomplete and noisy multi‐view data, improving clustering robustness ...
Ping Hu, Chenggang Lu, Rui Zhang, Haijun Yan, Yesong Xu   +4 more
wiley   +1 more source

Coupled Dynamics Between Networks and Fields in Physical Space: A Theoretical Perspective

Annalen der Physik, Volume 538, Issue 9, September 2026.
Networks embedded in physical space often communicate through dynamical fields that both mediate and respond to collective behavior. A unified theoretical perspective is presented for these network‐field systems, linking established mathematical frameworks and revealing how feedback between discrete interactions and continuous spatial processes can ...
Alex Arenas, Oriol Artime, Albert Díaz‐Guilera, Sergio Gómez, Clara Granell   +4 more
wiley   +1 more source

Area Law for the Entanglement Entropy of Free Fermions in Nonrandom Ergodic Field. [PDF]

Entropy (Basel)
Pastur L, Shamis M.
europepmc   +1 more source

Structural Network Optimization for the Liberalization of District Heating

Energy Technology, Volume 14, Issue 9, September 2026.
This study analyzes the liberalization of district heating through network interconnection using a combined framework of graph‐theoretic modeling, clustering, Cournot‐based competition, and multiobjective optimization. The results highlight structural trade‐o s between prices, producer profits, and infrastructure expenditure across alternative network ...
Gerhard Schöpping, Peter F. Pelz
wiley   +1 more source

The nonlinear porous medium equation for the <i>f</i>-Laplacian: Hamilton-Souplet-Zhang type gradient estimates and implications. [PDF]

Ann Mat Pura Appl
Taheri A, Vahidifar V.
europepmc   +1 more source

Demystifying Spatial Confounding—A Unified Analytical Framework for the Bias in Covariate Effect Estimates

Environmetrics, Volume 37, Issue 6, September 2026.
ABSTRACT In spatial regression models, unmeasured spatial variables, represented by spatial random effects, are typically not independent of observed covariates and can induce significant bias in covariate effect estimates. This fundamental problem, known as spatial confounding, has been extensively studied, but sometimes with puzzling and seemingly ...
Emiko Dupont, Isa Marques, Thomas Kneib
wiley   +1 more source

S2-HGNN: Scale-Aware Hypergraph Node Classification with Spectral Inductive Bias. [PDF]

Entropy (Basel)
Zhou J, Zhang S, Wu B, Wang Q, Wu C, Luo Z, Sun K, Mao H.   +7 more
europepmc   +1 more source

Inter‐Material Transfer Learning for Accelerated Nanofluid Heat Transfer Prediction: A Machine Learning Approach for Energy Systems

Energy Science &Engineering, Volume 14, Issue 9, Page 4316-4332, September 2026.
This study presents an inter‐material transfer learning framework for nanofluid heat transfer prediction in energy systems. By leveraging knowledge from Al2O3‐water data, the model accurately predicts hybrid Al2O3‐TiO2 nanofluid performance with only 20 simulations, achieving R2 = 0.985 and reducing computational requirements by 78. ABSTRACT This paper
Soumaya Hadj Salah, Aymen Ochi, Sonia Bezi   +2 more
wiley   +1 more source