Results 31 to 40 of about 3,784 (194)

Analogues of Waring-Girard formulas for the block-symmetric polynomials on the spaces $\ell_1(\mathbb{c}^2)$ and $\ell_p(\mathbb{c}^2)$

Visnyk Lvivskogo Universytetu Seriya Mekhaniko-Matematychna
Summary: Classical Waring-Girard formulas gives a representation of elementary and complete symmetric polynomials though the power symmetric polynomials. In this paper, we propose an analog of the Waring-Girard formulas for block-symmetric polynomials on the spaces \(\ell_1(\mathbb{C}^2)\) and \(\ell_p(\mathbb{C}^2)\) and show the application of the ...
Kravtsiv, Viktoriia, Martsinkiv, Mariia, Yaselskyi, Andrii   +2 more
openaire   +2 more sources

Microscale Mapping of Fiber Strain and Damage in Composite Wrinkled Laminates Using Computed Tomography Assisted Wide‐Angle X‐Ray Scattering

Advanced Science, EarlyView.
This study combines full‐field tomography with diffraction mapping to quantify radial (ε002$\varepsilon _{002}$) and axial (ε100$\varepsilon _{100}$) lattice strain in wrinkled carbon‐fiber specimens for the first time. Radial microstrain gradients (−14.5 µεMPa$\varepsilon \mathrm{MPa}$−1) are found to signal damage‐prone zones ahead of failure, which ...
Hoang Minh Luong, James Trevarthen, Igor Dolbnya, Wadwan Singhapong, Nicholas Pilato, Richard Butler, Jiraphant Srisuriyachot, Alexander J. G. Lunt   +7 more
wiley   +1 more source

Atomic Defects in Layered Transition Metal Dichalcogenides for Sustainable Energy Storage and the Intelligent Trends in Data Analytics

Advanced Science, EarlyView.
This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo, Ying Yang, Zizhen Fu, Fengqi Liu, Susu Fang, Kele Xu, Shanshan Wang   +6 more
wiley   +1 more source

A Generative Neuro‐Symbolic AI for Protein Sequence Design

Advanced Science, EarlyView.
We introduce EffieDes, a neuro‐symbolic framework coupling deep learning‐based fitness landscape parameterization with exact automated reasoning. Unlike greedy sampling, EffieDes identifies sequences that globally optimize fitness while satisfying intricate design constraints.
Marianne Defresne, Delphine Dessaux, Samuel Buchet, Lucie Barthe, Liza Ammar‐Khodja, Bessam Azizi, Valentin Durante, Gianluca Cioci, Simon de Givry, Alain Roussel, Luis F. Garcia‐Alles, Thomas Schiex, Sophie Barbe   +12 more
wiley   +1 more source

A Unifying Thermodynamic Model for Phase Separation and Aging of Biopolymers

Advanced Science, EarlyView.
Phase separation and aging of intrinsically disordered proteins are placed in a unifying framework. A thermodynamically consistent time‐dependent version of associating‐polymer theory shows how the processes are intricately coupled. Assuming aging to occur through interacting sites resulting from reversible conformational transitions, the model ...
Jasper J. Michels, Joana Caria, Edward A. Lemke   +2 more
wiley   +1 more source

Single‐Atom‐Enhanced Fully Inkjet‐Printed Electrochemical Sensor for Dopamine Detection

Advanced Science, EarlyView.
Fully inkjet‐printed paper‐based dopamine sensors are fabricated using nitrogen‐doped graphene acid functionalized with copper single atoms. The copper sites uniquely enhance dopamine oxidation through strong molecular adsorption, as verified by electrochemical measurements and DFT calculations. The approach highlights the potential of single‐atom inks
Martin‐Alex Nalepa, David Panáček, Vítězslav Hrubý, Matěj Jendrišák, Rostislav Langer, Michal Langer, Petr Jakubec, Ivan Dědek, Vojtěch Kupka, Michal Mazur, Radek Zbořil, Michal Otyepka   +11 more
wiley   +1 more source

Canonical Antibodies Adopt Distinct Binding Modes to Recognize Viral Glycan Shields

Advanced Science, EarlyView.
Canonical Y‐shaped antibodies recognize viral glycan shields through adaptive Fab assembly states shaped by glycan organization and somatic hypermutation. Structural analyses ofbroadly neutralizing antibodies VRC35 and VRC36 across glycoproteins of HIV‐1, influenza, SARS‐CoV‐2, and Lassa viruses reveal distinct Fab assembly states, spanning monovalent,
Jiaxuan Cheng, Evan M. Cale, Amirabbas Maghsoudi, Matthew S. Sutton, Nancy S. Longo, Rebecca A. Gillespie, Lingshu Wang, Ivan Kosik, Sue Chong, Atsuhiro Yasuhara, Tatsiana Bylund, Arne Schön, Prabhanshu Tripathi, Yaroslav Tsybovsky, Haotian Lei, Nicholas C. Morano, Allison B. Lupatkin, Abraham J. Morton, Zabrina C. Lang, Jordan E. Becker, Isabella R. Frascilla, Myungjin Lee, Ning Li, Cuiping Liu, Ryan S. Roark, Chen‐Hsiang Shen, I‐Ting Teng, David J. Van Wazer, Danyi Wang, Lingyuan Wu, Goran Ahlsen, Mike Castro, Haijuan Du, Michael J. Ernandes, Tiansheng Li, Bob C. Lin, Mark K. Louder, Krisha McKee, Jonah S. Merriam, Sijy O'Dell, Li Ou, Sergei Pletnev, David Prikryl, Qi Qiu, Sarah Rubin, Mallika Sastry, Stephen D. Schmidt, Asif Shajahan, Andrea R. Shiakolas, Sanjay Srivatsan, Baoshan Zhang, Qiong Zhou, Mark Connors, Jason G. Gall, Yicheng Guo, Rick K. Huang, Yaoxing Huang, Richard A. Koup, Q. Paula Lei, John R. Mascola, Reda Rawi, Leonid Serebryannyy, Lawrence Shapiro, Zizhang Sheng, David D. Ho, Patrick C. Wilson, Jonathan W. Yewdell, Theodore C. Pierson, Masaru Kanekiyo, Nicole A. Doria‐Rose, Peter D. Kwong, Tongqing Zhou   +71 more
wiley   +1 more source

Highly‐Uniform Passive Crossbar Arrays of Resistive Switching Random Access Memory (RRAM) for In‐Memory Computing Applications

Advanced Electronic Materials, EarlyView.
Passive resistive memory arrays promise efficient in‐memory computing but suffer from sneak paths and programming variability. Here, highly uniform 32 × 32 passive RRAM crossbars are programmed with multilevel precision below 3% error and 99.5% yield.
S. Ricci, P. Mannocci, M. Porzani, D. Bridarolli, F. Carletti, M. Farronato, D. Ielmini   +6 more
wiley   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Data‐Driven High‐Throughput Volume Fraction Estimation From X‐Ray Diffraction Patterns

Advanced Intelligent Discovery, EarlyView.
Long exposure times and the need for manual evaluation limit the use of X‐ray diffraction in high‐throughput applications. This study presents a data‐driven approach addressing both issues. HiVE (a method for High‐throughput Volume fraction Estimation) performs composition estimation for high‐noise XRD patterns produced using polychromatic emission ...
Hawo H. Höfer, André Orth, Robert Wang, Ben Breitung, Simon Schweidler, Jasmin Aghassi‐Hagmann, Markus Reischl   +6 more
wiley   +1 more source