Results 41 to 50 of about 3,765 (220)

Combining Spatial Multi‐Omics Data to Decipher Spatial Domains and Elucidate Cell Heterogeneity Based on Self‐Supervised Graph Learning

open access: yesAdvanced Science, EarlyView.
A self‐supervised multi‐view graph fusion framework integrates spatial multi‐omics, excelling in domain identification and denoising. It reconstructs spatial pseudo‐expression, jointly analyzes multi‐omics data, infers RNA velocity, predicts spatial omics features from single‐cell multi‐omics, and detects spatially dark genes and transcription factors,
Yuejing Lu   +8 more
wiley   +1 more source

On aNew Class of Meromorphically Univalent Functions with Applications to Geometric Functions [PDF]

open access: yesEngineering and Technology Journal, 2017
In this work, we inform a new class of meromorphic univalent function.We derive basic properties such ascoefficient estimates, convex set, extremepoints, radius of starlikeness and convexity, hadamard product, integraloperator,
M. Lafta, K. AL- Zubaidy
doaj   +1 more source

Mechanisms of Alkali Ionic Transport in Amorphous Oxyhalides Solid State Conductors

open access: yesAdvanced Energy Materials, EarlyView.
Large‐scale machine learning‐based molecular dynamics simulations are used to investigate isovalent amorphous oxyhalides, revealing a remarkable chemically independent ionic conductivity. A rigorous analysis of alkali residence times across different metal–anion environments identifies divalent anions as key diffusion bottlenecks.
Luca Binci   +3 more
wiley   +1 more source

Fuzzy Differential Subordination for Meromorphic Function Associated with the Hadamard Product

open access: yesAxioms, 2023
This paper is related to fuzzy differential subordinations for meromorphic functions. Fuzzy differential subordination results are obtained using a new operator which is the combination Hadamard product and integral operator for meromorphic function.
Sheza M. El-Deeb, Alina Alb Lupaş
doaj   +1 more source

Hadamard Products and Tilings

open access: yes, 2009
Louis W. Shapiro gave a combinatorial proof of a bilinear generating function for Chebyshev polynomials equivalent to the formula 1/(1-ax-x^2) * 1/(1-bx-x^2) = (1-x^2)/(1-abx-(2+a^2+b^2)x^2 -abx^3+x^4), where * denotes the Hadamard product. In a similar way, by considering tilings of a 2 by n rectangle with 1 by 1 and 1 by 2 bricks in the top row, and ...
openaire   +3 more sources

Partitioned and Hadamard product matrix inequalities [PDF]

open access: yesJournal of Research of the National Bureau of Standards, 1978
This note is partly expositor). Inequalities relating inversion with, respectively, extraction of principal submatriees and the Hadamard product in the two possible orders are developed in a simple and unified way for positive definite matrices. These inequalities are known, hut we also characterize the cases of equality and strict inequality.
openaire   +3 more sources

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley   +1 more source

Frequency-difference sparse Bayesian learning for unambiguous direction-of-arrival estimation [PDF]

open access: yesJASA Express Letters
The frequency-difference (FD) method uses the FD Hadamard product, comprising auto-products to model below-band acoustic fields and unintended cross-products, for efficient direction-of-arrival (DOA) estimation under spatial aliasing.
Ze Yuan   +3 more
doaj   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

open access: yesAdvanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour   +5 more
wiley   +1 more source

Hadamard and quasi-Hadamard properties for certain subclasses of analytic functions [PDF]

open access: yesSurveys in Mathematics and its Applications
In the present paper two subclasses \mathcalSq*(\itNe) and \mathcalKq(\itNe) of analytic functions are introduced by using q-derivative operator.
Shashi Kant   +2 more
doaj  

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