Results 121 to 130 of about 5,474,085 (350)
Percolation on random recursive trees [PDF]
Random Structures & Algorithms, 2015 We study Bernoulli bond percolation on a random recursive tree of size $n$ with percolation parameter $p(n)$ converging to $1$ as $n$ tends to infinity. The sizes of the percolation clusters are naturally stored in a tree. We prove convergence in distribution of this tree to the genealogical tree of a continuous-state branching process in discrete time.
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Spin Engineering of Dual‐Atom Site Catalysts for Efficient Electrochemical Energy Conversion
Advanced Materials, EarlyView.This review highlights recent progress in spin engineering of dual‐atom site catalysts (DASCs), emphasizing how spin‐related properties enhance electrocatalytic activity, selectivity, and stability. It summarizes cutting‐edge developments in dual‐atom catalysis, discusses the underlying spin‐catalysis mechanisms and structure–performance relationships,
Dongping Xue +5 more
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A survey of path planning of industrial robots based on rapidly exploring random trees. [PDF]
Front Neurorobot, 2023 Luo S, Zhang M, Zhuang Y, Ma C, Li Q.
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Advanced Materials, EarlyView.This review explores the transformative role of AI in biosensor technology and provides a holistic interdisciplinary perspective that covers a broader scope of AI‐enabled biosensor technologies across various sectors including healthcare, environmental monitoring, food safety, and agriculture. It also highlights the important role of novel materials in
Tuğba Akkaş +4 more
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Electronic Journal of Probability, 2011 Let $T$ be a tree with induced partial order $\preceq$. We investigate centered Gaussian processes $X=(X_t)_{t\in T}$ represented as $$ X_t= (t)\sum_{v \preceq t} (v) _v $$ for given weight functions $ $ and $ $ on $T$ and with $( _v)_{v\in T}$ i.i.d. standard normal.
Lifshits, Mikhail, Linde, Werner
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AI‐Driven Defect Engineering for Advanced Thermoelectric Materials
Advanced Materials, EarlyView.This review presents how AI accelerates the design of defect‐tuned thermoelectric materials. By integrating machine learning with high‐throughput data and physics‐informed representations, it enables efficient prediction of thermoelectric performance from complex defect landscapes.
Chu‐Liang Fu +9 more
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The Annals of Probability, 1991 Let $(\mathscr{R}(k), k \geq 1)$ be random trees with $k$ leaves, satisfying a consistency condition: Removing a random leaf from $\mathscr{R}(k)$ gives $\mathscr{R}(k - 1)$. Then under an extra condition, this family determines a random continuum tree $\mathscr{L}$, which it is convenient to represent as a random subset of $l_1$.
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Limits of Random Trees. II [PDF]
Acta Mathematica Hungarica, 2014 Local convergence of bounded degree graphs was introduced by Benjamini and Schramm. This result was extended further by Lyons to bounded average degree graphs. In this paper we study the convergence of random tree sequences with given degree distributions. Denote by ${\cal D}_n$ the set of possible degree sequences of a tree on $n$ nodes.
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Advanced Materials Technologies, Volume 10, Issue 6, March 18, 2025.The given research presents an innovative insole‐based device employing self‐powered triboelectric nanogenerators (TENG) for flatfoot detection. By integrating TENG tactile sensors within an insole, the device converts mechanical energy from foot movements to electrical signals analyzed via machine learning, achieving an 82% accuracy rate in flatfoot ...
Moldir Issabek +7 more
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