Results 121 to 130 of about 28,270 (263)

On the Martingale Property for Generalized Stochastic Processes [PDF]

, 1995
In the recent years, several groups have studied stochastic equations (e.g. SDE's, SPDE's, stochastic Volterra equations) outside the framework of the Itô calculus. Often, this led to solutions in spaces of generalized random processes or fields.
Benth, Fred Espen, Potthoff, Jürgen
core  

Allosteric DNAzyme‐Enabled Sensitive and Multiplex Detection of Biomarkers for Rapid Diagnosis of Urinary Tract Infections

Advanced Science, EarlyView.
A catalytic allosteric DNAzyme assay (SMART) is developed by engineering DNAzyme into a unimolecular biosensor, enabling highly sensitive and multiplex detection of small molecules and nucleic acids. SMART enables extraction‐free, one‐pot, one‐step, isothermal, and cost‐effective detection of biological markers, achieving >95% accuracy for rapid ...
Yanzhe Shen, Chenzhi Shi, Xiaowei Ma, Pengfei Hou, Haomin Zhang, Juanxiu Qin, Li Pan, Guilin Li, Lifei Gao, Qian Ma, Donglei Yang, Min Li, Pengfei Wang   +12 more
wiley   +1 more source

Wide sense one-dependent processes with embedded Harris chains and their applications in inventory management

In this paper we consider stochastic processes with an embedded Harris chain. The embedded Harris chain describes the dependence structure of the stochastic process.
Bazsa, E.M., Iseger, P. den
core  

Approximation of stochastic differential equations driven by alpha-stable Levy motion [PDF]

In this paper we present a result on convergence of approximate solutions of stochastic differential equations involving integrals with respect to alpha-stable Levy motion.
Aleksander Janicki, Zbigniew Michna, Aleksander Weron   +2 more
core  

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

Advanced 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, Rui Qiao, Ying Li, Junhong Li, Nuoya Yue, Jing Ge, Jiao Wang, Luonan Chen, Peiluan Li   +8 more
wiley   +1 more source

Modeling Heavy-Tailed Stock Index Returns Using the Generalized Hyperbolic Distribution [PDF]

In the present study, we estimate the parameters of the Generalized Hyperbolic Distribution for a series of stock index returns including the Romanian BETC and indexes from other two Eastern European countries, Hungary and the Czech Republic.
Necula, Ciprian
core  

Generalized stochastic processes: modelling and applications of noise processes

, 2018
This textbook shall serve a double purpose: first of all, it is a book about generalized stochastic processes, a very important but highly neglected part of probability theory which plays an outstanding role in noise modelling. Secondly, this textbook is
Schäffler, Stefan
core   +1 more source

Shear‐Induced Emergence of Aromatic Superlow‐Friction Interfaces in Amorphous Carbon: Triggering Chemical Impurities and Atomic‐Scale Mechanisms

Advanced Science, EarlyView.
High‐throughput quantum‐mechanical simulations reveal that amorphous carbon undergoes shear‐driven structural transformation into aromatic, graphene‐like interfaces. This mechanochemical process is governed by dopant chemistry: dopants with valency less than four promote the emergence of superlow‐friction amorphous graphene, whereas tetra‐valent ...
Takuya Kuwahara, Koki Horiguchi, Leonhard Mayrhofer, Gianpietro Moras, Michael Moseler   +4 more
wiley   +1 more source

Sampling Period Analysis for Deterministic and Stochastic Controllers: An Algorithmic Tool for Performance Optimization

IEEE Access
The leading contribution of this article is to describe the analysis of the sampling period in deterministic and stochastic pole-assignment-based digital controllers, by proposing an analysis tool that considers the Nyquist-Shannon frequency and ...
Maryson da Silva Araujo, Antonio da Silva Silveira   +1 more
doaj   +1 more source

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

Advanced Science, EarlyView.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu, Shuli Sun, Zhengliang Lv, Xinyu Liu, Yihua Wang, Bingqiang Liu   +5 more
wiley   +1 more source