Results 141 to 150 of about 17,625 (308)

A Non-autonomous Stochastic Discrete Time System with Uniform Disturbances

, 2015
International audienceThe main objective of this article is to present Bayesian optimal control over a class of non-autonomous linear stochastic discrete time systems with disturbances belonging to a family of the one parameter uniform distributions.
Szajowski, Krzysztof, J., Dassios, Ioannis, K.   +1 more
core   +1 more source

From Label‐Free Multiphoton Imaging to Pathological Reports: A Vision‐Language Breast Cancer Margin Pathological Diagnosis System

Advanced Science, EarlyView.
MarginPath is a novel vision‐language system that automates breast cancer margin assessment using a single label‐free multiphoton microscopy image. By integrating tumor‐associated collagen signatures with virtual H&E imaging, it generates accurate margin heatmaps and comprehensive diagnostic reports.
Shu Wang, Jingze Su, Xiahui Han, Deyong Kang, Xiao Zhang, Fei Xu, Changzu Liu, Junlin Pan, Xingfu Wang, Qiaohui Zhan, Aimin Wang, Feng Huang, Heping Cheng, Wenxi Liu, Ruolan Lin, Jianxin Chen   +15 more
wiley   +1 more source

Stochastic Improvement of Cyclic Railway Timetables

Real-time railway operations are subject to stochastic disturbances. However, a railway timetable is a deterministic plan. Thus a timetable should be designed in such a way that it can cope with the stochastic disturbances as well as possible.
Dekker, R., Vromans, M.J.C.M., Kroon, L.G., Retel Helmrich, M., Maroti, G.   +4 more
core  

A Data‐Driven Inverse Design Methodology for Magnetic Soft Millirobots Navigating in Confined Spaces

Advanced Science, EarlyView.
A data‐efficient inverse design framework automates the optimization of magnetic soft millirobots for confined‐space navigation. Integrating a physics‐based Cosserat rod model with Bayesian optimization efficiently identifies high‐performance geometries.
Ziyu Ren, Hong Wang, Chak Wang Tse, Yi Zheng, Xi Chen, Wenqi Hu   +5 more
wiley   +1 more source

Stochastic Index Numbers: A Review

The stochastic approach is a new way of viewing index numbers in which uncertainty and statistical ideas play a central role. Rather than just providing a single number for the rate of inflation, the stochastic approach provides the whole probability ...
E Antony Selvanathan, H.Y Izan, Kenneth W. Clements   +2 more
core  

Mixing‐Driven Defects and Composition Evolution in Multi‐Material Metal Additive Manufacturing

Advanced Science, EarlyView.
Operando synchrotron X‐ray imaging coupled with high‐fidelity multiphysics modeling uncovers how inter‐material mixing reshapes keyhole dynamics and drives distinct pore‐formation pathways in multi‐material laser powder bed fusion (LPBF). Composition‐dependent instabilities trigger defects, whereas rescanning suppresses porosity and homogenizes Cu ...
Zhilang Zhang, Steve Gaudez, Mario Togni, Steven Van Petegem, Markus Bambach, Mamzi Afrasiabi   +5 more
wiley   +1 more source

Beyond Percolation: Graphene‐Enabled Network Reinforcement Enhances Thermal Transport in Paraffin Phase‐Change Composites

Advanced Science, EarlyView.
Expanded‐graphite/graphene‐nanoplatelet hybrids deliver a near‐order‐of‐magnitude thermal‐conductivity enhancement in paraffin phase‐change materials. A microCT‐informed 3D modeling framework resolves the percolating EG backbone and captures sub‐voxel GNP enrichment, quantitatively linking microstructure to heat flow and revealing a graphene‐enabled ...
Thomas Hoke, Jackson Hoke, Shucheng Guo, Brittany Chan, Xi Chen   +4 more
wiley   +1 more source

Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form [PDF]

In this paper we replace the Gaussian errors in the standard Gaussian, linear state space model with stochastic volatility processes. This is called a GSSF-SV model.
Neil Shephard, Charles S. Bos
core  

Development of an Autotuner for Plants with Stochastic Disturbances

, 2017
The most universally exercised model which amply describes the transient behaviour of a wide range of chemical processes is the first-order-plus-deadtime model. An identification experiment must be conducted to generate the data required to fit an accurate first-order-plus deadtime model.
openaire   +2 more sources

PhosSight: A Unified Deep Learning Framework Boosting and Accelerating Phosphoproteome Identification to Enable Biological Discoveries

Advanced Science, EarlyView.
PhosSight is a unified deep‐learning framework for phosphoproteome identification, featured by a phosphorylation‐aware detectability predictor. It improves identification sensitivity in DDA through deep re‐localization and rescoring, accelerates DIA searches by detectability‐guided spectral library pruning, and expands phosphoproteome coverage to ...
Ben Wang, Zhiyuan Cheng, Chengying She, Hongwei Zhao, Jiahui Zhang, Lin Lv, Zhihao Yan, Hongwen Zhu, Lizhuang Liu, Yan Fu, Xinpei Yi   +10 more
wiley   +1 more source