Results 81 to 90 of about 568,969 (316)

Mechanical Properties of Architected Polymer Lattice Materials: A Comparative Study of Additive Manufacturing and CAD Using FEM and µ‐CT

open access: yesAdvanced Functional Materials, EarlyView.
This study examines how pore shape and manufacturing‐induced deviations affect the mechanical properties of 3D‐printed lattice materials with constant porosity. Combining µ‐CT analysis, FEM, and compression testing, the authors show that structural imperfections reduce stiffness and strength, while bulk material inhomogeneities probably enhance ...
Oliver Walker   +5 more
wiley   +1 more source

Inference for the limiting cluster size distribution of extreme values

open access: yes, 2009
Any limiting point process for the time normalized exceedances of high levels by a stationary sequence is necessarily compound Poisson under appropriate long range dependence conditions. Typically exceedances appear in clusters.
Robert, Christian Y.
core   +1 more source

Microporous Microgel Assemblies Facilitating the Recruitment and Osteogenic Differentiation of Progenitor Cells for Bone Regeneration

open access: yesAdvanced Functional Materials, EarlyView.
There is a significant need for biomaterials with well‐defined stability and bioactivity to support tissue regeneration. In this study, we developed a tunable microgel platform that enables the decoupling of stiffness from porosity, thereby promoting bone regeneration.
Silvia Pravato   +9 more
wiley   +1 more source

Are current microscopic traffic models capable of generating jerk profile consistent with real world observations?

open access: yesInternational Journal of Transportation Science and Technology
Microscopic behavior modeling plays a critical role in traffic flow analyais, simulation, and autonomous vehicle algorithm development. Numerous efforts are devoted to the development of it in both longitudinal and lateral dimensions.
Hongsheng Qi
doaj   +1 more source

Integral Options in Models with Jumps [PDF]

open access: yes
We present an explicit solution to the formulated in [17] optimal stopping problem for a geometric compound Poisson process with exponential jumps. The method of proof is based on reducing the initial problem to an integro-differential free-boundary ...
Pavel V. Gapeev
core  

Compound Compound Poisson Risk Model [PDF]

open access: yes, 2009
2000 Mathematics Subject Classification: 60K10, 62P05.The compound Poisson risk models are widely used in practice. In this paper the counting process in the insurance risk model is a compound Poisson process.
Minkova, Leda D.
core  

Miniature Nanomesh Mechano‐Acoustic Sensor with Wide Linear Dynamic Range, Broad Bandwidth, and Flat Frequency Response

open access: yesAdvanced Functional Materials, EarlyView.
A miniaturized mechano‐acoustic sensor is developed using an electrospun PVDF nanomesh as the diaphragm in a capacitive sensor structure. Unlike conventional nanomesh‐based sensors, it achieves high linear sensitivity, a broad and flat frequency response, and a compact form factor.
Jeng‐Hun Lee   +8 more
wiley   +1 more source

Statistical Models for High Frequency Security Prices [PDF]

open access: yes
This article studies two extensions of the compound Poisson process with iid Gaussian innovations which are able to characterize important features of high frequency security prices.
Roel C.A. Oomen
core  

Adiabatic reduction of a model of stochastic gene expression with jump Markov process

open access: yes, 2013
This paper considers adiabatic reduction in a model of stochastic gene expression with bursting transcription considered as a jump Markov process. In this model, the process of gene expression with auto-regulation is described by fast/slow dynamics.
Lei, Jinzhi   +3 more
core   +3 more sources

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

open access: yesAdvanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore   +7 more
wiley   +1 more source

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