Local analysis of Marangoni effects during and after droplet formation

The Marangoni effect on the critical heat flux (CHF) condition in pool boiling of binary mixtures has been identified and its effect has been quantitatively estimated with a modified model derived from hydrodynamics. The physical process of CHF in binary mixtures, and models used to describe it, are examined in the light of recent experimental evidence, accurate mixture properties, and phase equilibrium revealing a correlation to surface tension gradients and volatility. A correlation is developed from a heuristic model including the additional liquid restoring force caused by surface tension gradients. The CHF condition was determined experimentally for saturated methanol/water, 2-propanol/water, and ethylene glycol/water mixtures, over the full range of concentrations, and compared to the model. The evidence in this study demonstrates that in a mixture with large differences in surface tension, there is an additional hydrodynamic restoring force affecting the CHF condition.

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Strain uncertainties from two digital volume correlation approaches in prophylactically augmented vertebrae: Local analysis on bone and cement-bone microstructures

Journal of the Mechanical Behavior of Biomedical Materials ◽

10.1016/j.jmbbm.2016.12.006 ◽

2017 ◽

Vol 67 ◽

pp. 117-126 ◽

Cited By ~ 19

Author(s):

Gianluca Tozzi ◽

Enrico Dall’Ara ◽

Marco Palanca ◽

Marco Curto ◽

Federica Innocente ◽

...

Keyword(s):

Local Analysis ◽

Digital Volume Correlation

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Deformable Models for Segmentation Based on Local Analysis

Mathematical Problems in Engineering ◽

10.1155/2017/1646720 ◽

2017 ◽

Vol 2017 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Jimena Olveres ◽

Erik Carbajal-Degante ◽

Boris Escalante-Ramírez ◽

Enrique Vallejo ◽

Carla María García-Moreno

Keyword(s):

Local Binary Patterns ◽

Distance Measures ◽

Local Analysis ◽

Hermite Transform ◽

Active Shape Models ◽

Shape Models ◽

Segmentation Methods ◽

Segmentation Algorithms ◽

Image Modality ◽

New Algorithms

Segmentation tasks in medical imaging represent an exhaustive challenge for scientists since the image acquisition nature yields issues that hamper the correct reconstruction and visualization processes. Depending on the specific image modality, we have to consider limitations such as the presence of noise, vanished edges, or high intensity differences, known, in most cases, as inhomogeneities. New algorithms in segmentation are required to provide a better performance. This paper presents a new unified approach to improve traditional segmentation methods as Active Shape Models and Chan-Vese model based on level set. The approach introduces a combination of local analysis implementations with classic segmentation algorithms that incorporates local texture information given by the Hermite transform and Local Binary Patterns. The mixture of both region-based methods and local descriptors highlights relevant regions by considering extra information which is helpful to delimit structures. We performed segmentation experiments on 2D images including midbrain in Magnetic Resonance Imaging and heart’s left ventricle endocardium in Computed Tomography. Quantitative evaluation was obtained with Dice coefficient and Hausdorff distance measures. Results display a substantial advantage over the original methods when we include our characterization schemes. We propose further research validation on different organ structures with promising results.

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Intelligent IoT systems for civil infrastructure health monitoring: a research roadmap

Discover Internet of Things ◽

10.1007/s43926-021-00009-4 ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

E. Bertino ◽

M. R. Jahanshahi ◽

A. Singla ◽

R.-T. Wu

Keyword(s):

Data Acquisition ◽

Real Time ◽

Original Data ◽

Local Analysis ◽

Civil Infrastructure ◽

Time Requirements ◽

Iot Devices ◽

Learning Reinforcement ◽

The Times ◽

Acquisition Processes

AbstractThis paper addresses the problem of efficient and effective data collection and analytics for applications such as civil infrastructure monitoring and emergency management. Such problem requires the development of techniques by which data acquisition devices, such as IoT devices, can: (a) perform local analysis of collected data; and (b) based on the results of such analysis, autonomously decide further data acquisition. The ability to perform local analysis is critical in order to reduce the transmission costs and latency as the results of an analysis are usually smaller in size than the original data. As an example, in case of strict real-time requirements, the analysis results can be transmitted in real-time, whereas the actual collected data can be uploaded later on. The ability to autonomously decide about further data acquisition enhances scalability and reduces the need of real-time human involvement in data acquisition processes, especially in contexts with critical real-time requirements. The paper focuses on deep neural networks and discusses techniques for supporting transfer learning and pruning, so to reduce the times for training the networks and the size of the networks for deployment at IoT devices. We also discuss approaches based on machine learning reinforcement techniques enhancing the autonomy of IoT devices.

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