The effect of channel aspect ratio on air entrapment during imbibition in soil-on-a-chip micromodels with 2D and 2.5D pore structures

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Chia-Wen Tsao ◽  
Qun-Zhan Huang ◽  
Chang-Ye You ◽  
Markus Hilpert ◽  
Shao-Yiu Hsu ◽  
...  
Keyword(s):  
Aspect Ratio ◽  
Adhesive Bonding ◽  
Film Flow ◽  
Ct Images ◽  
Cnc Machining ◽  
Air Entrapment ◽  
Dry Adhesive ◽  
X Ray ◽  
Piston Displacement ◽  
Channel Aspect Ratio

“Soil-on-a-chip” micromodels designed with X-ray CT images were fabricated by tabletop CNC machining and dry adhesive bonding. The competition between film flow and piston displacement causes the air entrapment differences in 2D and 2.5D micromodels.

scholarly journals Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4595
Author(s):  
Parisa Asadi ◽  
Lauren E. Beckingham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ct Images ◽  
Ct Imaging ◽  
Learning Method ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

scholarly journals Vulnerability in Deep Transfer Learning Models to Adversarial Fast Gradient Sign Attack for COVID-19 Prediction from Chest Radiography Images

2021 ◽  
Vol 11 (9) ◽  
pp. 4233
Author(s):  
Biprodip Pal ◽  
Debashis Gupta ◽  
Md. Rashed-Al-Mahfuz ◽  
Salem A. Alyami ◽  
Mohammad Ali Moni
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Chest Radiography ◽  
High Sensitivity ◽  
Ct Images ◽  
Classification Performance ◽  
Learning Models ◽  
X Ray ◽  
Image Capturing ◽  
Fast Gradient

The COVID-19 pandemic requires the rapid isolation of infected patients. Thus, high-sensitivity radiology images could be a key technique to diagnose patients besides the polymerase chain reaction approach. Deep learning algorithms are proposed in several studies to detect COVID-19 symptoms due to the success in chest radiography image classification, cost efficiency, lack of expert radiologists, and the need for faster processing in the pandemic area. Most of the promising algorithms proposed in different studies are based on pre-trained deep learning models. Such open-source models and lack of variation in the radiology image-capturing environment make the diagnosis system vulnerable to adversarial attacks such as fast gradient sign method (FGSM) attack. This study therefore explored the potential vulnerability of pre-trained convolutional neural network algorithms to the FGSM attack in terms of two frequently used models, VGG16 and Inception-v3. Firstly, we developed two transfer learning models for X-ray and CT image-based COVID-19 classification and analyzed the performance extensively in terms of accuracy, precision, recall, and AUC. Secondly, our study illustrates that misclassification can occur with a very minor perturbation magnitude, such as 0.009 and 0.003 for the FGSM attack in these models for X-ray and CT images, respectively, without any effect on the visual perceptibility of the perturbation. In addition, we demonstrated that successful FGSM attack can decrease the classification performance to 16.67% and 55.56% for X-ray images, as well as 36% and 40% in the case of CT images for VGG16 and Inception-v3, respectively, without any human-recognizable perturbation effects in the adversarial images. Finally, we analyzed that correct class probability of any test image which is supposed to be 1, can drop for both considered models and with increased perturbation; it can drop to 0.24 and 0.17 for the VGG16 model in cases of X-ray and CT images, respectively. Thus, despite the need for data sharing and automated diagnosis, practical deployment of such program requires more robustness.

High length-to-width aspect ratio lead bromide microwires via perovskite-induced local concentration gradient for X-ray detection

CrystEngComm ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 2215-2221
Author(s):  
Emma Dennis ◽  
Soumya Kundu ◽  
Deepak Thrithamarassery Gangadharan ◽  
Jingjun Huang ◽  
Victor M. Burlakov ◽  
...  
Keyword(s):  
Aspect Ratio ◽  
Concentration Gradient ◽  
Width Ratio ◽  
Local Concentration ◽  
X Ray ◽  
Lead Bromide ◽  
High Length

Well-oriented PbBr2 microwires with a length-to-width ratio of up to 5000 were grown using a concentration gradient in co-crystallization with perovskite. Planar-integrated microwires showed a response to X-ray photons.

MICRO CAPILLARY ARRAY ELECTROPHORESIS CHIP BY MOVING MASK LIGA TECHNOLOGY

2004 ◽  
Vol 01 (01) ◽  
pp. 39-46
Author(s):  
HUI YOU ◽  
SHOUJI SHINOHARA ◽  
KENNICHI ENAMI ◽  
SHINSUKE SHIBATA ◽  
OSAMU TABATA ◽  
...  
Keyword(s):  
Adhesive Bonding ◽  
Dna Analysis ◽  
Adhesive Layer ◽  
Fabrication Process ◽  
Optical Components ◽  
X Ray ◽  
Laser Induced Fluorescence Detection ◽  
Capillary Array Electrophoresis ◽  
Liga Technology ◽  
Capillary Array

The concept and the fabrication process of a micro capillary array electrophoresis (μ-CAE) for DNA analysis were stated. The chip was mainly made of plastic and fabricated by the innovatory LIGA technology which included moving mask deep X-ray lithography, electroplating and hot embossing, and assembled by an adhesive bonding, in which the adhesive layer solidified and formed firm bond under UV exposure. The micro channel array with high aspect ratio is the key part, whose wall should have a slight inclination to ensure the demoulding. It was demonstrated that moving mask deep X-ray lithography (M2DXL) technology could successfully control the inclination and enabled to integrate micro optical components such as micro lens into the chip, which improved detection performance greatly. The fabrication process was experimented and some initial prototype chips have been obtained. Fluidic test and electrophoresis test with laser-induced fluorescence detection has been done on the prototype. The results confirm that the μ-CAE by moving mask LIGA process is realizable and it has great potential in high-throughput, which may lead to ultra-fast DNA analysis.

Fabrication of high aspect ratio nanoscale periodic structures by the soft X-ray interference lithography

2017 ◽  
Vol 170 ◽  
pp. 49-53 ◽  
Author(s):  
Jun Zhao ◽  
Yanqing Wu ◽  
Chaofan Xue ◽  
Shumin Yang ◽  
Liansheng Wang ◽  
...  
Keyword(s):  
Aspect Ratio ◽  
High Aspect Ratio ◽  
Periodic Structures ◽  
Interference Lithography ◽  
X Ray

Formation of GaN Self-Organized Nanotips by Nanomasking Effect

2001 ◽  
Vol 707 ◽  
Author(s):  
Harumasa Yoshida ◽  
Tatsuhiro Urushido ◽  
Hideto Miyake ◽  
Kazumasa Hiramtsu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Aspect Ratio ◽  
Formation Mechanism ◽  
High Aspect Ratio ◽  
Photoelectron Spectroscopy ◽  
Nanometer Scale ◽  
Xps Analysis ◽  
X Ray ◽  
Self Organized

ABSTRACTWe have successfully fabricated self-organized GaN nanotips by reactive ion etching using chlorine plasma, and have revealed the formation mechanism. Nanotips with a high density and a high aspect ratio have been formed after the etching. We deduce from X-ray photoelectron spectroscopy (XPS) analysis that the nanotip formation is attributed to nanometer-scale masks of SiO2 on GaN. The structures calculated by Monte Carlo simulation of our formation mechanism are very similar to the experimental nanotip structures.

THE COMPARISION OF RADIOLOGICAL DISTANCE DIFFERENCE BETWEEN THE PARS INTER-ARTICULARIS AND PEDICLE, AS A REPRODUCIBLE AND CONSISTENT GUIDE FOR PEDICULAR SCREW FIXATION TECHNIQUE, IN SPINAL LUMBAR SURGERIES.

2021 ◽  
pp. 61-63
Author(s):  
Bharath. V ◽  
Hemanth Kumar ◽  
Ashwanth Narayan ◽  
Venkatachalam .K ◽  
Ashwin. VY ◽  
...  
Keyword(s):  
Ct Images ◽  
Steady Increase ◽  
Lateral Border ◽  
X Ray ◽  
Screw Insertion ◽  
Pedicular Screw ◽  
Pars Interarticularis ◽  
Upward Direction ◽  
Anatomical Relationship ◽  
Distance Difference

The Inter-Pedicular and Inter-Pars distance was measured in a plain AP radiography (X-Ray) of 150 and 75 CT images normal patients between 18- 47 years of age. The aim of the study is to measure the normal Inter-Pedicular and Inter-Pars distance. We found that by studying the anatomical relationship between the inner or medial Pedicular border and the Pars outer or lateral border, gives the Orthopaedic Surgeon a reproducible and consistent guide towards exacting a pedicular screw placing. We found that both X-Ray and CT images shows steady increase in the Ipr and Ipd from L1 to L5, there is a minimal difference from L1-L2 and marked difference seen from L3 to L5, and showing the differences in distances are more in the males, compared to females. The Means of all the groups compared also proves that there is steady raise in the diameter of the IPR and IPD from L1 to L5, where there is dramatical and signicant change in the upward direction, noted from L3 to L5. The mean difference is almost constant from L1to L2. So this study, did essentially to help, establish that, the inner medial border of pedicle, is in near relationship to, the outer lateral border of the Pars-Interarticularis, which helps in establishing the latero-medial entry point for the pedicular screw insertion in the lumbar spine.

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