An analysis of bacteria separation and filtration from blood sample using passive methods

Panwala, Fenil C.; Kumar, R.; Shakeel, P. Mohamed

doi:10.1016/j.measurement.2019.02.037

Cited by 8 publications

(3 citation statements)

References 48 publications

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“…Initially,

R_{s}

records are arbitrarily (randomly) chosen with substitution to build a training dataset for each tree. Then, as proposed in [39], without substitution,

\sqrt{A}

arbitrary attributes are selected from

A

original attributes of the training dataset [22].…”

Section: Bci For Moocs Methodologymentioning

confidence: 99%

Brain–computer interface for assessment of mental efforts in e‐learning using the nonmarkovian queueing model

Balamurugan

Mullai

Soundararajan³

et al. 2020

Comp Applic In Engineering

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The rapid advancement in information and communication technology has made e‐learning an alternative learning method for many learners. In the last few years, a huge number of learners around the world have registered in massive open online courses (MOOCs) provided by various online learning platforms. However, MOOC platforms have a vital task for the online course provider to provide enhanced students' learning experiences and satisfaction. In this work, we developed a brain–computer interface for gathering data and detecting a learner's mental situation by observing MOOC videos and electroencephalogram (EEG) devices based on John Sweller's Cognitive Load Theory. The acquired EEG signals are preprocessed with two different normalization methods to scale signals. To validate the introduced framework, the system adopted three machine learning algorithms (random forest using non‐Markovian model, support vector machine, and k‐nearest neighbors) to develop a model with preprocessed training data and test the classifiers to validate their ensemble classifiers' performance. Finally, experimental analysis showed that the random forest classifier with the non‐Markovian approach achieved more than the other two techniques in the form of overall accuracy (99.15%) and F‐measures (99.21%).

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“…Initially,

R_{s}

records are arbitrarily (randomly) chosen with substitution to build a training dataset for each tree. Then, as proposed in [39], without substitution,

\sqrt{A}

arbitrary attributes are selected from

A

original attributes of the training dataset [22].…”

Section: Bci For Moocs Methodologymentioning

confidence: 99%

Brain–computer interface for assessment of mental efforts in e‐learning using the nonmarkovian queueing model

Balamurugan

Mullai

Soundararajan³

et al. 2020

Comp Applic In Engineering

View full text Add to dashboard Cite

show abstract

“…In another study, a label-free, size-based bacteria separation method from whole blood was achieved using elasto-inertial microfluidics [47]. In elasto-inertial microfluidics, the migration of blood cells into a non-Newtonian solution is facilitated by viscoelastic flow, while smaller bacteria remain in the streamline at the blood sample entrance and can be effectively separated.…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost Laser-Prototyped Microfluidic Device for Separating Cells and Bacteria

Gucluer,

Guler

2023

Applied Sciences

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Simple and rapid fabrication of microfluidic devices can enable widespread implementation of lab-on-chip devices in resource-limited environments. However, currently most of the microfluidic devices are fabricated in cleanroom facilities that are well-funded and not accessible to most of the researchers in developing countries. Herein, a simple, low-cost, and reliable method is shown to fabricate microfluidic devices for separating cells and bacteria-size microparticles. For this purpose, serpentine and spiral microfluidic channels are designed and fabricated using rapid laser prototyping. This single inlet microfluidic device is shown to successfully separate yeast cells and smaller microparticles with an efficiency of 85% which is very promising for many lab-on-chip applications including cell-based diagnostics and therapeutics.

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“…[3]. These design methods are commonly categorized into active ones which use external actuators (e.g., using pneumatic, electric, magnetic, acoustic, optical, centrifugal forces) [4][5][6], and passive ones by dint of specially designed channels [7][8][9]. Actuators provide better control flexibility, but external electric devices inevitably increase the system complexity.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Effects of Key Design Factors of a Negative-Differential-Resistance (NDR) Microfluidic Oscillator – An Equivalent-Circuit-Model Approach

Wu¹,

Xia²,

Wang³

et al. 2022

AAMM

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Numerical study on dynamic hydroelastic problems is usually rather complex due to the coupling of fluid and solid mechanics. Here, we demonstrate that the performance of a hydroelastic microfluidic oscillator can be analyzed using a simple equivalent circuit model. Previous studies reveal that its transition from the steady state to the oscillation state follows the negative-differential-resistance (NDR) mechanism. The performance is mainly determined by a bias fluidic resistor, and a pressurevariant resistor which further relates to the bending stiffness of the elastic diaphragm and the depth of the oscillation chamber. In this work, a numerical study is conducted to examine the effects of key design factors on the device robustness, the applicable fluid viscosity, the flow rate, and the transition pressure. The underlying physics is interpreted, providing a new perspective on hydroelastic oscillation problems. Relevant findings also provide design guidelines of the NDR fluidic oscillator.

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An analysis of bacteria separation and filtration from blood sample using passive methods

Cited by 8 publications

References 48 publications

Brain–computer interface for assessment of mental efforts in e‐learning using the nonmarkovian queueing model

Brain–computer interface for assessment of mental efforts in e‐learning using the nonmarkovian queueing model

A Low-Cost Laser-Prototyped Microfluidic Device for Separating Cells and Bacteria

Analyzing the Effects of Key Design Factors of a Negative-Differential-Resistance (NDR) Microfluidic Oscillator – An Equivalent-Circuit-Model Approach

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