(a) The proposed system for the parallel generation of ferrofluid droplets is inspired by cactus geometry. by Amirhossein Favakeh (21417884)

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Schematic of the experimental setup for the generation of ferrofluid droplets using a magnet. by Amirhossein Favakeh (21417884)

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The proposed system for the formation of water droplets under gravity. by Amirhossein Favakeh (21417884)

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(a) Dimensionless diameter and (b) droplet generation frequency versus the magnet distance from the vertex of the hydrophilic surface for different ferrofluid injection rates and t... by Amirhossein Favakeh (21417884)

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Parallel Ferrofluid Droplet Generation on a 2-D Nozzle with 2 Branches. by Amirhossein Favakeh (21417884)

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(a) Convex, triangular, and concave geometry at a constant vertex angle. by Amirhossein Favakeh (21417884)

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Process of Ferrofluid Droplet Generation on a Functional Wedged Surface at θ = 20°, Q = 5 ml h^-‍1 and L = 10 mm. by Amirhossein Favakeh (21417884)

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Values of geometry function for some typical hydrophilic surface geometries. by Amirhossein Favakeh (21417884)

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Non-dimensional diameter of droplets versus magnetic and gravitational Bond Number. by Amirhossein Favakeh (21417884)

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Comparison of measured contact angle of ferrofluid droplets on hydrophilic and hydrophobic surfaces. by Amirhossein Favakeh (21417884)

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Radial Ferrofluid Droplet Generation on a 2-D Star-Shaped Nozzle with 4 Vertices. by Amirhossein Favakeh (21417884)

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Radial Ferrofluid Droplet Generation on a 2-D Star-Shaped Nozzle with 3 Vertices. by Amirhossein Favakeh (21417884)

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Variations of (a) non-dimensional diameter and (b) the frequency of droplet generation in terms of the tilting angle for different water flow rates. by Amirhossein Favakeh (21417884)

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Example of sample data. by Xiying Wang (4859998)

“…The results reveal that as the number of nodes in the hidden layer increases, the model’s Mean Squared Error (MSE) and Relative Error (RE) show a decreasing trend, indicating an improvement in model prediction accuracy. …”

Structure of BPNN. by Xiying Wang (4859998)

“…The results reveal that as the number of nodes in the hidden layer increases, the model’s Mean Squared Error (MSE) and Relative Error (RE) show a decreasing trend, indicating an improvement in model prediction accuracy. …”

The workflow of EGA-BPNN. by Xiying Wang (4859998)

“…The results reveal that as the number of nodes in the hidden layer increases, the model’s Mean Squared Error (MSE) and Relative Error (RE) show a decreasing trend, indicating an improvement in model prediction accuracy. …”

S1 Data - by Xiying Wang (4859998)

“…The results reveal that as the number of nodes in the hidden layer increases, the model’s Mean Squared Error (MSE) and Relative Error (RE) show a decreasing trend, indicating an improvement in model prediction accuracy. …”

Algorithm flow of the GA-BPNN model. by Xiying Wang (4859998)

“…The results reveal that as the number of nodes in the hidden layer increases, the model’s Mean Squared Error (MSE) and Relative Error (RE) show a decreasing trend, indicating an improvement in model prediction accuracy. …”

Achieving Improved Ion Swarm Shaping Based on Ion Leakage Control in Ion Mobility Spectrometry by Jiyao Wang (2121157)

“…In Ion Mobility Spectrometry (IMS), ion gates are essential for controlling ion flow, significantly impacting detection sensitivity and resolution. …”

Multivariable analysis to identify significant factors for VTE in the training set. by Ligang Hao (626909)

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