Novel, Gasketless, Interconnect Using Parallel Superhydrophobic Surfaces for Modular Microfluidic Systems

2011 ◽  
Author(s):  
Christopher R. Brown ◽  
Bahador Farshchian ◽  
Pin-Chuan Chen ◽  
Taehyun Park ◽  
Sunggook Park ◽  
...  
Keyword(s):  
Steady State ◽  
Analytical Model ◽  
Liquid Bridge ◽  
Contact Angles ◽  
Superhydrophobic Surfaces ◽  
Maximum Pressure ◽  
The Novel ◽  
Microfluidic Systems ◽  
Order Of Magnitude ◽  
State Pressure

A novel, modular, microfluidic interconnect was developed using parallel superhydrophobic interfaces to facilitate the transport of fluids between component chips in modular microfluidic systems. A static analytical model, derived from the Laplace equation [1], approximates the maximum steady-state pressure of the liquid at the liquid bridge which forms across the gap between the chips. Preliminary experiments using parallel superhydrophobic surfaces on PMMA validated the concept. Additional experiments controlled the gap distance, measured contact angles of the superhydrophobic surfaces, gradually increased the pressure of the novel, gasketless, interconnect until rupture to find the maximum pressure across the liquid bridge and verify the model. The measured pressures were on the same order of magnitude (1–10 kPa) as estimated using the model for gap distances of 25 μm and 100 μm.

Analytical Model of Post-Impact Droplet Spreading on a Micro-Patterned Superhydrophobic Surface With Surface Slip

2014 ◽  
Author(s):  
Cristian E. Clavijo ◽  
Julie Crockett ◽  
Daniel Maynes
Keyword(s):  
Analytical Model ◽  
Contact Angles ◽  
Superhydrophobic Surfaces ◽  
Analytical Models ◽  
Impact Behavior ◽  
Droplet Deformation ◽  
Droplet Spreading ◽  
Surface Slip ◽  
Classical Energy ◽  
Dynamical Parameters

Several analytical models exist to predict droplet impact behavior on superhydrophobic surfaces. However, no previous model has rigorously considered the effect of surface slip on droplet spreading and recoiling that is inherent in many superhydrophobic surfaces. This paper presents an analytical model that takes into account surface slip at the solid-fluid interface during droplet deformation. The effects of slip are captured in terms that model the kinetic energy and viscous dissipation and are compared to a classical energy conservation model given by Attane et al. and experimental data from Pearson et al. A range of slip lengths, Weber numbers, Ohnesorge numbers, and contact angles are investigated to characterize the effects of slip over the entire range of realizable conditions. We find that surface slip does not influence normalized maximum spread diameter for low We but can cause a significant increase for We > 100. Surface slip affects dynamical parameters more profoundly for low Oh numbers (0.002–0.01). Normalized residence time and rebound velocity increase as slip increases for the same range of We and Oh. The influence of slip is more significantly manifested on normalized rebound velocity than normalized maximum spread diameter. Contact angles in the range of 150°–180° do not affect impact dynamics significantly.

Utilizing an Adaptive Controller (Azadi Controller) for Trajectory Planning of PUMA 560 Robot

2011 ◽  
Vol 403-408 ◽  
pp. 4880-4887
Author(s):  
Sassan Azadi
Keyword(s):  
Steady State ◽  
Trajectory Planning ◽  
Positive Feedback ◽  
Fuzzy Controller ◽  
Research Work ◽  
Adaptive Controller ◽  
The Novel ◽  
Transient Responses ◽  
Simulation Results ◽  
Good Substitute

This research work was devoted to present a novel adaptive controller which uses two negative stable feedbacks with a positive unstable positive feedback. The positive feedback causes the plant to do the break, therefore reaching the desired trajectory with tiny overshoots. However, the two other negative feedback gains controls the plant in two other sides of positive feedback, making the system to be stable, and controlling the steady-state, and transient responses. This controller was performed for PUMA-560 trajectory planning, and a comparison was made with a fuzzy controller. The fuzzy controller parameters were obtained according to the PSO technique. The simulation results shows that the novel adaptive controller, having just three parameters, can perform well, and can be a good substitute for many other controllers for complex systems such as robotic path planning.

Analytical Model to Predict Nonhomogeneous Soil Temperature Variation

1995 ◽  
Vol 117 (2) ◽  
pp. 100-107 ◽  
Author(s):  
M. Krarti ◽  
D. E. Claridge ◽  
J. F. Kreider
Keyword(s):  
Thermal Properties ◽  
Soil Temperature ◽  
Temperature Variation ◽  
Analytical Model ◽  
Soil Surface ◽  
Deep Soil ◽  
Soil Thermal Properties ◽  
Order Of Magnitude ◽  
Soil Surface Temperature ◽  
Annual Time

This paper presents an analytical model to predict the temperature variation within a multilayered soil. The soil surface temperature is assumed to have a sinusoidal time variation for both daily and annual time scales. The soil thermal properties in each layer are assumed to be uniform. The model is applied to two-layered, three-layered, and to nonhomogeneous soils. In case of two-layered soil, a detailed analysis of the thermal behavior of each layer is presented. It was found that as long as the order of magnitude of the thermal diffusivity of soil surface does not exceed three times that of deep soil; the soil temperature variation with depth can be predicted accurately by a simplified model that assumes that the soil has constant thermal properties.

scholarly journals RON-Gauss: Enhancing Utility in Non-Interactive Private Data Release

2019 ◽  
Vol 2019 (1) ◽  
pp. 26-46 ◽  
Author(s):  
Thee Chanyaswad ◽  
Changchang Liu ◽  
Prateek Mittal
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Differential Privacy ◽  
Real Data ◽  
The Novel ◽  
Private Data ◽  
Data Release ◽  
Machine Learning Applications ◽  
Order Of Magnitude ◽  
Real World Datasets

Abstract A key challenge facing the design of differential privacy in the non-interactive setting is to maintain the utility of the released data. To overcome this challenge, we utilize the Diaconis-Freedman-Meckes (DFM) effect, which states that most projections of high-dimensional data are nearly Gaussian. Hence, we propose the RON-Gauss model that leverages the novel combination of dimensionality reduction via random orthonormal (RON) projection and the Gaussian generative model for synthesizing differentially-private data. We analyze how RON-Gauss benefits from the DFM effect, and present multiple algorithms for a range of machine learning applications, including both unsupervised and supervised learning. Furthermore, we rigorously prove that (a) our algorithms satisfy the strong ɛ-differential privacy guarantee, and (b) RON projection can lower the level of perturbation required for differential privacy. Finally, we illustrate the effectiveness of RON-Gauss under three common machine learning applications – clustering, classification, and regression – on three large real-world datasets. Our empirical results show that (a) RON-Gauss outperforms previous approaches by up to an order of magnitude, and (b) loss in utility compared to the non-private real data is small. Thus, RON-Gauss can serve as a key enabler for real-world deployment of privacy-preserving data release.

scholarly journals The novel dipeptide ligand of TASP

2019 ◽  
Vol 484 (2) ◽  
pp. 228-232
Author(s):  
O. A. Deeva ◽  
A. S. Pantileev ◽  
I. V. Rybina ◽  
M. A. Yarkova ◽  
T. A. Gudasheva ◽  
...  
Keyword(s):  
Elevated Plus Maze ◽  
Open Field Test ◽  
Anxiolytic Activity ◽  
The Novel ◽  
Minimum Effective Dose ◽  
Elevated Plus Maze Test ◽  
Maze Test ◽  
Plus Maze ◽  
Order Of Magnitude ◽  
Preliminary Administration

Using the previously obtained first dipeptide ligand TSPO the N‑carbobenzoxy-L‑tryptophanyl-L‑isoleucine amide (GD‑23) as a basis, the new dipeptide was synthesized — the N‑phenylpropionyl–L‑tryptophanyl-L‑leucine amide (GD‑102). GD‑102 expressed anxiolytic activity in the open field test in BALB/c mice and in the elevated plus maze test in ICR mice. The minimum effective dose of GD‑102 was an order of magnitude lower than that of GD‑23. Preliminary administration of the TSPO selective antagonist, compound PK11195, completely blocked the anxiolytic activity of GD‑102, that indicated the participation of TSPO in the realization of the anxiolytic action GD‑102. The results were confirmed by molecular docking data.

scholarly journals TOXIFY: a deep learning approach to classify animal venom proteins

2019 ◽  
Author(s):  
T Jeffrey Cole ◽  
Michael S Brewer
Keyword(s):  
Protein Sequence ◽  
Software Package ◽  
Shotgun Proteomics ◽  
Learning Approach ◽  
The Novel ◽  
Venom Protein ◽  
Order Of Magnitude ◽  
Gated Recurrent Units ◽  
Venom Proteins ◽  
Generation Sequencing

In the era of Next-Generation Sequencing and shotgun proteomics, the sequences of animal toxigenic proteins are being generated at rates exceeding the pace of traditional means for empirical toxicity verification. To facilitate the automation of toxin identification from protein sequences, we trained Recurrent Neural Networks with Gated Recurrent Units on publicly available datasets. The resulting models are available via the novel software package TOXIFY, allowing users to infer the probability of a given protein sequence being a venom protein. TOXIFY is more than 20X faster and uses over an order of magnitude less memory than previously published methods. Additionally, TOXIFY is more accurate, precise, and sensitive at classifying venom proteins. Availability: https://www.github.com/tijeco/toxify

scholarly journals Predictive System of COVID -19 Using Response Based Analytical Model

2021 ◽  
pp. 05-10
Author(s):  
Pallavi Mirajkar ◽  
Rupali Dahake
Keyword(s):  
Analytical Model ◽  
Medical Services ◽  
The Novel ◽  
Response Data ◽  
Proposed Model ◽  
The World

The novel COVID sickness 2019 (COVID-19) pandemic caused by the SARS-CoV-2 keeps on representing a serious and vital threat to worldwide health. This pandemic keeps on testing clinical frameworks around the world in numerous viewpoints, remembering sharp increments in requests for clinic beds and basic deficiencies in clinical equipments, while numerous medical services laborers have themselves been infected. We have proposed analytical model that predicts a positive SARS-CoV-2 infection by considering both common and severe symptoms in patients. The proposed model will work on response data of all individuals if they are suffering from various symptoms of the COVID-19. Consequently, proposed model can be utilized for successful screening and prioritization of testing for the infection in everyone.

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