Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles

This paper proposes a vehicular edge federated learning (VEFL) solution, where an edge server leverages highly mobile connected vehicles' (CVs') onboard central processing units (CPUs) and local datasets to train a global model. Convergence analysis reveals that the VEFL training loss depends on the successful receptions of the CVs' trained models over the intermittent vehicle-to-infrastructure (V2I) wireless links. Owing to high mobility, in the full device participation case (FDPC), the edge server aggregates client model parameters based on a weighted combination according to the CVs' dataset sizes and sojourn periods, while it selects a subset of CVs in the partial device participation case (PDPC). We then devise joint VEFL and radio access technology (RAT) parameters optimization problems under delay, energy and cost constraints to maximize the probability of successful reception of the locally trained models. Considering that the optimization problem is NP-hard, we decompose it into a VEFL parameter optimization sub-problem, given the estimated worst-case sojourn period, delay and energy expense, and an online RAT parameter optimization sub-problem. Finally, extensive simulations are conducted to validate the effectiveness of the proposed solutions with a practical 5G new radio (5G-NR) RAT under a realistic microscopic mobility model

Parameter sensitivity analysis of surface plasmon resonance biosensor through numerical simulation

Surface based analytical tools have gained more importance for rapid, sensitive and label-free monitoring of molecular recognition events. Surface plasmon resonance (SPR) has played a prominent role in real time monitoring of surface binding events. SPR is increasing its significance especially for the study of ultrathin dielectric layer. This paper investigates the role of thin films of gold, silver and aluminium for protein detection in SPR biosensors. It is shown that the sensitivity, which is indicated by the shift of plasmon dip, is not linearly related to the thickness of protein but quadratic over a specific range. The approach involves a plot of a reflectivity curve as a function of the angle of incidence

3-D modeling of a carbon nanotube cantilever biosensor

In this paper, 3-D finite element modeling and simulations are carried out to investigate the bending deformation of a single-walled carbon nanotube cantilever biosensor due to mass attached, and addition of a nano-scale particles to the beam tip resulting from the bioparticle detection. In addition, an algorithm for an electrostaticmechanical coupled system is developed. The computed results are in excellent agreement with the well known electrostatic equations that govern the deformation.<br /

Differential Expressed Genes Identified Between African American and European American Keloid Fibroblasts

Keloids are benign fibroproliferative tumors due to dysregulation of collagen remodeling and abnormal wound healing. Although worldwide, there is a higher incidence of keloid disease (KD) in skin of color, little is known about this predisposition. In this study, we used one tissue micro array slide comprised of six AA and 6 EA punch biopsies of primary untreated keloid tissue from the head and neck area was created, following the NanoString® DSP Technology Access Program protocol. The GeoMx Human Whole Transcriptome Atlas Assay was performed, using morphology marker FAP. Polygonal region of interests selection strategy for Fibroblast Activation Protein (FAP) positive cells was conducted. Univariate analysis was performed, using linear regression models to identify differentially expressed genes (DEG) at a false discovery rate (FDR) of 0.05. Ingenuity pathway analysis (IPA) software was used to determine DEG pathway enrichment. 1,450 DEG were identified (p-va

Accelerated Transport through Sliding Dynamics of Rodlike Particles in Macromolecular Networks

Transport of rodlike particles in macromolecular networks is critical for many important biological processes and technological applications. Here, we report that speeding-up dynamics occurs once the rod length L reaches around integral multiple of the network mesh size ax. We find that such a fast diffusion follows the sliding dynamics and demonstrate it to be anomalous yet Brownian. The good agreement between theoretical analysis and simulations corroborates that sliding dynamics is an intermediate regime between hopping and Brownian dynamics, and suggests a mechanistic interpretation based on the rod-length dependent entropic free energy barrier. These theoretical findings are captured by the experimental observations of rods in synthetic networks, and bring new insight into the physics of the transport dynamics in confined media of networks

Photoacoustic Identification of Laser-induced Microbubbles as Light Scattering Centers for Optical Limiting in Liquid Suspension of Graphene Nanosheets

Liquid suspensions of carbon nanotubes, graphene and transition metal dichalcogenides have exhibited excellent performance in optical limiting. However, the underlying mechanism has remained elusive and is generally ascribed to their superior nonlinear optical properties such as nonlinear absorption or nonlinear scattering. Using graphene as an example, we show that photo-thermal microbubbles are responsible for the optical limiting as strong light scattering centers: graphene sheets absorb incident light and become heated up above the boiling point of water, resulting in vapor and microbubble generation. This conclusion is based on direct observation of bubbles above the laser beam as well as a strong correlation between laser-induced ultrasound and optical limiting. In-situ Raman scattering of graphene further confirms that the temperature of graphene under laser pulses rises above the boiling point of water but still remains too low to vaporize graphene and create graphene plasma bubbles. Photo-thermal bubble scattering is not a nonlinear optical process and requires very low laser intensity. This understanding helps us to design more efficient optical limiting materials and understand the intrinsic nonlinear optical properties of nanomaterials

A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images

Pathologists need to combine information from differently stained pathology slices for accurate diagnosis. Deformable image registration is a necessary technique for fusing multi-modal pathology slices. This paper proposes a hybrid deep feature-based deformable image registration framework for stained pathology samples. We first extract dense feature points via the detector-based and detector-free deep learning feature networks and perform points matching. Then, to further reduce false matches, an outlier detection method combining the isolation forest statistical model and the local affine correction model is proposed. Finally, the interpolation method generates the deformable vector field for pathology image registration based on the above matching points. We evaluate our method on the dataset of the Non-rigid Histology Image Registration (ANHIR) challenge, which is co-organized with the IEEE ISBI 2019 conference. Our technique outperforms the traditional approaches by 17% with the Average-Average registration target error (rTRE) reaching 0.0034. The proposed method achieved state-of-the-art performance and ranked 1st in evaluating the test dataset. The proposed hybrid deep feature-based registration method can potentially become a reliable method for pathology image registration.Comment: 22 pages, 12 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Nanoscale Graphene Nanoparticles Conductive Ink Mechanical Performance Based On Nanoindentation Analysis

Common conductive inks can be classified into three categories, which are noble metals, conductive polymers and carbon nanomaterials. Carbon nanomaterials offer many potential opportunities to be applied in printed and flexible electronics. Therefore, this paper aims to produce highly functional conductive ink using graphene nanoparticles with epoxy as a binder. As a baseline, graphene-filler conductive ink was formulated using a minimum percentage at the beginning. Then, the filler loading was increased based on the required conductivity level. This is to make sure the materials are in contact with each other and the movement of an electron will become easier. The formulation of ink, mixing process, printing process and curing process were performed to produce highly conductive graphene ink. The electrical and mechanical properties were assessed using a Four-point probe as per ASTM F390 and Dynamic Ultra Micro Hardness (DUMH) test as per ASTM E2546-1. Graphene Nanoplatelet (GNP) aggregates are unique nanoparticles consisting of shorts stacks of graphene sheets with platelets shape. They typically consist of aggregates of sub-micron platelets that have a particle diameter less than 2 microns, typical particle thickness of a few nanometers, a bulk density of 0.2 to 0.4 g/cc, an oxygen content of 98 wt%, and in the form of black granules. In this paper, the effect on sheet resistivity and nanoindentation for straight line-shape, curve-shape, square-shape and zigzag-shape circuits printed on Thermoplastic Polyurethane (TPU) substrate using Graphene Nanoparticles (GNPs) conductive ink as the connection material were investigated. The samples in this study were fabricated using a screen-printing method with a fixed circuit width of 1 mm, 2 mm and 3 mm. The straight-shape circuit, curve-shape, square-shape and zig-zag-shape circuits represent the electrical connection with 180°, A°, 90° and B° directional angles. The effect of varying circuit width on the sheet resistivity of all printed circuit mentioned before was later measured using Four-point probe. Nanoindentation was conducted using instrumental machines with indenter load and indenter displacement that can be continuously and simultaneously recorded during indenter loading and unloadin