Link and Network-wide Study of Incoherent GN/EGN Models

An unprecedented comparison of closed-form incoherent GN (InGN) models is presented with heterogeneous spans and partially loaded links in elastic optical networks. Results reveal that with accumulated dispersion correction and modulation format terms, the InGN shows higher accuracy

Optical Transmission Plasmonic Color Filter withWider ColorGamut Based on X-Shaped Nanostructure

Extraordinary Optical Transmission Plasmonic Color Filters (EOT-PCFs) with nanostructures have the advantages of consistent color, small size, and excellent color reproduction, making them a suitable replacement for colorant-based filters. Currently, the color gamut created by plasmonic filters is limited to the standard red, green, blue (sRGB) color space, which limits their use in the future. To address this limitation, we propose a surface plasmon resonance (SPR) color filter scheme, which may provide a RGB-wide color gamut while exceeding the sRGB color space. On the surface of the aluminum film, a unique nanopattern structure is etched. The nanohole functions as a coupled grating that matches photon momentum to plasma when exposed to natural light. Metals and surfaces create surface plasmon resonances as light passes through the metal film. The plasmon resonance wavelength can be modified by modifying the structural parameters of the nanopattern to obtain varied transmission spectra. The International Commission on Illumination (CIE 1931) chromaticity diagram can convert the transmission spectrum into color coordinates and convert the spectrum into various colors. The color range and saturation can outperform existing color filters.Funding: This project has received funding from Universidad Carlos III de Madrid and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant 801538

Breast Cancer Detection Based on Simplified Deep Learning Technique With Histopathological Image Using BreaKHis Database

Presented here are the results of an investigation conducted to determine the effectiveness of deep learning (DL)-based systems utilizing the power of transfer learning for detecting breast cancer in histopathological images. It is shown that DL models that are not specifically developed for breast cancer detection can be trained using transfer learning to effectively detect breast cancer in histopathological images. The outcome of the analysis enables the selection of the best DL architecture for detecting cancer with high accuracy. This should facilitate pathologists to achieve early diagnoses of breast cancer and administer appropriate treatment to the patient. The experimental work here used the BreaKHis database consisting of 7909 histopathological pictures from 82 clinical breast cancer patients. The strategy presented for DL training uses various image processing techniques for extracting various feature patterns. This is followed by applying transfer learning techniques in the deep convolutional networks like ResNet, ResNeXt, SENet, Dual Path Net, DenseNet, NASNet, and Wide ResNet. Comparison with recent literature shows that ResNext-50, ResNext-101, DPN131, DenseNet-169 and NASNet-A provide an accuracy of 99.8%, 99.5%, 99.675%, 99.725%, and 99.4%, respectively, and outperform previous studies

SSMS: A Split Step MultiBand Simulation Software

We introduce SSMS, a multiband optical fiber simulator entirely developed in MATLAB. SSMS solves the generalized nonlinear Schrödinger equation relying on the 4th order Runge-Kutta method in Interaction Picture (RK4IP) with adaptive step size approach and compare it with the widely used split-step Fourier method (SSFM). The simulator is validated considering S+C+L multiband transmission. Results show that the RK4IP method is approximately 10× faster than the traditional SSFM model for a similar level of accuracy

Preventing fibrinogen and HSA adsorption by coating surfaces with aqueous extracts of fish proteins

Abstract not available

The adsorption characteristics of osteopontin on hydroxyapatite and gold

The adsorption of osteopontin on hydroxyapatite (HA) and reference gold (Au) surfaces was studied at different protein bulk concentrations over the temperature range 295& 8211;317 K, using quartz crystal microbalance with dissipation (QCM-D) and X-ray photoelectron spectroscopy (XPS). The QCM-D protein adsorption studies were complemented with polyclonal antibodies to examine the availability of protein sequences on the resulting protein layer. The QCM-D and XPS results show that the osteopontin surface mass uptake is larger on Au as compared to HA surfaces within the range of experimental conditions examined (protein bulk concentrations and temperature range), in accordance with the formation of a more compact protein film on Au. The specific antibody binding to the resulting adsorbed osteopontin layer as measured by QCM-D further confirms that the protein packing and conformational/orientational changes occurring during OPN adsorption on Au and HA are different, since fewer antibodies are observed to bind per OPN molecule on Au as compared to HA. The adsorption process on the respective surfaces was modeled using both the Langmuir and Hill adsorption isotherms, and from these isotherm curves, the Gibbs free energy, ∆G, of the osteopontin adsorption was determined. The estimated ∆G values indicate that the osteopontin molecules have a high affinity towards Au, while a lower affinity is observed between osteopontin and HA. By examining the changes in ∆G as a function of temperature, we additionally find that the osteopontin adsorption on HA and Au is endothermic and driven by an increase in entropy