Artificial Neural Network Modelling for Optimizing the Optical Parameters of Plasmonic Paired Nanostructures

The Artificial Neural Network (ANN) has become an attractive approach in Machine Learning (ML) to analyze a complex data-driven problem. Due to its time efficient findings, it has became popular in many scientific fields such as physics, optics, and material science. This paper presents a new approach to design and optimize the electromagnetic plasmonic nanostructures using a computationally efficient method based on the ANN. In this work, the nanostructures have been simulated by using a Finite Element Method (FEM), then Artificial Intelligence (AI) is used for making predictions of associated sensitivity (S), Full Width Half Maximum (FWHM), Figure of Merit (FOM), and Plasmonic Wavelength (PW) for different paired nanostructures. At first, the computational model is developed by using a Finite Element Method (FEM) to prepare the dataset. The input parameters were considered as the Major axis, a, the Minor axis, b, and the separation gap, g, which have been used to calculate the corresponding sensitivity (nm/RIU), FWHM (nm), FOM, and plasmonic wavelength (nm) to prepare the dataset. Secondly, the neural network has been designed where the number of hidden layers and neurons were optimized as part of a comprehensive analysis to improve the efficiency of ML model. After successfully optimizing the neural network, this model is used to make predictions for specific inputs and its corresponding outputs. This article also compares the error between the predicted and simulated results. This approach outperforms the direct numerical simulation methods for predicting output for various input device parameters

A comprehensive deep learning method for empirical spectral prediction and its quantitative validation of nano-structured dimers

Nanophotonics exploits the best of photonics and nanotechnology which has transformed optics in recent years by allowing subwavelength structures to enhance light-matter interactions. Despite these breakthroughs, design, fabrication, and characterization of such exotic devices have remained through iterative processes which are often computationally costly, memory-intensive, and time-consuming. In contrast, deep learning approaches have recently shown excellent performance as practical computational tools, providing an alternate avenue for speeding up such nanophotonics simulations. This study presents a DNN framework for transmission, reflection, and absorption spectra predictions by grasping the hidden correlation between the independent nanostructure properties and their corresponding optical responses. The proposed DNN framework is shown to require a sufficient amount of training data to achieve an accurate approximation of the optical performance derived from computational models. The fully trained framework can outperform a traditional EM solution using on the COMSOL Multiphysics approach in terms of computational cost by three orders of magnitude. Furthermore, employing deep learning methodologies, the proposed DNN framework makes an effort to optimise design elements that influence the geometrical dimensions of the nanostructure, offering insight into the universal transmission, reflection, and absorption spectra predictions at the nanoscale. This paradigm improves the viability of complicated nanostructure design and analysis, and it has a lot of potential applications involving exotic light-matter interactions between nanostructures and electromagnetic fields. In terms of computational times, the designed algorithm is more than 700 times faster as compared to conventional FEM method (when manual meshing is used). Hence, this approach paves the way for fast yet universal methods for the characterization and analysis of the optical response of nanophotonic systems

Machine Learning Regression Approach to the Nanophotonic Waveguide Analyses

Machine learning is an application of artificial intelligence that focuses on the development of computer algorithms which learn automatically by extracting patterns from the data provided. Machine learning techniques can be efficiently used for a problem with a large number of parameters to be optimized and also where it is infeasible to develop an algorithm of specific instructions for performing the task. Here, we combine the finite element simulations and machine learning techniques for the prediction of mode effective indices, power confinement and coupling length of different integrated photonics devices. Initially, we prepare a dataset using COMSOL Multiphysics and then this data is used for training while optimizing various parameters of the machine learning model. Waveguide width, height, operating wavelength, and other device dimensions are varied to record different modal solution parameters. A detailed study has been carried out for a slot waveguide structure to evaluate different machine learning model parameters including number of layers, number of nodes, choice of activation functions, and others. After training, this model is used to predict the outputs for new input device specifications. This method predicts the output for different device parameters faster than direct numerical simulation techniques. Absolute percentage error of less than 5% in predicting an output has been obtained for slot, strip and directional waveguide coupler designs. This study pave the step towards using machine learning based optimization techniques for integrated silicon photonics devices

A Novel approach for Agrobacterium-mediated germ line transformation of Indian Bread wheat (Triticum aestivum) and Pasta wheat (Triticum durum)

Recalcitrance of wheat towards tissue culture procedures has hampered the wide use of conventional transformation techniques for its improvement. In the present study, a novel, non-tissue culture, cost effective approach has been established for the introduction of transgenes in wheat. Dry, mature seeds of two Indian varieties of wheat, Triticum aestivum cv. HD2329 (bread wheat), and Triticum durum cv. PDW215 (pasta wheat), were co-cultivated with Agrobacterium strain GV2260 (p35SGUSINT) and LBA4404 (pCAMBIA 3301), respectively, in the presence of 200 μM acetosyringone. The plantlets testing gus positive were raised till maturity in garden pots. T0 lines were screened by PCR for presence of selectable markers in the transformed plants followed by confirmation with Southern hybridization. In bread wheat, nptII was detected in five primary transformed lines (T0) (ws1, ws2, ws3, ws4, ws5) and the bar gene in three putatively transformed durum wheat lines (wsb1, wsb2, wsb3). The transformation efficiency was calculated as 1.16%, and 0.84% for T. aestivum and T. durum, respectively.Â

Comparison of distal radial access versus standard transradial access in patients with smaller diameter radial Arteries(The distal radial versus transradial access in small transradial ArteriesStudy: D.A.T.A - S.T.A.R study).

AIMS: To evaluate safety and efficacy of distal right radial access (DRRA) compared to right radial access (RRA), for coronary procedures, in patients with smaller diameter radial arteries (SDRA) (radial artery diameter (RAD) < 2.1 mm). METHODS AND RESULTS: This is a retrospective analysis of safety and efficacy of DRRA Vs. RRA in patients undergoing coronary procedures at our cardiac catheterization laboratories over a 10- month period between September 2017 and June, 2018 (first 5 calendar months with RRA-first; next 5 calendar months with DRRA-first). All patients underwent pre-procedure ultrasound of arm arteries. All patients had RAD<2.1 mm (mean RAD 1.63 ± 0.27 mm; RAD≤1.6 mm in 73.5%). Baseline characteristics were similar between groups. Primary end-point of puncture success was significantly lower in DRRA vs RRA group [79.5% vs 98.5%, p < 0.0001]. Puncture success was also lower in the subgroup of patients with RAD <1.6 mm Vs. ≥ 1.6 mm in the DRRA group (p < 0.0001). The secondary end-point of puncture time was significantly higher (2.1 ± 1.4 min vs. 1.0 ± 0.45 min, p < 0.00001) in the DRRA Vs. RRA group. The occurrence of vascular access site complications (including access site hematomas), radial artery occlusion (RAO) and distal RAO at day 1 and day 30 were similar between RRA and DRRA groups.Non-vascular access-site complication was seen only in the DRRA group. CONCLUSION: DRRA is a safe and effective access for coronary procedures; though technically challenging in patients with SDRA (RAD<2.1 mm; mean RAD 1.63 ± 0.27 mm), with lower puncture success and higher puncture time compared to RRA

Membrane interactions of latarcins: Antimicrobial peptides from spider venom

A group of seven peptides from spider venom with diverse sequences constitute the latarcin family. They have been described as membrane-active antibiotics, but their lipid interactions have not yet been addressed. Using circular dichroism and solid-state 15N-NMR, we systematically characterized and compared the conformation and helix alignment of all seven peptides in their membrane-bound state. These structural results could be correlated with activity assays (antimicrobial, hemolysis, fluorescence vesicle leakage). Functional synergy was not observed amongst any of the latarcins. In the presence of lipids, all peptides fold into amphiphilic α-helices as expected, the helices being either surface-bound or tilted in the bilayer. The most tilted peptide, Ltc2a, possesses a novel kind of amphiphilic profile with a coiled-coil-like hydrophobic strip and is the most aggressive of all. It indiscriminately permeabilizes natural membranes (antimicrobial, hemolysis) as well as artificial lipid bilayers through the segregation of anionic lipids and possibly enhanced motional averaging. Ltc1, Ltc3a, Ltc4a, and Ltc5a are efficient and selective in killing bacteria but without causing significant bilayer disturbance. They act rather slowly or may even translocate towards intracellular targets, suggesting more subtle lipid interactions. Ltc6a and Ltc7, finally, do not show much antimicrobial action but can nonetheless perturb model bilayers