Neutrosophic classifier: An extension of fuzzy classifer

Fuzzy classification has become of great interest because of its ability to utilize simple linguistically interpretable rules and has overcome the limitations of symbolic or crisp rule based classifiers. This paper introduces an extension to fuzzy classifier: a neutrosophic classifier, which would utilize neutrosophic logic for its working. Neutrosophic logic is a generalized logic that is capable of effectively handling indeterminacy, stochasticity acquisition errors that fuzzy logic cannot handle. The proposed neutrosophic classifier employs neutrosophic logic for its working and is an extension of commonly used fuzzy classifier. It is compared with the commonly used fuzzy classifiers on the following parameters: nature of membership functions, number of rules and indeterminacy in the results generated. It is proved in the paper that extended fuzzy classifier: neutrosophic classifier; optimizes the said parameters in comparison to the fuzzy counterpart. Finally the paper is concluded with justifying that neutrosophic logic though in its nascent stage still holds the potential to be experimented for further exploration in different domains.     

Zernike circle polynomials and optical aberrations of systems with circular pupils

Zernike circle polynomials, their numbering scheme, and relationship to balanced optical aberrations of systems with circular pupils are discussed.     

A Deep Learning Approach to Detection and Mitigation of Distributed Denial of Service Attacks in High Availability Intelligent Transport Systems

Mobile Networks and Applications - In the era of Internet of Things (IoT) powered by 5G technologies, Automobile Industry is headed towards a revolution. In Intelligent Transport Systems (ITS),...     

Green synthesis of highly stable carbon nanodots and their photocatalytic performance

The present study reports a novel, facile, biosynthesis route for the synthesis of carbon nanodots (CDs) with an approximate quantum yield of 38.5%, using Musk melon extract as a naturally derived‐precursor material. The synthesis of CDs was established by using ultraviolet–visible (UV–vis) spectroscopy, Dynamic light scattering, photoluminescence spectroscopy, X‐ray diffraction, transmission electron microscopy and Fourier transform infrared (FTIR) spectroscopy. The as‐prepared CDs possess an eminent fluorescence under UV–light (λ _ex  = 365 nm). The size range of CDs was found to be in the range of 5–10 nm. The authors further explored the use of such biosynthesised CDs as a photocatalyst material for removal of industrial dye. Degradation of methylene blue dye was performed in a photocatalytic reactor and monitored using UV–vis spectroscopy. The CDs show excellent dye degradation capability of 37.08% in 60 min and reaction rate of 0.0032 min⁻¹. This study shows that synthesised CDs are highly stable in nature, and possess potential application in wastewater treatment.Inspec keywords: carbon, nanostructured materials, nanofabrication, catalysis, photochemistry, ultraviolet spectra, visible spectra, photoluminescence, X‐ray diffraction, transmission electron microscopy, Fourier transform infrared spectra, fluorescence, dyesOther keywords: green synthesis, highly stable CD, photocatalytic performance, biosynthesis route, carbon nanodots, quantum yield, Musk melon extract, naturally derived‐precursor material, ultraviolet‐visible spectroscopy, dynamic light scattering, photoluminescence spectroscopy, X‐ray diffraction, transmission electron microscopy, Fourier transform infrared spectroscopy, FTIR spectroscopy, fluorescence, biosynthesised CD, photocatalyst material, industrial dye, methylene blue dye degradation, photocatalytic reactor, UV‐vis spectroscopy, wastewater treatment, size 5 nm to 10 nm, time 60 min     

Self-nanoemulsifying drug delivery system of docosahexanoic acid: development,in vitro,in vivo characterization

Context: Docosahexanoic acid (DHA) is an essential omega-3 fatty acid for normal brain development and its use has increased considerably in recent years.

Objective: The aim of this study is to develop and evaluate self-nanoemulsifying drug delivery systems (SNEDDS) of DHA for improved palatability, dispersibility and bioavailability.

Methods: The SNEDDS were prepared and evaluated for miscibility, employing different combinations of olive oil and soyabean oil as oil phase, Span 80, Span 20, soya phosphatidylcholine, Labrafil M 1944 CS as surfactants while Tween 80, PEG 400, Cremophor RH40 and propylene glycol as cosurfactants. Thermodynamically stable SNEDDS were characterized for dispersibility, self-emulsification time, droplet size, zeta potential along with sensory analysis. The optimized formulation was subjected to ex vivo and in vivo evaluation such as intestinal permeability, memory performance test, brain concentration and histopathology studies.

Results: The optimized SNEDDS formulation showed emulsification time of 27?±?4.7?s with droplet size of 17.6?±?3.5?nm and zeta potential of??37.6?±?0.5?mV. Intestinal absorption study depicted 18.3%, 21.5%, 41.5%, 98.7% absorption of DHA with SNEDDS-based formulation in comparison to 8.2%, 15.1%, 28.8%, 46.1% absorption of DHA with oil-based marketed formulation after 0.5, 1, 2 and 4?h. DHA concentration in brain homogenate was found to be increased to 2.6-fold in comparison to DHA-marketed formulation. This could be ascribed to enhanced dispersibility and bioavailability of DHA from nanosized formulation.

Conclusion: The developed formulation led to enhanced dispersibility and bioavailability of DHA due to the formation of nanodroplets.     

Artificial neural network based system identification and model predictive control of a flotation column

The paper describes the design of a neural network based model predictive controller for controlling the interface level in a flotation column. For the system identification, the tailings valve opening is subjected to a pseudo-random ternary signal and response of the interface level is recorded over a period of time. The data so generated is used to develop a dynamic feed forward neural network model. The model uses two past values and one present value of the tailings valve opening as well as interface level as inputs and predicts the future interface level. This model is used for the design of a model predictive controller to control the interface level. The controller was tested both for liquid–gas system as well as liquid–gas–solid system and was found to perform very satisfactorily. The performance of the controller was compared with that of a conventional PI controller for a two-phase system and was found to be better.     

A lumped-parameter model to investigate the effect of plantar pressure on arterial blood flow in a diabetic foot

This paper presents a lumped-parameter model for the big-toe region that investigates the effect of plantar pressure on the diameter of the blood vessels, specifically the arteries, in the presence of arterial and/or tissue changes. The model developed in this paper uses a multi-domain energy system approach to develop the lumped-parameter differential equations. Blood flow is modelled as fluidic flow through compliant pipes that have inertia, stiffness, and damping. The tissue material is treated as a soft compliant material that transmits the external force to the blood vessels. Conclusions have been drawn to show the effect of plantar pressure, tissue damage, and their combination on the diameter of the blood vessels. The principles used here can be used to model the entire foot and the model used to investigate the effect of plantar pressure, tissue damage, and arterial changes on different parts of the foot. The work presented here may also have applications in other vascular diseases.