RST-DE: Rough Sets-Based New Differential Evolution Algorithm for Scalable Big Data Feature Selection in Distributed Computing Platforms.

In data analysis, data scientists usually focus on the size of data instead of features selection. Owing to the extreme growth of internet resources data are growing exponentially with more features, which leads to big data dimensionality problems. The high volume of features contains much of redundant data, which may affect the feature classification in terms of accuracy.

Smartphone-integrated printed-paper sensor designed for on-site determination of dimethoate pesticide in food samples.

Herein, a user-friendly and portable smartphone-integrated printed-paper sensor was designed with Cu@Ag nanoparticles (NPs) for on-site monitoring of dimethoate pesticide in food samples, and the results obtained are compared with those obtained by UV-vis spectrophotometry. The working principle for identification of dimethoate pesticide is the change of yellow color NPs to reddish-yellow with associated bathochromic shift of absorption peak when pesticide introduced onto the fabricated paper or glass vial containing the NPs. A smartphone-color detector App and colorimetry were used for quantitative analysis of dimethoate in food samples.

Food safety monitoring of the pesticide phenthoate using a smartphone-assisted paper-based sensor with bimetallic Cu@Ag core-shell nanoparticles.

Presently, the use of several pesticides has been continuously rising owing to the increase in the production of food materials to meet the requirements of the growing population of the world. The safety of food materials with regards to pesticides is an important health concern for people. With this aim, we have developed a smartphone-assisted paper-based sensor impregnated with citrate capped Cu@Ag core-shell nanoparticles (NPs) for selective determination of phenthoate pesticides in water and food samples.

LSTM based prediction of malaria abundances using big data.

Malaria prevails in subtropical countries where health monitoring facilities are minimal. Time series prediction models are required to forecast malaria and minimize the effect of this disease on the population. This study proposes a novel scalable framework to predict the instances of malaria in selected geographical locations.

L-cysteine modified silver nanoparticles for selective and sensitive colorimetric detection of vitamin B1 in food and water samples.

The use of L-cysteine modified silver nanoparticles (Cys-capped AgNPs) as a colorimetric probe for determination of vitamin B1 (thiamine) is described in the present work. This method is based on the measurement of red shift of localized surface plasmon resonance (LSPR) band of Cys-capped AgNPs in the region of 200-800 nm. The color of Cys-capped AgNPs was changed from yellow to colorless by the addition of vitamin B1.

Colorimetric and smartphone-integrated paper device for on-site determination of arsenic (III) using sucrose modified gold nanoparticles as a nanoprobe.

An optical colorimetric and smartphone-integrated paper device (SIPD) is demonstrated for determination of As (III) in water and soil samples using sucrose modified gold nanoparticles (AuNPs/Suc) as a nanoprobe. The mechanism for determination of As(III) is experimentally validated by performing UV-Vis, transmission electron microscope (TEM), Fourier transforms infra-red spectroscopy (FTIR) and dynamic light scattering (DLS) measurements. The density function theory (DFT) calculations using B3LYP with 6-311G (2d,2p) and LANL2DZ basis sets is used to theoretically prove the mechanism for determination of As(III).

Management of cataract in Werner syndrome.

Werner syndrome (WS) is a rare progressive disorder. It is characterized by the appearance of unusually accelerated aging (progeria) including bilateral senile cataract. Here, we report a successful management of hypermature cataract in WS.

Sucrose capped gold nanoparticles as a plasmonic chemical sensor based on non-covalent interactions: Application for selective detection of vitamins B and B in brown and white rice food samples.

We report simple and selective method for detection of vitamins B and B in brown and white rice samples using localized surface plasmon resonance (LSPR) of sucrose capped gold nanoparticles (AuNPs) as a chemical sensor colorimetrically. Here, detection is based on the color change of AuNPs from pink to blue followed by a red shift of LSPR absorption band in UV-vis region with the addition of vitamins B and B into the NPs solution A good linear range was observed in the range of 25-1000 ngmL with detection limit of 8 ngmL for B and 50-1000 ngmL with detection limit of 15 ngmL for vitamins B. The employment of AuNPs for detection of B and B vitamins in rice food samples showed remarkable abilities in terms of the simplicity, low cost, stability, reproducibility and sensitivity.