Numerical analysis on the fate and transport of TiO nanoparticles in fractured porous media: Addressing intrinsic heterogeneities in sorptive mass transfer.

The aggregation and slow migration of nanoparticles in aqueous media have caused serious concerns about their fate and impacts in the subsurface environment. Anthropogenic release and distribution of TiO nanoparticles (TNP) have immense potential for surface adsorption, occlusion, impregnation, bioaccumulation, and phase partition into various environmental compartments, and the actual risks in their interactions are still unknown. In an attempt to realize the extent of source zone migration of TNP in a fracture-skin-matrix (F-S-M) medium, a numerical model is developed and analyzed for sensitivity of certain features of the flow field.

High frequency of transition to transversion ratio in the stem region of RNA secondary structure of untranslated region of SARS-CoV-2.

Introduction: The propensity of nucleotide bases to form pairs, causes folding and the formation of secondary structure in the RNA. Therefore, purine (R): pyrimidine (Y) base-pairing is vital to maintain uniform lateral dimension in RNA secondary structure. Transversions or base substitutions between R and Y bases, are more detrimental to the stability of RNA secondary structure, than transitions derived from substitutions between A and G or C and T.

Stochastic intelligent computing solvers for the SIR dynamical prototype epidemic model using the impacts of the hospital bed.

The present investigations are related to design a stochastic intelligent solver using the infrastructure of artificial neural networks (ANNs) and scaled conjugate gradient (SCG), i.e. ANNs-SCG for the numerical simulations of SIR dynamical prototype system based impacts of hospital bed.

Triazole-linked amidopyrene-tagged fluorometric probe for Au ions and pH control aggregation-induced emission.

In this paper, an amidopyrene-tagged reversible fluorescence probe 1 has been constructed for the detection of Au(III) ions in HO/CHCN (4/1, v/v). It is used to identify the Au(III) ions over several metal ions with excellent sensitivity (LOD: 0.061 µM).

Identification of breast lesion through integrated study of gorilla troops optimization and rotation-based learning from MRI images.

Breast cancer has emerged as the most life-threatening disease among women around the world. Early detection and treatment of breast cancer are thought to reduce the need for surgery and boost the survival rate. The Magnetic Resonance Imaging (MRI) segmentation techniques for breast cancer diagnosis are investigated in this article.

A comprehensive review of COVID-19 detection with machine learning and deep learning techniques.

Purpose: The first transmission of coronavirus to humans started in Wuhan city of China, took the shape of a pandemic called Corona Virus Disease 2019 (COVID-19), and posed a principal threat to the entire world. The researchers are trying to inculcate artificial intelligence (Machine learning or deep learning models) for the efficient detection of COVID-19. This research explores all the existing machine learning (ML) or deep learning (DL) models, used for COVID-19 detection which may help the researcher to explore in different directions.

Nitrate transport in a fracture-skin-matrix system under non-isothermal conditions.

Subsurface leaching of agricultural runoff has been identified to pose a serious hazard to the soil-water ecosystem and human health, mostly due to the associated contamination with nitrate. Our understanding of the nature of contaminant spread in the vadose and aquifer zones has been improved from recent mechanistic models on the flow and transport of contaminants through fractured porous media. The present study aims to explore the impacts of skin formation in a fracture-matrix aquifer system onto the nitrogen species transport under non-isothermal settings using numerical modeling.

Wavelet kernel least square twin support vector regression for wind speed prediction.

Wind energy is a powerful yet freely available renewable energy. It is crucial to predict the wind speed (WS) accurately to make a precise prediction of wind power at wind power generating stations. Generally, the WS data is non-stationary and wavelets have the capacity to deal with such non-stationarity in datasets.

Panoramic tongue imaging and deep convolutional machine learning model for diabetes diagnosis in humans.

Diabetes is a serious metabolic disorder with high rate of prevalence worldwide; the disease has the characteristics of improper secretion of insulin in pancreas that results in high glucose level in blood. The disease is also associated with other complications such as cardiovascular disease, retinopathy, neuropathy and nephropathy. The development of computer aided decision support system is inevitable field of research for disease diagnosis that will assist clinicians for the early prognosis of diabetes and to facilitate necessary treatment at the earliest.

SMF/FSO integrated dual-rate reliable and energy efficient WDM optical access network for smart and urban communities.

To handle the massive high-speed internet traffic, free space optics (FSO) or single-mode fiber (SMF) based fiber optic communication is being used everywhere across the world. These technologies are capable of providing huge bandwidth and transmitting the data at very high speed with low energy consumption. FSO is a very convenient technology to quickly expand the legacy network in the adverse geographical areas.

Identification of tyrosine kinase inhibitors from Panax bipinnatifidus and Panax pseudoginseng for RTK-HER2 and VEGFR2 receptors, by in silico approach.

Breast and stomach cancer is reported as a leading cause for human mortality across the world. The overexpression of receptor tyrosine kinase (RTK) proteins, namely the human epidermal growth factor receptor2 (HER2) and the vascular endothelial growth factor receptor2 (VEGFR2), is reported to be responsible for development and metastasis of breast and stomach cancer. Although several synthetic tyrosine kinase inhibitors (TKIs) as drug candidates targeting RTK-HER2 and VEGFR2 are currently available in the market, these are expensive with the reported side effects.

Numerical modelling of nitrate transport in fractured porous media under non-isothermal conditions.

Subsurface contamination is a frequent occurrence in fractured porous systems, posing a potential threat for the groundwater contamination. Tracking the movement of these contaminants is an inherent aspect of effective remediation strategy. The non-isothermal conditions prevailing in the subsurface environment further add to the complexity of the existing scenario.

Short-term wind speed prediction using hybrid machine learning techniques.

Wind energy is one of the potential renewable energy sources being exploited around the globe today. Accurate prediction of wind speed is mandatory for precise estimation of wind power at a site. In this study, hybrid machine learning models have been deployed for short-term wind speed prediction.

based identification of phytochemicals from Thunb. as potential inhibitors for overexpressed HER2 and VEGFR2 cancer genes.

Human epidermal growth factor receptor2 (HER2) and Vascular endothelial growth factor receptor2 (VEGFR2) - a tyrosine kinase receptors play a key role in breast and stomach cancers. The overexpression of HER2 and VEGFR2 genes increases the number of HER2 and VEGFR2 in the cell which initiates breast and stomach cancer respectively. The phytochemicals from traditional medicinal herb Thunb.

On Regularization Based Twin Support Vector Regression with Huber Loss.

Twin support vector regression (TSVR) is generally employed with -insensitive loss function which is not well capable to handle the noises and outliers. According to the definition, Huber loss function performs as quadratic for small errors and linear for others and shows better performance in comparison to Gaussian loss hence it restrains easily for a different type of noises and outliers. Recently, TSVR with Huber loss (HN-TSVR) has been suggested to handle the noise and outliers.

Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks.

Researchers around the world are applying various prediction models for COVID-19 to make informed decisions and impose appropriate control measures. Because of a high degree of uncertainty and lack of necessary data, the traditional models showed low accuracy over the long term forecast. Although the literature contains several attempts to address this issue, there is a need to improve the essential prediction capability of existing models.

Optimization of the operation of packed bed bioreactor to improve the sulfate and metal removal from acid mine drainage.

The present study discusses the potentiality of using anaerobic Packed Bed Bioreactor (PBR) for the treatment of acid mine drainage (AMD). The multiple process parameters such as pH, hydraulic retention time (HRT), concentration of marine waste extract (MWE), total organic carbon (TOC) and sulfate were optimized together using Taguchi design. The order of influence of the parameters towards biological sulfate reduction was found to be pH > MWE > sulfate > HRT > TOC.