VIT-AP University is a private university located in Amaravati, the capital of Andhra Pradesh, India. It is the first university located in the capital region. VIT-AP was established as a sister university of Vellore Institute of Technology..
The uncontrolled release of dyes and pathogens from textile effluents demands multifunctional materials for water remediation. In this study, TiO2 and 1%, 3%, and 5% Ce-doped TiO2 nanoparticles were synthesised via a Sol-Gel method. Structural and elemental analyses confirmed anatase-phase nanoparticles with successful Ce incorporation, mixed Ce3+/Ce4+ states, and oxygen-vacancy formation. Ce doping reduced the band gap from 3.11 eV (TiO2) to 2.92 eV (3% Ce-TiO2), accompanied by pronounced photoluminescence quenching. Among the synthesized samples, 3% Ce-TiO2 demonstrated superior photocatalytic performance. The catalyst efficiently degraded various cationic and anionic dyes, as well as real textile wastewater, achieving degradation efficiencies of 97% under UV irradiation and 94% under sunlight, while retaining its stability over five cycles. Treated wastewater showed reduced toxicity in Vigna radiata seed germination assays. The material also demonstrated antibacterial activity against Staphylococcus aureus and Escherichia coli, highlighting its potential for sustainable textile wastewater treatment.
The aim of this study was to investigate the simultaneous influence of B4C, BN and SiC reinforcement and heat treatment on the mechanical and tribological behavior of Al 7075 composite materials produced through Inert Gas Assisted Stir Casting. The developed composites showed a significant reduction in material density of 0.96
The diagnosis of illnesses involving myopathy and neuropathy largely depends on the accurate interpretation of surface electromyograms (sEMGs), which represent the electrical activity in muscles. However, sEMG recordings are often distorted by artifacts such as additive white Gaussian noise (AWGN), baseline wander (BW), electrode motion (EM), powerline interference (PLI), and ECG interference, compromising diagnostic accuracy. To ensure precise sEMG signal analysis and facilitate automated disease detection, this study proposes an improved, robust, and optimal adaptive noise cancellation (ANC) methodology. This enhanced ANC employs an efficient arithmetic optimization algorithm (AOA) to dynamically adjust filter coefficients, reducing the root mean square error (RMSE) between the target and filtered signals. Experiments conducted on real sEMG signals corrupted with AWGN, BW, EM, PLI, and ECG interference demonstrate the effectiveness of the AOA-based ANC. Rigorous experimental analysis shows that the proposed AOA-based ANC outperforms the bat algorithm (BATA), cuckoo search algorithm (CSA), particle swarm optimization (PSO), and starfish optimization algorithm (SFOA)-based ANCs, as well as other contemporary sEMG noise removal techniques, based on several standard evaluation metrics, including mean square error (MSE), correlation coefficient (CC), mean difference (MD), signal-to-noise ratio (SNR), maximum error (ME), and normalized root mean square error (NRMSE) under the given experimental conditions. To assess the practical use of the proposed ANC system, a multiclass EMG classification framework using superlet transform (SLT)-based spectrograms and the DenseNet-201 convolutional neural network (CNN) is developed to classify sEMG signals into thumb up (TU), pointing index (PI), and wrist extension with a closed hand (WEWCH) movements.
Stock market prediction plays an important role in economic decisions and informed investment, but it remains a challenging task due to the market’s nonlinear, dynamic and uncertain nature. Existing statistical and deep learning approaches often struggled with limited generalization, overfitting issues and inadequate feature representation. By motivating these issues, a novel StockGAN + + model is introduced by integrating generative adversarial learning and graph-based modeling for stock price prediction. Here, two different types of datasets were used, such as NASDAQ and the stock ticker dataset. At the initial stage, the input data is normalized by z-score normalization for preprocessing. High-level features are extracted from the preprocessed data using a stacked autoencoder module. Based on the collected features, stock prediction is performed by the StockGAN + + model, which combines a gated graph convolutional network (GGCN) and a temporal convolutional autoencoder (TCAE) as a discriminator and generator. The hyperparameters are dynamically tuned using improved chaotic assisted grasshopper optimization (Imp-CGop). The proposed model obtains lower MSE values of 0.0000364 and a correlation of 0.997 at the National Stock Exchange (NSE) dataset. The proposed model has obtained better performance when compared to the state-of-the-art models.
Sambhar Lake ecosystem, India’s largest inland saline wetland ecosystem, has undergone siginificant hydrological and land-use changes in recent decades. The current study integrates remote sensing and geospatial analysis to assess land use/land cover (LULC), water extent seasonality, algal bloom patterns, and long-term wetland transitions in Sambhar Lake under 2 km buffer. LULC classification for the monsoon season of 2023 identified eleven classes, with water accounting for the largest share (24.7
