Velalar College of Engineering and Technology

The g-C3N4-incorporated MnO2/MoS2 nanocomposite was successfully fabricated via a two-step hydrothermal approach. Structural and morphological analyses confirmed the uniform dispersion of g-C3N4 within the MnO2/MoS2 matrix, forming a porous interconnected framework that facilitates ion transport. Electrochemical analysis indicates improved charge transfer behaviour, suggesting enhanced electron transport within the composite. Electrochemical investigation in 1 M KOH shows that the MnO2/MoS2@g-C3N4 electrode delivers a high specific capacitance of 1535 F g⁻1 at 1 A g⁻1, outperforming the pristine MnO2/MoS2 electrode. The assembled asymmetric supercapacitor (MnO2/MoS2@g-C3N4//AC) exhibits an energy density of 60.6 Wh kg⁻1 at a power density of 800 W kg⁻1, along with 94.3

The preparation of nanoparticles through sustainable chemical practices offers an eco-friendly and feasible route to advanced nanomaterials. In this study, gold nanoparticles (Au-NPs) were synthesized using Garcinia mangostana (GM) peel extract, which is rich in antioxidants and provides biomolecules acting as bio-reductants, capping, and stabilizing agents. The synthesized Au-NPs were characterized by X-Ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), UV–Vis spectroscopy, scanning electron microscopy (SEM), and photoluminescence (PL) spectroscopy. XRD confirmed the face-centered cubic structure of Au-NPs, while UV–Vis spectra exhibited a characteristic absorption peak around 540 nm. TEM images revealed polydispersed nanoparticles with diverse shapes and sizes. FTIR analysis indicated the presence of phenols, flavonoids, benzophenones, and anthocyanins, suggesting their role in the reduction and stabilization of Au-NPs. The catalytic activity of Au-NPs was demonstrated by the effective degradation of Fuchsin Basic dye, achieving up to 89

Advancing supercapacitor (SC) technology demands smarter electroactive materials to achieve higher performance. Herein, we display the preparation of niobium pentoxide (Nb2O5) and its mesoporous carbon composite using a less toxic hexam- ethylenetetramine (HMTA) as structure directing agent. The prepared nanostructures were explored towards SC electrode applications in Potassium (K+) based electrolyte as an alternate for Lithium (Li) based electrolytes. The morphological investigations reveal the as prepared Nb2O5 exhibiting rod like morphology and the composite sample showing firm attachment of porous carbon. As a SC electrode material porous carbon embedded Nb2O5 nanorods showing specific capacitance of 518 Fg− 1 at a current rate of 1 Ag− 1 in 0.5 M K2SO4 electrolyte with good cyclic life (91

Water scarcity is an increasing challenge in arid and semi-arid regions, making desalination an essential solution for meeting rising freshwater demand. Effective placement and design of desalination plants within water distribution systems are crucial for ensuring long-term sustainability and cost efficiency. Focusing on Bhavnagar District, Gujarat, where rapid population growth, industrial development, and climate variability intensify water stress, this study introduces an integrated framework combining Geographic Information System (GIS), Remote Sensing (RS), and Random Forest (RF) machine learning to identify optimal desalination plant sites and design an efficient distribution network. Using multi-criteria spatial datasets-land use/land cover, slope, elevation, groundwater salinity, coastal proximity, and accessibility-the RF model classified site suitability with high accuracy (AUC > 0.9). The district (6514.33 km(2)) was categorized into five suitability zones: very low (21.87 %), low (29.05 %), moderate (20.36 %), high (16.69 %), and very high (12.02 %). Highly suitable zones were concentrated along coastal belts, supporting seawater intake needs and infrastructure efficiency. An optimized distribution network considering settlement density, demand centers, and pipeline accessibility was developed to minimize losses and costs. This integrated GIS-RS-RF framework provides a scalable model for desalination planning and offers actionable insights for enhancing water security in coastal, water-stressed districts.

Chromosomal abnormality detection is a fundamental task in clinical genetics, as accurate identification of structural and numerical defects is essential for reliable diagnosis and treatment planning. However, many existing learning-based approaches failed to effectively capture diverse discriminative features, limiting their classification performance. To address this challenge, this study proposes FLEMBODA AI, an advanced computational framework designed to enhance the efficiency, accuracy, and robustness of automated chromosome defect detection. At first, the karyotype image is collected. Then, the input is augmented and pre-processed using normalization and a G-bending enhancement approach to ensure that the model's effectiveness generalizes across diverse datasets. After that, the chromosomes are segmented from the pre-processed image using a U-Net approach to isolate chromosomes from the karyotype image. Then, the segmented chromosomes are given as input to the Hybrid Fuzzy-Convolutional Neural Network (Hybrid Fuzzy-CNN) approach. In this, the CNN model performs the Feature Extraction (FE) process, and the Fuzzy logic is used for the classification of chromosomal abnormalities. Here, the VGG-16 is used for weight assignment for the classification. Then, using the Mask Region-centric CNN (Mask R-CNN), chromosome defect localization is performed. Experimental results demonstrate that FLEMBODA AI achieves a recall of 95.3%, precision of 94.8%, and an F1-score of 95.0%, outperforming baseline models. Additionally, the U-Net segmentation model attains an accuracy of 93.8%, contributing significantly to improved abnormality localization and classification performance. Overall, the proposed FLEMBODA AI framework provides a reliable as well as effective solution for automated chromosomal abnormality detection, with strong potential for application in clinical diagnostics and future large-scale genetic analysis systems.