K. S. Rangasamy College of Technology (KSRCT) is an autonomous engineering college, affiliated to Anna University, Chennai, near Thiruchengode, Tamil Nadu, south India. The foundation stone of the college was laid in 1994, by Lion Dr K. S. Rangasamy, the institution offers 13 undergraduate and 9 postgraduate programmes and draws students from every state in India. It is a part of the KSR Group of Institutions. S.
Early diagnosis of cervical cancer is a long-term problem, specifically, under the conditions of low-resource environment, access to sophisticated diagnostic tools is limited. In order to overcome this challenge, this paper proposes a hybrid surface plasmon resonance (SPR) biosensor where detection of the Squamous Cell Carcinoma Antigen (SCC-Ag) as an important biomarker of cervical cancer becomes label-free with the embedding of machine learning techniques. The sensor design is constructed following a multilayer meta surface design containing graphene, MoS2 and MXenes which allow localization of the exciting electric fields and interaction with the biomolecules. Despite the fact the earlier versions have had a problem of signal reproducibility, complexity of fabrication, and real-time analysis, the limitations are overcome by implementation of intelligent modelling which uses 1D-Convolutional Neural Networks (1D-CNN) and analysis schemes. FDTD and TMM simulations allowed understanding and optimizing the proposed meta surface architecture, whereas AI models were then trained on sensor outputs and produced accurate prediction and classification. Findings indicate a low limit of detection of (0.02 ng/mL), a high value of sensitivity (186 deg/RIU) and a classification accuracy higher than 97
This study investigates the influence of Paper Mill Sludge Ash (PMSA) on the mechanical, microstructural, and durability properties of metakaolin-based geocrete. Metakaolin (MK) was partially substituted with PMSA at varying levels (5
In this study, we present a microwave-assisted synthesis to produce NiO, MWCNT@NiO, and rGO@NiO hybrid nanostructures efficiently. Comprehensive characterizations, including XRD, FTIR, FESEM, TEM, EDX, and BET confirmed the formation and structural integrity of MWCNT@NiO and rGO@NiO nanostructures. The nano-structures' electrochemical efficiency was examined in a 2 M KOH electrolyte. The specific capacitance of the rGO@NiO nanostructure is found to be the highest, with 491F/g at a current density of 1 A/g, compared to pure NiO (255F/g) and MWCNT@NiO (370F/g). This greater performance comes from the collaborative properties of the reduced graphene oxide, providing better ion diffusion, charge transfer efficiency, and active surface area with exemplary stability of capacitance of 91 % after 5000 cycles, which is much better cycling and mechanical stability than that of MWCNT@NiO and pure NiO. Furthermore, the electrochemical performance of the rGO@NiO ASC device was assessed using 1 M KOH as the electrolyte throughout a potential range of 0 to 1.2 V. At a current density of 1A/g, the device provided 31.92 Wh/kg energy density and 599.96 W/kg power density. The ASC device demonstrated good charge-discharge behavior, indicating superior capacitance properties and efficient ion transport. Thus, the rGO@NiO hybrid nanostructure can be a potential material for supercapacitor applications.
The project is to create a better online learning environment by using timely interventions to identify and assist students that are at risk of failure. The process starts with loading the training and testing datasets, which include study variables, engagement intensity, time-dependent factors, and assessment scores. Subsequently, various the prediction models are constructed with the help of machine-learning algorithms such as Random Forest, Support Vector Machine, K-Nearest Neighbors, and Extra Tree Classifier. In order to assign each student to a risk category, the data of each algorithm is divided into two parts-training and testing. Random Forest gets the highest prediction accuracy while other algorithms provide the insights for comparison. With the help of student behavior and engagement analysis, the instructors can take timely actions in support of the learning process. The early identification of at- risk students help to improve study habits, participation, and finally academic outcomes overall. The combination of ensemble learning and distance- based methods guarantees accuracy and dependability in predictions. The system is such that timely interventions are allowed, which in turn improve student engagement and performance. This whole process creates a nurturing online learning area and also brings about academic success.
High-performance supercapacitor electrodes were developed by integrating vanadium pentoxide (V2O5) with multi-walled carbon nanotubes (MWCNTs) to achieve a synergistic balance between high conductivity and enhanced energy storage capability. The incorporation of MWCNTs effectively improved the electrical pathways, while V2O5 provided abundant redox-active sites, resulting in superior charge storage behavior. V2O5/MWCNT nanocomposites were synthesized via a facile hydrothermal route and comprehensively characterized using x-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy, scanning electron microscopy (SEM), and transmission electron microscopy (TEM) analyses to confirm their crystalline structure, surface morphology, and compositional integrity. Electrochemical evaluation through cyclic voltammetry (CV), galvanostatic charge–discharge (GCD), and electrochemical impedance spectroscopy (EIS) in 3 M KOH electrolyte revealed remarkable capacitive characteristics and stability. The optimized electrode exhibited high specific capacitance of 89.654 F g−1 at 0.1 A g−1, retaining 91.56
