JSS Science and Technology University is a private university located in Mysore, Karnataka, India. Established in 1963 as SJCE has 12 departments in engineering, a Master of Computer Applications department. It was affiliated to the Visvesvaraya Technological University, Belgaum, but now it's a part of JSS Science and Technology University from 2016 - 2017 academic year. SJCE is accredited by the All India Council for Technical Education (AICTE), all its departments are accredited by the National Board of Accreditation (NBA). It was founded and is managed by the JSS Mahavidyapeetha.
A series of ethyl 3-benzoyl-7-(3-phenylpropyl)indolizine derivatives (5a-v) were efficiently synthesized via a 1-(2-oxo-2-(p-tolyl)ethyl)-4-(3-phenylpropyl)pyridin-1-ium bromide yield (3a-k) by [3 + 2] cycloaddition strategy, providing rapid access to a structurally diverse indolizine framework. The synthesized compounds were characterized by H-1 NMR, C-13 NMR, IR and HRMS. Their antibacterial activity was evaluated against Escherichia coli and Staphylococcus aureus; several derivatives exhibited significant inhibition, with enhanced potency against S. aureus (95-98%). Notably, compounds 3g, 3h, and 3k demonstrated antibacterial activity against E. coli with inhibition values of 60%, 25%, and 35%, respectively, and overall bactericidal efficiency comparable to gentamycin (similar to 90-97%). Structure-activity relationship (SAR) analysis revealed that electron-withdrawing substituents enhance antibacterial activity, whereas steric bulk reduces efficacy, and lipophilicity facilitates membrane penetration. Molecular docking and molecular dynamics simulations against the MurB enzyme indicated stable binding interactions, suggesting inhibition of bacterial cell wall biosynthesis. Furthermore, DFT studies supported favourable electronic properties. Overall, these findings highlight cationic pyridinium ylide derivatives as promising scaffolds for the development of MurB-targeted antibacterial agent.
A novel series of (S)-3-(5-fluoropyridin-2-yl)-5-(piperidin-3-yl)-1,2,4-oxadiazole derivatives (7a-s) was designed, synthesized, and evaluated for inhibitory activity against Vipera russellii venom phospholipase A2 fraction VIIIa (VRV-PL-VIIIa), a key enzyme implicated in inflammatory pathways. The structures of the synthesized compounds were confirmed by IR, 1H NMR, and 13C NMR spectral analyses. In vitro enzymatic assays demonstrated concentration-dependent inhibition of VRV-PL-VIIIa, with IC50 values ranging from 9.2 to 82 mu M. Among the series, compound 7k exhibited the most potent inhibitory activity (IC50 = 9.2 mu M). Furthermore, compound 7k displayed significant concentration-dependent inhibition of 5-lipoxygenase (5-LOX), indicating its potential as a dual anti-inflammatory agent. Molecular docking studies revealed favorable binding interactions within the sPLA2 active site, with binding affinities ranging from -5.39 to -7.65 kcal/mol. Density Functional Theory (DFT) calculations showed a moderate HOMO-LUMO energy gap (4.27 eV), suggesting an optimal balance between electronic stability and chemical reactivity. ADME predictions indicated favorable pharmacokinetic properties, including good oral bioavailability, membrane permeability, and compliance with Lipinski's rule of five. Additionally, 200 ns molecular dynamics simulations confirmed the stability of the 7k-sPLA2 complex, supported by persistent hydrogen-bonding and hydrophobic interactions within the active site. Collectively, these findings identify compound 7k as a promising lead scaffold for the development of potent sPLA2 inhibitors with potential anti-inflammatory activity.
Among a plethora of tactics, the electrochemical CO2-reduction method (ECO2RR) is a promising pathway to mitigate CO2 emissions and generate value-added chemicals simultaneously. Herein, a bimetallic NiO-HfO2-1 heterojunction catalyst was designed via the co-precipitation method, where the interfacial combination of NiO and HfO2 yields the remarkable synergistic effect due to strengthened interaction. The intrinsic structural and electronic properties were characterised using spectroscopic and analytical techniques. The as-designed catalyst was used as an electrode material for electrochemical analysis. NiO-HfO2-1 catalyst demonstrated a high current density (-33.68 mAcm- 2), a low overpotential and a low Tafel slope (83.38 mV dec-1), indicating that charge transfer through the electrode surface. An M-S plot gave evidence for the n-type semiconducting nature and the position of the band edges for the reduction of CO2. The catalyst was selective for formic acid with a Faradaic efficiency of over 96.08% in a long-term electrolysis test. The DFT computations promoted the experimental studies by showing that the NiO-HfO2-1 heterojunction interface is adept at lowering the energy barrier for CO2 activation. The combination of experimental electrochemical analysis, and DFT validation, which is used here to map the mechanistic route, reveals an intriguing catalyst design for sustainable energy conversion.
An eco-friendly and efficient route for synthesizing reduced graphene oxide (rGO) using Mimosa pudica leaf extract as a green reducing and stabilizing agent is reported. To address the limited understanding of structure–property relationships in green-synthesized rGO, a systematic study is performed. Analysis of graphene oxide (GO) and rGO using XRD, FTIR, Raman, and XPS confirms effective deoxygenation and restoration of sp2 domains. The spin-coated thin films of Mimosa pudica leaf extract reduced graphene oxide (MP-rGO) exhibit increased conductivity with a sheet resistance (Rs) of 373 ± 18 kΩsq−1 and high optical transmittance of 93.61
Reliable condition monitoring of rolling element bearings is essential for improving machinery availability and reducing unplanned downtime. Machine learning (ML) techniques have been widely applied for vibration-based bearing fault diagnosis; however, reported performance is often difficult to interpret due to variations in datasets, operating conditions, and validation protocols. This study presents a controlled experimental benchmark evaluating three classical ML algorithms—Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Artificial Neural Network (ANN)—for vibration-based fault classification of cylindrical roller bearings. Experiments were conducted on SKF N204 bearings using a machinery fault simulator under three bearing conditions (healthy, outer race defect, and roller defect) at four rotational speeds (100–400 RPM). Artificial defects were introduced using electrical discharge machining (EDM), and vibration signals were acquired using a National Instruments data acquisition system. Root mean square (RMS), kurtosis, and crest factor were extracted as interpretable statistical features, resulting in a dataset of 119 feature samples. Model performance was evaluated using stratified train–validation–test splits, with k-fold cross-validation applied uniformly across all classifiers, including the ANN, to ensure fair comparison. Under the present laboratory conditions, the ANN exhibited the most consistent performance across validation and testing, while SVM and k-NN showed slightly lower validation accuracy but comparable testing behavior. The results demonstrate that high classification accuracy can be achieved using simple statistical features and classical ML models in a controlled setting. However, the findings should be interpreted as upper-bound diagnostic performance under laboratory conditions, rather than direct indicators of industrial-scale generalization. The study provides a transparent benchmarking reference and highlights the trade-off between interpretability, data efficiency, and classification performance in bearing health monitoring applications.
