A series of pyridine-based 1,2,3-triazole derivatives were synthesized using both microwave-assisted and conventional methods employing green solvents in the presence of CuSO4 as a catalyst. The synthesized compounds were thoroughly characterized by 1H, 13C NMR, IR, and mass spectroscopic techniques to confirm their structural identities. Furthermore, all compounds were evaluated for their anticancer and antimicrobial activities. Most of the synthesized derivatives exhibited significant biological activity, demonstrating both anticancer and antimicrobial potential. Most of the compounds displayed exceptional cytotoxic activity against the tested cancer cell lines and showed strong inhibitory effects against all evaluated bacterial and fungal strains, indicating their broad-spectrum biological efficacy.
Proposed research presents a data-driven framework for forecasting municipal solid waste (MSW) generation and emission dynamics in Erode City, India, by employing supervised machine learning algorithms. Leveraging a five-year dataset (2019-2024) comprising socio-economic variables, zonal waste typologies, and historical waste volumes, the model integrates Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) classifiers. Feature selection and proximity ranking techniques were applied to identify high-impact variables, with plastic and organic waste emerging as dominant predictors. Data pre-processing included normalization, missing value imputation, and spatial zoning analysis. The model was validated through cross-validation with an 80:20 training-to-testing ratio. Among the tested models, SVM exhibited Superior performance, achieving a prediction accuracy of 96%, lowest mean squared error (MSE = 4860), and minimal computational latency (0.67 seconds), indicating suitability for real-time deployment. The integration of proximity matrix analysis and zonal feature clustering enhanced interpretability and robustness. The proposed framework demonstrates significant potential for scalable waste forecasting applications, enabling emission quantification and strategic decision-making. Future work includes the incorporation of real-time sensor data, temporal decomposition, and hybrid deep learning architectures to optimize waste handling and carbon mitigation strategies.
The present study deals with the Love wave propagation in a dry sandy layer sandwiched between upper monoclinic elastic layer and lower inhomogeneous isotropic elastic half-space. Rectangular type irregularity is considered at the interface of sandy layer and half-space. Analytical expressions for displacement fields are derived by adopting the variable separation method. Dispersion equation for the phase velocity of Love wave is also derived by using suitable boundary conditions and validation with standard Love wave equation is also verified, discussed in particular case. Significant effect of parameter like sandiness, irregularity, inhomogeneity and thickness ratio is observed by analyzing the dispersion equation graphically using MATLAB software. Results obtained regarding Love wave propagation in the considered model serve as major highlights having significant relevance in the field of geophysics, soil mechanics and earthquake engineering.
Identifying influential nodes that can maximize information diffusion is a fundamental problem in social network analysis, commonly referred to as influence maximization. Diffusion model-based and traditional greedy and heuristic algorithms tend to be expensive to calculate, or poorly aware of the network structure, especially when dealing with large networks. To overcome these weaknesses, this paper will introduce a hybrid architecture that will combine Graph Neural Networks (GNNs) with the Ant Colony Optimization (ACO) towards influence maximization. The proposed approach works as follows, where the GNN learns influence representations on the node level, which jointly models both local and higher-order structural dependencies in the network, and ACO uses these learned representations to assemble a non-redundant and diverse seed set. The chosen seed sets are tested on the basis of the Independent Cascade diffusion model where the spread of influence is taken as the key performance measure. The experiments on the real-world social network data indicate that the proposed GNN-ACO framework is steadily more influential in spreading than the current baseline methods and that it has comparative computational efficiency at varying levels of seed set sizes. These findings demonstrate the advantage of optimizing influence maximization by using representation learning with a seed selection strategy based on optimization.
Hydrogen embrittlement (HE) is a widely recognised phenomenon that can expressively impact high-strength materials. In particular, high-strength steels are susceptible to delayed fracture, a form of hydrogen embrittlement that may occur during vehicle service. HE can trigger fracture, accelerate subcritical crack growth, and result in catastrophic failure, ultimately reducing mechanical properties like strength, toughness, and ductility. Hydrogen can penetrate materials either through electrochemical reactions or exposure to high-pressure hydrogen environments. To evaluate its impact on mechanical properties and measure the absorbed hydrogen content, several techniques are employed, such as Slow Strain Rate Testing (SSRT), Linearly Increasing Stress Testing (LIST), and Thermal Desorption Spectroscopy (TDS). In automotive steels, a typical amount of hydrogen can be found in the range of 1 to 5 ppm (parts per million) by weight, but this can vary depending on the type of steel and its processing. The extent of mechanical degradation is not a standard metric but can evident as a significant loss of ductility, such as a loss in ductility by 30
