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.
Aim: This study aims to address existing gaps in air quality research by assessing the association between Air Quality Index (AQI) and traffic volume across eight road sites in Greater Noida, India, using the National Ambient Air Quality Standards (NAAQS) as benchmarks over multiple seasons. Notably, this work is one among several systematic studies to comprehensively correlate AQI and traffic patterns in Greater Noida, enhancing the understanding through multi-seasonal data. Methods: Ambient air pollutants including SO2, NO2, PM10, and PM25 were monitored at eight different sites throughout Greater Noida for one year. The Exceedance Factor technique and Air Quality Index methodology were applied to evaluate air quality status. Traffic volume was manually counted per hour at each site. Pearson Linear Correlation was used to analyze the relationship between traffic density and average AQI. Results: Particulate matter levels consistently exceeded the Central Pollution Control Board (CPCB) guidelines. AQI ratings varied from unhealthy to hazardous, with a strong positive correlation (r = 0.909) observed between AQI values and traffic density. Seasonal variation affected pollutant concentrations, with worse air quality typically observed in winter. Implications: Findings emphasize the significant impact of vehicular traffic on ambient air quality and associated health risks in rapidly urbanizing regions. The study highlights the need for comprehensive real-time monitoring and robust air quality management strategies to mitigate pollution and protect public health in Greater Noida and similar urban settings.
Electronic Health Records (EHRs) represent an important constituent of modern healthcare, providing support for medical procedures, maintaining records about patient health for much extended time periods, and conducting analytical research in medicine. Despite the broad use of EHRs, the overwhelming demand exists for them to be shared among different health environments securely and in an interoperable manner due to restrictions imposed by these systems regarding privacy preservation, security, and auditability. The research outlines a patient-centred, blockchain-based architecture that guarantees patient data confidentiality and privacy when it is shared via EHRs. A consensus technique called Proof of Stake (PoS) is presented in order to solve the scalability problems of conventional proof-based consensus algorithms. By using this method along with smart contracts and cryptographic primitives, patient-focused access control policies can give patients full control over who owns their data and who can access it. The proposed framework was developed with the aim of making it easy and convenient for various stakeholders in the health care sector to communicate with each other, thus facilitating the exchange of data safely without the involvement of middlemen. Gas cost analysis, transaction latency analysis, and comparative analysis with the existing EHR management systems have been adopted as performance analysis. Through the adoption of the above approach with the aim of improving the data sharing process using EHR systems in the health care sector, the simulation outcome has ensured improved access governance, low computational cost, and secure medical data through the blockchain technology approach.
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.
