Priyadarshini Engineering College

In the current work, the malachite green (MG) dye was removed from synthetic wastewater by employing a sustainable adsorbent Cajanus cajan (Tur Dal Husk) as an adsorbent. The activated carbon based on Tur Dal Husk (TDH) was prepared and analyzed by analytical techniques methods like the analyzes surface area involving Brunauer-Emmett-Teller (BET) analysis, scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR). The impacts of multiple causes, specifically pH, adsorbent quantity, contact duration, and the concentration of dye was investigated about the elimination of MG dye. The equilibrium isotherms underwent analysis through the Freundlich and Langmuir models. The highest ability to absorb was obtained as 24.81 mg/g, 30.95 mg/g, and 36.49amg/g at 303K, 313K, and 323K respectively. The separation factor value confirmed that the adsorption was beneficial at the adsorption conditions. The kinetics exhibited behavior consistent with pseudo-second-order kinetics. The thermal variables, involving entropy (ΔS), Gibbs free energy (ΔG), and enthalpy (ΔH), revealed that adsorption is a process that occurs naturally and absorbs heat during the process.

Concrete strength prediction is of great relevance for construction safety and quality assurance; however, these methods often trade-off their accuracy or interpretability, especially when it comes to the use of supplementary cementitious materials like fly ash in process. This study aims to build an interpretable, highly accurate model for predicting the compressive and tensile strength of concrete with a hybrid approach based on gradient boosting (XGBoost), deep neural networks (DNNs), and optimization via AutoGluon Process. The model is put into a multitask learning (MTL) framework that includes mix design variables, environmental factors, and non-destructive testing (NDT) data samples. The interpretation of model predictions is accomplished through SHAP and LIME to quantify global and local importance. Results show an impressive R² score of 0.91 on the test set with a 23% reduction in MSE and LIME fidelity exceeding 0.87. This shows a 10-15% increase in the mean-squared error, surpassing existing models. Feature analysis shows that fly ash percentage contributes around 25% to the predictions. The proposed solution thus offers a robust interpretability platform for concrete strength prediction and further shows great promise for optimization in material design and structural integrity assurances. This work serves as a landmark in bridging the gap between hybrid modeling with automated optimization and explainability for concrete strength predictions.

Wireless sensor networks (WSNs) are gaining attention in modern technology, and are valued for their affordability and versatility. However, energy efficiency is a still major challenge, especially since sensor nodes are often deployed in remote areas where replacing or recharging batteries is difficult. While clustering algorithms and cluster head (CH) selection methods have been recognized as key strategies for extending network lifetime, existing studies often lack a comprehensive evaluation of hybrid CH selection techniques. This paper addresses these gaps by presenting a detailed analysis of clustering algorithms aimed at optimizing energy consumption in WSNs and evaluating various hybrid CH selection procedures. A key contribution of this work is the development of a framework that compares these methods across multiple performance metrics, including Packet Delivery Ratio (PDR), energy consumption, delay, network lifetime, and routing efficiency, using robust simulation tools. The findings demonstrate that hybrid CH selection techniques can significantly enhance energy efficiency while maintaining or improving network performance. By providing a thorough quantitative analysis and actionable insights, this research advances the state of the art in WSN energy management and offers practical implications for designing more sustainable and efficient sensor networks. The proposed methodologies and findings can serve as a foundation for future studies and real-world deployments, addressing critical challenges in resource-constrained environments.

Global industrialization and fossil fuel consumption have elevated CO2 levels and caused global warming, causing catastrophes worldwide. Given this reality, removing CO2 from the atmosphere and transforming acquired CO2 into value-added resources like fuels and chemical feedstock is imperative to build a circular economy. The use of MOFs and their derivatives in CO2 photocatalytic conversion is eco-friendly and could alleviate future fuel shortages. Modern photocatalysts MOFs, consist of hybrid organic ligand, and inorganic nodal metals with customizable morphology. MOF flexible rational design allows several active sites to be added to a framework, generating a complex photocatalytic system. The first half critically studied morphology, photocatalytic reduction mechanism, pristine MOF applications, and MOF modification/functionalization for photocatalytic reduction of CO2. Their catalytic performance is currently insufficient for industrial usage because to poor light-harvesting and electron-hole separation. Researchers created MOF nanocomposites with many benefits by adding guest compounds. Morphological alterations, sensitization with polymers, plasmonic metals, and heterojunction generation with type I, type II, type III, and Z-scheme for MOF nanocomposites represent the current status of this large and significant research topic in the second half of paper. We presented the progress in photocatalytic CO2 reduction into fuels and chemical feedstock using MOF nanocomposites. This review discussed MOF nanocomposites' photocatalytic merits and cons and stimulate the advancement of more efficient and broadly applicable photocatalysts. The broad range of work assembled in this review will be very valuable to a wide spectrum of research on worldwide CO2 abatement.

This work looks into the potential of fly ash additives, which is a by-product of coal combustion, to improve strengths in concrete. Very few of the existing studies consider the comprehensive multivariate approach of interactions between different types and proportions of fly ash additives in relation to various measures of concrete strength. Hence, this paper intends to surmount these limitations with the adoption of an integrated multivariate analytical methodology that involves Multivariate Analysis of Variance (MANOVA), multiple regression analysis, PCA, and correlation analysis using canonical correlation analysis. MANOVA is applied for the significance of fly ash additives with respect to multiple concrete strength dependent variables. Those significant predictors and their interactions drastically identified through the MANOVA are taken forward for analysis by Multiple Regression, followed by quantification for their effect on individual measures of strength. The dimensions will now be reduced using PCA to identify principal components that capture most of the variance. Finally, the relationships between sets of fly ash components and concrete strength measures are examined and quantified by CCA. The conjoined application of those methods has advantages in that MANOVA gives overall information on the effect of fly ash additives, Multiple Regression quantifies the contributions unique to each additive, PCA retains important information while simplifying the data, and CCA explains the complicated interrelations amongst variables for this process. The results indicate that specific fly ash additives, at optimal proportions, significantly enhance concrete strength, as revealed by high R-squared values in regression models that ensure a strong predictive power. This work contributes to valuable insight into the optimization of fly ash mixture variables for the development of more sustainable and robust concrete formulations, hence advancing the field of construction materials and environmental sustainability.