AISSMS College of Engineering

The detection of toxic gases at trace levels is crucial for environmental monitoring and human safety. In this work, we report the synthesis and gas-sensing performance of a novel redox-engineered nanocomposite comprising gold (Au ^0 ) nanoparticle cores encapsulated within benzene sulphonic acid (BSA) doped polypyrrole (PPy) shells, denoted as Au ^0 @PPy ^+· C_6H_5SO_3^- . The nanocomposites were synthesized via in situ oxidative polymerization of pyrrole in the presence of HAuCl _4 and BSA, resulting in a uniform core–shell architecture with enhanced redox activity. The synergistic combination of catalytic Au ^0 cores, conductive PPy shells, and acid doping yielded a material with superior surface reactivity and charge transport properties. Chemiresistive sensors fabricated using this composite exhibited highly sensitive, reversible, and reproducible responses toward selected toxic gases, including Cl _2 , NH _3 , CO, SO _2 , NO _2 , and LPG under ambient conditions. Notably, the sensor displayed a rapid increase in resistance upon exposure to oxidizing gases (Cl _2 , SO _2 , NO _2 ), and a sharp decrease upon interaction with reducing gases (NH _3 , CO, LPG), confirming the p-type nature of the composite. The device demonstrated excellent sensitivity, long-term stability, and full recovery upon air purging, highlighting its potential for real-time, low-power toxic gas monitoring applications. This study provides valuable insight into redox modulation and core-shell engineering strategies for the design of high-performance gas-sensing materials.

The application of remote sensing data, specifically Nighttime Light (NTL) imagery derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), has rapidly emerged as an indispensable methodology for measuring fine-grained economic activity. This approach is particularly critical in developing nations like India, where traditional socioeconomic data may be sparse, outdated, or inconsistently reported across heterogeneous sub-national regions. The inherent objective nature of NTL data offers a crucial advantage over conventional survey or census methods, providing a reliable and frequently updated proxy for urban expansion, electrification, and consumption.While existing academic literature has effectively established correlations between NTL and broad macroeconomic indicators, such as national GDP or overall crime rates, a significant research gap persists. The majority of these studies rely on coarse state or district-level aggregations and older, less precise DMSP-OLS data. This level of aggregation critically fails to capture the spatial inequality, heterogeneity, and highly localized development that occurs within India’s rapidly growing metropolitan areas. Furthermore, relying predominantly on traditional econometric models overlooks the enhanced predictive and pattern-recognition capabilities offered by modern ensemble machine learning techniques.To bridge this crucial gap, this paper introduces a novel machine learning-based framework designed for assessing regional economic development at a granular, sub-district resolution. The core methodology involves the fusion of multi-source geospatial data: monthly VIIRS NTL composites (serving as a high-frequency economic proxy) are integrated with specific ground-truth indicators derived from OpenStreetMap (OSM) Points-of-Interest (POI) data, which track concentrated consumption and nightlife amenities.The analytical pipeline is multi-faceted and robust: it includes rigorous preprocessing and noise filtering, followed by density-based spatial clustering (DBSCAN) for reliable hotspot detection and temporal decomposition (STL) for isolating seasonality and identifying anomalies. The core predictive component utilizes an ensemble regression model (XGBoost) to forecast short-term economic momentum (proxied by radiance growth). The empirical validation achieved a strong predictive accuracy (R2 = 0.6828) on the unseen test set, confirming the superiority of the machine learning ensemble approach for this complex time-series forecasting task. Ultimately, this work contributes an open-source reproducible pipeline and yields actionable insights critical for evidence-based urban governance and policymaking.

This study presents a thermo-mechanical simulation of multi-pass butt-welding of AISI 316L stainless steel using Finite Element Analysis (FEA). A three-dimensional model of the welded joint was developed in CATIA and analysed in Visual Weld using a sequentially coupled thermo-mechanical approach. The simulation was carried out to predict the temperature distribution, residual stress, and welding-induced deformation during the multi-pass welding process. The results indicate a peak temperature of approximately 1580 °C, a maximum residual stress of 600 MPa, and a maximum total deformation of 0.496 mm near the free edge of the welded plate after completion of the final welding pass. The study demonstrates that mesh refinement, sequential heat loading, and appropriate welding parameters play an important role in accurately predicting the thermo-mechanical response of multi-pass welded joints. The developed finite element model provides a reliable and efficient approach for analysing welding behaviour and can support the optimization of welding processes in precision engineering applications.

Underwater image analysis often suffers from low visibility, color distortion, and scattering effects, which reduce the performance of object classification and detection models. We propose a hybrid augmentation pipeline combining Gaussian Blur, Gaussian Noise, and Generative Adversarial Networks (GANs) to synthetically enhance training datasets. The proposed method first applies Gaussian-based degradations to simulate real-world underwater distortions and then employs an Attention-Guided Self-Adaptive GAN (AGSA-GAN) based generator to produce realistic augmented samples. This improves dataset diversity and robustness against varying underwater conditions. Experimental evaluation on Ocean Dark dataset (low light images) demonstrates improvements in object detection. The proposed augmentation approach is particularly effective for deep learning model such as YOLO, making it suitable for underwater robotics, marine debris detection, and ecological monitoring. Ablation study is done on augmented images using YOLO model and evaluation metrics, Precision, Recall and mAP are measured which shows better performance on combined images of Gaussian blur and noise when applied to AGSA-GAN, Evaluation metrics obtained are precision, Recall and F1 score, mAP0.5, mAP0.5-0.95 and Accuracy as 0.853, 0.849, 0.851, 0.867, 0.548, and 0.718 respectively.

This study investigates an efficient hydrometallurgical route for the recovery of Pb from spent Pb–acid battery paste using nitric acid leaching followed by carbonate precipitation. Effect of different inorganic and organic acid were investigated. It was observed that nitric acid exhibited highest recovery among other acids. Response Surface Methodology (RSM) was applied to optimize process based on a Box–Behnken design by evaluating the effects of acid concentration, particle size, and stirring time. A second-order polynomial model was developed, and analysis of variance confirmed the adequacy and significance of the model with a high coefficient of determination (R2 = 0.9795). Particle Size was identified as the most influential parameter. Under optimized conditions such as acid concentration 3.8134 M, particle size 150 μm and stirring time 210 min, a maximum recovery of Pb, approximately 91.21