Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Optimization of electrophoretic deposition of alumina onto steel substrates from its suspension in iso-propanol using statistical design of experiments(2008) Mohanty, G.; Besra, L.; Bhattacharjee, S.; Singh, B.P.Statistical design of experiments was used to investigate the effect the process parameters on electrophoretic deposition (EPD) of alumina onto steel substrates from its suspension in iso-propanol. The process parameters considered were (i) concentration of particles in the suspension (solid loading), (ii) electrode separation, (iii) applied potential, and (iv) deposition time on the quantity of ceramic particles electrophoretically deposited. A 24 full factorial matrix, with four repetitions of the center point, was used to develop the predictive regression equation for deposition of alumina per unit area of the electrode in the design space. The results show that particle concentration has the most dominant effect with more than 50% contribution to the deposited amount. A good correlation was obtained between predicted and experimental values suggesting that the model can predict data accurately in the experimental matrix. © 2007 Elsevier Ltd. All rights reserved.Item Tropospheric NO2 and O3 Response to COVID-19 Lockdown Restrictions at the National and Urban Scales in Germany(John Wiley and Sons Inc, 2021) Balamurugan, V.; Chen, J.; Qu, Z.; Bi, X.; Gensheimer, J.; Shekhar, A.; Bhattacharjee, S.; Keutsch, F.N.This study estimates the influence of anthropogenic emission reductions on nitrogen dioxide ((Formula presented.)) and ozone ((Formula presented.)) concentration changes in Germany during the COVID-19 pandemic period using in-situ surface and Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) satellite column measurements and GEOS-Chem model simulations. We show that reductions in anthropogenic emissions in eight German metropolitan areas reduced mean in-situ (& column) (Formula presented.) concentrations by 23 (Formula presented.) (& 16 (Formula presented.)) between March 21 and June 30, 2020 after accounting for meteorology, whereas the corresponding mean in-situ (Formula presented.) concentration increased by 4 (Formula presented.) between March 21 and May 31, 2020, and decreased by 3 (Formula presented.) in June 2020, compared to 2019. In the winter and spring, the degree of (Formula presented.) saturation of ozone production is stronger than in the summer. This implies that future reductions in (Formula presented.) emissions in these metropolitan areas are likely to increase ozone pollution during winter and spring if appropriate mitigation measures are not implemented. TROPOMI (Formula presented.) concentrations decreased nationwide during the stricter lockdown period after accounting for meteorology with the exception of North-West Germany which can be attributed to enhanced (Formula presented.) emissions from agricultural soils. © 2021. The Authors.Item GSI: An Influential Node Detection Approach in Heterogeneous Network Using Covid-19 as Use Case(Institute of Electrical and Electronics Engineers Inc., 2023) Shetty, R.D.; Bhattacharjee, S.; Dutta, A.; Namtirtha, A.The growth of COVID-19, caused by the SARS-CoV-2 virus, has turned into an unprecedented pandemic in the last century. It is crucial to identify superspreading nodes to prevent the pandemic's progress. Most available superspreader identification techniques consider only a single or few network metrics related to the complex network's topological structure. Furthermore, it is more challenging to determine influential spreaders from heterogeneous structures of networks. In a disease transmission network, the degree of heterogeneity is essential to locate the path of the infection spread. Therefore, it is required to have an extended degree of centrality to collect information from various neighborhood levels. This article presents an approach, namely, global structure influence (GSI), which considers network nodes' local and global influence. This method can gather information from multiple levels of the neighborhood. Evaluation of our proposed method is done by considering different types of networks, i.e., social networks, highly heterogeneous human contact networks, and epidemiological networks, and also by using the benchmark susceptible-infected-recovered (SIR) epidemic model. The GSI technique provides real-spreading dynamics across various network structures and has outperformed the baseline techniques with an average Kendall's τ improvement range from 0.017 to 0.278. This study will help to identify the superspeaders in real applications, where pathogens spread quickly because of close contact, such as the recently witnessed COVID-19 pandemic. © 2014 IEEE.Item Downscaled XCO2 Estimation Using Data Fusion and AI-Based Spatio-Temporal Models(Institute of Electrical and Electronics Engineers Inc., 2024) Pais, S.M.; Bhattacharjee, S.; Anand Kumar, M.; Chen, J.One of the well-known greenhouse gases (GHGs) produced by anthropogenic human activity is carbon dioxide (CO2). Understanding the carbon cycle and how negatively it affects the ecosystem requires analysis of the rise in CO2 concentration. This work aims to map CO2 concentration for the entire surface, making it useful for regional carbon cycle analysis. Here, column-averaged CO2 dry mole fraction, called XCO2, measured by the orbiting carbon observatory-2 (OCO-2) satellite, is used. Because of spectral interference by the clouds and aerosols, there are many missing footprints in the Level-2 swath of OCO-2, making it disruptive to understand any assessment related to the carbon cycle. The objective of this work is to predict 1 km2 XCO2 using data resampling and machine learning models. This work achieves a minimum mean absolute error (MAE) and root mean square error (RMSE) of 0.3990 and 0.8090 ppm, using the monthly models. © 2004-2012 IEEE.Item Node Classification in Weighted Complex Networks Using Neighborhood Feature Similarity(Institute of Electrical and Electronics Engineers Inc., 2024) Shetty, R.D.; Bhattacharjee, S.; Thanmai, K.The potential of graph representation learning schemes has attained great acceptance in diverse, complex network applications. Most of the existing Graph Neural Network (GNN) architectures explore the node features aggregation and feature transformation within the neighborhoods, mainly performed on the unweighted graphs. Also, the existing GNN architectures consider all sets of neighborhood features, which are computationally expensive tasks. Practically, most of the real-world graphs are weighted graphs, and it is important to learn the representation of weighted graphs. In this work, we generate and leverage information of the best possible feature combination from the multiple levels of the networks. Edge weights and the connection structure are considered for generating node embedding, and classifying the node more accurately. The proposed framework, Similarity Feature Embedding GNN (SFEGNN), can be efficiently used for node classification in the weighted networks by leveraging feature overlap similarity from the network structure. This novel approach is helpful in modeling weighted networks for node classification and determining how strongly the neighborhood features are correlated. We validate the efficacy of SFEGNN on six benchmark datasets with varying degrees of homophily ratio and found that it is effective even for highly heterophily networks. Our model has empirically outperformed the state-of-the-art GNN framework with the highest accuracy improvement of 28.88%. © 2017 IEEE.Item NDWC: Global Scaling With Reducing Factor for Influence Ranking in Weighted Complex Networks(Institute of Electrical and Electronics Engineers Inc., 2025) Shetty, R.D.; Manoj, T.; Bhattacharjee, S.; Vasudeva, n.Optimal nodes identification in weighted complex networks is an essential task across diverse areas such as epidemiology, social media, infrastructure, and information diffusion. Traditional centrality measures often fail to capture the nuanced influence of a node when edge weights vary significantly across the network. In the scope of this study, propose a novel centrality measure, Normalized Degree and Weight Centrality (NDWC), that incorporates global scaling and a reducing factor to better assess the importance of nodes in weighted networks. NDWC integrates both structural (degree-based) and strength-based (edge weight) contributions, normalized using global standard deviations to ensure fair comparisons. Furthermore, a reducing factor is introduced to penalize nodes with skewed edge weight distributions, enhancing robustness against local heterogeneity. By combining these elements, NDWC provides a more balanced and representative ranking of nodes. Experimental validation on widely used datasets demonstrates that NDWC outperforms several state-of-the-art methods in identifying influential nodes, particularly in weighted networks. © 2013 IEEE.Item Forecasting of Fine-Grained SIF of OCO-2 Using Multi-source Data and AI-Based Techniques(Springer, 2025) Pais, S.M.; Bhattacharjee, S.; Anand Kumar, M.The emission of unused energy absorbed from the sunlight by plants and other photosynthetic organisms is known as solar-induced fluorescence (SIF). SIF is a direct proxy for the photosynthetic activity of the plants used to monitor drought, crop yield estimation, ecological processes, and carbon cycles. Comprehending the SIF dynamics beforehand helps gain an understanding of vegetation dynamics, carbon cycle, and crop phenology. This study explores the potential of using Orbiting Carbon Observatory-2 (OCO-2) SIF data for forecasting SIF at regional scales. The research utilizes machine learning models and data fusion to forecast the SIF data, by establishing relationships between observed SIF from past timestamps and the Enhanced Vegetation Index. The lasso regression achieves minimal error of RMSE 0.0355 Wm-2nm-1sr-1 and MAPE of 16.9093% for forecasting monthly SIF data. In contrast, the light gradient boosting machine model (LG) performs well for a larger non-linear dataset, i.e., seasonal models achieving a RMSE of 0.0389 Wm-2nm-1sr-1 and MAPE of 17.4895%, respectively. Karnataka and Maharastra, the two Indian states, are considered as the study areas for this work for a temporal window of 2017–2019. Fine-grained, uniformly distributed SIF forecasting provides valuable insights for understanding vegetation responses to environmental changes, optimizing agricultural practices, and developing climate change mitigation strategies. © Indian Society of Remote Sensing 2025.Item High-Resolution Soil Moisture Estimation: A Case Study in Coastal India(Springer, 2025) Sudhakara, B.; Bhattacharjee, S.The high-resolution soil moisture (SM) is significant for agricultural production, hydrological modelling, weather prediction, and climate studies at the regional scale. Current satellite and derived SM products span a wide range of coarse spatial resolutions. This work predicts high-resolution and precise SM estimates for the USA and the western coastal regions of India by utilizing remote sensing data. The study implements and compares five models for SM prediction in the study regions, including machine learning and deep learning models. The extra trees regressor model outperformed all other models in the USA and India datasets. It achieved the best performance metrics, with an MAE of 1.080 mm, RMSE of 1.715 mm, and R2 of 0.958 for the USA, and an MAE of 0.847 mm, RMSE of 1.554 mm, and R2 of 0.955 for India. The study validates the predicted data using 20 stations from the International Soil Moisture Network in the USA. It aims to support decision-making for agricultural practices, hydrology, and climate adaptation by providing valuable insights into the area’s spatio-temporal dynamics of SM. © Indian Society of Remote Sensing 2025.Item High-Spatial-Resolution Estimation of XCO2 Using a Stacked Ensemble Model(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Pais, S.M.; Bhattacharjee, S.; Anand Kumar, M.; Balamurugan, V.; Chen, J.Highlights: What are the main findings? The study develops a customized stacked ensemble model that generalizes (Formula presented.) predictions across multiple country, such as Germany, France, and Japan. It produces gap-filled high-resolution monthly, seasonal, and yearly maps, highlighting vegetation dynamics and seasonal cycles. What is the implication of the main finding? The customized stacked ensemble model provides reliable cross-country (Formula presented.) predictions at 1 (Formula presented.) resolution, validated against TCCON and CAMS, supporting large-scale environmental monitoring. Seasonal and yearly analyses show vegetation dynamics and photosynthetic activity significantly influence (Formula presented.), enhancing the model’s adaptability for agriculture, different climate assessments, and future global mapping. One of the leading causes of climate change and global warming is the rise in carbon dioxide ((Formula presented.)) levels. For a precise assessment of (Formula presented.) ’s impact on the climate and the creation of successful mitigation methods, it is essential to comprehend its distribution by analyzing (Formula presented.) sources and sinks, which is a challenging task using sparsely available ground monitoring stations and airborne platforms. Therefore, the data retrieved by the Orbiting Carbon Observatory-2 (OCO-2) satellite can be useful due to its extensive spatial and temporal coverage. Sparse and missed retrievals in the satellite make it challenging to perform a thorough analysis. This work trains machine learning models using the Orbiting Carbon Observatory-2 (OCO-2) (Formula presented.) retrievals and auxiliary features to obtain a monthly, high-spatial-resolution, gap-filled (Formula presented.) concentration distribution. It uses a multi-source aggregated (MSD) dataset and the generalized stacked ensemble model to predict country-level high-resolution (1 (Formula presented.)) (Formula presented.). When evaluated with TCCON, this country-level model can achieve an RMSE of 1.42 ppm, a MAE of 0.84 ppm, and (Formula presented.) of 0.90. © 2025 by the authors.Item Pertinence of contact duration as edge feature for epidemic spread analysis(Nature Research, 2025) Shetty, R.D.; Bhattacharjee, S.Identifying superspreading nodes has attracted greater attention because of its wide practical significance in various applications. Existing studies consider the edges mostly equally while designing the algorithms for the unweighted contact networks, where each connection explicitly shows whether the individuals are in contact or not. It will not consider other relevant information in the context of epidemiology study or infectious disease spread, such as proximity or total time spent between the contact nodes. The recent studies focused on the weighted network, where most of the methods have computed the edge weights by utilizing degree and k-shell measure, which captures the topological structure of the network but not the interaction duration between pair of contacts. In this study, we mainly aim to generate weighted networks to model the pathogen spread by optimal calculation of the edge weight in terms of contact duration (time spent) between individual contacts. Leveraging this interaction duration as the edge weight, we further design a novel technique, namely Real Weighted Influence (RWInf), for identifying the superspreading nodes during an epidemic outbreak. The empirical study revealed that the proposed approach outperforms with an improvement of 0.146–0.473 kendall’s score in comparison with baseline approaches. © The Author(s) 2025.