Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Modelling soil moisture under different land covers in a sub-humid environment of Western Ghats, India(Indian Academy of Sciences, 2011) Venkatesh, B.; Nandagiri, L.; Purandara, B.K.; Reddy, V.B.The objective of this study is to apply and test a simple parametric water balance model for prediction of soil moisture regime in the presence of vegetation. The intention was to evaluate the differences in model parameterization and performance when applied to small watersheds under three different types of land covers (Acacia, degraded forest and natural forest). The watersheds selected for this purpose are located in the sub-humid climate within the Western Ghats, Karnataka, India. Model calibration and validation were performed using a dataset comprising depth-averaged soil moisture content measurements made at weekly time steps from October 2004 to December 2008. In addition to this, a sensitivity analysis was carried out with respect to the water-holding capacity of the soils with the aim of explaining the suitability and adaptation of exotic vegetation types under the prevailing climatic conditions. Results indicated reasonably good performance of the model in simulating the pattern and magnitude of weekly average soil moisture content in 150 cm deep soil layer under all three land covers. This study demonstrates that a simple, robust and parametrically parsimonious model is capable of simulating the temporal dynamics of soil moisture content under distinctly different land covers. Also, results of sensitivity analysis revealed that exotic plant species such as Acacia have adapted themselves effectively to the local climate. © Indian Academy of Sciences.Item Studies on the Site-specific PEGylation Induced Interferences Instigated in Uricase Quantification Using the Bradford Method(Springer Netherlands, 2016) Nanda, P.; JagadeeshBabu, P.E.Uricase from Bacillus fastidiosus was site-specifically PEGylated using methoxypolyethyleneglycol-maleimide (mPEG-mal) of different molecular weights (750 Da, 5 kDa, 10 kDa) via Thiol PEGylation strategy. The obtained monoPEGylated uricase conjugates were characterized using sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) and the molecular weight of single subunit of the conjugate was found to be 42.6, 48.1 and 56.3 kDa with respect to different molecular weights of m-PEG-mal. The influence of PEGylation on the quantification of uricase using protein quantification techniques like Bradford assay, RP-HPLC detection and Dumbroff method has been evaluated. A gradual decline in the absorbance value was observed with the advancement of the PEGylation reaction, indicating an interferences in the protein quantification due to PEGylation. The extent of interference highly dependence on mPEG-mal concentration and its chain length. The present study indicates that the quantification of PEGylation induced interferences caused in protein measured ought to be prudently measured at every discrete step of the PEGylation process. © 2016, Springer Science+Business Media New York.Item Predictive Simulation of Seawater Intrusion in a Tropical Coastal Aquifer(American Society of Civil Engineers (ASCE) onlinejls@asce.org, 2016) Lathashri, U.A.; Mahesha, A.The solute transport in a tropical, coastal aquifer of southern India is numerically simulated considering the possible cases of aquifer recharge, freshwater draft, and seawater intrusion using numerical modeling software. The aquifer considered for the study is a shallow, unconfined aquifer with lateritic formations having good monsoon rains up to about 3,000 mm during June to September and the rest of the months almost dry. The model is calibrated for a two-year period and validated against the available dataset, which gave satisfactory results. The groundwater flow pattern during the calibration period shows that for the month of May a depleted water table and during the monsoon month of August a saturated water table was predicted. The sensitivity analysis of model parameters reveals that the hydraulic conductivity and recharge rate are the most sensitive parameters. Based on seasonal investigation, the seawater intrusion is found to be more sensitive to pumping and recharge rates compared to the aquifer properties. The water balance study confirms that river seepage and rainfall recharge are the major input to the aquifer. The model is used to forecast the landward movement of seawater intrusion because of the anticipated increase in freshwater draft scenarios in combination with the decreased recharge rate over a longer period. The results of the predictive simulations indicate that seawater intrusion may still confine up to a distance of approximately 450-940 m landward for the scenarios considered and thus are sustainable. © 2015 American Society of Civil Engineers.Item Prediction accuracy of soil organic carbon from ground based visible near-infrared reflectance spectroscopy(Springer, 2018) Minu, S.; Shetty, A.The present study was conducted to predict soil organic carbon (SOC) from ground visible near-infrared (Vis-NIR, 400- 2500 nm) spectroradiometer reflectance spectra. The objective was to study the effect of various pre-processing methods and prediction models on the accuracy of SOC estimated. Measured SOC content and reflectance spectra from pasture and cotton fields of Narrabri, Australia were used in the analysis. Reflectance spectra were pretreated with different smoothing methods such as: moving average, median filtering, gaussian smoothing and Savitzky Golay smoothing. A comparison between principal component regression, partial least square regression (PLSR) and artificial neural network models was carried out to get an optimum model for organic carbon prediction. The results indicate that PLSR model performs better with Savitzky Golay as the best pre-processing method for the study area, yielding R2cal = 0:84, RPDcal = 2.55 and RPIQcal = 4.02 in the calibration set and R2val = 0:77, RPDval = 2.17 and RPIQval = 3.19 in the validation set. The study recommends a suitable method in case of limited number of soil data. Based on the study, it can be said that properly pretreated reflectance spectra show tremendous potential in soil organic carbon prediction. © Indian Society of Remote Sensing 2017.Item Simulation of coastal aquifer using mSim toolbox and COMSOL multiphysics(Springer, 2020) Kumar, S.S.; Deb Barma, S.; Mahesha, M.Fluctuations in groundwater levels along the coast have a significant impact on the extent of saltwater intrusion into freshwater aquifers. This study aims to simulate the groundwater flow and solute transport in the region by using the mSim toolbox in the MATLAB and COMSOL Multiphysics. The investigation is focussed on a micro-basin of Pavanje river located along the west coast of India. The model results are calibrated and validated against the field observations. The results show that the variation of the water table over the year is significant and range from about 3–14 m. There exists a reasonable correlation between the simulated and observed values of groundwater level and salinity. The wells that are most vulnerable to seawater intrusion in the region are identified. The COMSOL model estimated a salinity range of 0–20 mol/m3. Additionally, the model is used to understand the response of coastal aquifer to various stress scenarios. The study reveals that reduced recharge rate with increased pumping has a serious impact on aquifer system. © 2020, Indian Academy of Sciences.Item Groundwater level modeling using Augmented Artificial Ecosystem Optimization(Elsevier B.V., 2023) Nguyen, N.; Deb Barma, S.D.; van Lam, T.; Kisi, O.; Mahesha, A.Nature-inspired optimization is an active area of research in the artificial intelligence (AI) field and has recently been adopted in hydrology for the calibration (training) of both process-based and statistical models. This study proposes an improved AI model, Augmented Artificial Ecosystem Optimization-based Multi-Layer Perceptron (AAEO-MLP), to build a monthly groundwater level (GWL) forecasting model. AAEO-MLP model is built on the novel Augmented version of Artificial Ecosystem Optimization and traditional MLP network. In AAEO, Levy-flight trajectory and Gaussian random are utilized in exploration and exploitation to improve the optimizing ability. The AAEO-MLP model is tested on two time-series (1989–2012) datasets collected at two wells in India. Various explanatory variables such as monthly cumulative precipitation, mean temperature, tidal height, and previous measurements of GWL were considered for predicting 1-month ahead of GWL. The performance of AAEO-MLP was benchmarked against 17 different models (original AEO, 3 different variants of AEO, and 13 well-known models) in terms of forecasting accuracy based on six metrics (e.g., mean absolute error, root mean square error, Kling–Gupta efficiency, normalized Nash–Sutcliffe efficiency, Pearson's correlation index, a20 index). Furthermore, convergence analysis and model stability are employed to indicate the effectiveness of AAEO-MLP. The compared results express that the AAEO-MLP is superior to other models in terms of prediction accuracy, convergence, and stability. Overall, the results depict that the AAEO is a promising approach for optimizing machine learning models (e.g., MLP) and should be explored for other hydrological forecasting applications (e.g., streamflow, rainfall) to further assess its strengths over existing methods. © 2022 Elsevier B.V.Item A novel Deep Learning architecture for lung cancer detection and diagnosis from Computed Tomography image analysis(Elsevier Inc., 2024) Crasta, L.J.; Neema, R.; Pais, A.R.Timely identification of lung nodules, which are precursors to lung cancer, and their evaluation can significantly reduce the incidence rate. Computed Tomography (CT) is the primary technique used for lung cancer screening due to its high resolution. Identifying white, spherical shadows as lung nodules in CT images is essential for accurately detecting lung cancer. Convolutional Neural Network (CNN)-based methods have performed better than traditional techniques in various medical image applications. However, challenges still need to be addressed due to insufficient annotated datasets, significant intra-class variations, and substantial inter-class similarities, which hinder their practical use. Manually labeling the position of nodules on CT slices is critical for distinguishing between benign and malignant cases, but it is an unreliable and time-consuming process. Insufficient data and class imbalance are the primary factors that may result in overfitting and below-par performance. The paper presents a novel Deep Learning (DL) framework to detect and classify lung cancer in input CT images. It introduces a 3D-VNet architecture for accurate segmentation of pulmonary nodules and a 3D-ResNet architecture designed for their classification. The segmentation model achieves a Dice Similarity Coefficient (DSC) of 99.34% on the LUNA16 dataset while reducing false positives to 0.4%. The classification model shows performance metrics with accuracy, sensitivity, and specificity of 99.2%, 98.8%, and 99.6%, respectively. The 3D-VNet network outperforms previous segmentation methods by accurately calibrating lung nodules of various sizes and shapes with excellent robustness. The classification model's metrics show that the suggested method outperforms current approaches regarding accuracy, specificity, sensitivity and F1-Score. © 2024 The Authors