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Institution

National Institute of Technology, Karnataka

EducationMangalore, Karnataka, India
About: National Institute of Technology, Karnataka is a education organization based out in Mangalore, Karnataka, India. It is known for research contribution in the topics: Corrosion & Cloud computing. The organization has 5017 authors who have published 7057 publications receiving 70367 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the interaction between the microstructures of Hayward-AdS black holes using Ruppeiner geometry was investigated and it was shown that the dominant interaction between black hole molecules is attractive in most part of the parametric space of temperature and volume, as in van der Waals system.

27 citations

Journal ArticleDOI
TL;DR: A good concurrence was found between the experimental and predicted values, representing that the unstructured models were able to illustrate the fermentation profile effectively.
Abstract: The production of cell-associated camptothecin (CPT) from an endophytic fungus Fusarium oxysporum NFX06 isolated from Nothapodytes foetida and its kinetics studies were proposed. Response surface methodology (RSM) based on central composite design (CCD) was used to construct a model to describe the effects of substrate concentration. Three independent variables (dextrose, peptone, and MgSO4) were successfully employed to study the yield of CPT under submerged fermentation. The maximum yield of CPT obtained from CCD was about 598.0 ng/g biomass. The model-validated optimum predicted CPT yield and experimental CPT yield from the biomass were found to be 628.08 ng/g and 610.09 ng/g at the concentrations of dextrose 42.64 (g/L), peptone 9.23 (g/L), and MgSO4 0.26 (g/L) respectively. The predicted yield of CPT was 4.90% higher than the value obtained from CCD and 2.85% higher than the value obtained from experiment conducted at optimum conditions. The kinetic parameters, maximum specific growth rate μmax=1.212 day(-1), growth-associated CPT production coefficient (α=29.35 ng/g biomass), and non-growth-associated CPT production coefficient (β=0.03 ng CPT/g biomass-day) were obtained. The logistic model was found suitable to predict mycelial growth with a high determination coefficient (R2). Luedeking-Piret and modified Luedeking-Piret models were employed to represent the product kinetics and substrate consumption kinetics. A good concurrence was found between the experimental and predicted values, representing that the unstructured models were able to illustrate the fermentation profile effectively.

27 citations

Journal ArticleDOI
TL;DR: This work has trained a novel hybrid skip connection-based FCN architecture from scratch for the detection of FCD from 3 T MRI 3D FLAIR images and conducted 5-fold cross-validation to evaluate the model.
Abstract: In this work, we have focused on the segmentation of Focal Cortical Dysplasia (FCD) regions from MRI images. FCD is a congenital malformation of brain development that is considered as the most common causative of intractable epilepsy in adults and children. To our knowledge, the latest work concerning the automatic segmentation of FCD was proposed using a fully convolutional neural network (FCN) model based on UNet. While there is no doubt that the model outperformed conventional image processing techniques by a considerable margin, it suffers from several pitfalls. First, it does not account for the large semantic gap of feature maps passed from the encoder to the decoder layer through the long skip connections. Second, it fails to leverage the salient features that represent complex FCD lesions and suppress most of the irrelevant features in the input sample. We propose Multi-Res-Attention UNet; a novel hybrid skip connection-based FCN architecture that addresses these drawbacks. Moreover, we have trained it from scratch for the detection of FCD from 3 T MRI 3D FLAIR images and conducted 5-fold cross-validation to evaluate the model. FCD detection rate (Recall) of 92% was achieved for patient wise analysis.

27 citations

Journal ArticleDOI
TL;DR: In this article, a multilayered composite of Mg-2%Zn/Al-7075 was developed by accumulative roll bonding (ARB) of wrought Mg 2% Zn and aluminum 7075 alloy.
Abstract: Multilayered composite of Mg-2%Zn/Al-7075 was developed by accumulative roll bonding (ARB) of wrought Mg-2%Zn and aluminum 7075 alloy. The Mg-2%Zn/Al-7075 multilayered composite exhibited density of 2295 kg/m3 and an average grain size of 1 and 1.3 μm in Mg-2%Zn and Al-7075 layers, respectively. A thorough microstructural characterization was performed on the composites by scanning electron microscope, electron backscatter diffraction (EBSD), transmission electron microscope and phase analysis by x-ray diffraction. In addition, mechanical properties were evaluated by microhardness and tensile tests. Corrosion behavior of the multilayered composite was examined using electrochemical polarization test. EBSD analysis showed the presence of ultrafine grains with high-angle grain boundaries. The composite exhibited a significant improvement in ultimate tensile strength (~1.82 times) and elongation (~1.5 times) as compared with Mg-2%Zn alloy, after four-pass ARB process.

27 citations

Journal ArticleDOI
TL;DR: In this paper, the effectiveness of CHIRPS satellite rainfall data in comparison with IMD gridded Rainfall Data and development of various flow forecasting models is evaluated in the Nethravathi Basin, Karnataka, India.
Abstract: Streamflow forecasting can offer valuable information for optimal management of water resources, flood mitigation, and drought warning. This research aims in evaluating the effectiveness of CHIRPS satellite rainfall data in comparison with IMD gridded Rainfall Data and development of various flow forecasting models. Daily rainfall data for three decades (1983–2012) over the Nethravathi Basin, Karnataka, India is used for analysis. The analysis is carried out for the monsoon season (June–September), out of which 70% data considered for training the model and remaining for testing. Different input combinations are developed, and soft-computing methods like ANFIS, GRNN, PSO-ANN, and ELM are applied for flow forecasting on a temporal scale. The model performance is evaluated using various statistical indices like NNSE, RRMSE, and MAE. The results indicate that CHIRPS rainfall showed better performance in comparison with IMD data. ELM expressed an enhanced effect when compared to all other methods. The usefulness and effectiveness of CHIRPS data compared to IMD data has been explored.

27 citations


Authors

Showing all 5100 results

NameH-indexPapersCitations
Ajay Kumar5380912181
Bhiksha Raj5135913064
Alexander P. Lyubartsev491849200
Vijay Nair4742510411
Sukumar Mishra444057905
Arun M. Isloor382616272
Vinay Kumaran362624473
M. C. Ray301152662
Airody Vasudeva Adhikari301192832
Ian R. Lane271292947
D. Krishna Bhat26951715
Anurag Kumar261262276
Soma Biswas251272195
Chandan Kumar25661806
H.S. Nagaraja23901609
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202351
2022175
2021938
2020893
2019838
2018740