http://nopr.niscpr.res.in/handle/123456789/62854 2026-10-09T19:43:23Z http://nopr.niscpr.res.in/handle/123456789/62864 Title: Mining of Potential Antifungal Molecules for Control of Fusarium fujikuroi in Rice using in silico and in vitro Analysis Authors: Kumar, Randeep; Mandal, Abhishek; Kundu, Aditi; Bashyal, Bishnu Maya; Patanjali, Neeraj; Dutta, Anirban; S, Gopala Krishnan; Singh, A K; Singh, Anupama Abstract: A library of 170 fungicidal molecules of different functional moieties were subjected to in-silico assessment of their relative potential to inhibit ten vital targets of the Fusarium fujikuroi, bakanae disease causative pathogen in rice. Targets chosen were tubulin proteins (α-, β- and γ-tubulin) and NRPS31 gene cluster (FFUJ_00005, FFUJ_00006, FFUJ_00007, FFUJ_00008, FFUJ_00010, FFUJ_00011, FFUJ_00013). In-silico findings were validated with the help of in vitro analysis of the molecules to predict the most effective compound(s) relative to carbendazim (positive control). Most effective molecules were selected based on their chemical characteristics and Lipinski’s rule. One each of the natural and synthetic origin molecules was selected for the molecular dynamics and in-vitro analysis. β-Caryophyllene came out as the most potential molecule followed by flusilazole. The extent of inhibition of α-tubulin by these two molecules was significantly higher than by carbendazim. In-vitro bioassay validated the in-silico findings with LC50 values of 3.29, 64.12, and 178.77 μg/mL for β-caryophyllene, flusilazole and carbendazim, respectively. Further, molecular dynamics also revealed the selected molecular complex as highly effective with time when analyzed using Root Mean Square Deviation (RMSD) and Radius of Gyration (Rg). Page(s): 1117-1133 2023-11-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/62863 Title: Comparative Analysis of Porous Titanium Spinal Cage with Conventional Spinal Cages: A Finite Element Study Authors: Kumar, Prashant; Bhardwaj, Rahul; Matharu, Amrit Lal; Meena, Vijay Kumar Abstract: The objective of this study is to compare the stress shielding effect of various conventional as well as modified additive manufactured porous materials used for spinal cages. A finite element study was performed by changing the design (fully porous and hybrid) and the materials (PEEK, CFR-PEEK, Titanium) of spinal cages. All the models were simulated under uniaxial compression, to study the stress shielding effect. The Finite Element Analysis results showed that the hybrid spinal cage transfers more stress to its adjacent vertebrae than the other design configurations under uniaxial compression. The hybrid titanium cage was most effective in reducing the stress shielding effect. The hybrid cage is stronger than PEEK & CFR-PEEK cages, however, due to the porous structure reduced stress shielding was observed. Page(s): 1134-1142 2023-11-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/62862 Title: Deep Neural Network Based Modelling of Chemisorption Process on Surface of Oxide Based Gas Sensors Authors: Gupta, Rahul; Kumar, Pradeep; Kumar, Dinesh Abstract: The sensor response of the metal oxide based gas sensor has been simulated using Deep Neural Network (DNN) model. The neural network designed for the modelling of the sensor has single input layer, three hidden layers and single output layer. The linear regression algorithm has been used to compute the electrical conductance of the sensor at given temperature and pressure. The data generated through modified Wolkenstein method has been used for training, validation and testing of the developed network. The data for materials Tin (IV) oxide (SnO2), Tin (II) oxide (SnO) and Copper (I) oxide (Cu2O) with different Eg values has been utilized. The other input parameters like Temperature, ND, NC, NV, EF−ESSand ECS−EF are varied for the specific range to collect a variety of data for calculation of electrical conductance of the sensor. The total data used for training, validation and testing was 1,90,512 data points. The plots for training, validation and testing phase have been plotted. The sensor response computed through the proposed model is validated with the results of already published mathematical model. The sensor response shows steep change when the gas concentration of the target gas reaches above 10−8 atm. The proposed model can be retrained or transfer learning can be applied for using the same model for other types of materials for gas sensing applications. Page(s): 1143-1151 2023-11-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/62861 Title: Predicting Student Performance with Adaptive Aquila Optimization-based Deep Convolution Neural Network Authors: Lu, Jiayi; Singh, Vineeta; Singh, Suruchi; Kumar, Alok; Pandey, Saurabh; Verma, Deepak Kumar; Kaushik, Vandana Dixit Abstract: Predicting student performance is the major problem for enhancing the educational procedures. A level of student’s performance may be influenced by several factors like job of parents, sexual category and average scores obtained in prior years. Student’s performance prediction is a challenging chore, which can help educational staffs and students of educational institutions to follow the progress of students in their academic activities. Student performance enhancement and progress in educational quality are the most vital part of educational organizations. Presently, it is essential for an educational organization to predict the performance of students. Existing methods utilized only previous student performances for prediction without including other significant behaviors of students. For addressing such problems, a proficient model is proposed for prediction of student performance utilizing proposed Adaptive Aquila Optimization-allied Deep Convolution Neural Network (DCNN). In this process, data transformation is initiated using the Yeo-Johnson transformation method. Subsequently, feature selection is performed using Fisher Score to identify the most relevant features. Following feature selection, data augmentation techniques are applied to enhance the dataset. Finally, student performance is predicted through the utilization of a DCNN, with a focus on fine-tuning the network parameters for optimal performance. This fine-tuning is achieved through the use of the Adaptive Aquila Optimizer (AAO), ensuring the network is poised to deliver the best possible results in predicting student outcomes. Proposed AAO-based DCNN has achieved minimal error values of Mean Square Error, Root Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, Mean Absolute Relative Error, Mean Squared Relative Error, and Root Mean Squared Relative Error, respectively. Page(s): 1152-1164 2023-11-01T00:00:00Z