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| 1 | Recent advances in solid-state fermentation显示文摘 | Reeta Rani Singhania Anil Kumar Patel Carlos R. Soccol Ashok Pandey | 2008 | Biochemical Engineering Journal2008，，1： | 1 |
| 2 | The classification of clubfoot a practical approach显示文摘 | Sureshwar Pandey Anil Kumar Pandey | 2003 | The Foot2003，13，： | 1 |
| 3 | Genome-Wide Comparative in silico Analysis of Calcium Transporters of Rice and Sorghum显示文摘The mechanism of calcium uptake, translocation and accumulation in Poaceae has not yet been fully understood. To address this issue, we conducted genome-wide comparative in silico analysis of the calcium (Ca2+) transporter gene family of two crop species, rice and sorghum. Gene annotation, identification of upstream cis-acting ele- ments, phylogenetic tree construction and syntenic mapping of the gene family were performed using several bio- informatics tools. A total of 31 Ca2+ transporters, distributed on 9 out of 12 chromosomes, were predicted from rice genome, while 28 Ca2+ transporters predicted from sorghum are distributed on all the chromosomes except chromosome 10 (Chr 10). Interestingly, most of the genes on Chr 1 and Chr 3 show an inverse syntenic relation- ship between rice and sorghum. Multiple sequence alignment and motif analysis of these transporter proteins re- vealed high conservation between the two species. Phylogenetic tree could very well identify the subclasses of channels, ATPases and exchangers among the gene family. The in silico cis-regulatory element analysis suggested diverse functions associated with light, stress and hormone responsiveness as well as endosperm- and meris- tem-specific gene expression. Further experiments are warranted to validate the in silico analysis of the predicted transporter gene family and elucidate the functions of Ca2+ transporters in various biological processes. | Anshita Goel Gohar Taj Dinesh Pandey Sanjay Gupta Anil Kumar | 2011 | Genomics, Proteomics & Bioinformatics2011，9，4： | 0 |
| 4 | A multiwavelength view of a classical T Tauri star CV Cha显示文摘Using long-term optical, ultraviolet(UV) and X-ray data, we present a study of a classical T Tauri star CV Cha. The V-band light curve obtained from the All Sky Automated Survey(ASAS) shows short as well as long-term variability. The short-term variability could be due to rotational modulation of CV Cha. We derive the rotational period of 3.714 ± 0.001 d for CV Cha. UV light curves obtained from Swift also show the variations. X-ray light curves from XMM-Newton and Swift do not show any significant short as well as long-term variability. However, the light curve from Chandra appears to be variable, which could be due to the emergence of flaring activities. X-ray spectra from all observations are explained well by the single temperature plasma of 0.95 keV with X-ray luminosity of 1030.4erg s-1in the 0.5–7.5 keV energy band. It appears that variability in optical and UV bands could be due to the presence of both hot and cool spots on the surface, while X-ray emission is dominated by magnetic processes. | Jeewan Chandra Pandey Subhajeet Karmakar Arti Joshi Saurabh Sharma Shashi Bhushan Pandey Anil Kumar Pandey | 2019 | Research in Astronomy and Astrophysics2019，19，1： | 0 |
| 5 | Insight into the relationship between non-linear mixed convection and thermal radiation:The case of Newtonian fluid flow due to non-linear stretching显示文摘The current research focuses the light on the characterization of buoyancy-driven non-linear mixed convection and non-linear radiation in a Newtonian flow over a nonlinearly stretching vertical sheet,and this type of flow has useful applications in many industrial processes,such as the paper and pulp industry,polymer industry,electronic device cooling,solar collectors,gas turbine plants,and nuclear power.Using appropriate transformations,governing PDEs for non-linear mixed convection are reduced to higher-order non-linear ODEs and those are numerically solved.Along with tabular presentations of computed results,the graphical representations are generated to elucidate the effects of involved parameters on convection transport properties and their inter-relations.It demonstrates that flow velocity increases near the surface and decreases away from the surface as the non-linear convection parameter increases.Furthermore,increments in the thermal buoyancy,temperature ratio and non-linear radiation parameters result in the boost of velocity.The temperature decreases as linear and non-linear buoyancy-related parameters(non-linear convection and thermal buoyancy parameters)are of higher levels.In contrast,the temperature rises with two non-linear thermal radiation-related parameters(thermal ratio and non-linear radiation parameters).For greater values of the non-linear stretching related parameter,a lower velocity and a higher temperature are witnessed.The non-linear convection,thermal buoyancy,thermal ratio and non-linear radiation parameters contribute toward the reduction of the magnitude of surface-drag force and growth of the surface cooling rate.But,with the non-linearity in surface stretching there are significant percentage hikes of surface-drag force magnitude and surface cooling rate. | Amit Kumar Pandey Krishnendu Bhattacharyya Anil Kumar Gautam Sohita Rajput Mani Shankar Mandal Ali J.Chamkha Dhananjay Yadav | 2023 | Propulsion and Power Research2023，12，1： | 0 |
| 6 | A robust deep structured prediction model for petroleum reservoir characterization using pressure transient test data显示文摘A robust deep learning model consisting of long short-term memory and fully connected neural net-works has been proposed to automatically interpret homogeneous petroleum reservoirs having infinite,no flow,and constant pressure outer boundary conditions.The pressure change data recorded during the well test operation along with its derivative is input into the model to perform the classification for identifying the reservoir model and,further,regression to estimate output parameter.Gaussian noise was added to analytical models while generating the synthetic training data.The hyperparameters were regulated to perform model optimization,resulting in a batch size of 64,Adam optimization algorithm,learning rate of 0.01,and 80:10:10 data split ratio as the best choices of hyperparameters.The perfor-mance accuracy also increased with an increase in the number of samples during training.Suitable classification and regression metrics have been used to evaluate the performance of the models.The paper also demonstrates the prediction performance of the optimized model using simulated and actual oil well pressure drawdown test cases.The proposed model achieved minimum and maximum relative errors of 0.0019 and 0.0308,respectively,in estimating output for the simulated test cases and relative error of 0.0319 for the real test case. | Rakesh Kumar Pandey Anil Kumar Ajay Mandal | 2022 | Petroleum Research2022，7，2： | 0 |
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