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Your search keyword '"Sharma, Vaibhav"' showing total 273 results
Start Over Author "Sharma, Vaibhav" Publication Type Academic Journals
273 results on '"Sharma, Vaibhav"'

14. Fluorescence-tagged salivary small extracellular vesicles as a nanotool in early diagnosis of Parkinson’s disease

Author

Rastogi, Simran, Rani, Komal, Rai, Sanskriti, Singh, Rishabh, Bharti, Prahalad Singh, Sharma, Vaibhav, Sahu, Jyoti, Kapoor, Vrinda, Vishwakarma, Poorvi, Garg, Sumit, Gholap, Shivajirao Lahu, Inampudi, Krishna Kishore, Modi, Gyan Prakash, Rani, Neerja, Tripathi, Madhavi, Srivastava, Achal, Rajan, Roopa, Nikolajeff, Fredrik, and Kumar, Saroj

Published
2023
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18. A neurodevelopmental epigenetic programme mediated by SMARCD3–DAB1–Reelin signalling is hijacked to promote medulloblastoma metastasis

Author

Zou, Han, Poore, Bradley, Brown, Emily E., Qian, Jieqi, Xie, Bin, Asimakidou, Evridiki, Razskazovskiy, Vladislav, Ayrapetian, Deanna, Sharma, Vaibhav, Xia, Shunjin, Liu, Fei, Chen, Apeng, Guan, Yongchang, Li, Zhengwei, Wanggou, Siyi, Saulnier, Olivier, Ly, Michelle, Fellows-Mayle, Wendy, Xi, Guifa, Tomita, Tadanori, Resnick, Adam C., Mack, Stephen C., Raabe, Eric H., Eberhart, Charles G., Sun, Dandan, Stronach, Beth E., Agnihotri, Sameer, Kohanbash, Gary, Lu, Songjian, Herrup, Karl, Rich, Jeremy N., Gittes, George K., Broniscer, Alberto, Hu, Zhongliang, Li, Xuejun, Pollack, Ian F., Friedlander, Robert M., Hainer, Sarah J., Taylor, Michael D., and Hu, Baoli

Published
2023
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30. BiBitcoin Price Prediction using Recurrent Neural Networks and Long Short-Term Memory

Author

Sudhakaran Pradeep, Sharma Vaibhav, and Khandelwa Shreyanshi

Subjects
bitcoin, decentralization of cryptocurrency, long short-term memory (lstm), recurrent neural networks, currency, machine learning, deep learning, Environmental sciences, GE1-350
Abstract

Bitcoin's decentralized nature has made it a popular mode of payment for buyers and sellers, but its highly volatile nature poses a challenge for investors. This study aims to predict future Bitcoin prices using a combination of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. The non-stationary nature of Bitcoin prices is addressed using RNN, which is particularly useful for analyzing sequential data.The dataset used in this research is sourced from the Kraken exchange and includes various factors that are believed to influence Bitcoin prices, such as transaction volume, hash rate, and Google search trends. The data is preprocessed and cleaned to ensure accuracy, and then fed into the RNN and LSTM models for training and testing.The study's use of RNN and LSTM algorithms demonstrates the effectiveness of these methods in predicting Bitcoin prices, particularly in the context of sequential data. The results of the study, provide insights into potential future trends in Bitcoin prices and identify key indicators that significantly influence Bitcoin prices. The findings of this research have important implications for investors and traders looking to make informed decisions in the cryptocurrency market, as well as for researchers seeking to improve our understanding of Bitcoin's price dynamics. By predicting future prices, the study provides insights that can mitigate the risks associated with Bitcoin's volatility, making it a more viable investment option.

Published
2024
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35. Helical spin ordering in room-temperature metallic antiferromagnet Fe3Ga4

Author

Wilfong, Brandon, Fedorko, Adrian, Baigutlin, Danil R., Miroshkina, Olga N., Zhou, Xiuquan, Stephen, Gregory M., Friedman, Adam L., Sharma, Vaibhav, Bishop, Omar, Barua, Radhika, Bennett, Steven P., Chung, Duck Young, Kanatzidis, Mercouri G., Buchelnikov, Vasiliy D., Sokolovskiy, Vladimir V., Barbiellini, Bernardo, Bansil, Arun, Heiman, Don, and Jamer, Michelle E.

Published
2022
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43. Densities and volumetric properties of pyridine with 2-dimethylaminoethanol and 2-diethylaminoethanol at temperatures from 293.15 to 313.15 K.

Author

Nath, Narendra, Awasthi, Anjali, Dwivedi, Swati, Sharma, Vaibhav, and Awasthi, Aashees

Subjects
MOLECULAR volume, MOLECULAR interactions, INTERMOLECULAR interactions, PYRIDINE, DILUTION, BINARY mixtures
Abstract

The densities $\rho $ ρ of binary mixtures of 2-(dimethylamino)ethanol and 2-diethylaminoethanol with pyridine were measured over the entire concentration range at 293.15, 303.15 and 313.15 K. The values of excess molar volume, $V_m^E$ V m E , apparent molar volume, ${V_{\varphi ,DMAE}}$ V φ , DMAE and ${V_{\varphi ,DEAE}}$ V φ , DEAE ; partial molar volumes, ${\bar V_{m,1}}$ V ˉ m , 1 and ${\bar V_{m,2}}$ V ˉ m , 2 , excess partial molar volumes, $\bar V_{m,1}^E$ V ˉ m , 1 E and $\bar V_{m,2}^E$ V ˉ m , 2 E have been computed using the experimental observations. The partial molar volumes, $\bar V_{m,1}^o$ V ˉ m , 1 o and $\bar V_{m,2}^o$ V ˉ m , 2 o ; excess partial molar volumes, $\bar V_{m,1}^{oE}$ V ˉ m , 1 oE and $\bar V_{m,2}^{oE}$ V ˉ m , 2 oE at infinite dilution were also calculated. The variations of these parameters as a function of composition and temperature are discussed in terms of the intermolecular interactions in the studied binary mixtures. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Synthesis, Characterization, and Magnetocaloric Properties of the Ternary Boride Fe 2 AlB 2 for Caloric Applications.

Author

Sharma, Vaibhav and Barua, Radhika

Subjects
*MAGNETIC transitions, *MAGNETOCALORIC effects, *SUSTAINABILITY, *HEAT pumps, *POROUS materials
Abstract

The ternary transition metal boride Fe2AlB2 is a unique ferromagnetic "MAB" phase that demonstrates a sizable magnetocaloric effect near room temperature—a feature that renders this material suitable for magnetic heat pump devices (MHP), a promising alternative to conventional vapor compression technology. Here, we provide a comprehensive review of the material properties of Fe2AlB2 (magnetofunctional response, transport properties, and mechanical stability) and discuss alloy synthesis from the perspective of shaping these materials as porous active magnetic regenerators in MHPs. Salient aspects of the coupled magnetic and structural phase transitions are critically assessed to elucidate the fundamental origin of the functional response. The goal is to provide insight into strategies to tune the magnetofunctional response via elemental substitution and microstructure optimization. Finally, outstanding challenges that reduce the commercial viability of Fe2AlB2 are discussed, and opportunities for further developments in this field are identified. [ABSTRACT FROM AUTHOR]

Published
2024
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50. On a composition of Ismail–May operator.

Author

Gupta, Vijay and Sharma, Vaibhav

Abstract

In this paper, we address a discrete operator based on the composition of Ismail–May operator and Szász–Mirakjan operator and study its approximation properties for various classes of continuous functions using Taylor's formula. We estimate its moments and further give direct theorems employing Peetre's K‐functional and the modulus of continuity. Furthermore, Voronovskaja‐type asymptotic theorems are given. Additionally, we provide further compositions and give a comparison among their approximation properties. [ABSTRACT FROM AUTHOR]

Published
2024
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