Rajiv Ranjan

Indranil Bhattacharyya executive director of RBI 's Monetary Policy Department, is said to join the Monetary Policy Committee MPC ), replacing Rajiv Ranjan , as reported by ET Now citing sources.

Open Access Article by Joel Anyam Joel Anyam SciProfiles Scilit Preprints.org Google Scholar

The New Development Bank (NDB) said it has appointed Rajiv Ranjan , a career central banker who has served on the rate-setting panel of the Reserve Bank of India

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Epilepsy affects over 50 million people globally, with accurate seizure type classification directly influencing treatment selection as different seizure types respond to specific antiepileptic medications. Manual electroencephalogram (EEG) interpretation remains time-intensive and requires specialized expertise, creating clinical workflow bottlenecks. This work presents EEG-ARCNet, an attention-residual convolutional network integrating residual connections with channel attention mechanisms to extract discriminative temporal and spectral features from multi-channel EEG recordings. The model combines nine statistical temporal features with five frequency-band power measures through Welch's spectral decomposition, processed through attention-enhanced convolutional pathways. Evaluated on the Temple University Hospital Seizure Corpus, EEG-ARCNet achieved 99.65% accuracy with 99.59% macro-averaged F1-score across five seizure types (absence, focal non-specific, simple partial, tonic-clonic, and tonic). To validate practical deployment, the model was implemented on Raspberry Pi 4, achieving a 2.06 ms average inference time per 10 s segment with 35.4% CPU utilization and 499.4 MB memory consumption. The combination of high classification accuracy and efficient edge deployment demonstrates technical feasibility for resource-constrained seizure-monitoring applications.

Abstract: The security of code generated by large language models (LLMs) is a significant concern, as studies indicate that such code often contains

Ranchi, Dec 6: Jharkhand Chief Minister Hemant Soren appeared before the MP/MLA court in Ranchi on Saturday in a case related to alleged non-compliance with Enforcement Directorate summons.

Molybdenum is a critical raw material for advanced alloys and the chemical industry; however, its supply is increasingly constrained by geopolitical and resource limitations. An efficient recycling of molybdenum from secondary resources is therefore essential to mitigate the supply risk and promote maximum utilization of resources. In this study, a low-energy hydrometallurgical process is proposed for the recovery of molybdenum from spent iron-molybdate catalysts, integrating ammoniacal leaching with anti-solvent crystallization. Conventional leaching using ammonia solution alone achieved less than 90% efficiency and required a high ammonia concentration of 5.0 mol/L. This consumption was significantly reduced to only 2.0 mol/L by employing an ammonia-ammonium solution. The required NH4OH-(NH4)2SO4 ratio was optimized to achieve selective molybdenum dissolution that resulted in 92% leaching efficiency while limiting iron co-dissolution to below 5%. The residual iron, governed by the quasi-equilibrium of iron pentaamine complexation, was effectively supressed through controlled settling of the leachate. Subsequently, ethanol-assisted anti-solvent crystallization, performed at an aqueous-to-organic volumetric ratio of 1:2, enabled the direct recovery of high-purity (NH4)6Mo7O24(H2O)4 with 95% yield under ambient conditions. This work demonstrates a scalable and resource-efficient strategy for molybdenum recovery through integrating the selective leaching approach with low-energy crystallization technique.