Rajiv Ranjan

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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Content 16 slides Get More When you purchase this document, the purchase price can be applied to the cost of an annual subscription, giving you access to more research for your investment.

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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.

Background: Preeclampsia (PE) is a major contributor to maternal and perinatal morbidity in India. While traditional history-based screening is standard, it often lacks sensitivity. This study evaluates the effectiveness of the Fetal Medicine Foundation (FMF) calculator-based screening, utilizing a multi-parametric approach without biochemical markers, against traditional maternal history-based screening in a North Indian population.Methods: A prospective observational study was conducted at Dr. Baba Saheb Ambedkar Medical College and Hospital (September 2022 to May 2023). Four hundred women at 11 to 13+6 weeks gestation (crown-rump length (CRL) 45-84 mm) were randomized into Group A (FMF calculator-based screening using maternal history, mean arterial pressure (MAP), and uterine artery pulsatility index (UtA-PI)) and Group B (maternal history-based screening). High-risk cases (Group A cutoff: ≥1 in 150) received 150 mg of aspirin daily until 36 weeks.Results: The incidence of preterm PE was significantly lower in Group A (9, 4.9%), compared to Group B (23, 12.4%) (p = 0.011). The total PE incidence was 14 (7.6%) in Group A and 34 (18.3%) in Group B (p = 0.002). Among women initially classified as low risk, 24 (16.0%) in the history-based group developed PE compared with six (4.3%) in the calculator group (p = 0.001). Group A also showed a significant reduction in NICU admissions, i.e., 14 (7.6%) versus 28 (15.1%) in Group B (p = 0.025).Conclusion: First-trimester screening using the FMF calculator without biochemical markers is significantly more effective than history-based checklists. This protocol facilitates a viable, resource-based screening strategy for low- and middle-income countries.

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