International Indian Scientist Awards

Next-generation (next-gen) optical fiber represents a shift from traditional solid-glass strands to innovative structures and materials designed to overcome the capacity limits of existing networks. As data-heavy applications like AI training and 6G networks grow, these advanced fibers aim to provide petabit-per-second transmission speeds and ultra-low latency. Key Next-Gen Fiber Technologies Hollow-Core Fiber (HCF): Unlike legacy fiber that uses solid glass, HCF transmits light through an air-filled channel. Because light travels faster in air than glass, it reduces signal delay (latency) by approximately 30–35%, making it critical for high-performance computing and financial trading. Space Division Multiplexing (SDM): This technology increases capacity by adding more "lanes" for data within a single strand.Multi-Core Fiber (MCF): Packs multiple independent glass cores into one fiber, multiplying throughput without increasing the cable's physical size. Multi-Mode Fiber ...

Recursive iterative P rincipal Component Analysis (RIPCA) is a specialized technique developed for real-time model identification and error variance estimation in time-varying processes. It combines the recursive update capabilities of Recursive PCA (RPCA) with the noise-handling power of Iterative PCA (IPCA). Core Components The RIPCA algorithm operates by integrating three distinct methodologies: Iterative PCA (IPCA): Originally designed by Narasimhan and Shah to solve the "errors-in-variables" problem, where both input and output measurements contain noise. It iteratively estimates both the linear model and the specific error variances (heteroskedastic noise) for each variable. Recursive PCA (RPCA): A technique that updates the data covariance matrix incrementally as new samples arrive. This eliminates the need to store massive amounts of past data, making it ideal for online monitoring. The Recursive Iterative Approach: In RIPCA, these are combined to track changes in a...

A novel superplastic micro-extrusion (SME) technology has been developed to manufacture high-strength, lightweight magnesium micro-components, specifically addressing the challenges of limited room-temperature formability in magnesium alloys. This technology utilizes microstructural engineering to enable large plastic deformations at the microscale, resulting in miniaturized parts like micro-pins and micro-cups with homogeneous mechanical properties. Key Features of the SME TechnologyMicrostructural Engineering: The process starts by engineering an ultrafine grained (UFG) microstructure in Mg-RE (Rare Earth) alloys using Friction Stir Processing (FSP). This produces equiaxed grains (typically <1 µm) with thermally stable intermetallic phases ( ) that pinning grain boundaries. Dual-Mode Extrusion: The technology supports both Micro-Forward Extrusion (MFE) to create pins and Micro-Backward Extrusion (MBE) for cup-like structures. Governing Mechanism: The primary deformation mechanism...

  Ligustilide is a major bioactive phthalide primarily found in plants of the Apiaceae family, such as Angelica sinensis (Dong Quai) and Ligusticum chuanxiong. It is a volatile, lipophilic compound that serves as a key quality marker for these medicinal herbs. Key Biological Activities Ligustilide is recognized for its broad pharmacological potential, often attributed to its ability to modulate multiple biological pathways simultaneously. 1Neuroprotection: It crosses the blood-brain barrier and has shown potential in treating brain injuries and neurodegenerative diseases like Alzheimer's by reducing neuroinflammation and oxidative stress. Anti-inflammatory & Antioxidant: It inhibits pro-inflammatory cytokines (like TNF-α and IL-6) and activates the Nrf2/HO-1 defense pathway to combat oxidative damage. Gastrointestinal Health: Recent studies suggest it may treat GI disorders by targeting proteins like PTGS2 (COX-2) and EGFR to reduce inflammation and promote healing. Anticancer:...

Advancements in medical image segmentation have been heavily driven by the integration of Transformer models, which address the limitations of Convolutional Neural Networks (CNNs) in capturing long-range dependencies and global context. While CNNs excel at local feature extraction, Transformers utilize self-attention mechanisms to model relationships between distant pixels, crucial for identifying complex anatomical structures. Key Advancements in Architectures Recent trends show a shift toward hybrid architectures, blending the local focus of CNNs with the global understanding of Transformers. Hybrid CNN-Transformer Models: These are currently the dominant approach, combining convolutional encoders for low-level details (edges, texture) with Transformer encoders for high-level semantic information. Key examples include TransUNet, which hybridizes Transformers and U-Net, and Swin-Unet, the first pure transformer-based U-shaped architecture using hierarchical attention. Hierarchical ...

As of early 2026, the hydrogen energy landscape has shifted from a period of high expectations to a "year of reckoning" and consolidation. While global hydrogen demand has exceeded 95 million tonnes per year, it remains dominated by fossil-fuel-based "grey" hydrogen used as an industrial feedstock for refining and ammonia production. However, the pipeline for low-carbon "green" hydrogen has surged to over 1,500 projects, signaling a transition toward commercial-scale industrial use. Current Landscape (2026) The present market is characterized by a move from speculative vision to practical feasibility. De Nora Production Dominance: Steam methane reforming (natural gas) and coal gasification still account for approximately 75% and 23% of global production, respectively. Green Hydrogen Growth: Green hydrogen, though representing less than 1% of current total production, is seeing extraordinary growth rates exceeding 45–50% annually. Key Hubs:China: Dominates...