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Subject: Science and Technology

  • BEL–Bellatrix Partnership to Develop VLEO Satellite Systems

    Why in the News

    India’s defence PSU Bharat Electronics Limited (BEL) and space-tech startup Bellatrix Aerospace have signed an MoU to jointly develop Very Low Earth Orbit (VLEO) satellite systems.

    What is VLEO (Very Low Earth Orbit)?

    • Altitude: About 150 km to 450 km above Earth.
    • Lower than Low Earth Orbit (LEO) satellites.
    • Satellites experience thin atmospheric drag, requiring propulsion systems to maintain orbit.

    How VLEO Satellites Work

    • At low altitude, satellites face aerodynamic drag from the upper atmosphere.
    • Advanced propulsion systems provide continuous thrust to maintain orbital position.
    • Bellatrix will use electric/green propulsion technologies for station-keeping.

    Key Features of VLEO Systems

    • High-Resolution Imaging: Closer proximity to Earth enables sub-meter imaging using smaller sensors.
    • Ultra-Low Latency Communication: Shorter signal distance enables faster data transmission and real-time communication.
    • Lower Launch Costs: Lower orbit requires less fuel to deploy satellites.
    • Reduced Space Debris: Failed satellites naturally re-enter and burn up due to atmospheric drag.

    Aim of the Partnership

    • Develop indigenous VLEO satellite platforms and payloads.
    • Provide solutions for defence and civilian applications.
    • Combine PSU manufacturing capability with startup innovation.

    Strategic Significance

    • Strengthens India’s self-reliance in space technology.
    • Enables high-resolution surveillance and intelligence gathering.
    • Useful for:
      • Border monitoring
      • Earth observation
      • Real-time communication systems.

    Prelims Pointers

    • Bharat Electronics Limited (BEL) operates under the Ministry of Defence.
    • Bellatrix Aerospace develops satellite propulsion systems.
    • VLEO satellites orbit at lower altitude than conventional Earth-observation satellites, offering improved imaging and reduced debris risk.
    [2011] An artificial satellite orbiting around the Earth does not fall down. This is so because the attraction of Earth (a) does not exist at such a distance. (b) is neutralized by the attraction of the moon. (c) provides the necessary speed for its steady motion. (d) provides the necessary acceleration for its motion

  • How do astronauts return from space and survive re-entry

    Why in the News?

    India is advancing its human spaceflight ambitions under ISRO’s Gaganyaan programme, with successful Crew Escape System tests and re-entry validation experiments demonstrating safe atmospheric descent capability. Since re-entry involves extreme heat (over 1,500°C) and velocities exceeding 25,000 km/h, mastering this phase is a critical milestone that places India closer to joining the limited group of nations capable of independently returning astronauts safely from space.

    What is spacecraft re-entry?

    Spacecraft re-entry is the critical process of a vehicle returning from space, passing through a planet’s atmosphere to land on the surface. It is a controlled deceleration process in which a spacecraft transitions from orbital velocity to safe landing conditions.It involves using atmospheric drag and heat shielding to dissipate immense kinetic energy (approx. mph) while managing temperatures up to caused by compressed air.

    Key aspects of re-entry include:

    1. Deceleration and Heating: As the spacecraft hits the dense atmosphere, it experiences extreme deceleration and intense heat, often creating a “wall of fire” around the craft.
    2. Thermal Protection: Vehicles use specialized heat shields, such as ablative materials, to protect against temperatures exceeding 1650 degree celsius.
    3. Methods: Re-entry can be controlled (using engines for precise, safe, or targeted landing) or uncontrolled (naturally falling back).
    4. Phases: It typically involves deorbiting, atmospheric entry, and landing (often using parachutes).
    5. Challenges: The “entry corridor” must be precisely navigated; entering too steeply causes excessive heat, while too shallow causes the craft to skip back into space

    Why is Re-entry Considered the Most Critical Phase of Spaceflight?

    1. Orbital Velocity: Spacecraft travel at ~7.8 km/s in Low Earth Orbit, generating extreme kinetic energy during descent.
    2. Thermal Load: Atmospheric compression produces temperatures above 1,500°C, sufficient to melt structural metals.
    3. Deceleration Stress: Astronauts experience high G-forces due to rapid velocity reduction.
    4. Historical Precedent: Early scientific belief held that re-entry survival was impossible due to predicted structural failure from heat loads.

    How Does a Spacecraft Dissipate Immense Heat During Re-entry?

    1. Blunt Body Design: Rounded capsule structure disperses heat around the vehicle rather than allowing penetration.
    2. Aerodynamic Braking (Aerobraking): Uses atmospheric drag to systematically reduce speed without propulsion fuel.
    3. Thermal Protection System (TPS): Shields internal structure from heat exposure.
    4. Ablation Mechanism: Outer material chars and erodes, carrying heat away from the capsule.
    5. Heat Shield Materials: Designed to prevent thermal transfer to primary structure and crew module.

    What is the “Re-entry Corridor” and Why is It Crucial?

    1. Optimal Angle Window: Ensures safe atmospheric penetration between overshoot and undershoot limits.
    2. Overshoot Risk: Too shallow angle causes the capsule to skip back into space.
    3. Undershoot Risk: Too steep angle results in excessive heating and structural stress.
    4. Precision Navigation: Onboard guidance systems adjust trajectory within strict tolerances.

    Why Does Communication Blackout Occur During Re-entry?

    1. Plasma Formation: Extreme heat ionizes surrounding air, forming an electrically charged plasma layer.
    2. Signal Obstruction: Plasma sheath blocks radio communication between crew and ground stations.
    3. Blackout Duration: Persists until velocity reduces sufficiently for plasma dissipation.
    4. Mitigation Strategy: Use of relay satellites and high-frequency transmission pathways through thinner plasma regions.

    How Do Parachutes Enable Safe Landing?

    1. Terminal Velocity Reduction: Atmospheric drag alone remains insufficient for safe splashdown.
    2. Multi-stage Deployment: Drogue parachutes stabilize descent; main parachutes reduce final speed.
    3. Controlled Splashdown: Ensures low-impact landing in designated sea recovery zones.
    4. Landing Example: Bay of Bengal identified as primary splashdown zone for Indian missions.

    How Will India’s Gaganyaan Crew Module Execute Re-entry?

    1. Crew Module (CM): Maintains trajectory within re-entry corridor and survives thermal stress.
    2. Service Module (SM): Provides propulsion during orbital phase; separates before re-entry.
    3. Controlled Manoeuvres: Adjusts lift-to-drag ratio for precise landing.
    4. Thermal Validation: Crew Module Atmospheric Re-entry Experiment validated full-scale heat shield.
    5. Operational Significance: Positions India among nations capable of independent human re-entry systems.

    Conclusion

    Safe atmospheric re-entry represents the ultimate test of a nation’s human spaceflight capability, demanding mastery over thermal protection, trajectory precision, communication resilience, and controlled descent systems. As India advances toward operationalizing Gaganyaan, successful re-entry validation will not only ensure astronaut safety but also strengthen technological sovereignty, strategic autonomy, and India’s position among leading spacefaring nations.

    PYQ Relevance

    [UPSC 2017] India has achieved remarkable successes in unmanned space missions including the Chandrayaan and Mars Orbiter Mission, but has not ventured into manned space mission. What are the main obstacles to launching a manned space mission, both in terms of technology and logistics? Examine critically.

    Linkage: This GS-3 question examines the technological and logistical challenges in shifting from unmanned missions to human spaceflight. It directly links to Gaganyaan, especially re-entry systems, crew safety, and human-rated launch capability.

  • President Undertakes Sortie in LCH Prachand

    Why in the News

    President Droupadi Murmu undertook a sortie in the indigenous Light Combat Helicopter Prachand at Air Force Station Jaisalmer on February 27, 2026.

    About LCH Prachand

    • India’s indigenously developed Light Combat Helicopter.
    • Designed for high altitude warfare and desert operations.
    • Equipped with:
      • Air to ground missiles
      • Rocket systems
      • 20 mm turret gun
    • Developed by Hindustan Aeronautics Limited

    Significance

    • Highlights indigenous defence capability.
    • Demonstrates operational readiness of the Indian Air Force.
    • Symbolic boost to Aatmanirbhar Bharat in defence manufacturing.

    Prelims Pointers

    • LCH Prachand inducted into Indian Air Force in 2022.
    • Designed for operations at high altitude including Himalayan region.
    • Air Force Station Jaisalmer is a key western sector air base.
    • President is Supreme Commander of the Armed Forces under Article 53.
    [2025] With reference to India’s defence, consider the following pairs: Aircraft type : Description 

    I. Dornier-228 : Maritime patrol aircraft 

    II. IL-76 : Supersonic combat aircraft 

    III. C-17 Globemaster : Military transport aircraft 

    How many of the pairs given above are correctly matched? 

    (a) Only one (b) Only two (c) All the three (d) None

  • INS Anjadip Commissioned

    Why in the News

    The Indian Navy commissioned INS Anjadip, the fourth indigenously designed and built Anti Submarine Warfare Shallow Water Craft, at Chennai Port.

    About INS Anjadip

    • Type: Anti Submarine Warfare Shallow Water Craft
    • Length: 77 metres
    • Built by: Garden Reach Shipbuilders & Engineers at Kattupalli
    • Named after: Anjadip Island off Karwar coast

    Key Capabilities

    • Designed for shallow and coastal waters
    • Detect, track and neutralise enemy submarines
    • Equipped with:
      • Shallow water sonars
      • Lightweight torpedoes
      • Anti submarine rockets
      • Combat management system

    Operational Roles

    • Anti submarine warfare in littoral zones
    • Coastal surveillance
    • Low intensity maritime operations
    • Search and rescue missions

    Significance

    • Enhances India’s anti submarine warfare capability
    • Strengthens coastal defence architecture
    • Reflects Aatmanirbhar Bharat in naval shipbuilding
    • Boosts indigenous defence manufacturing ecosystem
    [2016] Which one of the following is the best description of ‘INS Astradharini’, that was in the news recently? (a) Amphibious warfare ship 

    (b) Nuclear-powered submarine 

    (c) Torpedo launch and recovery vessel 

    (d) Nuclear-powered aircraft carrier

  • Have AI products/LLMs started to disrupt the software services industry?

    Why in the News?

    India’s $250+ billion IT services industry is witnessing structural churn due to rapid enterprise adoption of Artificial Intelligence (AI) and Large Language Models (LLMs). AI has rapidly moved from pilot projects to full-scale deployment in India’s IT services industry. Companies are restructuring teams and changing billing models as automation begins to reduce dependency on large manpower-based delivery.

    Is AI-driven productivity restructuring India’s traditional labour-arbitrage IT model?

    1. Labour Arbitrage Model: India’s IT growth historically depended on low-cost skilled manpower and time-and-material billing structures.
    2. AI-Enabled Productivity Gains: Generative AI assists coding, testing, documentation, and DevOps processes, reducing manual effort.
    3. Reduced Headcount Dependency: Tasks earlier requiring 8-10 engineers may now require significantly fewer personnel.
    4. Shift in Developer Roles: Engineers increasingly supervise AI outputs instead of manually writing baseline code.
    5. Enterprise Adoption: AI tools are embedded in workflow systems rather than treated as experimental add-ons.

    Does AI disproportionately impact entry-level and BPO/KPO employment structures?

    1. Routine Automation: Repetitive and well-defined tasks in BPO/KPO segments are highly automatable.
    2. Entry-Level Vulnerability: Coding support, documentation drafting, and testing roles face reduction.
    3. Reskilling Imperative: Demand shifts toward prompt engineering, AI model supervision, and domain integration.
    4. Net Employment Effect: Overall revenue per engineer may increase, but entry pathways narrow.
    5. Mid-Level Stability: Complex integration, client management, and architecture roles remain comparatively resilient.

    Is the IT services billing architecture shifting from manpower-based to outcome-based pricing?

    1. Traditional Pyramid Model: Revenue historically linked to number of deployed engineers.
    2. Automation Impact: AI reduces billable hours while increasing efficiency.
    3. Outcome-Based Pricing: Clients demand delivery linked to quality, productivity, and time benchmarks.
    4. Margin Preservation: Firms attempt to maintain profitability despite lower headcount expansion.
    5. Service Model Transformation: Predictable delivery replaces volume-based staffing.

    Are Indian IT firms building foundational AI capabilities or remaining service integrators?

    1. Foundational Model Ownership: Major LLM development remains concentrated in US and Chinese firms.
    2. Service-Dominant Strategy: Indian companies focus on AI integration, customization, and enterprise embedding.
    3. Infrastructure Constraints: Limited domestic investment in compute capacity and advanced semiconductor ecosystems.
    4. Strategic Choice: Debate between investing in sovereign AI models versus deepening service specialization.
    5. Global Competitiveness: Scaling, execution efficiency, and process rigour remain India’s strengths.

    Does AI transformation necessitate new regulatory and social protection frameworks?

    1. Employment Transition Risks: Automation may temporarily increase unemployment in routine segments.
    2. Skill Certification Gap: Absence of standardized AI skill accreditation mechanisms.
    3. Data Governance Concerns: AI deployment raises issues of data privacy, algorithmic bias, and compliance.
    4. Energy & Environmental Costs: Data centres increase electricity consumption and water usage.
    5. Policy Preparedness: Need for labour transition planning, digital skilling missions, and regulatory clarity.

    Is AI replacing software engineers or redefining their functional role?

    1. Task Automation vs Role Elimination: AI reduces repetitive coding but increases need for oversight.
    2. AI-Assisted Development: Engineers validate AI-generated code for architectural integrity.
    3. Domain Integration: Banking, healthcare, and financial services require contextual expertise.
    4. Product Engineering Shift: Movement from services to proprietary frameworks and tools.
    5. Horizontal Skill Structure: Less hierarchical team pyramids.

    Conclusion

    AI-led transformation marks a structural shift in India’s IT services growth model from labour arbitrage to productivity arbitrage. The challenge is not technological disruption itself, but managing its employment, skill, and regulatory implications. A calibrated approach that combines innovation, large-scale reskilling, data governance, and employment-sensitive growth strategy will determine whether AI becomes a source of competitive advantage or structural imbalance.

    PYQ Relevance

    [UPSC 2022] ‘Economic growth in the recent past has been led by increase in labour productivity.’ Explain this statement. Suggest the growth pattern that will lead to creation of more jobs without compromising labour productivity.

    Linkage: This question links directly to GS-3 themes of jobless growth, labour productivity, digitalisation, and structural transformation of the Indian economy, especially in the context of AI-driven automation. It is also highly relevant for Essays on “Growth vs Employment,” “Technology and Jobs,” and “Inclusive Development in the Age of AI.”

  • How are India firms training LLMs?

    Why in the News?

    India has made its first major push into foundational AI model training by releasing domestically developed 35B and 105B parameter LLMs using subsidised Graphics Processing Unit (GPU) infrastructure under the IndiaAI Mission. With over 36,000 GPUs commissioned and 4,096 allocated to select firms, the move marks a strategic shift from dependence on foreign frontier models to state-supported indigenous AI capability.

    Why Is Training Large Language Models on Indian Soil Financially and Logistically Challenging?

    1. GPU Dependence: Requires high-end Graphics Processing Units for model training and inference; combined hardware and electricity costs run into millions of dollars.
    2. Electricity Intensity: Compute-heavy training increases power consumption and operational expenses.
    3. Capital Requirements: Large upfront investment limits private-sector experimentation in foundational AI.
    4. Data Constraints: Internet training corpora disproportionately represent English and European languages.
    5. Token Inefficiency: Indian language tasks require more tokens due to translation layers, increasing inference cost.

    How Has the IndiaAI Mission Lowered Entry Barriers for Domestic AI Firms?

    1. Public Compute Infrastructure: Commissioned 36,000+ GPUs in domestic data centres operated by firms such as Yotta.
    2. Cluster Allocation: Provided 4,096 GPUs through a shared government compute facility.
    3. Subsidised Access: Enabled startups and researchers to train and deploy models at relatively nominal fees.
    4. Institutional Facilitation: Ministry of Electronics and Information Technology supports long-term indigenous AI capacity.
    5. Ecosystem Development: Encourages domestic research, experimentation, and AI entrepreneurship.

    How Does the Mixture of Experts (MoE) Architecture Improve Cost Efficiency in Model Deployment?

    1. Selective Activation: Activates only a fraction of parameters during inference rather than the full network.
    2. Compute Reduction: Lowers electricity consumption compared to dense models.
    3. Inference Efficiency: Enables large models such as 105B parameters to run at lower operational cost.
    4. Scalable Design: Allows domestic firms to optimise performance without matching trillion-parameter scale.
    5. Cost Competitiveness: Enhances feasibility of AI deployment in education, healthcare, and governance contexts.

    Does Parameter Size Alone Determine Strategic AI Capability?

    1. Model Scale: Domestic models at 35B and 105B parameters remain smaller than global frontier systems.
    2. Contextual Alignment: Designed for Indian languages and domestic sectoral use.
    3. Sector-Specific Model: A 17B multilingual model developed for education and healthcare applications.
    4. Incremental Scaling Strategy: Prioritises contextual performance before expanding model size.
    5. Capability Gap: Comparative benchmarking with frontier systems remains limited.

    How Does Linguistic Data Imbalance Affect Digital Inclusion?

    1. Language Dominance: English and European languages dominate global internet datasets.
    2. Indian Language Underrepresentation: Limits model accuracy in vernacular contexts.
    3. Translation Dependence: Machine translation remains inferior to native-language modelling.
    4. Governance Impact: Weak vernacular performance may affect citizen-facing digital services.
    5. Inclusion Objective: Indigenous LLMs aim to strengthen equitable AI access.

    What Transparency and Accountability Concerns Arise from Publicly Funded AI Infrastructure?

    1. Open-Source Ambiguity: Models described as open but not fully accessible on major global platforms.
    2. Limited Independent Scrutiny: Restricted external evaluation affects benchmarking.
    3. Public Investment Oversight: Large-scale GPU subsidies require measurable performance assessment.
    4. Benchmark Transparency: Absence of publicly standardised comparison metrics.
    5. Energy Governance: Limited disclosure of sustainability audits for compute-intensive infrastructure.

    Way Forward: Strengthening Indigenous AI Capacity

    1. Transparent Benchmarking: Establishes clear performance metrics for publicly funded LLMs against global standards to ensure accountability.
    2. Green Compute Standards: Mandates energy-efficiency norms and renewable integration for GPU-intensive data centres.
    3. Vernacular Data Expansion: Builds high-quality Indian language datasets through public–private collaboration.
    4. Outcome-Linked Subsidy: Links GPU allocation and funding to measurable innovation and adoption outcomes.
    5. Regulatory Framework: Defines standards for data governance, algorithmic transparency, and institutional accountability.

    Conclusion

    India’s entry into foundational LLM training marks a shift from AI consumption to domestic capability creation. Public compute subsidies under the IndiaAI Mission reduce entry barriers but require transparent benchmarking, fiscal oversight, and sustainability safeguards. Long-term competitiveness will depend on strengthening vernacular data ecosystems, improving cost-efficient architectures, and institutionalising regulatory accountability.

    PYQ Relevance

    [UPSC 2023] Introduce the concept of Artificial Intelligence (AI). How does AI help clinical diagnosis? Do you perceive any threat to privacy of the individual in the use of AI in healthcare?

    Linkage: Indigenous LLM development strengthens AI capability for governance and sectoral applications such as healthcare diagnostics. It simultaneously raises concerns of data protection, algorithmic transparency, and privacy, core issues highlighted in the 2023 AI question.

  • AI and the brain: similar in scale, different in design

    Why in the News?

    GPT-4 introduced a new design that activates only selected parts of its system for specific tasks, similar to how the human brain works. At the same time, AI models are now approaching the brain in scale but consume far more energy. This contrast between similar size and very different efficiency has made the AI-brain comparison a major policy and technological issue.

    How does the scale convergence between AI models and the human brain raise governance and infrastructure challenges?

    1. Parameter Expansion: GPT-3 contains 175 billion parameters; newer models approach trillions, nearing the brain’s ~100 trillion synapses. Scale increases computational dependency and infrastructure concentration.
    2. Data Centre Energy Demand: Training and operating large AI models require megawatts of electricity. Ensures rising carbon footprint and grid stress.
    3. Hardware Dependence: AI training relies on high-performance GPUs originally developed for video gaming. Strengthens semiconductor concentration risks.
    4. Digital Infrastructure Concentration: Massive parallel computation requires clustered data centres. Facilitates market dominance by few global technology firms.
    5. Strategic Autonomy Concern: Nations lacking advanced chip fabrication capacity face technological dependence. Impacts India’s semiconductor mission and AI self-reliance goals.

    In what ways does mixture-of-experts architecture influence regulatory and accountability frameworks?

    Mixture-of-Experts (MoE) is a type of Artificial Intelligence model design where: instead of using the entire neural network for every task and the system activates only a few specialised parts (“experts”) for each input.

    1. Selective Activation: GPT-4 activates specialised network portions for specific tasks. Enhances computational efficiency but complicates traceability
    2. Modular Processing: Resembles the brain’s region-specific activation (language, vision, movement). Raises issues of explainability in AI outputs.
    3. Sparse Routing Mechanism: Routes input through selected pathways rather than full network. Challenges transparency audits.
    4. Task-Based Resource Allocation: Adjusts computational effort based on difficulty. Requires regulatory standards for algorithmic accountability.
    5. Governance Implication: Fragmented internal processing complicates liability assignment in AI-generated harms.

    Why does energy efficiency disparity between AI and the human brain matter for sustainability policy?

    1. Metabolic Efficiency: Human brain operates at ~20 watts of power. Demonstrates biological optimisation.
    2. Event-Driven Signalling: Biological neurons activate selectively and sparsely. Conserves energy.
    3. Digital Arithmetic Dependence: AI systems perform continuous high-precision computation. Increases electricity consumption.
    4. Carbon Footprint Risk: Large-scale AI training elevates emissions through energy-intensive data centres.
    5. Green AI Imperative: Necessitates energy-efficient chip design, including neuromorphic hardware and spike-like operations.

    How do differences in feedback mechanisms and learning processes impact ethical and institutional oversight?

    1. Deep Feedback Loops: Brain processes signals forward, backward, and laterally. Enables contextual interpretation.
    2. Contextual Meaning Formation: Human cognition integrates prior knowledge. Reduces rigid output behaviour.
    3. Feed-Forward Architecture: Most LLMs rely on stacked layers without true recurrence. Limits adaptive contextual reasoning.
    4. Statistical Learning Model: AI identifies probabilistic patterns from text corpora. Does not “understand” meaning intrinsically.
    5. Regulatory Concern: Absence of embodied cognition raises risks of hallucinations, misinformation, and biased outputs.

    What are the implications of AI’s divergence from biological intelligence for public policy and strategic planning?

    1. Non-Biological Scaling: Machines are not constrained by evolutionary limits. Enables rapid parameter expansion.
    2. Super-Computational Potential: AI may surpass humans in speed and pattern recognition.
    3. Efficiency Trade-off: AI sacrifices energy efficiency for computational speed.
    4. Neuromorphic Research: Attempts to mimic spike-based operations to reduce power usage.
    5. Policy Imperative: Requires anticipatory regulation balancing innovation and risk mitigation.

    How does AI’s hardware dependency influence economic concentration and digital sovereignty?

    1. GPU Dominance: AI training dependent on limited global chip manufacturers.
    2. Capital Intensity: High infrastructure cost restricts entry to large corporations.
    3. Data Concentration: Models trained on massive datasets inaccessible to smaller players.
    4. Regulatory Challenge: Ensures competition law scrutiny in AI markets.
    5. National Security Dimension: AI capability linked to defence, cyber security, and economic competitiveness.

    Conclusion 

    AI is approaching the human brain in scale but remains fundamentally different in design and efficiency. While the brain operates with minimal energy and deep contextual feedback, AI depends on massive computation and data infrastructure.

    The key policy challenge lies in balancing innovation with sustainability, accountability, and digital sovereignty. Future AI development must focus not just on scale, but on efficiency, transparency, and alignment with human values.

    PYQ Relevance

    [UPSC 2023] Introduce the concept of Artificial Intelligence (AI). How does AI help clinical diagnosis? Do you perceive any threat to privacy of the individual in the use of AI in healthcare?

    Linkage: Directly linked to GS-3 (Science & Technology) under AI applications and data governance, and GS-4 (Ethics) regarding privacy, accountability, and algorithmic decision-making. The AI-brain debate strengthens this theme by highlighting efficiency, bias, and regulatory concerns in healthcare systems.

  • Proteins Tweaked as Quantum Sensors Inside the Body

    Why in the News

    Two recent studies published in Nature in February 2026 have demonstrated that fluorescent proteins can be genetically engineered to function as quantum sensors inside living cells, detecting magnetic fields and radio waves.

    Background

    • The discovery of Green Fluorescent Protein revolutionised biology by allowing scientists to visualise cellular processes. This breakthrough was recognised with the Nobel Prize in Chemistry in 2008.
    • Now, researchers have shown that such proteins can be modified to detect quantum level signals inside cells.

    Core Scientific Principle

    When a fluorescent protein absorbs light:

    1. An electron moves to a higher energy state.
    2. It usually returns, emitting light.
    3. In some cases, a radical pair forms with unpaired electrons.
    4. Their spin states are influenced by weak magnetic fields.
    5. Changes in spin alter fluorescence intensity.

    This is known as optically detected magnetic resonance, a quantum phenomenon.

    Key Research Findings

    1. Enhanced Yellow Fluorescent Protein

    • Exhibits a metastable triplet state
    • Spin state controlled using laser pulses and microwaves
    • Demonstrated qubit like behaviour inside cells
    • Observed in human kidney cells and in Escherichia coli at room temperature

    2. MagLOV Proteins

    • Engineered from plant light sensing proteins
    • Magneto sensitive fluorescent variants
    • Show stable magnetic resonance inside living bacterial cells
    • Genetically encodable and biologically compatible
    [2022] Which one of the following is the context in which the term “qubit” is mentioned? (a) Cloud Services 

    (b) Quantum Computing 

    (c) Visible Light Communication Technologies 

    (d) Wireless Communication Technologies

  • AI’s workhorse: What is a GPU? How does it work?

    Why in the News?

    European regulators are examining Nvidia’s dominance in AI GPUs amid concerns of anti-competitive practices and software lock-in through CUDA. The NVIDIA CUDA ecosystem is a comprehensive, proprietary parallel computing platform and programming model that enables GPUs to perform general-purpose computing (GPGPU). Nvidia holds nearly 90% of the discrete AI GPU market, creating high entry barriers. AI training workloads rely on thousands of GPUs operating continuously, raising electricity demand and carbon concerns. The transition from CPU-centric to GPU-centric computing marks a structural shift in global digital infrastructure with strategic and regulatory implications.

    Introduction

    It is a specialised processor designed to execute large numbers of parallel computations simultaneously. Initially developed for rendering computer graphics, GPUs now form the backbone of artificial intelligence (AI), machine learning, simulations, and high-performance computing.

    The Story So Far

    1. 1999 Launch: Nvidia marketed GeForce 256 as the first GPU.
    2. Shift in Function: Moved from video game graphics to AI infrastructure.
    3. Current Role: Powers generative AI, data centres, scientific simulations, defence modelling.

    What is a Graphics Processing Unit (GPU)?

    1. Parallel Compute Engine: Contains thousands of smaller cores performing repetitive calculations simultaneously.
    2. Workload Design: Optimised for image rendering, matrix multiplication, and tensor operations.
    3. High Bandwidth Memory: Ensures rapid movement of large datasets.
    4. Data-Heavy Efficiency: Suitable for neural networks with millions or billions of parameters.

    How Does a GPU Work? 

    GPU rendering operates through a structured sequence called the rendering pipeline:

    1. Vertex Processing
      1. Function: Processes vertices (corner points of 3D objects).
      2. Operation: Applies mathematical transformations to determine position, rotation, scaling, and camera perspective.
      3. Outcome: Converts 3D coordinates into screen-space positions.
    2. Rasterisation
      1. Function: Converts geometric shapes into pixels.
      2. Operation: Determines which pixels on the screen are covered by each triangle.
      3. Outcome: Transforms vector graphics into a pixel grid.
    3. Fragment Processing
      1. Function: Determines final colour and appearance of each pixel.
      2. Operation: Applies lighting, textures, shading, shadows, reflections.
      3. Outcome: Produces realistic visual effects.
    4. Frame Buffer Writing
      1. Function: Stores processed pixel data in memory.
      2. Operation: Writes final image data into frame buffer for display output.
      3. Outcome: Displays rendered image on screen.

    How Do GPUs Enable Artificial Intelligence?

    1. Matrix Operations: Neural networks multiply large grids of numbers repeatedly.
    2. Tensor Operations: Handles multi-dimensional data structures beyond 2D matrices.
    3. Tensor Cores: Specialised hardware (e.g., Nvidia H100) capable of ~1.9 quadrillion operations per second.
    4. Parallelism: Enables simultaneous processing of thousands of data inputs.
    5. Training Efficiency: Reduces time required for large model training.

    Where is the GPU Located?

    1. Discrete GPU: Separate graphics card connected to CPU via high-speed interface.
    2. Integrated GPU: Embedded within CPU chip.
    3. Data Centre Clusters: Installed in racks powering AI training and inference systems.

    How Are GPUs Different from Central Processing Units?

    1. CPU Architecture: Few powerful cores; optimised for sequential logic and control tasks.
    2. GPU Architecture: Many smaller cores; optimised for repetitive parallel workloads.
    3. Control Logic vs Compute Throughput: CPU manages system operations; GPU maximises computation throughput.
    4. Use Case Distinction: CPUs handle operating systems and general tasks; GPUs handle AI training and graphics.

    How Much Energy Do GPUs Consume?

    1. Board Power: Nvidia A100 consumes ~250 W during training.
    2. Continuous Operation: AI training can run for 12 hours or longer.
    3. Energy Estimate: Four GPUs operating continuously consume ~6 kWh per day (excluding server overhead).
    4. Infrastructure Overhead: Additional 30-60% energy required for cooling, CPUs, networking.
    5. Climate Implication: Data centre expansion increases electricity demand and carbon emissions.

    Does Nvidia Have a Monopoly?

    1. Market Share: Nearly 90% of discrete AI GPU market.
    2. CUDA Ecosystem: Proprietary software platform increases switching costs.
    3. Hardware Performance Edge: High-performance GPUs strengthen dominance.
    4. Regulatory Scrutiny: European authorities examining potential anti-competitive practices.
    5. Entry Barriers: Semiconductor fabrication requires high capital and advanced manufacturing ecosystems.

    Governance and Policy Implications

    1. Competition Regulation: Requires anti-trust oversight to prevent abuse of dominant position.
    2. Digital Sovereignty: Countries dependent on foreign AI chips face strategic vulnerability.
    3. Energy Governance: Necessitates integration of renewable energy and green data centre norms.
    4. Export Controls: Advanced chips increasingly subject to geopolitical restrictions.
    5. Industrial Policy: Encourages domestic semiconductor ecosystem development.

    Conclusion

    GPUs have become foundational to artificial intelligence and modern digital infrastructure. Their dominance raises concerns of market concentration, energy sustainability, and strategic dependence. Effective competition regulation, green computing standards, and domestic semiconductor capacity are essential to ensure technological growth remains inclusive, secure, and sustainable.

    PYQ Relevance

    [UPSC 2020] What do you understand by nanotechnology and how is it helping in health sector?

    Linkage: Both nanotechnology and GPU-based AI fall under GS-3 emerging technologies and test conceptual clarity about hardware-driven technological transformation.

  • ISRO to test improved fire detection algorithm during rabi harvest  

    Why in the News?

    Indian Space Research Organisation will pilot a modified algorithm to better detect farm fire events during the upcoming wheat harvesting season. The move follows discrepancies between satellite detected fires and ground reports flagged by the Commission for Air Quality Management.

    Background: Stubble Burning

    • Paddy stubble generated within a 30 day window in Punjab, Haryana and western UP.
    • Farmers burn residue due to:
      • Short gap between harvest and next sowing cycle
      • Low cost and quick clearance
    • Burning releases PM2.5 and gaseous pollutants.
    • During peak season, farm fires can contribute up to 40 percent of Delhi pollution load.

    Satellite Monitoring Mechanism

    • Fire data based on sun synchronous polar orbiting satellites:
      • NASA Terra and Aqua using MODIS sensor
      • National Oceanic and Atmospheric Administration Suomi NPP using VIIRS sensor
    • Issue identified:
      • Peak burning time shifted from around 1.30 pm in 2020 to nearly 5 pm in 2024.
      • Late evening fires may escape detection due to fixed satellite overpass timings.
    • Rabi Season Focus
      • Wheat harvesting: Late March to May.
    • 2025 data recorded:
      • Punjab: 10,207 fire events
      • Haryana: 1,832
      • NCR districts of UP: 259
    • For first time, CAQM directing monitoring of summer wheat stubble burning.
    [2019] For the measurement/estimation of which of the following are satellite images/remote sensing data used? 1. Chlorophyll content in the vegetation of a specific location 

    2. Greenhouse gas emissions from rice paddies of a specific location 

    3. Land surface temperatures of a specific location 

    Select the correct answer using the code given below: 

    (a) 1 only (b) 2 and 3 only (c) 3 only (d) 1, 2 and 3