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Subject: Emerging Technologies

  • 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.

  • 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.

  • AI Mission 2.0 and Expansion of Common Compute

    Why in the News?

    At the AI Impact Summit in New Delhi, the Union IT Minister announced the launch of AI Mission 2.0 and the addition of 20,000 GPUs to the government’s common compute infrastructure under the IndiaAI Mission.

    What is the Common Compute Cluster?

    • Government supported shared AI infrastructure
    • Objective: Democratise access to expensive AI computing resources and reduce entry barriers.
    • Provides access to high performance GPUs
    • Open to:
      • Startups
      • Researchers
      • Academia
      • Indian AI firms

    Key Announcements

    • Addition of 20,000 GPUs

      • To be installed within six months
      • Strengthens national AI compute capacity
      • Supports training of large language models and advanced AI systems

    • AI Mission 2.0

      • Greater focus on:
      • AI research and development
      • Innovation ecosystem
      • AI diffusion across sectors
      • Strengthening public digital infrastructure

    • Indigenous Foundational Model

      • A foundational large language model from an Indian firm expected
      • Aim: Build applications with real public impact
    [2025] Consider the following statements: I. It is expected that Majorana 1 chip will enable quantum computing. 

    II. Majorana 1 chip has been introduced by Amazon Web Services (AWS). 

    III. Deep learning is a subset of machine learning. 

    Which of the statements given above are correct? 

    (a) I and only I (b) II and III only (c) I and III only (d) I, II and III

  • Sodium Ion Batteries and India’s Battery Strategy  

    Why in the News?

    A recent policy focused analysis highlighted sodium ion battery technology as a strategic alternative for India to reduce dependence on lithium ion batteries, strengthen energy security and address critical mineral supply risks.

    Background

    • Batteries are central to EVs, renewable energy storage and digital devices
    • Lithium ion batteries currently dominate due to high energy density and long cycle life
    • India faces high import dependence for lithium, cobalt and nickel

    India’s Current Battery Push

    • Advanced Chemistry Cell manufacturing supported under PLI scheme
    • About 40 GWh capacity allocated, but limited domestic upstream ecosystem
    • Heavy reliance on imported raw materials and components

    What are Sodium Ion Batteries

    • Batteries that use sodium instead of lithium as the charge carrier
    • Sodium is abundant and widely available
    • Compatible with existing lithium ion manufacturing lines with minor changes

    Performance Comparison

    • Lower energy density than lithium ion batteries
    • Suitable for grid storage, two wheelers and stationary applications

    Global Status

    • Around 70 GWh sodium ion capacity operational globally in 2025
    • Expected to reach nearly 400 GWh by 2030
    [2025] In the context of electric vehicle batteries, consider the following elements: I. Cobalt 

    II. Graphite 

    III. Lithium 

    IV. Nickel 

    How many of the above usually make up battery cathodes? 

    (a) Only one (b) Only two (c) Only three (d) All the four

  • Solid Fuel Ducted Ramjet (SFDR) Technology Test 2026

    Why in the News?

    Defence Research & Development Organisation successfully demonstrated Solid Fuel Ducted Ramjet (SFDR) technology on February 03, 2026 from Integrated Test Range, marking India’s entry into an elite group of nations with this advanced missile propulsion capability.

    About Solid Fuel Ducted Ramjet (SFDR)

    • An advanced air breathing propulsion system for long range air to air missiles
    • Uses solid fuel with controlled airflow for sustained thrust
    • Allows missiles to maintain high speed during terminal phase
    • Significantly increases range and no escape zone

    Key Highlights of the Test

    • All subsystems including nozzle less booster, SFDR motor and fuel flow controller performed as expected
    • Missile was boosted to the required Mach number before ramjet ignition
    • Performance validated through tracking instruments along the coast of the Bay of Bengal
    • Successful data capture confirmed stable combustion and thrust control

    Strategic Significance

    • Enables development of next generation long range air to air missiles
    • Provides major tactical advantage against hostile aircraft
    • Strengthens indigenous defence research and manufacturing
    • Reduces dependence on imported propulsion technologies
    [2023] Consider the following statements: 1. Ballistic missiles are jet-propelled at subsonic speeds throughout their flights, while cruise missiles are rocket-powered only in the initial phase of flight

    2. Agni-V is a medium-range supersonic cruise missile, while BrahMos is a solid-fuelled intercontinental ballistic missile

    Which of the statements given above is/are correct? 

    (a) 1 only (b) 2 only (c) Both 1 and 2 (d) Neither 1 nor 2

  • Reconstructing the Sun’s Invisible Magnetic Fields

    Why in the News?

    Researchers from Indian Institute of Technology Kanpur have reconstructed the internal magnetic fields of the Sun using 30 years of surface data, improving the ability to predict solar cycles and space weather. The study was published on January 20 in Astrophysical Journal Letters.

    Background

    • The Sun follows an ~11 year solar activity cycle driving sunspots, solar flares and coronal mass ejections
    • These events affect satellites, navigation systems, power grids and communications on Earth
    • Prediction is difficult because magnetic fields originate deep inside the Sun, beyond direct observation

    What is New in This Study?

    • Instead of relying on simplified theoretical assumptions, researchers used a data driven solar dynamo model
    • Real observations of surface magnetic fields were fed into a 3D computer simulation
    • This allowed reconstruction of the invisible magnetic fields inside the Sun

    Key Scientific Findings

    • Successfully reproduced the Butterfly Diagram, showing sunspot migration from high latitudes to the equator
    • Revealed behaviour of the toroidal magnetic field in the Sun’s convection zone
    • Internal magnetic field strength matched actual Solar Cycles 23, 24 and 25

    Significance

    • Advances understanding of solar dynamo physics
    • Enhances space weather forecasting accuracy
    • Critical for protecting space based assets and communication infrastructure
    [2022] If a major solar storm (solar flare) reaches the Earth, which of the following are the possible effects on the Earth? 

    1. GPS and navigation systems could fail. 

    2. Tsunamis could occur at equatorial regions. 

    3. Power grids could be damaged. 

    4. Intense auroras could occur over much of the Earth. 

    5. Forest fires could take place over much of the planet. 

    6. Orbits of the satellites could be disturbed. 

    7. Shortwave radio communication of the aircraft flying over polar regions could be interrupted. Select the correct answer using the code given below: 

    (a) 1, 2, 4 and 5 only (b) 2, 3, 5, 6 and 7 only (c) 1, 3, 4, 6 and 7 only (d) 1, 2, 3, 4, 5, 6 and 7

  • Deuteron

    Why in the News?

    A recent study by the ALICE Collaboration at Large Hadron Collider, CERN has explained how deuterons survive ultra high energy particle collisions.

    About Large Hadron Collider

    • World’s largest and most powerful particle accelerator
    • Located near Geneva, on the France Switzerland border
    • Circular tunnel of 27 km circumference
    • Operated by CERN
    • Collides protons and heavy ions at near speed of light

    About Deuteron

    • Deuteron is the nucleus of deuterium, a stable isotope of hydrogen
    • Contains one proton + one neutron
    • Denoted by ²H or D
    • Simplest composite nucleus after hydrogen
    • Found in trace amounts in natural water
    • Present in atmospheres of Jupiter and Saturn

    Why Deuteron Survival Was a Puzzle

    • LHC collisions create extreme temperature and energy
    • Deuterons should theoretically break apart
    • Yet deuterons and anti deuterons are observed repeatedly

    Key Scientific Finding

    • Deuterons mainly form through coalescence mechanism
    • Protons and neutrons form first, then bind together later
    • Pions act as energy carriers enabling binding
    • Formation happens away from the most violent collision zone
    • Explains survival despite low binding energy

    Applications of Deuteron

    • Production of heavy water (D₂O) used as moderator in nuclear reactors
    • Used in fusion research as a fuel source
    • Used in tritium production
    • Important in nuclear physics experiments
    [2011] The function of heavy water in a nuclear reactor is to? 

    (a) Slow down the speed of neutrons

    (b) Increase the speed of neutrons

    (c) Cooldown the reactor

    (d) Stop the nuclear reaction.

  • Long Range Anti Ship Hypersonic Glide Missile (LR AShM)

    Why in the News?

    India will publicly debut its Long Range Anti Ship Hypersonic Glide Missile (LR AShM) at the 77th Republic Day parade, marking India’s entry into the elite hypersonic anti ship weapons club.

    What is LR AShM?

    • Indigenous hypersonic glide missile (More than Mach 5 Speed)
    • Designed to engage high value naval targets such as aircraft carrier battle groups
    • Capable of very long range strikes with extreme speed and manoeuvrability

    Developed By

    • Defence Research and Development Organisation
    • For the Indian Navy
    • Intended mainly for coastal battery and maritime strike roles

    Aim

    • Enhance maritime deterrence in the Indian Ocean Region
    • Neutralise enemy surface combatants at stand off distances
    • Strengthen A2 AD Anti Access Area Denial capabilities through shore based mobile launchers
    [2023] Consider the following statements: 

    1. Ballistic missiles are jet-propelled at subsonic speeds throughout their flights, while cruise missiles are rocket-powered only in the initial phase of flight

    2. Agni-V is a medium-range supersonic cruise missile, while BrahMos is a solid-fuelled intercontinental ballistic missile

    Which of the statements given above is/are correct? 

    (a) 1 only (b) 2 only (c) Both 1 and 2 (d) Neither 1 nor 2