New emerging technologies

Beyond Lithium: India’s Emerging Sodium-Ion Battery Roadmap

Context: With rapid growth in electric vehicles (EVs) and the expanding need for renewable energy storage, India is reassessing its dependence on lithium-ion batteries. In this context, India is increasingly exploring sodium-ion battery technology as a safer and strategically resilient alternative.

Lithium-Ion Batteries: Basics

A Lithium-Ion Battery (LiB) is a rechargeable electrochemical battery where lithium ions act as charge carriers. During discharge, ions move from anode to cathode, and during charging the flow reverses through an electrolyte medium.

Key components include:

Anode: Graphite-based lithium storage
Cathode: Lithium Iron Phosphate (LFP) or Nickel Manganese Cobalt (NMC)
Electrolyte: Lithium salt solution enabling ion transport

Why India Must Reduce Overdependence on Lithium-Ion

India’s battery expansion is constrained by mineral supply risks:

Supply concentration risk: Over 70% of lithium processing and major cobalt refining are concentrated in a few countries, increasing geopolitical vulnerability.
Import dependence: Though India has allocated around 40 GWh Advanced Chemistry Cell (ACC) capacity under PLI, raw material supply chains remain largely imported.
Price volatility: Rising global EV demand is expected to intensify pressure on critical minerals like lithium, cobalt, and nickel.

This makes lithium-ion dominance a strategic and economic challenge.

Why Sodium-Ion Batteries are a Strong Alternative

Sodium-ion batteries (SiBs) use sodium ions instead of lithium. Sodium is widely available and can be derived from soda ash, making it less geopolitically sensitive.

Advantages include:

Mineral-light chemistry: Many SiBs avoid cobalt, nickel, and copper.
Manufacturing compatibility: Existing Li-ion factories can be adapted with limited retrofitting.
High safety: Lower thermal runaway risks and safer transport; can be stored at zero volts.
Rapid scaling potential: Global SiB capacity is projected to rise from ~70 GWh (2025) to ~400 GWh by 2030.

Limitations of Sodium-Ion Technology

Despite promise, SiBs face challenges:

Lower energy density, reducing performance for long-range EVs.
Early commercial stage, with limited large-scale deployment compared to lithium-ion.

Sodium-Ion vs Lithium-Ion: Key Differences

Raw materials: Sodium is abundant; lithium and cobalt are limited.
Energy density: Lithium-ion remains superior.
Safety: Sodium-ion is more stable and less fire-prone.
Supply chain: Sodium-ion has lower geopolitical vulnerability.
Charging & cycle life: Sodium-ion can offer faster charging and higher cycle life in some configurations.

Way Forward for India

India’s battery strategy should focus on diversification:

Technology-neutral incentives: Expand PLI to include sodium-ion chemistry.
Domestic upstream ecosystem: Promote local production of sodium-based cathodes, anodes, and electrolytes.
Regulatory readiness: Update BIS safety standards to certify sodium-ion batteries.
Global collaboration: Build partnerships with EU and East Asian innovators for technology transfer and joint R&D.

Conclusion

Sodium-ion batteries may not replace lithium-ion entirely, but they offer India a strong opportunity to build a safer, cheaper, and geopolitically resilient energy storage ecosystem, critical for EV growth and renewable integration.

Li-Fi Internet System: A Breakthrough in Wireless Communication

Context: Gujarat-based Nav Wireless Technologies has achieved a major milestone by successfully deploying the United States’ first commercial Li-Fi internet system in New York City. This marks a significant step towards transforming how wireless communication operates globally.

About Li-Fi Technology

Li-Fi (Light Fidelity) is a wireless optical communication technology that uses light waves from Light Emitting Diodes (LEDs) to transmit data at extremely high speeds.

Working Mechanism: Li-Fi works by modulating the intensity of LED light at rapid speeds—imperceptible to the human eye—to encode data.
Receiver Setup: A photodiode captures these light signals and converts them into electrical signals, which are then processed into usable data such as audio, video, or text.
Comparison with Wi-Fi: Unlike Wi-Fi, which relies on radio waves, Li-Fi operates using visible, infrared, and ultraviolet light, offering faster and more secure data transmission.

Advantages of Li-Fi Internet

High Speed: Li-Fi can deliver speeds exceeding 100 Gbps, outperforming most Wi-Fi systems.
Enhanced Security: As light cannot pass through walls, Li-Fi signals are naturally confined to a space, reducing the risk of external hacking.
Large Bandwidth: The visible light spectrum is nearly 10,000 times wider than the radio spectrum, significantly improving data capacity and reducing network congestion.
Electromagnetic Safety: Li-Fi avoids interference with medical or aviation instruments, making it ideal for hospitals, aircraft, and industrial environments.
Energy Efficiency: Dual-use LED lighting systems can both illuminate and transmit data, reducing energy and infrastructure costs.

Limitations of Li-Fi

Line-of-Sight Dependency: Li-Fi requires a direct line of sight between the transmitter and receiver; obstruction can weaken the connection.
Limited Range: Since light cannot penetrate walls, each room needs its own Li-Fi transmitter for complete coverage.
Ambient Light Interference: Bright ambient or sunlight may distort signals, making Li-Fi less effective outdoors.

Significance and Way Forward

Li-Fi represents a paradigm shift in communication technology, offering ultra-fast, secure, and eco-friendly connectivity. As smart cities, healthcare, and aviation sectors look for interference-free and high-speed data networks, India’s growing role in Li-Fi innovation positions it as a leader in next-generation communication solutions.

CEREBO: Portable Device for Brain Injury Detection

Context: Indian Council of Medical Research (ICMR) and partner institutes have developed CEREBO, a hand-held, non-invasive indigenous diagnostic device for rapid detection of Traumatic Brain Injuries (TBIs).

Relevance of the Topic: Prelims: About CEREBO.

Key Features of CEREBO

CEREBO is a novel hand-held, portable, non-invasive diagnostic device designed for the detection of Traumatic Brain Injuries (TBIs).
It utilises advanced near-infrared spectroscopy combined with machine learning to detect intracranial bleeding and brain swelling within one minute.

Benefits of CEREBO:

Can be used by paramedic staff as well as unskilled personnel.
Provides colour-coded, radiation-free results, making it safe for infants and pregnant women.
Cost-effective (reduces imaging costs) as compared to conventional imaging tools.
Offers an emergency diagnostic option when advanced tools like CT or MRI scans are inaccessible or delayed. Can be deployed in ambulances, rural clinics, military healthcare systems and disaster response units.
Enhances early detection of TBI and improves patient outcomes.
Reduces dependence on expensive, imported diagnostic tools.

Traumatic Brain Injury (TBI)

TBI is a condition caused by sudden trauma or injury to the head, which disrupts normal brain function. The injury may range from mild (concussion) to severe, often leading to long-term physical, cognitive, emotional, and behavioural impairments.
Nearly 1.5-2 million persons are injured every year and one million die annually in India due to TBIs. Road traffic injuries (60%) are the leading cause, followed by falls and violence.

AI in Judiciary: Promise and Challenges

Context: In July 2025, the Kerala High Court issued India’s first policy on the use of Artificial Intelligence (AI) in the district judiciary, highlighting both its potential to tackle the backlog of over 5 crore cases and the risks of errors, bias, and accountability gaps.

Relevance of the Topic: Mains: Use of AI in Judiciary: Promises and Challenges.

AI in Judiciary

The judiciary faces longstanding challenges such as case backlogs, language barriers, and the need for digital modernisation.

AI in Judiciary including Machine Learning (ML), Natural Language Processing (NLP), Optical Character Recognition (OCR), and Predictive Analytics are being leveraged to automate administrative tasks, improve case tracking, and enhance crime prevention.
Initiatives like e-Courts Project Phase III, AI-assisted legal translation, predictive policing, and AI-driven legal chatbots are reshaping the legal landscape, making processes faster, smarter, and more transparent.
The Kerala High Court’s July 2025 guidelines on AI use in district judiciary marked the first official policy in India addressing AI adoption in courts.

Promise of AI in Judiciary:

Speed and Efficiency:
- Translation of documents into regional languages can help judges and litigants overcome language barriers.
- Automated transcription of oral arguments and witness depositions saves manual effort.
- Defect identification in filings ensures faster case listing and reduces delays.
Enhanced Legal Research: AI enables quick scanning of vast legal databases, saving time and supporting more focused, substantive legal analysis.
Improved Accessibility: AI-based tools can simplify judgments into easy-to-read summaries for litigants. Translation features enhance access to justice in regional languages.
Administrative Support: AI can assist registries in case classification, docket management, and scheduling, helps reduce the burden on court staff and ensures smoother case flow.
Potential Cost Reduction: By saving time and resources in transcription, research, and filing checks, AI can lower litigation costs, making justice more affordable.

Problems with AI in Judiciary

While AI promises efficiency and accessibility, its deployment in the judiciary raises serious legal, ethical, and technical concerns.

Translation and Transcription Errors: E.g., “Leave granted” translated as “holiday approved” in Hindi. In Noel Anthony Clarke vs Guardian News & Media Ltd. (2025), the claimant’s name “Noel” was repeatedly transcribed as “no.” Such errors, though small, can distort meaning and impact case outcomes.
AI Hallucinations: A study published in theJournal of Empirical Legal Studies found that legal Large Language Models (LLM) can make up case laws and cite incorrect sources to substantiate claims. E.g., OpenAI’s Whisper has been reported to “hallucinate” entire phrases or sentences, especially when speakers pause during speech.
Search Engine Bias: AI-powered legal research may reflect user behaviour patterns, not objective comprehensiveness. Risk of “invisibilising” important precedents, skewing legal arguments and judgments.
Loss of Human Nuance: Judicial decision-making requires context, empathy, and balancing of equities. Over-reliance on AI risks reducing adjudication to mechanical rule-based inferences.
Data Privacy and Security: Use of sensitive, non-public, or personal data in AI systems lacks a clear framework. Risk of data leaks, misuse, or surveillance by private vendors supplying AI tools.
Infrastructure Deficits: Many courts in India still rely heavily on paper-based processes. Weak internet connectivity, lack of digitisation, and poor hardware are major obstacles to AI deployment.

Courts are not just service providers; they are custodians of justice. Over-reliance on AI risks undermining fairness, transparency, and human judgment. Hence, AI must be adopted with caution, transparency and safeguards.

Role of Decoys in Contemporary Warfare

Context: In contemporary warfare, as jets, tanks, and warships have become more sophisticated; the methods used to shield them from detection and attack have greatly evolved. Decoys have emerged as vital tools to protect assets through deception.

Relevance of the Topic: Prelims & Mains: Decoys - working, applications, examples; Role of Decoys in Contemporary warfare.

What are Decoys ?

A decoy in warfare is a deliberately created false target (physical or electronic) that imitates real military assets with the objective of misleading enemy sensors and weapons, thereby protecting actual platforms, wasting adversary munitions, and buying time for counteraction.

Types of Decoys in Modern Warfare

Airborne Decoys:
- Fibre-Optic Towed Decoys (FOTD): E.g., Rafael’s X-Guard, Raytheon’s AN/ALE-50/55.
- Expendable Active Decoys (EAD): E.g., Leonardo’s BriteCloud, that imitate aircraft signatures.
- Stand-in Decoys: E.g., US MALD series, acting as mini-jammers or fake aircraft.
Land-Based Decoys: Inflatable tanks, artillery, or missile batteries with radar/thermal emissions. E.g., Russia’s Inflatech, Ukraine’s wooden/3D-printed systems, US Army’s fake vehicles.
Naval Decoys: Chaff, acoustic emitters, and self-propelled active decoys. E.g.,
- Nulka (Australia-US), mimicking large vessels to mislead anti-ship missiles.
- India: Kavach (chaff decoy) & Maareech (anti-torpedo system).

India’s Deployment of Decoys

X-Guard Fibre-Optic Towed Decoy (FOTD):
- During Operation Sindoor, the Indian Air Force reportedly deployed the X-Guard Fibre-Optic Towed Decoy (FOTD) on its Rafale jets.
- These decoys are believed to have misled Pakistan’s J-10C fighters and their PL-15E beyond-visual-range missiles, resulting in false kill claims by the adversary.
- Integrated with the SPECTRA Electronic Warfare (EW) suite, the X-Guard provided an additional protective layer, enhancing the survivability of Rafales.
- Following the operation, the Ministry of Defence began fast-tracking the emergency procurement of additional X-Guard units to strengthen the Air Force’s defensive capabilities.
T-90 Tank Decoys: In 2025, the Indian Army issued a Request for Information (RFI) to domestic vendors for the development of T-90 tank decoys. These decoys are required to replicate not only the physical dimensions but also the thermal and acoustic signatures of real tanks.
Kavach Decoy System: Indian Navy has inducted the Kavach decoy system, designed to protect warships by diverting radar-guided anti-ship missiles.
Maareech Advanced Torpedo Defence System (ATDS): Indian Navy has also operationalised the Maareech Advanced Torpedo Defence System (ATDS), jointly developed by DRDO and BEL, which detects incoming torpedoes and deploys decoys to neutralise them effectively.

Role of Decoys in Contemporary Warfare:

Protection of High-Value Assets: Decoys act as the first line of defence for expensive platforms like fighter jets, tanks, and warships. E.g., Indian Air Force reportedly used X-Guard Fibre-Optic Towed Decoys on Rafales during Operation Sindoor to protect jets from Pakistan’s J-10C fighters and PL-15E missiles.
Confusing and Misleading Enemy Sensors: They replicate radar, thermal, and acoustic signatures to misguide surveillance and targeting systems. E.g., X-Guard mimics a Rafale’s radar cross-section and Doppler velocity, making it hard for missiles to distinguish between real and fake targets.
Wastage of Enemy Munitions: By drawing enemy fire onto false targets, decoys force adversaries to expend costly missiles and bombs. E.g., Ukraine has used wooden and 3D-printed decoys of artillery and missile systems to make Russia waste drones and precision strikes.
Buying Time for Counteraction: Decoys delay enemy decision-making and create windows for evasion or retaliation. E.g., In naval warfare, Australia-US Nulka active decoy draws incoming missiles away from warships, giving them time to maneuver or launch countermeasures.
Force Multiplication in Ground Warfare: Ground decoys simulate massed formations, creating the illusion of greater strength. E.g., Russia’s Inflatech decoys can quickly create fake tank or artillery formations; Indian Army in 2025 issued an RFI for T-90 tank decoys with thermal and acoustic signatures to deceive drones.
Multi-Layered Defence Systems: Decoys work in tandem with Electronic Warfare (EW) suites to form a layered shield. E.g.,
- On Rafales, SPECTRA EW suite + X-Guard FOTD together provide both onboard jamming and an external trailing shield.
- Indian Navy’s Kavach chaff system and Maareech ATDS provide similar protection against radar-guided missiles and torpedoes.
Psychological and Strategic Impact: Decoys undermine the enemy’s confidence in their own sensors and kill claims, adding to the fog of war.

Decoys, across air, land, and sea, have become indispensable to modern war fighting. For a relatively low investment, they deliver high-impact protection.

Need for Climate-Smart Fabrics in Heat Action Plans

Context: India needs climate-smart fabrics to cope with intensifying heatwaves, as traditional clothing offers limited protection against extreme heat and humidity.

Relevance of the Topic: Prelims: Key facts about Climate Smart Fabrics.

What are Climate Smart Fabrics?

Climate Smart Fabrics, also known as Smart textiles, are textiles designed to adapt to environment conditions.
These fabrics integrate technologies like- sensors, microchips, and conductive fibres which enables them to monitor, react to, and even change their properties in response to stimuli like temperature, moisture etc.
Examples:
- New Phase Change Materials (PCMs) integrated into fabrics can absorb excess heat and release it when things cool down.
- Scientists at Stanford University developed a textile that is transparent to infrared wavelengths and radiates heat away from the body.

Key Features:

Thermal Regulation: Absorb and release heat to maintain optimal body temperature.
Moisture-Wicking: Pull sweat away from the skin and enable faster evaporation.
UV Protection: Shield against harmful ultraviolet rays.

Smart Fabrics use Important Technologies to function:

Nanotechnology: Developments in nanotechnology allow fabrics to be treated or engineered at a molecular level to give them unique properties like water resistance or enhanced durability. E.g., Graphene Modified Protective Clothing.
Miniaturised Electronics enable the embedding of sensors and circuits directly into textiles without affecting their flexibility or comfort.
Wireless Technologies like Bluetooth and NFC (Near Field Communication) facilitate the communication of smart textiles with smartphones and other devices, enabling real-time data tracking and interaction.
Thin and flexible batteries or solar cells: Improvements in energy harvesting and storage technologies are crucial to power these smart textiles.

Why India Needs Climate-Smart Fabrics?

India is experiencing record-breaking heatwaves. For instance, Delhi's heat index touched 54°C, Ooty witnessed its warmest day in 73 years and Kashmir had its hottest June in five decades.
As heatwaves intensify and humidity levels rise across India, especially in the Indo-Gangetic plain, traditional cotton clothing is proving inadequate.
- In high humidity, cotton dries slowly, sticks to the body, traps heat, and raises the risk of skin infections.
- Natural fibres like cotton offer little protection against harmful UV rays, increasing the risk of skin-related illnesses, including cancer.
Over 50% of India’s workforce is engaged in outdoor occupations such as farming, construction, and street vending, making them highly vulnerable to extreme heat exposure.
Vulnerable groups often lack access to appropriate protective clothing. For instance, in Varanasi, Blinkit delivery partners recently went on strike, demanding cotton uniforms to cope with the summer heat.
An analysis from Down to Earth estimates that a single five-day heat wave leads to 30,000 excess deaths in summer.

Despite growing threats, India's Heat Wave Action Plan lacks longterm, science-backed interventions such as climate-smart clothing.

It relies only on a combination of early warning systems, public awareness campaigns, emergency medical response, and structural interventions like cool roofs and shaded public spaces.

Way Forward

Integrate smart fabrics into Heat Action Plans.
India’s new Research, Development and Innovation (RDI) Scheme (₹1 lakh crore outlay) should prioritise affordable wearable technologies and mass production of climate-adaptive fabrics.

AI in Warfare and India’s Preparedness

Context: According to a research report by Delhi-based Centre for Joint Warfare Studies, an autonomous think tank, Artificial Intelligence (AI) is set to rapidly transform the landscape of warfare with deeptech being deployed for tasks ranging from autonomous weapons systems to intelligence gathering and cybersecurity.

Relevance of the Topic: Mains: How AI is transforming the landscape of warfare and India’s Preparedness.

Use case of AI in Warfare includes

Development of autonomous weapons systems that can select and engage targets without human intervention.
Analysing vast amounts of data to identify potential threats.
Tracking enemy movements, and forecasting future attacks.
Creating realistic battlefield simulations to enable field evaluation trials as well as allowing soldiers to train in virtual environments to prepare for real-world combat scenarios.

Countries around the world have started integrating AI in Warfare

China

China is using the AI models to improve artillery systems by reducing the time between shots and increasing accuracy.
Chinese military drones are equipped with generative AI that allows them to detect and destroy enemy radars automatically.
China combines AI across land, air, sea, space, cyberspace, and electromagnetic spectrum. This gives it a strong edge in multi-domain operations.

Pakistan

Pakistan’s Air Force set up a Centre of Artificial Intelligence and Computing (CAIC) in 2020.
During Operation Sindoor, Pakistan likely received LIVE satellite images and data from China. AI may have been used to quickly process this data, helping Pakistan track Indian troop movements in real-time.

Ukraine

Ukraine has equipped its long-range drones with AI that can autonomously identify terrain and military targets, using them to launch successful attacks against Russian refineries.

Israel

Israel has also used its Lavender AI system in the conflict in Gaza to identify 37,000 Hamas targets. As a result, the current conflict between Israel and Hamas has been dubbed the first “AI war”.

India

The Defence Research and Development Organisation (DRDO) established the Centre for Artificial Intelligence and Robotics (CAIR) in 1986, with the aim of developing autonomous technologies for military use.
CAIR has worked on a wide range of applications including combat systems, path planning, sensor integration, target identification, underwater mine detection, patrolling, logistics, and localisation.

However, despite this early start, India faces several key challenges in effectively harnessing AI for modern warfare.

Challenges for India in AI Warfare

Lack of Energy Infrastructure
- AI technologies need continuous, high-power electricity for data centres and simulations. India has low nuclear power capacity (around 7.5 GW), much less than countries like South Korea.
- Overdependence on solar and wind energy without backup storage makes the power grid unstable.

Inadequate AI Infrastructure: India lacks large-scale, defence-specific AI data centres. Limited access to high-performance computing for real-time battlefield analysis and decision-making.
Fragmented Research & Development: Agencies like DRDO’s CAIR have been working since 1986, but progress has been slow. No large-scale, coordinated national mission focused on AI for defence.
Weak Civil-Military Fusion: Unlike China or the U.S., India does not have strong collaboration between private tech firms, startups, academia, and the military. Defence R&D is mostly government-driven, limiting innovation speed.
Lag in C4ISR, Space, Cyber, and Electromagnetic Domains: India lags behind China in C4ISR capabilities- Command, Control, Communication, Computers, Intelligence, Surveillance, and Reconnaissance, particularly in the domains of space, cyberspace, and the electromagnetic spectrum.
Lack of National Policy or defence doctrine on AI integration: No clear national policy or defence doctrine on AI integration in military strategy. Regulatory and bureaucratic delays slow down tech adoption in defence forces.
Limited Private Sector Participation: Private sector involvement in nuclear energy and AI defence systems is limited. Without private innovation and investment, India cannot scale up AI infrastructure quickly.

AI is transforming modern warfare into an “agentic” battlefield, where autonomous systems, rapid decision-making, and multi-domain dominance decide outcomes.

Stratospheric Aerosol Injection

Context: A recent study presents a novel approach to Stratospheric Aerosol Injection (SAI) as a potential means to directly cool the Earth.

Relevance of the Topic: Prelims: Concept and Mechanism of Stratospheric Aerosol Injection Method.

What is Stratospheric Aerosol Injection (SAI)?

SAI is a proposed method of cooling the planet and reducing the impacts of climate change by adding a layer of tiny reflective particles (aerosols) to the high atmosphere.
Aerosols reflect sunlight back into space, increasing Earth’s albedo and lowering surface temperatures by reducing the amount of sunlight reaching the earth.
The method was inspired by volcanic eruptions, which have been known to have a cooling effect on the planet by spewing aerosols into the air.
How well SAI works depends on the type of material injected, the timing of the injection, and the location.

Key Findings:

Injecting 12 million tonnes of sulphur dioxide every year at an altitude of 13 km in the local spring and summer seasons of each hemisphere could cool the planet by approximately 0.6 degrees Celsius.
To achieve 1°C cooling, 21 million tonnes/year of sulphur dioxide would be required.
If the particles were injected at an even higher altitude in the subtropics, only 7.6 million tonnes would be required annually.
Higher altitude injection is more effective because particles stay for longer. At lower altitudes particles are more likely to be caught in clouds and washed out by rain. Despite this, researchers are exploring low-altitude spraying because it is technically less challenging.

While there are some benefits to this method, using three times the usual amount of aerosols carries greater risk.

Risks and Side effects of SAI

Social and geopolitical risks: If one country injects aerosols into the stratosphere, all countries will be affected, it could affect global climate patterns, leading to conflicts.
Delayed recovery of the ozone hole and increased risk of acid rain.
Cooling could mask warming on the ground and make countries complacent about curtailing emissions.

GS1 to roll out next-generation Barcodes by 2027

Context: Global Standards 1 (GS1) is preparing to replace the current barcodes with next-generation formats such as QR Codes powered by GS1 and GS1 DataMatrix. The global rollout is expected by 2027, and aims to improve product traceability and supply chain transparency.

Relevance of the Topic:Prelims: Key facts about bar code; next-generation Barcodes.

What is Barcode?

Barcode is the small image of lines (bars) and spaces of varying widths that is used to identify a particular product number, person or location.
The bars are used to represent the binary digits 0 and 1, sequences of which in turn can represent numbers from 0 to 9 and be processed by a digital computer.
This encoded information can be interpreted by a barcode scanner. The scanner shines a laser or LED light on the barcode, and detects the reflected light. The reflected light is converted into a digital signal, and interpreted.

What is QR Code?

QR Code (quick-response code) is a two-dimensional bar code. It consists of a printed square pattern of small black and white squares that encode data which can be scanned into a computer system.
The black and white squares can represent numbers from 0 to 9, letters from A to Z, or characters.
QR codes can encode URLs, text, payment information, geo-location etc.
Applications: Digital payments, inventory management, government IDs, medical certificates, boarding passes etc.

Next-generation Barcodes:

The upgraded barcodes will be structured to:
- enhance supply chain traceability, information-sharing and support recall tracking.
- support integration with technologies such as artificial intelligence and blockchain, especially for applications requiring secure and multi-party data sharing.
The barcodes would be compatible with smartphone cameras, allowing broader accessibility without the need for proprietary scanners.

Applications:

The upgraded barcode -

Would enable businesses, consumers and regulators to access product-specific information- including expiry dates, sourcing data and recall notices through a single scan.
Would help track the movement of medicines, medical devices and agricultural goods through standardised labelling.
Tool to verify product authenticity in sectors such as pharmaceuticals. Integrated into Ayushman Bharat for inventory management and cost tracking.

Global Standards 1 (GS1)

GS1 is a not-for-profit entity that develops and maintains open standards used across retail, logistics, healthcare, agriculture and other sectors.
In India, GS1 standards are already used in national programmes such as FASTag, Ayushman Bharat and ROHINI (a hospital registry).

Mission Mausam: Utilising AI in Weather Forecasting

Context: As weather patterns grow more unpredictable due to the climate crisis, India has launched Mission Mausam to improve weather understanding and forecasting through expanded observation networks, better modelling and advanced tools like AI and machine learning.

Relevance of the topic:

Prelims: Key facts about Mission Mausam.

Mains: Applications of AI: Weather Forecasting.

Mission Mausam

Launched in 2024 with a budget of Rs 2000 crores over two years.
Aim: To improve weather and climate services, and forecast information for multiple sectors, including agriculture, disaster management, and rural development. The long-term goal is to make India weather-ready and climate-smart.
Initiative by: Ministry of Earth Sciences

Objectives of Mission Mausam:

Strengthening observations (in-situ & remote sensing) networks with advanced radars, satellites, and automated weather stations.
Improve Model/ Data Assimilation/ HPC for giving accurate information to the Public and stakeholders (Numerical + Artificial Intelligence and Machine Learning).
Enhance India's capability in weather forecasting across various scales — short-term, medium-term, extended-range, and seasonal.
Provide actionable advisories for agriculture, water resources, energy, health, and disaster management sectors.

Mission Mausam Implementation Strategy

Mission Mausam adopts a multi-pronged approach to achieve its objectives:

Infrastructure Development: Installation of Doppler Weather Radars, Automatic Weather Stations, and rain gauges across the country.
Supercomputing Power: Leveraging high-performance computing systems like Pratyush and Mihir for advanced climate modelling.
Collaborative Research: Partnerships with global organisations like the World Meteorological Organisation to enhance forecasting techniques.
Public Outreach: Dissemination of user-friendly advisories through mobile apps (E.g., Mausam App), SMS services, and Media channels.

Implementation Phases:

The five-year mission would be implemented in two phases.
- First phase (until March 2026): Focus on expanding the observation network. This includes adding around 70 Doppler radars, high-performance computers and setting up 10 wind profilers and 10 radiometers.
- Second phase: Focus on adding satellites and aircraft to further enhance observational capabilities.

Cloud Chamber:

Under the mission, a cloud chamber will be established at the Indian Institute of Meteorology (IITM) in Pune, within the next one and a half years.
Aim: To study the processes occurring within clouds in the context of rising temperatures.

Working:

Artificial clouds will be created inside a laboratory at the IITM and conduct experiments. This will help the scientists better understand:
- which types of clouds can be seeded (a process where substances are added to clouds to make them produce rain)
- what materials should be used for seeding
- how much seeding is needed to either increase rain or even prevent rain.
Rising temperatures lead to clouds becoming taller and more electrically active, while their horizontal spread may shrink. This can result in stronger thunderstorms and more frequent lightning events and impact rainfall dynamics. The insights gained from the cloud chamber will help improve the parameterisation of weather models and help to artificially enhance or suppress rain and hail within the next five years.

Mission Mausam envisages augmenting the entire observational network (surface as well as upper-air), numerical modelling framework, incorporating AI/ML techniques, enhancing the computing power to mitigate the impact of climate change-induced extreme weather events. "Mausam GPT" is being designed to provide quick and reliable weather-related information in both text and audio forms.

Traditional vs AI-based Weather Forecasting

Traditional Weather Forecasting: These models simulate atmospheric processes using equations and data from weather stations and satellites (E.g., temperature, wind). These models are computationally intensive, time-consuming, and sometimes limited in capturing localised phenomena due to the chaotic and non-linear nature of weather systems.
AI-Based Forecasting: Unlike traditional models, AI/ML techniques adopt a data-first approach. They learn from historical and real-time data, identifying correlations between input variables (E.g., wind, humidity, ocean temperature) and outcomes (E.g., rainfall, cyclones). AI can uncover hidden patterns and non-linear relationships not captured by physics-based models.

Challenges in AI-based Weather Forecasting:

Data Quality and Availability: AI models need large, consistent, and high-quality datasets. Issues like sensor errors, inconsistent formats, and lack of real-time or historical data complicate training. While data availability has improved tenfold but gaps remain in sensor networks, especially in remote areas.
Human Resource Gap: A critical shortage of experts skilled in both climate science and AI/ML.
Interpretability and Trust: AI models are like black boxes - it is hard to explain why they make a certain prediction. This makes it difficult for non-experts to trust or verify the results.
Infrastructure and Computation: AI models, especially for high-resolution forecasting, require GPU-based computing and significant infrastructure investment.

To bridge the gaps, scientists are increasingly turning towards hybrid models that combine the interpretability of physics-based models and adaptability of AI/ML.

Vehicle-to-Grid Technology

Context: The Kerala State Electricity Board (KSEB) and the Indian Institute of Technology Bombay (IIT Bombay) have initiated a pilot project to explore the implementation of Vehicle-to-Grid (V2G) technology across the State. This collaboration aims to assess the feasibility of integrating Electric Vehicles (EVs) into the State’s power grid.

Relevance of the topic:

Prelims: Key facts related to V2G Technologies.

Mains: V2G Technology - Status, benefits, global application, challenges.

Vehicle-to-Grid (V2G) Technology

Vehicle-to-Grid (V2G) technology allows Electric Vehicles (EVs) to not only draw electricity from the grid (for charging) but also send electricity back to the grid when required.
When an EV is not in use (e.g., parked at home), and is connected to a bi-directional charger, it can interact with the grid by discharging power back into it.
This transforms EVs from mere transportation devices into decentralised energy storage units, which can be instrumental in balancing electricity supply and demand.
The ability of EV batteries to transfer power encompasses many possibilities such as V2G, Vehicle to Home (V2H), Vehicle to Vehicle (V2V) etc. Among these, V2G is the most popular use case.

Benefits of V2G Technology

Decentralised storage: EVs are mobile energy storage units that can operate independently of centralised power plants.
Facilitates Renewable Energy Integration: EVs can store surplus energy generated during high RE output periods and feed it back during low generation times. Provides backup during peak load conditions.
Emergency power source: In scenarios of climate-induced disasters (E.g., floods, storms), EVs can act as emergency power sources for homes, hospitals, or relief centers.

Global Application of V2G

EVs have emerged as a cost-effective form of distributed energy storage, with owners incentivised to supply power back to the grid.

V2G Technologies have gained significant attraction in mature EV markets such as Europe and the U.S.
In the U.K. and the Netherlands, EV owners are compensated for supplying excess energy back to the grid during peak hours.
In places like California, EV users are actively encouraged to participate in the ancillary services segment of the electricity market, helping improve grid stability and reliability.

Status of V2G Technology in India

In India, V2G integration is currently in a nascent stage, facing regulatory and infrastructure challenges.

Most of the current planning around EVs in India is aimed at expanding EV charging networks rather than enabling bidirectional power flow.
Some Distribution Companies (DISCOMs) have initiated pilot projects focusing on smart charging and exploring V2G capabilities.
The Central Electricity Authority (CEA) has constituted a committee to frame guidelines for reverse charging — that is, enabling electricity to flow from EV batteries back to the grid. This committee has highlighted smart charging as a key enabler to ensure EV growth with minimal impact on the grid.

Challenges

India’s electricity market is highly centralised and regulated and the current structure is not suited for decentralised solutions like EVs to send power back to the grid.
Variable nature of RE and mismatches between electricity supply and demand.
Most EV charging stations in India are unidirectional, and there is limited availability of bi-directional chargers compatible with V2G technology.
There is no clear compensation mechanism for EV owners who might supply power back to the grid, making the proposition unattractive for consumers.

To mainstream Vehicle-to-Grid (V2G) Technology, supportive regulatory changes are needed.

Facial Recognition Technology

Context: Delhi Police is planning a city-wide rollout of facial recognition technology (FRT) later this year in 2025. Experts warn that the increasing integration of such technology across platforms may come at a cost.

Relevance of the Topic: Prelims: About Facial recognition technology.

Facial Recognition Technology:

Facial recognition is a cutting-edge biometric technology that identifies or verifies an individual by analysing their facial features.
The algorithm-based technology creates a unique digital map of a person’s face by detecting and analysing facial features such as the distance between the eyes, shape of the jaw etc. This faceprint is then compared to a database of stored images for identity verification or identification.

Automated Facial Recognition System (AFRS)

AFRS uses a large database containing millions of facial images including those from CCTV footage, social media, and official records.
When an unidentified image is captured (E.g., from a surveillance camera), AFRS uses artificial intelligence to find a matching pattern in the database and identify the person.

There are two types of Matching:

1:1 Verification: Confirms if the face matches a single image (e.g., unlocking your phone).
1:N Identification: Compares the face to an entire database to identify an unknown individual (E.g., identifying suspects in law enforcement). Delhi Police usually use FRT for 1:N identification.

Limitations of FRT

Accuracy Issue: The system may wrongly identify someone (false positive) or fail to recognise the correct person (false negative). Accuracy drops with poor angles, low light, or occlusions like masks or sunglasses.
Limited Datasets: Studies have shown higher error rates for women, children, and people with darker skin tones, especially, when systems are trained on datasets lacking diversity. Delhi Police treat matches above 80% similarity as positive results, while matches below 80% as false positive results which require additional corroborative evidence.

Facial Recognition System in Delhi:

Since 2018, the Delhi Police has been using the Facial Recognition System (an Israeli software) to monitor high-security events in the Capital.
FRS vans are armed with cameras, computers, and automatic number plate readers (configured to scan faces instead of license plates) and stationed in different parts of the two districts every day, scanning faces and alerting them of potential hits.
Apart from fixed cameras, Prakhar vans with mobile cameras scan crowds and crime-prone areas.

Safe City Project

Delhi Police plans to expand FRS under the Safe City Project with 10,000 high-resolution CCTV cameras across the capital, whose LIVE feed will be beamed directly to a command centre at the police headquarters.
Implementation: Centre for Development of Advanced Computing, under the Ministry of Electronics and Information Technology.
CDAC will be responsible for setting up C41 (Integrated Command, Control, Communication & Computer Centre) where integrated video feeds will be beamed. These feeds will be analysed in real time, with AI models capable of identifying over 20 faces in a crowd, even under partial visibility or disguised appearances.

However, its use raises serious concerns about privacy and misuse. Without a clear legal framework, it has a chilling effect on civil liberties, there is a risk of misidentifying individuals, profiling, and violating fundamental rights.