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  may be more vulnerable to UV degradation than many models have assumed.While most performance models focus on heat, mo...
23/04/2026

may be more vulnerable to UV degradation than many models have assumed.

While most performance models focus on heat, moisture, and mechanical wear, exposure is emerging as an increasingly important factor in how solar panels degrade over time. Recent research suggests current testing and lifespan models may not always capture real-world UV conditions well enough, especially for some newer photovoltaic technologies.

Constant exposure to ultraviolet radiation can slowly break down materials within the panel, affecting encapsulation layers and backsheets, weakening durability, and contributing to performance loss over time.

This is important because solar energy systems are designed with long lifespans in mind, often around 20 to 30 years. If degradation accelerates earlier than predicted, it affects energy output and long-term return on investment.

For large scale solar deployments, even small shifts in lifespan assumptions can translate into significant economic and operational consequences.

The focus is now shifting toward better material design and more accurate lifespan modeling, where environmental factors like UV are treated as major variables rather than secondary ones.

அனைவருக்கும் மகிழ்ச்சியான சிங்கள மற்றும் தமிழ் சித்திரைப் புத்தாண்டு நல்வாழ்த்துக்கள்! இந்த வருடம் புதிய நம்பிக்கையையும்...
13/04/2026

அனைவருக்கும் மகிழ்ச்சியான சிங்கள மற்றும் தமிழ் சித்திரைப் புத்தாண்டு நல்வாழ்த்துக்கள்!
இந்த வருடம் புதிய நம்பிக்கையையும், பிரகாசமான வாய்ப்புகளையும் கொண்டு வரட்டும். நீங்களும் உங்கள் அன்புக்குரியவர்களும் அமைதி, செழிப்பு மற்றும் அர்த்தமுள்ள முன்னேற்றத்துடன் நிறைந்த ஒரு பயணத்தை தொடர வாழ்த்துகிறோம்.

උදා වන නව වසර නව බලාපොරොත්තු සහ සාර්ථකත්වය රැගෙන එන සුභ අලුත් අවුරුද්දක් වේවා! ඔබගේත් ඔබගේ ආදරණීයයන්ගේත් ජීවිත සාමයෙන්, ...
13/04/2026

උදා වන නව වසර නව බලාපොරොත්තු සහ සාර්ථකත්වය රැගෙන එන සුභ අලුත් අවුරුද්දක් වේවා! ඔබගේත් ඔබගේ ආදරණීයයන්ගේත් ජීවිත සාමයෙන්, සෞභාග්‍යයෙන් සහ අර්ථවත් ප්‍රගතියකින් පිරී ඉතිරේවා යි අපි ප්‍රාර්ථනා කරමු.

ADME wishes everyone a very Happy Sinhala and Tamil New Year!May this Avurudu bring fresh hope and brighter opportunitie...
13/04/2026

ADME wishes everyone a very Happy Sinhala and Tamil New Year!

May this Avurudu bring fresh hope and brighter opportunities ahead. Wishing you and your loved ones a season filled with peace, prosperity and meaningful progress in the year to come.

A new system called   is improving how robots grasp transparent and reflective objects…  have long struggled with object...
08/04/2026

A new system called is improving how robots grasp transparent and reflective objects…

have long struggled with objects they cannot clearly measure.

HEAPGrasp is changing that by enabling robots to handle transparent and reflective objects more reliably, something that has long been a challenge in . The method was developed to address cases where conventional measurement systems struggle with materials like glass, clear plastics, and shiny metal surfaces.

Instead of relying on expensive sensors, this approach uses a standard hand-eye camera combined with intelligent planning, allowing robots to better interpret difficult surfaces through silhouette-based 3D measurement and optimized camera movement.

The result is not just improved accuracy, reaching 96% success in reported real robot experiments, but also faster and more efficient operation, reducing camera trajectory length by 52% and ex*****on time by 19% compared with a baseline method.

This is important because real world environments are rarely ideal.

Many industrial and everyday objects do not present clear visual cues to conventional depth-based systems, making reliable handling difficult for automation in settings such as logistics, food service, and manufacturing.

By improving how robots deal with these edge cases, systems like this move closer to being practical, adaptable, and better suited for less controlled environments.

The more advanced our machines become, the more important it is to remember what gave progress its direction long before...
06/04/2026

The more advanced our machines become, the more important it is to remember what gave progress its direction long before ever existed.

Human intelligence built the foundations of the modern world without algorithmic assistance. It mapped the laws of motion, uncovered , developed vaccines, split the atom, decoded , and carried people to the Moon. These were not the products of automated systems deciding what mattered. They came from curiosity, imagination, courage, and the distinctly human ability to ask questions no machine had been trained to answer.

That remains true even now.

AI can analyse medical images, generate , optimise supply chains, and surface patterns across vast datasets. However, it does not decide what is worth healing, building, protecting, or pursuing.

A may support a diagnosis, yet a doctor must still weigh uncertainty and human consequence…

A may recommend who gets shortlisted for a role, yet fairness and judgment cannot be reduced to a score…

A may produce thousands of design options, but it cannot decide which one carries meaning or vision...

This is where human intelligence continues to matter most. Not as a fallback when automation stops, but as the source of intention behind it. imagines what does not yet exist. distinguishes what is useful from what is merely possible. asks whether progress is serving the right end at all.

Technology can extend human capability, but we must not forget that it is in fact human intelligence that gives progress its purpose.

Silicon is no longer the only path for high performance computing.A new generation of   circuits, built using  , is push...
03/04/2026

Silicon is no longer the only path for high performance computing.

A new generation of circuits, built using , is pushing the boundaries of speed, efficiency, and signal integrity in optical systems.

Unlike traditional electronic chips, these devices use light instead of electricity to process and transmit information, significantly reducing energy loss while enabling faster data transfer.

Lithium tantalate, a material known for its strong electro optic properties, allows precise control of light signals on chip. This makes it especially promising for applications in high speed telecommunications, advanced computing, and next generation sensing technologies.

By minimizing signal loss and improving performance at scale, these photonic circuits could play a key role in addressing growing demands for data processing and energy efficiency.

The shift here isn’t just incremental it’s architectural. Moving from electrons to photons opens new possibilities for how information is processed, transmitted, and scaled.

  performance is no longer limited only by models.Rather it is increasingly limited by how fast   can be accessed and mo...
31/03/2026

performance is no longer limited only by models.

Rather it is increasingly limited by how fast can be accessed and moved.

As AI systems scale, the real challenge is not just building better # algorithms, but feeding them with massive volumes of data efficiently. Traditional storage systems were not designed for this level of parallel, data intensive demand.

This is where modern object storage platforms are becoming critical.

They allow organizations to store, retrieve and stream large datasets at scale, enabling faster training cycles, smoother deployment and more responsive AI systems. Instead of creating bottlenecks in data pipelines, companies can reduce them and move toward more continuous, near real time intelligence.

The shift is subtle but important.

Better models require better data infrastructure, and in many cases, the advantage is not in the algorithm alone, but in how effectively data can be delivered to it.

As AI adoption grows, storage architecture is becoming one of the most decisive factors in performance.

High performance   may no longer be completely dependent on perfect data.A recent research preprint introduces  , a syst...
30/03/2026

High performance may no longer be completely dependent on perfect data.

A recent research preprint introduces , a system that taught a G1 humanoid robot to play tennis using fragmented and imperfect human motion data.

Rather than relying on full, clean motion capture from real matches, it learned from motion fragments covering skills like forehands, backhands, lateral shuffles and crossover steps. In testing, it achieved up to 96.5 percent success on a defined ball return task and sustained real world multi shot rallies with human players.

Traditionally, training for dynamic physical tasks has depended on clean, highly structured datasets. However, real world data is rarely perfect. It is noisy, incomplete and inconsistent.

This system changes that.

By learning from imperfect inputs, the robot still developed precise timing, coordination and reactive decision making for fast, unpredictable scenarios like tennis rallies.

The significance goes beyond the sport.

If robots can learn effectively from imperfect data, it could lower the cost of data collection and expand AI deployment into environments where clean data is difficult to obtain.

This brings robotics closer to a more practical model of learning, adapting from incomplete information rather than relying entirely on perfect examples. Still, this is an early result from a March 2026 preprint, and its broader impact will depend on how well the approach transfers to other real world tasks.

Battery powered exploration may not be the only path forward.A new   wind powered  , developed by   and called  , is dem...
27/03/2026

Battery powered exploration may not be the only path forward.

A new wind powered , developed by and called , is demonstrating how machines can move through harsh, windy environments without using a to power locomotion.

Instead of relying on conventional onboard power storage, the system uses a simple mechanical design that converts airflow into movement. This allows the robot to continue navigating terrain as long as wind is available, addressing one of the major constraints in long duration exploration, energy for movement.

This approach is especially relevant in environments where recharging or replacing batteries is impractical, such as remote deserts, polar regions, or even other planetary environments.

By reducing dependence on onboard energy storage for movement, robots like this could potentially operate for extended periods without needing to stop and recharge.

The focus here is not speed or precision, but persistence, the ability to keep moving and exploring as long as environmental conditions allow.

  is preparing to send humans around the Moon again for the first time in over fifty years.The Space Launch System rocke...
26/03/2026

is preparing to send humans around the Moon again for the first time in over fifty years.

The Space Launch System rocket and spacecraft for II have now rolled back to Launch Pad 39B as preparations continue for launch opportunities beginning in April 2026.

This mission marks a major step in NASA’s Artemis program. Unlike Artemis I, which was uncrewed, Artemis II will carry four astronauts on a lunar flyby, testing critical systems and hardware needed for future human lunar exploration missions.

The rollout to the launch pad is not just a routine step. It shows that vehicle processing, ground systems, and mission planning are moving into the final phase ahead of one of the most significant human spaceflight missions in decades.

Artemis II is part of NASA’s broader effort to establish a long-term human presence on the Moon, laying the groundwork for future exploration, including missions to Mars.

After decades without human missions beyond low Earth orbit, NASA’s human program is steadily returning to deep space.

Solar efficiency has just crossed a threshold that was once considered difficult to achieve in advanced  .A new perovski...
25/03/2026

Solar efficiency has just crossed a threshold that was once considered difficult to achieve in advanced .

A new perovskite silicon triple junction solar cell has pushed efficiency beyond 30 percent, marking a significant step forward in photovoltaic performance.

Traditional silicon cells are limited in how much of the solar spectrum they can convert into electricity. Triple-junction designs address this by stacking multiple layers, each tuned to capture different wavelengths of light, reducing energy losses and improving overall conversion efficiency.

The addition of materials is key here. They are highly tunable and promising for integration with silicon technologies, making them strong candidates for next generation high efficiency solar cells.

Crossing the 30 percent mark is an important laboratory milestone. Higher efficiency means more power generated from the same surface area, strengthening the long term case for solar in space constrained or high demand applications.

The focus now shifts from proving performance to making these designs stable, manufacturable and cost effective at scale.

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