A sweet scent typically lingers around in the air at Kannauj, an ancient city in India’s most populous state of Uttar Pradesh. It’s an imprint of the countless occasions when it had rained, of roses that bloomed at dawn, and of sandalwood trees that once breathed centuries of calm.. Though mushy smells are not unique to Kannauj, the city utilized traditional distillation methods to make perfume out of these earthly scents.

Kannauj has had a longstanding tradition in perfume-making since four centuries ago. The city, colloquially known as the country’s ancient perfume capital, still uses rustic copper stills, wood-fired ovens, and bamboo pipes leading to sandalwood oil-filled vessels, or attar as it is colloquially known, to make their perfume. Though it gives a pre-industrial look, a closer peek would reveal an ecosystem of complex thermal regulation, plant chemistry, sustainability science, and hydro-distillation chemistry at work.

When synthetically-made but sustainable perfumes, and AI-generated ones share the spotlight today, Kannauj’s tryst with perfumes offer an alternative, sustainable model in traditional distillation, which is inherently low-carbon, zero-waste, and follow principles of a circular economy; all in alignment with sustainable development goals.

Traditional perfume-making is naturally sustainable

In industrial processing, hydro-distillation is a commonly done to separate substances with different boiling points. Heating the liquids produce vapors, which can later be liquefied in a separate chamber. Perfumers in Kannauj follow the same practice, except it promises to be more sustainable with the copper stills, a process colloquially known as dheg-bhakpa hydro-distillation.

There’s no alcohol or synthetic agents in use. Instead, they heat up raw botanicals – such as roses, vetiver roots, jasmine, or even sunbaked clay – to precise temperatures well short of burning, thereby producing fragrant vapor. The vapors are then guided into cooling chambers, where they condense and bond with a natural fixative, often sandalwood oil. Plant residue is the only byproduct, which finds use as organic compost to cultivate another generation of crops.

Trapping earthly scent to make perfume

In the past five years, Kannauj’s veteran perfumers noticed a quiet, but steady shift in their timely harvest and produce. Rose harvests have moved earlier by weeks. Vetiver roots grow shallower due to erratic rainfall. Jasmine yields are fluctuating wildly. The local Ganges river, which influences humidity levels essential for distillation timing, is no longer as predictable. For an entire natural aromatic economy built on seasonal synchrony, this uncertainty has rung alarm bells.

“The scent of a flower depends not just on the flower itself,” Vipin Dixit, a third-generation attar-maker whose family has distilled fragrance for decades, said to EdPublica.

“It depends on the weather the night before, on the heat at sunrise, on the moisture in the air. Even the soil has a scent-memory.”

As a result, perfumers in Kannauj have begun to adapt, applying traditional wisdom through a modern scientific lens. Local distillers are now working with botanists and environmental scientists to study soil microbiomes, measure scent compounds using chromatography, and develop community-based rainwater harvesting to ensure sustainable crop health.

One of the most surprising innovations is trapping petrichor — the scent of first rain — through earth attars. Clay is baked during extreme heat waves, mimicking summer conditions, then distilled to trap the scent of rain hitting dry soil. This aroma, called mitti attar, is one of the few scents in the world created from an environmental phenomenon; and not a flower.

At a time when the world is scrambling to save biodiversity, the humble attar may become a template for green chemistry — one that doesn’t just preserve scent, but also restores the relationship between science, nature, and soul.

As rescue and recovery operations continue following the June 12, 2025, plane crash in Ahmedabad, aviation safety experts are now focusing on the technical investigation phase. With 241 lives lost, the search for the cause isn’t just about accountability—it’s about prevention.

The Black Box: Aviation’s Memory Keeper

1. What Is the Black Box?

Despite the name, the black box is actually orange — for visibility. It consists of two components:

• Cockpit Voice Recorder (CVR): Captures conversations and audio from the flight deck.
• Flight Data Recorder (FDR): Logs dozens to hundreds of parameters — speed, altitude, engine status, control inputs.

These devices are housed in titanium or steel and can withstand:

• Temperatures above 1,000°C
• Underwater pressures up to 20,000 feet
• Crashes with up to 3,600 G-force

They also emit underwater locator beacons for up to 30 days.

2. Forensic Engineering & Flight Reconstruction

Beyond black boxes, investigators use:

• Radar data and air traffic control logs
• Wreckage analysis for structural failure clues
• Satellite-based tracking systems like ADS-B
• Weather data for turbulence or wind shear insights

Forensic teams often reconstruct the flight path virtually or even physically using recovered debris to determine failure points.

3. Human Factors & AI in Modern Investigation

New tools like machine learning and human factors analysis are used to identify procedural errors or lapses in judgement.

In many modern investigations, AI helps:

• Filter large datasets (e.g., over 1,000 flight parameters per second)
• Detect patterns missed by the human eye
• Predict similar risk scenarios in future flights

What Happens Next in the Ahmedabad Crash?

Authorities, in coordination with the Directorate General of Civil Aviation (DGCA), are likely:

• Retrieving and analyzing the black box
• Interviewing air traffic controllers
• Reconstructing the aircraft’s final seconds using both data and simulation

The final report may take months, but it will be critical in issuing safety directives or revising standard procedures.

In a breakthrough that could transform the future of wireless communication and edge computing, engineers at MIT have developed a novel AI hardware accelerator capable of processing wireless signals at the speed of light. The new optical chip, built for signal classification, achieves nanosecond-level performance—up to 100 times faster than conventional digital processors—while consuming dramatically less energy.

With wireless spectrum under growing strain from billions of connected devices, from teleworking laptops to smart sensors, managing bandwidth has become a critical challenge. Artificial intelligence offers a path forward, but most existing AI models are too slow and power-hungry to operate in real time on wireless devices.

The MIT solution, known as MAFT-ONN (Multiplicative Analog Frequency Transform Optical Neural Network), could be a game-changer.

“There are many applications that would be enabled by edge devices that are capable of analyzing wireless signals,” said Prof. Dirk Englund, senior author of the study, in a media statement. “What we’ve presented in our paper could open up many possibilities for real-time and reliable AI inference. This work is the beginning of something that could be quite impactful.”

Published in Science Advances, the research describes how MAFT-ONN classifies signals in just 120 nanoseconds, using a compact optical chip that performs deep-learning tasks using light rather than electricity. Unlike traditional systems that convert signals to images before processing, the MIT design processes raw wireless data directly in the frequency domain—eliminating delays and reducing energy usage.

“We can fit 10,000 neurons onto a single device and compute the necessary multiplications in a single shot,” said Ronald Davis III, lead author and recent MIT PhD graduate.

The device achieved over 85% accuracy in a single shot, and with multiple measurements, it converges to above 99% accuracy, making it both fast and reliable.

Beyond wireless communications, the technology holds promise for edge AI in autonomous vehicles, smart medical devices, and future 6G networks, where real-time response is critical. By embedding ultra-fast AI directly into devices, this innovation could help cars react to hazards instantly or allow pacemakers to adapt to a patient’s heart rhythm in real-time.

Future work will focus on scaling the chip with multiplexing schemes and expanding its ability to handle more complex AI tasks, including transformer models and large language models (LLMs).