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Category: food – Page 3

A team from the University of Córdoba is developing an autonomous tractor with three different steering modes, allowing it to drive in straight lines, make turns efficiently, and shift modes in response to its trajectories.

One of the possible meanings of the name Sergius is “one who serves,” hence the name of the robotic tractor that can autonomously perform agricultural tasks in fields of woody crops. This one-of-a-kind vehicle, designed by the University of Córdoba, is part of an Agriculture 4.0 context in which agricultural tasks are being automated.

The researchers, with the Rural Mechanization and Technology Group at the University of Córdoba, Sergio Bayano and Rubén Sola, designed the vehicle from the ground up, in collaboration with two companies charged with its mechanical manufacturing and programming. The paper is published in the journal Computers and Electronics in Agriculture.

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Researchers at Korea’s Daegu Gyeongbuk Institute of Science and Technology (DGIST) have developed a porous laser-induced graphene (LIG) sensor array that functions as a “next-generation AI electronic nose” capable of distinguishing scents like the human olfactory system does and analyzing them using artificial intelligence.

This technology converts scent molecules into electrical signals and trains AI models on their unique patterns. It holds great promise for applications in personalized health care, the cosmetics industry, and environmental monitoring.

While conventional electronic noses (e-noses) have already been developed and used in areas such as food safety and gas detection in industrial settings, they struggle to distinguish subtle differences between similar smells or analyze complex scent compositions. For instance, distinguishing among floral perfumes with similar notes or detecting the faint odor of fruit approaching spoilage remains challenging for current systems. This gap has driven demand for next-generation e-nose technologies with greater precision, sensitivity, and adaptability.

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A research team has developed a “next-generation AI electronic nose” capable of distinguishing scents like the human olfactory system does and analyzing them using artificial intelligence. This technology converts scent molecules into electrical signals and trains AI models on their unique patterns. It holds great promise for applications in personalized health care, the cosmetics industry, and environmental monitoring.

The study is published in the journal ACS Nano. The team was led by Professor Hyuk-jun Kwon of the Department of Electrical Engineering and Computer Science at DGIST, with integrated master’s and Ph.D. student Hyungtae Lim as first author.

While conventional electronic noses (e-noses) have already been deployed in areas such as and gas detection in industrial settings, they struggle to distinguish subtle differences between similar smells or analyze complex scent compositions. For instance, distinguishing among floral perfumes with similar notes or detecting the faint odor of fruit approaching spoilage remains challenging for current systems. This gap has driven demand for next-generation e-nose technologies with greater precision, sensitivity, and adaptability.

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Urea, with the formula CO(NH2)2, is a chemical compound that is widely used in a range of sectors, including manufacturing, agriculture and various industries. Conventionally, this compound is produced via a two-step process that entails the synthesis of ammonia from nitrogen (N₂) and its subsequent reaction with carbon dioxide (CO₂).

This reaction occurs at and under , leading to the formation of a compound called ammonium carbamate. This compound is then decomposed at lower pressures, which ultimately produces and water.

Traditional processes for producing urea are very energy intensive, meaning that to produce desired amounts of urea they consume a lot of electrical power. Over the past few years, some engineers have thus been trying to devise more energy-efficient strategies to synthesize urea.

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Scientists looking to tackle our ongoing obesity crisis have made an important discovery: Intermittent calorie restriction leads to significant changes both in the gut and the brain, which may open up new options for maintaining a healthy weight.

Researchers from China studied 25 volunteers classed as obese over a period of 62 days, during which they took part in an intermittent energy restriction (IER) program – a regime that involves careful control of calorie intake and relative fasting on some days.

Not only did the participants in the study lose weight – 7.6 kilograms (16.8 pounds) or 7.8 percent of their body weight on average – there was also evidence of shifts in the activity of obesity-related regions of the brain, and in the make-up of gut bacteria.

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Unmanned aerial vehicles (UAVs), commonly known as drones, have already proved to be valuable tools for a wide range of applications, ranging from film and entertainment production to defense and security, agriculture, logistics, construction and environmental monitoring. While these technologies are already widely used in many countries worldwide, engineers have been trying to enhance their capabilities further so that they can be used to tackle even more complex problems.

Researchers at Pohang University of Science and Technology and the Agency for Defense Development (ADD)’s AI Autonomy Technology Center in South Korea recently developed a drone with foldable wings that could be more maneuverable than conventional . Their drone draws inspiration from the winged flying squirrel, a type of squirrel that uses loose flaps of skin attached from their wrists to their ankles to glide from tree to tree.

“The flying squirrel drone is inspired by the movements of flying squirrels, particularly their ability to rapidly decelerate by spreading their wings just before landing on trees,” Dohyeon Lee, Jun-Gill Kang and Soohee Han, co-authors of the paper, told Tech Xplore. “We initiated this research with the belief that, like flying squirrels, drones could expand their dynamic capabilities by utilizing .”

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Leading health experts have warned that the US is staring down the barrel of another pandemic as bird flu spirals out of control on US farms.

So far, the H5N1 outbreak has affected nearly 1,000 dairy cow herds and resulted in more than 70 human cases, including the first confirmed death.

The US poultry industry is at significant risk, say experts from the Global Virus Network (GVN), particularly in areas with high-density farming and where personal protective practices may be lacking.

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Plants are susceptible to a wide range of pathogens. For the common potato plant, one such threat is Pectobacterium atrosepticum, a bacterium that causes stems to blacken, tissues to decay, and often leads to plant death, resulting in significant agricultural losses each year.

In 2012, researchers isolated a new virus that infects and kills this bacterium—a bacteriophage named φTE (phiTE). Now, for the first time, scientists have uncovered the atomic structure of φTE, revealing a possible mechanism of infection that may be more complex than previously thought.

The study, published earlier this month in Nature Communications, is the result of a multidisciplinary collaboration between researchers from the Okinawa Institute of Science and Technology (OIST) and the University of Otago. It brings together expertise across several fields, including virology, , , protein engineering, biochemistry, and biophysics.

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