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The silent conversations of plants

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The silent conversations of plants


plants
Credit: CC0 Public Domain

This morning, my six-year-old came into our bedroom and started reading a story from a book. She followed each word on the page, slowly forming full sentences. Sometimes she stumbled and asked for help with some “funny words,” but by the end of the book, she had told us a story about a bear in the snow.

Verbal communication is one of the many reasons humans have become so successful as a species. From warning each other of danger to communicating complex information, our ability to speak has been crucial.

But it’s not just humans and other animals who have developed sophisticated communication. A lot of people think of plants as passive, but they have their own way of interacting with each other. The idea has been around for a while, even inspiring Hollywood movies like “Avatar.”

However, recent science is showing plant communication systems may be more complex than we imagined.

These communication networks are sensitive and in balance. Imagine how disrupted our world would be if global network systems suddenly broke down. The recent CrowdStrike IT outages are just one example of how delicate these systems are and how important communication is—and that’s the case for plants too.

To grasp how organisms who can’t speak pass on information to each other, it’s important to understand that humans also have a non-verbal communication system. This includes our senses of sight, smell, hearing, taste and touch.

For example, natural gas companies add a chemical called mercaptan to natural gas, giving it that distinctive “rotten egg” smell to warn us of leaks. Think also of how we have developed sign language, while many people are skilled lip readers.

In addition to these senses, we also have equilibrioception (the ability to maintain balance and body posture), proprioception (the sense of the relative position and strength of our body parts), thermoception (sense of temperature changes), and nociception (ability to sense pain). All these abilities have enabled humans to become highly sophisticated in communication and engagement with the natural world.

Other species, particularly plants, use their senses to spread information in their own way.

What are the neighbors up to?

Most of us are familiar with the smell of freshly cut grass. The volatiles, or chemical substances, released by the grass plants, which we associate with that smell, are one way they communicate to other nearby plants that a predator—or in this case a lawnmower—is present, prompting an adjustment in plant defenses. Rather than using auditory cues, plants use chemical-induced communication. However, plant communication doesn’t end with volatiles.

Recently, scientists discovered just how well-connected plants are and how efficiently they can send messages to their peers via their roots, electrical signals, a network of underground fungi and soil microbes. The nosy plant neighborhood watch was discovered.

For example, electrophysiology is a relatively new scientific discipline that studies how electrical signals in and between plants are communicated and interpreted. With major advances in technology and artificial intelligence (AI), we have seen significant accelerated growth in this area of research in the past few years.

Scientists could be on the verge of remarkable discoveries, with recent advances integrating electrical signal communication within and between plants into modern greenhouses to monitor and control crop watering or detect nutritional deficiencies.

Scientists achieve this by inserting small electrical probes, similar to acupuncture needles, to test how changes in electrical signals relate to plant performance, such as transporting water and nutrients, and converting light into important sugars.

Researchers have even influenced plant behavior by sending electrical signals from mobile phones, making them perform basic responses like opening or closing leaves in a Venus flytrap.

Soon we may be able to fully translate the language of our crops.

A great deal of plant-to-plant communication happens below ground, facilitated by large fungal networks known as the “wood-wide web.” This network of fungi connects trees and plants underground, allowing them to share resources like water, nutrients and information. Through this system, older trees can help younger ones grow, and trees can warn each other about dangers such as pests.

It’s like an underground internet for trees and plants, helping them support and communicate with each other. The network is extensive, with over 80% of plants believed to be connected, making it one of the oldest communication systems in the world.

Just as the internet enables us to connect, share ideas, knowledge and information that can influence decision-making, the “wood-wide web” allows plants to use symbiotic fungi to prepare for environmental changes.

However, disturbing the soil through chemicals, deforestation or climate change can disrupt the communication nodes through affecting water and nutrient cycles in these networks, making plants less informed and connected. Not much research has yet been carried out into the effects of disrupting these networks.

But we know that plants’ responsive behavior, such as defense responses and gene regulation, can be altered by their fungal network if they are connected to one.

So this communication-disconnect might make them more vulnerable, making it more difficult to protect and restore ecosystems around the world. There is still a lot scientists have to learn about these highly complex networks

We know it’s important to help children learn to read so that they can navigate the world around them. It is just as important as to ensure we do not disconnect plant communication. After all, we depend upon plants for our well-being and survival.

Provided by
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This article is republished from The Conversation under a Creative Commons license. Read the original article.The Conversation

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The silent conversations of plants (2024, September 16)
retrieved 16 September 2024
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Solving long-standing challenge in semiconductor manufacturing—a refined algorithm for detecting wafer defects

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Solving long-standing challenge in semiconductor manufacturing—a refined algorithm for detecting wafer defects


Minting wafer thin defect detection
Illustration of SPD-Conv structure with partition factor P = 2. Credit: International Journal of Information and Communication Technology (2024). DOI: 10.1504/IJICT.2024.141433

Research published in the International Journal of Information and Communication Technology may soon help solve a long-standing challenge in semiconductor manufacture: the accurate detection of surface defects on silicon wafers. Crystalline silicon is the critical material used in the production of integrated circuits and in order to provide the computing power for everyday electronics and advanced automotive systems needs to be as pristine as possible prior to printing of the microscopic features of the circuit on the silicon surface.

Of course, no manufacturing technology is perfect and the intricate process of fabricating semiconductor chips inevitably leads to some defects on the silicon wafers. This reduces the number of working chips in a batch and leads to a small, but significant proportion of the production line output failing.

The usual way to spot defects on silicon wafers has been done manually, with human operators examining each wafer by eye. This is both time-consuming and error-prone due to the fine attention to detail required. As wafer production has ramped up globally to meet demand and the defects themselves have become harder to detect by eye, the limitations of this approach have become more apparent.

Chen Tang, Lijie Yin and Yongchao Xie of the Hunan Railway Professional Technology College in Zhuzhou, Hunan Province, China explain that automated detection systems have emerged as a possible solution. These too present efficiency and accuracy issues in large-scale production environments. As such, the team has turned to deep learning, particularly convolutional neural networks (CNNs), to improve wafer defect detection.

The researchers explain that CNNs have demonstrated significant potential in image recognition. They have now demonstrated that this can be used to identify minute irregularities on the surface of a silicon wafer. The “You Only Look Once” series of object detection algorithms is well known for being able to balances accuracy against detection speed.

The Hunan team has taken the YOLOv7 algorithm a step further to address the specific problems faced in wafer defect detection. The main innovation in the work lies in using SPD-Conv, a specialized convolutional operation to enhance the ability of the algorithm to extract fine details from images of silicon wafers. Additionally, the researchers incorporated a Convolutional Block Attention Module (CBAM) into the model to sharpen the system’s focus on smaller defects that are often missed in manual inspection or by other algorithms.

When tested on the standard dataset (WM-811k) for assessing wafer defect detection systems, the team’s refined YOLOv7 algorithm achieved a mean average precision of 92.5% and had a recall rate of 94.1%. It did this quickly, at a rate of 136 images per second, which is faster than earlier systems.

More information:
Chen Tang et al, Wafer surface defect detection with enhanced YOLOv7, International Journal of Information and Communication Technology (2024). DOI: 10.1504/IJICT.2024.141433

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Solving long-standing challenge in semiconductor manufacturing—a refined algorithm for detecting wafer defects (2024, September 16)
retrieved 16 September 2024
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TikTok battles US ban threat in court

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TikTok battles US ban threat in court


Social media app TikTok has come under scrutiny from the US government
Social media app TikTok has come under scrutiny from the US government.

TikTok faced pushback in a federal court on Monday in its efforts to stop a law that requires the app to divest from its Chinese ownership or face a ban in the United States.

A three-judge panel of the US Court of Appeals in Washington heard arguments from TikTok, its owner ByteDance, and a group of users claiming that the ban violates free speech and is unconstitutional.

The US government alleges TikTok allows Beijing to collect data and spy on users. It also says TikTok is a conduit to spread propaganda. China and the company strongly deny these claims.

TikTok has until January to find a buyer or face the ban, which would likely provoke a strong response from the Chinese government and further strain US-China relations.

It would also upend the social media business and rile many of the app’s 170 million US users.

ByteDance, TikTok’s parent company, has stated it has no plans to sell TikTok, leaving the app’s legal appeal—focused on US guarantees for free speech—as its only option for survival.

“The law before this court is unprecedented. Its effect would be staggering,” said Andrew Pincus, the lawyer arguing on behalf of the wildly popular video-sharing app.

“For the first time in history, Congress has expressly targeted a specific US speaker (i.e., TikTok U.S.),” he added.

In their questions, the judges challenged this argument, comparing it to earlier cases in US jurisprudence.

This included a case from the 1980s where closing the Palestine Information Office in Washington DC was deemed legal because it was backed by the PLO, an organization officially designated as a terrorist group.

TikTok’s lawyer countered: “Mere foreign ownership can’t possibly be a justification, because it would turn the First Amendment (protecting free speech) on its head.”

He added that seeing foreign ownership alone as criteria for forced divestiture “would be a pretty shocking change here,” citing other foreign-owned media companies such as Politico, Al Jazeera, and the BBC.

The lawyer also questioned why the US law did not target e-commerce sites with similar Chinese ownership.

Pincus said that if you followed the US government’s logic, which he disagreed with, “certainly those sites could well be susceptible to (China’s) action, but they’ve been excluded by Congress (in the law).”

‘Important questions’

The judges grilled the US government on whether TikTok U.S., a US-based company, should be denied its free speech rights.

The US government lawyer, Daniel Tenny, insisted that the content being targeted was a recommendations algorithm based at ByteDance in China, not anything created by US users, and that it was therefore out of reach of free speech considerations.

“There’s really no dispute here that the recommendation engine is maintained, developed, and written by ByteDance, rather than TikTok US, and that is what’s being targeted,” Tenny argued.

The trio of judges will decide the case in the coming weeks or months, but regardless of their decision, the case is likely to reach the US Supreme Court, experts said.

“After listening to the oral arguments, I am more convinced that this case will end up in the Supreme Court,” said Sarah Kreps, director of Cornell’s Tech Policy Institute.

“Overall, the judges sounded more skeptical of the TikTok case but also raised important questions about the First Amendment, foreign influence and standards of scrutiny that I do not think were clearly resolved with today’s exchanges,” she added.

The fate of Americans’ access to TikTok has become a prominent issue in the country’s political debates, with Republican presidential candidate Donald Trump opposing a ban.

Democratic President Joe Biden, whose vice president Kamala Harris is running against Trump, signed the law that gives TikTok until January to shed its Chinese ownership or be expelled from the US market.

© 2024 AFP

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TikTok battles US ban threat in court (2024, September 16)
retrieved 16 September 2024
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An AI tool for scanning sand grains opens windows into recent time and the deep past

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An AI tool for scanning sand grains opens windows into recent time and the deep past


Introducing SandAI: A tool for scanning sand grains that opens windows into recent time and the deep past
Scanning electron microscopy reveals the shape and texture of a quartz sand grain from the Mississippi River. The pictured sand grain is about 200 micrometers in length. Credit: Michael Hasson/Stanford University

Stanford researchers have developed an artificial intelligence-based tool—dubbed SandAI—that can reveal the history of quartz sand grains going back hundreds of millions of years. With SandAI, researchers can tell with high accuracy if wind, rivers, waves, or glacial movements shaped and deposited motes of sand.

The tool gives researchers a unique window into the past for geological and archaeological studies, especially for eras and environments where few other clues, such as fossils, are preserved through time. SandAI’s approach, called microtextural analysis, can also help with modern-day forensic investigations into illegal sand mining and related issues.

“Working on sedimentary deposits that haven’t been disturbed or deformed feels about as close as you can get to being in a time machine—you’re seeing exactly what was on the surface of Earth, even hundreds of millions of years ago. SandAI adds another layer of detail to the information we can pull from them,” said Michael Hasson, a Ph.D. candidate with Mathieu Lapôtre, an assistant professor of Earth and planetary sciences at the Stanford Doerr School of Sustainability.

Hasson is lead author of a new study demonstrating the tool, published in Proceedings of the National Academy of Sciences.

Telltale signatures

Historically, microtextural analysis has been done by hand and eye, using magnifying glasses and microscopes to attempt to draw inferences about sand grains’ histories.

Modern science has validated the approach, showing that transport mechanisms do indeed impart telltale signatures—for example, grains that traveled farther often appear more rounded because they’ve had their sharp corners dulled; waves and wind also leave distinctive abrasion patterns.

However, traditional microtextural analysis is highly subjective, time-consuming, and scattershot across different studies. Thanks to the new tool, which leverages the power of machine learning to deeply scrutinize microscopic images of sand grains, microtextural analysis can now be far more quantitative, objective, and potentially useful across a wide range of applications. It also analyzes individual sand grains instead of lumping multiple grains into a single category, offering a more complete evaluation.

“Instead of a human going through and deciding what one texture is versus another for sand grains, we are using machine learning to make microtextural analysis more objective and rigorous,” said Lapôtre, who is senior author of the paper. “Our tool is opening doors for microtextural analysis applications that were not available before.”

Worldwide, sand is the most used resource, after water, and is critical in the construction industry. Materials such as concrete, mortar, and some plasters require angular sand for proper adhesion and stability. Gauging the origins of sand, however, to ensure ethical and legal sourcing is challenging, so the researchers hope SandAI can bolster traceability. For example, SandAI could help forensics investigators crack down on illegal sand mining and dredging.

Training the tool

To build SandAI, the researchers employed a neural network that “learns” in a manner akin to the human brain, where correct answers strengthen connections between artificial neurons, or nodes, in the program, enabling the computer to learn from its mistakes.

Introducing SandAI: A tool for scanning sand grains that opens windows into recent time and the deep past
The SandAI neural network was trained using modern quartz sand and can help unravel the histories encoded in ancient rocks. Shown here are ancient ripples formed by water currents being reworked by modern wind-blown sediment in Oman. Credit: Mathieu Lapôtre/Stanford University

With help from collaborators around the world, Hasson assembled hundreds of scanning electron microscope images of sand grains, representing material from the most common terrestrial environments: fluvial (rivers and streams), eolian (windblown sediments, such as sand dunes), glacial, and beach.

“We wanted this method to work across geological time, but also across all of the geography that we have on Earth,” said Hasson. “So, for example, the windblown dunes class was designed to include examples that are wet and dry, large and small. We needed the classes to be as diverse as they possibly could be.”

SandAI analyzed this set of images to train itself to predict the sand grains’ histories based on features that human researchers might not ever discern. The tool naturally made errors and would then iteratively improve. Once SandAI reached a robust 90% prediction accuracy, the researchers introduced new samples the model had not previously seen.

With images of sandstones from well-characterized environments ranging from the current day back to roughly 200 million years into the Jurassic era, SandAI performed well, correctly elucidating the grains’ transport histories.

Novel science and applications

Next, the researchers challenged the tool with images of sand grains collected from Norway that date back more than 600 million years to the Cryogenian period. Better known as the time of “Snowball Earth,” this was when ice sheets are thought to have covered the whole planet, before plants and animals had evolved. The origin of the sample in question, called the BrÃ¥vika Member, has been contested, with various research groups coming to different conclusions.

“With this Cryogenian sample, we were seeing how far we can push SandAI and really using it to do new science rather than just verifying that the tool worked,” Hasson said.

Intriguingly, SandAI surmised that the ancient sand grains had been shaped and deposited as part of a windblown sand dune—in agreement with some manual microtextural studies. Moreover, because the tool analyzes individual sand grains, versus lumping multiple grains into a single category, other details emerged.

While the dominant signature indeed indicated wind transport, a secondary signature that manual techniques would likely miss pointed to glacial sand. Together, those signals paint a portrait of sand dunes running somewhere near a glacier, as might well be expected during the Snowball Earth period.

To evaluate those findings further, Hasson and colleagues looked for a potential modern analog of this Cryogenian geological scene. The researchers ran windblown sand grains from Antarctica through SandAI and, sure enough, arrived at the same result.

“These findings from SandAI suggest that Antarctica really is a good modern analog to the environment represented by the BrÃ¥vika Member,” Hasson said. “They are a really strong piece of evidence that the signal we got from the Cryogenian deposits isn’t just a fluke.”

The researchers have made SandAI available online for anyone to use. They plan to continue developing it based on user feedback and look forward to seeing the tool applied in a range of contexts.

“The fact that we can now offer detailed conclusions about geological deposits that weren’t knowable before I find kind of mind-blowing,” said Hasson. “We’re looking forward to seeing what else SandAI can do.”

More information:
Michael Hasson et al, Automated determination of transport and depositional environments in sand and sandstones, Proceedings of the National Academy of Sciences (2024). DOI: 10.1073/pnas.2407655121

Citation:
An AI tool for scanning sand grains opens windows into recent time and the deep past (2024, September 16)
retrieved 16 September 2024
from https://phys.org/news/2024-09-ai-tool-scanning-sand-grains.html

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Researchers find golden eagles improve their flight skills with age

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Researchers find golden eagles improve their flight skills with age


golden eagle
Credit: Pixabay/CC0 Public Domain

Researchers at the Max Planck Institute of Animal Behavior in Germany, in collaboration with the Swiss Ornithological Institute in Switzerland and the University of Vienna in Austria, investigated how young golden eagles improve their flight skills as they age.

Their results, published in eLife, show that as golden eagles improve their flying skills, they become able to explore a broader area within their range in the central European Alps. Apparently, even seemingly instinctive behaviors require at least some learning in young animals.

Golden eagles are soaring birds. They ride on upward-moving air currents with open wings, which helps them conserve energy while covering large distances. “However, locating these invisible uplifts and positioning their bodies within the uplifts to gain height is not a simple task. The eagles literally need to learn to fly, at least when it comes to using uplifts,” explains Elham Nourani, the lead author of the study.

The team used GPS tracking technology to monitor 55 juvenile golden eagles from nests in Switzerland, Italy, Germany, Slovenia, and Austria. The eagles were tracked for up to three years after leaving their parents’ territories, as they flew freely across the central European Alps.

Increased habitat

The team found evidence of this learning process by observing a shift in the eagles’ flight patterns. Initially, the young birds flew close to mountain ridges, where winds deflect and move upwards, creating reliably predictable soaring conditions. Over time, they increasingly dared to fly in more open areas where uplifts are less predictable.






The model predictions for every week since independence from parents. The increase in areas in red indicates increased flyability in the landscape. This matches the increase of the habitats used by golden eagles in nature. Credit: Max Planck Society

This transition suggests that as the eagles aged, their ability to find and utilize uplifts improved, making them less dependent on mountain ridges for flight.

The researchers estimated that the flyable areas for the eagles expanded more than 2,000-fold over three years as the birds’ honed their flight skills. “Flying is an eagle’s defining behavioral trait and you would think that when they fledged they should take to it like fish to water.

“But apparently they need experience and learning to extract energy from the atmospheric flows, shaping and changing how they move and where they go,” explains Kamran Safi, research group leader at the Max Planck Institute in Konstanz.

The relationship between age and use of the landscape can have implications for wildlife management practices. “We depend on wildlife distribution and movement maps to mitigate human-wildlife conflicts” says Nourani.

“Our study suggests that such maps should be viewed as dynamic, changing across various factors, including age.” This insight will enable more accurate predictions of potential overlaps between human activities and eagle behavior, particularly their use of the landscape at different stages of their development.

More information:
Elham Nourani et al, Developmental stage shapes the realized energy landscape for a flight specialist, eLife (2024). DOI: 10.7554/eLife.98818.2

Journal information:
eLife


Provided by
Max Planck Society


Citation:
Researchers find golden eagles improve their flight skills with age (2024, September 16)
retrieved 16 September 2024
from https://phys.org/news/2024-09-golden-eagles-flight-skills-age.html

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