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Computer Programming

Physics of Irregular Objects on Inclined Planes Unveiled

How gravity causes a perfectly spherical ball to roll down an inclined plane is part of elementary school physics canon. But the world is messier than a textbook. Scientists have sought to quantitatively describe the much more complex rolling physics of real-world objects. They have now combined theory, simulations, and experiments to understand what happens when an imperfect, spherical object is placed on an inclined plane.

Published

5 months ago

March 29, 2025

Mona V

The physics of everyday objects has long been a staple of elementary school education. However, real-world objects rarely conform to the idealized shapes and scenarios presented in textbooks. A team of scientists at Harvard’s John A. Paulson School of Engineering and Applied Sciences (SEAS) has taken on the challenge of understanding the complex rolling physics of imperfect, spherical objects placed on inclined planes.

Led by L. Mahadevan, a Lola England de Valpine Professor of Applied Mathematics, Physics, and Organismic and Evolutionary Biology in SEAS and FAS, the researchers combined theory, simulations, and experiments to gain insights into this phenomenon. Their findings were published in Proceedings of the National Academy of Sciences and offer fundamental implications for various fields, including nanoscale cellular transport and robotics.

“We often overlook the intricacies of the world around us,” Mahadevan said. “By pausing to wonder, we can learn about ourselves and the world.” The team’s exploration of this simple problem drew connections between different mathematical disciplines, making it both enjoyable and potentially useful in the long run.

The researchers began with simulations of slightly irregular objects (spheres or cylinders) rolling down various inclines, noting that an irregularly shaped object does not always roll. As the ramp became steeper, the likelihood of the object rolling increased, while a flattening ramp led to a decrease in rolling speed. The critical angle of inclination marked the transition from non-rolling to rolling behavior, where some fascinating physics emerged.

First author Daoyuan Qian described the terminal rolling speed near this critical point as “a simple measure of order,” which varied depending on factors such as object dimensions and inertia. For example, the time period of rolling diverged near the transition, while stable rolling motion was established away from the critical point. Cylindrical objects were predicted to behave differently from spherical ones due to differences in rotational dynamics.

The researchers tested their calculations with experiments observing irregular rolling cylinders and spheres on different inclines, verifying that their results matched theoretical predictions for behavior near the onset of motion.

A surprising observation emerged when experimenting with irregularly shaped spheres: the motion exhibited a periodic pattern, repeating itself indefinitely once reaching steady state. This was an unexpected finding, but upon reflection, it made sense – a sphere rolling jerkily forward would resemble a dung beetle’s trajectory, seeming to be completely random and requiring complex mathematical descriptions.

However, mapping out the motions of the balls as distinct trajectories revealed a pattern: regardless of irregularity, the motion was periodic. Furthermore, the ball rolled over itself twice in each period of motion before returning to its original state.

These results provided vivid physical manifestations of topological theorems, including a demonstration of the “Hairy Ball Theorem,” which states that you cannot comb the hair on a sphere without a cowlick. This was seen in how the rolling trajectories looked on the surface of the sphere. The experiments also served to illustrate Dirac’s Plate Trick, positing that a rotating object with strings must rotate twice to return to its original state.

“It’s quite interesting how we can see these kinds of abstract mathematics made visible with this simple experiment,” said co-author and postdoctoral fellow Yeonsu Jung. “And then the question could be, ‘What else can we do?’ … Maybe we could explore something that hasn’t been studied by mathematicians yet.”

The study was funded by Transition Bio Ltd, Cambridge University, the National Research Foundation of Korea, the Simons Foundation, and the Henri Seydoux Fund.

Source:Harvard John A. Paulson School of Engineering and Applied Sciences

Original Link:https://www.sciencedaily.com/releases/2025/03/250327164525.htm

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The Quiet Threat to Trust: How Overreliance on AI Emails Can Harm Workplace Relationships

AI is now a routine part of workplace communication, with most professionals using tools like ChatGPT and Gemini. A study of over 1,000 professionals shows that while AI makes managers’ messages more polished, heavy reliance can damage trust. Employees tend to accept low-level AI help, such as grammar fixes, but become skeptical when supervisors use AI extensively, especially for personal or motivational messages. This “perception gap” can lead employees to question a manager’s sincerity, integrity, and leadership ability.

Published

4 days ago

August 13, 2025

Mona V

The use of artificial intelligence (AI) in writing and editing emails has become a common practice among professionals, with over 75% of them utilizing tools like ChatGPT, Gemini, Copilot, or Claude in their daily work. While these generative AI tools can make writing easier, research reveals that relying on them too heavily can undermine trust between managers and employees.

A study conducted by researchers Anthony Coman and Peter Cardon surveyed 1,100 professionals about their perceptions of emails written with low, medium, and high levels of AI assistance. The results showed a “perception gap” in messages written by managers versus those written by employees. When evaluating their own use of AI, participants tended to rate it similarly across different levels of assistance. However, when rating others’ use, the magnitude of AI assistance became important.

The study found that low levels of AI help, such as grammar or editing, were generally acceptable. However, higher levels of assistance triggered negative perceptions, especially among employees who perceived their managers’ reliance on AI-generated content as laziness or a lack of caring. This perception gap had a substantial impact on trust: only 40% to 52% of employees viewed supervisors as sincere when they used high levels of AI, compared to 83% for low-assistance messages.

The findings suggest that managers should carefully consider message type, level of AI assistance, and relational context before using AI in their writing. While AI may be suitable for informational or routine communications, relationship-oriented messages requiring empathy, praise, congratulations, motivation, or personal feedback are better handled with minimal technological intervention.

In essence, the quiet threat to trust posed by overreliance on AI emails is a reminder that while technology can enhance productivity and efficiency, it cannot replace human touch and emotional intelligence in workplace relationships.

Computer Programming

Revolutionizing Materials Discovery: AI-Powered Lab Finds New Materials 10x Faster

A new leap in lab automation is shaking up how scientists discover materials. By switching from slow, traditional methods to real-time, dynamic chemical experiments, researchers have created a self-driving lab that collects 10 times more data, drastically accelerating progress. This new system not only saves time and resources but also paves the way for faster breakthroughs in clean energy, electronics, and sustainability—bringing us closer to a future where lab discoveries happen in days, not years.

Published

1 month ago

July 15, 2025

Mona V

The article you provided showcases a groundbreaking achievement in materials discovery research. A team of scientists has developed an AI-powered laboratory that can collect at least 10 times more data than previous techniques, drastically expediting the process while slashing costs and environmental impact. This self-driving laboratory combines machine learning and automation with chemical and materials sciences to discover materials more quickly.

The innovation lies in the implementation of dynamic flow experiments, where chemical mixtures are continuously varied through the system and monitored in real-time. This approach generates a vast amount of high-quality data, which is then used by the machine-learning algorithm to make smarter, faster decisions, honing in on optimal materials and processes.

The results are staggering: the self-driving lab can identify the best material candidates on its very first try after training, reducing the number of experiments needed and dramatically cutting down on chemical use and waste. This breakthrough has far-reaching implications for sustainable research practices and society’s toughest challenges.

The article highlights the work of Milad Abolhasani, corresponding author of the paper, who emphasizes that this achievement is not just about speed but also about responsible research practices. The future of materials discovery, he says, is not just about how fast we can go, but also about how responsibly we get there.

The paper, “Flow-Driven Data Intensification to Accelerate Autonomous Materials Discovery,” was published in the journal Nature Chemical Engineering and showcases a collaborative effort from multiple researchers and institutions. The work has been supported by the National Science Foundation and the University of North Carolina Research Opportunities Initiative program.

Computer Programming

Revolutionizing AI Efficiency: Breakthrough in Spin Wave Technology

A groundbreaking step in AI hardware efficiency comes from Germany, where scientists have engineered a vast spin waveguide network that processes information with far less energy. These spin waves quantum ripples in magnetic materials offer a promising alternative to power-hungry electronics.

Published

1 month ago

July 11, 2025

Mona V

The rapid advancement of Artificial Intelligence (AI) has put an immense strain on our energy resources. In response, researchers are racing to find innovative solutions that can make AI more efficient and sustainable. A groundbreaking discovery in spin wave technology could be the game-changer we’ve been waiting for. A team from the Universities of Münster and Heidelberg, led by physicist Prof. Rudolf Bratschitsch, has successfully developed a novel way to produce waveguides that enable spin waves to travel farther than ever before.

The scientists have created the largest spin waveguide network in history, with 198 nodes connected by high-quality waveguides. This achievement is made possible by using yttrium iron garnet (YIG), a material known for its low attenuation properties. The team employed a precise technique involving a silicon ion beam to inscribe individual spin-wave waveguides into a thin film of YIG, resulting in complex structures that are both flexible and reproducible.

One of the key advantages of this breakthrough is the ability to control the properties of the spin wave transmitted through the waveguide. Researchers were able to accurately alter the wavelength and reflection of the spin wave at specific interfaces, paving the way for more efficient AI processing. This innovation has the potential to revolutionize the field of AI by making it 10 times more efficient.

The study was published in Nature Materials, a prestigious scientific journal. The project received funding from the German Research Foundation (DFG) as part of the Collaborative Research Centre 1459 “Intelligent Matter.” This groundbreaking discovery is poised to take AI to new heights and make our energy resources go further than ever before.