Inspired by the human eye, our biomedical engineering lab at Georgia Tech has designed an adaptive lens made of soft, light-responsive, tissuelike materials. Our study is published in the journal Science Robotics.

Adjustable camera systems usually require a set of bulky, moving, solid lenses and a pupil in front of a camera chip to adjust focus and intensity. In contrast, human eyes perform these same functions using soft, flexible tissues in a highly compact form.

Our lens, called the photo-responsive hydrogel soft lens, or PHySL, replaces rigid components with soft polymers acting as artificial muscles. The polymers are composed of a hydrogel—a water-based polymer material. This hydrogel muscle changes the shape of a soft lens to alter the lens’s focal length, a mechanism analogous to the ciliary muscles in the human eye.

The hydrogel material contracts in response to light, allowing us to control the lens without touching it by projecting light onto its surface. This property also allows us to finely control the shape of the lens by selectively illuminating different parts of the hydrogel. By eliminating rigid optics and structures, our system is flexible and compliant, making it more durable and safer in contact with the body.

Why it matters

Artificial vision using cameras is commonplace in a variety of technological systems, including robots and medical tools. The optics needed to form a visual system are still typically restricted to rigid materials using electric power. This limitation presents a challenge for emerging fields, including soft robotics and biomedical tools that integrate soft materials into flexible, low-power and autonomous systems. Our soft lens is particularly suitable for this task.

Soft robots are machines made with compliant materials and structures, taking inspiration from animals. This additional flexibility makes them more durable and adaptive. Researchers are using the technology to develop surgical endoscopes, grippers for handling delicate objects and robots for navigating environments that are difficult for rigid robots.

The same principles apply to biomedical tools. Tissuelike materials can soften the interface between body and machine, making biomedical tools safer by making them move with the body. These include skinlike wearable sensors and hydrogel-coated implants.

What other research is being done in this field

This work merges concepts from tunable optics and soft “smart” materials. While these materials are often used to create soft actuators—parts of machines that move—such as grippers or propulsors, their application in optical systems has faced challenges.

Many existing soft lens designs depend on liquid-filled pouches or actuators requiring electronics. These factors can increase complexity or limit their use in delicate or untethered systems. Our light-activated design offers a simpler, electronics-free alternative.

What’s next

We aim to improve the performance of the system using advances in hydrogel materials. New research has yielded several types of stimuli-responsive hydrogels with faster and more powerful contraction abilities. We aim to incorporate the latest material developments to improve the physical capabilities of the photo-responsive hydrogel soft lens.

We also aim to show its practical use in new types of camera systems. In our current work, we developed a proof-of-concept, electronics-free camera using our soft lens and a custom light-activated, microfluidic chip. We plan to incorporate this system into a soft robot to give it electronics-free vision. This system would be a significant demonstration for the potential of our design to enable new types of soft visual sensing.

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

A recent study shows that AI-powered social media bots can increase user engagement on posts, but they fall short of encouraging users to post more overall.

The study, “Does Social Bot Help Socialize? Evidence from a Microblogging Platform,” focused on user engagement with CommentRobot, a large language model–powered bot launched on Weibo, China’s leading microblogging platform.

The work is published in the journal Information Systems Research.

At the core of the research project, the social bot automatically generated comments on users’ posts in public threads on the platform. The researchers found that when human posts receive bot comments, their peers are more likely to engage with those posts, but human authors of focal posts (hereafter posters) were not any more likely to increase their social media activity.

The study was conducted by Yang Gao of the University of Illinois Urbana-Champaign, Maggie Mengqing Zhang of the University of Virginia, and Mikhail Lysyakov of the University of Rochester.

Key findings were that when people receive bot-generated comments, their posts receive 23% more comments, and 11% more likes.

“Our research studied the bots at several complex levels, from bot comment quality to which users were targeted and how human peers responded to the public interactions between the bot and the poster,” said Gao.

Gao said that the quality of the bot comments matters. Social bot comments that were considered relevant and included certain social cues were more likely to generate engagement. The researchers detected a pattern where social bots often prioritized less active users, but that it was active users who more significantly benefited from receiving bot comments.

“It’s often assumed that people are more likely to engage with other people and not bots, but what we found is that when the bots are able to integrate relevant social cues into their comments, this stimulates a response from people,” said Zhang. “This in turn increases engagement.”

“What may be most interesting about this dynamic,” said Lysyakov, “is that the subsequent engagement is often not directly with the bot’s comments, but rather with other human users who also decided to engage in discussion.”

While all of this heightens user activity and engagement around a single social media post, the study authors found that overall, this did not increase the likelihood that they would become more active on the platform as posters.

The researchers analyzed over 106,000 posts by 64,000 users on Weibo in January 2024, focusing on first-time interactions with CommentRobot. They used econometric models, instrumental variable analysis, robustness checks and an online randomized experiment with 348 active Weibo users to confirm their findings.

“All of this suggests that while AI-powered social bots can help increase visibility and engagement around posts, platforms should refine their deployment strategies,” said Gao. “Poorly targeted or low-quality comments may limit their effectiveness, and platforms cannot assume bots will increase overall user activity.”

Bio-inspired computational methods have gained popularity recently. These methods mimic the seemingly complex behavior of organisms to tackle difficult and often overwhelming problems. For example, algorithms have been inspired by honeybees’ flight patterns when searching for nectar, ants’ social foraging strategies, the evasive murmurations of birds and fish, and even the growth patterns of slime molds. By modeling these natural processes mathematically, researchers can develop innovative solutions to complex challenges.

Work published in the International Journal of Bio-Inspired Computation has turned to fireflies and how they seek out the brightest of their number to address the classic knapsack problem. This problem involves making optimal choices about resource allocation under specific constraints. Using the firefly algorithm, researchers have explored how this natural behavior might be used to guide decision-making in modern financial systems.

Conventional optimization techniques, such as dynamic programming, often struggle with the scale and volatility of real-world finance. When objectives such as profitability, regulatory compliance, and ethical considerations must all be balanced, those methods often fall short.

Inspired by the firefly’s attraction to brighter individuals, the firefly algorithm provides an adaptive strategy that can explore and exploit potential solutions, even in complex, dynamic environments. The integration of machine learning helps handle noisy and rapidly changing data, both of which are characteristics of financial markets.

The researchers specifically used the dual search pattern firefly algorithm (DSPFA), which combines Gaussian distributions with Lévy flights. This mathematical approach models both small incremental adjustments and rare, large jumps. This allows the algorithm to adapt in real time to changing financial conditions. It can dynamically balance risk and return while also accounting for environmental, social, and governance considerations.

Simulations demonstrated that this approach can effectively handle a variety of constraints, such as liquidity limits and regulatory requirements. At the same time, it maintains computational efficiency and produces decisions that are relatively easy to audit.