The world of sports has long been fascinated by the unique charm of boccia – a Paralympic game that transcends age and ability barriers. A team from Osaka Metropolitan University has taken this phenomenon a step further with Extended Reality (XR) Boccia, an innovative rehabilitation program that combines physical exercise with emotional benefits for older adults. Developed by Associate Professor Masataka Kataoka’s research group, XR Boccia offers a fresh alternative to traditional boccia and treadmill walking, making it perfect for environments with limited space.

The researchers conducted an intriguing study to investigate the effects of XR Boccia on participants over 65. The findings reveal that both XR Boccia and traditional boccia showed significant improvements in mood, vitality, and energy among participants after experiencing these programs. Notably, there was no substantial difference in lower limb muscle activity during any of the exercises, although a notable increase in rectus femoris muscle activity (which helps extend the knee) was observed in both types of boccia compared to treadmill walking.

The implications of this research are groundbreaking. Associate Professor Kataoka noted that XR Boccia could be an effective rehabilitation exercise for older adults, boasting both physical and emotional benefits. Given its adaptability and practicality, it’s suitable for indoor environments like hospitals and nursing care facilities. The researchers aim to further investigate long-term results in a larger population of older adults and continue updating the XR program.

The study was published in PLOS One, shedding light on this innovative approach to rehabilitation. With XR Boccia, we may be witnessing a new chapter in the journey towards better health and happiness for older adults, one game at a time.

Researchers at Caltech have made a breakthrough in developing a simple algorithm for underwater robots to harness the power of ocean currents. Led by John Dabiri, the Centennial Professor of Aeronautics and Mechanical Engineering, the team has successfully created a system that allows small autonomous underwater vehicles (AUVs) to ride on turbulent water currents rather than fighting against them.

The researchers began by studying how jellyfish navigate through the ocean using their unique ability to traverse and plumb the depths. They outfitted these creatures with electronics and prosthetic “hats” to carry small payloads and report findings back to the surface. However, they soon realized that jellyfish do not have a brain and therefore cannot make decisions about how to navigate.

To address this limitation, Dabiri’s team developed what would be considered the equivalent of a brain for an AUV using artificial intelligence (AI). This allowed the robots to make decisions underwater and potentially take advantage of environmental flows. However, they soon discovered that AI was not the most efficient solution for their problem.

Enter Peter Gunnarson, a former graduate student who returned to Dabiri’s lab with a simpler approach. He attached an accelerometer to CARL-Bot, an AUV developed years ago as part of his work on incorporating artificial intelligence into its navigation technique. By measuring how CARL-Bot was being pushed around by vortex rings (underwater equivalents of smoke rings), Gunnarson noticed that the robot would occasionally get caught up in a vortex ring and be propelled clear across the tank.

The team then developed simple commands to help CARL-Bot detect the relative location of a vortex ring and position itself to catch a ride. Alternatively, the bot can decide to get out of the way if it does not want to be pushed by a particular vortex ring. This process involves elements of biomimicry, mimicking nature’s ability to use environmental flows for energy conservation.

Dabiri hopes to marry this work with his hybrid jellyfish project, which aims to demonstrate a similar capability to take advantage of environmental flows and move more efficiently through the water. With this breakthrough, underwater robots can now ride the tides, reducing energy expenditure and increasing their efficiency in navigating the ocean depths.

The collaboration between trailblazing engineers and industry professionals has led to a groundbreaking technique called Skia, which may transform the future of computing efficiency for modern data centers.

In data centers, large computers process massive amounts of data, but often struggle to keep up due to taxing workloads. This results in slower performance, causing search engines to generate answers more slowly or not at all. To address this issue, researchers at Texas A&M University have developed Skia in collaboration with Intel, AheadComputing, and Princeton.

The team includes Dr. Paul V. Gratz, a professor in the Department of Electrical and Computer Engineering, Dr. Daniel A. Jiménez, a professor in the Department of Computer Science and Engineering, and Chrysanthos Pepi, a graduate student in the Department of Electrical and Computer Engineering.

Processing instructions has become a major bottleneck in modern processor design,” Gratz said. “We developed Skia to better predict what’s coming next and alleviate that bottleneck.” Skia can not only help better predict future instructions but also improve the throughput of instructions on the system, leading to quicker performance and less power consumption for the data center.

Think of throughput in terms of being a server in a restaurant,” Gratz said. “You have lots and lots of jobs to do. How many tasks can you complete or how many instructions can you execute per unit time? You want high throughput, especially for computing.”

Improving throughput can lead to quicker performance and less power consumption for the data center. In fact, making it up to 10% more efficient means a company previously needing to make 100 data centers around the country now only needs to make 90, which is 10 fewer data centers. That’s pretty significant. These data centers cost millions of dollars, and they consume roughly the equivalent of the entire output of a power plant.

Skia identifies and decodes these shadow branches in unused bytes, storing them in a memory area called the Shadow Branch Buffer, which can be accessed alongside the BTB. What makes this technique interesting is that most of the future instructions were already available, and we demonstrate that Skia, with a minimal hardware budget, can make data centers more efficient, nearly twice the performance improvement versus adding the same amount of storage to the existing hardware as we observe,” Pepi said.

Their findings, “Skia: Exposing Shadow Branches,” were published in one of the leading computer architecture conferences, the ACM International Conference on Architectural Support for Programming Languages and Operating Systems. The team also traveled to the Netherlands to present their work to colleagues from around the globe.

Funding for this research is administered by the Texas A&M Engineering Experiment Station (TEES), the official research agency for Texas A&M Engineering.