Koki Ho, Georgia Institute of Technology and Mariel Borowitz, Georgia Institute of Technology

Most space mission systems historically have used one spacecraft designed to complete an entire mission independently. Whether it was a weather satellite or a human-crewed module like Apollo, nearly every spacecraft was deployed and performed its one-off mission completely on its own.

But today, space industry organizations are exploring missions with many satellites working together. For example, SpaceX's Starlink constellations include thousands of satellites. And new spacecraft could soon have the capabilities to link up or engage with other satellites in orbit for repairs or refueling.

Some of these spacecraft are already operating and serving customers, such as Northrop Grumman's mission extension vehicle. This orbiting craft has extended the lives of multiple communications satellites.

These new design options and in-orbit capabilities make space missions look more like large logistics operations on Earth.

We're researchers who have studied the space industry for years. We've studied how the space sector could learn lessons from companies like Amazon or FedEx about managing complex fleets and coordinating operations.

Lessons from the ground transportation network

Space mission designers plan their routes in order to deliver their payloads to the Moon or Mars, or orbit efficiently within a set of cost, timeline and capacity constraints. But when they need to coordinate multiple space vehicles working together, route planning can get complicated.

Logistics companies on the ground solve similar problems every day and transport goods and commodities across the globe. So, researchers can study how these companies manage their logistics to help space companies and agencies figure out how to successfully plan their mission operations.

One NASA-funded study in the early 2000s had an idea for simulating space logistics operations. These researchers viewed orbits or planets as cities and the trajectories connecting them as routes. They also viewed the payload, consumables, fuel and other items to transport as commodities.

This approach helped them reframe the space mission problem as a commodity flow problem – a type of question that ground logistics companies work on all the time.

Lessons from ground logistics infrastructure

New capabilities for refueling and repairing spacecraft in orbit create new opportunities as well as challenges.

Namely, space operators don't usually know which satellite will be the next one to fail or when that will happen. For these new technologies to be useful, space mission designers would need to come up with an infrastructure system. That could look like a fleet of service vehicles and depots in space that quickly respond to any unpredictable events.

Fortunately, space mission designers can learn from operations on the ground. City planners and emergency response organizations think through these types of challenges while determining where to locate hospitals or fire departments. They also consider these facilities' capacities to respond to unpredictable calls.

We can draw an analogy between a ground logistics system design and an in-space servicing system design. This way, researchers can leverage theories developed for ground logistics to improve the space mission design practice.

One study published in November 2020 developed a framework for servicing spacecraft on orbit using what logistics experts call spatial queuing theory. Researchers most commonly use this modeling theory to analyze the performance of a ground logistics system.

Lessons from ground warehouse management

In the past, individual spacecraft carried out their missions independently, so if a satellite failed, its mission engineers had to develop and send a replacement.

Now, for missions with multiple satellites, such as the Iridium satellite constellation, operators often maintain one or more spares on orbit.

This becomes complicated for constellations made up of hundreds or thousands of spacecraft. Mission designers want to ensure they have enough spare satellites in orbit so they don't have to interrupt the mission if one breaks. But sending too many spare satellites gets expensive.

When dealing with these types of large constellations, mission designers can learn from the methods Amazon and other ground companies use to manage their warehouses. Amazon puts these warehouses in specific places and stocks them with certain items to make sure the deliveries are handled efficiently.

Inventory management theories on the ground can help inform how space companies tackle these challenges.

A study published in November 2019 developed an approach that space companies could use to manage their spare strategies. This approach can help them decide where in orbit to allocate their spare satellites to meet their needs while minimizing any service interruptions.

International dimensions

Spacecraft operate in a complex and rapidly changing environment. Operators need to know where other missions are operating and what rules they should follow when refueling or repairing in space. In space, however, nobody has defined these rules yet.

Ships, aircraft and ground vehicles all have clear rules of the road to follow when interacting with other vehicles. For example, civilian ships and aircraft have to share their location with other vehicles and officials to help manage traffic.

Some researchers are examining what similar rules could look like for space. One study examined how developing rules based on a spacecraft's size, age or other attributes might help future space operations run more smoothly. For example, one rule might be that the spacecraft that launched most recently should take responsibility for maneuvering when there's another craft in its path.

With more satellites and spacecraft launching now than ever, companies and government agencies will need new technologies and policies to coordinate them. As space activity becomes more complex, researchers can continue to apply what they've learned on the ground to new missions in space.

Koki Ho, Associate Professor of Aerospace Engineering, Georgia Institute of Technology and Mariel Borowitz, Associate Professor of International Affairs, Georgia Institute of Technology

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

Shaoyu Yuan, Rutgers University – Newark

In the competitive arena of global technology, China's ambitions in artificial intelligence stand out – not just for their scale but for their distinct strategic approach.

In 2017, the Chinese Communist Party declared its intent to surpass the United States to become the world leader in AI by 2030. This plan, however, is less about pioneering novel technologies and more about strategically adapting existing ones to serve state economic, political and social objectives.

While both China and the United States are actively pursuing AI technologies, their approaches differ significantly. The U.S. has traditionally led in fundamental AI research and innovation, with institutions such as Massachusetts Institute of Technology and Stanford and tech giants such as Google and Microsoft driving breakthroughs in machine learning. This innovation-first approach contrasts with China's focus on adaptation and application of existing technologies for specific state objectives.

The United States' AI development is primarily driven by a decentralized network of academic institutions, private companies and government agencies, often with competing interests and a focus on commercial applications. In contrast, China's AI strategy is more centralized and state directed, with a clear focus on supporting government initiatives such as social control and economic planning.

AI for ideological control

At the heart of China's AI strategy lies its effort to embed the technology in the machinery of the government's ideological control. A prime example is the Xue Xi chatbot developed by researchers at China's top-ranked university, Tsinghua University. Unlike Western AI models designed to foster open-ended dialogue, Xue Xi was trained in part on “Xi Jinping Thought” to indoctrinate users – likely initially to be party members in government – with Communist Party ideology.

This isn't just a singular initiative but part of a broader trend. AI-driven surveillance systems, like the facial recognition technology deployed across the Xinjiang region of China, enable the government to maintain tight control over the area's minority Muslim Uyghur population.

These technologies are not groundbreaking. They build on existing innovations but are finely tuned to serve the Communist Party's efforts to maintain social order and prevent dissent. China's AI prowess comes not by creating the newest technology but by mastering and deploying AI in ways that align with its ideological imperatives.

AI for economic control

China's AI strategy is also deeply intertwined with its economic ambitions. Faced with slowing growth, the Communist Party views technology as the essential tool for pulling the country out of its economic slowdown. This is particularly evident in sectors such as manufacturing and logistics, where AI is used to drive efficiencies and maintain China's competitive edge in global supply chains. For example, companies such as online retail giant Alibaba have developed AI-driven logistics platforms that optimize delivery routes and manage warehouse operations, ensuring that China remains the factory of the world.

Additionally, China's social credit system, which rates citizens on their civic and financial behavior, represents a significant strategic initiative where AI plays an increasingly crucial role. China's system is designed to monitor and influence citizen behavior on a massive scale. Although AI is not yet fully implemented across the entire social credit system, it is being integrated to track and analyze vast amounts of data, such as financial transactions, online interactions and social relationships in real time.

This data is then used to assign scores that can affect various aspects of life, from loan approvals to travel permissions. As AI becomes increasingly embedded in the system, it is likely, I believe, to further reinforce state control and ensure societal compliance, prioritizing government oversight over personal autonomy.

Strategic exports

On the international stage, China is exporting its AI technologies to expand its influence, particularly in developing nations. Through the Belt and Road Initiative, Chinese tech giants such as Huawei and ZTE are providing AI-driven surveillance systems to governments in Africa, Southeast Asia and Latin America. These systems, often framed as tools for improving public safety, are part of a larger strategy to export China's governance model.

For instance, in Zimbabwe, Chinese firms have helped implement a nationwide facial recognition system under the guise of combating crime. Political activists in Zimbabwe fear that technology is being used to monitor political opponents and activists, mirroring its use in China. By exporting AI technologies that are tightly integrated with state control, China is not only expanding its market share but also promoting its authoritarian model as a viable alternative to Western democracy.

AI for strategic military advantage

China's military ambitions are also tightly linked to its AI strategy. The People's Liberation Army, China's military, is investing heavily in AI-driven autonomous systems, including drones and robotic platforms. These technologies are not necessarily the most advanced in the world, but China is adapting them to fit its strategic needs.

China is developing AI systems to support its naval operations in the South China Sea, a region of significant geopolitical tension. China is deploying autonomous submarines and surveillance drones to monitor and potentially disrupt foreign military activities in the region. This strategic use of AI in military applications highlights China's focus on using existing technologies to achieve specific geopolitical objectives, rather than seeking innovation for its own sake.

Calculated strategy

China's approach to AI is a calculated strategy of adaptation and application, rather than raw innovation. By mastering the use of existing technologies and aligning them with state objectives, China is not only bolstering its domestic control but also reshaping global power dynamics.

Whether through ideological indoctrination, economic control, strategic exports or military advancements, China's AI playbook is a powerful reminder that in the realm of technology, how tools are used can be just as transformative as the tools themselves.

Shaoyu Yuan, Dean's Fellow at the Division of Global Affairs, Rutgers University – Newark

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

Abdon Pena-Francesch, University of Michigan

When you think of a fearsome, sharp-toothed predator, a squid probably isn't the first animal that comes to mind. But these complex creatures have sophisticated eyesight, a strong beak to crush shells and agile tentacles that help them snatch up prey.

Oh, and they have teeth in their suckers. The serrated teeth inside the suction cups on their tentacles allow them to latch onto prey.

While most hard tissues in animals are mineralized, with calcium fortifying their bones, shells or teeth, the squids' sucker teeth are instead composed of structural proteins. Scientists don't really understand how these teeth are made.

By looking inside a squid sucker using an electron microscope, our team of scientists captured an image that shows the cell tissue that grows the teeth. The cells located in the inside walls of the suction cup secrete proteins that bind to each other and form complex teethed-ring structures.

High-strength proteins in squid sucker teeth

Squid sucker teeth have some outstanding properties. They're resistant to compression, yet they're flexible and can conform to the shape of their prey. Our team's research tries to understand not only how these teeth are made, but also where their unique properties come from.

The teeth are composed of a family of structural proteins, which have a mechanical function rather than a biological function. Some examples include keratin, which makes up hair and nails, or silk, which gives structure to spider webs and silkworm cocoons. In squids, these sucker teeth catch and grip onto prey.

Proteins are made of amino acids arranged in a specific order, and that order defines their structure. Sucker teeth proteins have amino acids that form hard, tiny crystals called nanocrystals in the material. These nanocrystals connect the protein strands in a network – similar to knots in a fishing net.

These nanocrystals come together to form nanotubes inside the material, like tiny honeycomb structures. When we look at them through an electron microscope, we can see a tooth cut in half, revealing the intricate internal structure with long but tiny nanotubes. Thanks to these nanostructures, the squid protein teeth have strength, toughness and a flexibility that outperforms many synthetic polymers and modern materials.

Squid-inspired new materials

Scientists and engineers can take inspiration from biology and use unique natural structures to model and develop new types of materials. For example, squid sucker ring teeth have inspired the development of self-healing materials that can repair their own cuts, punctures or scratches.

The nanocrystals that hold together the squid teeth proteins can reform after they break. Materials made in our lab inspired by squid nanocrystals could lead to self-repairing medical devices or robots. These materials would last longer and require less upkeep, which would be useful in dangerous environments or inside the human body.

These squid-inspired materials could also assemble and disassemble by themselves. Materials with this property could be recycled or degraded without leaving behind any waste. That would make this sort of material a promising bio-based alternative to single-use plastic.

Abdon Pena-Francesch, Assistant Professor of Materials Science and Engineering, University of Michigan

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