Generative artificial intelligence models have left such an indelible impact on digital content creation that it’s getting harder to recall what the internet was like before it. You can call on these AI tools for clever projects such as videos and photos — but their flair for the creative hasn’t quite crossed over into the physical world just yet. So why haven’t we seen generative AI-enabled personalized objects, such as phone cases and pots, in places like homes, offices, and stores yet? According to MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers, a key issue is the mechanical integrity of the 3D model.While AI can help generate personalized 3D models that you can fabricate, those systems don’t often consider the physical properties of the 3D model. MIT Department of Electrical Engineering and Computer Science (EECS) PhD student and CSAIL engineer Faraz Faruqi has explored this trade-off, creating generative AI-based systems that can make aesthetic changes to designs while <a href="https://news.mit.edu/2023/ai-driven-tool-personalize-3d-printable-models-0915">preserving functionality,</a> and another that modifies structures with the desired <a href="https://news.mit.edu/2025/3d-modeling-you-can-feel-0422">tactile properties</a> users want to feel.<h2>Making it real</h2>Together with researchers at Google, Stability AI, and Northeastern University, Faruqi has now found a way to make real-world objects with AI, creating items that are both durable and exhibit the user’s intended appearance and texture. With the AI-powered “<a href="https://dl.acm.org/doi/10.1145/3745778.3766655" target="_blank">MechStyle</a>” system, users simply upload a 3D model or select a preset asset of things like vases and hooks, and prompt the tool using images or text to create a personalized version. A generative AI model then modifies the 3D geometry, while MechStyle simulates how those changes will impact particular parts, ensuring vulnerable areas remain structurally sound. When you’re happy with this AI-enhanced blueprint, you can 3D print it and use it in the real world.<a></a><iframe width="640" height="360" src="https://www.youtube.com/embed/3BEZYZ86DNQ?&amp;wmode=opaque" frameborder="0"></iframe>You could select a model of, say, a wall hook, and the material you’ll be printing it with (for example, plastics like polylactic acid). Then, you can prompt the system to create a personalized version, with directions like, “generate a cactus-like hook.” The AI model will work in tandem with the simulation module and generate a 3D model resembling a cactus while also having the structural properties of a hook. This green, ridged accessory can then be used to hang up mugs, coats, and backpacks. Such creations are possible thanks, in part, to a stylization process, where the system changes a model’s geometry based on its understanding of the text prompt, and working with the feedback received from the simulation module.According to CSAIL researchers, 3D stylization used to come with unintended consequences. Their formative study revealed that only about 26 percent of 3D models remained structurally viable after they were modified, meaning that the AI system didn’t understand the physics of the models it was modifying.“We want to use AI to create models that you can actually fabricate and use in the real world,” says Faruqi, who is a lead author on a <a href="https://dl.acm.org/doi/10.1145/3745778.3766655">paper</a> presenting the project. “So MechStyle actually simulates how GenAI-based changes will impact a structure. Our system allows you to personalize the tactile experience for your item, incorporating your personal style into it while ensuring the object can sustain everyday use.”This computational thoroughness could eventually help users personalize their belongings, creating a unique pair of glasses with speckled blue and beige dots resembling fish scales, for example. It also produced a pillbox with a rocky texture that’s checkered with pink and aqua spots. The system’s potential extends to crafting unique home and office decor, like a lampshade resembling red magma. It can even design assistive technology fit to users’ specifications, such as finger splints to aid with dexterous injuries and utensil grips to aid with motor impairments.In the future, MechStyle could also be useful in creating prototypes for accessories and other handheld products you might sell in a toy shop, hardware store, or craft boutique. The goal, CSAIL researchers say, is for both expert and novice designers to spend more time brainstorming and testing out different 3D designs, instead of assembling and customizing items by hand.<h2>Staying strong</h2>To ensure MechStyle’s creations could withstand daily use, the researchers augmented their generative AI technology with a type of physics simulation called a finite element analysis (FEA). You can imagine a 3D model of an item, such as a pair of glasses, with a sort of heat map indicating which regions are structurally viable under a realistic amount of weight, and which ones aren’t. As AI refines this model, the physics simulations highlight which parts of the model are getting weaker and prevent further changes. Faruqi adds that running these simulations every time a change is made drastically slows down the AI process, so MechStyle is designed to know when and where to do additional structural analyses. “MechStyle’s adaptive scheduling strategy keeps track of what changes are happening in specific points in the model. When the genAI system makes tweaks that endanger certain regions of the model, our approach simulates the physics of the design again. MechStyle will make subsequent modifications to make sure the model doesn’t break after fabrication.”Combining the FEA process with adaptive scheduling allowed MechStyle to generate objects that were as high as 100 percent structurally viable. Testing out 30 different 3D models with styles resembling things like bricks, stones, and cacti, the team found that the most efficient way to create structurally viable objects was to dynamically identify weak regions and tweak the generative AI process to mitigate its effect. In these scenarios, the researchers found that they could either stop stylization completely when a particular stress threshold was reached, or gradually make smaller refinements to prevent at-risk areas from approaching that mark.The system also offers two different modes: a freestyle feature that allows AI to quickly visualize different styles on your 3D model, and a MechStyle one that carefully analyzes the structural impacts of your tweaks. You can explore different ideas, then try the MechStyle mode to see how those artistic flourishes will affect the durability of particular regions of the model.CSAIL researchers add that while their model can ensure your model remains structurally sound before being 3D printed, it’s not yet able to improve 3D models that weren’t viable to begin with. If you upload such a file to MechStyle, you’ll receive an error message, but Faruqi and his colleagues intend to improve the durability of those faulty models in the future.What’s more, the team hopes to use generative AI to create 3D models for users, instead of stylizing presets and user-uploaded designs. This would make the system even more user-friendly, so that those who are less familiar with 3D models, or can’t find their design online, can simply generate it from scratch. Let’s say you wanted to fabricate a unique type of bowl, and that 3D model wasn’t available in a repository; AI could create it for you instead. “While style-transfer for 2D images works incredibly well, not many works have explored how this transfer to 3D,” says Google Research Scientist Fabian Manhardt, who wasn’t involved in the paper. “Essentially, 3D is a much more difficult task, as training data is scarce and changing the object’s geometry can harm its structure, rendering it unusable in the real world. MechStyle helps solve this problem, allowing for 3D stylization without breaking the object’s structural integrity via simulation. This gives people the power to be creative and better express themselves through products that are tailored towards them.” Farqui wrote the paper with senior author Stefanie Mueller, who is an MIT associate professor and CSAIL principal investigator, and two other CSAIL colleagues: researcher Leandra Tejedor SM ’24, and postdoc Jiaji Li. Their co-authors are Amira Abdel-Rahman PhD ’25, now an assistant professor at Cornell University, and Martin Nisser SM ’19, PhD ’24; Google researcher Vrushank Phadnis; Stability AI Vice President of Research Varun Jampani; MIT Professor and Center for Bits and Atoms Director Neil Gershenfeld; and Northeastern University Assistant Professor Megan Hofmann. Their work was supported by the MIT-Google Program for Computing Innovation. It was presented at the Association for Computing Machinery’s Symposium on Computational Fabrication in November.

MIT CSAIL researchers have found a way to make real-world objects with AI, creating durable items that exhibit the user’s intended appearance and texture. Image: Alex Shipps/MIT CSAIL, with assets from the researchers and Pexels.

"MechStyle" allows users to personalize 3D models, while ensuring they're physically viable after fabrication, producing unique personal items and assistive technology.

Generative AI tool helps 3D print personal items that sustain daily use

<h2 id="isPasted">Interview Summary: Delta Line at SPS</h2>At SPS, Giovanni and Andrea from Delta Line spoke about the company’s positioning in the motion control market, recent product innovations, and its growing focus on intralogistics applications. They explained that Delta Line is a Swiss-based specialist in complete motion solutions, covering motors, drives, gearboxes, and fully integrated systems. With manufacturing facilities in Switzerland, Italy, and China, and a co-owned electronics company in Italy, Delta Line maintains full control over design, quality, and lead times. The company serves key sectors including logistics, robotics, automation, medical devices, and industrial machinery.When discussing differentiation, Giovanni and Andrea highlighted Delta Line’s vertically integrated structure as a core advantage. Owning both mechanical and electronic development enables a cost-effective setup, consistent quality, and fast response times. This is complemented by lean, application-focused technical support, allowing Delta Line to quickly adapt solutions to customer-specific requirements.Andrea outlined the main innovations presented at SPS, focusing on the expansion of Delta Line’s integrated servo motor family. New 100 mm and 110 mm square flange servo motors were introduced, extending the existing 60 mm and 80 mm range. These motors deliver up to 2,000 W of power and more than 60 Nm of torque, feature IP65 protection, support optional brakes and multiple encoder types, and comply with relevant safety standards, making them suitable for demanding industrial and mobile robotics applications.The conversation also addressed intralogistics as a rapidly growing market for Delta Line since 2020. Andrea explained that the company has worked closely with customers developing shuttles, AGVs, sorters, and automated handling systems. Through these projects, Delta Line gained insight into key challenges such as efficiency, energy consumption, footprint constraints, and reliability. This experience led to the development of dedicated solutions, including the IBX57 and IVX60 integrated servo motors, optimized for short, high-power duty cycles, compact designs, and cost-effective brushless operation.Giovanni concluded by providing insight into Delta Line’s electronics strategy. All drives are designed and produced in Italy, fully controlled by Delta Line from concept to release. Newly introduced drive platforms now cover motor sizes up to 130 mm flange and support major industrial communication protocols and functional safety requirements, enabling Delta Line to address applications ranging from intralogistics to machining and automation with a unified electronics portfolio.

Integrated motion solutions built for speed, efficiency, and customization.

Delta Line on their Product Innovations at SPS 2025

A new machine-learning-based approach to mapping real-time tumor metabolism in brain cancer patients, developed at the University of Michigan, could help doctors discover which treatment strategies are most likely to be effective against individual cases of glioma. The team verified the accuracy of the model by comparing it against human patient data and running mouse experiments.The study, published in Cell Metabolism, builds on previous research showing that <a href="https://www.michiganmedicine.org/health-lab/dietary-changes-could-provide-therapeutic-avenue-brain-cancer" target="_blank" rel="noreferrer noopener">some gliomas can be slowed down through the patient’s diet</a>. If a patient isn’t consuming certain protein building blocks, called amino acids, then some tumors are unable to grow. However, other tumors can produce these amino acids for themselves, and can continue growing anyway. Until now, there was no easy way to tell which patients would benefit from dietary restrictions.The digital twin’s ability to map metabolic activity in tumors also helped determine whether a drug that prevents tumors from producing a building block for replicating and repairing DNA would work, as some cells can obtain that molecule from their environments.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1768432470142-braintwin-gemini-3x2-featured.jpg" style="width:100%px" class="fr-fic fr-dib" />Illustration depicts a brain with a tumor and its digital twin. The glioma digital twin developed at U-M maps tumor metabolism and could help doctors discover whether a specific drug or diet is likely to slow tumor growth in an individual patient. Credit: Google GeminiTo overcome challenges in mapping tumor metabolism inside the brain, the Michigan team developed a computer-based “digital twin” that can predict how an individual patient’s brain tumor will react to each treatment. They were primarily funded by the National Institutes of Health, particularly the National Cancer Institute.“Typically, metabolic measurements during surgeries to remove tumors can’t provide a clear picture of tumor metabolism—surgeons can’t observe how metabolism varies with time, and labs are limited to studying tissues after surgery. By integrating limited patient data into a model based on fundamental biology, chemistry and physics, we overcame these obstacles,” said <a href="https://bme.umich.edu/people/nagrath-deepak/" target="_blank" rel="noreferrer noopener">Deepak Nagrath</a>, U-M professor of biomedical engineering and co-corresponding author of the study.The digital twin uses patient data obtained through blood draws, metabolic measurements of the tumor tissue and the tumor’s genetic profile. The digital twin then calculates the speed at which the cancer cells consume and process nutrients, known as metabolic flux. “This is the first time a machine learning and AI-based approach has been used to measure metabolic flux directly in patient tumors,” said <a href="https://nagrath.bme.umich.edu/profile/baharan-meghdadi/" target="_blank" rel="noreferrer noopener">Baharan Meghdadi</a>, a doctoral student in chemical engineering and co-first author of the study.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1768432493048-brain-twin-abstract.jpg" style="width:100%px" class="fr-fic fr-dib" />The illustration depicts a brain with a glioma and its digital twin. Credit: Baharan Meghdadi, Systems Biology of Human Disease Group, University of Michigan.The researchers built a type of deep learning model called a convolutional neural network and trained it on synthetic patient data, generated based on known biology and chemistry and constrained by measurements from eight patients with glioma who were infused with labeled glucose during surgery. By comparing their computer models with different data from six of those patients, they found the digital twins could predict metabolic activity with high accuracy. In experiments conducted on mice, the team confirmed that the diet only slowed tumor growth in mice that the digital twin had identified as good candidates for the treatment.“These results are exciting. The ability to measure metabolic activity in patient tumors could allow us to predict which metabolic therapies might work best for each patient,” said <a href="https://medschool.umich.edu/profile/2410/daniel-r-wahl" target="_blank" rel="noreferrer noopener">Daniel Wahl</a>, the Achtenberg Family Professor of Radiation Oncology and a co-corresponding author of the study. The digital twin also predicted how tumors would respond to the drug mycophenolate mofetil, which targets how cancer cells build DNA. The digital twins correctly identified that some tumors could bypass the drug’s effects by using a “salvage pathway” to grab nutrients from their surroundings. Again, the team confirmed the predictions with mouse experiments.“This amazing tool could help doctors avoid prescribing treatments that a specific tumor is already equipped to resist, and is a way for us to move towards more targeted and personalized treatments for our patients,” said <a href="https://medschool.umich.edu/profile/6398/wajd-n-al-holou" target="_blank" rel="noreferrer noopener">Wajd N. Al-Holou</a>, assistant professor of neurosurgery and co-first author of the study. A doctor could use a patient’s digital twin to test whether a specific diet or drug would actually starve the cancer before the patient changes their meal plan or starts a new medication.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1768432520248-braintwin-illustration.jpg" style="width:100%px" class="fr-fic fr-dib" />Illustration depicts a patient and his digital twin. The glioma digital twin developed at U-M maps tumor metabolism and could help doctors discover whether a specific drug or diet is likely to slow tumor growth in an individual patient. Credit: Ella Maru Studio“This work moves us closer to truly personalized cancer care—not just for brain cancer, but eventually for a variety of tumors. By simulating different therapies virtually, we hope to spare patients from unnecessary treatments and focus on those likely to help,” said <a href="https://medschool.umich.edu/profile/3802/costas-lyssiotis" target="_blank" rel="noreferrer noopener">Costas Lyssiotis</a>, the Maizel Research Professor of Oncology and co-corresponding author of the study.Additional support for the research was provided by the Damon Runyon Cancer Foundation, Forbes Scholar Award, Rogel Scholar Award (U-M), Sontag Foundation, Ivy Glioblastoma Foundation, Alex’s Lemonade Stand Foundation, Chad Tough Defeat DIPG Foundation, National Institute of Neurological Disorders and Stroke (NIH), American Cancer Society and the B*Cured Foundation.Researchers from the University of Alabama, Birmingham, and the Mayo Clinic also contributed to the study.The team has applied for patent protection with the assistance of U-M Innovation Partnerships and is seeking partners to bring the technology to market. Nagrath is also a professor of chemical engineering. Wahl is also an associate professor of neurosurgery. Lyssiotis is also a professor of molecular and integrative physiology, and internal medicine, and is co-director of the Rogel and Blondy Center for Pancreatic Cancer.

Illustration depicts a brain with a tumor and its digital twin. The glioma digital twin developed at U-M maps tumor metabolism and could help doctors discover whether a specific drug or diet is likely to slow tumor growth in an individual patient. Credit: Google Gemini

A simulation of an individual patient's brain tumors, kept up to date with readily available data, can identify whether dietary treatments and drugs are likely to work.

Brain tumor digital twin predicts metabolic treatment outcomes

Palo Alto, California – January 14, 2026 – <a href="https://monogoto.io/" target="_blank" rel="noopener">Monogoto</a> announced today that Monogoto Software-Defined Connectivity Platform has received a 2025 IoT Platforms Leadership Award from IoT Evolution World, the leading online publication covering IoT technologies.The IoT Platforms Leadership Awards honor solutions that provide the foundational technology required to build, manage, and operate IoT ecosystems — from device onboarding and connectivity to analytics, automation, and lifecycle management.“We’re building connectivity for a software-defined world, where networks are intelligent, adaptive, and secure by design,” said Itamar Kunik, co-founder and CEO of Monogoto. “This is about giving devices the autonomy to connect, adapt, and operate globally without friction. As IoT systems become more distributed and AI-driven, connectivity has to keep up. We’re proud to be recognized by IoT Evolution World as we continue pushing the boundaries of what connectivity can do.”Monogoto’s Software-Defined Connectivity Platform redefines how enterprises build and operate connected systems by treating connectivity as software first, not just infrastructure. The platform unifies private, public, and satellite connectivity with zero-trust security, programmable APIs, and developer-first tools. Designed for intelligent, distributed IoT environments, it empowers customers to deploy secure, scalable connectivity globally with real-time visibility, automation, and performance built for the AI era.“The solutions selected for the IoT Evolution IoT Platforms Leadership Award reflect innovation driving the fast-growing Internet of Things marketplace. It is my honor to congratulate Monogoto for their innovative work and contribution to this rapidly evolving industry,” said Carl Ford, Community Developer, IoT Evolution World.“It is my pleasure to recognize Monogoto Software-Defined Connectivity Platform, an innovative solution that earned Monogoto the 2025 IoT Evolution IoT Platforms Leadership Award,” said Rich Tehrani, CEO, <a href="http://www.tmcnet.com/" target="_blank" rel="noopener external noreferrer">TMC</a>. “I look forward to seeing more innovation from Monogoto in the future.”<h3>About Monogoto</h3>Monogoto is a cloud-based connectivity provider offering secure, programmable, and global cellular connectivity for IoT, Private LTE, and 5G applications. Built on a zero-trust architecture and API-first design, Monogoto enables developers and enterprises to provision, control, and scale connectivity like software — across public networks, private infrastructure, and satellite links. With presence in 200+ countries, Monogoto powers next-generation applications across FinTech, supply chain &amp; logistics, smart utilities, manufacturing, agriculture and more.Learn more at <a href="http://www.monogoto.io/" target="_blank" rel="noopener">www.monogoto.io</a><h3>About Crossfire Media</h3><a href="http://www.crossfiremedia.com/" target="_blank" rel="noopener external noreferrer">Crossfire Media</a>, co-publishers of IoT Evolution, is an integrated marketing company with a core focus on future trends in technology. We service communities of interest with conferences, tradeshows, webinars and newsletters. Crossfire Media has a partnership with Technology Marketing Corporation (TMC) to produce events and websites related to disruptive technologies. Crossfire Media is a division of Crossfire Consulting, a full service Information Technology company based in New York. <h3>About TMC</h3>TMC provides global buyers with valuable insights to make informed tech decisions through our editorial platforms, live events, webinars, and online advertising. Leading vendors trust TMC, thought leadership, and our events for branding, thought leadership, and lead generation. Our live events, like the <a href="http://www.itexpo.com/" target="_blank" rel="noopener external noreferrer">ITEXPO #TECHSUPERSHOW</a>, deliver unmatched visibility, while our custom lead generation programs and webinars ensure a steady flow of sales opportunities. Display ads on trusted sites generate millions of impressions, boosting brand reputations. TMC offers a complete 360-degree marketing solution, from event management to content creation, driving SEO, branding, and marketing success. Learn more at <a href="http://www.tmcnet.com/" target="_blank" rel="external noopener noreferrer">www.tmcnet.com</a> and follow @tmcnet on <a href="https://www.facebook.com/tmcnetcom" target="_blank" rel="external noopener noreferrer">Facebook</a>, <a href="https://www.linkedin.com/company/tmc_60911/" target="_blank" rel="external noopener noreferrer">LinkedIn</a>, and <a href="https://twitter.com/tmcnet" target="_blank" rel="external noopener noreferrer">X</a>.For media inquiries, please contact:Carla Deisenroth SVP, Marketing <a target="_blank">carla@monogoto.io</a>

Monogoto Software-Defined Connectivity Platform recognized for innovation in scalable, secure IoT infrastructure

Monogoto Receives 2025 IoT Platforms Leadership Award from IoT Evolution World

<h2 id="isPasted">Reliability is a Design Discipline</h2>Much of semiconductor innovation is measured by what a device can do at its limits. SimpleChips measures innovation by what a system can be trusted to do over time.With high-voltage, electronics problems surface slowly through drift, nonlinearity, thermal behavior, or edge conditions that only appear after years in the field. In critical systems, from electrified powertrains to medical devices, these effects often outweigh peak performance. They determine whether a system remains predictable, serviceable, and safe long after it has been deployed.SimpleChips was built around this reality. One of the company’s focuses is on high-voltage mixed-signal ICs for environments where tolerance stackups, component aging, and system-level interactions define success or failure. Its work is shaped by applications where reliability is not a specification but a requirement, and where simplifying a system adds value by making it more reliable.That perspective is grounded in experience. It reflects decades of experience designing and manufacturing silicon for demanding environments, and a deliberate choice to work on problems few others are willing or able to address.<h2>Reliability Understanding Starts Where Things Broke</h2>This systems-oriented mindset is closely tied to the background of SimpleChips’ founder, CEO, and CTO, Alain R. Comeau (M. Sc.). Trained as a physicist at the University of Sherbrooke, Mr. Comeau began his career inside semiconductor fabrication facilities, working in yield engineering and process integration. Those early years left a lasting impression.Working in fabs meant tracing problems from observed symptoms back to their root causes. It meant seeing how small variations in process or design assumptions could propagate into field failures. Mr. Comeau often describes this as “detective work,” an exercise which forces engineers to look beyond nominal operation and focus on where systems break down. Over time, this experience built a global view of semiconductor behavior across design, manufacturing, and long-term reliability.That perspective, and the related successes attached to it, later carried into senior technical roles across multiple organizations (Mitel, EG&amp;G, Zarlink, Microsemi), where Mr. Comeau was responsible for resolving process and yield issues from design to production. The cumulative effect of that work was a clear understanding of how tightly yield and reliability are linked, especially at the moment a product enters service.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1768403654641-1768403654641.jpeg" width="604" height="404" class="fr-fic fr-dii" alt="Alain R. Comeau (M. Sc.), Founder, CEO, and CTO at SimpleChips (Credit: SimpleChips)" />Alain R. Comeau (M. Sc.), Founder, CEO, and CTO at SimpleChips (Credit: SimpleChips)<h2>Beyond the Design Kit: When Physics Defines the Real Limit</h2>SimpleChips operates on the premise that design and manufacturing decisions must be evaluated together. Technology selection is guided by a disciplined assessment of risk, performance, and cost across the full lifecycle of a device. Improving yield is viewed as a direct path to improving reliability, particularly at time zero.This way of thinking also directly impacts how SimpleChips approaches analog and mixed-signal designs. Many engineers work within the boundaries defined by standard process design kits. These kits provide structure and consistency while also defining what is considered feasible. SimpleChips routinely questions those boundaries when application requirements demand it.According to Mr. Comeau, the limitations are not always a limitation of physics. It is more sometimes the result of constraints imposed by tooling and preexisting established libraries. When required, the team extends the process’ components’ libraries by developing new resistive, capacitive, or transistor structures, guided by a clear understanding of the risks involved and supported by extensive internal libraries built over time.<h2>Simpler Architectures for Reliability</h2>SimpleChips’ devices are designed for use in EV powertrains, industrial systems, medical equipment, and aerospace applications. These systems operate across wide voltage and temperature ranges. They are also expected to perform consistently over decades.One example illustrates how this thinking translates into silicon. In developing a high-voltage monitor, SimpleChips needed a monolithic resistor network capable of maintaining an extremely good linearity of +/-0.1% from 0 to 1200 volt (or less than +/-0.60ppm/V). Mr. Comeau stated that such a unique component (p/n 18SCT005) allows designers to build accurate and compact high-voltage sensing networks without the use of bulky external components such as high-voltage resistors or strings of low-voltage resistors. He also acknowledged that while this innovation “would not win a Nobel Prize,” its value lies in its practicality.The 18SCT005 enables simpler system architectures that would otherwise require large networks of discrete components, each introducing additional tolerances and potential failure points. By integrating this behavior into silicon, SimpleChips simplifies high-voltage sensing and improves system predictability and compactness. Engineers evaluating these devices can reference detailed performance data published on the company’s website to assess suitability for their applications.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1768403872647-Screenshot+2026-01-14+151635.png" width="299" height="398" class="fr-fic fr-dii" alt="The 18SCT005 high-voltage precision monitor IC (Credit: SimpleChips)" />The 18SCT005 high-voltage precision monitor IC (Credit: SimpleChips)<h2>Solving the Near-Impossible and Delivering the Essential</h2>SimpleChips’ current product direction is rooted in a long history of custom ASIC development. Founded in 2002, the company initially focused on IP development and licensing before transitioning in 2015 to IC manufacturing and expanding operations from Canada to the United States. Over the years, SimpleChips has built a strong reputation in enabling custom solutions for end-of-life products and legacy systems. Several of these solutions (40+) were innovative enough to warrant successful patent applications. As a result of these strong credentials and reputation, SimpleChips can now count on some of the largest U.S. system manufacturers as returning Customers.These returning Customers demanded more than component-level expertise. They demanded system-level understanding. These “resurrected chips” simply have to be dropped into existing unchanged systems and work the very first time. Inputs, outputs, timing relationships, and environmental constraints all have to be considered. This work shaped the company’s ability to deliver digital, analog, and mixed-signal functionality within tightly constrained systems on-time.<h2>Good Information Leads to Good Decisions</h2>Certain patterns of information have emerged across products, and clear challenges have appeared repeatedly. As Mr. Comeau puts it, “good information leads to good decisions”. As a result of good information, better solutions have been created. Problems exist as a result of a chain of events such as “problemA = eventB and eventC.” If eventB is eliminated, then problemA can never happen. This rationale was fundamental in understanding root-causes behind some of the most complex problems SimpleChips has resolved.SimpleChips began to formalize these recurring solutions into standard products, building a product and technology portfolio grounded in real applications. The 18SCT005 reflects this evolution. It replaces tens or hundreds of discrete resistors with a single drop-in IC, reducing assembly complexity and minimizing potential failure points. These positive impacts are equally felt at the system level. Designs become easier to validate and maintain. This improves system reliability. The value becomes what is removed from the system rather than what is added.<h2>Engineering with Consequences</h2>The environments where SimpleChips’ devices operate leave little room for error. Some of these devices are used in medical systems implanted in the human body. One chip has been deployed in defibrillators for a decade, where its purpose is to restart the heart, without a single failure.Mr. Comeau has spoken candidly about the responsibility that comes with such applications. Failure in this context carries direct human consequences. This reality reinforces the company’s emphasis on rigor, accountability, and careful decision-making. Engineering choices are made with a clear understanding of where devices will be used and what is at stake.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1768403654763-1768403654763.jpeg" width="528" height="298" class="fr-fic fr-dii" alt="High-voltage monitoring in implantable cardiac devices like pacemakers (Credit: SimpleChips)" />High-voltage monitoring in implantable cardiac devices like pacemakers (Credit: SimpleChips)<h2>Redefining the “Company”</h2>SimpleChips operates a small international team of professionals. The cultural perspective, brought in by professionals coming from 8 different countries, is viewed as sources of technical breadth and innovation. Mr. Comeau often reflects on the meaning of the word “company” as meaning to “work in the company of others”. This definition considers Customers as colleagues for the purpose of a project. Customer interaction plays a defining role in how SimpleChips operates. The company prioritizes rapid response and treats communication as an engineering input. Customers are included in feedback loops, and their insights shape both architecture and product direction. Although syntactically incorrect, SimpleChips always capitalizes the “C” in “Customers” because Customers are the most important “working-in-company-of” team member and always drive business forward.<h2>Long-Term Engineering and Reliable Electrification</h2>From a business standpoint, SimpleChips maintains both custom and standard product lines. This dual structure provides financial stability and reduces sensitivity to market fluctuations. Custom projects continue to shape product development, while standard products expand the company’s reach. This balance enables long-term engineering investment. It allows the company to focus on solving difficult problems without being driven by short-term cycles.Against this backdrop of continuous investment, power management continues to drive the electronics landscape. Electrification across transportation, industries, and infrastructures (AI) are accelerating. SimpleChips operates as an enabler within this evolution, with precision, stability, and reliability as core priorities.Mr. Comeau often speaks about positive creativity, teamwork and the responsibility that comes with building technologies for critical systems. SimpleChips’ work reflects that outlook. It is shaped by experience, informed by real applications, and guided by a clear understanding of what is at stake and what is important (root-causes) when failure is not an option.To see detailed performance data and explore the SimpleChips product portfolio, visit <a href="https://www.simplechips.com/" target="_blank">simplechips.com</a>

How SimpleChips designs high-voltage silicon for systems that must perform reliably over decades?

SimpleChips: A Systems-First Approach to High-Voltage IC Design

Whether for AI-supported analyses, remote patient monitoring, or general process improvement in hospitals, telemedicine technology, the IoT and devices networked within it are transforming our healthcare. M2M SIM cards are an important component of telemedical networking applications.<h2>Modern Health Economy: M2M Applications in the Healthcare Sector</h2>Machine-to-machine communication (M2M) refers to the automated exchange of data between devices, among themselves or from a device to a central system. However, not only devices but also robots, vehicles, machines, and production systems can collect and share data thanks to M2M technologies and wireless communication.M2M applications in medical technology include, for example, ECG devices and emergency vehicles. These transmit data without human intervention so that information is available on another end device when required. M2M solutions are also increasingly being used in home care. These technologies transmit data from patient-measuring devices, for example, and thus support remote patient monitoring. Data is usually transmitted via the internet.Medical devices can be connected by cable or equipped with M2M SIM cards that enable wireless data transmission. The expansion of 5G will play a major role for M2M SIM cards for telemedicine applications in particular. With 5G, it will be possible to exchange large amounts of data in near real time.<h2>Advantages of M2M Medical Technology in Healthcare</h2>Regardless of whether the connection is made by cable or M2M SIM card. In medical technology, communication between devices has considerable advantages. Automated data exchange between machines is always faster, more effective, and less prone to error than manual transfer of information. For example, the networking of surgical instruments or devices in wards that would otherwise have to be monitored by personnel.However, telehealth is not always just about speed. Telemedicine applications have been driven primarily by situations in which the doctor or specialist and patient are physically separated. This includes residents of remote villages as well as space travel with astronauts in space and researchers on an expedition in the Arctic. Sensors that monitor selected bodily functions in implants or wearable patient devices also offer great potential.The values collected are, in turn, automatically transferred to patient databases. Other benefits that can be achieved through automation and digitalization through M2M applications in the healthcare sector include:Selected advantages of M2M for Telehealth at a glance:<ul><li>Accelerated emergency communication</li><li>Increased flexibility for medical staff</li><li>Locating patients in emergencies</li><li>More efficient organisation &amp; management, e.g. of free beds</li><li>Cost reductions through time savings</li><li>Faster access to patient data</li><li>Patient monitoring for more safety</li><li>Reduction of error sources through increased automation</li></ul><h2>More Flexibility through Telemedicine &amp; M2M Applications</h2>Reduced budgets and fewer, and fewer, urgently needed staff are unfortunately becoming increasingly commonplace in hospitals and care facilities. M2M-based medical technology enables medical and nursing staff to provide high-quality patient care remotely. This not only relieves the burden on specialist staff, but can also save time and money by optimizing processes.With eHealth and telemedicine, patients can learn to measure values such as pulse or blood pressure independently and forward this data directly to their doctor's system.The benefits of M2M communication in practice: the journey to the doctor or patient is saved. Patients enjoy a higher quality of life thanks to fewer stressful visits to the doctor and the minimization of long waiting times and journeys from rural areas. In the same way, medical staff are relieved and can work less overtime or concentrate on other patients.<h2>Data Transmission: M2M SIM Cards in Telemedicine</h2>Mobile communication between devices, apps, practice or hospital systems, and central offices or databases relies on different wireless technologies. For example, the company offers an M2M SIM card for telemedicine applications that enables devices at any location to transmit data securely.Their M2M SIM cards enable your devices to connect to the mobile network in order to transmit data automatically. Unlike smartphone SIM cards, for example, their M2M SIMs can use the mobile networks of different network operators nationwide and worldwide. It is therefore often referred to as a multi-network SIM or roaming SIM. As there are usually a large number of M2M SIM cards in use, they are managed via an online portal tailored to the requirements of M2M communication.You can use their comprehensive API to transfer data directly into your own systems!<h2>M2M SIM Cards for Telehealth: The Best Connection Everywhere with National Roaming</h2>Mobile communications for M2M telemedicine has the advantage that the technology is established, standardized and the infrastructure is available almost everywhere — and not just in Germany, but worldwide, across national borders. This nationwide availability of mobile communications is particularly important for sensors located on patients, for example, which are therefore constantly on the move.But not all mobile communications are the same. The Federal Network Agency has concluded that there is still a lot of catching up to do in terms of network coverage by individual network operators: only one of the major network operators' cell phone networks is available in 4G in around 7.2% of Germany. The risk of connection interruptions and failures is high there. If sensitive health data must be transmitted quickly, it becomes dangerous.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzY4MjYzMDkyNzMyLTE3NjgyNjMwOTI3MzIuanBlZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" width="602" height="317" class="fr-fic fr-dii" />The availability of the networks varies in individual federal states. In Bavaria, for example, network availability is worse than average: the network of at most one mobile network operator is available in 15.5% of the entire area. This patchy coverage shows the relevance of using appropriate M2M SIM cards for IoT and M2M projects in the healthcare sector. This is because they can use the mobile networks of different network operators.Their M2M SIM cards enable IoT devices to connect to the strongest network at a location. So it is not the network provider that counts, but only the network strength.<h2>The Right M2M SIM for Telemetry and Telemedicine</h2>Independence and flexibility are highly relevant in the medical field of M2M technology. Whether a patient is located in the countryside or lives in the middle of the city: in order to be able to react to changes in condition or emergencies in real time as far as possible, data must be reliably transmitted everywhere at all times. They offer the right IoT SIM cards for this, in combination with flexible M2M tariffs — always geared to the needs of your project.Their M2M SIM cards are available in all common SIM formats as well as industrial M2M SIMs and fixed eSIMs.<a href="https://en.whereversim.de/" target="_blank">Read More at the Wherever Sim Homepage! </a>

M2M SIM cards enable secure wireless connectivity for telemedicine and IoT healthcare devices, supporting remote patient monitoring, reliable data transfer, and flexible nationwide roaming.

M2M Communication in Healthcare: Wireless Connection via SIM Cards in Telemedicine

In an advance that could help ensure people are taking their medication on schedule, MIT engineers have designed a pill that can report when it has been swallowed.The new reporting system, which can be incorporated into existing pill capsules, contains a biodegradable radio frequency antenna. After it sends out the signal that the pill has been consumed, most components break down in the stomach while a tiny RF chip passes out of the body through the digestive tract.This type of system could be useful for monitoring transplant patients who need to take immunosuppressive drugs, or people with infections such as HIV or TB, who need treatment for an extended period of time, the researchers say.“The goal is to make sure that this helps people receive the therapy they need to help maximize their health,” says Giovanni Traverso, an associate professor of mechanical engineering at MIT, a gastroenterologist at Brigham and Women’s Hospital, and an associate member of the Broad Institute of MIT and Harvard.Traverso is the senior author of the new study, which <a href="https://www.nature.com/articles/s41467-025-67551-5" target="_blank">appears today in Nature Communications</a>. Mehmet Girayhan Say, an MIT research scientist, and Sean You, a former MIT postdoc, are the lead authors of the paper.<h2>A pill that communicates</h2><iframe width="640" height="360" src="https://www.youtube.com/embed/dn821xdp3EY?&amp;wmode=opaque&amp;rel=0" frameborder="0"> </iframe>Patients’ failure to take their medicine as prescribed is a major challenge that contributes to hundreds of thousands of preventable deaths and billions of dollars in health care costs annually.To make it easier for people to take their medication, Traverso’s lab has worked on <a href="https://news.mit.edu/2025/weekly-pill-schizophrenia-shows-promise-clinical-trials-0610" target="_blank">delivery capsules</a> that can remain in the digestive tract for days or weeks, releasing doses at predetermined times. However, this approach may not be compatible with all drugs.“We’ve developed systems that can stay in the body for a long time, and we know that those systems can improve adherence, but we also recognize that for certain medications, we can’t change the pill,” Traverso says. “The question becomes: What else can we do to help the person and help their health care providers ensure that they’re receiving the medication?”In their new study, the researchers focused on a strategy that would allow doctors to more closely monitor whether patients are taking their medication. Using radio frequency — a type of signal that can be easily detected from outside the body and is safe for humans — they designed a capsule that can communicate after the patient has swallowed it.There have been previous efforts to develop RF-based signaling devices for medication capsules, but those were all made from components that don’t break down easily in the body and would need to travel through the digestive system.To minimize the potential risk of any blockage of the GI tract, the MIT team decided to create an RF-based system that would be bioresorbable, meaning that it can be broken down and absorbed by the body. The antenna that sends out the RF signal is made from zinc, and it is embedded into a cellulose particle.“We chose these materials recognizing their very favorable safety profiles and also environmental compatibility,” Traverso says.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1768262332759-MIT-Medi-Adherence-02-press.jpg" style="width:100%px" class="fr-fic fr-dib" />Two photos show the gelatin-coated capsules (left) and the capsule without the coating (right). The capsule can be broken down and absorbed by the body. Credit: Courtesy of the researchersThe zinc-cellulose antenna is rolled up and placed inside a capsule along with the drug to be delivered. The outer layer of the capsule is made from gelatin coated with a layer of cellulose and either molybdenum or tungsten, which blocks any RF signal from being emitted.Once the capsule is swallowed, the coating breaks down, releasing the drug along with the RF antenna. The antenna can then pick up an RF signal sent from an external receiver and, working with a small RF chip, sends back a signal to confirm that the capsule was swallowed. This communication happens within 10 minutes of the pill being swallowed.The RF chip, which is about 400 by 400 micrometers, is an off-the-shelf chip that is not biodegradable and would need to be excreted through the digestive tract. All of the other components would break down in the stomach within a week.“The components are designed to break down over days using materials with well-established safety profiles, such as zinc and cellulose, which are already widely used in medicine,” Say says. “Our goal is to avoid long-term accumulation while enabling reliable confirmation that a pill was taken, and longer-term safety will continue to be evaluated as the technology moves toward clinical use.”<h2>Promoting adherence</h2>Tests in an animal model showed that the RF signal was successfully transmitted from inside the stomach and could be read by an external receiver at a distance up to 2 feet away. If developed for use in humans, the researchers envision designing a wearable device that could receive the signal and then transmit it to the patient’s health care team.The researchers now plan to do further preclinical studies and hope to soon test the system in humans. One patient population that could benefit greatly from this type of monitoring is people who have recently had organ transplants and need to take immunosuppressant drugs to make sure their body doesn’t reject the new organ.“We want to prioritize medications that, when non-adherence is present, could have a really detrimental effect for the individual,” Traverso says.Other populations that could benefit include people who have recently had a stent inserted and need to take medication to help prevent blockage of the stent, people with chronic infectious diseases such as tuberculosis, and people with neuropsychiatric disorders whose conditions may impair their ability to take their medication.

The outer layer of the capsule is made from gelatin coated with materials that block any radio frequency signal from being emitted (top left). Once the capsule is swallowed, the coating breaks down, releasing the drug along with the RF antenna (top right and bottom left). The materials are bioresorbable (bottom right). Credit: Courtesy of the researchers

MIT engineers designed capsules with biodegradable radio frequency antennas that can reveal when the pill has been swallowed.

Pills that communicate from the stomach could improve medication adherence

Before we step into 2026, we’re taking a moment to look back at everything we built, shipped, secured, optimized, and of course, connected in 2025.<h4>Connectivity Everywhere, and Then Some</h4>In 2025, our global footprint expanded faster than ever. We powered secure, reliable connectivity in 200+ countries, ensuring devices could go further and work smarter, no matter where they are. Across those countries, we saw an average of 5 million data points transmitted per month. From smart cities and precision agriculture, to fleet logistics and construction sites, our connectivity found its way into every corner of the world.<h4>A Big Year for Innovation</h4>Our Software-Defined Connectivity Cloud didn’t just scale, it shined! As a matter of fact, it took home two major industry awards, recognizing both our <a href="https://monogoto.io/2025/08/19/monogoto-receives-2025-iot-evolution-product-of-the-year-award/" target="_blank" rel="noopener">cutting-edge innovation</a> and <a href="https://monogoto.io/2025/11/17/monogoto-receives-2025-security-excellence-award-from-iot-evolution/" target="_blank" rel="noopener">built-in security</a>.Behind the scenes our team kept busy:<ul><li>Launching the <a href="https://www.linkedin.com/newsletters/connected-by-monogoto-7382062424666865664/" target="_blank" rel="noopener external noreferrer">Connected. by Monogoto</a> Newsletter</li><li>Hitting the road for 9 events</li><li>Speaking on 2 of panels</li><li><a href="https://www.youtube.com/watch?v=0sEwJC3gFKc&amp;list=PLOb9qX96-TT9NVHwzWC_MNMC60c-stFba" target="_blank" rel="noopener external noreferrer">Showcasing</a> Monogoto Edge, Private LTE, and IoT intelligence in motion</li></ul><a href="https://www.youtube.com/watch?v=7c75z7hDMsA" target="_blank" rel="noopener external noreferrer">Watch our 2025 Year in Review&gt;&gt;</a><h4>Looking Ahead to 2026</h4>It’s been a year of growth, innovation, and new possibilities here at Monogoto, and we couldn’t have done it without you. As we look ahead to 2026, we anticipate smarter networks, faster decisions, more visibility and more connectivity, everywhere.

Take a moment to look back at everything we built, shipped, secured, optimized, and of course, connected in 2025.

Monogoto Wrapped 2025: A Year of Smarter, Stronger, Everywhere Connectivity

Palo Alto, California – January 8, 2026 – OQ Technology, the Luxembourg-based 5G NTN satellite operator for NB-IoT and D2D, and Monogoto, a leading provider of cloud-based hybrid connectivity, today announced a strategic partnership at CES in Las Vegas, the world’s largest and most influential technology trade show. The collaboration adds OQ’s Low Earth Orbit (LEO) satellite constellation to Monogoto’s existing hybrid ecosystem, which already includes partnerships with GEO satellite networks, and creates one of the most comprehensive multi-layer connectivity offerings in the market.With this expansion, Monogoto’s customers can seamlessly connect devices across public and private cellular networks, Wi-Fi, GEO, and LEO satellites, all through a single SIM, unified IP addressing, and consistent APIs. This makes it easier than ever to maintain reliable connectivity for devices in remote, mobile, or intermittently connected environments.“OQ Technology’s mission is to extend cellular Internet of Things (IoT) and Direct-to-Device (D2D) beyond terrestrial boundaries,” said Omar Qaise, Founder &amp; CEO, OQ Technology. “By partnering with Monogoto, our LEO constellation becomes part of a truly hybrid connectivity platform, bringing satellite IoT to enterprises and operators worldwide by leveraging our valuable spectrum and landing rights.”The partnership integrates OQ’s reliable and active 3GPP-compliant 5G NTN NB-IoT service into Monogoto’s platform, extending coverage and reliability for industries such as energy, logistics, maritime, agriculture, and utilities that operate beyond the reach of terrestrial networks.“For the first time, LEO is joining our hybrid connectivity landscape,” said Maor Efrati, Co-founder &amp; CTO, Monogoto. “We believe LEO will drive a fundamental shift in global communications. Our mission is to do what we do best, lower the barriers to adoption and empower customers to explore and deploy LEO connectivity alongside their existing cellular, Wi-Fi, and GEO satellite networks.”<h2>About Monogoto</h2>Monogoto is a cloud-based connectivity provider offering secure, programmable, and global cellular connectivity for IoT, Private LTE, and 5G applications. Built on a zero-trust architecture and API-first design, Monogoto enables developers and enterprises to provision, control, and scale connectivity like software — across public networks, private infrastructure, and satellite links. With presence in 200+ countries, Monogoto powers next-generation applications across FinTech, supply chain &amp; logistics, smart utilities, manufacturing, agriculture and more. Learn more at <a href="http://www.monogoto.io/" target="_blank" rel="noopener">www.monogoto.io</a>.<h2>About OQ Technology</h2>OQ Technology is a Luxembourg-based satellite operator pioneering 5G Non-Terrestrial Network (NTN) connectivity through its direct-to-device (D2D) global satellite network. The company is the first in Europe to operate a LEO constellation dedicated to D2D services, using its 60 MHz of MSS S-band spectrum rights to deliver secure, seamless, and standards-based connectivity to unmodified smartphones and IoT devices.OQ Technology bridges the gap between terrestrial and space-based mobile networks, enabling real-time, 3GPP-compliant 5G NTN communications for governments, enterprises, and consumers worldwide.Learn more at <a href="https://www.oqtec.com/" target="_blank" rel="noopener external noreferrer">https://www.oqtec.com</a>.

Partnership adds Non-Terrestrial Network (NTN) coverage from OQ Technology to the Wi-Fi, and GEO satellite connectivity offerings from Monogoto

Monogoto Expands Hybrid Global Connectivity by Adding LEO Constellation from OQ Technology

<h2 id="isPasted"> </h2>Over the past decade, artificial intelligence has largely existed within digital interfaces, such as phones, computers, and smart displays, responding to prompts and executing isolated commands. As connected environments become more common, expectations for AI are changing. Users increasingly want assistants that operate continuously, adapt to routines, and interact with the physical world rather than remaining confined to screens.As smart homes enter the era of agentic AI, several structural limitations remain unresolved. Unstable latency in real-world networks undermines continuous decision-making. The absence of true long-term memory prevents assistants from learning household routines over time. And fragmented device orchestration means user intent rarely translates into coordinated, verifiable action across devices. Together, these gaps keep most smart assistants confined to isolated commands rather than end-to-end execution.It is within this context that Hey Tuya is built, on top of Tuya Smart’s Physical AI Engine (PAE), a foundational “driving system” designed to support AI hardware operating in complex, real-world environments. Rather than treating intelligence as a purely digital layer, PAE is intended to enable AI models to transition from reasoning on screens to acting reliably within physical spaces.<h2>Multi-Device Collaboration: Turning Intent into Action</h2>Hey Tuya is designed to become a comprehensive AI life assistant capable of sensing environments, learning user habits, and coordinating with physical devices. Unlike traditional AI assistants confined to individual devices, Hey Tuya aims to break the boundaries of screen-based interaction. Users will be able to summon their AI assistant from anywhere, whether through the Tuya App, smart speakers, control hubs, or even AI toys and wearables. It will enable multi-device collaboration, orchestrating smart devices and equipment to deliver a connected and intelligent experience throughout daily life.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1767972675503-96c7be29-266e-4afc-bc9e-c10d264da631.png" style="width:100%px" class="fr-fic fr-dib" />Hey Tuya is expected to feature short-term and long-term memory, designed to learn and adapt to users’ routines, environmental preferences, and habits. Rather than reacting solely to direct commands, the assistant is intended to anticipate needs, such as preparing the home environment ahead of arrival or adjusting settings based on learned habits, and enabling interactions that feel more continuous and contextual.<h2>Expanding Smart Life Beyond the Home</h2>Initially, AI assistants were designed to cater to specific environments, mostly confined to single-use applications such as answering questions, setting alarms, or playing music. However, with Hey Tuya, the platform seeks to break out of these limited boundaries and establish a fully connected environment that stretches beyond the home. While much of the focus remains on residential settings, the underlying architecture behind Hey Tuya can also extend into commercial spaces where coordinated device intelligence and automated decision-making are increasingly relevant. This wide applicability makes Hey Tuya a future-proof solution, capable of evolving alongside advancements in AIoT.<iframe id="6964da7f4fbcb9a97e6ac387" width="250" height="440" src="https://wevolver.com/iframe/specs/hey-tuya?iframe=true" frameborder="0" scrolling="no"></iframe> <h2>Full-Scenario Coverage: From Home to Office and Beyond</h2>Hey Tuya’s capabilities are designed to span diverse life scenarios, taking on different roles:<h3>Home Security Guardian</h3>Expected to provide round-the-clock security, with image recognition and retrieval, converting results into easy-to-understand voice or text alerts for clear safety status. This will enable households to feel safe and secure with minimal effort, as Hey Tuya continuously learns and adapts to each home’s unique characteristics, evolving to anticipate potential security risks.<h3>Energy-Saving Expert</h3>Aimed at monitoring and analysing energy consumption in real time, proactively suggesting strategies like turning off idle devices, shutting off appliances during weekend trips, or adjusting AC temperatures according to weather and seasonal changes, enabling users to save energy effortlessly. The AI will continuously optimize energy use based on a family’s routines, seasonal weather, and real-time environmental data, significantly reducing energy costs while maintaining optimal comfort.<h3>Health Coach</h3>Expected to focus on both physical and mental well-being, offering support for calorie recognition, tracking sleep and exercise data, and generating personalized health reports. These capabilities will be powered by cutting-edge AI models that analyse vast amounts of health-related data, enabling Hey Tuya to make personalised recommendations and track progress over time.<h3>Efficient Work Assistant</h3>Hey Tuya offers services like meeting minutes generation, real-time multilingual translation, content summarization, and mind mapping to enhance productivity. A live, work-focused assistant will help users streamline their professional lives, reduce workload stress, and improve efficiency, all while learning about their preferences and adapting to the evolving work landscape.<h3>Personal Life Secretary</h3>Expected to manage daily tasks such as reminders, holiday alerts, pet and plant care, and family health monitoring for an organized life. By integrating with personal calendars, smart appliances, and health monitoring systems, Hey Tuya can provide a comprehensive assistant that keeps daily tasks in check while learning and adapting to users' daily rhythms.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1767968719873-a9ff6969-adc0-46f5-a527-19b4883a9a52.png" style="width:100%px" class="fr-fic fr-dib" /><h2>Powered by Tuya’s Physical AI Engine</h2>Deploying AI in physical environments introduces challenges that do not exist in purely digital systems, including unstable connectivity, fragmented execution across devices, and the need for memory that persists beyond individual sessions. Tuya’s Physical AI Engine (PAE) is designed as a system-level response to these constraints, enabling AI systems to move from digital cognition to physical execution.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1768216551766-PAE+Architecture.jpg" style="width:100%px" class="fr-fic fr-dib" alt="PAE Architecture" />PAE ArchitectureOne fundamental requirement for agentic AI is long-term, individual memory. PAE’s OmniMem Individual Long-Term Memory Engine is designed to support a persistent and evolving memory system across real-world usage scenarios, allowing the assistant to build continuity over time rather than resetting with each interaction.Real-time execution presents another major bottleneck for Physical AI. PAE incorporates real-time communication engines, including AI-Device Real-Time Network (AD-RTN) and Tuya Real-Time Communication (T-RTC), to accelerate AI-device interactions and maintain stable performance across complex and variable network environments.Beyond individual device intelligence, Physical AI requires orchestration, meaning the ability to translate intent into coordinated action across systems. PAE’s Dynamic Orchestration Agent (DOA) is designed to support this layer, enabling AI agents to reason, plan, and execute actions across multiple devices rather than operating in isolation.By integrating an AI capability chain spanning conversational intelligence, vision-based perception, and IoT intelligence, PAE enables flexible and customizable model combinations suited to diverse scenarios. This approach prioritizes adaptability and execution over reliance on any single model or modality.<h2>Designing for Continuous, Environment-Aware Intelligence</h2>Recent demonstrations of Physical AI systems emphasize a move away from single-interface control toward continuous, environment-aware execution. In this model, AI operates quietly in the background, coordinating devices and responding to context rather than waiting for explicit commands.Hey Tuya reflects this design philosophy by focusing less on interaction mechanics and more on system-level intelligence, where sensing, reasoning, and action are tightly integrated across the physical environment.With this strategic rollout, Tuya ensures that Hey Tuya is ready to meet the varied demands of global users, addressing their needs for an AI life assistant that learns and evolves with them, integrating across devices, environments, and routines.

Hey Tuya showcases how agentic Physical AI evolves assistants from isolated commands into systems that learn context, coordinate devices, and act reliably across real-world environments.

From Isolated Commands to Agentic Systems: What Agentic Living Reveals About the Evolving AI Roles

Detecting cancer in the earliest stages could dramatically reduce cancer deaths because cancers are usually easier to treat when caught early. To help achieve that goal, MIT and Microsoft researchers are using artificial intelligence to design molecular sensors for early detection.The researchers developed an AI model to design peptides (short proteins) that are targeted by enzymes called proteases, which are overactive in cancer cells. Nanoparticles coated with these peptides can act as sensors that give off a signal if cancer-linked proteases are present anywhere in the body.Depending on which proteases are detected, doctors would be able to diagnose the particular type of cancer that is present. These signals could be detected using a simple urine test that could even be done at home.“We’re focused on ultra-sensitive detection in diseases like the early stages of cancer, when the tumor burden is small, or early on in recurrence after surgery,” says Sangeeta Bhatia, the John and Dorothy Wilson Professor of Health Sciences and Technology and of Electrical Engineering and Computer Science at MIT, and a member of MIT’s Koch Institute for Integrative Cancer Research and the Institute for Medical Engineering and Science (IMES).Bhatia and Ava Amini ’16, a principal researcher at Microsoft Research and a former graduate student in Bhatia’s lab, are the senior authors of the study, which <a href="https://www.nature.com/articles/s41467-025-67226-1" target="_blank">appears today in Nature Communications</a>. Carmen Martin-Alonso PhD ’23, a founding scientist at Amplifyer Bio, and Sarah Alamdari, a senior applied scientist at Microsoft Research, are the paper’s lead authors.<h2>Amplifying cancer signals</h2>More than a decade ago, Bhatia’s lab came up with the idea of using protease activity as a <a href="https://news.mit.edu/2012/noninvasive-diagnostics-for-cancer-1216" target="_blank">marker</a> of early cancer. The human genome encodes about 600 proteases, which are enzymes that can cut through other proteins, including structural proteins such as collagen. They are often overactive in cancer cells, as they help the cells escape their original locations by cutting through proteins of the extracellular matrix, which normally holds cells in place.The researchers’ idea was to coat nanoparticles with peptides that can be cleaved by a specific protease. These particles could then be ingested or inhaled. As they traveled through the body, if they encountered any cancer-linked proteases, the peptides on the particles would be cleaved.Those peptides would be secreted in the urine, where they could be detected using a paper strip similar to a pregnancy test strip. Measuring those signals would reveal the overactivity of proteases deep within the body.“We have been advancing the idea that if you can make a sensor out of these proteases and multiplex them, then you could find signatures of where these proteases were active in diseases. And since the peptide cleavage is an enzymatic process, it can really amplify a signal,” Bhatia says.The researchers have used this approach to demonstrate diagnostic sensors for <a href="https://news.mit.edu/2020/urine-sensor-test-detect-lung-tumors-0401" target="_blank">lung</a>, <a href="https://news.mit.edu/2017/new-technology-detect-tiny-ovarian-tumors-0410" target="_blank">ovarian</a>, and <a href="https://news.mit.edu/2014/a-paper-diagnostic-for-cancer-0224" target="_blank">colon</a> cancers.However, in those studies, the researchers used a trial-and-error process to identify peptides that would be cleaved by certain proteases. In most cases, the peptides they identified could be cleaved by more than one protease, which meant that the signals that were read could not be attributed to a specific enzyme.Nonetheless, using “multiplexed” arrays of many different peptides yielded distinctive sensor signatures that were diagnostic in animal models of many different types of cancer, even if the precise identity of the proteases responsible for the cleavage remained unknown.In their new study, the researchers moved beyond the traditional trial-and-error process by developing a novel AI system, named CleaveNet, to design peptide sequences that could be cleaved efficiently and specifically by target proteases of interest.Users can prompt CleaveNet with design criteria, and CleaveNet will generate candidate peptides likely to fit those criteria. In this way, CleaveNet enables users to tune the efficiency and specificity of peptides generated by the model, opening a path to improving the sensors’ diagnostic power.“If we know that a particular protease is really key to a certain cancer, and we can optimize the sensor to be highly sensitive and specific to that protease, then that gives us a great diagnostic signal,” Amini says. “We can leverage the power of computation to try to specifically optimize for these efficiency and selectivity metrics.”For a peptide that contains 10 amino acids, there are about 10 trillion possible combinations. Using AI to search that immense space allows for prediction, testing, and identification of useful sequences much faster than humans would be able to find them, while also considerably reducing experimental costs.<h2>Predicting enzyme activity</h2>To create CleaveNet, the researchers developed a protein language model to predict the amino acid sequences of peptides, analogous to how large language models can predict sequences of text. For the training data, they used publicly available data on about 20,000 peptides and their interactions with different proteases from a family known as matrix metalloproteinases (MMPs).Using these data, the researchers trained one model to generate peptide sequences that are predicted to be cleaved by proteases. These sequences could then be fed into another model that predicted how efficiently each peptide would be cleaved by any protease of interest.To demonstrate this approach, the researchers focused on a protease called MMP13, which cancer cells use to cut through collagen and help them metastasize from their original locations. Prompting CleaveNet with MMP13 as a target allowed the models to design peptides that could be cut by MMP13 with considerable selectivity and efficiency. This cleavage profile is particularly useful for diagnostic and therapeutic applications.“When we set the model up to generate sequences that would be efficient and selective for MMP13, it actually came up with peptides that had never been observed in training, and yet these novel sequences did turn out to be both efficient and selective,” Martin-Alonso says. “That was very exciting to see.”This kind of selectivity could help to reduce the number of different peptides needed to diagnose a given type of cancer, to identify novel biomarkers, and to provide insight into specific biological pathways for study and therapeutic testing, the researchers say.Bhatia’s lab is currently part of an ARPA-H funded project to create reporters for an at-home diagnostic kit that could potentially detect and distinguish between 30 different types of cancer, in early stages of disease, based on measurements of protease activity. These sensors could include detection of not only MMP-mediated cleavage, but other enzymes such as serine proteases and cysteine proteases.Peptides designed using CleaveNet could also be incorporated into cancer therapeutics such as antibody treatments. Using a specific peptide to attach a therapeutic such as a cytokine or small molecule drug to a targeting antibody could enable the medicine to be released only when the peptides are exposed to proteases in the tumor environment, improving efficacy and reducing side effects.Beyond direct applications in diagnostics and therapeutics, combining efforts from the ARPA-H work with this modeling framework could enable the creation of a comprehensive “protease activity atlas” that spans multiple protease classes and cancers. Such a resource could further accelerate research in early cancer detection, protease biology, and AI models for peptide design.

Nanoparticles coated with AI-generated peptides can act as sensors that signal if cancer-linked proteases are present in the body. Image: MIT News

Nanoparticles coated with molecular sensors could be used to develop at-home tests for many types of cancer.

AI-generated sensors open new paths for early cancer detection

Have you exercised today? How about training together with a robot?
Please use robots in a friendly and safe manner, and keep a safe distance.

Unitree Humanoid Robot Daily Training

Hospitals want more efficient and personalized care, but traditional technologies ( e.g., BLE, Wi-Fi, RFID) often lack the precision and reliability necessary to deliver it. To support a new generation of precision-aware patient care, many designers are turning toward ultra-wideband (UWB) technology as a new solution.UWB is highly sought after due to its unique ability to provide high-ranging and angular accuracy, robust security, and advanced sensing capabilities that surpass those of existing systems. Rather than simply offering precision, UWB excels at accurately estimating both the distance and direction between devices, which is simply unmatched by other short-range wireless technologies For these reasons, major enterprise vendors like Cisco and Juniper are already embedding UWB radios directly into next-generation Wi-Fi access points, enabling hospitals to deploy precision location services without standalone infrastructure. With these capabilities, UWB can enhance hospital operations with a multitude of new features and functionalities. <h2>Why Hospitals Need Precision-Aware Infrastructure?</h2>Time is of the essence in hospital care, as even a few minutes or seconds in care delivery can significantly impact a patient's health. Yet modern hospitals are also uniquely dynamic and high-density environments. Crowded wards, staff shortages, and equipment bottlenecks often create inefficiencies that can hinder care delivery. To organize and orchestrate, hospitals have long relied on location systems that track the movement of patients, staff, and equipment across facilities. These systems, often referred to as Real-Time Location Systems (RTLS), feed data into dashboards that help administrators manage patient flow, locate equipment, and monitor staff activity for both efficiency and safety.Current RTLS deployments typically operate using Wi-Fi triangulation, RFID tags, or BLE beacons. While useful, these methods usually provide accuracy only within a few meters. In tangible terms, hospital workers might be able to identify that an infusion pump is on a particular floor, but not which storage closet it occupies. Or they could confirm that a patient is in the right wing, but not which room. With limited granularity, location data is ineffective, causing delays, equipment misplacement, and potential safety risks.  <img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzY3NzgxNjkyMTA1LTE3Njc3ODE2OTIxMDUucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" width="624" height="293" class="fr-fic fr-dii" alt="UWB transmits ultra-short pulses across a wide spectrum to calculate time-of-flight with centimeter-level precision" />UWB transmits ultra-short pulses across a wide spectrum to calculate time-of-flight with centimeter-level precision<a href="https://www.qorvo.com/design-hub/blog/the-future-of-positioning-exploring-uwb-and-bluetooth-channel-sounding" target="_blank">UWB technology</a> resolves these issues with its design specifically for high-precision positioning and sensing. Where Wi-Fi, BLE, or RFID are designed for other applications and are retrofitted for location tracking, UWB was engineered from the start to measure distance and position with extreme accuracy. By transmitting ultra-short pulses across a wide spectrum, UWB calculates the time of flight between anchors and tags, thereby determining both range and angle with centimeter-level accuracy. This gives it the unique ability to provide fine-grained location data even in crowded, interference-heavy hospital environments. UWB also maintains accuracy in non-line-of-sight conditions, penetrating walls and equipment clutter that can confound other systems.<h2>Real-Time Patient &amp; Staff Location Tracking </h2>The ability to locate patients and staff with room-level granularity is not enough for modern healthcare. With its improved performance, UWB unlocks continuous, precise tracking that can distinguish between individuals in crowded spaces and provide exact location data within a few centimeters.Such a level of precise tracking direct impacts patient safety. In a dementia ward, for instance, badges equipped with UWB can issue alerts the moment a patient tries to exit a secure area. During an emergency code, the same system can identify the closest staff member and direct them straight to the patient’s location. Beyond safety, the technology also supports efficiency by allowing hospitals to allocate tasks in real time based on where staff members are and who is available.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzY3NzgxNjkyMTExLTE3Njc3ODE2OTIxMTEucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" width="624" height="463" class="fr-fic fr-dii" alt="UWB can be used to track patients and navigate visitors" />UWB can be used to track patients and navigate visitors<a href="https://www.qorvo.com/design-hub/blog/revolutionizing-european-football-with-uwb-micro-location" target="_blank">A useful parallel comes from professional sports</a>. During the COVID-19 pandemic, the European League of Football used UWB micro-location to track player proximity in real time and identify exposure chains. Hospitals can adapt the same tag-and-anchor model to monitor patient movement and run contact tracing during outbreaks.Ultimately, UWB-enhanced location precision translates into better patient outcomes. Faster response times, improved safety protocols, and optimized staff workflows simultaneously reduce risk and improve overall quality of care.<h2>Vital Signs Monitoring with UWB Radar Sensing </h2>Beyond tracking, UWB also functions as a radar sensing technology with medical applications. By transmitting ultra-short pulses and analyzing their reflections, UWB radar algorithms can detect the subtle chest movements caused by breathing or the micro-motions associated with a beating heart. As a result, UWB-based radar can measure respiration, heart rate, and movement without wearable devices.UWB broadens how hospitals monitor patients. Conventional systems use contact sensors such as pulse oximeters or chest straps that can irritate skin and restrict movement. UWB radar senses respiration and micro-motion through bedding and clothing, meaning patients can stay covered in isolation rooms and during post-operative recovery. Typical radar modes draw less than 10 mW, so engineers can easily integrate UWB into enterprise access points or compact bedside monitors.Applications include post-operative monitoring for apnea, where continuous observation without tethered devices improves patient comfort. In isolation wards, UWB reduces the need for repeated staff contact and minimizes exposure risks. And in neonatal and elderly care, fragile patients can be monitored without harm from adhesive-based monitors.UWB radar benefits are augmented when integrated with AI-driven analytics platforms, where predictive models can detect anomalies such as irregular breathing or early signs of distress. By combining continuous, contactless monitoring with predictive intelligence, hospitals can embrace more proactive care strategies.<h2>Asset &amp; Equipment Visibility </h2>For hospitals, accurate and real-time inventory is tantamount to patient safety and operational efficiency. For example, in the case of a patient emergency, mission-critical devices such as ventilators and defibrillators must be located within seconds. In general, hospitals lose significant time and resources each year due to misplaced or unavailable equipment.UWB’s ranging and angular accuracy solves this challenge. Devices tagged with UWB can be tracked across multiple floors and through structural barriers, giving staff immediate visibility into their exact location via centralized dashboards. The primary and most obvious benefit is improved patient outcomes due to faster emergency response times. Beyond patient health, however, hospitals also benefit from avoiding unnecessary over-purchasing. Where hospitals often purchase excess equipment to compensate for losses, UWB improves the utilization of existing assets and reduces redundant costs.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1767782465837-UWB+Radar+image+2.png" width="624" height="245" class="fr-fic fr-dii" style="width:802px" alt="UWB Radar detects object by simultaneously transmitting and receiving multipath signal reflections." />UWB Radar detects object by simultaneously transmitting and receiving multipath signal reflections.UWB also supports compliance and auditing by maintaining historical records of the movement of sensitive equipment and pharmaceuticals. Just as factories use UWB-enabled digital twins to model workflows and optimize logistics, hospitals can construct real-time digital models of their assets and equipment. Improved visibility ensures that resources are present and correctly deployed, resulting in higher operational efficiency and readiness.<h2>Indoor Navigation for Patients &amp; Visitors</h2>Navigating large hospitals is often compared to navigating airports. Long hallways, multiple wings, and complex layouts can leave patients and visitors disoriented. Missed appointments and late arrivals are common consequences.UWB-enabled navigation systems can change this experience by providing turn-by-turn indoor directions with accuracy down to the room level. Where Wi-Fi triangulation only provides approximate locations, UWB can directly guide a patient to the correct department or waiting area. Visitors can locate ICU rooms, labs, or pharmacies without confusion, and staff can optimize routes for transporting patients or retrieving equipment. Hospitals are also beginning to deploy autonomous mobile robots (AMRs) for the delivery of medications, lines, and supplies, and UWB can be used to enhance the navigation accuracy. This reduces human workload and guarantees timely deliveries in complex facilities.If device compatibility is a concern, operators can combine Wi-Fi and UWB within the same ecosystem rather than choosing one or the other. In a hybrid approach, Wi-Fi handles broad coverage and basic connectivity, and UWB supplies the fine-grained positioning layer. A patient’s phone, for example, might use Wi-Fi to connect to the hospital’s digital navigation app. But once indoors, UWB anchors can refine that position to the exact hallway or room. Staff tablets could similarly rely on Wi-Fi for communications but tap into UWB data for precise equipment retrieval or patient handoff. By layering the two technologies, hospitals marry the universal reach of Wi-Fi with the centimeter-level precision of UWB. The result is fewer missed appointments, smoother patient navigation, and higher throughput across operations.<h2>Infrastructure &amp; Ecosystem Readiness </h2>For hospitals considering adoption, one of the biggest enablers is that UWB is no longer a standalone technology requiring a separate network. <a href="https://community.cisco.com/t5/wireless-mobility-blogs/precision-meets-scale-cisco-brings-uwb-to-indoor-location/ba-p/5295608" target="_blank">Cisco’s Wi-Fi 7 Catalyst</a> access points and <a href="https://www.juniper.net/documentation/us/en/software/mist/mist-wireless/topics/topic-map/wireless-network-overview.html" target="_blank">Juniper’s Mist ecosystem</a> are early examples of this integration, where UWB radios are co-located with Wi-Fi radios to deliver precision location capabilities at the infrastructure level. This level of broad adoption removes deployment barriers by allowing hospitals to extend precision location services on top of their existing wireless networks. It also means that hospitals can adopt UWB without the prohibitive costs and barriers typically associated with integrating new enterprise technologies into existing IT and OT.The broader ecosystem also already supports UWB adoption, as smartphones, wearables, and IoT devices are already shipping with embedded UWB chips, such as Apple’s U2 and equivalent Android platforms. As the technology is more widely adopted by hardware giants, costs will decrease, and adoption will be more feasible.  One last reason to adopt UWB is the enhanced security levels it uniquely offers. Specifically, UWB incorporates encrypted timestamp sequences (STS) and AES-based cryptography to protect against spoofing and relay attacks. These built-in features make UWB compliant with tight healthcare security requirements and give hospitals confidence in deploying the technology at scale.<h2>Conclusion </h2>Legacy RTLS and monitoring systems are no longer sufficient for the accuracy and responsiveness demanded by modern healthcare. UWB is a ready-to-deploy technology that fills this gap with centimeter-level location ranging accuracy, radar-based vital sign monitoring, and secure, scalable infrastructure.As UWB integrates into enterprise Wi-Fi access points, hospitals can create more precision-aware environments that can anticipate needs, minimize risks, and deliver safer, faster, and more personalized outcomes.The next-generation of patient care will depend on precision. With UWB, hospitals are finally equipped to deliver it.

Ultra wideband enables centimetre-accurate location radar sensing and secure RTLS in hospitals, improving patient safety, staff efficiency, asset tracking, and navigation.

UWB in the Enterprise Hospital - Enabling Precision-Aware Patient Care

High-strength aluminum alloys are critical for making cars and planes more lightweight and fuel-efficient, but manufacturers struggle to process them consistently. A University of Michigan-led team, in collaboration with General Motors Research &amp; Development,  developed a computationally efficient, multiscale model that offers a predictive pathway to tune the chemistry and cooling of high strength aluminum alloys for optimized performance.The ability to design less intensive manufacturing processes is critical to bringing lightweight aluminum alloys into broader use in automotive applications.“This framework both improves our understanding of high-strength aluminum alloys and opens the door to modeling complex behaviors in many advanced alloys used for lightweight and sustainable manufacturing,” said <a href="https://midas.umich.edu/directory/liang-qi/">Liang Qi</a>, an associate professor of materials science and engineering and U-M and corresponding author of the study published in npj Computational Materials.Because of the potential energy and safety advantages in expanding the use of lightweight metals, the study was funded by the National Science Foundation in collaboration with General Motors Research &amp; Development.“Ultimately, it gives researchers and engineers a powerful tool to design better materials more efficiently,” added Qi.<h2 id="isPasted">Simplifying processing for car manufacturing</h2>The research team focused on 7000-series aluminum-magnesium-zinc (Al-Mg-Zn) alloys which were originally developed for aerospace applications. Tiny particles of magnesium and zinc, which substitute into the aluminum matrix, form precipitates that reinforce the aluminum to create exceptional strength at low weights.However, the alloy has been limited to aerospace applications because its strengthening process—especially the natural aging step that occurs at room temperature—is highly unpredictable. Aerospace manufacturers avoid natural aging using costly, specialized processing steps like high-temperature deformation or low-temperature storage. While an effective workaround, these methods are unfavorable for large-scale vehicle manufacturing.To reduce these costs and expand the material’s integration in autobody structures, the research team aims to understand the hardening processes on the microscale during natural aging—when the metal sits at room temperature over a period of time. “Our work helps engineers better understand how tiny defects and atomic movements affect the strengthening of advanced aluminum alloys, especially natural aging of Al-Mg-Zn alloys. The results of the research provide a pathway to understanding how to improve the formability of these alloys for automotive applications,” said Louis G. Hector Jr., a senior technical fellow at General Motors Research &amp; Development and co-author of the study.<h2>Processing strong, lightweight alloys</h2>To strengthen Al-Mg-Zn alloys, the material is first heated to around 500 °C to ensure that all the zinc and magnesium atoms dissolve into the aluminum. Then, the alloy is rapidly cooled, a process known as quenching, and aged at room temperature for about one to three days.The temperature drops so quickly during quenching that atoms do not have time to rearrange themselves to match the new equilibrium. This traps vacancies, which are missing atoms in the metal’s crystal structure. Trapped vacancies influence how fast clusters of zinc and magnesium atoms, called solutes, move during cooling steps. This, in turn, shapes where precipitates form and the resulting strength of the material.<h2>Modeling vacancy diffusion in multicomponent alloys</h2>Modeling vacancies and solute diffusion can help optimize performance, but this is extremely computationally expensive because it happens at the atomic scale over several days. At the atomic scale, vacancies hop between neighboring atoms and each hop depends on the local chemical environment. In alloys with three components, such as Al-Mg-Zn alloys, the number of possible solute configurations is enormous, which makes conventional atomistic simulations slow and unable to reach the timescales relevant for natural aging. Further, clusters can grow and evolve over seconds to hours, far beyond the reach of standard kinetic Monte Carlo methods for atomistic simulations.To sidestep these issues, the researchers developed a multiscale framework that links atomic behavior to long-term aging kinetics.A Markov chain model describes how long vacancies remain trapped inside clusters. Instead of modeling every atom, the model treats vacancy motion like a guided random walk through an energy landscape. These trapping behaviors are then fed into a mesoscale cluster-dynamics model that predicts how clusters evolve over hours of natural aging.“We can now simulate hours or even days of material aging in just minutes, whereas traditional methods might struggle to simulate even a few seconds of real-time precipitate evolution,” said <a href="https://www.linkedin.com/in/zhucongx/" target="_blank" rel="noreferrer noopener">Zhucong Xi</a>, a postdoctoral fellow of materials science and engineering at U-M and lead author of the study.<h2>Quench rate leaves a vacancy fingerprint</h2>The multiscale framework found that quench rate leaves a long-lived “vacancy fingerprint” that controls natural aging.Faster cooling leaves more mobile vacancies in the aluminum matrix, resulting in faster solute diffusion and cluster kinetics during natural aging. Slower cooling allows larger precipitates to form during quenching, which trap vacancies and extend the natural aging time frame. The model accurately predicts natural-aging behavior over days and shows how alloy chemistry, cooling and aging can be tuned to control the evolution of precipitates and, ultimately, the mechanical properties of aluminum alloys.“By capturing vacancy–cluster interactions across length and time scales, we can now predict aging behavior that previously required intensive trial-and-error experimentation,” said Qi.This research was funded by the National Science Foundation Grant Opportunities for Academic Liaison with Industry (190542).Solute clusters kinetics during quenching and aging were observed at the <a href="https://mc2.engin.umich.edu/" target="_blank" rel="noreferrer noopener">Michigan Center for Materials Characterization</a>, which is operated and maintained with support from indirect cost allocations in federal grants.

A new multiscale materials modeling framework predicts microstructures to control the final strength of lightweight aluminum alloys for fuel-efficient vehicles.

Model predicts best cooling and aging regimen to form strong alloys

Why did humans evolve the eyes we have today?While scientists can’t go back in time to study the environmental pressures that shaped the evolution of the diverse vision systems that exist in nature, a new computational framework developed by MIT researchers allows them to explore this evolution in artificial intelligence agents.The framework they developed, in which embodied AI agents evolve eyes and learn to see over many generations, is like a “scientific sandbox” that allows researchers to recreate different evolutionary trees. The user does this by changing the structure of the world and the tasks AI agents complete, such as finding food or telling objects apart.This allows them to study why one animal may have evolved simple, light-sensitive patches as eyes, while another has complex, camera-type eyes.The researchers’ experiments with this framework showcase how tasks drove eye evolution in the agents. For instance, they found that navigation tasks often led to the evolution of compound eyes with many individual units, like the eyes of insects and crustaceans.On the other hand, if agents focused on object discrimination, they were more likely to evolve camera-type eyes with irises and retinas.This framework could enable scientists to probe “what-if” questions about vision systems that are difficult to study experimentally. It could also guide the design of novel sensors and cameras for robots, drones, and wearable devices that balance performance with real-world constraints like energy efficiency and manufacturability.“While we can never go back and figure out every detail of how evolution took place, in this work we’ve created an environment where we can, in a sense, recreate evolution and probe the environment in all these different ways. This method of doing science opens to the door to a lot of possibilities,” says Kushagra Tiwary, a graduate student at the MIT Media Lab and co-lead author of a paper on this research.He is joined on the paper by co-lead author and fellow graduate student Aaron Young; graduate student Tzofi Klinghoffer; former postdoc Akshat Dave, who is now an assistant professor at Stony Brook University; Tomaso Poggio, the Eugene McDermott Professor in the Department of Brain and Cognitive Sciences, an investigator in the McGovern Institute, and co-director of the Center for Brains, Minds, and Machines; co-senior authors Brian Cheung, a postdoc in the  Center for Brains, Minds, and Machines and an incoming assistant professor at the University of California San Francisco; and Ramesh Raskar, associate professor of media arts and sciences and leader of the Camera Culture Group at MIT; as well as others at Rice University and Lund University. The research <a href="https://doi.org/10.1126/sciadv.ady2888" target="_blank">appears today in Science Advances</a>.<h2>Building a scientific sandbox</h2>The paper began as a conversation among the researchers about discovering new vision systems that could be useful in different fields, like robotics. To test their “what-if” questions, the researchers decided to <a href="https://mit-genai.pubpub.org/pub/bcfcb6lu/release/3" target="_blank">use AI to explore the many evolutionary possibilities</a>.“What-if questions inspired me when I was growing up to study science. With AI, we have a unique opportunity to create these embodied agents that allow us to ask the kinds of questions that would usually be impossible to answer,” Tiwary says.To build this evolutionary sandbox, the researchers took all the elements of a camera, like the sensors, lenses, apertures, and processors, and converted them into parameters that an embodied AI agent could learn.They used those building blocks as the starting point for an algorithmic learning mechanism an agent would use as it evolved eyes over time.“We couldn’t simulate the entire universe atom-by-atom. It was challenging to determine which ingredients we needed, which ingredients we didn’t need, and how to allocate resources over those different elements,” Cheung says.In their framework, this evolutionary algorithm can choose which elements to evolve based on the constraints of the environment and the task of the agent.Each environment has a single task, such as navigation, food identification, or prey tracking, designed to mimic real visual tasks animals must overcome to survive. The agents start with a single photoreceptor that looks out at the world and an associated neural network model that processes visual information.Then, over each agent’s lifetime, it is trained using reinforcement learning, a trial-and-error technique where the agent is rewarded for accomplishing the goal of its task. The environment also incorporates constraints, like a certain number of pixels for an agent’s visual sensors.“These constraints drive the design process, the same way we have physical constraints in our world, like the physics of light, that have driven the design of our own eyes,” Tiwary says.Over many generations, agents evolve different elements of vision systems that maximize rewards.Their framework uses a genetic encoding mechanism to computationally mimic evolution, where individual genes mutate to control an agent’s development.For instance, morphological genes capture how the agent views the environment and control eye placement; optical genes determine how the eye interacts with light and dictate the number of photoreceptors; and neural genes control the learning capacity of the agents.<h2>Testing hypotheses</h2>When the researchers set up experiments in this framework, they found that tasks had a major influence on the vision systems the agents evolved.For instance, agents that were focused on navigation tasks developed eyes designed to maximize spatial awareness through low-resolution sensing, while agents tasked with detecting objects developed eyes focused more on frontal acuity, rather than peripheral vision.Another experiment indicated that a bigger brain isn’t always better when it comes to processing visual information. Only so much visual information can go into the system at a time, based on physical constraints like the number of photoreceptors in the eyes.“At some point a bigger brain doesn’t help the agents at all, and in nature that would be a waste of resources,” Cheung says.In the future, the researchers want to use this simulator to explore the best vision systems for specific applications, which could help scientists develop task-specific sensors and cameras. They also want to integrate LLMs into their framework to make it easier for users to ask “what-if” questions and study additional possibilities.“There’s a real benefit that comes from asking questions in a more imaginative way. I hope this inspires others to create larger frameworks, where instead of focusing on narrow questions that cover a specific area, they are looking to answer questions with a much wider scope,” Cheung says.This work was supported, in part, by the Center for Brains, Minds, and Machines and the Defense Advanced Research Projects Agency (DARPA) Mathematics for the Discovery of Algorithms and Architectures (DIAL) program.

Researchers developed a computational framework that enables them to explore and probe the evolution of vision systems over millions of years using embodied AI agents. Image: iStock

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