In the fictional nation of Beryllia, the 2026 World Chalice Games were set to begin as the country faced an unrelenting heat wave. The grid, already under strain from the circumstances, was dealt a further blow when a coordinated set of attacks including vandalism, drone, and ballistic attacks by an adversary, Crimsonia, crippled the grid’s physical infrastructure.
This scenario, inspired by the upcoming 2026 World Cup and the 2028 Olympic Games in Los Angeles, was an exercise in studying how utilities can prevent and mitigate, among other dangers, physical attacks on power grids. Called GridEx, the exercise was hosted by the Electricity Information Sharing and Analysis Center (E-ISAC) from 18 to 20 November, 2025, and was described in a report released on 2 March. GridEx has been held every two years since 2011.
“We know that threat actors look to exploit certain circumstances,” says Michael Ball, CEO of E-ISAC, which is a program of the North American Electric Reliability Corporation (NERC), about designing the Beryllia scenario. “The Chalice Games became a good example of how we could build a scenario around a threat actor.”
Physical attacks on the grid are rising in the U.S., and GridEx attendance was up in November as utilities grapple with how to prevent and mitigate attacks. Participation in the exercise was at its highest level since 2019, according to the new report. Given the number of organizations present, GridEx estimates that more than 28,000 individual players participated, including utility workers and government partners, an all-time high since the exercise began.
Rising Physical Threats to Power Grids
The U.S. and Canadian grids face growing security issues from physical threats, including vandalism, assault of utility workers, intrusion of property, and theft of components, like copper wiring. NERC’s 2025 E-ISAC end of year report cites more than 3,500 physical security breaches that calendar year, about 3 percent of which disrupted electricity. That’s up from 2,800 events cited in the 2023 report (3 percent of those also resulted in electricity disruptions). Yet despite a number of recent high-profile attacks in the U.S., physical attacks on the grid are happening worldwide.
“They’re not uniquely a U.S. thing,” says Danielle Russo, executive director of the Center for Grid Security at Securing America’s Future Energy, a nonpartisan organization focused on advancing national energy security. Russo says that while attacks are common in places like Ukraine, they’re not limited to wartime scenarios. “Other countries that are not experiencing direct conflict are experiencing increasing amounts of physical attacks on their energy infrastructure,” she says. Take Germany for example: On 3 January, an arson attack by left-wing activists in Berlin caused a five-day blackout impacting 45,000 households. That comes after a suspected arson attack on two pylons in September 2025 left 50,000 Berlin households without power. Some German officials cite domestic extremism and fears of Russian sabotage in recent years as reasons for heightened security concerns over critical infrastructure.
The uptick in attacks on the U.S. grid has been anchored by a number of incidents in recent years. In December 2025, an engineer in San Jose, California was sentenced to 10 years in prison for bombing electric transformers in 2022 and 2023. A Tennessee man was arrested in November 2024 for attempting to attack a Nashville substation using a drone armed with explosives. And in 2023, a neo-Nazi leader was among two arrested in a plot to attack five substations around Baltimore with firearms, part of an increasing trend in white supremacist groups planning to attack the U.S. energy sector.
“Since [E-ISAC] started publishing data back in 2016, we’ve seen a large and consistent increase in the number of reported physical security incidents per year,” says Michael Coe, the vice president of physical and cyber security programs at the American Public Power Association, a trade group that works with E-ISAC to plan GridEx. While not all data is publicly available, Coe says there’s been a “tenfold” increase over the past decade in the number of reported physical attacks on the grid.
Drone Attacks: A Grid Security Challenge
During the fictional World Chalice Games scenario, drone attacks destroyed Beryllia’s substation equipment, highlighting a threat that’s gained traction as more drones enter the airspace.
“The question we get all the time is, how do you tell if it’s a bad actor, or if it’s a 12-year-old kid that got the drone for their birthday?” says Erika Willis, the program manager for the substations team at the Electric Power Research Institute (EPRI).
One strategy to track and alert utilities to potential threats such as drones is called sensor fusion. The system includes a pan-tilt-zoom camera capable of 360-degree motion mounted on top of a tripod or pole with four installed radars. The radars combine with the camera for a dual system that can track drones even if they’re obstructed from view, says Willis. For instance, if a nearby drone flies behind a tree, hidden from the camera, the radars will still pick up on it. The technology is currently being tested at EPRI’s labs in Charlotte, North Carolina and Lenox, Massachusetts.
EPRI is also exploring how robotics and AI can improve security systems, Willis says. One approach involves integrating AI analysis into robotic technology already surveilling substation perimeters. Using AI can improve detection of break-ins and damage to fencing around substations, Willis says. “As opposed to a human having to go through 200 images of a fence, you can have the AI overlays do some of those algorithms…If the robot has done the inspection of the substation 100 times, it can then relay to you that there’s an anomaly,” Willis says.
Prisma Photonics deploys fiber sensing technology that uses reflected optical signals to detect perturbations from vehicles and other sources near underground fiber cable.Prisma Photonics
Already, a number of utilities in the U.S. are using AI integrations in their security and monitoring processes. That’s thanks in part to the Tel Aviv, Israel-based Prisma Photonics, a software company that launched in 2017 and has since deployed its fiber sensing technology across thousands of miles of transmission infrastructure in the U.S., Canada, Europe, and Israel. A file-cabinet-sized unit plugs into a substation and sends light pulses down existing fiber optic cables 30 miles in each direction. As the pulses travel down the cables, a tiny fraction of the light is reflected back to the substation unit. An AI model processes the results and can classify events based on patterns in the optical signal as a result of perturbations happening around the fiber cable.
“If we identify an event that we don’t have a classification for, and we get a feedback from a customer saying, ‘oh, this was a car crash,’ then we can classify that in the model to say this is actually what happened,” says Tiffany Menhorn, Prisma Photonics’ vice president of North America.
As preparations get underway for the ninth GridEx in 2027, Ball says participation in the exercises alone isn’t enough to bolster grid security. Instead, he wants utilities to take what they learn from the training and apply it in their own operations. “It’s the action of doing it, versus our statistic of saying, ‘here’s what our growth was.’ That growth should relate to the readiness and capability of the industry.”
I changed the tense on this because the subsequent sentences use past tense. It seemed weird to switch from present tense in the first sentence to past tense in the rest of the paragraph, but I could be mistaken.
A trade association of cloud service providers (CSPs) filed an antitrust complaint today with the European Union’s European Commission (EC) over Broadcom's shuttering of VMware’s CSP partner program this year.
There are even fewer CSP partners working with VMware today. Broadcom introduced a requirement that CSP partners operate at least 3,500 cores, rendering hundreds of CSPs ineligible for partnership. Before Broadcom bought VMware, the virtualization company had over 4,000 CSP partners, per a February 2024 report from The Register. Today, VMware reportedly has 19 CSP partners in the US and about nine in the United Kingdom, The Register reported.
A growing number of Nigerian companies are turning to kit-based assembly to bring electric vehicles to market in Africa. Lagos-basedSaglev Micromobility Nigeriarecently partnered withDongfeng Motor Corporation, in Wuhan, China, to assemble 18-seat electric passenger vans from imported kits.
Kit-based assembly allows Nigerian firms to reduce costs, create jobs, and develop local technical expertise—key steps toward expanding EV access. Fully assembled and imported EVs face high tariffs that put them out of reach for many African consumers, whereas kit-based approaches make electric mobility more affordable today. Saglev’s initiative reflects a broader trend: CIG Motors, NEV Electric, and regional players in Cote D’Ivoire, Ghana, and Kenya are also leveraging imported kits to build local EV ecosystems, signaling that parts of West Africa are intent on catching up with global electrification efforts.
Expanding the Local EV Ecosystem
CIG Motors operates a kit-assembly plant in Lagos producing vehicles from Chinese automakers GAC Motor and Wuling Motors. These vehicles include the Wuling Bingo, a compact five-door electric hatchback, and the Hongguang Mini EV Macaron, a microcar with roughly 200 kilometers of range aimed at ride-share operators looking for ultralow-cost urban transport. NEV Electric focuses on electric buses and three-wheelers for urban transit and last-mile delivery.
Saglev’s CEO, Olu Faleye, emphasizes that Nigeria’s EV transition addresses both practical economic needs in addition to environmental goals. Beyond passenger transport, electric vehicles could help reduce one of Nigeria’s persistent agricultural challenges: post-harvest spoilage. Nigeria loses an estimated 30–40 million tonnes of food annually because of weak logistics and limited refrigeration infrastructure, according to the Organization for Technology Advancement of Cold Chain in West Africa.
Electric vans, mini-trucks, and three-wheel cargo vehicles could help close this gap because their batteries can power refrigeration systems during transport without relying on costly diesel fuel. As EV adoption grows and charging infrastructure expands, temperature-controlled transport could become more affordable, reducing spoilage, improving farmer incomes, and helping stabilize food supplies, the organization says.
“I don’t believe that the promised land is making a fully built EV on the ground here.”
–Olu Faleye, Saglev CEO
Beyond Nigeria, Mombasa, Kenya–based Associated Vehicle Assemblers has begun assembling electric taxis and minibuses from imported kits, and Ghana’s government is spurring kit-car assembly there under its national Automotive Development Plan. In Ghana, assemblers benefit from import-duty exemptions on kits and equipment, corporate tax breaks, and access to industrial infrastructure. Saglev is already availing itself of those benefits, at its kit-assembly plant in Accra. The company says it also plans to expand its assembly operations to Cote D’Ivoire.
Many early EV adopters therefore charge vehicles using gasoline or diesel generators. Faleye notes that Nigerians have long relied on such workarounds and expects fossil fuels to remain part of the EV charging equation for the foreseeable future—at least until falling costs for solar panels and battery storage make cleaner charging viable.
He acknowledges that charging EVs using hydrocarbons is fraught from an environmental perspective, but he points out that the practice at least brings other benefits of EVs, including lower maintenance costs and the EVs’ synergies with refrigeration and transportation logistics. And he points to a 2020 peer-reviewed study in the journal Environmental and Climate Technologies that compared the overall efficiency of internal combustion vehicles and electric vehicles across the full well-to-wheel energy chain. The study’s conclusion: Even after accounting for conversion losses, generating electricity with a diesel or gasoline generator to power an electric vehicle can remain just as efficient overall as burning the same fuel directly in a vehicle’s internal combustion engine.
Scalable EV Adoption in Nigeria
The approach taken by Saglev and other Nigerian kit-car builders shows how local assembly can advance EV adoption even where infrastructure remains unreliable. By starting with kits, companies can deploy practical electric mobility solutions now while building the supply chains and technical expertise needed for more resource-intensive localized production.
Still, when asked whether Saglev plans to eventually move beyond kit assembly to independent design and manufacturing of EVs, Faleye calls such a move impractical.
“I don’t believe that the promised land is making a fully built EV on the ground here,” he says. “For me to do efficient vehicle manufacturing, I’d need a lot of robotics and 3D printing. That expense is unnecessary—it would just increase costs and make EVs more expensive.”
In a country where electricity can disappear for days, Nigeria’s kit-based EV strategy highlights a practical truth: incremental progress and ingenuity may matter more than perfect infrastructure. For Saglev, every kit-based vehicle rolling off the line is not just a van or bus—it’s a step toward an EV ecosystem that works for Nigeria’s realities today.
One morning in May 2019, a cardiac surgeon stepped into the operating room at Boston Children’s Hospital more prepared than ever before to perform a high-risk procedure to rebuild a child’s heart. The surgeon was experienced, but he had an additional advantage: He had already performed the procedure on this child dozens of times—virtually. He knew exactly what to do before the first cut was made. Even more important, he knew which strategies would provide the best possible outcome for the child whose life was in his hands.
How was this possible? Over the prior weeks, the hospital’s surgical and cardio-engineering teams had come together to build a fully functioning model of the child’s heart and surrounding vascular system from MRI and CT scans. They began by carefully converting the medical imaging into a 3D model, then used physics to bring the 3D heart to life, creating a dynamic digital replica of the patient’s physiology. The mock-up reproduced this particular heart’s unique behavior, including details of blood flow, pressure differentials, and muscle-tissue stresses.
This type of model, known as a virtual twin, can do more than identify medical problems—it can provide detailed diagnostic insights. In Boston, the team used the model to predict how the child’s heart would respond to any cut or stitch, allowing the surgeon to test many strategies to find the best one for this patient’s exact anatomy.
That day, the stakes were high. With the patient’s unique condition—a heart defect in which large holes between the atria and ventricles were causing blood to flow between all four chambers—there was no manual or textbook to fully guide the doctors. The condition strains the lungs, so the doctors planned an open-heart surgery to reroute deoxygenated blood from the lower body directly to the lungs, bypassing the heart. Typically with this kind of surgery, decisions would be made on the fly, under demanding conditions, and with high uncertainty. But in this case, the plan had been tested in advance, and the entire team had rehearsed it before the first incision. The surgery was a complete success.
Such procedures have become routine at the Boston hospital. Since that first patient, nearly 2,000 procedures have been guided by virtual-twin modeling. This is the power of the technology behind the Living Heart Project, which I launched in 2014, five years before that first procedure. The project started as an exploratory initiative to see if modeling the human heart was possible. Now with more than 150 member organizations across 28 countries, the project includes dozens of multidisciplinary teams that regularly use multiscale virtual twins of the heart and other vital organs.
This technology is reshaping how we understand and treat the human body. To reach this transformative moment, we had to solve a fundamental challenge: building a digital heart accurate enough—and trustworthy enough—to guide real clinical decisions.
A father’s concern
Now entering its second decade, the Living Heart Project was born in part from a personal conviction. For many years, I had watched helplessly as my daughter Jesse faced endless diagnostic uncertainty due to a rare congenital heart condition in which the position of the ventricles is reversed, threatening her life as she grew. As an engineer, I understood that the heart was an array of pumping chambers, controlled by an electrical signal and its blood flow carefully regulated by valves. Yet I struggled to grasp the unique structure and behavior of my daughter’s heart well enough to contribute meaningfully to her care. Her specialists knew the bleak forecast children like her faced if left untreated, but because every heart with her condition is anatomically unique, they had little more than their best guesses to guide their decisions about what to do and when to do it. With each specialist, a new guess.
Then my engineering curiosity sparked a question that has guided my career ever since: Why can’t we simulate the human body the way we simulate a car or a plane?
At a visualization center in Boston, VR imagery helps the mother of a young girl with a complex heart defect understand the inner workings of her child’s heart. Dassault Systèmes
I had spent my career developing powerful computational tools to help engineers build digital models of complex mechanical systems, using models that ranged from the interactions of individual atoms to the components of entire vehicles. What most of these models had in common was the use of physics to predict behavior and optimize performance. But in medicine today, those same physics-based approaches rarely inform decision-making. In most clinical settings, treatment decisions still hinge on judgments drawn from static 2D images, statistical guidelines, and retrospective studies.
This was not always the case. Historically, physics was central to medicine. The word “physician” itself traces back to the Latin physica, which translates to “natural science.” Early doctors were, in a sense, applied physicists. They understood the heart as a pump, the lungs as bellows, and the body as a dynamic system. To be a physician meant you were a master of physics as it applied to the human body.
As medicine matured, biology and chemistry grew to dominate the field, and the knowledge of physics got left behind. But for patients like my daughter, that child in Boston, and millions like them, outcomes are governed by mechanics. No pill or ointment—no chemistry-based solution—would help, only physics. While I did not realize it at the time, virtual twins can reunite modern physicians with their roots, using engineering principles, simulation science, and artificial intelligence.
A decade of progress
The LHP concept was simple: Could we combine what hundreds of experts across many specialties knew about the human heart to build a digital twin accurate enough to be trusted, flexible enough to personalize, and predictive enough to guide clinical care?
We invited researchers, clinicians, device and drug companies, and government regulators to share their data, tools, and knowledge toward a common goal that would lift the entire field of medicine. The Living Heart Project launched with a dozen or so institutions on board. Within a year, we had created the first fully functional virtual twin of the human heart.
The Living Heart was not an anatomical rendering, tuned to simply replicate what we observed. It was a first-principles model, coupling the network of fibers in the heart’s electrical system, the biological battery that keeps us alive, with the heart’s mechanical response, the muscle contractions that we know as the heartbeat.
The Living Heart virtual twin simulates how the heart beats, offering different views to help scientists and doctors better predict how it will respond to disease or treatment. The center view shows the fine engineering mesh, the detailed framework that allows computers to model the heart’s motion. The image on the right uses colors to show the electrical wave that drives the heartbeat as it conducts through the muscle, and the image on the left shows how much strain is on the tissue as it stretches and squeezes. Dassault Systèmes
Academic researchers had long explored computational models of the heart, but those projects were typically limited by the technology they had access to. Our version was built on industrial-grade simulation software from Dassault Systèmes, a company best known for modeling tools used in aerospace and automotive engineering, where I was working to develop the engineering simulation division. This platform gave teams the tools to personalize an individual heart model using the patient’s MRI and CT data, blood-pressure readings, and echocardiogram measurements, directly linking scans to simulations.
Surgeons then began using the Living Heart to model procedures. Device makers used it to design and test implants. Pharmaceutical companies used it to evaluate drug effects such as toxicity. Hundreds of publications have emerged from the project, and because they all share the same foundation, the findings can be reproduced, reused, and built upon. With each application, the research community’s understanding of the heart snowballed.
Early on, we also addressed an essential requirement for these innovations to make it to patients: regulatory acceptance. Within the project’s first year, the U.S Food and Drug Administration agreed to join the project as an observer. Over the next several years, methods for using virtual-heart models as scientific evidence began to take shape within regulatory research programs. In 2019, we formalized a second five-year collaboration with the FDA’s Center for Devices and Radiological Health with a specific goal.
That goal was to use the heart model to create a virtual patient population and re-create a pivotal trial of a previously approved device for repairing the heart’s mitral valve. This helped our team learn how to create such a population, and let the FDA experiment with evaluating virtual evidence as a replacement for evidence from flesh-and-blood patients. In August 2024, we published the results, creating the first FDA-led guidelines for in silico clinical trials and establishing a new paradigm for streamlining and reducing risk in the entire clinical-trial process.
In 10 years, we went from a concept that many people doubted could be achieved to regulatory reality. But building the heart was only the beginning. Following the template set by the heart team, we’ve expanded the project to develop virtual twins of other organs, including the lungs, liver, brain, eyes, and gut. Each corresponds to a different medical domain, which has its own community, data types, and clinical use cases. Working independently, these teams are progressing toward a breakthrough in our understanding of the human body: a multiscale, modular twin platform where each organ twin could plug into a unified virtual human.
How a digital twin of the heart is constructed
A cardiac digital twin starts with medical imaging, typically MRI, CT, or both. The slices are reconstructed into the 3D geometry of the heart and connected vessels. The geometry of the whole organ must then be segmented into its constituent parts, so each substructure—atria, ventricles, valves, and so on—can be assigned their unique properties.
At this point, the object is converted to a functional, computational model that can represent how the various cardiac tissues deform under load—the mechanics. The complete digital twin model becomes “living” when we integrate the electrical fiber network that drives mechanical contractions in the muscle tissue.
Each part of the heart, such as the left ventricle [left], is superimposed with a detailed digital mesh to re-create its physiology. These pieces come together to form an anatomically accurate rendering of the whole organ [right].Dassault Systèmes
To simulate circulation, the twin adds computational models of hemodynamics, the physics of blood flow and pressure. The model is constrained by boundary conditions of blood flow, valve behavior, and vascular resistance set to closely match human physiology. This lets the model predict blood flow patterns, pressure differentials, and tissue stresses.
Finally, the model is personalized and calibrated using available patient data, such as how much the volume of the heart chambers changes during the cardiac cycle, pressure measurements, and the timing of electrical pulses. This means the twin reflects not only the patient’s anatomy but how their specific heart functions.
Building bigger cohorts with generative AI
When the FDA in silico clinical trial initiative launched in 2019, the project’s focus shifted from these handcrafted virtual twins of specific patients to cohorts large enough to stand in for entire trial populations. That scale is feasible today only because virtual twins have converged with generative AI. Modeling thousands of patients’ responses to a treatment or projecting years of disease progression is prohibitively slow with conventional digital-twin simulations. Generative AI removes that bottleneck.
AI boosts the capability of virtual twins in two complementary ways. First, machine learning algorithms are unrivaled at integrating the patchwork of imaging, sensor, and clinical records needed to build a high-fidelity twin. The algorithms rapidly search thousands of model permutations, benchmark each against patient data, and converge on the most accurate representation. Workflows that once required months of manual tuning can now be completed in days, making it realistic to spin up population-scale cohorts or to personalize a single twin on the fly in the clinic.
Second, enriching AI models’ training sets with data from validated virtual patients grounds the AI simulations in physics. By contrast, many conventional AI predictions for patient trajectories rely on statistical modeling trained on retrospective datasets. Such models can drift beyond physiological reality, but virtual twins anchor predictions in the laws of hemodynamics, electrophysiology, and tissue mechanics. This added rigor is indispensable for both research and clinical care—especially in areas where real-world data are scarce, whether because a disease is rare or because certain patient populations, such as children, are underrepresented in existing datasets.
Enabling in silico clinical trials
On the research side, the FDA-sponsored In Silico Clinical Trial Project that we completed in 2024 opened a new world for medical innovations. A conventional clinical trial may take a decade, and 90 percent of new drug treatments fail in the process. Virtual twins, combined with AI methods, allow researchers to design and test treatments quickly in a simulated human environment. With a small library of virtual twins, AI models can rapidly create expansive virtual patient cohorts to cover any subset of the general population. As clinical data becomes available, it can be added into the training set to increase reliability and enable better predictions.
The Living Heart Project has expanded beyond the heart, modeling organs throughout the body. The 3D brain reconstruction [top] shows major pathways in the brain’s white matter connecting color-coded regions of the brain. The lung virtual twin [middle] combines the organ’s geometry with a physics-based simulation of air flowing down the trachea and into the bronchi. And the cross section of a patient’s foot [bottom] shows points of strain in the soft tissue when bearing weight. Dassault Systèmes
Virtual twin cohorts can represent a realistic population by building individual “virtual patients” that vary by age, gender, race, weight, disease state, comorbidities, and lifestyle factors. These twins can be used as a rich training set for the AI model, which can expand the cohort from dozens to hundreds of thousands. Next the virtual cohort can be filtered to identify patients likely to respond to a treatment, increasing the chances of a successful trial for the target population.
The trial design can also include a sampling of patient types less likely to respond or with elevated risk factors, thus allowing regulators and clinicians to understand the risks to the broader population without jeopardizing overall trial success. This methodology enhances precision and efficiency in clinical research, providing population-level insights previously available only after many years of real-world evidence.
Of course, though today’s heart digital twins are powerful, they’re not perfect replicas. Their accuracy is bounded by three main factors: what we can measure (for example, image resolution or the uncertainty of how tissue behaves in real life), what we must assume about the physiology, and what we can validate against real outcomes. Many inputs, like scarring, microvascular function, or drug effects are difficult to capture clinically, so models often rely on population data or indirect estimation. That means predictions can be highly reliable for certain questions but remain less certain for others. Additionally, today’s digital twins lack validation for predicting long-term outcomes years in the future, because the technology has been in use for only a few years.
Over time, each of these limitations will steadily shrink. Richer, more standardized data will tighten personalization of the models. AI tools will help automate labor-intensive steps. And the collection of longitudinal data will improve the model’s ability to reliably predict how the body will evolve over time.
How virtual twins will change health care
Throughout modern medicine, new technologies have sharpened our ability to diagnose, providing ever-clearer images, lab data, and analytics that tell physicians what is presently happening inside a patient’s body. Virtual twins shift that paradigm, giving clinicians a predictive tool.
This “Living Lung” virtual-twin simulation shows strain patterns during breathing. Mona Eskandari/UC Riverside
Early demonstrations are already appearing in many areas of medicine, including cardiology, orthopedics, and oncology. Soon, doctors will also be able to collaborate across specialties, using a patient-specific virtual twin as the common ground for discussing potential interactions or side effects they couldn’t predict independently.
Although these applications will take some time to become the standard in clinical care, more changes are on the horizon. Real-time data from wearables, for example, could continuously update a patient’s personalized virtual twin. This approach could empower patients to understand and engage more deeply in their care, as they could see the direct effects of medical and lifestyle changes. In parallel, their doctors could get comprehensive data feeds, using virtual twins to monitor progress.
Imagine a digital companion that shows how your particular heart will react to different amounts of salt intake, stress, or sleep deprivation. Or a visual explanation of how your upcoming surgery will affect your circulation or breathing. Virtual twins could demystify the body for patients, fostering trust and encouraging proactive health decisions.
How are virtual twins being used in medicine?
Virtual twins have guided cardiovascular surgeries, providing predictions and exposing hidden details that even expert clinicians might miss, such as subtle tissue responses and flow dynamics.
Oncologists are modeling tumor growth and the body’s response to different therapies, reducing the uncertainty in choosing the best treatment path for both medical and quality-of-life metrics.
Orthopedic specialists are personalizing implants to deliver custom-made solutions, considering not only the local environment but also the overall body kinematics that will govern long-term outcomes.
A new era of healing
With the Living Heart Project, we’re bringing physics back to physicians. Modern physicians won’t need to be physicists, any more than they need to be chemists to use pharmacology. However, to benefit from the new technology, they will need to adapt their approach to care.
This means no longer seeing the body as a collection of discrete organs and considering only symptoms, but instead viewing it as a dynamic system that can be understood, and in most cases, guided toward health. It means no longer guessing what might work but knowing—because the simulation has already shown the result. By better integrating engineering principles into medicine, we can redefine it as a field of precision, rooted in the unchanging laws of nature. The modern physician will be a true physicist of the body and an engineer of health.
A technical examination of the sensing, motion control, power, and thermal challenges facing humanoid robotics engineers — with component-level design strategies for real-world deployment.
What Attendees will Learn
Why motion control remains the hardest unsolved problem — Explore the modelling complexity, real-time feedback requirements, and sensor fusion demands of maintaining stable bipedal locomotion across dynamic environments.
How sensing architectures enable perception and safety — Understand the role of inertial measurement units, force/torque feedback, and tactile sensing in achieving reliable human-robot interaction and collision avoidance.
What power and thermal constraints mean for system design — Examine the trade-offs in battery chemistry selection (LFP vs. NCA), DC/DC converter topologies, and thermal protection strategies that determine operational endurance.
How the industry is transitioning from prototype to mass production — Learn about the shift toward modular architectures, cost-driven component selection, and supply chain readiness projected for the late 2020s.
Happy 80th anniversary, ENIAC! The Electronic Numerical Integrator and Computer, the first large-scale, general-purpose, programmable electronic digital computer, helped shape our world.
On 15 February 1946, ENIAC—developed in the Moore School of Electrical Engineering at the University of Pennsylvania, in Philadelphia—was publicly demonstrated for the first time. Although primitive by today’s standards, ENIAC’s purely electronic design and programmability were breakthroughs in computing at the time. ENIAC made high-speed, general-purpose computing practicable and laid the foundation for today’s machines.
On the eve of its unveiling, the U.S. Department of War issued a news release hailing it as a new machine “expected to revolutionize the mathematics of engineering and change many of our industrial design methods.” Without a doubt, electronic computers have transformed engineering and mathematics, as well as practically every other domain, including politics and spirituality.
ENIAC’s success ushered the modern computing industry and laid the foundation for today’s digital economy. During the past eight decades, computing has grown from a niche scientific endeavor into an engine of economic growth, the backbone of billion-dollar enterprises, and a catalyst for global innovation. Computing has led to a chain of innovations and developments such as stored programs, semiconductor electronics, integrated circuits, networking, software, the Internet, and distributed large-scale systems.
Inside the ENIAC
The motivation for developing ENIAC was the need for faster computation during World War II. The U.S. military wanted to produce extensive artillery firing tables for field gunners to quickly determine settings for a specific weapon, a target, and conditions. Calculating the tables by hand took “human computers” several days, and the available mechanical machines were far too slow to meet the demand.
In 1942 John Mauchly, an associate professor of electrical engineering at Penn’s Moore School, suggested using vacuum tubes to speed up computer calculations. Following up on his theory, the U.S. Army Ballistic Research Laboratory, which was responsible for providing artillery settings to soldiers in the field, commissioned Mauchly and his colleagues J. Presper Eckert and Adele Katz Goldstine, to work on a new high-speed computer. Eckert was a lab instructor at Moore, and Goldstine became one of ENIAC’s programmers. It took them a year to design ENIAC and 18 months to build it.
The computer contained about 18,000 vacuum tubes, which were cooled by 80 air blowers. More than 30 meters long, it filled a 9 m by 15 m room and weighed about 30 kilograms. It consumed as much electricity as a small town.
Programming the machine was difficult. ENIAC did not have stored programs, so to reprogram the machine, operators manually reconfigured cables with switches and plugboards, a process that took several days.
By the 1950s, large universities either had acquired or built their own machines to rival ENIAC. The schools included Cambridge (EDSAC), MIT (Whirlwind), and Princeton (IAS). Researchers used the computers to model physical phenomena, solve mathematical problems, and perform simulations.
After almost nine years of operation, ENIAC officially was decommissioned on 2 October 1955.
In the early 1970s, there was a controversy over who invented the electronic computer and who would be assigned the patent. In 1973 Judge Earl Richard Larson of U.S. District Court in Minnesota ruled in the Honeywell v. Sperry Rand case that Eckert and Mauchly did not invent the automatic electronic digital computer but instead had derived their subject matter from a computer prototyped in 1939 by John Vincent Atanasoff and Clifford Berry at Iowa State College (now Iowa State University). The ruling granted Atanasoff legal recognition as the inventor of the first electronic digital computer.
IEEE’s ENIAC Milestone
In 1987 IEEE designated ENIAC as an IEEE Milestone, citing it as “a major advance in the history of computing” and saying the machine “established the practicality of large-scale electronic digital computers and strongly influenced the development of the modern, stored-program, general-purpose computer.”
The commemorative Milestone plaque is displayed at the Moore School, by the entrance to the classroom where ENIAC was built.
“The ENIAC legacy heralded the computer age, transforming not only science and industry but also education, research, and human communication and interaction.”
“The Second Life of ENIAC,” an article published in the annals in 2006, covers a lesser-known chapter in the machine’s history, about how it evolved from a static system—configured and reconfigured through laborious cable plugging—into a precursor of today’s stored-program computers.
“These six women found out what it took to run this computer, and they really did incredible things,” a Penn professor, Mitch Marcus, said in a 2006 PhillyVoice article. Marcus teaches in Penn’s computer and information science department.
Two other women contributed to the programming. Goldstine wrote ENIAC’s five-volume manual, and Klára Dán von Neumann, wife of John von Neumann, helped train the programmers and debug and verify their code.
To honor the women of ENIAC, the IEEE Computer Society established the annual Computer Pioneer Award in 1981. Eckert and Mauchly were among the award’s first recipients. In 2008 Bartik was honored with the award. Nominations are open to all professionals, regardless of gender.
An ENIAC replica
Last year a group of 80 autistic students, ages 12 to 16, from PS Academy Arizona, in Gilbert, recreated the ENIAC using 22,000 custom parts. It took the students almost six months to assemble.
A ceremony was held in January to display their creation. The full-scale replica features actual-size panels made from layered cardboard and wood. Although all electronic components are simulated, they are not electrically active. The machine, illuminated by hundreds of LEDs, is accompanied by a soundtrack that simulates the deep hum of ENIAC’s transformers and the rhythmic clicking of relays.
“Every major unit, accumulators, function tables, initiator, and master programmer is present and placed exactly where it was on the original machine,” Tom Burick, the teacher who mentored the project, said at the ceremony.
The replica, still on display at the school, is expected to be moved to a more permanent spot in the near future.
ENIAC’s legacy
ENIAC’s significance is both technical and symbolic. Technically, it marks the beginning of the chain of innovations that created today’s computational infrastructure. Symbolically, it made governments, militaries, universities, and industry view computation as a tool for improvement and for innovative applications that had previously been impossible. It marked a tectonic shift in the way humans approach problem-solving, modeling, and scientific reasoning.
The ENIAC legacy heralded the computer age, transforming not only science and industry but also education, research, and human communication and interaction.
As Eckert is reported to have said, “There are two epochs in computer history: Before ENIAC and After ENIAC.”
Coevolution of programming languages
The remarkable evolution of computer hardware during the past 80 years has been sparked by advances in programming languages—the essential drivers of computing.
From the manual rewiring of ENIAC to the orchestration of intelligent, distributed systems, programming languages have steadily evolved to make computers more powerful, expressive, and accessible.
Lessons From Computing’s Remarkable Journey
Computing history teaches us that flexibility, accessibility, collaboration, sound governance, and forward thinking are essential for sustained technological progress. In a recent Communications of the ACM article, Richa Gupta identified four historic shifts that led to computing’s rapid, transformative progress:
Programmable machines taught us that flexibility is key; technologies that adapt and are repurposed scale better.
The Internet showed that connection and standard protocols drive explosive growth but also bring new risks such as data security issues, invasion of privacy, and misuse.
Personal computers illustrated that accessibility and usability matter more than raw power. When nonexperts can use a tool easily, adoption rises.
The open-source movement revealed that collaborative innovation accelerates growth and helps spot problems early.
Predictions for computing in the decades ahead
The evolution of computing will continue along multiple trajectories, with the emphasis moving from generalization to specialization (for AI, graphics, security, and networking), from monolithic system design to modular integration, and from performance-centric metrics alone to energy efficiency and sustainability as primary objectives.
Increasingly, security will be built into hardware by design. Computing paradigms will expand beyond traditional deterministic models to embrace probabilistic, approximate, and hybrid approaches for certain tasks.
Those developments will usher in a new era of computing and a new class of applications.
In late 2024, the federal government’s cybersecurity evaluators rendered a troubling verdict on one of Microsoft’s biggest cloud computing offerings.
The tech giant’s “lack of proper detailed security documentation” left reviewers with a “lack of confidence in assessing the system’s overall security posture,” according to an internal government report reviewed by ProPublica.
Or, as one member of the team put it: “The package is a pile of shit.”
Prisma Photonics deploys fiber sensing technology that uses reflected optical signals to detect perturbations from vehicles and other sources near underground fiber cable.Prisma Photonics
At a visualization center in Boston, VR imagery helps the mother of a young girl with a complex heart defect understand the inner workings of her child’s heart. Dassault Systèmes
Each part of the heart, such as the left ventricle [left], is superimposed with a detailed digital mesh to re-create its physiology. These pieces come together to form an anatomically accurate rendering of the whole organ [right].Dassault Systèmes
The Living Heart Project has expanded beyond the heart, modeling organs throughout the body. The 3D brain reconstruction [top] shows major pathways in the brain’s white matter connecting color-coded regions of the brain. The lung virtual twin [middle] combines the organ’s geometry with a physics-based simulation of air flowing down the trachea and into the bronchi. And the cross section of a patient’s foot [bottom] shows points of strain in the soft tissue when bearing weight. Dassault Systèmes
This “Living Lung” virtual-twin simulation shows strain patterns during breathing. Mona Eskandari/UC Riverside
