When the first electric school buses rolled into his rural South Carolina yard, Karim Johnson already knew what to expect. He’d learned those lessons hundreds of miles north, in a suburban district in upstate New York, where early adoption meant long nights, slow chargers and plenty of guesswork.
But this time it was different. In New York, he had to do it all: secure grants, navigate RFPs, scrap old buses and install chargers that took eight hours to recharge after a single route. In South Carolina, the buses arrived, chargers installed, paperwork complete and keys ready to be handed over. Yet, despite the smoother rollout, one challenge remains the same: selecting the routes the ESBs would run on, and in South Carolina, that’s a 175-mile rural route.
Johnson, the current director of transportation at Dorchester School District 4 in South Carolina, said at his previous school district — Bethlehem Central School District in New York — ESB adoption was much more nuanced. He recalled overseeing everything from applying for grants to infrastructure set up. When he started the ESB journey in 2021, the technology was still considered new and limited charging options were available, leaving him to select Level 2 chargers with no charge management software.
“There was a lot of community support for it,” he said of purchasing EVs, noting it was a process. “Those buses were purchased through [The New York State Energy Research and Development Authority] NYSERDA, and we were able to work with our bus dealership, which was Matthews Bus in New York State. They were a really big resource.”
However, he said the NYSERDA Grant had stipulations of what routes the buses needed to be on. So, instead of placing buses on the shortest district routes, they had to be placed on Johnson’s longest routes in New York. The district purchased two more EVs outside of the grant, which they deployed on shorter routes.
In South Carolina, Johnson said the state was awarded funding from Round 1 of the Environmental Protection Agency Clean School Bus Program. And because the state owns and maintains all school buses, it handled bus procurement, infrastructure setup, charger selection, and utility collaboration. Dorchester was awarded eight ESBs. Six have already been delivered, with four currently on the road and two awaiting minor work. However, once the buses were delivered, Johnson was able to immediately start driver training and route assignments.
Routes in Dorchester, however, are long, ranging from 90 to 175 miles a day, far above the typical ESB ideal use case. The lack of shorter routes, Johnson said, creates deployment constraints. Though with the DC fast chargers installed, buses can charge in the middle of the day, allowing them to be used in both a morning and afternoon route. This was a huge difference, as in New York his chargers would take eight hours to charge, making them sometimes unable to be used during the afternoon runs. In contrast, the DC fast chargers take around three hours and only run into challenges if there’s a mid-day route that needs coverage.
In addition to their ESB operation training, drivers must ensure the buses are plugged in after each route and have sufficient charge before each trip. Plus, in New York, drivers had to consider the weather, as the range dropped about 20 miles in colder months.
“I have no surprises now when it comes to the EV buses,” Johnson said. “When something comes up, I know the steps to go through, from working with the dealership, or … with the bus distributor.”
Johnson advised transportation directors looking to implement ESBs to plan thoroughly and early, evaluating everything from route lengths to charging times and dwell windows. Plus, he said, it’s important to choose the right infrastructure where fast chargers may be essential for rural or long-mileage districts. He also advised directors to understand the grant requirements.
He noted that while initial development will reveal operational challenges, it’s a learning curve and soon directors will be familiar with the ESBs like they are with their diesel buses. Overall, he concluded, with the right planning and charging strategy, EVs don’t need to be limited to the shortest routes.
There is growing attention on the links between artificial intelligence and increased energy demands. But while the power-hungry data centers being built to support AI could potentially stress electricity grids, increase customer prices and service interruptions, and generally slow the transition to clean energy, the use of artificial intelligence can also help the energy transition.
For example, use of AI is reducing energy consumption and associated emissions in buildings, transportation, and industrial processes. In addition, AI is helping to optimize the design and siting of new wind and solar installations and energy storage facilities.
On electric power grids, using AI algorithms to control operations is helping to increase efficiency and reduce costs, integrate the growing share of renewables, and even predict when key equipment needs servicing to prevent failure and possible blackouts. AI can help grid planners schedule investments in generation, energy storage, and other infrastructure that will be needed in the future. AI is also helping researchers discover or design novel materials for nuclear reactors, batteries, and electrolyzers.
Researchers at MIT and elsewhere are actively investigating aspects of those and other opportunities for AI to support the clean energy transition. At its 2025 research conference, MITEI announced the Data Center Power Forum, a targeted research effort for MITEI member companies interested in addressing the challenges of data center power demand.
Controlling real-time operations
Customers generally rely on receiving a continuous supply of electricity, and grid operators get help from AI to make that happen — while optimizing the storage and distribution of energy from renewable sources at the same time.
But with more installation of solar and wind farms — both of which provide power in smaller amounts, and intermittently — and the growing threat of weather events and cyberattacks, ensuring reliability is getting more complicated. “That’s exactly where AI can come into the picture,” explains Anuradha Annaswamy, a senior research scientist in MIT’s Department of Mechanical Engineering and director of MIT’s Active-Adaptive Control Laboratory. “Essentially, you need to introduce a whole information infrastructure to supplement and complement the physical infrastructure.”
The electricity grid is a complex system that requires meticulous control on time scales ranging from decades all the way down to microseconds. The challenge can be traced to the basic laws of power physics: electricity supply must equal electricity demand at every instant, or generation can be interrupted. In past decades, grid operators generally assumed that generation was fixed — they could count on how much electricity each large power plant would produce — while demand varied over time in a fairly predictable way. As a result, operators could commission specific power plants to run as needed to meet demand the next day. If some outages occurred, specially designated units would start up as needed to make up the shortfall.
Today and in the future, that matching of supply and demand must still happen, even as the number of small, intermittent sources of generation grows and weather disturbances and other threats to the grid increase. AI algorithms provide a means of achieving the complex management of information needed to forecast within just a few hours which plants should run while also ensuring that the frequency, voltage, and other characteristics of the incoming power are as required for the grid to operate properly.
Moreover, AI can make possible new ways of increasing supply or decreasing demand at times when supplies on the grid run short. As Annaswamy points out, the battery in your electric vehicle (EV), as well as the one charged up by solar panels or wind turbines, can — when needed — serve as a source of extra power to be fed into the grid. And given real-time price signals, EV owners can choose to shift charging from a time when demand is peaking and prices are high to a time when demand and therefore prices are both lower. In addition, new smart thermostats can be set to allow the indoor temperature to drop or rise — a range defined by the customer — when demand on the grid is peaking. And data centers themselves can be a source of demand flexibility: selected AI calculations could be delayed as needed to smooth out peaks in demand. Thus, AI can provide many opportunities to fine-tune both supply and demand as needed.
In addition, AI makes possible “predictive maintenance.” Any downtime is costly for the company and threatens shortages for the customers served. AI algorithms can collect key performance data during normal operation and, when readings veer off from that normal, the system can alert operators that something might be going wrong, giving them a chance to intervene. That capability prevents equipment failures, reduces the need for routine inspections, increases worker productivity, and extends the lifetime of key equipment.
Annaswamy stresses that“figuring out how to architect this new power grid with these AI components will require many different experts to come together.” She notes that electrical engineers, computer scientists, and energy economists “will have to rub shoulders with enlightened regulators and policymakers to make sure that this is not just an academic exercise, but will actually get implemented. All the different stakeholders have to learn from each other. And you need guarantees that nothing is going to fail. You can’t have blackouts.”
Using AI to help plan investments in infrastructure for the future
Grid companies constantly need to plan for expanding generation, transmission, storage, and more, and getting all the necessary infrastructure built and operating may take many years, in some cases more than a decade. So, they need to predict what infrastructure they’ll need to ensure reliability in the future. “It’s complicated because you have to forecast over a decade ahead of time what to build and where to build it,” says Deepjyoti Deka, a research scientist in MITEI.
One challenge with anticipating what will be needed is predicting how the future system will operate. “That’s becoming increasingly difficult,” says Deka, because more renewables are coming online and displacing traditional generators. In the past, operators could rely on “spinning reserves,” that is, generating capacity that’s not currently in use but could come online in a matter of minutes to meet any shortfall on the system. The presence of so many intermittent generators — wind and solar — means there’s now less stability and inertia built into the grid. Adding to the complication is that those intermittent generators can be built by various vendors, and grid planners may not have access to the physics-based equations that govern the operation of each piece of equipment at sufficiently fine time scales. “So, you probably don’t know exactly how it’s going to run,” says Deka.
And then there’s the weather. Determining the reliability of a proposed future energy system requires knowing what it’ll be up against in terms of weather. The future grid has to be reliable not only in everyday weather, but also during low-probability but high-risk events such as hurricanes, floods, and wildfires, all of which are becoming more and more frequent, notes Deka. AI can help by predicting such events and even tracking changes in weather patterns due to climate change.
Deka points out another, less-obvious benefit of the speed of AI analysis. Any infrastructure development plan must be reviewed and approved, often by several regulatory and other bodies. Traditionally, an applicant would develop a plan, analyze its impacts, and submit the plan to one set of reviewers. After making any requested changes and repeating the analysis, the applicant would resubmit a revised version to the reviewers to see if the new version was acceptable. AI tools can speed up the required analysis so the process moves along more quickly. Planners can even reduce the number of times a proposal is rejected by using large language models to search regulatory publications and summarize what’s important for a proposed infrastructure installation.
Harnessing AI to discover and exploit advanced materials needed for the energy transition
“Use of AI for materials development is booming right now,” says Ju Li, MIT’s Carl Richard Soderberg Professor of Power Engineering. He notes two main directions.
First, AI makes possible faster physics-based simulations at the atomic scale. The result is a better atomic-level understanding of how composition, processing, structure, and chemical reactivity relate to the performance of materials. That understanding provides design rules to help guide the development and discovery of novel materials for energy generation, storage, and conversion needed for a sustainable future energy system.
And second, AI can help guide experiments in real time as they take place in the lab. Li explains: “AI assists us in choosing the best experiment to do based on our previous experiments and — based on literature searches — makes hypotheses and suggests new experiments.”
He describes what happens in his own lab. Human scientists interact with a large language model, which then makes suggestions about what specific experiments to do next. The human researcher accepts or modifies the suggestion, and a robotic arm responds by setting up and performing the next step in the experimental sequence, synthesizing the material, testing the performance, and taking images of samples when appropriate. Based on a mix of literature knowledge, human intuition, and previous experimental results, AI thus coordinates active learning that balances the goals of reducing uncertainty with improving performance. And, as Li points out, “AI has read many more books and papers than any human can, and is thus naturally more interdisciplinary.”
The outcome, says Li, is both better design of experiments and speeding up the “work flow.” Traditionally, the process of developing new materials has required synthesizing the precursors, making the material, testing its performance and characterizing the structure, making adjustments, and repeating the same series of steps. AI guidance speeds up that process, “helping us to design critical, cheap experiments that can give us the maximum amount of information feedback,” says Li.
“Having this capability certainly will accelerate material discovery, and this may be the thing that can really help us in the clean energy transition,” he concludes. “AI [has the potential to] lubricate the material-discovery and optimization process, perhaps shortening it from decades, as in the past, to just a few years.”
MITEI’s contributions
At MIT, researchers are working on various aspects of the opportunities described above. In projects supported by MITEI, teams are using AI to better model and predict disruptions in plasma flows inside fusion reactors — a necessity in achieving practical fusion power generation. Other MITEI-supported teams are using AI-powered tools to interpret regulations, climate data, and infrastructure maps in order to achieve faster, more adaptive electric grid planning. AI-guided development of advanced materials continues, with one MITEI project using AI to optimize solar cells and thermoelectric materials.
Other MITEI researchers are developing robots that can learn maintenance tasks based on human feedback, including physical intervention and verbal instructions. The goal is to reduce costs, improve safety, and accelerate the deployment of the renewable energy infrastructure. And MITEI-funded work continues on ways to reduce the energy demand of data centers, from designing more efficient computer chips and computing algorithms to rethinking the architectural design of the buildings, for example, to increase airflow so as to reduce the need for air conditioning.
In addition to providing leadership and funding for many research projects, MITEI acts as a convenor, bringing together interested parties to consider common problems and potential solutions. In May 2025, MITEI’s annual spring symposium — titled “AI and energy: Peril and promise” — brought together AI and energy experts from across academia, industry, government, and nonprofit organizations to explore AI as both a problem and a potential solution for the clean energy transition. At the close of the symposium, William H. Green, director of MITEI and Hoyt C. Hottel Professor in the MIT Department of Chemical Engineering, noted, “The challenge of meeting data center energy demand and of unlocking the potential benefits of AI to the energy transition is now a research priority for MITEI.”
A Fleet Electrification Accelerator launched by Connecticut Green Bank earlier this year provides school districts with a free planning and deployment support program for local school bus fleets, with a focus on distressed municipalities.
The program is designed to help Connecticut school districts transition to electric school buses by offering technical, financial and operational guidance. It helps school districts overcome the various barriers to electrification such as EV adoption, infrastructure planning, vehicle procurement and cost analysis.
As of Tuesday, Preston Public Schools and the Connecticut Technical Education and Career System joined the Fleet Electrification Accelerator Fall Cohort. CTECS serves over 11,000 students across 17 technical high schools and one technical education center.
“The Green Bank is proud of the electric school bus investments we have made in 2025 through the Fleet Electrification Accelerator, which will help communities strive towards a cleaner future,” stated Bryan Garcia, president and CEO of the Connecticut Green Bank. “The participation of Preston Public Schools and CTECS reflects a growing commitment among Connecticut’s educational institutions to reduce emissions and deliver public health benefits through improved air quality while continuing to focus on the safe, reliable transportation of their students to and from school.”
Through the program, Preston Public Schools and CTECS will receive everything from electric school bus basics, vehicle and charger recommendations, a road map, on site assessment, procurement timelines and funding and incentive opportunities.
“Preston Public Schools is one of approximately 10 school districts that owns and operates its fleet of school buses,” said Roy Seitsinger, Preston’s superintendent of schools. “The initial assessment shows the location of our transportation department to be the third best location statewide to provide the necessary power for electrifying our future fleet needs. We are proud to be one of the first school districts to partner with Connecticut Green Bank.”
Recruitment for the spring 2025 Fleet Electrification Accelerator is currently underway. The program is sponsored by CALSTART.
A new report reaffirms California’s commitment to zero-emission vehicle adoption and deployment despite attempts by Congress and the Trump administration to remove federal waivers that provide the state authority to cut pollution levels within its borders and elsewhere.
The state currently is involved in multiple lawsuits challenging the administration’s efforts to revoke the waivers approved by the Biden administration’s Environmental Protection Agency and others.
The California Air Resources Board (CARB) released the report last week in response to Gov. Gavin Executive Order N-27-25 in June that directs CARB and several other state agencies to recommend strategies that make clean transportation more affordable, reliable and accessible. The report outlines strategies to expand the adoption of Zero-Emission Vehicles (ZEVs) across all vehicle types, including school buses, as part of the state’s broader effort to combat air pollution and climate change.
The report highlights California’s leadership in clean transportation, noting that the state has already surpassed its goal of deploying 2 million ZEVs. With 56 ZEV manufacturers operating in the state and nearly 178,000 public or shared private electric vehicle chargers installed, California is setting the pace for the nation.
However, the CARB report notes that five of the 10 most polluted cities in the U.S. are in California, and millions of residents still live in areas with dangerously high ozone levels, especially in the Los Angeles area and the San Joaquin Valley.
To address these challenges, CARB recommends actions across six key areas: Private investment, incentives, infrastructure, fuel pricing, regulations, and procurement. CARB seeks to sustain the Low Carbon Fuel Standard (LCFS) program that utilizes credits from 200 participating companies and from utilities to subsidize clean fuels like renewable diesel and to continue taking advantage of other existing funding programs. It recommends backfilling the federal clean air vehicle tax credits that are set to expire at the end of next month and providing “reliable and consistent funding” to the agency and the California Energy Commission for ZEV deployment and infrastructure incentive programs.
Noting that infrastructure remains one of the largest barriers to ZEV adoption, the report highlights the need for increased reliability of and access to EV chargers, including timely repair. CARB also recommends streamlining permitting processes and utility energization timelines. This includes implementing flexible service connections and other strategies to eliminate delays in EV charging installation.
CARB also calls for unlocking the benefits of V2G by improving the energization process to enable vehicles to power homes and businesses or to export power to the grid during peak demand periods. This includes developing utility rates “that align EV charging and discharging with grid needs” and establishing incentives to automakers that build EVs that can provide backup power. CARB also writes that standards are needed for chargers to enable the use of vehicle-grid integration.
School buses are directly impacted by the state’s push for ZEV adoption. The report emphasizes the need for incentives and infrastructure to support the transition to zero-emission buses. For school districts, this could mean additional access to funding programs that make it easier to replace aging diesel buses with electric or even hydrogen-powered alternatives. Additionally, CARB says the focus on building reliable charging infrastructure could alleviate concerns about fueling capacity and range limitations.
For companies operating school buses, the report’s recommendations present both opportunities and challenges. The emphasis on private investment through programs like the LCFS could provide financial incentives for operators to transition their fleets. Additionally, the state’s focus on workforce development could help create a pipeline of skilled workers to maintain and operate ZEVs.
However, the transition will require careful planning. CARB states operators will need to navigate new regulations, invest in charging or fueling infrastructure and ensure their fleets meet the state’s reliability and durability standards. Collaboration with state agencies and local governments will be key to overcoming these hurdles.
The CARB report also notes 17 other states and the District of Columbia have chosen to adopt at least part of California’s vehicle standards. The demand in these states for clean transportation collectively represents 40 percent of the nation’s new light-duty vehicle market and 25 percent of the nation’s new heavy-duty vehicle market, which are three to four times that of California alone. In addition, three of these states have established complementary regulations similar to California’s LCFS to further advance the clean vehicle market.
The U.S. Department of Energy (DOE) warns blackouts could increase by 100 times in 2030 if the nation “continues to shutter reliable power sources and fails to add additional firm capacity.” The forecast is a driving factor for school transportation departments seeking to incorporate cleaner alternatives for fueling buses.
The DOE report “Evaluating U.S. Grid Reliability and Security” released July 7, fulfills Section 3(b) of President Donald Trump’s Executive Order “Strengthening The Reliability and Security of the United States Electric Grid,” designed to deliver a uniform methodology to identify at-risk regions and guide federal reliability interventions.
The report finds the current path—retiring more generations without dependable replacements—threatens both grid reliability and the ability to meet growing AI-driven energy demand. Without intervention, the bulk power system cannot support AI growth, maintain reliability, or keep energy affordable.
Projected load growth is too large and fast for existing grid management and capacity planning methods to handle. A transformative shift is urgently needed.
The retirement of 104 giga-watts (GW) of firm capacity by 2030, without one-to-one replacement, worsens the resource adequacy challenge. Loss of this generation could cause major outages during unfavorable weather for wind and solar.
While 209 GW of new generation is projected by 2030, only 22 GW would be firm baseload power. Even without retirements, the model found increased risk of outages in 2030 by a factor of 34.
Current methods for assessing resource adequacy are outdated. Modern evaluations must consider not just peak demand, but also the frequency, magnitude and duration of outages, and model increasing interdependence with neighboring grids.
“Though demands on the electric grid are increasing, we do not foresee a meaningful logistics problem for school transportation directors,” noted Michelle Levinson, the World Resources Institute’s senior manager of eMobility Finance and Policy. “The report headline averages numbers across the whole of the U.S. The risk of additional outages is low and is brought up by high assumed data center demand in Electric Reliability Council of Texas and in PJM South (Virginia and Maryland).”
Levinson commented that the most recent data from the U.S. Energy Information Administration indicates electricity customers on average experienced approximately 5.5 hours of electricity interruptions in 2022.
“Even if all these outages occur on school days, which is unlikely, outages would account for only 0.19 percent of the hours when a bus is in the yard and potentially charging,” she added. “Luckily, transportation directors are already accustomed to navigating the impacts of electric outages on their fueling capabilities through their experience with liquid fossil fuel pumps, which also needs electricity to function.”
Levinson acknowledged change can be “scary” and the transition to electric school buses requires a shift in logistics but should not be a problem in and of itself and as with all logistics comes down to planning.
Overnight and midday down times of most school buses offer substantial opportunities for directors to charge batteries in advance of any conditions that might indicate higher grid risks, such as extreme weather events, she added.
However, others warn that even a short outage will greatly disrupt transportation operations. The DOE’s predicted blackout rate “introduces serious questions about how to keep buses moving in the face of growing grid instability,” noted Joel Stutheit, senior manager of autogas business development at the Propane Education & Research Council (PERC).
“The school day is built around a routine,” he continued. “Imagine what happens to that routine if the grid goes down as often as this DOE report suggests. If a transportation director is relying on an electric school bus fleet, blackouts could leave them unable to charge buses and reliably transport students. Even a short-term outage could introduce last-minute scheduling changes, rerouting [of] buses, and adding extra pressure on drivers and operations teams.”
Transportation directors need to shift from thinking about the electric grid as a guarantee to thinking about it as a variable for which they must plan, Stutheit said.
Ewan Pritchard, the chief subject matter expert on school bus electrification for consultant Energetics, said he believes the intent of the report was to make electric vehicles look bad.
“The DOE’s report is politically charged,” he shared. “My company is the evaluator for the electric vehicle infrastructure program for the state of California. My team is collecting data from all the vehicle charging stations across the state of California that are put in by the electric utilities. We track the time of usage of all of those stations, and we issue a report annually on the progress.”
The team’s work, he said, demonstrates electric school buses can benefit the utility grid — a shoring-up effect in the sense that it depends on when a school bus is plugged in.
For example, it can be a problem if school districts charge electric vehicles between 4 p.m. to 9 p.m., actively drawing power from the utility grid during peak demand times when usage and prices are highest, he noted.
Instead, Pritchard recommended school transportation departments would do well to use charge management systems, which essentially keep track of the strain on the utility grid, the cost of electricity and carbon production.
Doing so saves districts money, he added.
“We’re seeing tremendous change in the way people are charging vehicles, especially when it comes to school buses, because school buses have a very predictable schedule,” Pritchard said. “There’s plenty of time between 9 p.m. and 6 a.m. to recharge their vehicles.”
A Back Up Plan?
The challenge of student safety is “likely not as extreme as the report makes it seem,” Levinson agreed.
“If operators have not charged their vehicles ahead of a significant outage event, battery capacities may be low or zero, meaning this particular type of transport would not be able to run its typical route,” she pointed out. “School may not be in session in the event of such a significant outage.”
Alternatively, schools districts may find that electric buses can provide an additional level of safety and resiliency for students and communities during extreme events when the larger grid is out, Levinson said.
“Localized microgrid capabilities that connect bi-directional buses and essential school or community facilities are especially relevant in situations where extreme weather conditions isolate people and businesses,” she added.
PERC’s Stutheit, who previously was the director of transportation for Bethel School District in Washington, noted students are immediately impacted if buses can’t operate due to a power outage as “many students rely on transportation to and from school not only for their education, but to access meals and other essential services.”
If the grid goes down due to severe weather, the stakes are even higher for transportation directors to provide evacuations or emergency transportation, Stutheit said, adding student transporters need reliably-powered school buses that can respond quickly to keep students safe.
“Propane autogas buses provide that layer of resiliency,” he argued. “These buses can operate and refuel even when the grid is down. In the event of an emergency evacuation or shelter-in-place situation, propane autogas buses allow districts to respond without waiting on fuel deliveries or power restoration. That kind of reliability supports student safety.”
Pritchard noted most schools have backup generators if power goes out. He said the real student safety issue is when the tailpipe of a combustion vehicle is putting out emissions at that student’s height, adding studies show the concentration of pollutants inside of a vehicle are worse than the concentration outside of a vehicle when it comes to school buses.
“I think it’s more of a student safety issue to not electrify your fleet,” he added.
And then there is the possibility of using electric school buses to power microgrids available to provide surplus power to school buildings.
Getting Smart
To mitigate challenges, school districts should implement smart charging strategies and familiarize themselves with charge management tools and capabilities, Levinson said, adding it is best to charge when the grid is least constrained, such as overnight or midday when there is the most solar production.
“School districts can also create standard operating procedures and emergency management procedures. They can also conduct emergency preparedness drills to practice for such scenarios and identify places for procedural improvements,” she added.
Other steps include identifying additional charging locations beyond the primary charging yard and installing site-level resilience via batteries, solar and/or generators.
Stutheit shared that propane also complements EVs as part of a multi-fuel strategy, as it can be go-to energy in emergency situations when the grid is down. It can also provide transportation directors with an affordable option that won’t need infrastructure updates to keep up with grid instability.
There are ways to lessen the risk from outages that apply to both diesel and electric school buses, involving alternative power from outside the grid, Levinson said, adding grid outages affect all functions, not just charging buses.
“In cases in which electric school buses are vehicle-to-load or vehicle-to-building capable, they can be a potential asset to provide site power to run phones, computers, and HVAC systems during an outage. Increasingly electric vehicles, such as electric school buses, can be part of the grid support solution.”
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