Federal Executive Branch
Here's a look at documents from the U.S. Executive Branch
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White House Fact Sheet: Strengthening National Defense With Reliable Coal Power
WASHINGTON, Feb. 12 -- The White House issued the following fact sheet on Feb. 11, 2026:
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President Donald J. Trump Strengthens United States National Defense with America's Beautiful Clean Coal Power Generation Fleet
STRENGTHENING NATIONAL DEFENSE WITH RELIABLE COAL POWER: Today, President Donald J. Trump signed an Executive Order directing the Department of War to prioritize long-term Power Purchase Agreements with America's beautiful, clean coal fleet to ensure military installations and critical defense facilities have uninterrupted, on-demand baseload power.
* The Order directs the
... Show Full Article
WASHINGTON, Feb. 12 -- The White House issued the following fact sheet on Feb. 11, 2026:
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President Donald J. Trump Strengthens United States National Defense with America's Beautiful Clean Coal Power Generation Fleet
STRENGTHENING NATIONAL DEFENSE WITH RELIABLE COAL POWER: Today, President Donald J. Trump signed an Executive Order directing the Department of War to prioritize long-term Power Purchase Agreements with America's beautiful, clean coal fleet to ensure military installations and critical defense facilities have uninterrupted, on-demand baseload power.
* The Order directs theSecretary of War, in coordination with the Secretary of Energy, to approve long-term Power Purchase Agreements or similar contracts with coal-fired energy production facilities to serve Department of War installations and other mission-critical facilities.
* Priority will be given to projects that enhance grid reliability and blackout prevention, on-site fuel security, and mission assurance for defense and intelligence capabilities.
ENSURING A RESILIENT GRID: President Trump is committed to strengthening the electric grid and believes that beautiful, clean coal plays a critical role in ensuring reliable energy, national security, and economic stability.
* Baseload power and a reliable grid are vital to powering military installations, operations, defense-industrial production, and ensuring the safety of the American people; any prolonged disruption threatens operational readiness and national security.
* It is imperative that the Department of War strategically utilize America's vast coal resources that have proven reliability in providing continuous, on-demand baseload power.
* Intermittent sources like wind and solar are unreliable in extreme weather, leaving the grid and our defense installations that rely on them vulnerable to interruptions in power.
MAKING AMERICA ENERGY DOMINANT: President Trump believes it is vital for America to be energy dominant and energy secure.
* On the campaign trail, President Trump warned, "you have a grid system in this country that's obsolete and a disaster," underscoring his urgency to act.
* On Day One, President Trump declared a National Energy Emergency to ensure that the Administration can bring all available authorities to bear to improve the integrity of our Nation's electrical grid.
* He is revitalizing America's beautiful clean coal industry in order to provide Americans with access to reliable, affordable power and support the thousands of American jobs that depend on the industry.
* In April 2025, President Trump signed a series of Executive Orders to designate coal as a mineral, lift barriers to coal mining on Federal lands, and grant regulatory relief from burdensome Biden-era restrictions on certain coal-fired power plants.
* President Trump's actions have stopped the closure of 17 gigawatts of coal power and resulted in new investments and build of affordable, baseload power generation across the country.
* President Trump is cutting red tape and rolling back regulations that hinder coal, oil, and natural gas production.
* Last summer, the Administration renewed the charter for the National Coal Council (NCC)--a Federal Advisory Committee terminated during the Biden Administration--and convened its first meeting just last month.
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Original text here: https://www.whitehouse.gov/fact-sheets/2026/02/fact-sheet-president-donald-j-trump-strengthens-united-states-national-defense-with-americas-beautiful-clean-coal-power-generation-fleet/
Federal Reserve Bank of New York: Statement Regarding Repurchase Agreement Small Value Exercise
NEW YORK, Feb. 12 -- The Federal Reserve Bank of New York issued the following statement:
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Statement Regarding Repurchase Agreement Small Value Exercise
The Open Market Trading Desk (the Desk) at the Federal Reserve Bank of New York undertakes small value open market transactions for the purpose of testing operational readiness to implement existing and potential policy directives from the Federal Open Market Committee (FOMC). The FOMC directs the Desk to conduct these exercises to test its operational readiness in the Authorizations and Continuing Directives for Open Market Operations.
In
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NEW YORK, Feb. 12 -- The Federal Reserve Bank of New York issued the following statement:
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Statement Regarding Repurchase Agreement Small Value Exercise
The Open Market Trading Desk (the Desk) at the Federal Reserve Bank of New York undertakes small value open market transactions for the purpose of testing operational readiness to implement existing and potential policy directives from the Federal Open Market Committee (FOMC). The FOMC directs the Desk to conduct these exercises to test its operational readiness in the Authorizations and Continuing Directives for Open Market Operations.
Inconnection with these directives, the Desk intends to conduct a small value forward start overnight repo operation with Primary Dealers. The bid submission process will be conducted from 10:30 AM ET to 10:45 AM ET on Wednesday, February 18, 2026. All counterparties will be limited to one $1 million proposition per tranche during the operation. Results will be posted on the New York Fed's website following the completion of the operation. The operation details are as follows:
Repurchase Agreement Operation:
OPERATION TENOR/TYPE ... ELIGIBLE COUNTERPARTIES ... OPERATION DATE ... SETTLEMENT DATE ... MATURITY DATE ... SECURITY TYPE ... MINIMUM BID RATE ... MAXIMUM VALUE OF OPERATION
Forward Start Overnight Repo ... Primary Dealers ... Wed, Feb 18, 2026 ... Thu, Feb 19, 2026 ... Fri, Feb 20, 2026 ... Multi-tranche: Treasury, Agency, Agency MBS ... SRP Rate on Feb 18, 2026 ... $100 million
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Original text here: https://www.newyorkfed.org/markets/opolicy/operating_policy_260211
DOE Lawrence Berkeley National Laboratory: AI Sensor 'Sniffs' Out Spectral Targets
WASHINGTON, Feb. 12 (TNSjou) -- The U.S. Department of Energy Lawrence Berkeley National Laboratory issued the following news:
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New AI Sensor 'Sniffs' Out Spectral Targets
A first-of-its-kind smart sensor developed at Berkeley Lab performs AI tricks to identify targets while it captures spectral images
By Rachel Berkowitz
Meet the sniffer dog of spectroscopy tools: an AI-enhanced sensor that can "sniff and seek" target objects in real-time.
Spectral imaging tools -- cameras that capture colors beyond the RGB spectrum visible to our eyes -- are vital for gleaning information about an
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WASHINGTON, Feb. 12 (TNSjou) -- The U.S. Department of Energy Lawrence Berkeley National Laboratory issued the following news:
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New AI Sensor 'Sniffs' Out Spectral Targets
A first-of-its-kind smart sensor developed at Berkeley Lab performs AI tricks to identify targets while it captures spectral images
By Rachel Berkowitz
Meet the sniffer dog of spectroscopy tools: an AI-enhanced sensor that can "sniff and seek" target objects in real-time.
Spectral imaging tools -- cameras that capture colors beyond the RGB spectrum visible to our eyes -- are vital for gleaning information about anobject's material and structural properties. Marrying them with machine learning has provided a powerful pipeline for identifying features in real-world applications including semiconductor fabrication, pollutant tracking, and crop monitoring. By folding AI algorithms into the camera's sensor itself, researchers at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have now eliminated a data-processing bottleneck that has long plagued the performance of spectral imaging technology. The result is an intelligent sensor capable of identifying chemicals and characterizing materials quickly and efficiently.
"We focused on enhancing the speed, resolution, and power efficiency of existing spectral machine vision technologies by more than two orders of magnitude," said Ali Javey, the scientist who led the Science study (https://www.science.org/doi/10.1126/science.ady6571) reporting the device. Javey is a senior faculty scientist at Berkeley Lab and a professor of materials science and engineering at UC Berkeley. The work was performed in close collaboration with Aydogan Ozcan at UCLA.
The sensor design illustrates how novel functionality can be built into semiconductor devices themselves to improve their efficiency and utility, and enable a new class of AI vision hardware.
Building algorithms with light
Today's spectral imaging technologies have separate sensor and computational modules. The sensor first captures a stack of images, each of which corresponds to a certain color. Then the dense image stack gets sent to a digital processor for further computation, which produces the object-identification results. That's where the problems arise.
"The sensors must collect and send much more data to the digital processor than normal cameras, roughly ten- to hundred-times larger in volume," said Dehui Zhang, a postdoc in Berkeley Lab's Materials Sciences Division and the lead author on the study. Consequently, the sensor and computer hardware are often overwhelmed, making object-recognition tasks extremely slow and power-hungry.
Instead, the Berkeley Lab team developed sensors that perform AI computation and spectral analysis during the image capturing -- or photodetection -- process itself.
"Photodetection can be perceived as an automatic physical computational process," explained Zhang. When light hits the sensor, its intensity automatically gets mapped to the strength of an electrical current. Because the sensor's responsivity to light can easily be adjusted, the researchers have a tuning knob for selecting which spectral signatures get highlighted and which get suppressed. The current that departs the sensor to be read by a circuit, therefore, serves as an inference about the image's spectral content.
"We proved that the computational process mathematically resembles an algorithm typically used for digital machine learning," said Zhang. This analogy made it possible to use the sensor as a machine learning computer and perform the machine learning computations on the incoming light itself.
Training the machine
Any AI or machine vision model first needs to learn what it's supposed to identify. That means "showing" it enough examples of the spectral signatures of interest -- say, the infrared patterns that come from a real leaf versus an artificial one; or the pixels in an image that belong to a bird's plumage versus a tree's similarly colored bark -- that it can find these signatures in an untrialed test case.
In the training step, the researchers showed the sensor dozens of images of colorful birds in wooded settings. Rather than examining every pixel of each image, the sensor "sniffed" a random selection of pixels, each of which was labeled as belonging either to the bird or to the unwanted background. An external computer sent an electrical signal to the sensor commanding it to "identify bird" or "identify background," and recorded the sensor's output for each command. Software then determined the best command combination for teaching the sensor to highlight the bird region while suppressing everything else.
In the test step, they showed the sensor a new image and told it to find a bird, using the command combination developed during training. The sensor gave positive output signals only for pixels that belonged to the bird. This result meant the sensor had learned from the examples to identify target objects, even when they belonged to an image it had never seen before.
"For me, the most exciting part is the concept of giving intelligence to sensors," said Javey. Normal sensors simply collect raw environmental information, leaving the intelligent recognition tasks to digital processors.
By co-designing the semiconductor materials, devices and the algorithms, the team enabled the sensors to learn and compute without the need for digital post-processing of data.
But applications for the technology go way beyond identifying birds. Using photodiodes of black phosphorus (capable of detecting mid-infrared light with tunable responsivity), the researchers experimentally demonstrated several other intriguing possibilities. They successfully identified oxide layer thicknesses in semiconductor samples -- which manufacturing giants need to be perfectly uniform -- as well as hydration states in different plant leaves, object segmentation in optical images, and transparent chemicals in a petri dish.
"I'm also optimistic about the future of such devices for broader applications," Javey said. In the future, the smart sensors could find a home not only in spectral machine vision but in "other advanced optical sensing and beyond."
The work was funded by the US Department of Energy's Office of Basic Energy Sciences. It received support from the DOE's Microelectronics Energy Efficiency Research Center for Advanced Technologies, one of the DOE's three Microelectronics Science Research Centers.
For information about licensing this technology, contact UC Berkeley.
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Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab's expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab's world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy's Office of Science.
DOE's Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
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Original text here: https://newscenter.lbl.gov/2026/02/11/new-ai-sensor-sniffs-out-spectral-targets/
DOE Lawrence Berkeley National Laboratory: 'Robot Pizza Chef' Serving Up Better Quantum Computers
WASHINGTON, Feb. 12 (TNSjou) -- The U.S. Department of Energy Lawrence Berkeley National Laboratory issued the following news:
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A 'Robot Pizza Chef' Serving Up Better Quantum Computers
A new cluster tool at Berkeley Lab gives researchers a faster way to find the ideal recipes for superconducting qubits, sensors, and other ingredients in a quantum computer.
Key Takeaways
* The new QIS cluster tool at the Molecular Foundry lets researchers experiment with dozens of materials and methods for making qubit components in a single automated system, accelerating discoveries for long-lived quantum
... Show Full Article
WASHINGTON, Feb. 12 (TNSjou) -- The U.S. Department of Energy Lawrence Berkeley National Laboratory issued the following news:
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A 'Robot Pizza Chef' Serving Up Better Quantum Computers
A new cluster tool at Berkeley Lab gives researchers a faster way to find the ideal recipes for superconducting qubits, sensors, and other ingredients in a quantum computer.
Key Takeaways
* The new QIS cluster tool at the Molecular Foundry lets researchers experiment with dozens of materials and methods for making qubit components in a single automated system, accelerating discoveries for long-lived quantumdevices.
* By combining fabrication and analysis tools in one connected, clean environment under vacuum, the QIS cluster tool helps researchers grow diverse materials in ways they couldn't before.
* Automation through the cluster tool will produce enormous datasets that can train AI models on what makes a successful qubit, improving future designs.
By Lauren Biron
Quantum computers could revolutionize how we design materials, secure information, and discover new drugs -- but only if we can make their fragile building blocks, qubits, more stable, reliable, and controllable. To quickly figure out the best recipes for qubits, researchers at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) are putting a new robotic system to work.
The quantum information science (QIS) cluster tool sits within a cleanroom at the Molecular Foundry, a DOE user facility dedicated to nanoscience that supports around a thousand researchers from around the world each year -- many of whom will bring their unique ideas for how to design better qubits. But given how exquisitely sensitive quantum components are to their environment, building and testing individual designs (and moving the sample from one tool to another) is slow and error-prone.
The QIS cluster tool dramatically speeds up and standardizes the process by combining multiple instruments for fabrication and analysis into one closed vacuum system. That means researchers can grow many types of materials using different techniques, one on top of another, without ever leaving the system, resulting in pristine interfaces. This is not possible with conventional fabrication methods, and drastically reduces the chance that quantum components are contaminated during production.
At the cluster tool's hub, a robotic arm shuttles an 8-inch disc called a wafer in and out of the surrounding ring of stations. Some deposit atom-thin layers of material, while others check each step for quality.
"It's like a robot pizza chef sitting in the middle with a spatula," said Aeron Tynes Hammack, a Berkeley Lab scientist who works on the cluster tool. "The exciting thing is that it automates processes in a fully clean environment to make complex materials. You can do it very reliably, very reproducibly, and fine-tune the recipes. It gives you insights you would never have if you were human-in-the-loop limited, making one sample at a time."
By automatically collecting AI-compatible data during quantum device fabrication and linking it to which qubits perform the best, researchers can then apply artificial intelligence to accelerate the search for the best materials, device design, and production methods for the next generation of quantum components.
Made to order
The QIS cluster tool excels at developing a tiny device at the heart of most quantum computers: the Josephson junction, a sandwich of two superconductors separated by an ultrathin insulating layer. This structure taps into the strange rules of the quantum world: pairs of electrons can "tunnel" through the barrier, even though they don't have the energy to cross over it in the classical sense.
Josephson junctions are combined with other components to form circuits that act as qubits, the basic units of quantum information. By sending carefully tuned microwave pulses into the circuit, qubits can then be manipulated to perform operations, similar to the bits in classic computers. But because they exploit quantum effects, they are not restricted to a binary set of states, opening the door to new types of computation. As the technology matures, quantum computers could tackle problems that are far too large or complex for today's machines, such as simulating molecules and optimizing massive networks (like the electric grid, supply chains, or traffic flow).
It's fitting that a cluster tool specializing in Josephson junctions is now at Berkeley Lab; John Clarke and his fellow laureates conducted their Nobel-Prize winning work on the technology at the lab, building the predecessors of today's superconducting qubits and paving the way for quantum computing.
Cooking with gas
Tiny changes to the materials, how they are layered and deposited, or accidental impurities can vastly change how a Josephson junction performs and how long a qubit can perform useful calculations.
With the QIS cluster tool, experimenters can choose from multiple different materials (such as aluminum, niobium, titanium, or compounds of those metals combined with oxygen or nitrogen) and methods to fabricate their quantum components. Those techniques include painting atoms on layer by layer, sputtering atoms from a target, evaporating and condensing materials, and etching the surface with a beam of ions. The tool is extremely precise, able to make features that are just a few atoms wide. Specialized software optimizes the workflow so multiple samples can move through the cluster tool simultaneously.
The cluster tool also has several ways to analyze the materials, using electrons, x-rays, lasers, and infrared light. These can identify what (and how many) molecules were deposited and any impurities. If something goes wrong, researchers can stop the run early on, rather than wasting weeks or months of effort only to find the final qubit is damaged.
"Slight imperfections on the atomic level can destroy the delicate coordinated dance of electrons that give rise to special quantum properties," said Jim Ciston, deputy director of the Molecular Foundry. "There are so many different variables, from materials to temperatures to patterns, that you could possibly try. We need a tool that will autonomously explore and refine recipes for making these interfaces that lead to high-reliability, long-lived qubits."
A recipe for success
While bringing the cluster tool online, researchers at the Molecular Foundry have focused on making high-quality versions of traditional aluminum Josephson junctions. They've also collaborated with experts in Berkeley Lab's physics division to try different materials and test what happens at the quantum level in a junction made with two different kinds of metals. And in a recent study, researchers showed they could make high-quality Josephson junctions out of the element hafnium, carried out the first in-depth tests of the devices, and found they could be useful for supersensitive qubit-based particle detectors (capable of searching for low-energy signals expected from dark matter).
"We're exploring the different types of materials that we can deposit and how the processes influence their grain structure, composition, superconducting temperature transitions, and tolerance to magnetic fields...the 'boring' material science stuff," Hammack said. "But, you know, modern life is made out of really basic material science stuff, and the really basic material science stuff is what we have full carte blanche to explore in a way that you just tend to not have the bandwidth for in industry."
Some Molecular Foundry researchers who use the cluster tool are members of the Quantum Systems Accelerator (QSA), a DOE National Quantum Information Science Research Center led by Berkeley Lab since 2020. After fabricating Josephson junctions for quantum computers at the cluster tool, they'll assemble and test the final qubits in QSA's new dilution refrigerator in the Molecular Foundry. This setup will create a fast feedback loop that links how the components are made to the final qubit performance, speeding progress toward more reliable quantum computers.
Every experiment adds to a growing dataset that can train artificial intelligence models on what makes a successful qubit. Right now, the cluster tool can flag if one of the fabrication steps goes wrong, but the goal is smart, autonomous, AI-advised operation, "so that the machine can tell us from a recipe whether it is likely to produce high-quality qubits," Hammack said.
Secret sauce
Cluster tools are common in industry, but typically focus on production rather than exploration.
"I'm coming back to the Foundry from industry, and one of the challenges in industry is you wind up locked to the processes in your past that have been successful," Hammack said. "There are a lot of different materials that exhibit superconducting behavior that you can make Josephson junctions and resonators out of. What the national lab ecosystem and the user science facilities offer to the global community is that we have a mandate and writ to explore the basic science and which materials have different properties that might be compelling."
Those discoveries will then be shared publicly, enabling others to adopt successful recipes on their own systems and giving industry new options to pursue.
Josephson junctions are the first order up at the QIS cluster tool, but it can also develop precision pieces for microelectronics or other parts of a quantum computer, such as resonators and capacitors. The same devices underlying quantum computers can also serve as extraordinarily sensitive sensors in other fields.
"When you're building quantum logic gates or other computational interfaces, you're also getting really, really good sensors for free," Hammack said.
Such sensors could aid the search for dark matter, detect single molecules, or even help scientists identify and track new viruses, improving how we respond to future health challenges.
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Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab's expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab's world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy's Office of Science.
DOE's Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
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REPORT: https://arxiv.org/pdf/2510.25203
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Original text here: https://newscenter.lbl.gov/2026/02/11/a-robot-pizza-chef-serving-up-better-quantum-computers/
BLS Western Region Issues Report on Employer-Reported Workplace Injuries and Illnesses in Oregon 2024
SAN FRANCISCO, California, Feb. 12 (TNSLrpt) -- Employer-reported workplace injuries and illnesses in Oregon 2024 - A report from U.S. Department of Labor Bureau of Labor Statistics Western Region - Feb. 11, 2026
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Private industry employers reported 44,600 nonfatal workplace injuries and illnesses in Oregon in 2024, the U.S. Bureau of Labor Statistics reported today. (See table A.) Regional Commissioner Chris Rosenlund noted that this resulted in a total recordable cases (TRC) incidence rate of 3.3 cases per 100 full-time equivalent workers; the national rate was 2.3. The estimates in this
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SAN FRANCISCO, California, Feb. 12 (TNSLrpt) -- Employer-reported workplace injuries and illnesses in Oregon 2024 - A report from U.S. Department of Labor Bureau of Labor Statistics Western Region - Feb. 11, 2026
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Private industry employers reported 44,600 nonfatal workplace injuries and illnesses in Oregon in 2024, the U.S. Bureau of Labor Statistics reported today. (See table A.) Regional Commissioner Chris Rosenlund noted that this resulted in a total recordable cases (TRC) incidence rate of 3.3 cases per 100 full-time equivalent workers; the national rate was 2.3. The estimates in thisrelease are from the Survey of Occupational Injuries and Illnesses (SOII).
Oregon's findings from the 2024 Survey of Occupational Injuries and Illnesses for private industry include:
* TRC incidence rates ranged from 0.3 in information to 4.5 in trade, transportation, and utilities. (See table 1.)
* Two supersectors, with 41 percent of employment, accounted for 53 percent of the occupational injuries and illnesses: trade, transportation, and utilities and education and health services. (See table 2.)
* The natural resources and mining industry TRC rate was 3.4 in 2024 and 4.9 in 2023. Other services, except public administration TRC rates were 2.2 and 1.4 in 2024 and 2023, respectively. (See table 3.)
* The TRC injury and illness incidence rate ranged from 1.6 for establishments employing fewer than 11 workers to 4.2 for establishments employing 50 to 249 workers. (See table 4.)
* Injuries accounted for 41,400 (92.8 percent) of total recordable cases; illnesses were an additional 3,200 cases.
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Table A. Number and rate of nonfatal occupational injuries and illnesses in private industry, United States and Oregon, 2024
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Private industry injury and illness case types
Of the 44,600 private industry injury and illness cases reported in Oregon, 27,000 were of a more severe nature, involving days away from work, job transfer, or restriction while recuperating--commonly referred to as DART cases. These cases occurred at a rate of 2.0 cases per 100 full-time workers; nationally the rate was 1.4.
Other recordable cases (those not involving days away from work, job transfer, or restriction) accounted for the remaining 17,600 cases in Oregon, at a rate of 1.3. The national rate for other recordable cases was 1.0.
State and local government injury and illness cases
In the state and local government sector in Oregon, 6,700 injury and illness cases were reported in 2024, resulting in a rate of 3.7 cases per 100 full-time workers. Nationally, the rate was 4.4. Eighty-four percent of injuries and illnesses reported in Oregon's public sector occurred among local government workers.
State estimates
Private industry estimates are available for 42 states, the District of Columbia, and three territories. (See map 1.) Factors such as differences in the composition of industry employment may influence state incidences rates and should be considered whenever comparing rates among different states.
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Map 1. Incidence rates of nonfatal occupational injuries and illnesses by state and selected industries, 2024
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Additional information
The Survey of Occupational Injuries and Illnesses (SOII) is a Federal/State cooperative program that presents estimates on nonfatal workplace injuries and illnesses. For more information on the SOII program, scope, and sampling methodology, see the national Employer-Reported Workplace Injuries and Illnesses news release and the SOII Handbook of Methods.
Incidence rates and counts by industry and case type published by the SOII are rounded. As a result, some components may not add to totals. See the effects of rounding on estimates for more information.
Data for U.S. territories are not included in the national SOII estimates. Employment data used in this release are from the Quarterly Census of Employment and Wages (QCEW) program.
If you are deaf, hard of hearing, or have a speech disability, please dial 7-1-1 to access telecommunications relay services.
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Table 1. Incidence rates of nonfatal occupational injuries and illnesses by selected industries and case type, Oregon, 2024
Table 2. Numbers of nonfatal occupational injuries and illnesses by selected industries and case types, Oregon, 2024 (numbers in thousands)
Table 3. Incidence rates of nonfatal occupational injuries and illnesses by selected industries and selected case type, Oregon, 2023-24
Table 4. Incidence rates of nonfatal occupational injuries and illnesses by selected industries and employment size, Oregon, 2024
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View original text plus charts and tables here: https://www.bls.gov/regions/west/news-release/2026/workplaceinjuriesandillnesses_oregon_20260211.htm
BLS Western Region Issues Report on Employer-Reported Workplace Injuries and Illnesses in Arizona 2024
SAN FRANCISCO, California, Feb. 12 (TNSLrpt) -- Employer-reported workplace injuries and illnesses in Arizona 2024 - A report from U.S. Department of Labor Bureau of Labor Statistics Western Region - Feb. 11, 2026
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Private industry employers reported 59,600 nonfatal workplace injuries and illnesses in Arizona in 2024, the U.S. Bureau of Labor Statistics reported today. (See table A.) Regional Commissioner Chris Rosenlund noted that this resulted in a total recordable cases (TRC) incidence rate of 2.6 cases per 100 full-time equivalent workers; the national rate was 2.3. The estimates in
... Show Full Article
SAN FRANCISCO, California, Feb. 12 (TNSLrpt) -- Employer-reported workplace injuries and illnesses in Arizona 2024 - A report from U.S. Department of Labor Bureau of Labor Statistics Western Region - Feb. 11, 2026
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Private industry employers reported 59,600 nonfatal workplace injuries and illnesses in Arizona in 2024, the U.S. Bureau of Labor Statistics reported today. (See table A.) Regional Commissioner Chris Rosenlund noted that this resulted in a total recordable cases (TRC) incidence rate of 2.6 cases per 100 full-time equivalent workers; the national rate was 2.3. The estimates inthis release are from the Survey of Occupational Injuries and Illnesses (SOII).
Arizona's findings from the 2024 Survey of Occupational Injuries and Illnesses for private industry include:
* TRC incidence rates ranged from 0.8 in financial activities to 4.0 in leisure and hospitality. (See table 1.)
* Two supersectors, with 41 percent of employment, accounted for 55 percent of the occupational injuries and illnesses: trade, transportation, and utilities and education and health services. (See table 2.)
* The construction industry TRC rates were 2.6 and 2.1 in 2024 and 2023, respectively. The financial activities industry TRC rate was 0.8 in both 2024 and 2023. (See table 3.)
* The TRC injury and illness incidence rate ranged from 0.9 for establishments employing fewer than 11 workers to 3.1 for establishments employing 50 to 249 workers. (See table 4.)
* Injuries accounted for 56,200 (94.3 percent) of total recordable cases; illnesses were an additional 3,500 cases.
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Table A. Number and rate of nonfatal occupational injuries and illnesses in private industry, United States and Arizona, 2024
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Private industry injury and illness case types
Of the 59,600 private industry injury and illness cases reported in Arizona, 35,500 were of a more severe nature, involving days away from work, job transfer, or restriction while recuperating--commonly referred to as DART cases. These cases occurred at a rate of 1.6 cases per 100 full-time workers; nationally the rate was 1.4.
Other recordable cases (those not involving days away from work, job transfer, or restriction) accounted for the remaining 24,100 cases in Arizona, at a rate of 1.1. The national rate for other recordable cases was 1.0.
State and local government injury and illness cases
In the state and local government sector in Arizona, 11,900 injury and illness cases were reported in 2024, resulting in a rate of 4.4 cases per 100 full-time workers, the same as the national rate. Ninety-one percent of injuries and illnesses reported in Arizona's public sector occurred among local government workers.
State estimates
Private industry estimates are available for 42 states, the District of Columbia, and three territories. (See map 1.) Factors such as differences in the composition of industry employment may influence state incidences rates and should be considered whenever comparing rates among different states.
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Map 1. Incidence rates of nonfatal occupational injuries and illnesses by state and selected industries, 2024
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Additional information
The Survey of Occupational Injuries and Illnesses (SOII) is a Federal/State cooperative program that presents estimates on nonfatal workplace injuries and illnesses. For more information on the SOII program, scope, and sampling methodology, see the national Employer-Reported Workplace Injuries and Illnesses news release and the SOII Handbook of Methods.
Incidence rates and counts by industry and case type published by the SOII are rounded. As a result, some components may not add to totals. See the effects of rounding on estimates for more information.
Data for U.S. territories are not included in the national SOII estimates. Employment data used in this release are from the Quarterly Census of Employment and Wages (QCEW) program.
Information in this release will be made available to individuals with sensory impairments upon request. Voice phone: (202)-691-5200; Telecommunications Relay Service: 7-1-1.
If you are deaf, hard of hearing, or have a speech disability, please dial 7-1-1 to access telecommunications relay services.
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Table 1. Incidence rates of nonfatal occupational injuries and illnesses by selected industries and case type, Arizona, 2024
Table 2. Numbers of nonfatal occupational injuries and illnesses by selected industries and case types, Arizona, 2024 (numbers in thousands)
Table 3. Incidence rates of nonfatal occupational injuries and illnesses by selected industries and selected case type, Arizona, 2023-24
Table 4. Incidence rates of nonfatal occupational injuries and illnesses by selected industries and employment size, Arizona, 2024
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View original text plus charts and tables here: https://www.bls.gov/regions/west/news-release/2026/workplaceinjuriesandillnesses_arizona_20260211.htm
BLS Western Region Issues Report on Employer-Reported Workplace Injuries and Illnesses in Alaska 2024
SAN FRANCISCO, California, Feb. 12 (TNSLrpt) -- Employer-reported workplace injuries and illnesses in Alaska 2024 - A report from U.S. Department of Labor Bureau of Labor Statistics Western Region - Feb. 11, 2026
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Private industry employers reported 5,900 nonfatal workplace injuries and illnesses in Alaska in 2024, the U.S. Bureau of Labor Statistics reported today. (See table A.) Regional Commissioner Chris Rosenlund noted that this resulted in a total recordable cases (TRC) incidence rate of 2.8 cases per 100 full-time equivalent workers; the national rate was 2.3. The estimates in this
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SAN FRANCISCO, California, Feb. 12 (TNSLrpt) -- Employer-reported workplace injuries and illnesses in Alaska 2024 - A report from U.S. Department of Labor Bureau of Labor Statistics Western Region - Feb. 11, 2026
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Private industry employers reported 5,900 nonfatal workplace injuries and illnesses in Alaska in 2024, the U.S. Bureau of Labor Statistics reported today. (See table A.) Regional Commissioner Chris Rosenlund noted that this resulted in a total recordable cases (TRC) incidence rate of 2.8 cases per 100 full-time equivalent workers; the national rate was 2.3. The estimates in thisrelease are from the Survey of Occupational Injuries and Illnesses (SOII).
Alaska's findings from the 2024 Survey of Occupational Injuries and Illnesses for private industry include:
* TRC incidence rates ranged from 1.0 in professional and business services to 5.2 in manufacturing. (See table 1.)
* Two supersectors, with 47 percent of employment, accounted for 54 percent of the occupational injuries and illnesses: trade, transportation, and utilities and education and health services. (See table 2.)
* The natural resources and mining industry TRC rate was 1.2 in 2024 and 1.3 in 2023. (See table 3.)
* The TRC injury and illness incidence rate ranged from 2.4 for establishments employing 11 to 49 workers to 5.1 for establishments employing 1,000 or more workers. (See table 4.)
* Injuries accounted for 5,400 (91.5 percent) of total recordable cases; illnesses were an additional 400 cases.
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Table A. Number and rate of nonfatal occupational injuries and illnesses in private industry, United States and Alaska, 2024
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Private industry injury and illness case types
Of the 5,900 private industry injury and illness cases reported in Alaska, 3,300 were of a more severe nature, involving days away from work, job transfer, or restriction while recuperating--commonly referred to as DART cases. These cases occurred at a rate of 1.5 cases per 100 full-time workers; nationally the rate was 1.4.
Other recordable cases (those not involving days away from work, job transfer, or restriction) accounted for the remaining 2,600 cases in Alaska, at a rate of 1.2. The national rate for other recordable cases was 1.0.
State and local government injury and illness cases
In the state and local government sector in Alaska, 1,100 injury and illness cases were reported in 2024, resulting in a rate of 2.8 cases per 100 full-time workers. Nationally, the rate was 4.4. Eighty-two percent of injuries and illnesses reported in Alaska's public sector occurred among local government workers.
State estimates
Private industry estimates are available for 42 states, the District of Columbia, and three territories. (See map 1.) Factors such as differences in the composition of industry employment may influence state incidences rates and should be considered whenever comparing rates among different states.
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Map 1. Incidence rates of nonfatal occupational injuries and illnesses by state and selected industries, 2024
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Additional information
The Survey of Occupational Injuries and Illnesses (SOII) is a Federal/State cooperative program that presents estimates on nonfatal workplace injuries and illnesses. For more information on the SOII program, scope, and sampling methodology, see the national Employer-Reported Workplace Injuries and Illnesses news release and the SOII Handbook of Methods.
Incidence rates and counts by industry and case type published by the SOII are rounded. As a result, some components may not add to totals. See the effects of rounding on estimates for more information.
Data for U.S. territories are not included in the national SOII estimates. Employment data used in this release are from the Quarterly Census of Employment and Wages (QCEW) program.
Information in this release will be made available to individuals with sensory impairments upon request. Voice phone: (202)-691-5200; Telecommunications Relay Service: 7-1-1.
If you are deaf, hard of hearing, or have a speech disability, please dial 7-1-1 to access telecommunications relay services.
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Table 1. Incidence rates of nonfatal occupational injuries and illnesses by selected industries and case type, Alaska, 2024
Table 2. Numbers of nonfatal occupational injuries and illnesses by selected industries and case types, Alaska, 2024 (numbers in thousands)
Table 3. Incidence rates of nonfatal occupational injuries and illnesses by selected industries and selected case type, Alaska, 2023-24
Table 4. Incidence rates of nonfatal occupational injuries and illnesses by selected industries and employment size, Alaska, 2024
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View original text plus charts and tables here: https://www.bls.gov/regions/west/news-release/2026/workplaceinjuriesandillnesses_alaska_20260211.htm
