RSS feed source: Federal Emergency Management Agency

BOTHELL, Wash. –  The Federal Emergency Management Agency (FEMA) authorized the use of federal funds to help with firefighting costs for the Highland Fire burning in Crook County, Oregon. 

The state of Oregon’s request for a declaration under FEMA’s Fire Management Assistance Grant (FMAG) program was approved by FEMA Region 10 Acting Administrator Vincent J. Maykovich on Saturday July, 12, 2025, at 10:58 p.m. PT. He determined that the Highland Fire threatened to cause such destruction as would constitute a major disaster. This is the fourth FMAG declaration in 2025 to help fight Oregon wildfires. 

At the time of the state’s request, the wildfire threatened homes in and around the community of Prineville Lake Acres. The fire was also threatening roads, infrastructure, utilities, a watershed, and wildlife resources.  

FMAGs make funding available to pay up to 75 percent of a state’s eligible firefighting costs for fires that threaten to become major disasters. Eligible items can include expenses for field camps, equipment use, materials, supplies and mobilization and demobilization activities attributed to fighting the fire. These grants do not provide assistance to individual home or business owners and do not cover other infrastructure damage caused by the fire.  

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Follow FEMA Region 10 on X and LinkedIn for the latest updates and visit FEMA.gov for more information.

FEMA’s mission is helping people before, during, and after disasters.

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RSS feed source: Federal Emergency Management Agency

After Tennesseans apply for FEMA disaster assistance for the April 2-24 severe storms, a home inspection may be necessary to help determine whether the home is safe, sanitary and livable.

Information collected during the inspection is among the criteria FEMA uses to determine if applicants are eligible for federal assistance. Inspectors do not make decisions on eligibility for assistance.

The inspector will consider:

The structural soundness of the home, both inside and outside.Whether the electrical, gas, heat, plumbing and sewer or septic systems are all in working order.Whether the home is safe to live in and can be entered and exited safely.

Inspectors will call or text applicants to make an appointment to meet at the home. They will already have the applicant’s FEMA application number. They will leave messages or texts at the phone number listed on the FEMA application. These communications may come from unfamiliar phone numbers. It is important that applicants respond so their application can be processed.

Inspectors carry photo identification and will show it to the applicant. For security reasons, federal identification may not be photographed. Inspectors’ service is free and they will never ask for, or accept, money.

A typical home inspection takes about 30 to 45 minutes to complete. After the inspection, applicants should allow seven to 10 days for processing. If you have questions about the status of your application, you can

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RSS feed source: Federal Emergency Management Agency

FEMA’s Mobile Disaster Recovery Centers in Dickson and Cheatham counties are closing permanently Saturday, July 12. The deadline for homeowners and renters in Cheatham, Davidson, Dickson, Dyer, Hardeman, McNairy, Montgomery, Obion and Wilson counties to apply for FEMA assistance is Aug. 19.

Mobile Disaster Recovery Centers Closing:

Cheatham County: Kingston Springs City Hall, 396 Spring Street, Kingston Springs, TN 37082
Hours: 8 a.m. – 6 p.m. Saturday, July 12. Dickson County: Dickson County Government Building, 303 Henslee Drive, Dickson, TN 37005
Hours: 8 a.m. – 1 p.m. Saturday, July 12.

Open locations:

Dyer County: Bogota Community Center, 78 Sandy Lane, Bogota, TN 38007
Hours: 8 a.m.–6 p.m. CT Monday-SundayHardeman County: Safehaven Storm Shelter, 530 Madison Ave W., Grand Junction, TN 38039
Hours: 8 a.m.–6 p.m. CT Monday-SundayMcNairy County: Latta Theatre, 205 W. Court Ave., Selmer, TN 38375
Hours: 8 a.m.–6 p.m. CT Monday-SundayMontgomery County: Montgomery County Library, 350 Pageant Lane, Clarksville, TN 37040
Hours: 9 a.m.–8 p.m. CT Monday-Thursday; 9 a.m.–6 p.m. CT Friday-Saturday; 
1 p.m.–5 p.m. CT SundayObion County: Obion County Library, 1221 E. Reelfoot Ave., Union City, TN 38261
Hours: 8 a.m.–6 p.m. CT Monday-Saturday; closed Sunday

Can’t make it to a center? Apply online at DisasterAssistance.gov, use the FEMA App for mobile devices or call the FEMA Helpline at 800-621-3362. Lines are open from 6 a.m. to 11 p.m. CT seven days a week and specialists speak many languages. To view an accessible video on how to apply, visit Three Ways to Apply for FEMA Disaster Assistance – YouTube.

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RSS feed source: Federal Emergency Management Agency

Managing diabetes is a daily challenge faced by nearly 40 million Americans. It involves tracking food intake, timing medication and engaging in physical activity. Getting it wrong can lead to serious health issues; therefore, developing better prediction tools is a vital part of effective diabetes care.

To support better diabetes management, researchers funded by multiple U.S. National Science Foundation grants are developing innovative tools that help patients predict blood sugar levels more precisely without compromising the privacy of their health data. This cutting-edge approach could transform how people with diabetes monitor and manage their condition in real-time.

At the core of this technology is a method called federated learning, which allows artificial intelligence models to be trained across many patients’ devices without sending any personal data to a central server. This setup is ideal for healthcare, where data privacy is paramount and patients often use battery- and memory-limited smart devices. But early federated learning systems struggled to adapt to individual differences, like how people eat, move or react to insulin.

To address this challenge, the research team grouped patients based on their carbohydrate (e.g., sugar and starch) intake levels. The idea is that people who eat in similar ways tend to show similar glucose patterns. By training the AI on these grouped behaviors, the model became more effective at making personalized blood glucose predictions.

To test

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