Author: WeWorldMedia-admin

FEMA Authorizes Funds to Fight Utah’s Monroe Fire

Published: 17 July 2025
Categories: FEMA News

RSS feed source: Federal Emergency Management Agency

DENVER – This afternoon FEMA authorized the use of federal funds to help with firefighting costs for the Monroe Fire burning in Sevier County, Utah. The fire started July 13, 2025 and is 0-percent contained.  

Acting FEMA Region 8 Administrator Katherine Fox approved the state’s request for a federal Fire Management Assistance Grant (FMAG) this afternoon after determining the fire threatened such destruction that it would constitute a major disaster.

At the time of the request, the fire had burned 8000 acres and was threatening critical infrastructure including essential communications as well as the local watershed. There are also several other large fires burning uncontrolled within the state of Utah and fire weather conditions remain a concern. 

The authorization makes FEMA funding available to pay 75 percent of the state’s eligible firefighting costs under an approved grant for managing, mitigating and controlling designated fires. These grants do not provide assistance to individual home or business owners and do not cover other infrastructure damage caused by the fire.

Fire Management Assistance Grants are provided through the President’s Disaster Relief Fund and are made available by FEMA to assist in fighting fires that threaten to cause a major disaster. Eligible items can include expenses for field camps; equipment use, repair and replacement; mobilization and demobilization activities; and tools, materials and supplies. 

For more information on FMAGs, visit  https://www.fema.gov/fire-management-assistance-grants-program-details.

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New AI model could revolutionize U.S manufacturing

Published: 17 July 2025
Categories: National Science Foundation

RSS feed source: Federal Emergency Management Agency

Artificial intelligence has transformed fields like medicine and finance, but it hasn’t gained much traction in manufacturing. Factories present a different challenge for AI: They are structured, fast-paced environments that rely on precision and critical timing. Success requires more than powerful algorithms; it demands deep, real-time understanding of complex systems, equipment and workflow. A new AI model designed specifically for manufacturing, seeks to address this challenge and revolutionize how factories operate.

With support from the U.S. National Science Foundation, a team led by California State University Northridge’s Autonomy Research Center for STEAHM has developed MaVila — short for Manufacturing, Vision and Language — an intelligent assistant that combines image analysis and natural language processing to help manufacturers detect problems, suggest improvements and communicate with machines in real time. Their goal is to create smarter, more adaptive manufacturing systems that can better support one of the most important sectors of the U.S. economy.

MaVila takes a different approach. Instead of relying on outside data, like information on the internet, it is trained with manufacturing-specific knowledge from the start. It learns directly from visual and language-based data in factory settings. The tool can “see” and “talk” — analyzing images of parts, describing defects in plain language, suggesting fixes and even communicating with machines to carry out automatic adjustments.

MaVila was trained using a specialized approach that required

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Fully Funded PhD Position on Machine Learning for Scientific Computing

Published: 17 July 2025
Categories: Eng Jobs Academic Keys

RSS feed source: Federal Emergency Management Agency

A fully supported PhD position on Machine Learning for Scientific Computing is available in the Department of Mechanical and Aerospace Engineering at Rutgers University. The successful applicant will work under the supervision of Dr. George Moutsanidis and the desired start date is January 2026.

The PhD project will focus on the development and application of advanced machine learning techniques on computational fluid dynamics and computational solid mechanics. This includes both data-driven and physics-informed approaches, with potential applications in optimization, surrogate modeling, and uncertainty quantification.

Required Qualifications:

MS degree in Engineering, Applied Mathematics, or related field Solid academic background in the theory and application of novel machine learning techniques, such as, deep operator networks, graph neural networks, and generative AI methods. Solid academic background on scientific computing Excellent coding skills Exceptional analytical and problem-solving skills Strong communication and teamwork capabilities

Why join Rutgers

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