Author: WeWorldMedia-admin

Be Alert to Fraud After a Disaster

Published: 14 July 2025
Categories: FEMA News

RSS feed source: Federal Emergency Management Agency

AUSTIN, Texas – Texas survivors should be aware that con artists and criminals may try to obtain money or steal personal information through fraud after the storms and flooding that began July 2. In some cases, thieves try to apply for FEMA assistance using names, addresses and Social Security numbers they have stolen from survivors.

If a FEMA inspector contacts you or comes to your home and you did not submit a FEMA application, your information may have been used without your knowledge to create a FEMA application. If so, inform the inspector that you did not apply for FEMA assistance. The inspector will request a stop to the processing of your application.

If you did not apply for assistance and receive a letter from FEMA, or if you suspect fraudulent activity involving FEMA, you can report it to the FEMA Fraud Branch at  [email protected]. You may also write to FEMA Fraud and Internal Investigation Division, 400 C Street SW Mail Stop 3005, Washington, DC 20472-3005.

If you applied for FEMA assistance and received a notice that you already applied or that your application is being processed, you can visit a Disaster Recovery Center to receive in-person assistance. A recovery center is open from 8 a.m. to 7 p.m. daily at the First Baptist Church at 625 Washington St. in Kerrville.

You should also report suspicious activity to the

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AI that delivers smarter glucose predictions without compromising privacy

Published: 14 July 2025
Categories: National Science Foundation

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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Most of the planned coal capacity retirements are in the Midwest or Mid-Atlantic regions

Published: 14 July 2025
Categories: US Energy Information Administration

RSS feed source: Federal Emergency Management Agency

In-brief analysis

July 14, 2025

Based on what power plant owners and operators have reported to EIA, the total operating capacity of U.S. coal-fired power plants is scheduled to fall from 172 gigawatts (GW) in May 2025 to 145 GW by the end of 2028, according to our Preliminary Monthly Electric Generator Inventory. On a regional basis, 58% of the planned coal capacity retirements are in the Midwest and Mid-Atlantic regions.

Coal consumption in the U.S. electric power sector has fallen since its peak in the late 2000s because of increased competition from other electricity sources, especially from natural gas and renewables. Furthermore, coal-fired power plants have been subject to regulations regarding emissions that require plants to add equipment, modify processes, or stop operation.

Our inventory of operating capacity and planned retirements reflects power plant operators’

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Data Fusion and Distributed Monitoring for Ocean Engineering

Published: 14 July 2025
Categories: Eng Jobs Academic Keys

RSS feed source: Federal Emergency Management Agency

The project goal is to specify, design and implement an environment to develop and deploy Machine Learning (ML) models. Therefore, it will be necessary to understand the requirements defined by the different project groups, which develop different ML models for ocean energy systems. It will be necessary to research alternatives and adapt them to the context, involving research in the area of ​​Software Engineering and ML, following MLOps concepts. We expect to prepare an MLOps environment that meets these needs, defines and executes a process for deploying these models.

Requirements for the candidate

·        Have extensive knowledge of programming, preferably in Python language

·        Knowledge of Software Engineering

·        Knowledge of Machine Learning

·        Knowledge of the English language

REQUIRED DOCUMENTS FOR APPLICATION

Single-page presentation letter. Introduce yourself and share your

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