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Rapid Response Grant Yields Oil Patch Stories, Book Chapter

Rapid Response Grant Yields Oil Patch Stories, Book Chapter
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Portland , Oregon , United-states , Watford-city , North-dakota , Erika-bolstad , Tom-brooks-of-tiger-well-service , Energy-states , Oil-bust , Society-of-environmental-journalists , Rapid-response-grant-yields-oil-patch-stories , Fund-for-environmental-journalism

OpenAI Q Star theoretical AI model explained

Learn about the as yet unreleased OpenAI Q Star project, expected to take us one step closer to Artificial General Intelligence (AGI) in the ....

David-shapiro , Energy-states , Low-energy-states ,

"A Cherry On Top." CCI Boasts About Targeting Energy States for Climate Lawsuits

The Center for Climate Integrity – the Rockefeller-funded activist group pushing climate litigation across the country – is openly boasting of targeting energy producing states with lawsuits in blatant attempt to undermine the U.S. oil and. ....

Utica , Pennsylvania , United-states , Ohio , Energy-in-depth , Energy-states , Climate-integrity , Richard-wiles , Utica-shale , Cherry-on-top , About-targeting-energy-states

Compare Electricity Rates by State [2022 Best Prices]

Are you overpaying for electricity? Click here to compare the best electricity rates and companies in America. ....

New-york , United-states , New-hampshire , Texas , Washington , Delaware , Rhode-island , California , San-antonio , Oregon , Michigan , Connecticut

"Energy Aware Irregular Slotted Aloha Methods for Wireless Powered IoT " by Yiwei Li and Kwan Wu Chin

This paper considers Radio Frequency (RF) energy harvesting devices that use an Irregular Slotted Aloha (IRSA) channel access protocol to transmit their data to a Hybrid Access Point (HAP). Specifically, it addresses the fundamental problem of optimizing the number of packet replicas transmitted by each device in each time frame. Unlike prior works, it considers a learning approach to optimize the number of replicas according to the energy level of devices. This paper first uses a model-based Markov Decision Process (MDP) to study the problem at hand. Then it proposes a model-free, centralized and a distributed Q-learning-based solution that aim to maximize the number of successful transmissions in each time frame. Our simulation results show that our centralized and distributed solutions respectively achieve up to 38% and 29% more successful transmissions than conventional Aloha. ....

Radio-frequency , Irregular-slotted-aloha , Hybrid-access-point , Markov-decision-process , Batteries , Channel-access , Energy-states , Nternet-of-things , Rregular-aloha , Markov-processes , Receivers