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"Characterisation of MOSFET dosimeters for alpha particle therapy" by Fang Yi Su, Giordano Biasi et al.

Alpha particle therapy, such as diffusing alpha-emitters radiation therapy (DaRT) and targeted alpha-particle therapy (TAT), exploits the short range and high linear energy transfer (LET) of alpha particles to destroy cancer cells locally with minimal damage to surrounding healthy cells. Dosimetry for DaRT and TAT is challenging, as their radiation sources produce mixed radiation fields of α particles, β particles and γ rays. There is currently no dosimeter for real-time in vivo dosimetry of DaRT or TAT. Metal-oxide-semiconductor field-effect transistors (MOSFETs) have features that are ideal for this scenario. They can be read out in real-time non-destructively, store information permanently, and their sensitivity to radiation can be varied by changing the bias on the gate during irradiation. Moreover, owing to their compactness, MOSFETs can fit into fine-gauge needle applicators, such as those used to carry radioactive seeds into the tumour. This study characterised the response o ....

University Of Wollongong , Centre For , Radiation Physics , Alpha Particles , Alpha Particles , Logic Gates , Medical Treatment , Etal Oxide Semiconductor Field Effect Transistors Mosfets , Radiation Effects ,

"A Distributed Device Selection Method to Minimize AoI in RF-Charging N" by Lei Zhang and Kwan Wu Chin

This letter considers optimizing information freshness in a network with Radio Frequency (RF)-energy harvesting wireless devices. A Hybrid Access Point (HAP) charges these devices and instructs a subset of devices to carry out sampling and transmit their sample. We outline a Distributed Q-Learning (DQL) algorithm that allows the HAP to select devices without knowing their uplink channel state and battery state. Our results show that DQL achieves at most 48%, 57%, and 61% lower average AoI than Round Robin (RR), Random Pick (RP), and AoI-Greedy (AG), respectively. The average AoI of DQL is only around 7% higher than the optimal selection strategy. ....

Radio Frequency , Hybrid Access Point , Distributedq Learning , Round Robin , Random Pick , Device Selection , Nformation Freshness , Logic Gates , Markov Processes , Radio Frequency , Signal To Noise Ratio , Wireless Power ,