Publications by authors named "P Byrne"

Using first principles calculations, we show that [Formula: see text] materials have strong electron-phonon coupling, with many having a superconducting critical temperature ([Formula: see text]) that exceeds that of the more familiar [Formula: see text] at ambient pressure. In particular, we find that [Formula: see text] is the most stable member of the family, with [Formula: see text] whilst the peak [Formula: see text] is with [Formula: see text] which has [Formula: see text]. Our results reveal that these materials are both thermodynamically and dynamically stable, with strong electron-phonon coupling, indicating significant potential for practical superconducting applications.

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The aim of this study was to compare the acute effect of three cluster set (CS) intra-set rest intervals (15 s, 30 s, and 45 s) on mechanical performance measures during a flywheel resistance training session. Twelve amateur male field sport athletes attended three training measurement sessions (separated by 14 days of wash-out), consisting of four sets of nine repetitions (as cluster-blocks: 3 + 3 + 3), using a 0.050 kg·m inertial load.

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This work evaluates a new prototype of a mobile wick solar still with a passive external condenser designed and manufactured by the company IPFH2O. The system's purpose is to distil non-potable water into potable water using solar energy. It is intended for populations in areas where access to drinking water remains difficult.

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Background: Misinformation represents a serious and growing concern for public health and healthcare health; and has attracted much interest from researchers, media, and the public over recent years. Despite increased concern about the impacts of misinformation on health and wellbeing, however, the concept of health misinformation remains underdeveloped. In particular, there is a need to clarify how certain types of health information come to be designated as "misinformation," what characteristics are associated with this classification, and how the concept of misinformation is applied in health contexts.

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Article Synopsis
  • Heart failure is prevalent among hemodialysis patients, and high flow arteriovenous fistulas (AVFs) may be a risk factor; this study aims to create a predictive model to identify patients at risk of developing high flow AVFs.
  • Using data from 366 patients, the study compared various machine learning models to predict high flow AVF development, finding that the bootstrap forest model was the most effective.
  • Results showed that while 31.4% of patients had high flow AVFs, and 24.9% developed heart failure after AVF creation, no direct correlation was found between AVF flow and the onset of heart failure or death post-creation.
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