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James Sweeney

Researcher at University of Limerick

Publications -  30
Citations -  376

James Sweeney is an academic researcher from University of Limerick. The author has contributed to research in topics: Evolutionary computation & Climate change. The author has an hindex of 8, co-authored 29 publications receiving 186 citations. Previous affiliations of James Sweeney include University College Dublin & Royal College of Surgeons in Ireland.

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AI-based modeling and data-driven evaluation for smart manufacturing processes

TL;DR: The objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.
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Bayesian inference for palaeoclimate with time uncertainty and stochastic volatility

TL;DR: A Bayesian model is proposed and fit to infer palaeoclimate over several thousand years, using data that arise as ancient pollen counts taken from sediment cores together with radiocarbon dates which provide (uncertain) ages.
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Consequences of Abrupt Cessation of Alpha1-Antitrypsin Replacement Therapy

TL;DR: Stopping Therapy for Alpha1-Antitrypsin Deficiency In Ireland, augmentation treatment of patients with genetic alpha1-antitriesin deficiency was abruptly discontinued when government–industry price...
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Doctor Retention: A Cross-sectional Study of How Ireland Has Been Losing the Battle.

TL;DR: Ireland’s doctor retention strategy has not addressed the root causes of poor training and working experiences in Irish hospitals and needs a more diversified retention strategy that addresses under-staffing, facilitates circular migration by younger trainees who choose to train abroad, identifies and addresses specialty-specific factors, and builds mentoring linkages between trainees and senior specialists.
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Joint palaeoclimate reconstruction from pollen data via forward models and climate histories

TL;DR: In this paper, a method and software for reconstructing palaeoclimate from pollen data with a focus on accounting for and reducing uncertainty is presented, which can provide most probable climate estimates with uncertainty intervals.