Background: The failure of some high-income countries to retain their medical graduates is one driver of doctor immigration from low- and middle-income countries. Ireland, which attracts many international medical graduates, implemented a doctor retention strategy from early 2015. This study measures junior doctors’ migration intentions, the reasons they leave and likelihood of them returning. The aim is to identify the characteristics and patterns of doctors who plan to emigrate to inform targeted measures to retain these doctors. Methods: A national sample of 1148 junior hospital doctors completed an online survey in early 2018, eliciting their experiences of training and working conditions. Respondents were asked to choose between the following career options: remain in Ireland, go and return, go and stay away, or quit medicine. Bivariate analyses and a two-stage multivariable analysis were used to model the factors associated with these outcomes. Results: 45% of respondents planned to remain in Ireland, 35% leave but return later, 17% leave and not return; and 3% to quit medicine. An intention to go abroad versus remain in Ireland was independently associated (P<.05) with the doctor being under 30 years (odds ratio [OR]=1.09 per year under 30), a non-European Union (EU) national (OR=1.54), a trainee (OR=1.50), and with hospital specialization, especially in Anesthesiology (OR=5.09). Respondents were more likely to remain if they had experienced improvements in supervision and training costs. Intention to go abroad and not return versus go and return was independently associated (P<.05) with: age over 30 years (OR=1.16 per year over 30); being a non-EU (OR=9.85) or non-Irish EU (OR=3.42) national; having trained through a graduate entry pathway (OR=2.17), specializing in Psychiatry (OR=4.76) and reporting that mentoring had become worse (OR=5.85). Conclusion: Ireland’s doctor retention strategy has not addressed the root causes of poor training and working experiences in Irish hospitals. It 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.

Summary We propose and fit a Bayesian model to infer palaeoclimate over several thousand years. The data that we use arise as ancient pollen counts taken from sediment cores together with radiocarbon dates which provide (uncertain) ages. When combined with a modern pollen–climate data set, we can calibrate ancient pollen into ancient climate. We use a normal–inverse Gaussian process prior to model the stochastic volatility of palaeoclimate over time, and we present a novel modularized Markov chain Monte Chain algorithm to enable fast computation. We illustrate our approach with a case‐study from Sluggan Moss, Northern Ireland, and provide an R package, Bclim, for use at other sites.

Smart Manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying Industrial Internet of Things (IIoT) sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management. Embracing Machine Learning and Artificial Intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on Evolutionary Computing and Deep Learning algorithms toward making semiconductor manufacturing smart. We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes and to address various challenges. We elaborate on the utilization of a Genetic Algorithm and Neural Network to propose an intelligent feature selection algorithm. Our 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.

Here, we present experimental evidence of the direct piezoelectric effect in the globular protein, lysozyme. Piezoelectric materials are employed in many actuating and sensing applications because they can convert mechanical energy into electrical energy and vice versa. Although originally studied in inorganic materials, several biological materials including amino acids and bone, also exhibit piezoelectricity. The exact mechanisms supporting biological piezoelectricity are not known, nor is it known whether biological piezoelectricity conforms strictly to the criteria of classical piezoelectricity. The observation of piezoelectricity in protein crystals presented here links biological piezoelectricity with the classical theory of piezoelectricity. We quantify the direct piezoelectric effect in monoclinic and tetragonal aggregate films of lysozyme using conventional techniques based on the Berlincourt Method. The largest piezoelectric effect measured in a crystalline aggregate film of lysozyme was approximately 6.5 pC N−1. These findings raise fundamental questions as to the possible physiological significance of piezoelectricity in lysozyme and the potential for technical applications.

Observers were consistent in grading URT disorders. However, significant disparity in grading existed between observers for some conditions affecting reliability.