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Health lifestyles in China reflect complex interplays of various structural forces, yielding intricate and evolving patterns. Leveraging data from the 2004-2015 China Health and Nutrition Survey ( = 9,986), this study discerns latent health lifestyles, tracks transitional dynamics, and probes socioeconomic disparities in these shifts. Three distinct lifestyle categories emerge: 'high risk', 'overall healthy but inactive', and 'modernized and active'.

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With the ongoing development of renewable energy sources, information technologies and physical energy systems are further integrated, which leads to challenges in ensuring the secure and stable operation of renewable energy power systems in the face of potential cyber threats. The strengths of blockchain in cybersecurity make it a promising solution to these challenges. However, existing blockchains are not well-suited for control tasks due to their low real-time performance.

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Raw data and noise in spectrophotometry.

Anal Chim Acta

January 2025

GH and Q Services Limited, West Studios, Sheffield Road, Chesterfield, S41 7LL, United Kingdom; College of Science and Engineering, University of Derby, Markeaton Street, Derby, DE22 3AW, United Kingdom.

Background: Spectrophotometers are ubiquitous in chemical and biological science; however, their precision limits are under-appreciated. Rules-of-thumb and IUPAC referenced guidance restricting absorbance ranges to minimize uncertainty are based on historically important instruments which are no longer as widely used. Advances over the last half-century changed most "raw" data from absorbance and transmittance values directly produced in analog electronics to digitized intensities.

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Continuous professional development (CPD) is crucial for physicians to maintain and enhance their skills. In response to the changing context of CPD and health care, this study applied a design thinking approach to transform and modernise the Royal College of Physicians and Surgeons of Canada's Maintenance of Certification (MOC) Program. A member-wide survey and co-design sessions with physicians, CPD leaders, and patient representatives were conducted, emphasising the importance of their insights and experiences.

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Predicting future hospital antimicrobial resistance prevalence using machine learning.

Commun Med (Lond)

October 2024

Modernising Medical Microbiology, Experimental Medicine, Nuffield Department of Medicine, Level 7 Research Offices, John Radcliffe Hospital, Headley Way, University of Oxford, Oxford, UK.

Article Synopsis
  • The study aims to predict antimicrobial resistance (AMR) at the hospital level in England using machine learning techniques, specifically focusing on historical data of AMR and antimicrobial usage over multiple years.
  • The research employs an Extreme Gradient Boosting (XGBoost) model and compares its predictive capability against other methods, finding XGBoost to offer the best performance, particularly in hospitals experiencing significant changes in AMR prevalence.
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