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Purpose: Brachial plexus traction injuries have conventionally been categorized as involving the C5-C6, C5-C7, C5-T1, and C8-T1 roots. In this article, we report a distinct clinical presentation of brachial plexus injury characterized by intact finger flexion with signs of complete brachial plexus injury.

Methods: From 2010 to 2022, 989 patients who sustained brachial plexus injuries were examined and underwent surgery.

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Hepatitis C virus (HCV) infection is a significant risk factor for liver cirrhosis and hepatocellular carcinoma (HCC). Traditionally, the primary prevention strategy for HCV-associated HCC has focused on removing infection through antiviral regimes. Currently, highly effective direct-acting antivirals (DAAs) offer extraordinary success across all patient categories, including cirrhotics.

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As we commemorate 50 years of the Expanded Programme on Immunization (EPI), the global mission to eradicate polio stands at a critical juncture. While remarkable progress has been made over the past decades, ensuring a steady supply of polio vaccines remains a significant challenge that could undermine these achievements. This manuscript aims to address the complexities of polio vaccine security within the context of the Immunization Agenda 2030 (IA2030) and the Global Polio Eradication Strategy 2022-2029, proposing actionable strategies to strengthen the vaccine supply.

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Application of Machine Learning to Predict CO Emissions in Light-Duty Vehicles.

Sensors (Basel)

December 2024

Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK.

Climate change caused by greenhouse gas (GHG) emissions is an escalating global issue, with the transportation sector being a significant contributor, accounting for approximately a quarter of all energy-related GHG emissions. In the transportation sector, vehicle emissions testing is a key part of ensuring compliance with environmental regulations. The Vehicle Certification Agency (VCA) of the UK plays a pivotal role in certifying vehicles for compliance with emissions and safety standards.

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This paper proposes a green computing strategy for low Earth orbit (LEO) satellite networks (LSNs), addressing energy efficiency and delay optimization in dynamic and energy-constrained environments. By integrating a Markov Decision Process (MDP) with a Double Deep Q-Network (Double DQN) and introducing the Energy-Delay Ratio (EDR) metric, this study effectively quantifies and balances energy savings with delay costs. Simulations demonstrate significant energy savings, with reductions of up to 47.

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