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Introduction: Ready-to-administer formulations for intravenous administration of noradrenaline are now broadly recommended and predicted to reduce pressure on critical care nursing. This analysis sought to quantify the nurse resource released from national level transition. Methods: The annual number of noradrenaline support days for hypotensive shock was determined and the administration of noradrenaline was simulated over 24 h using a decision tree. A 'best-practice' ready-to-administer strategy (RtA) of volumetrically pumped noradrenaline was compared to a 'nil uptake' strategy (AfC) of bedside prepared solution delivered either volumetrically or using a double syringe pump. A mix of noradrenaline concentrations, flow rates, product sizes, and preferences for ampoule pooling, preparation volume, and sterility were included. The consumption of nurse days and product units was then projected over 1 year for a population of adults in critical care in England. Results: Noradrenaline was administered over 231,011 days per year across 4123 critical care beds in England. Implementing a transition from AfC to RtA strategies on this scale released 35,791 nurse days or 176 whole-time nurse equivalents at 50/50 NHS band 5 and 6, a monetised release of £11.6 million. There was an increase in drug acquisition cost of £2.1 million using the licensed commercial product Sinora®. Annual net monetary benefit was + £9.5 million, or + £65,961 per critical care unit (CCU) of 29 beds, equivalent to one nurse released per unit for patient care. Conclusions: This modelling of ready-to-administer noradrenaline with volumetric delivery quantifies and bears out the recommendations of the Lord Carter review, the Royal Pharmaceutical Society, and the NHS Specialist Pharmacy Service in their encouragement of ready-to-administer formulations for safe and resource-effective critical care. [ABSTRACT FROM AUTHOR]

With great changes in production requirements, in-service machine tools may be unable to meet new production requirements. Aiming for this problem, a sustainable resource-saving and high-valued recovery approach for in-service machine tools is proposed, which integrates remanufacturing and product-service systems (PSS). In-service machine tool remanufacturing is defined as a new remanufacturing model based on condition monitoring and diagnosis, which is different from traditional used machine tool remanufacturing and new machine tool manufacturing mentioned in the current literature. Procedure framework of in-service machine tool remanufacturing is proposed, including condition monitoring and diagnosis, matching analysis, remanufacturability evaluation and decision-making, identification of potential problems, individualized redesign, disassembly, cleaning, inspection and classification, performance improvement as well as reassembly and inspection. Combining the remanufacturing practice of an in-service heavy-duty horizontal lathe, the comprehensive resource-efficient benefits of in-service machine tool remanufacturing are illustrated. The results show that the proposed remanufacturing model can restore in-service machine tools to like-new or better performance and upgrade their functionality, with great economic and social benefits. For the implementation of this remanufacturing model, an in-depth analysis of the supporting technologies such as condition monitoring and diagnosis, decision-making analysis, etc., should be done in future research to guarantee the production capacities of in-service machine tools. [ABSTRACT FROM AUTHOR]

In this study, under the constraint of resource-saving and environment-friendliness objective, based on multi-agent genetic algorithm, multi-objective spatial optimization (MOSO) model for land use allocation was developed from the view of simulating the biological autonomous adaptability to environment and the competitive-cooperative relationship. The model was applied to solve the practical multi-objective spatial optimization allocation problems of land use in the core region of Changsha, Zhuzhou, Xiangttan city cluster in China. The results has indicated that MOSO model has much better performance than GA for solving complex multi-objective spatial optimization allocation problems and it is a promising method for generating land use alternatives for further consideration in spatial decision-making. [ABSTRACT FROM AUTHOR]