Background: Poor oral health is common among older adults residing in care homes impacting their diet, quality of life, self-esteem, general health and well-being. The care home setting is complex and many factors may affect the successful implementation of oral care interventions. Exploring these factors and their embedded context is key to understanding how and why interventions may or may not be successfully implemented within their intended setting.
Objectives: This methodology paper describes the approach to a theoretically informed process evaluation alongside a pragmatic randomised controlled trial, so as to understand contextual factors, how the intervention was implemented and important elements that may influence the pathways to impact.
Materials And Methods: SENIOR is a pragmatic randomised controlled trial designed to improve the oral health of care home residents in the United Kingdom. The trial uses a complex intervention to promote and provide oral care for residents, including education and training for staff.
Results: An embedded, theoretically informed process evaluation, drawing on the PAHRIS framework and utilising a qualitative approach, will help to understand the important contextual factors within the care home that influence both the trial processes and the implementation of the intervention.
Conclusion: Utilising an implementation framework as the basis for a theoretically informed process evaluation provides an approach that specifically focuses on the contextual factors that may influence and shape the pathways to impact a given complex intervention a priori, while also providing an understanding of how and why an intervention may be effective. This contrasts with the more common post hoc approach that only focuses on implementation after the empirical results have emerged.
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Sci Rep
December 2024
School of Public Administration, Guangzhou University, Guangzhou, 510006, China.
The randomness and volatility of existing clean energy sources have increased the complexity of grid scheduling. To address this issue, this work proposes an artificial intelligence (AI) empowered method based on the Environmental, Social, and Governance (ESG) big data platform, focusing on multi-objective scheduling optimization for clean energy. This work employs a combination of Particle Swarm Optimization (PSO) and Deep Q-Network (DQN) to enhance grid scheduling efficiency and clean energy utilization.
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December 2024
Faculty of Chemistry, Jagiellonian University, Gronostajowa 2, 30-387, Kraków, Poland.
Tin sulphide compounds (SnS, x = 1, 2) are potential anode materials for potassium-ion batteries (PIBs) due to their characteristic layered structure, high theoretical capacity, non-toxicity and low production cost. However, they suffer from significant volume changes resulting in poor performance of such anodes. In this work incorporation of SnS into the carbon structure was expected to overcome these disadvantages.
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December 2024
School of Management Science and Engineering, Shandong Jianzhu University, Jinan, 250101, China.
This study seeks to improve urban supply chain management and collaborative governance in the context of public health emergencies (PHEs) by integrating fuzzy theory with the Back Propagation Neural Network (BPNN) algorithm. By combining these two approaches, an early warning mechanism for supply chain risks during PHEs is developed. The study employs Matlab software to simulate supply chain risks, incorporating fuzzy inference techniques with the adaptive data modeling capabilities of neural networks for both training and testing.
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December 2024
School of Information Engineering, Yangzhou University, Yangzhou, Jiangsu, China.
Consensus algorithms play a critical role in maintaining the consistency of blockchain data, directly affecting the system's security and stability, and are used to determine the binary consensus of whether proposals are correct. With the development of blockchain-related technologies, social choice issues such as Bitcoin scaling and main chain forks, as well as the proliferation of decentralized autonomous organization (DAO) applications based on blockchain technology, require consensus algorithms to reach consensus on a specific proposal among multiple proposals based on node preferences, thereby addressing the multi-value consensus problem. However, existing consensus algorithms, including Practical Byzantine Fault Tolerance (PBFT), do not support nodes expressing preferences.
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December 2024
College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang, 47100, China.
Tea bud detection technology is of great significance in realizing automated and intelligent plucking of tea buds. This study proposes a lightweight tea bud identification model based on modified Yolov5 to increase the picking accuracy and labor efficiency of intelligent tea bud picking while lowering the deployment pressure of mobile terminals. The following methods are used to make improvements: the backbone network CSPDarknet-53 of YOLOv5 is replaced with the EfficientNetV2 feature extraction network to reduce the number of parameters and floating-point operations of the model; the neck network of YOLOv5, the Ghost module is introduced to construct the ghost convolution and C3ghost module to further reduce the number of parameters and floating-point operations of the model; replacing the upsampling module of the neck network with the CARAFE upsampling module can aggregate the contextual tea bud feature information within a larger sensory field and improve the mean average precision of the model in detecting tea buds.
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