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[Display omitted] With the sustainable development of advanced manufacturing technology, the important link affecting the final quality of complex products has been transferred from design and manufacturing to the assembly process. However, traditional pre-defined assembly parameters could not respond to changes in the assembly process caused by assembly errors, leading to unstable assembly quality and poor consistency. To address these issues, this paper proposes a novel online modeling and control method for complex assembly process based on digital twins (DT) and an extended particle swarm optimization algorithm, which could dynamically adjust the assembly process parameters to quickly respond to changes in the assembly process. Firstly, a DT-enabled autonomous assembly framework is proposed for the online analysis, optimization and control of assembly quality, by considering the coupling effect of multi-dimensional assembly errors on assembly quality. Then, two key enabling technologies of the framework, including the multi-dimensional assembly error-fused DT modeling method and DT-driven online optimization and control method of assembly errors are introduced in detail. Finally, a smart assembly prototype is implemented, where its application examples and evaluation results show the superiority of the proposed approach. Compared with traditional method, the proposed approach could significantly reduce the iteration time by 64% and improve the assembly quality by 14.05%. [ABSTRACT FROM AUTHOR]

Design for manufacturability is crucial to ensure high-quality products. It fully considers manufacturing-related factors in a collaborative design manner. However, since manufacturing knowledge is unstructured and hard to reuse, designers mainly depend on frequent design iterations to meet complex manufacturing constraints. Such insufficient design-manufacturing collaboration is not conducive to design efficiency improvement. To alleviate the problem, this paper proposes a novel graph embedding-based approach of Explainable Manufacturing Knowledge Recommendation (XMKR) for collaborative product design, which could achieve designer-oriented manufacturing knowledge reuse to avoid design errors proactively and minimize excessive iterations. Firstly, a graph embedding model based on the graph neural network (MKGE-GNN) is proposed to learn implicit semantic information of manufacturing knowledge. Secondly, with MKGE-GNN learning results, the potential manufacturing knowledge preferences of designers are identified in the design context graph by Personalized PageRank to achieve context-aware explainable manufacturing knowledge recommendation. Finally, a car body collaborative design case is conducted to validate the efficacy of the proposed approach, which not only provides accurate manufacturing knowledge in designers' decision-making, but also enhances designers' cognitions for manufacturability in the dynamic design context. [ABSTRACT FROM AUTHOR]

The potential role of krill oil (KO) supplementation on cardiovascular health are inconsistent in several clinical trials. Therefore, our present meta-analysis aimed to systematically evaluate the impacts of supplementation of KO on cardiovascular disease risk factors (CVDRFs). Intervention trials assessing KO supplementation on cardiovascular disease (CVD) outcomes were systematically retrieved for pooling. The primary outcome was lipid profile. Secondary outcomes were consisted by blood pressure, glycemic indices, body composition together with inflammatory markers. We synthesized the effect sizes with 95% confidence intervals and weighted mean difference. To explore the heterogeneity source, we employed meta-regression and subgroup analysis. Quality assessment, publication bias, sensitivity-analysis and the certainty of evidence were also carried out. We included 14 trials (18 treatment arms) with 1458 participants. KO supplementation had beneficial effects on total cholesterol (P = 0.01), low-density lipoprotein cholesterol (P = 0.006), and triglycerides (P = 0.0005). However, no effects were found for other CVDRFs, such as blood pressure, glycemic control, body composition as well as inflammatory markers. Subgroup analyses indicated that these notably favorable effects were observed in trials with a parallel design, treatment duration <8 weeks and subjects with baseline body mass index <28 kg/m2. The above findings remained consistent in the sensitivity analysis, without obvious publication bias detected. The current evidence demonstrated that daily KO supplementation may as a candidate for lipid management strategies. In future, studies should pay attention to the relationships of KO intake with the incidence of CVD events or all-cause mortality. • Krill oil supplementation significantly lowered total cholesterol, low-density lipoprotein cholesterol, and triglycerides. • Krill oil supplementation did not significantly affect the blood pressure, blood glucose and body composition. • Krill oil were well tolerated and generally safe, and no serious adverse events occurred during the follow-up. [ABSTRACT FROM AUTHOR]

Carbon emissions have drawn widely attention due to the worsening climate changes. In a machining process, a reasonable selection of cutting parameters can not only save the production cost and time, but also reduce the production carbon emissions. In addition, the wear conditions of different cutting tools also have great influence on cutting parameters selection as they could cause huge difference on carbon emissions. However, in traditional optimization methods of cutting parameters, the tool wear conditions are always ignored. Thus, in order to overcome this limitation, an optimization method of cutting parameters considering the tool wear conditions is developed. Firstly, the quantified relationships among cutting parameters, tool wear and production indexes (production carbon emissions, cost and time) are analysed. Then, a multi-objective cutting parameters optimization model is established based on the above production indexes to determine the optimal cutting parameters and tools. Thirdly, a modified NSGA-II algorithm is used to resolve the proposed model. Finally, a case study is designed to demonstrate the advantages and feasibility of the proposed approach. The results show that (i) the optimal cutting parameters change with the tool wear conditions; (ii) For the same type of cutting tools with different wear conditions, the optimal values of production carbon emissions, cost and time increase along with the raise of the tool wear conditions; (iii) For different available cutting tools with different tool wear conditions, it is necessary to apply a multi-objective optimization method to decide the optimal production carbon emissions, cost and time. [ABSTRACT FROM AUTHOR]