Eliasen Ewing (crowdrouter20)

In this paper, we present two metaheuristic evolutionary algorithms-based approaches to position the customer order decoupling point (CODP) in smart mass customization (SMC). SMC tries to autonomously mass customize and produce products per customer needs in Industry 4.0. SMC shown here is from the perspective of arriving at a CODP during manufacturing process flow designs meant for fast moving and complex product variants. Learning generally needs several repetitive cycles to break the complexity barrier. We make use of fruit fly and particle swarm optimization (PSO) evolutionary algorithms with the help of MATLAB programming to constantly search better fitting consecutive process modules in manufacturing chain. CODP is optimized by increasing modularity and reducing complexity through evolutionary concept. Learning-based PSO iterations are performed. The methods shown here are recommended for process flow design in a learning-oriented supply chain organization which can involve in-house and outsourced manufacturing steps. Finally, a complexity reduction model is presented which can aid in deploying this concept in design of supply chain and manufacturing flows. The online version contains supplementary material available at 10.1007/s00521-020-05657-1. The online version contains supplementary material available at 10.1007/s00521-020-05657-1.To predict the mortality of patients with coronavirus disease 2019 (COVID-19). We collected clinical data of COVID-19 patients between January 18 and March 29 2020 in Wuhan, China . Gradient boosting decision tree (GBDT), logistic regression (LR) model, and simplified LR were built to predict the mortality of COVID-19. We also evaluated different models by computing area under curve (AUC), accuracy, positive predictive value (PPV), and negative predictive value (NPV) under fivefold cross-validation. A total of 2924 patients were included in our evaluation, with 257 (8.8%) died and 2667 (91.2%) survived during hospitalization. Upon admission, there were 21 (0.7%) mild cases, 2051 (70.1%) moderate case, 779 (26.6%) severe cases, and 73 (2.5%) critically severe cases. The GBDT model exhibited the highest fivefold AUC, which was 0.941, followed by LR (0.928) and LR-5 (0.913). The diagnostic accuracies of GBDT, LR, and LR-5 were 0.889, 0.868, and 0.887, respectively. In particular, the GBDT model demonstrated the highest sensitivity (0.899) and specificity (0.889). VER155008 The NPV of all three models exceeded 97%, while their PPV values were relatively low, resulting in 0.381 for LR, 0.402 for LR-5, and 0.432 for GBDT. Regarding severe and critically severe cases, the GBDT model also performed the best with a fivefold AUC of 0.918. In the external validation test of the LR-5 model using 72 cases of COVID-19 from Brunei, leukomonocyte (%) turned to show the highest fivefold AUC (0.917), followed by urea (0.867), age (0.826), and SPO2 (0.704). The findings confirm that the mortality prediction performance of the GBDT is better than the LR models in confirmed cases of COVID-19. The performance comparison seems independent of disease severity. The online version contains supplementary material available at(10.1007/s00521-020-05592-1). The online version contains supplementary material available at(10.1007/s00521-020-05592-1).The increasing popularity of social media platforms has simplified the sharing of news articles that have led to the explosion in fake news. With the emergence of fake news at a very rapid rate, a serious concern has produced in our society because of enormous fake content dissemination. The quality of the news content is questionable and there exists a necessity for an automated tool for the detection. Existing studies primarily focus on utilizing information extracted from the news content. We suggest that user-based engagements and the context related group of people (echo-chamber) sharing the same opinions can play a vital role in the fake news