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Processes or products are typically complex systems with numerous interrelated procedures and interdependent components. This results in complex relationships between responses and input factors, as well as complex nonlinear correlations among multiple responses. If the two types of complex correlations in the quality design cannot be properly dealt with, it will affect the prediction accuracy of the response surface model, as well as the accuracy and reliability of the recommended optimal solutions. In this paper, we combine kernel trick-based kernel principal component analysis, spline-based Bayesian semiparametric additive model, and normal boundary intersection-based evolutionary algorithm to address these two types of complex correlations. The effectiveness of the proposed method in modeling and optimisation is validated through a simulation study and a case study. The results show that the proposed Bayesian semiparametric additive model can better describe the process relationships compared to least squares regression, random forest regression, and support vector basis regression, and the proposed multi-objective optimisation method performs well on several indicators mentioned in the paper. [ABSTRACT FROM AUTHOR]

Hybrid heterojunction solar cells (HHSCs) using different active materials as hole‐selective transport layers and electron‐selective transport layers show great potential in both low cost and high power conversion efficiency (PCE). Herein, PEDOT:PSS/p‐Si/ZnO backcontact heterojunction solar cells are prepared using the simple low‐temperature solution method. PEDOT:PSS is introduced into p‐Si‐based solar cells as an antireflective and passivation layer to optimize device performance. The effect of the work function (WF) of ZnO on device performance is investigated, and a PCE of 10.74% is achieved using low‐WF ZnO. In addition, the PEDOT:PSS layer is doped with Nafion, and the surface passivation quality of the p‐Si is dramatically enhanced. By optimizing the doping concentration of Nafion, the PCE of the optimized device reaches 11.43%, which is currently the highest PCE for p‐Si‐based solar cells using solution method. The findings provide a simple and promising method to achieve high‐performance PEDOT:PSS/p‐Si/ZnO HHSCs. [ABSTRACT FROM AUTHOR]

The conventional approaches to constructing Prediction Intervals (PIs) always follow the principle of 'high coverage and narrow width'. However, the deviation information has been largely neglected, making the PIs unsatisfactory. For high-risk forecasting tasks, the cost of forecast failure may be prohibitive. To address this, this work introduces a multi-objective loss function that includes Prediction Interval Accumulation Deviation (PIAD) within the Lower Upper Bound Estimation (LUBE) framework. The proposed model can achieve the goal of 'high coverage, narrow width, and small bias' in PIs, thus minimizing costs even in cases of prediction failure. A salient feature of the LUBE framework is its ability to discern uncertainty without explicit uncertainty labels, where the data uncertainty and model uncertainty are learned by Deep Neural Networks (DNN) and a model ensemble, respectively. The validity of the proposed method is demonstrated through its application to the prediction of carbon prices in China. Compared with conventional uncertainty quantification methods, the improved interval optimization method can achieve narrower PI widths. [ABSTRACT FROM AUTHOR]

Accurate soybean futures price prediction is critical to related agricultural production, warehousing, and trading. Interval forecasting can avoid the loss of fluctuation information and evaluate the uncertainty of futures prices. However, most previous studies only consider the single‐type auxiliary variable, which will cause the deficiency of valued information. Moreover, the research concentrating on internet search index ignores the search habits of investors, resulting in subjectivity in keyword selection. Therefore, a novel multisource data‐driven combined forecasting model is proposed that consists of four parts: unstructured data processing, interval multi‐scale decomposition, interval combination forecasting, and model evaluation. First, sentiment analysis technology is used to convert news text into sentiment scores. The internet search keyword screening method based on latent Dirichlet allocation is then constructed to achieve the quantification of investor attention. Second, a decomposition method is applied to decompose the original interval‐valued series into finite more stationary components. Third, interval prediction results are obtained by machine learning‐based multiple predictors. Finally, the model evaluation module comprising error evaluation indicators and comparison experiments is presented to verify the effectiveness. The experimental results show that the proposed model has higher prediction accuracy, which indicates that multisource data and designed keyword screening methods can enhance the forecasting performance of the model. [ABSTRACT FROM AUTHOR]