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Ship energy consumption and emission prediction are critical for ship energy efficiency management and pollution gas emission control, both of which are major concerns for the shipping industry and hence continue to attract global attention and research interest. This article examined the energy efficiency data sources, big data analysis for energy efficiency, and analyzed the ship energy consumption and emission prediction models. The ship energy consumption and pollution gas emission prediction models are comprehensively summarized based on the modeling method and principles. The theoretical analysis and artificial intelligence-based ship energy consumption model, as well as the top-down and bottom-up ship emission prediction models, are thoroughly examined in terms of influencing factors, model accuracy, data sources, and practical applications. On this basis, the challenges of ship energy consumption and emission prediction are discussed, and future research suggestions are proposed, providing a foundation for the development of ship energy consumption and emission prediction technologies. The analysis results show that the principles, parameters of concern, and data quality all have a significant impact on the performance of the prediction models. Consequently, the prediction model's accuracy can be improved by combining intelligent algorithms and machine learning. In the future, high precision, self-adapting, ship fuel consumption and emission prediction models based on artificial intelligence technology should be further studied, in order to improve their prediction performance, and thus providing solid foundations for the optimization management and control of the ship energy consumption and emissions. • The progress on the prediction of the ship's energy consumption and pollution gas emissions has been summarized in detail. • The characteristics and performance of different prediction models have been discussed. • The challenges are deeply analyzed, and the prospects are put forward. • This work will be regarded as an essential guidance for future research on the optimization methods and decision-making system development of ship energy consumption and emissions. [ABSTRACT FROM AUTHOR]

The accurate prediction of energy consumption is significant for ship energy efficiency optimization. However, the existing prediction methods of ship energy consumption based on a single algorithm have limitations in adaptability and accuracy. Therefore, a novel high-precision and self-adaptive prediction method based on the multi-model fusion approach is investigated in this paper. Firstly, the data processing and analysis are carried out. Then, the Stacking-based fusion model is established and the prediction performance is analyzed. On this basis, an adaptive fusion model based on the Self-adaptive Parameter Optimization (SPO) method is established. Finally, an Intelligent Selection based Self-adaptive Hybrid (ISSH) method is proposed. The study results indicate that the proposed ISSH method can predict ship energy consumption more accurately, with the Mean Square Error (MSE) reduced by 66.7 % and the Mean Absolute Error (MAE) reduced by 12.7 % compared to the optimal single prediction model. In addition, the ISSH-based fusion model can reduce the MSE by 50.0 % and the MAE by 9.9 %, compared to the Stacking-based fusion model without parameter optimization. Moreover, the ISSH method can achieve self-adaptive prediction of ship energy consumption under diverse scenarios by adopting the intelligent selection strategy (ISS) method of basic models, which is significant to achieve dynamic optimization of ship energy efficiency. The accurate prediction of energy consumption is significant for ship energy efficiency optimization. However, the existing prediction methods of ship energy consumption based on a single algorithm have limitations in adaptability and accuracy. Therefore, a novel high-precision and self-adaptive prediction method based on the multi-model fusion approach is investigated in this paper. Firstly, the data processing and analysis are carried out. Then, the Stacking-based fusion model is established and the prediction performance is analyzed. On this basis, an adaptive fusion model based on the Self-adaptive Parameter Optimization (SPO) method is established. Finally, an Intelligent Selection based Self-adaptive Hybrid (ISSH) method is proposed. The study results indicate that the proposed ISSH method can predict ship energy consumption more accurately, with the Mean Square Error (MSE) reduced by 66.7 % and the Mean Absolute Error (MAE) reduced by 12.7 % compared to the optimal single prediction model. In addition, the ISSH-based fusion model can reduce the MSE by 50.0 % and the MAE by 9.9 %, compared to the Stacking-based fusion model without parameter optimization. Moreover, the ISSH method can achieve self-adaptive prediction of ship energy consumption under diverse scenarios by adopting the intelligent selection strategy (ISS) method of basic models, which is significant to achieve dynamic optimization of ship energy efficiency. [Display omitted] • An ISS is proposed to evaluate and select basic models for ship energy consumption prediction. • A Stacking-based fusion mode for ship energy consumption prediction is constructed. • The SPO is proposed to optimize the fusion model to achieve better prediction performance of ship energy consumption. • The proposed ISSH method can achieve high-precision and self-adaptive prediction of ship energy consumption. [ABSTRACT FROM AUTHOR]

Previous studies suggest that the osteoprotegerin gene ( OPG ) plays an important role in the development of osteoporosis. This study aims to investigate the potential association between OPG genetic polymorphisms and bone mineral density (BMD) and osteoporosis in postmenopausal women. 938 Chinese postmenopausal women were enrolled. The lumbar spine (L 2–4 ) BMD, neck BMD, and total hip BMD were measured by dual energy X-ray absorptiometry (DEXA). The genotypes of OPG genetic polymorphisms were evaluated by the created restriction site-polymerase chain reaction (CRS-PCR), PCR-restriction fragment length polymorphism (PCR-RFLP), and DNA sequencing methods. Our data indicated that subjects with genotype TT of the g.26395T>C genetic polymorphism showed a significantly higher adjusted value of BMD when compared with those of genotypes TC and CC. Subjects with genotype AA of the g.27649A>G genetic polymorphism showed a significantly higher adjusted value of BMD than those of genotypes AG and GG. These findings suggest that the OPG genetic polymorphisms may affect BMD and osteoporosis in Chinese postmenopausal women. [ABSTRACT FROM AUTHOR]