Analysis and Prediction of Rural Residents’ Living Consumption Growth in Sichuan Province Based on Markov Prediction and ARMA Model

I select 32 samples concerning per capita living consumption of rural residents in Sichuan Province during the period 1978-2009. First, using Markov prediction method, the growth rate of living consumption level in the future is predicted to largely range from 10% to 20%. Then, in order to improve the prediction accuracy, time variable t is added into the traditional ARMA model for modeling and prediction. The prediction results show that the average relative error rate is 1.56%, and the absolute value of relative error during the period 2006-2009 is less than 0.5%. Finally, I compare the prediction results during the period 2010-2012 by Markov prediction method and ARMA model, respectively, indicating that the two are consistent in terms of growth rate of living consumption, and the prediction results are reliable. The results show that under the similar policies, rural residents' consumer demand in Sichuan Province will continue to grow in the short term, so it is necessary to further expand the consumer market.

Machine learning and Bayesian optimization for performance prediction of proton-exchange membrane fuel cells

Proton-exchange membrane fuel cells (PEMFCs) are critical components of zero-emission electro-hydrogen generators. Accurate performance prediction is vital to the optimal operation management and preventive maintenance of these generators. Polarization curve remains one of the most important features representing the performance of PEMFCs in terms of efficiency and durability. However, predicting the polarization curve is not trivial as PEMFCs involve complex electrochemical reactions that feature multiple nonlinear relationships between the operating variables as inputs and the voltage as outputs. Herein, we present an artificial-intelligence-based approach for predicting the PEMFCs’ performance. In that way, we propose first an explainable solution for selecting the relevant features based on kernel principal component analysis and mutual information. Then, we develop a machine learning approach based on XGBRegressor and Bayesian optimization to explore the complex features and predict the PEMFCs’ performance. The performance and the robustness of the proposed machine learning based prediction approach is tested and validated through a real industrial dataset including 10 PEMFCs. Furthermore, several comparison studies with XGBRegressor and the two popular machine learning-based methods in predicting PEMFC performance, such as artificial neural network (ANN) and support vector machine regressor (SVR) are also conducted. The obtained results show that the proposed approach is more robust and outperforms the two conventional methods and the XGBRegressor for all the considered PEMFCs. Indeed, according to the coefficient of determination criterion, the proposed model gains an improvement of 6.35%, 6.8%, and 4.8% compared with ANN, SVR, and XGBRegressor respectively.

AN INVESTIGATION ON ASM TURBULENCE MODEL IN PREDICTIING NONISOTROPIC TURBULENT FLOWS

Non-isotropy is evident for flows with large streamline curvature. Algebraic stress model can fairly simulate non-isotropy of turbulence. Some flow situations were investigated with a well developed ASM model. The calcu- lated results show that the distributions of Reynolds stresses in different directions are obviously different. The non-isotropy of the flow is evident. The effect of streamline curvature on the flow feature of submerged water jet restricted by solid boundaries were studied, and its application in the design of energy dissipators of free trajectory jet type was disscused.

Challenges and perspectives for structural biology of IncRNAs-the example of the Xist IncRNA A-repeats

Following the discovery of numerous long non-coding RNA(IncRNA)transcripts in the human genome,their important roles in biology and human disease are emerging.Recent progress in experimental methods has enabled the identification of structural features of IncRNAs.However,determining high-resolution structures is challenging as IncRNAs are expected to be dynamic and adopt multiple conformations,which may be modulated by interaction with protein binding partners.The X-inactive specific transcript(Xist)is necessary for X inactivation during dosage compensation in female placental mammals and one of the beststudied IncRNAs.Recent progress has provided new insights into the domain organization,molecular features,and RNA binding proteins that interact with distinct regions of Xist.The A-repeats located at the 5'end of the transcript are of particular interest as they are essential for mediating silencing ofthe inactive X chromosome.Here,we discuss recent progress with elucidating structural features of the Xist IncRNA,focusing on the A-repeats.We discuss the experimental and computational approaches employed that have led to distinct structural models,likely reflecting the intrinsic dynamics of this RNA.The presence of multiple dynamic conformations may also play an important role in the formation ofthe associated RNPs,thus influencing the molecular mechanism underlying the biological function of the Xist A-repeats.We propose that integrative approaches that combine biochemical experiments and high-resolution structural biology in vitro with chemical probing and functional studies in vivo are required to unravel the molecular mechanisms of IncRNAs.