DataSheet1_A novel machine learning ensemble forecasting model based on mixed frequency technology and multi-objective optimization for carbon trading price.PDF

Carbon trading prices are crucial for carbon emissions and transparent carbon market pricing. Previous studies mainly focused on data mining in the prediction direction to quantify carbon trading prices. Although the prospect of high-frequency data forecasting mechanisms is considerable, more mixed-frequency ensemble forecasting is needed for carbon trading prices. Therefore, this article designs a new type of ensemble prediction model to increase the scope of model research. The module is divided into three parts: data denoising, mixed frequency and machine learning, multi-objective optimization, and ensemble forecasting. Precisely, the data preprocessing technology enhanced by adopting a self-attention mechanism can better remove noise and extract effective features. Furthermore, mixed frequency technology is introduced into the machine learning model to achieve more comprehensive and efficient prediction, and a new evaluation criterion is proposed to measure the optimal submodel. Finally, the ensemble model based on deep learning strategy can effectively integrate the advantages of high-frequency and low-frequency data in complex datasets. At the same time, a new multi-objective optimization algorithm is proposed to optimize the parameters of the ensemble model, significantly improving the predictive ability of the integrated module. The results of four experiments and the Mean Absolute Percent Error index of the proposed model improved by 28.3526% compared to machine learning models, indicating that the ensemble model established can effectively address the time distribution characteristics and uncertainty issues predicted by carbon trading price models, which helps to mitigate climate change and develop a low-carbon economy.</p

Table2_A novel machine learning ensemble forecasting model based on mixed frequency technology and multi-objective optimization for carbon trading price.XLSX

Carbon trading prices are crucial for carbon emissions and transparent carbon market pricing. Previous studies mainly focused on data mining in the prediction direction to quantify carbon trading prices. Although the prospect of high-frequency data forecasting mechanisms is considerable, more mixed-frequency ensemble forecasting is needed for carbon trading prices. Therefore, this article designs a new type of ensemble prediction model to increase the scope of model research. The module is divided into three parts: data denoising, mixed frequency and machine learning, multi-objective optimization, and ensemble forecasting. Precisely, the data preprocessing technology enhanced by adopting a self-attention mechanism can better remove noise and extract effective features. Furthermore, mixed frequency technology is introduced into the machine learning model to achieve more comprehensive and efficient prediction, and a new evaluation criterion is proposed to measure the optimal submodel. Finally, the ensemble model based on deep learning strategy can effectively integrate the advantages of high-frequency and low-frequency data in complex datasets. At the same time, a new multi-objective optimization algorithm is proposed to optimize the parameters of the ensemble model, significantly improving the predictive ability of the integrated module. The results of four experiments and the Mean Absolute Percent Error index of the proposed model improved by 28.3526% compared to machine learning models, indicating that the ensemble model established can effectively address the time distribution characteristics and uncertainty issues predicted by carbon trading price models, which helps to mitigate climate change and develop a low-carbon economy.</p

Table3_A novel machine learning ensemble forecasting model based on mixed frequency technology and multi-objective optimization for carbon trading price.XLSX

Carbon trading prices are crucial for carbon emissions and transparent carbon market pricing. Previous studies mainly focused on data mining in the prediction direction to quantify carbon trading prices. Although the prospect of high-frequency data forecasting mechanisms is considerable, more mixed-frequency ensemble forecasting is needed for carbon trading prices. Therefore, this article designs a new type of ensemble prediction model to increase the scope of model research. The module is divided into three parts: data denoising, mixed frequency and machine learning, multi-objective optimization, and ensemble forecasting. Precisely, the data preprocessing technology enhanced by adopting a self-attention mechanism can better remove noise and extract effective features. Furthermore, mixed frequency technology is introduced into the machine learning model to achieve more comprehensive and efficient prediction, and a new evaluation criterion is proposed to measure the optimal submodel. Finally, the ensemble model based on deep learning strategy can effectively integrate the advantages of high-frequency and low-frequency data in complex datasets. At the same time, a new multi-objective optimization algorithm is proposed to optimize the parameters of the ensemble model, significantly improving the predictive ability of the integrated module. The results of four experiments and the Mean Absolute Percent Error index of the proposed model improved by 28.3526% compared to machine learning models, indicating that the ensemble model established can effectively address the time distribution characteristics and uncertainty issues predicted by carbon trading price models, which helps to mitigate climate change and develop a low-carbon economy.</p

Table1_A novel machine learning ensemble forecasting model based on mixed frequency technology and multi-objective optimization for carbon trading price.XLSX

Carbon trading prices are crucial for carbon emissions and transparent carbon market pricing. Previous studies mainly focused on data mining in the prediction direction to quantify carbon trading prices. Although the prospect of high-frequency data forecasting mechanisms is considerable, more mixed-frequency ensemble forecasting is needed for carbon trading prices. Therefore, this article designs a new type of ensemble prediction model to increase the scope of model research. The module is divided into three parts: data denoising, mixed frequency and machine learning, multi-objective optimization, and ensemble forecasting. Precisely, the data preprocessing technology enhanced by adopting a self-attention mechanism can better remove noise and extract effective features. Furthermore, mixed frequency technology is introduced into the machine learning model to achieve more comprehensive and efficient prediction, and a new evaluation criterion is proposed to measure the optimal submodel. Finally, the ensemble model based on deep learning strategy can effectively integrate the advantages of high-frequency and low-frequency data in complex datasets. At the same time, a new multi-objective optimization algorithm is proposed to optimize the parameters of the ensemble model, significantly improving the predictive ability of the integrated module. The results of four experiments and the Mean Absolute Percent Error index of the proposed model improved by 28.3526% compared to machine learning models, indicating that the ensemble model established can effectively address the time distribution characteristics and uncertainty issues predicted by carbon trading price models, which helps to mitigate climate change and develop a low-carbon economy.</p

The scatter plot showing the association between individual FD values and PIQ>VIQ profiles for ASD boys.

Data shown are from ASD participants with higher PIQs relative to their VIQ scores (N = 17 out of 20 ASD participants have PIQ>VIQ scores). Overall FD is higher for ASD participants who have a higher, wider PIQ>VIQ spread compared to ASD participants with a lower or narrower PIQ>VIQ difference (P = 0.023). The size of the marker denotes the corresponding root mean square error (RMSE) value to the log-log line of best fit for each participant; a smaller marker indicates a lower error value to the line of best fit and a larger marker indicates a higher value. Note. PIQ: Performance Intelligence Quotient; VIQ: Verbal Intelligence Quotient. All IQ subtest scores are within the normal range, above 70.</p

The scatter plot shows individual fractal dimension (FD) values for the right cerebellar cortex.

The left panel shows FD values for typically developing (TD) children (N = 18) and the right panel FD for ASD children (N = 20). **denotes P<0.05, Bonferroni correction.</p

Understanding the Product Selectivity of Syngas Conversion on ZnO Surfaces with Complex Reaction Network and Structural Evolution

Recently, a bifunctional oxide–zeolite (OX-ZEO) catalyst was widely studied experimentally, which can selectively convert syngas to light olefins. The performance of OX-ZEO is exceptional, while the mechanism is controversial. In this work, we have first developed an algorithm based on graph theory to establish a complete reaction network for syngas conversion to methanol, ketene, and methane. Combined with density functional theory (DFT) calculations, the activity and selectivity of syngas conversion over zinc oxide (ZnO) are systematically studied by a reaction phase diagram. The key intermediate, ketene, is observed in experiments, which has been first confirmed theoretically in this work. The evolution of ZnO surfaces is found to be a key factor of diverse product selectivity. It is found that methanol production is more favored over the ZnO surfaces with a low oxygen vacancy concentration. As the oxygen vacancy increases, the main product evolves gradually from methanol to ketene and finally to methane. Accordingly, the overall reaction activity increases too. Our prediction from the reaction phase diagram is finally verified by microkinetic modeling

Demographic characteristics of the sample.

<p>Demographic characteristics of the sample.</p

Computational Insights on Electrocatalytic Synthesis of Methylamine from Nitrate and Carbon Dioxide

Carbon dioxide (CO2) and nitrate (NO3–) co-reduction to valuable chemicals, e.g., methylamine (MMA) production, by electricity generated from renewable energy sources, has been considered as a promising route for electrochemical synthesis. However, the mechanism of C–N coupling in the process is key and remains unclear. Herein, we studied the (quasi) activity trend over a set of metal phthalocyanine (MPc) via density functional theory (DFT) calculations and reaction phase diagram analysis. Surprisingly, we found that the lowest barrier for C–N coupling is via desorbed HCHO and NH2OH rather than either being adsorbed on the catalyst. Then, we performed kinetic barrier calculations and microkinetic modeling over CoPc–NH2, which was previously observed with the capability of producing MMA, to understand the Faradaic efficiency trends of MMA and other products at different potentials. Our kinetic model provides important insights for improving the MMA activity and selectivity and is useful for catalyst design for C–N coupling in general

Reduced structural complexity of the right cerebellar cortex in male children with autism spectrum disorder - Fig 3

<p><b>Illustration of subtle surface non-linearities in the right cerebellar cortex of two individual participants (one TD and one ASD) as captured by the FD measure (<i>D</i></b>_<b>2</b><b>), (A)</b> TD male child (9.21 years old, UCLA 0051278 in ABIDE) and <b>(B)</b> ASD male child (10 years old, Yale 0050602 in ABIDE. The left panel shows the bilateral cerebellums in the coronal plane whereas the right panel shows a rendering of the right cerebellar cortex for each participant and its corresponding log-log plot. Note that the final slope estimate (i.e., the FD value) is higher for the TD participant, with higher <i>R</i>² and lower root mean square error (RMSE), indicating a better fit for TD individual relative to the participant with ASD.</p