A CNN-LSTM based deep learning model with high accuracy and robustness for carbon price forecasting: A case of Shenzhen's carbon market in China.

Accurately predicting carbon trading prices using deep learning models can help enterprises understand the operational mechanisms and regulations of the carbon market. This is crucial for expanding the industries covered by the carbon market and ensuring its stable and healthy development. To ensure the accuracy and reliability of the predictions in practical applications, it is important to evaluate the model's robustness. In this paper, we built models with different parameters to predict carbon trading prices, and proposed models with high accuracy and robustness. The accuracy of the models was assessed using traditional survey indicators. The robustness of the CNN-LSTM model was compared to that of the LSTM model using Z-scores. The CNN-LSTM model with the best prediction performance was compared to a single LSTM model, resulting in a 9% reduction in MSE and a 0.0133 shortening of the Z-score range. Furthermore, the CNN-LSTM model achieved a level of accuracy comparable to other popular models such as CEEMDAN, Boosting, and GRU. It also demonstrated a training speed improvement of at least 40% compared to the aforementioned methods. These results suggest that the CNN-LSTM enhances model resilience. Moreover, the practicality of using Z-score to evaluate model robustness is confirmed.

Prediction of nitrous oxide emission of a municipal wastewater treatment plant using LSTM-based deep learning models.

Accurate assessment of greenhouse gas emissions from wastewater treatment plants is crucial for mitigating climate change. N2O is a potent greenhouse gas that is emitted from wastewater treatment plants during the biological denitrification process. In this study, we developed and evaluated deep learning models for predicting N2O emissions from a WWTP in Switzerland. Six key parameters were selected to obtain the optimal LSTM model by adjusting experimental parameter conditions. The optimal parameter condition was achieved with 150 neurons, the tanh activation function, the RMSprop optimization algorithm, a learning rate of 0.001, no dropout regularization, and a batch size of 128. Under the same conditions, we compared the performance of recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks. We found that LSTM models outperformed RNN models in predicting N2O emissions. The optimal LSTM model achieved a 36% improvement in mean absolute error (MAE), a 19% improvement in root mean squared error (RMSE), and a 6.92% improvement in R2 score compared to the RNN model. Additionally, LSTM models demonstrated better resilience to sudden changes in the target sequence, exhibiting a 9.54% higher percentage of explained variance compared to RNNs. These results highlight the potential of LSTM models for accurate and robust prediction of N2O emissions from wastewater treatment plants, contributing to effective greenhouse gas mitigation strategies.

Fabrication of Naturally Derived Double-Network Hydrogels With a Sustained Aspirin Release System for Facilitating Bone Regeneration.

Continuous efforts on pursuit of effective drug delivery systems for engineering hydrogel scaffolds is considered a promising strategy for the bone-related diseases. Here, we developed a kind of acetylsalicylic acid (aspirin, ASA)-based double-network (DN) hydrogel containing the positively charged natural chitosan (CS) and methacrylated gelatin (GelMA) polymers. Combination of physical chain-entanglement, electrostatic interactions, and a chemically cross-linked methacrylated gelatin (GelMA) network led to the formation of a DN hydrogel, which had a suitable porous structure and favorable mechanical properties. After in situ encapsulation of aspirin agents, the resulting hydrogels were investigated as culturing matrices for adipose tissue-derived stromal cells (ADSCs) to evaluate their excellent biocompatibility and biological capacities on modulation of cell proliferation and differentiation. We further found that the long-term sustained ASA in the DN hydrogels could contribute to the anti-inflammation and osteoinductive properties, demonstrating a new strategy for bone tissue regeneration.