Performance investigation of a solar-driven cascaded phase change heat storage cross-seasonal heating system for plateau applications.

The mismatch between solar radiation resources and building heating demand on a seasonal scale makes cross-seasonal heat storage a crucial technology, especially for plateau areas. Utilizing phase change materials with high energy density and stable heat output effectively improves energy storage efficiency. This study integrates cascaded phase change with a cross-seasonal heat storage system aimed at achieving low-carbon heating. The simulation analyzes heat distribution and temperature changes from the heat storage system to the heating terminal. The results indicate that although the solar collectors operate for 26.3% of the total heat storage and heating period, the cumulative heat stored is 45.4% higher than the total heating load. Heat transferred by the cross-seasonal heat storage system accounts for up to 61.2% of the total heating load. Therefore, the system reduces fuel consumption by 77.6% compared to conventional fossil fuel heating systems. Moreover, radiant floor heating terminals, with a wide range of operating temperatures, match well with cascaded phase change heat storage and can reduce operation time by 19.5% and heat demand by 5.2% compared to conventional radiators. In addition to demonstrating the feasibility of applying cascaded phase change technology in cross-seasonal heat storage heating, this study reveals the lifecycle sustainability due to the shortened heat storage period. The configuration, parameters, and simulation results provide a reference basis for system application and design.

Variation pattern, influential factors, and prediction models of PM2.5 concentrations in typical urban functional zones of northeast China.

This study investigated the spatial and temporal variations of PM2.5 concentrations in Harbin, China, under the influence of meteorological parameters and gaseous pollutants. The complex relationship between meteorological parameters and pollutants was explored using Pearson correlation analysis and interaction effect analysis. Using the correlation analysis and interaction analysis methods, four mechanical learning models, PCC-Is-CNN, PCC-Is-LSTM, PCC-Is-CNN-LSTM and PCC-Is-BP neural network, were developed for predicting PM2.5 concentration in different time scales by combining the long-term and short-term data with the basic mechanical learning models. The results show that the PCC-Is-CNN-LSTM model has superior prediction performance, especially when integrating short-term and long-term historical data. Meanwhile, applying the model to cities in other climatic zones, the results show that the model performs well in the Dwa climatic zone, while the prediction performance is lower in the CWa climatic zone. This suggests that although the model is well adapted in regions with a similar climate to Harbin, model performance may be limited in areas with complex climatic conditions and diverse pollutant sources. This study emphasizes the importance of considering meteorological and pollutant interactions to improve the accuracy of PM2.5 predictions, providing valuable insights into air quality management in cold regions.

3D-QSAR Studies on High-affinity Phosphodiestera.

BACKGROUND: Recent studies have found that Phosphodiesterase-4 (PDE4) is closely related to the pathogenesis of depression, cognitive impairment and neurological impairment. OBJECTIVE: Our objective is to develop potent inhibitors of the high-affinity phosphodiesterase 4D isoform (PDE4D) that can serve as radioligands for Positron Emission Tomography (PET) imaging, thereby advancing research in the field of neurological diseases. METHODS: We employed a multi-step approach combining three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling, molecular docking, classification techniques, and CoMSIA analysis to investigate the conformational relationship of highaffinity PDE4D inhibitors as PET ligands. ADMET and Drug-likeness predictions were also conducted. By utilizing these methods, our aim was to identify more potent PDE4D inhibitors. RESULTS: The results showed that the CoMSIA model with the best principal component scores (n=7) had a cross-validated Q2 value of 0.602 and a non-cross-validated R2 value of 0.976. These results affirmed the excellent predictive capability of the established CoMSIA model. Analysis of the generated 3D-QSAR contour plots highlighted specific regions in the molecular structure of the compounds that can be further optimized and modified. Guided by the contour plots, we designed 100 novel PDE4D inhibitors, and molecular docking was performed for the top 4 compounds with high activity. The molecular docking scores were promising, and ADMET and drug similarity predictions yielded satisfactory results. Taking into consideration these factors, compound 51c was determined to be the optimal compound, laying a solid foundation for further research. CONCLUSION: For the continued development of PDE4D PET radioligand, these models and new compounds' developing methodology offer a theoretical foundation and crucial references.