Eucommia ulmoides gum (EUG) was applied in blend rubber with a heavily limited amount because of its duality of rubber and plastic, and an efficient way of triazolinedione (TAD)-based Alder-ene reaction was used to improve the elastic properties of EUG. Binary modification of EUG with two TADs containing hexyl and polyhedral oligomeric silsesquioxane (POSS) groups were conducted to generate the modified EUG elastomers with tunable mechanical properties and good compatibility by varying TAD contents. When the low hexyl (1%) and POSS (0.2%) TADs incorporated, the modified EUGs displayed high tensile strength of 36.57 MPa with the elongation at break of 876%, and thus high toughness of 152.14 MJ m −3 . If high contents of hexyl (20%) and POSS (0.2%) TADs employed, the modified EUGs exhibited excellent elongation at break of 1165% and recovery rate of 60%, and especially its loss factor reached up to 0.83-0.65 at 20-70 C. Therefore, the modified EUGs containing the polar urazole and POSS groups should be a novel elastomer with good compatibility, wear resistance, and damping properties.

Block copolymers with push–pull azobenzene pendants and core–shell nanostructures exhibited high and regulated dielectric constants by photoisomerization of azobenzene groups, low dielectric loss, and high energy density.

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In this study, we focus on the preparation, microstructures, and properties of polyurethane (PU) elastomers by using polyether glycol, toluene diisocyanate, and extender chain reagent. We find that the elastomers prepared by using extender chain reagent exhibit the most excellent mechanical properties. Furthermore, by an optimum synthetic route, hard segments can be formed uniformly in the soft segments, leading to the perfect micro-phase separation. We also find that the relative molecular weight of polyether glycol can affect the mechanical properties of the synthesized PU elastomers, i.e., the more relative molecular weight, the more soft segments, and the smaller tensile strength, rupture strength, and rigidity.

Temperature prediction is significant for precise control of the greenhouse environment. Traditional machine learning methods usually rely on a large amount of data. Therefore, it is difficult to make a stable and accurate prediction based on a small amount of data. This paper proposes a temperature prediction method for greenhouses. With the prediction target transformed to the logarithmic difference of temperature inside and outside the greenhouse,the method first uses XGBoost algorithm to make a preliminary prediction. Second, a linear model is used to predict the residuals of the predicted target. The predicted temperature is obtained combining the preliminary prediction and the residuals. Based on the 20-day greenhouse data, the results show that the target transformation applied in our method is better than the others presented in the paper. The MSE (Mean Squared Error) of our method is 0.0844, which is respectively 20.7%, 76.0%, 10.2%, and 95.3% of the MSE of LR (Logistic Regression), SGD (Stochastic Gradient Descent), SVM (Support Vector Machines), and XGBoost algorithm. The results indicate that our method significantly improves the accuracy of the prediction based on the small-scale data.