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• High atmospheric p CO 2 levels, constrained by the paired C-isotopic compositions of carbonate and organic carbon from ∼1.6 Ga Gaoyuzhuang Formation, could have played an important role in compensating for the low solar luminosity and maintaining the temperature of the Mesoproterozoic Earth's surface. It is hypothesized that the Mesoproterozoic was characterized by a prevailing warm climate and a high atmospheric p CO 2 level. However, quantitative constraints on Mesoproterozoic atmospheric p CO 2 are scarce. Here, we report high-resolution organic and inorganic carbon isotope records from sediment samples aged ∼1.6 Ga, collected from Hebei Province in North China. The δ13C org values of these Mesoproterozoic samples vary from −28.4‰ to −34.3‰, and the δ13C carbonate values range from −2.1‰ to 0.4‰. The paired carbonate and organic carbon isotope records can be used as a proxy to quantify the atmospheric CO 2 concentration during the Mesoproterozoic. According to the calibration between the photosynthetic isotopic effect and atmospheric CO 2 , atmospheric CO 2 concentrations are estimated to be within the range of 910–18,800 ppmv, ∼2–50 times greater than the present atmospheric level. The results support the previous hypothesis which suggests that high levels of atmospheric CO 2 prevailed in the Early Mesoproterozoic. High atmospheric pCO 2 levels could have played an important role in compensating for the possible lower solar luminosity and maintaining the temperature of the Mesoproterozoic Earth's surface. [ABSTRACT FROM AUTHOR]

• To effectively and practically solve the dynamic economic optimal control problem in greenhouse environments, an improved genetic algorithm with engineering constraint rules is proposed. • Engineering constraint rules greatly improve the algorithm optimization performance, and the practicability for the optimal control confines. • The setting of Nc is investigated, not only from the algorithm performance, but also from the practicality of control profiles. To effectively and practically solve the dynamic economic optimal control problem in greenhouse environments, an improved genetic algorithm with engineering constraint rules (R-GA) is proposed. Based on a dynamic greenhouse-crop model and control vector parameterization (CVP) method to discretize the control variables, the economic optimal control problem is transformed into a nonlinear programming (NLP) problem with finite-dimension parameters, and then R-GA is used to effectively solve the NLP problem. Three components were investigated, such as a smooth penalty function to deal with state variable path constraints, engineering constraint rules to improve the optimization performance and the algorithm feasibility, and the number of collocation points ( Nc ) to meet the actual control laws. The simulation results demonstrate that the proposed approach greatly improves the effectiveness and feasibility to solve the dynamic optimal control problem in greenhouse environments. [ABSTRACT FROM AUTHOR]

• The light intensity over 507 W ∙ m-2 did not have a significant effect on the transpiration rate increase. • The relationship between VPD and transpiration rate was expressed by Gauss peak fitting method. • Tomato transpiration model considering the specific environment in the greenhouse was developed. Since transpiration model of tomato is a main index for precise irrigation control in greenhouse soilless culture, more precise transpiration model needs to be developed for tomato cultivation. Although various models have been developed, they are limited under low VPD condition due to high light intensity in greenhouse, especially during the daytime at summer season of Korea. Therefore, the objective of this study was to develop an improved model considering the above unusual environment in greenhouse. Experiments were conducted between 2017 and 2018. The actual transpiration rate was measured by a load cell, weight measuring device, and used as data for model development. In 2018 experiment, an environment suitable for the growth of crops was established through shading film and humidification. The model equation was developed by calibrating parameters within the VPD range suitable for crop growth with compensated radiation under high light conditions. Experimental results showed that the transpiration rate had exponential increase to the maximum with respect to radiation and transpiration rate to VPD was fitted to gaussian peak shape. The developed model equation was verified by comparing with measured transpiration rate. Verification result showed that the developed model had a higher correlation coefficient than the existing one. In summary, a tomato transpiration model considering the specific environment in the greenhouse was developed in this study. It could be used to precision irrigation and environment control in greenhouse tomato cultivation. [ABSTRACT FROM AUTHOR]