The water uptake inducing cracking of sweet cherry fruit (Prunus avium L.) was investigated in relation to the respiratory characteristics of the fruit. Respiratory quotient (CO2: O2 ratio; RQ) decreased from 0.63 on 22 days after bloom (DAB) to 0.36 on 30 DAB when fruit susceptibility to cracking was generally highest, while it increased to 0.45 on 35 DAB. Water uptake per fruit was higher in the fruits in which the RQ was lower, demonstrating a negative correlation with RQ. Multiple regression analysis using RQ, stomatal density and total soluble solids content as independent variables, while water uptake per fruit as dependent variable indicated that RQ seemed to be predominant factor for water uptake of fruit. An RQ value lower than 1 may induce reduction in pressure in the intercellular spaces as compared to atmospheric pressure when the stomatal pore is covered with water; therefore, water on the fruit surface may be forced into the intercellular spaces of the mesocarp through the stomatal pore.
Acoustic emission (AE) occurs in the xylem during cavitation. In fact, AE behavior is influenced by changes in drought stress. For this study, four AE sensors were attached to a miniature tomato plants to investigate AE behavior during a long-term (32 d) change of drought stress. The change ratio of the AE occurrence rate from nighttime to daytime, RDAE roughly corresponded to the soil moisture when the soil moisture was less than 20% (VMC) though only one sample was used for the long-term measurement. Then, AE behavior occurring concomitantly with a rapid change of drought stress, induced by stem cutting, was investigated. The change ratio of AE occurrence rate caused by stem cutting corresponded to the drought stress, which was influenced by the cutting timing and soil moisture. The experimental results were explained using the model in which severe drought stress induced a transition of the embolism from temporary to permanent status. A parameter incorporating the change ratio of the AE occurrence rate caused by stem cutting well indicated the degree of embolism risk.
This study focuses on generating and controlling air flow caused by temperature differences and increasing the greening rate in urban areas by means of biowalls. In this study, computational fluid dynamics (CFD) software and an artificial neural network (ANN) inverse model were used to study generating and controlling air flow. First, an ANN inverse model was trained and tested using the data obtained from the CFD simulation. Then, the trained ANN inverse model recommended greening patterns to generate the desired air flow. Finally, a model study was conducted under similar conditions on the greening patterns recommended by the ANN inverse model. The most highly recommended greening pattern was whole-greening, in which the average temperature of 35.5°C would generate ascending air flow at a rate of 0.3 m • s−1. Wind velocity in the model study of a whole-greening pattern in which average temperature was 33.8°C, was 0.29 m • s−1 which is close to the desired wind velocity in the ANN inverse model. This result shows that it is possible to generate and control air flow near bio-greening caused by temperature differences, and this method which used CFD simulation and ANN inverse model is applicable.
This paper highlighted a new methodology for quality control of an Affective Bio-greening Material (ABGM). ABGM is a bio-greening material which is produced in non-uniform controlled environment to generate the dynamic appearance for satisfying the different consumer. In this methodology, the consumer evaluated easily the material affectivity using its appearance in the questionnaires. Subsequently, this appearance was detected in production using textural features extracted from grey level co-occurrences texture analysis and water content. Hence, quality control was defined as the function of irreversible change of textural appearance which was stimulated by water content. Artificial Neural Network (ANN) was used to develop an intelligent watchdog model. The model was defined that material affectivity can be controlled from textural images and water content in a given local temperature and relative humidity. The model was demonstrated via a case study of Sunagoke moss greening (Rhacomitrium japonicum). The trained ANN model generated satisfied correlation between measured and predicted values and minimum inspection error. The research results concluded the possibility to control the affectivity of a bio-greening material in a non-uniform environment based plant factory production using an intelligent watchdog model.
A dynamic model for simulating recombinant protein production in transgenic lettuce for optimum environmental control was developed. The model comprised three main parameters, which were leaf fresh weight, the number of cells, and amount of protein in each leaf. The differential equations of the model were integrated using the modeling software STELLATM. The model was calibrated based on the measured data of transgenic lettuce plants which were grown in a plant factory for 50 days from seeding. The plants contained an introduced Cauliflower mosaic virus-35S promoter fused onto the β-glucuronidase gene. Simulations were conducted to investigate the effect of plant growth control on the productivity of recombinant proteins which have various stabilities. The simulations indicated the importance of recombinant protein stability. If the parameters corresponding to the production and degradation of a recombinant protein can be identified, the simulation will be a strong tool for devising an environmental control strategy for highly efficient recombinant protein production systems.