Role of Hydraulic Signal and ABA in Decrease of Leaf Stomatal and Mesophyll Conductance in Soil Drought-Stressed Tomato

Drought reduces leaf stomatal conductance (gs) and mesophyll conductance (gm). Both hydraulic signals and chemical signals (mainly abscisic acid, ABA) are involved in regulating gs. However, it remains unclear what role the endogenous ABA plays in gm under decreasing soil moisture. In this study, the responses of gs and gm to ABA were investigated under progressive soil drying conditions and their impacts on net photosynthesis (An) and intrinsic water use efficiency (WUEi) were also analyzed. Experimental tomato plants were cultivated in pots in an environment-controlled greenhouse. Reductions of gs and gm induced a 68–78% decline of An under drought conditions. While soil water potential (Ψsoil) was over −1.01 MPa, gs reduced as leaf water potential (Ψleaf) decreased, but ABA and gm kept unchanged, which indicating gs was more sensitive to drought than gm. During Ψsoil reduction from −1.01 to −1.44 MPa, Ψleaf still kept decreasing, and both gs and gm decreased concurrently following to the sustained increases of ABA content in shoot sap. The gm was positively correlated to gs during a drying process. Compared to gs or gm, WUEi was strongly correlated with gm/gs. WUEi improved within Ψsoil range between −0.83 and −1.15 MPa. In summary, gs showed a higher sensitivity to drought than gm. Under moderate and severe drought at Ψsoil ≤ −1.01 MPa, furthermore from hydraulic signals, ABA was also involved in this co-ordination reductions of gs and gm and thereby regulated An and WUEi.

Monitoring of Deep Processing of Oil De-Noising Technique

Oil for deep processing of the signal contains a lot of pulse interference monitoring and random non-stationary noise characteristics; this paper presents a wavelet-based moving average against impulse noise filter. First on the hydraulic signal wavelet de-noising, wavelet processing and then the results after anti-pulse moving average processing. The simulation signals and hydraulic monitoring signal de-noising experiments show that: the use of wavelet technology and anti-pulse interference filter combination of moving average method has good oil pressure monitoring signal filtering, to achieve deep process debris blocking fault laid the foundation for monitoring.

Method Based on Knowledge Modules of Hydraulic Fault and it’s Application

According to various problems by using traditional redundant fault-tolerance technology and information fault-tolerant technology, a novel method of knowledge modules of hydraulic fault (KMHF) was proposed in this paper. The principle of the method was that new type of fluid information expression units were designed to perceive and process the fault information of the system, then the information was expressed as mechanical signal, hydraulic signal or electrical signal, which could be used to implement fault tolerance at power stage. The engineering application of the method of KMHF can solve the problems of the whole fault tolerance in the hydraulic system, moreover, with it the structure of hydraulic system can be greatly simplified.