Biostimulants are materials that when applied in small amounts are capable of promoting plant growth. Nanoparticles (NPs) and nanomaterials (NMs) can be considered as biostimulants since, in specific ranges of concentration, generally in small levels, they increase plant growth. Pristine NPs and NMs have a high density of surface charges capable of unspecific interactions with the surface charges of the cell walls and membranes of plant cells. In the same way, functionalized NPs and NMs, and the NPs and NMs with a corona formed after the exposition to natural fluids such as water, soil solution, or the interior of organisms, present a high density of surface charges that interact with specific charged groups in cell surfaces. The magnitude of the interaction will depend on the materials adhered to the corona, but high-density charges located in a small volume cause an intense interaction capable of disturbing the density of surface charges of cell walls and membranes. The electrostatic disturbance can have an impact on the electrical potentials of the outer and inner surfaces, as well as on the transmembrane electrical potential, modifying the activity of the integral proteins of the membranes. The extension of the cellular response can range from biostimulation to cell death and will depend on the concentration, size, and the characteristics of the corona.
This paper proposes a nonlinear synchronization controller for a swarm of unicycle robots performing a cooperative task, i.e., following a desired trajectory per robot while maintaining a prescribed formation. The effect of communication between robots is analyzed and several network topologies are investigated, e.g., all‐to‐all, ring type, undirected, among others. The stability analysis of the closed loop system is provided using the Lyapunov method. Experiments with four unicycle robots are presented to validate the control law and communication analysis. Accumulated errors over the experiment time are presented in order to determine which topology is most efficient.
This paper is addressed to the problem of designing an adaptive cascade controller to stabilize a jacketed continuous chemical reactor whose isothermal dynamics are globally stable. Assuming that the heat generation and transfer models are uncertain, the controller is built on the basis of back-stepping procedure in conjunction with the mass and energy conservation principles that underlie the reactor behaviour. The corresponding closed-loop dynamics are studied yielding a semiglobal stability criterion coupled with a systematic construction-tuning procedure. The resulting cascade controller has aPI-type structure with an antireset windup scheme, and is put in perspective with its industrial-type counterpart. A simulated example is used to illustrate the functioning of the proposed controller.
Tomato is one the most important vegetables worldwide and mineral nutrition in tomato crops is considered as the second most important factor in crop management after water availability. Mathematical modeling techniques allow us to design strategies for nutrition management. In order to generate the necessary information to validate and calibrate a dynamic growth model, two tomato crop cycles were developed. Several mineral analyses were performed during crop development to determine the behavior of N, P, K, Ca, Mg and S in different organs of the plant. Regression models were generated to mimic the behavior of minerals in tomato plants and they were included in the model in order to simulate their dynamic behavior. The results of this experiments showed that the growth model adequately simulates leaf and fruit weight (EF > 0.95 and Index > 0.95). As for harvested fruits and harvested leaves, the simulation was less efficient (EF < 0.90 and Index < 0.90). Simulation of minerals was suitable for N, P, K and S as both, the EF and the Index, had higher values than 0.95. In the case of Ca and Mg, simulations showed indices below 0.90. These models can be used for planning crop management and to design more appropriate fertilization strategies.
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