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Featured Application: Conidiospores of Trichoderma harzianum can be applied as biocontrols for plant diseases, which are eco-friendly and safe for human health. The best model obtained in this work is used to find the optimal conditions to produce the maximum number of conidiospores. An alternative to facing plagues without affecting ecosystems is the use of biocontrols that keep crops free of harmful organisms. There are some studies showing the use of conidiospores of Trichoderma harzianum as a medium for the biological control of plagues. To find the optimal parameters to maximize the production of conidiospores of Trichoderma harzianum in barley straw, this process is modeled in this work through artificial neural networks and response surface modeling. The data used in this modeling include the amount of conidiospores in grams per milliliter, the culture time from 48 to 136 h in intervals of 8 h, and humidity percentages of 70%, 75%, and 80%. The surface response model presents R2 = 0.8284 and an RMSE of 4.6481. On the other hand, the artificial neural network with the best performance shows R2 = 0.9952 and RMSE = 0.7725. The modeling through both methodologies can represent the behavior of the Trichoderma harzianum conidiospores growth in barley straw, showing that the artificial neural network has better goodness of fit than the response surface methodology, and it can be used for obtaining the optimal values for producing conidiospores. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) facilitate complex activities and are widely used for aerial transport. Quadrotor UAVs (QUAV), the most popular UAV containing four motors, are characterised by higher control properties since they have fewer actuators than degrees of freedom, implying a nonlinear underactuated system. In addition, the coupling of dynamics, flaws while modelling and parameter uncertainty are the factors that hinder the design and implementation of a controller. Here, we present the modelling, optimisation, simulation, and implementation methodology for controllers, proportional-integralderivative (PID), and super-twisting-sliding mode control (ST-SMC). We carry out the parameterisation problem of controllers using the hunger game search (HGS) metaheuristic algorithm. This process was developed offline, and the values obtained were successfully implemented in simulation and experimental form. The testing platform comprises a motion capture system, Vicon® Bonita cameras, linked by ROS, that allows the known position and the attitude of a Parrot® QUAV bebop1. The whole six dynamics of the QUAV are included in the implementation, translational trajectories X-Y are trapezoidal, and the altitude trajectory is a ramp. The results enabled the comparison of the statistics calculation of each controller. Successful tracking trajectories were obtained even with disturbance when the ST-SMC algorithm was implemented with root mean square error (RMSE)=0.0176. [ABSTRACT FROM AUTHOR]

The one-dimensional cutting-stock problem (1D-CSP) consists of obtaining a set of items of different lengths from stocks of one or different lengths, where the minimization of waste is one of the main objectives to be achieved. This problem arises in several industries like wood, glass, and paper, among others similar. Different approaches have been designed to deal with this problem ranging from exact algorithms to hybrid methods of heuristics or metaheuristics. The African Buffalo Optimization (ABO) algorithm is used in this work to address the 1D-CSP. This algorithm has been recently introduced to solve combinatorial problems such as travel salesman and bin packing problems. A procedure was designed to improve the search by taking advantage of the location of the buffaloes just before it is needed to restart the herd, with the aim of not to losing the advance reached in the search. Different instances from the literature were used to test the algorithm. The results show that the developed method is competitive in waste minimization against other heuristics, metaheuristics, and hybrid approaches. [ABSTRACT FROM AUTHOR]

The development of quadrotor unmanned aerial vehicles (QUAVs) is a growing field due to their wide range of applications. QUAVs are complex nonlinear systems with a chaotic nature that require a controller with extended dynamics. PD and PID controllers can be successfully applied when the parameters are accurate. However, this parameterization process is complicated and time-consuming; most of the time, parameters are chosen by trial and error without guaranteeing good performance. The originality of this work is to present a novel nonlinear mathematical model with aerodynamic moments and forces in the Newton–Euler formulation, and identify metaheuristic algorithms applied to parameter optimization of compensated PD and PID controls for tracking the trajectories of a QUAV. Eight metaheuristic algorithms (PSO, GWO, HGS, LSHADE, LSPACMA, MPA, SMA and WOA) are reported, and RMSE is used to measure each dynamic performance of the simulations. For the PD control, the best performance is obtained with the HGS algorithm with an RMSE = 0.037247252379126. For the PID control, the best performance is obtained with the HGS algorithm with an RMSE = 0.032594309723623. Trajectory tracking was successful for the QUAV by minimizing the error between the desired and actual dynamics. [ABSTRACT FROM AUTHOR]

Permutation flow shop scheduling problem deals with the production planning of a number of jobs processed by a set of machines in the same order. Several metaheuristics have been proposed for minimizing the makespan of this problem. Taking as basis the previous Alternate Two-Phase PSO (ATPPSO) method and the neighborhood concepts of the Cellular PSO al- gorithm proposed for continuous problems, this paper proposes the improvement of ATPPSO with a simple adaptive local search strategy (called CAPSO-SALS) to enhance its performance. CAPSO-SALS keeps the simplicity of ATPPSO and boosts the local search based on a neigh- borhood for every solution. Neighbors are produced by interchanges or insertions of jobs which are selected by a linear roulette scheme depending of the makespan of the best personal positions. The performance of CAPSO-SALS is evaluated using the 12 different sets of Taillard's benchmark problems and then is contrasted with the original and another previous enhancement of the ATPPSO algorithm. Finally, CAPSO-SALS is compared as well with other ten classic and state-of-art metaheuristics, obtaining satisfactory results. [ABSTRACT FROM AUTHOR]

A Petri net (PN) is a directed graph which consists of two kinds of nodes called places and transitions. Besides their graphical representation, PN possess a mathematical formalism based on the incidence matrix and the state equation. In this paper we show that PN can be used as a general tool to represent the evolution of any elementary cellular automaton (ECA). This is performed by matrix operations obtained from the state equation of the PN which represent the cellular automaton and the use of a logical operator. It is presented an algorithm to construct a PN for any ECA and we give some comparative examples between the evolution of markings of the PN and the evolution of the respective ECA. [ABSTRACT FROM PUBLISHER]

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