As the Earth warms, alternatives to traditional farming are crucial. Exploring fungi, especially poly extremophilic and extremotolerant species, to be used as plant probiotics, represents a promising option. Extremophilic fungi offer avenues for developing and producing innovative biofertilizers, effective biocontrol agents against plant pathogens, and resilient enzymes active under extreme conditions, all of which are crucial to enhance agricultural efficiency and sustainability through improved soil fertility and decreased reliance on agrochemicals. Yet, extremophilic fungi’s potential remains underexplored and, therefore, comprehensive research is needed to understand their roles as tools to foster sustainable agriculture practices amid climate change. Efforts should concentrate on unraveling the complex dynamics of plant-fungi interactions and harnessing extremophilic fungi’s ecological functions to influence plant growth and development. Aspects such as plant’s epigenome remodeling, fungal extracellular vesicle production, secondary metabolism regulation, and impact on native soil microbiota are among many deserving to be explored in depth. Caution is advised, however, as extremophilic and extremotolerant fungi can act as both mitigators of crop diseases and as opportunistic pathogens, underscoring the necessity for balanced research to optimize benefits while mitigating risks in agricultural settings.

The main contribution of this paper is the design of an event-triggered formation control for leader-following consensus in second-order multi-agent systems (MASs) under communication faults. All the agents must follow the trajectories of a virtual leader despite communication faults considered as smooth time-varying delays dependent on the distance between the agents. Linear matrix inequalities (LMIs)-based conditions are obtained to synthesize a controller gain that guarantees stability of the synchronization error. Based on the closed-loop system, an event-triggered mechanism is designed to reduce the control law update and information exchange in order to reduce energy consumption. The proposed approach is implemented in a real platform of a fleet of unmanned aerial vehicles (UAVs) under communication faults. A comparison between a state-of-the-art technique and the proposed technique has been provided, demonstrating the performance improvement brought by the proposed approach.

In this paper, we present the design of a fractional-order sliding mode observer (FO-SMO) for actuator fault estimation in a quadrotor unmanned aerial vehicle (QUAV) system. Actuator faults can significantly compromise the stability and performance of QUAV systems; therefore, early detection and compensation are crucial. Sliding mode observers (SMOs) have recently demonstrated their accuracy in estimating faults in QUAV systems under matched uncertainties. However, existing SMOs encounter difficulties associated with chattering and sensitivity to initial conditions and noise. These challenges significantly impact the precision of fault estimation and may even render fault estimation impossible depending on the magnitude of the fault. To address these challenges, we propose a new fractional-order SMO structure based on the Caputo derivative definition. To demonstrate the effectiveness of the proposed FO-SMO in overcoming the limitations associated with classical SMOs, we assess the robustness of the FO-SMO under three distinct scenarios. First, we examined its performance in estimating actuator faults under varying initial conditions. Second, we evaluated its ability to handle significant chattering phenomena during fault estimation. Finally, we analyzed its performance in fault estimation under noisy conditions. For comparison purposes, we assess the performance of both observers using the Normalized Root-Mean-Square Error (NRMSE) criterion. The results demonstrate that our approach enables more accurate actuator fault estimation, particularly in scenarios involving chattering phenomena and noise. In contrast, the performance of classical (non-fractional) SMO suffers significantly under these conditions. We concluded that our FO-SMO is more robust to initial conditions, chattering phenomena, and noise than the classical SMO.