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Fiber hybridization is suggested to enhance the properties of fiber‐reinforced composites. Regarding the matrix, thermoplastic alternatives to conventional thermosets are needed to balance mechanical performance and sustainability. We combined woven flax and carbon fibers with a polyamide 11 matrix into a hybrid biocomposite and studied its manufacturing process as well as its impact, tensile, and damping properties. Mechanical damage was investigated using scanning electron microscopy and X‐ray computed tomography. Hybridization significantly improved the tensile properties: 233% higher modulus and 432% higher strength than pure flax composites and 19% higher failure strain than pure carbon composites. Additionally, the hybrid composites exhibited a positive hybrid effect with respect to the impact resistance, characterized by higher displacement at maximum impact force and occurrence of combined damage mechanisms. Although the damping behavior of the hybrid composites remained inferior to that of pure flax composites, their damping factor was 20% higher than that of pure carbon composites. These results provide valuable insights into the mechanical performance of carbon‐flax hybrid composites. Highlights: Enhanced mechanical performance and impact resistance in woven flax‐carbon/PA11 hybrid biocomposite compared to nonhybrid reference composites.Superior tensile modulus and strength of hybrid biocomposite than pure flax fiber composite, and greater failure strain than pure carbon fiber composite.A positive hybridization effect in impact properties with a greater displacement at maximum force and higher dissipated impact energy, indicating enhanced ductility and fracture toughness.Improved damping behavior in hybrid biocomposite compared to the pure carbon composite. [ABSTRACT FROM AUTHOR]

This study presents a novel control strategy for managing nonlinear systems in the presence of mismatched uncertainties. Dealing with the uncertainties that do not satisfy the so‐called matching conditions is an ongoing issue in control engineering. In this regard, and for the first time, a disturbance observer (DO)‐based hybrid control system, which considers robustness as well as control signal optimality, is developed in this article. For this purpose, and with the aim of robustly managing uncertain nonlinear systems and achieving optimized control effort, an optimal control law based on the state‐dependent Riccati equation is integrated with a DO‐based second‐order sliding mode controller. The Lyapunov stability theory is applied to verify the asymptotic stability of the designed control system. Computer simulations are performed to demonstrate the efficacy and the superiority of our novel controller over two other existing methods (DO‐based sliding mode control [SMC] and DO‐based adaptive SMC). The simulation results show that the presented method is capable of managing uncertain nonlinear systems robustly and with much less control effort than the two mentioned methods. [ABSTRACT FROM AUTHOR]

Analyzing the structure and function of the brain from a network perspective has increased considerably over the past two decades, with regional subnetwork analyses becoming prominent in the recent literature. However, despite the fact that the brain, as a complex system of interacting subsystems (i.e., subnetworks), cannot be fully understood by analyzing its constituent parts as independent elements, most studies extract subnetworks from the whole and treat them as independent networks. This approach entails neglecting their interactions with other brain regions and precludes identifying potential compensatory mechanisms outside the analyzed subnetwork. In this study, using simulated and empirical data, we show that the analysis of brain subnetworks within the context of their whole‐brain networks, that is, including their interactions with other brain regions, can yield different outcomes when compared to analyzing them as independent networks. We also provide a multivariate mixed‐effects modeling framework that allows analyzing subnetworks within the context of their whole‐brain networks, and show that it can better disentangle global (whole‐brain) and local (subnetwork) differences when compared to standard t‐test analyses. T‐test analyses may produce misleading results in identifying complex global and local level differences. The provided multivariate model is an extension of a previously developed model for global, system‐level hypotheses about the brain. The modified version detailed here provides the same utilities as the original model—quantifying the relationship between phenotypes and brain connectivity, comparing brain networks among groups, predicting brain connectivity from phenotypes, and simulating brain networks—but for local, subnetwork‐level hypotheses. [ABSTRACT FROM AUTHOR]

Complex brain networks formed via structural and functional interactions among brain regions are believed to underlie information processing and cognitive function. A growing number of studies indicate that altered brain network topology is associated with physiological, behavioral, and cognitive abnormalities. Graph theory is showing promise as a method for evaluating and explaining brain networks. However, multivariate frameworks that provide statistical inferences about how such networks relate to covariates of interest, such as disease phenotypes, in different study populations are yet to be developed. We have developed a freely available MATLAB toolbox with a graphical user interface that bridges this important gap between brain network analyses and statistical inference. The modeling framework implemented in this toolbox utilizes a mixed‐effects multivariate regression framework that allows assessing brain network differences between study populations as well as assessing the effects of covariates of interest such as age, disease phenotype, and risk factors on the density and strength of brain connections in global (i.e., whole‐brain) and local (i.e., subnetworks) brain networks. Confounding variables, such as sex, are controlled for through the implemented framework. A variety of neuroimaging data such as fMRI, EEG, and DTI can be analyzed with this toolbox, which makes it useful for a wide range of studies examining the structure and function of brain networks. The toolbox uses SAS, R, or Python (depending on software availability) to perform the statistical modeling. We also provide a clustering‐based data reduction method that helps with model convergence and substantially reduces modeling time for large data sets. [ABSTRACT FROM AUTHOR]