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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 trial was designed to evaluate the effect of a standardized capsule of Aloe vera gel (AVG) on the quality of life (QOL) in patients with systolic heart failure (HF). Forty‐two patients were randomly divided into two groups to receive either AVG 150 mg or harmonized placebo capsules twice a day for 8 weeks. The patients were evaluated before and after the intervention using the Minnesota Living with Heart Failure Questionnaire (MLHFQ), New York Heart Association (NYHA) functional class, six‐minute walk test (6MWT), Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI) and STOP‐BANG questionnaires. Post‐intervention, AVG group indicated a significant reduction in the total score of MLHFQ (p < 0.001). The changes in MLHFQ and NYHA class were statistically significant after taking medication (p < 0.001 and p = 0.004, respectively). The change of 6MWT in the AVG group was more advanced; however, it was not statistically significant (p = 0.353). Moreover, in the AVG group, the severity of insomnia and obstructive sleep apnea decreased (p < 0.001, p = 0.01 respectively) and the sleep quality improved as well (p < 0.001). There were significantly fewer adverse events reported in the AVG group (p = 0.047). Therefore, AVG combined with standard medical therapy could provide more clinical benefits for patients with systolic HF. [ABSTRACT FROM AUTHOR]

Objective: The goal of this study was to determine whether the degree of weight loss after 6 months of a behavior-based intervention is related to baseline connectivity within two functional networks (FNs) of interest, FN1 and FN2, in a group of older adults with obesity.Methods: Baseline functional magnetic resonance imaging data were collected following an overnight fast in 71 older adults with obesity involved in a weight-loss intervention. Functional brain networks in a resting state and during a food-cue task were analyzed using a mixed-regression framework to examine the relationships between baseline networks and 6-month change in weight.Results: During the resting condition, the relationship of baseline brain functional connectivity and network clustering in FN1, which includes the visual cortex and sensorimotor areas, was significantly associated with 6-month weight loss. During the food-cue condition, 6-month weight loss was significantly associated with the relationship between baseline brain connectivity and network global efficiency in FN2, which includes executive control, attention, and limbic regions.Conclusion: These findings provide further insight into complex functional circuits in the brain related to successful weight loss and may ultimately aid in developing tailored behavior-based treatment regimens that target specific brain circuitry. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) comprise the most prevalent causes of morbidity and mortality in both men and women worldwide. CVDs are associated with several risk factors such as hyperlipidemia, diabetes mellitus, hypertension, obesity, tobacco smoking and an unhealthy diet. Currently, in addition to the use of related pharmacological treatments in the management of CVDs, the investigation of other suitable healthcare approaches for these disorders such as the identification of herbal medicines has been considered in the scientific communities. Aloe vera (L.) Burm.f. is a perennial medicinal plant. The innermost leaf layer of this plant contains transparent gel, which is used as food. Pre‐clinical studies have shown several biological activities of A. vera gel (AVG), including antidiabetic, lipid‐lowering, antioxidant, antiinflammatory, hepatoprotective, and immunomodulatory effects. Other pharmacological activities of AVG such as anti‐fibrotic, anti‐hypertensive, and anti‐atherosclerotic effects have been reported. Moreover, several clinical studies have demonstrated the ameliorating effects of AVG on some markers of CVDs risk factors. Thus, this study was conducted to review clinical trials besides in vitro and in vivo studies on the cardiac beneficial effects of AVG. However, further high‐quality studies are needed to firmly establish the clinical efficacy of the plant. [ABSTRACT FROM AUTHOR]

In the present work, a novel method is devised for controlling an uncertain wheeled mobile robot (WMR) in a desired path. To achieve this objective, an optimal and robust control system with adaptive gains is combined to benefit from the advantages of both methods. In fact, applying this controller not only controls a nonlinear system robustly against uncertainties and external disturbances, but also optimizes a quadratic cost function. Also, since the upper bound of uncertainty is determined by an adaptive law, it does not need to be adjusted manually. To ensure the designed controller's finite time stability, Lyapunov theory is used. Finally, to illustrate the effectiveness of the proposed control method, a case study involving a WMR is presented. By comparing the results of this approach with those of an adaptive sliding mode control (ASMC), it is shown that the proposed controller uses less control effort, relative to the ASMC controller, to stabilize the uncertain WMR in the presence of external disturbances. [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]

Objective: The purpose of this study was to determine whether baseline measures of hedonic hunger—the Power of Food Scale—and self‐control for food consumption—the Weight Efficacy Lifestyle Questionnaire—were associated with network topology within two sets of brain regions (regions of interest [ROIs] 1 and 2) in a group of older adults with obesity. These previously identified brain regions were shown in a different cohort of older adults to be critical for discriminating weight loss success and failure. Methods: Baseline functional magnetic resonance imaging data (resting state and food cue task) were collected in a novel cohort of 67 older adults with obesity (65‐85 years, BMI = 35‐42 kg/m2) participating in an 18‐month randomized clinical trial on weight regain. Results: The Power of Food Scale was most related to ROI 1, which includes the visual cortex and sensorimotor processing areas during only the food cue state. During both the food cue and resting conditions, the Weight Efficacy Lifestyle Questionnaire was associated with ROI 2, which includes areas of the attention network and limbic circuitry. Conclusions: Our findings show critical, distinct links between brain network topology with self‐reported measures that capture hedonic hunger and the confidence that older adults have in resisting the consumption of food because of both intrapersonal and social/environmental cues. [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]

The equations of motion for a viscoelastic polymeric piezoelectric microplate are established based on the thin plate theory and Kelvin-Voigt laws. Polyvinylidene fluoride is chosen as the polymeric piezoelectric material. The plate is assumed to be rectangular and the boundary conditions are clamped at all edges. Liquid is modelled as a damping foundation beneath the plate. The equations are solved using assumed-mode method along with Newmark's β method. The effects of variation in the input voltage, damping coefficient, viscoelastic parameter, and excitation frequency are discussed. The results are compared with the developed finite element method. An excellent agreement is observed between the two methods. [ABSTRACT FROM AUTHOR]

A group of patients with pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were reported from China in December 2019. Although several antiviral drugs are widely tested, none of them has been approved as specific antiviral therapy for coronavirus disease 2019 (COVID-19). Accumulating evidence established a hyperinflammatory states or cytokine storm in COVID-19. Among these cytokines, IL-6 plays a key role in cytokine storm and can predict the adverse clinical outcomes and fatality in these patients. Based on the evidence of the significant role of IL-6 in cytokine storm, diabetes mellitus, and cardiovascular diseases as principal comorbidities, it seems that anti-cytokine therapy may be useful in patients with severe COVID-19 to reduce mortality. Recent studies demonstrated that herbal-derived natural products had immunosuppressive and anti-inflammatory properties and exhibited exceptional act on mediators of inflammation. Parthenolide is the principal sesquiterpene lactones and the main biologically active constituent Tanacetum parthenium (commonly known as feverfew) which has could significantly reduce IL-1, IL-2, IL-6, IL-8, and TNF-α production pathways established in several human cell line models in vitro and in vivo studies. Therefore, parthenolide may be one of the herbal candidates for clinical evaluation. [ABSTRACT FROM AUTHOR]