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Abstract Background Experiences during childhood and adolescence have enduring impacts on physical and mental well-being, overall quality of life, and socioeconomic status throughout one’s lifetime. This underscores the importance of prioritizing the health of children and adolescents to establish an impactful healthcare system that benefits both individuals and society. It is crucial for healthcare providers and policymakers to examine the relationship between COVID-19 and the health of children and adolescents, as this understanding will guide the creation of interventions and policies for the long-term management of the virus. Methods In this umbrella review (PROSPERO ID: CRD42023401106), systematic reviews were identified from the Cochrane Database of Systematic Reviews; EMBASE (OvidSP); and MEDLINE (OvidSP) from December 2019 to February 2023. Pairwise and single-arm meta-analyses were extracted from the included systematic reviews. The methodological quality appraisal was completed using the AMSTAR-2 tool. Single-arm meta-analyses were re-presented under six domains associated with COVID-19 condition. Pairwise meta-analyses were classified into five domains according to the evidence classification criteria. Rosenberg’s FSN was calculated for both binary and continuous measures. Results We identified 1551 single-arm and 301 pairwise meta-analyses from 124 systematic reviews that met our predefined criteria for inclusion. The focus of the meta-analytical evidence was predominantly on the physical outcomes of COVID-19, encompassing both single-arm and pairwise study designs. However, the quality of evidence and methodological rigor were suboptimal. Based on the evidence gathered from single-arm meta-analyses, we constructed an illustrative representation of the disease severity, clinical manifestations, laboratory and radiological findings, treatments, and outcomes from 2020 to 2022. Additionally, we discovered 17 instances of strong or highly suggestive pairwise meta-analytical evidence concerning long-COVID, pediatric comorbidity, COVID-19 vaccines, mental health, and depression. Conclusions The findings of our study advocate for the implementation of surveillance systems to track health consequences associated with COVID-19 and the establishment of multidisciplinary collaborative rehabilitation programs for affected younger populations. In future research endeavors, it is important to prioritize the investigation of non-physical outcomes to bridge the gap between research findings and clinical application in this field.

This paper investigates the attitude controller design for an underactuated reusable launch vehicle (RLV) with nonminimum phase property. To address the nonminimum phase problem, a zero dynamic active suppression control (ZDASC) method based on Byrnes-Isidori (B-I) standard model transformation is proposed. First, a computable criterion for the nonminimum phase property of RLVs is presented, by which we can effectively determine the nonminimum phase property based on aerodynamic parameters in the RLV model. Second, an augmented system consisting of internal dynamics and dynamic compensator is constructed, and a ZDASC method is proposed for the RLV attitude control system with nonminimum phase property, which can enable adaptive stabilization of the RLV’s zero dynamics under different operating conditions and uncertainties. ZDASC achieves output accurate tracking without solving the ideal internal dynamics, which reduces the computational complexity and enhances system robustness. Finally, simulation results of the RLV attitude control are provided to verify the effectiveness and robustness of the proposed controller in tracking the guidance commands as well as stabilizing the internal dynamics.

Abstract Resection of oral and maxillofacial tumors is often accompanied by the inferior alveolar nerve neurectomy, resulting in abnormal sensation in lower lip. It is generally believed that spontaneous sensory recovery in this nerve injury is difficult. However, during our follow-up, patients with inferior alveolar nerve sacrifice showed different degrees of lower lip sensory recovery. In this study, a prospective cohort study was conducted to demonstrate this phenomenon and analyze the factors influencing sensory recovery. A mental nerve transection model of Thy1-YFP mice and tissue clearing technique were used to explore possible mechanisms in this process. Gene silencing and overexpression experiments were then conducted to detect the changes in cell morphology and molecular markers. In our follow-up, 75% of patients with unilateral inferior alveolar nerve neurectomy had complete sensory recovery of the lower lip 12 months postoperatively. Patients with younger age, malignant tumors, and preservation of ipsilateral buccal and lingual nerves had a shorter recovery time. The buccal nerve collateral sprouting compensation was observed in the lower lip tissue of Thy1-YFP mice. ApoD was demonstrated to be involved in axon growth and peripheral nerve sensory recovery in the animal model. TGF-β inhibited the expression of STAT3 and the transcription of ApoD in Schwann cells through Zfp423. Overall, after sacrificing the inferior alveolar nerve, the collateral compensation of the ipsilateral buccal nerve could innervate the sensation. And this process was regulated by TGF-β-Zfp423-ApoD pathway.

X-ray inspections of contraband are widely used to maintain public transportation safety and protect life and property when people travel. To improve detection accuracy and reduce the probability of missed and false detection, a contraband detection algorithm YOLOv8s-DCN-EMA-IPIO* based on YOLOv8s is proposed. Firstly, the super-resolution reconstruction method based on the SRGAN network enhances the original data set, which is more conducive to model training. Secondly, DCNv2 (deformable convolution net v2) is introduced in the backbone network and merged with the C2f layer to improve the ability of the feature extraction and robustness of the model. Then, an EMA (efficient multi-scale attention) mechanism is proposed to suppress the interference of complex background noise and occlusion overlap in the detection process. Finally, the IPIO (improved pigeon-inspired optimization), which is based on the cross-mutation strategy, is employed to maximize the convolutional neural network’s learning rate to derive the optimal group’s weight information and ultimately improve the model’s detection and recognition accuracy. The experimental results show that on the self-built data set, the mAP (mean average precision) of the improved model YOLOv8s-DCN-EMA-IPIO* is 73.43%, 3.98% higher than that of the original model YOLOv8s, and the FPS is 95, meeting the deployment requirements of both high precision and real-time.

During the Northridge earthquake, extensive brittle failures on the weld zones of the beam bottom flanges in the rigidity connection of steel special moment frames (SMFs) were detected. One of the primary reasons is the high-tensile strain demand created at the beam bottom flange zones due to positive bending. The weak panel zone of the I-section column exhibits more shear deformation, which promotes and accelerates the brittle fracture of the beam bottom flange weld zones. A box-strengthened panel zone can minimize the shear deformation of the panel zone of the I-section column, which may also reduce the inter-story displacement of steel SMFs and enhance their seismic behavior. In order to investigate this fact, in this research we carried out a model test of a steel frame with a box-strengthened panel zone to examine SMFs’ seismic performance and inter-story displacement, as well as testing the contribution of panel zone shear deformation to inter-story drift. Numerical methods were then used to investigate the influence of the axial compression ratio and beam-to-column linear stiffness ratio on the effect of shear deformation on the box-strengthened panel zone. Design recommendations are given based on the research results.

Citrus honey (CH) is rich in nutrients that have a wide variety of biological functions, such as antibacterial, anti–inflammatory, and antioxidant activities, and which demonstrate therapeutic properties, such as anti–cancer and wound–healing abilities. However, the effects of CH on alcohol–related liver disease (ALD) and the intestinal microbiota remain unknown. This study aimed to determine the alleviating effects of CH on ALD and its regulatory effects on the gut microbiota in mice. In total, 26 metabolites were identified and quantified in CH, and the results suggested that the primary metabolites were abscisic acid, 3,4–dimethoxycinnamic acid, rutin, and two markers of CH, hesperetin and hesperidin. CH lowered the levels of aspartate aminotransferase, glutamate aminotransferase, and alcohol–induced hepatic edema. CH could promote the proliferation of Bacteroidetes while reducing the abundance of Firmicutes. Additionally, CH also showed some inhibitory effects on the growth of Campylobacterota and Turicibacter. CH enhanced the secretion of short–chain fatty acids (SCFAs), such as acetic acid, propionic acid, butyric acid, and valeric acid. Given its alleviating functions in liver tissue damage and its regulatory effects on the gut microbiota and SCFAs, CH could be a promising candidate for the therapeutic treatment of ALD.

High-temperature superconducting (HTS) magnetic levitation (maglev) trains for designed high speed need a non-contact braking method that can produce stable and sufficient braking forces to ensure the safety of the train during emergency braking. In order to study the braking effects of permanent magnet eddy current braking (PMECB) used in HTS maglev vehicles and its effects on the levitation performance of HTS maglev vehicles, an equivalent two-dimensional simulation model of PMECB for a HTS maglev test vehicle under different working air gaps of 5 mm, 10 mm, 15 mm and 20 mm was established in Maxwell software. Then, a 6 degree of freedom dynamic model of the vehicle was established in Universal Mechanism software. In the dynamic simulation, the normal force of PMECB was not considered, and only the detent force of PMECB was taken as the excitation of the vehicle. The simulation results show that PMECBs can reduce the vehicle to relatively low speed in a few seconds. During the operation of PMECBs, the levitation height and levitation force of the maglev Dewar will be affected, and maximum variations in levitation heights and levitation forces occur on the Dewars at both ends of the vehicle. These help us to understand the braking and levitation performance of HTS maglev vehicles under the action of PMECBs and enrich the design idea of braking and levitation systems of HTS maglev vehicles equipped with PMECBs.

Background: Pneumatic muscle actuator (PMA) actuated multisection continuum arms are widely applied in various fields with high flexibility and bionic properties. Nonetheless, their kinematic modeling and control strategy proves to be extremely challenging tasks. Methods: The relationship expression between the deformation parameters and the length of PMA with the geometric method is obtained under the assumption of piecewise constant curvature. Then, the kinematic model is established based on the improved D-H method. Considering the limitation of PMA telescopic length, an impedance iterative learning backstepping control strategy is investigated. For one thing, the impedance control is utilized to ensure that the ideal static balance force is maintained constant in the Cartesian space. For another, the iterative learning backstepping control is applied to guarantee that the desired trajectory of each PMA can be accurately tracked with the output-constrained requirement. Moreover, iterative learning control (ILC) is implemented to dynamically estimate the unknown model parameters and the precondition of zero initial error in ILC is released by the trajectory reconstruction. To further ensure the constraint requirement of the PMA tracking error, a log-type barrier Lyapunov function is employed in the backstepping control, whose convergence is demonstrated by the composite energy function. Results: The tracking error of PMA converges to 0.004 m and does not exceed the time-varying constraint function through cosimulation. Conclusion: From the cosimulation results, the superiority and validity of the proposed theory are verified.

In this paper, a finite-time control strategy composed of disturbance observer and sliding mode controller is investigated for five-degree of freedom (5-DOF) upper-limb exoskeleton subject to system uncertainties and external disturbances. First, a new finite-time disturbance observer (FTDO) is constructed to handle unknown disturbances and modeling uncertainties. Second, a nonsingular fast terminal sliding mode (TSM) approach is presented to follow the desired joint angles. By combining the novel FTDO and the nonsingular fast TSM technology, the exoskeleton angle tracking errors can be forced to zero within a finite time. Furthermore, the stability of the exoskeleton system is illustrated via Lyapunov. Finally, the tracking performance of the proposed control scheme is verified by simulations.

An output feedback finite-time control strategy for five degree of freedoms (DOF) upper-limb exoskeleton is investigated in this paper. First, a multivariable second-order sliding mode control is proposed to improve the response speed and stability of the exoskeleton system with parameter uncertainties and external disturbances. Furthermore, for the sake of high control precision with limited information, a finite-time linear extended state observer is constructed to estimate both the unmeasured states and the lumped perturbations. Finally, by utilizing the 5-DOF upper-limb model, simulation results are presented to demonstrate the effectiveness of the control strategy.

Multivariate time series data, which comprise a set of ordered observations for multiple variables, are pervasively generated in weather conditions, traffic, financial stocks, etc. Therefore, it is of great significance to analyze the correlation between multiple time series. Financial stocks generate a significant amount of multivariate time series data that can be used to build networks that reflect market behavior. However, traditional commercial complex networks cannot fully utilize the multiple attributes of stocks and redundant filter relationships and reveal a more authentic financial stock market. We propose a fusion similarity of multiple time series and construct a threshold network with similarity. Furthermore, we define the connectivity efficiency to choose the best threshold, establishing a high connectivity efficiency network with the optimal network threshold. By searching the central node in the threshold network, we have found that the network center nodes constructed by our proposed method have a more comprehensive industry coverage than the traditional techniques to build the systems, and this also proves the superiority of this method.

Diffusion bonding of V–4Cr–4Ti alloy to pure titanium (Ti) was carried out by hot forging in the temperature range between 1150 °C and 950 °C. The microstructure and mechanical properties of the bonded joint were determined by using light optical microscopy (OM), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), micro-hardness measurements, and shear tests. The results indicate that the sound dissimilar joint can be obtained through hot forging processes. The interface has a width of about 100 μm and can be divided into two distinctive zones: a Widmanstatten structure zone and a β-Ti phase structure zone. According to the micro-hardness distribution profile across the substrates and the interface, the largest hardness of 332 HV was measured in a narrow region between the Ti substrate and the joint, which is remarkably higher than that of the substrates (Ti of 190 HV and V–4Cr–4Ti alloy of 258 HV). The lowest hardness of only 182 HV was found in a region in the V–4Cr–4Ti substrate adjacent to the joint. The shear test showed that the joint has a bonding strength higher than 165 MPa and a fracture of the joint took place in the region with the highest hardness.

The defects have been confirmed to activate catalytic sites and significantly enhance electrocatalytic activity. However, the influence of defects on the electronic spin state of catalytic active sites and their impact on catalytic behavior are still in the early stages of research. Spin behavior is a fundamental property of the electrons. Herein, using NiFe-LDH nanosheets as the model structure, we constructed trivalent cation vacancy models to gain a deeper understanding of the intrinsic relationship among defects, spin, and catalytic activity from the perspective of the electronic spin state. The presence of defects leads to an increased proportion of the dz² orbital perpendicular to the plane and the dxy orbital parallel to the LDH layer, which enhancement effectively improves the capture of out-of-plane oxygen intermediates and facilitates electron transfer within the plane. As a result, the catalytic activity for the oxygen evolution reaction (OER) is greatly improved. In addition, magnetic field experiments may also be used to better understand the role of spin in the catalytic process. With the increase of defect concentration, the spin-magnetic response intensity of the OER can be effectively enhanced, which is related to the defect-induced spin single electron. This work explains how defects can effectively modulate the electron spin properties of active centers, thereby achieving enhanced catalytic reaction kinetics. By shedding light on the principles governing catalytic sites at the electron spin level, we would aid in the understanding and design of catalysts at the spintronic level. [ABSTRACT FROM AUTHOR]