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Abstract The ankle–foot prosthesis aims to compensate for the missing motor functions by fitting the motion characteristics of the human ankle, which contributes to enabling the lower-limb amputees to take care of themselves and improve mobility in daily life. To address the problems of poor bionic motion of the ankle–foot prosthesis and the lack of natural interaction among the patient, prosthesis, and the environment, we developed a complex reverse-rolling conjugate joint based on the human ankle–foot structure and motion characteristics, the rolling joint was used to simulate the rolling-sliding characteristics of the knee joint. Meanwhile, we established a segmental dynamics model of the prosthesis in the stance phase, and the prosthetic structure parameters were obtained with the optimal prosthetic structure dimensions and driving force. In addition, a carbon fiber energy-storage foot was designed based on the human foot profile, and the dynamic response of its elastic strain energy at different thicknesses was simulated and analyzed. Finally, we integrated a bionic ankle–foot prosthesis and experiments were conducted to verify the bionic nature of the prosthetic joint motion and the energy-storage characteristics of the carbon fiber prosthetic foot. The proposed ankle–foot prosthesis provides ambulation support to assist amputees in returning to social life normally and has the potential to help improve clinical viability to reduce medical rehabilitation costs.

Abstract The feasibility of aqueous zinc‐ion batteries for large‐scale energy storage is hindered by the inherent challenges of Zn anode. Drawing inspiration from cellular mechanisms governing metal ion and nutrient transport, erythritol is introduced, a zincophilic additive, into the ZnSO4 electrolyte. This innovation stabilizes the Zn anode via chelation interactions between polysaccharides and Zn2+. Experimental tests in conjunction with theoretical calculation results verified that the erythritol additive can simultaneously regulate the solvation structure of hydrated Zn2+ and reconstruct the hydrogen bond network within the solution environment. Additionally, erythritol molecules preferentially adsorb onto the Zn anode, forming a dynamic protective layer. These modifications significantly mitigate undesirable side reactions, thus enhancing the Zn2+ transport and deposition behavior. Consequently, there is a notable increase in cumulative capacity, reaching 6000 mA h cm⁻2 at a current density of 5 mA cm−2. Specifically, a high average coulombic efficiency of 99.72% and long cycling stability of >500 cycles are obtained at 2 mA cm−2 and 1 mA h cm−2. Furthermore, full batteries comprised of MnO2 cathode and Zn anode in an erythritol‐containing electrolyte deliver superior capacity retention. This work provides a strategy to promote the performance of Zn anodes toward practical applications.

The study of the polarimetric target decomposition algorithm with physical scattering models has contributed to the development of the field of remote sensing because of its simple and clear physical meaning with a small computational effort. However, most of the volume scattering models in these algorithms are for forests or crops, and there is a lack of volume scattering models for grasslands. In order to improve the accuracy of the polarimetric target decomposition algorithm adapted to grassland data, in this paper, a novel volume scattering model is derived considering the characteristics of real grassland plant structure and combined with the backward scattering coefficients of grass, which is abstracted as a rotatable ellipsoid of variable shape. In the process of rotation, the possibility of rotation is considered in two dimensions, the tilt angle and canting angle; for particle shape, the anisotropy degree A is directly introduced as a parameter to describe and expand the applicability of the model at the same time. After obtaining the analytical solution of the parameters and using the principle of least negative power to determine the optimal solution of the model, the algorithm is validated by applying it to the C-band AirBorne dataset of Hunshandak grassland in Inner Mongolia and the X-band Cosmos-Skymed dataset of Xiwuqi grassland in Inner Mongolia. The performance of the algorithm with five polarimetric target decomposition algorithms is studied comparatively. The experimental results show that the algorithm proposed in this paper outperforms the other algorithms in terms of grassland decomposition accuracy on different bands of data.

The complex influence of various disturbances on an inertially stabilized platform (ISP) restricts the further improvement of its servo performance. This article investigates the mapping relationship between internal and external disturbances and servo performance by establishing a high-precision dynamics model of the servo device with simulation and experiment. For internal disturbances, a nonlinear model of friction and backlash is established based on a BP neural network, and the transmission error is reconstructed based on the principle of main order invariance. For external disturbances, the road disturbance torque, changing inertia, and mass imbalance torque are modeled. The quantitative mapping relationship between internal and external disturbances and servo performance is obtained through simulation, in which friction and road disturbance are the largest internal and external factors affecting the servo performance, respectively. These conclusions are verified by load simulation experiments on a certain type of servo device, in which the servo performance is improved by 17% for every 25% reduction of friction torque, and the servo performance is reduced by 12% for every 33% increase of road disturbance torque. The research results provide a reference for servo device selection, performance indicator assignment, and performance prediction of the ISP.

ObjectiveCompared with the light-flashing paradigm, the ring-shaped motion checkerboard patterns avoid uncomfortable flicker or brightness modulation, improving the practical interactivity of brain-computer interface (BCI) applications. However, due to fewer harmonic responses and more concentrated frequency energy elicited by the ring-shaped checkerboard patterns, the mainstream untrained algorithms such as canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA) methods have poor recognition performance and low information transmission rate (ITR).MethodsTo address this issue, a novel untrained SSVEP-EEG feature enhancement method using CCA and underdamped second-order stochastic resonance (USSR) is proposed to extract electroencephalogram (EEG) features.ResultsIn contrast to typical unsupervised dimensionality reduction methods such as common average reference (CAR), principal component analysis (PCA), multidimensional scaling (MDS), and locally linear embedding (LLE), CCA exhibits higher adaptability for SSVEP rhythm components.ConclusionThis study recruits 42 subjects to evaluate the proposed method and experimental results show that the untrained method can achieve higher detection accuracy and robustness.SignificanceThis untrained method provides the possibility of applying a nonlinear model from one-dimensional signals to multi-dimensional signals.

Polarimetric target decomposition algorithms have played an important role in extracting the scattering characteristics of buildings, crops, and other fields. However, there is limited research on the scattering characteristics of grasslands and a lack of volume scattering models established for grasslands. To improve the accuracy of the polarimetric target decomposition algorithm applicable to grassland environments, this paper proposes an adaptive polarimetric target decomposition algorithm (APD) based on the anisotropy degree (A). The adaptive volume scattering model is used in APD to model volume scattering in forest and grassland regions separately by adjusting the value of A. When A > 1, the particle shape becomes a disk, and the grassland canopy is approximated as a cloud layer composed of randomly oriented disk particles; when A < 1, the particle shape is a needle, simulating the scattering mechanism of forests. APD is applied to an L-band AirSAR dataset from San Francisco, a C-band AirSAR dataset from Hunshandak grassland in Inner Mongolia Autonomous Region, and an X-band COSMO-SkyMed dataset from Xiwuqi grassland in Inner Mongolia Autonomous Region to verify the effectiveness of this method. Comparison studies are carried out to test the performance of APD over several target decomposition algorithms. The experimental results show that APD outperforms the algorithms tested in terms of this study in decomposition accuracy for grasslands and forests on different bands of data.

IntroductionSugar-sweetened beverage (SSB) intake is associated with an increased risk of cardiometabolic diseases. However, evidence regarding associations of artificially sweetened beverages (ASBs) and fruit juices with cardiometabolic diseases is mixed. In this study, we aimed to investigate the association between the SSB, ASB and fruit juice consumption with the incidence of cardiometabolic conditions and mortality.MethodsRelevant prospective studies were identified by searching PubMed, Web of Science, Embase, and Cochrane Library until December 2022 without language restrictions. The pooled relative risk (RR) and 95% confidence intervals (CIs) were estimated for the association of SSBs, ASBs, and fruit juices with the risk of type 2 diabetes (T2D), cardiovascular disease (CVD), and mortality by using random-effect models.ResultsA total of 72 articles were included in this meta-analysis study. Significantly positive associations were observed between the consumption of individual beverages and T2D risk (RR: 1.27; 95% CI: 1.17, 1.38 for SSBs; RR: 1.32; 95% CI: 1.11, 1.56 for ASBs; and RR:0.98; 95% CI: 0.93, 1.03 for fruit juices). Moreover, our findings showed that intakes of SSBs and ASBs were significantly associated with risk of hypertension, stroke, and all-cause mortality (RR ranging from 1.08 to 1.54; all p

In order to improve the state monitoring and adaptive control capability of inertial stabilization platforms (ISPs) with unknown loads, it is necessary to estimate the dynamic parameters comprehensively online. However, most current online estimation methods regard the system as a linear dual-inertia model which neglects the backlash and nonlinear friction torque. It reduces the accuracy of the model and leads to incomplete and low accuracy of the estimated parameters. The purpose of this research is to achieve a comprehensive and accurate online estimation of multiple parameters of ISPs and lay a foundation for state monitoring and adaptive control of ISPs. First, a dual-inertia model containing backlash and nonlinear friction torque of the motor and load is established. Then, the auto-regressive moving average (ARMA) model of the motor and load is established by the forward Euler method, which clearly expresses the online identification formula of the parameters. On this basis, the adaptive identification method based on the recursive extended least squares (RELS) algorithm is used to realize the online estimation of multiple parameters. The simulation and experimental results show that the proposed adaptive multi-parameter estimation method can realize the simultaneous online identification of the moment of inertia of the load, the damping coefficient of motor and load, the transmission stiffness, the Coulomb friction torque of motor and load, and the backlash, and the steady-state error is less than 10%. Compared with the traditional linear dual-inertia model, the similarity between the model based on the proposed adaptive parameter estimation algorithm and the actual system is increased by 65.3%.

Compared with zero-shot learning (ZSL), the generalized zero-shot learning (GZSL) is more challenging since its test samples are taken from both seen and unseen classes. Most previous mapping-based methods perform well on ZSL, while their performance degrades on GZSL. To solve this problem, inspired by the ensemble learning, this paper proposes a model with cooperative coupled generative networks (CCGN). Firstly, to alleviate the hubness problem, the reverse visual feature space is taken as the embedding space, with the mapping achieved by a visual feature center generation network. To learn a proper visual representation of each class, we propose a coupled of generative networks, which cooperate with each other to synthesize a visual feature center template of the class. Secondly, to improve the generative ability of the coupled networks, we further employ a deeper network to generate. Meanwhile, to alleviate loss semantic information problem caused by multiple network layers, a residual module is employed. Thirdly, to mitigate overfitting and to increase scalability, an adversarial network is introduced to discriminate the generation of visual feature centers. Finally, a reconstruction network, which reverses the generation process, is employed to restrict the structural correlation between the generated visual feature center and the original semantic representation of each class. Extensive experiments on five benchmark datasets (AWA1, AWA2, CUB, SUN, APY) demonstrate that the proposed algorithm yields satisfactory results, as compared with the state-of-the-art methods.

Twenty-two novel longifolene-derived diphenyl ether-carboxylic acid compounds 7a–7v were synthesized from renewable biomass resources longifolene, and their structures were confirmed by FT-IR, 1H NMR, 13C NMR, and HRMS. The preliminary evaluation of in vitro antifungal activity displayed that compound 7b presented inhibition rates of 85.9%, 82.7%, 82.7%, and 81.4% against Alternaria solani, Cercospora arachidicola, Rhizoctonia solani, and Physalospora piricola, respectively, and compound 7l possessed inhibition rates of 80.7%, 80.4%, and 80.3% against R. solani, C. arachidicola, P. piricola, respectively, exhibiting excellent and broad-spectrum antifungal activities. Besides, compounds 7f and 7a showed significant antifungal activities with inhibition rates of 81.2% and 80.7% against A.solani, respectively. Meanwhile, a reasonable and effective 3D-QSAR mode (r2 = 0.996, q2 = 0.572) has been established by the CoMFA method. Furthermore, the drug-loading complexes 7b/MgAl-LDH were prepared and characterized. Their pH-responsive controlled-release behavior was investigated as well. As a result, complex 7b/MgAl-LDH-2 exhibited excellent controlled-releasing performance in the water/ethanol (10:1, v:v) and under a pH of 5.7.

Cytochrome bc1 complex is an important component of cellular respiratory chain, and it is also an important target enzyme to inhibit the growth of plant pathogens. Using cytochrome bc1 complex as the target enzyme, twenty-three novel nopol-based 1,2,4-triazole-thioether compounds were designed and synthesized from natural preponderant resource β-pinene, and their structures were confirmed by FT-IR, NMR, ESI-MS and elemental analysis. The in vitro antifungal activity of the target compounds 5a-5w was preliminarily evaluated against eight plant pathogens at the concentration of 50 µg/ml. The bioassay results showed that the target compounds exhibited the best antifungal activity against Physalospora piricola, in which compounds 5b (R= o-CH3 Ph), 5e (R= o-OCH3 Ph), 5h (R= o-F Ph), 5m (R= o-Br Ph), 5o (R= m,m-OCH3 Ph), and 5r (R= p-OH Ph) had inhibition rates of 91.4, 83.3, 86.7, 83.8, 91.4 and 87.3%, respectively, much better than that of the positive control chlorothalonil. Also, compound 5a (R= Ph) had inhibition rate of 87.9% against Rhizoeotnia solani, and compound 5b (R= o-CH3 Ph) had inhibition rates of 87.6 and 89% against Bipolaris maydis and Colleterichum orbicala, respectively. In order to develop novel and promising antifungal compounds against P. piricola, the analysis of three-dimensional quantitative structure-activity relationship (3D-QSAR) was carried out using the CoMFA method on the basis of their antifungal activity data, and a reasonable and effective 3D-QSAR model (r2 = 0.944, q2 = 0.685) has been established. In addition, the theoretical study of molecular docking revealed that the target compounds could bind to and interact with the site of cytochrome bc1 complex.

The dual-motor precision transmission mechanism (DMPTM) is an alternative way to eliminate backlash while ensuring the stiffness of the servo system. However, most of the established models of DMPTM are not accurate enough, and are not conducive to the optimization of system performance and the design of high-precision controllers. In this paper, based on the detailed linear model of the single components of the DMPTM, the dead-zone model, considering the time-varying stiffness, is proposed to describe the backlash of the two transmission chains, and the friction of the mechanism is depicted by the Stribeck model. Then, a high-precision dynamic model of the DMPTM is formed. Finally, the model validation experiments for the open-loop and closed-loop are carried out in the time domain and frequency domain. The experimental results show that the proposed model can accurately describe the nonlinear characteristics of the mechanism. The Pearson correlation coefficient between the proposed model and the actual system is ropen-loop > 99.41%, for the open-loop, and rclosed-loop > 83.7%, for the closed-loop, and these results are both better than those of the existing model. In the frequency domain, whether it is the open-loop or closed-loop model, the frequency response of the proposed model also reproduces the actual system well, which verifies the accuracy of the model.

Dental pulp stem cells (DPSCs) and their exosomes (Exos) are effective treatments for regenerative medicine. Hypoxia was confirmed to improve the angiogenic potential of stem cells. However, the angiogenic effect and mechanism of hypoxia-preconditioned DPSC-Exos are poorly understood. We isolated exosomes from DPSCs under normoxia (Nor-Exos) and hypoxia (Hypo-Exos) and added them to human umbilical vein endothelial cells (HUVECs). HUVEC proliferation, migration and angiogenic capacity were assessed by CCK-8, transwell, tube formation assays, qRT-PCR and Western blot. iTRAQ-based proteomics and bioinformatic analysis were performed to investigate proteome profile differences between Nor-Exos and Hypo-Exos. Western blot, immunofluorescence and immunohistochemistry were used to detect the expression of lysyl oxidase-like 2 (LOXL2) in vitro and in vivo. Finally, we silenced LOXL2 in HUVECs and rescued tube formation with Hypo-Exos. Hypo-Exos enhanced HUVEC proliferation, migration and tube formation in vitro superior to Nor-Exos. The proteomics analysis identified 79 proteins with significantly different expression in Hypo-Exos, among which LOXL2 was verified as being upregulated in hypoxia-preconditioned DPSCs, Hypo-Exos, and inflamed dental pulp. Hypo-Exos partially rescued the inhibitory influence of LOXL2 silence on HUVEC tube formation. In conclusion, hypoxia enhanced the angiogenic potential of DPSCs-Exos and partially altered their proteome profile. LOXL2 is likely involved in Hypo-Exos mediated angiogenesis.

Secretory IgA is critical to prevent the invasion of pathogens via mucosa. However, the key factors and the mechanisms of IgA generation in the porcine gut are not well-understood. In this study, a panel of factors, including BAFF, APRIL, CD40L, TGF-β1, IL-6, IL-10, IL-17A, and IL-21, were employed to stimulate IgM+ B lymphocytes from porcine ileum Peyer's patches. The results showed that IL-21 significantly upregulated IgA production of B cells and facilitated cell proliferation and differentiation of antibody-secreting cells. In addition, three transcripts in porcine IgA class switch recombination (CSR), germ-line transcript α, post-switch transcript α, and circle transcript α, were first amplified by (nest-)PCR and sequenced. All these key indicators of IgA CSR were upregulated by IL-21 treatment. Furthermore, we found that IL-21 predominantly activated JAK1, STAT1, and STAT3 proteins and confirmed that the JAK-STAT signaling pathway was involved in porcine IgA CSR. Thus, IL-21 plays an important role in the proliferation and differentiation of IgA-secreting cells in porcine Peyer's patches through the JAK-STAT signaling pathway. These findings provide insights into the mucosal vaccine design by regulation of IL-21 for the prevention and control of enteric pathogens in the pig industry.

In recent years, with the continuous improvement of urban public transportation capacity, citizens’ travel has become more and more convenient, but there are still some potential problems, such as morning and evening peak congestion, imbalance between the supply and demand of vehicles and passenger flow, emergencies, and social local passenger flow surged due to special circumstances such as activities and inclement weather. If you want to properly guide the local passenger flow and make a reasonable deployment of operating buses, it is necessary to grasp the changing law of public transportation short-term passenger flow. This paper builds a short-term passenger flow prediction model for urban public transportation based on the idea of integrated learning. The goal is to use the integrated model to accurately predict the short-term passenger flow of urban public transportation, using Multivariable Linear Regression (MLR), K-Nearest Neighbor (KNN), eXtreme Gradient Boosting (XGBoost), and Gated Recurrent Unit (GRU) as the four seed models, and then use regression algorithm to integrate the model and predict the passenger flow, station boarding and landing, and cross-sectional passenger flow data of the typical representative line 428 in the “Huitian Area” of Beijing from January 1, 2020, to May 31, 2020. Finally, the prediction results of the submodels are compared with those of the integrated model to verify the superiority of the integrated model. The research results of this paper can enrich the short-term passenger flow forecasting system of urban public transportation and provide effective data support and scientific basis for the passenger flow, vehicle management, and dispatch of urban public transportation.

This paper proposes a new multi-objective bi-level programming model for the ring road bus lines and fare design problems. The proposed model consists of two layers: the traffic management operator and travelers. In the upper level, we propose a multi-objective bus lines and fares optimization model in which the operator’s profit and travelers’ utility are set as objective functions. In the lower level, evolutionary multi agent model of travelers’ bounded rational reinforcement learning with social interaction is introduced. A solution algorithm for the multi-objective bi-level programming is developed on the basis of the equalization algorithm of OD matrix. A numerical example based on a real case was conducted to verify the proposed models and solution algorithm. The computational results indicated that travel choice models with different degrees of rationality significantly changed the optimization results of bus lines and the differentiated fares; furthermore, the multi-objective bi-level programming in this paper can generate the solution to reduce the maximum section flow, increase the profit, and reduce travelers’ generalized travel cost.

Purple sweet potato starch is a potential resource for resistant starch production. The effects of heat-moisture treatment (HMT) and enzyme debranching combined heat-moisture treatment (EHMT) on the morphological, crystallinity and thermal properties of PSP starches were investigated. The results indicated that, after HMT or EHMT treatments, native starch granules with smooth surface was destroyed to form a more compact, irregular and sheet-like structure. The crystalline pattern was transformed from C-type to B-type with decreasing relative crystallinity. Due to stronger crystallites formed in modified starches, the swelling power and solubility of HMT and EHMT starch were decreased, while the transition temperatures and gelatinization enthalpy were significantly increased. In addition, HMT and EHMT exhibited greater effects on the proliferation of bifidobacteria compared with either glucose or high amylose maize starch.

ObjectiveTo investigate the expression of transcription factor forkhead box protein O (FOXO) genes in gastric cancer tissues and their correlation and clinical significance with Helicobacter pylori (Hp) infection. MethodsThe expression levels of FOXOs genes (including FOXO1, FOXO3, FOXO4, and FOXO6) were detected by immunohistochemistry in cancer tissues and paracancerous tissues from 41 gastric cancer patients with Hp (−) and 29 gastric cancer patients with Hp (+) , as well as in gastric tissues from 30 healthy individuals. The correlation between FOXOs expression and Hp infection, clinical pathological features was analyzed. The relationship between FOXOs expression and survival prognosis of gastric cancer patients was analyzed using the Kaplan-Meier plotter. ResultsCompared with those in the Hp(−) gastric cancer tissues, the expression levels of FOXO1, FOXO4, and FOXO6 were higher in the Hp(+) gastric cancer tissues (P