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Abstract Background Childhood obesity is increasingly recognized as a major public health challenge worldwide, and excessive sedentary screen time is emerging as a key risk factor. This study aimed to assess the recreational screen sedentary time of Chinese primary school-aged children and investigate the relationship between screen-related family factors and the outcome variable. Methods Our study used data from a cross-sectional survey collected from fifth-grade students and their parents in Beijing, China, from April to May 2018 (n = 2,373). The questions included basic demographic information, family socioeconomic status, students’ and parents’ sedentary and exercising habits, within-family communicational factors, and health belief patterns. The recreational screen sedentary time of the children was compared across demographic groups. The study employed multivariate linear regression models to examine associations between children’s screen time and various family factors, as well as the moderating effect of overall family communication. Results Our findings revealed an average daily recreational screen sedentary time of 2.4 h among participants. Screen time significantly varied across demographic categories, including children’s sex, age, residence, parents’ education, household income, family size, and primary family member. After adjustment, the proportion of child-owned digital devices (p

Background: During the COVID-19 pandemic, numerous studies focused on mental health, but few considered both positive and negative aspects within the dual-factor model of psychological well-being. In China, a highly populous country, limited evidence exists regarding mental health and its associated factors following the surge and decline of COVID-19 cases after the loosening of COVID-19 control measures. This study aims to investigate the mental health status of Chinese residents in the aftermath of the pandemic and factors influencing positive and negative indicators using the System-Based Model of Stress. Methods: A cross-sectional online survey of 1,026 participants was conducted in China from March 2–31, 2023, using quota sampling. Structural equation modeling (SEM) was performed to test the conceptual model, where social support, perceived susceptibility, perceived severity, pandemic-related events, coping style, and concern about COVID-19 were considered as predictors, and psychological distress and subjective well-being as outcomes. Results: The results revealed high prevalence rates of psychological distress (23 %) with either of anxiety (15 %) or depression (20 %), and poor subjective well-being (23 %) among Chinese residents after the COVID-19 pandemic. Social support was negatively correlated with psychological distress, and negative coping style, pandemic-related events, and concern about COVID-19 were positively correlated with psychological distress. Moreover, social support was positively correlated with subjective well-being, and negative coping style and pandemic-related events were negatively correlated with subjective well-being. Conclusions: These findings enhance our understanding of the differing correlates of positive and negative mental health, suggesting targeted psychological interventions for post-pandemic and future public health events.

Observer-based control is the most commonly used method in the control of electro-hydraulic servo system (EHSS) with uncertainties, but it suffers from the drawback of low accuracy under the influence of large external load forces and disturbances. To address this problem, this paper proposes a novel compensation function observer-based backstepping sliding-mode control (BSMC) approach to achieve high-accuracy tracking control. In particular, the model uncertainties, including nonlinearities, parameter perturbations and external disturbances are analyzed and treated together as a lumped disturbance. Then, a fourth-order compensation function observer (CFO) is constructed, which fully utilizes the system state information to accurately estimate the lumped disturbance. On this basis, the estimate of the lumped disturbance is incorporated into the design of a backstepping sliding-mode controller, allowing the control system to compensate for the disturbance effect. The stability of the closed-loop control system under the CFO and BSMC is rigorously proven through the use of the Lyapunov theory, which guarantees that all the tracking error signals converge exponentially to the origin. Comparative simulations are carried out to show the effectiveness and efficiency of the proposed approach, i.e., compared with PID and ESO-based BSMC methods, the tracking accuracy is respectively improved by 94.86% and 88.19% under the influence of large external load forces and disturbances.

In this paper, a gradient dynamics-based control method is proposed to directly tackle the singularity problem in the backstepping control design of the TORA system. This method is founded upon the construction of an energy-like positive function, which includes an auxiliary variable in terms of the intermediate virtual control law. On this basis, a gradient dynamics is created to obtain a new virtual control command, which is capable of making the auxiliary variable gradually approach zero, thereby mitigating the issue of division by zero. The core innovation is the integration of the gradient dynamics into the recursive backstepping design to overcome the singularity problem and stabilize the system at the equilibrium quickly. In addition, it rigorously proves that all the signals in the closed-loop control system are uniformly ultimately bounded, and the tracking errors converge to a small neighborhood around zero through a Lyapunov-based stability analysis. Comparative simulations demonstrate that the proposed approach not only avoids the singularity issue, but also achieves a better transient performance over other methods.

Currently, intelligent robotics is supplanting traditional industrial applications. It extends to business, service and care industries, and other fields. Stable robot grasping is a necessary prerequisite for all kinds of complex application scenarios. Herein, we propose a method for preparing an elastic porous material with adjustable conductivity, hardness, and elastic modulus. Based on this, we design a soft robot tactile fingertip that is gentle, highly sensitive, and has an adjustable range. It has excellent sensitivity (~1.089 kpa−1), fast response time (~35 ms), and measures minimum pressures up to 0.02 N and stability over 500 cycles. The baseline capacitance of a sensor of the same size can be increased by a factor of 5–6, and graphene adheres better to polyurethane sponge and has good shock absorption. In addition, we demonstrated the application of the tactile fingertip to a two-finger manipulator to achieve stable grasping. In this paper, we demonstrate the great potential of the soft robot tactile finger in the field of adaptive grasping for intelligent robots.

Abstract Background The popularization of health and medical informatics yields huge amounts of data. Extracting clinical events on a temporal course is the foundation of enabling advanced applications and research. It is a structure of presenting information in chronological order. Manual extraction would be extremely challenging due to the quantity and complexity of the records. Methods We present an recurrent neural network- based architecture, which is able to automatically extract clinical event expressions along with each event’s temporal information. The system is built upon the attention-based and recursive neural networks and introduce a piecewise representation (we divide the input sentences into three pieces to better utilize the information in the sentences), incorporates semantic information by utilizing word representations obtained from BioASQ and Wikipedia. Results The system is evaluated on the THYME corpus, a set of manually annotated clinical records from Mayo Clinic. In order to further verify the effectiveness of the system, the system is also evaluated on the TimeBank _Dense corpus. The experiments demonstrate that the system outperforms the current state-of-the-art models. The system also supports domain adaptation, i.e., the system may be used in brain cancer data while its model is trained in colon cancer data. Conclusion Our system extracts temporal expressions, event expressions and link them according to actually occurring sequence, which may structure the key information from complicated unstructured clinical records. Furthermore, we demonstrate that combining the piecewise representation method with attention mechanism can capture more complete features. The system is flexible and can be extended to handle other document types.

Abstract Background Semantic resources such as knowledge bases contains high-quality-structured knowledge and therefore require significant effort from domain experts. Using the resources to reinforce the information retrieval from the unstructured text may further exploit the potentials of such unstructured text resources and their curated knowledge. Results The paper proposes a novel method that uses a deep neural network model adopting the prior knowledge to improve performance in the automated extraction of biological semantic relations from the scientific literature. The model is based on a recurrent neural network combining the attention mechanism with the semantic resources, i.e., UniProt and BioModels. Our method is evaluated on the BioNLP and BioCreative corpus, a set of manually annotated biological text. The experiments demonstrate that the method outperforms the current state-of-the-art models, and the structured semantic information could improve the result of bio-text-mining. Conclusion The experiment results show that our approach can effectively make use of the external prior knowledge information and improve the performance in the protein-protein interaction extraction task. The method should be able to be generalized for other types of data, although it is validated on biomedical texts.

A method of local anode effect prediction is proposed for the problem that it is difficult to detect the local anode effect in large aluminum reduction cell in real time. Firstly, a fuzzy classification of local anode effect prediction in terms of fuzziness level is proposed considering various working conditions of anode current in the region. Secondly, a current volatility detection method based on time-sliding window density is designed from the problem of uneven current distribution in the region, and the anode currents in the region are classified and tracked for prediction according to the different current volatility. Thirdly, an improved Gated Recurrent Unit (GRU) neural network structure is proposed to improve the prediction accuracy of fluctuating currents. Finally, simulation experiments are conducted based on actual data, and compared with Long Short-Term Memory (LSTM) and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), the proposed method has certain advantages in both prediction time, training time, and the mean absolute error (MAE) and mean square error (MSE), which verifies the effectiveness of the proposed method.

This review intends to introduce the application of lignin-derived catalyst for green organic synthesis over latest two decades and aims to present a renewable alternative for conventional catalyst for future industry application. The structure of lignin is initially introduced in this review. Then, various pretreatment and activation technologies of lignin are systematically presented, which includes physical activation for the formation of well-developed porosity and chemical activation to introduce catalytic active sites. Finally, the catalytic performances of various lignin-derived catalysts are rationally assessed and compared with conventional catalysts, which involves lignin-derived solid acids for hydrolysis, hydration, dehydration (trans)esterification, multi-component reaction and condensation, lignin-derived solid base for Knoevenagel reaction, lignin-derived electro-catalysts for electro-oxidation, oxygen reduction reaction, and lignin-derived supported transition metal catalysts for hydrogenation, oxidation, coupling reaction, tandem reaction, condensation reaction, ring-opening reaction, Friedel-Crafts-type reaction, Fischer–Tropsch synthesis, click reaction, Glaser reaction, cycloaddition and (trans)esterification. The above lignin-derived catalysts thus successfully promote the transformations of organic compounds, carbon dioxide, biomass-based cellulose, saccharide and vegetable oil into valuable chemicals and fuels. At the end of this review, some perspectives are given on the current issues and tendency on the lignin-derived catalysts for green chemistry. Keywords: Catalyst, Green chemistry, Lignin, Pretreatment, Synthesis

In this paper, we consider the robust stabilization control problem of underactuated translational oscillator with a rotating actuator (TORA) system in the presence of unknown matched disturbances by employing continuous control inputs. A nonlinear continuous robust control approach is proposed by integrating the techniques of backstepping and linear extended state observer (LESO). Specifically, based on the backstepping design methodology, a hyperbolic tangent virtual control law is designed for the first subsystem of the cascaded TORA model, via which an integral chain error subsystem is subsequently constructed and the well-known LESO technique is easy to implement. Then, an LEO is designed to estimate the lumped matched disturbances in real-time, and the influence of the disturbances is compensated by augmenting the feedback controller with the disturbance estimation. The convergence and stability of the entire control system are rigorously proved by utilizing Lyapunov theory and LaSalle’s invariance principle. Unlike some existing methods, the proposed controller is capable of generating robust and continuous control inputs, which guarantee that both the rotation and translation of TORA systems are stabilized at the origin simultaneously and smoothly, attenuating the influence of disturbances. Comparative simulation results are presented to demonstrate the effectiveness and superior control performance of the proposed method.

Flexible-joint manipulators (FJMs) have been widely used in the fields of industry, agriculture, medical service, aerospace, etc. However, the FJMs in practical applications inevitably encounter various uncertainties including matched and mismatched disturbances. In this paper, we consider the high precision tracking control problem of FJMs in the presence of unknown lumped matched and mismatched disturbances. An efficient model-assisted composite control approach is proposed by integrating two reduced-order extended state observers (RESOs), a second-order command filtered backstepping (SCFB) technique and an error compensation dynamic system. Unlike some existing methods, the RESOs constructed with partial known model information are capable of estimating and compensating the matched and mismatched disturbances simultaneously. In addition, by employing the SCFB with an error compensation system, the proposed approach can not only overcome the problem of “explosion of complexity” inherent in backstepping, but also reduce the filtering errors arising from the command filters. The stability of the resulting control system and the convergence of error signals are guaranteed by Lyapunov stability theory. Comparative simulations are conducted for a single-link FJM with both matched and mismatched disturbances, and the results show that the proposed approach achieves a better tracking performance, i.e., compared with conventional backstepping method and adaptive fuzzy command filtered control method, the tracking accuracy is improved by 99.5% and 99.2%, respectively.

After the construction of the Three Gorges Dam (TGD) in China, the downstream has been affected by the reduction in sediment discharge and regulation of flow processes, which have resulted in severe scouring and changes hydrological regime. Consequently, the spawning ground of Chinese sturgeon distributed along the downstream Yichang reach could be affected. This study examined the effects of TGD on the streamflow, sediment load and channel morphology downstream based on in situ measured data. Results showed that, after the impoundment of the TGD, sediment load at the downstream Yichang hydrological station decreased significantly, and the Yichang reach continued to be scoured. The distribution of erosion was uneven, and the scouring mainly occurred in the branching channels. The channel gradient and riverbed roughness increased with the erosion of the river cross section. After more than 10 years of erosion, the riverbed scouring and armouring in the Yichang reach was basically completed, thus we expected that the spawning grounds of Chinese sturgeon could be retain as the riverbed tends to be stable. The findings in this work have implications in the protection of the critically endangered Chinese sturgeon.

The Qinghai-Tibetan Plateau, known as the world’s “third pole”, is home to several large rivers in Asia. Its geomorphology is exceptionally vulnerable to climate change, which has had a significant impact on historical riverbed development through runoff and sedimentation processes. However, there is limited research combining climate change, sedimentology, and chronology with river dynamics to investigate riverbed evolution patterns in geological-historical time scales and their changes in overland flow capacity. In the current study, the evolution of a representative portion of the river channel in the Nangqian basin in the Lancang River headwaters was investigated to explore the reaction of the riverbed to climatic change during the geological period via field surveys, riverbed drilling, optically stimulated luminescence (OSL) dating and bankfull channel geometry parameters. The generalized channel section of the historical period was obtained by linking sedimentary layers of the same age on the distribution map of borehole sections, and the bankfull area of the river was computed accordingly. The restored bankfull areas can effectively reflect the ability of historical river channels to transport water and sediment, thus reflecting the climate change at that time. The findings showed that river morphology in the mounded river section could be successfully reconstructed using OSL dating and sedimentary records and that the conceptual sections of the historical warm periods at 2000 years (2 ka) and 0.7 ka can be recovered. Based on the reconstruction, the calculated bankfull areas during the two warm events were larger than present by factors of 1.28 and 1.9, respectively, indicating a stronger capacity for transporting water and sediments. This is the first trial in the Lancang headwaters to investigate the response of river morphology to climate change on a geological time scale.

The horizontal translational oscillator with a rotational actuator (TORA) is a typical underactuated mechanical system, whose control problem is still open and theoretically challenging. At present, the existing control methods are structurally complicated and require an exact knowledge of the system parameters. Moreover, few works have considered the output feedback stabilization of the TORA system subject to practical constraints of input saturation and angular velocity unmeasurement. To address these problems, this paper proposes a novel model-free amplitude-limited control approach to stabilize the TORA system at the origin using only angle feedback. Firstly, the passivity of the horizontal TORA system is analyzed, based on which a novel Lyapunov function augmented with an auxiliary signal is constructed by taking the input saturation into account. Then, an amplitude-limited control law is derived in a straightforward manner. In order to make the control law independent of velocity feedback, the auxiliary signal is designed in terms of the ball rotational angle and an output of a dynamic system. The asymptotic stability of the entire control system is rigorously guaranteed by utilizing Lyapunov theory and LaSalle’s invariance principle. Finally, simulation results with comparisons to existing methods demonstrate the effectiveness and superiority of the proposed control approach.

Physical contact inevitably occurs during robot interaction with outside environments. A robot should have the ability to detect and distinguish whether a physical interaction between a human and the robot is contact or collision, so as to ensure human safety and improve interaction performance. In this paper, a virtual sensor that can detect and distinguish contact and collision between humans and industrial robots is proposed. Based on the generalized momentum of the robot, two observers with low-pass and band-pass filter characteristics were designed in this virtual sensor to realize the robot collision detection. Using the different frequency distribution ranges of the lighter contact force signal and the heavier collision force signal, the filter parameters in the two observers were appropriately selected to distinguish between collisions and contacts in close interaction between humans and robots. The virtual sensor does not require acceleration information or inverse dynamics calculations. It only needs to sample the motor driving current and position information of the robot joint, and can easily be applied to conventional industrial robots. The experimental results show that the low-pass and band-pass torque observers can detect different force signals in real-time, and the proposed virtual sensor can be used for collision detection and distinction in human−robot interactions.

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Background: The concurrent effects of particulate matter (PM) on both blood pressure (BP) and short-chain fatty acids (SCFAs) are insufficiently explored, with limited research on the potential mediating roles of SCFAs. Methods: In this prospective panel study with 4 follow-ups, we recruited 40 college students in Hefei, China, to assess the impacts of short-term exposure to PM (aerodynamic diameter ≤10 μm (PM10), ≤2.5 μm (PM2.5), and ≤1 μm (PM1)) on BP and SCFAs, along with potential mechanisms. Real-time PM data, urinary SCFAs levels, and BP indicators were systematically collected. Linear mixed-effects models assessed the relationships between PM, SCFAs, and BP. Mediation analyses explored SCFAs’ mediating role in the PM-BP association. Results: PM exposure was positively linked to BP and negatively associated with SCFAs. For a 10 μg/m3 rise in PM10 at lag 0–72 h, there were notable reductions of 0.0019 % (95 %CI: −0.0028, −0.0010) in Acetic acid, 0.0262 % (-0.0369, −0.0155) in Propionic acid, and 0.0702 % (-0.1025, −0.0378) in Butyric acid. Systolic BP, diastolic BP, and mean arterial pressure (MAP) increased by 2.60 mmHg (0.96, 4.25), 2.24 mmHg (1.18, 3.31), and 2.36 mmHg (1.20, 3.53), respectively, per 10-μg/m3 rise in PM1 at lag 0–24 h. Decreased SCFAs levels explained significant portions (24.69–31.80 %) of the elevated MAP due to PM10. Stronger associations were found in females and individuals with abnormal BMI. Conclusions: Our study shows that PM exposure decreases urinary SCFAs levels, which partially mediate the impact of PM on elevated BP. These findings enhance our comprehension of the pathways linking PM exposure to BP changes.