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The development of a soft crawling robot (SCR) capable of quick folding and recovery has important application value in the field of biomimetic engineering. This article proposes an origami-inspired vacuum-actuated foldable soft crawling robot (OVFSCR), which is composed of entirely soft foldable mirrored origami actuators with a Kresling crease pattern, and possesses capabilities of realizing multimodal locomotion incorporating crawling, climbing, and turning movements. The OVFSCR is characterized by producing periodically foldable and restorable body deformation, and its asymmetric structural design of low front and high rear hexahedral feet creates a friction difference between the two feet and contact surface to enable unidirectional movement. Combining an actuation control sequence with an asymmetrical structural design, the body deformation and feet in contact with ground can be coordinated to realize quick continuous forward crawling locomotion. Furthermore, an efficient dynamic model is developed to characterize the OVFSCR's motion capability. The robot demonstrates multifunctional characteristics, including crawling on a flat surface at an average speed of 11.9 mm/s, climbing a slope of 3°, carrying a certain payload, navigating inside straight and curved round tubes, removing obstacles, and traversing different media. It is revealed that the OVFSCR can imitate contractile deformation and crawling mode exhibited by soft biological worms. Our study contributes to paving avenues for practical applications in adaptive navigation, exploration, and inspection of soft robots in some uncharted territory. [ABSTRACT FROM AUTHOR]

Oxygen evolution reaction (OER) plays a crucial role in developing energy conversion and adjusting electronic structure of the electrocatalysts can effectively improve the catalytic activity and stability. However, it is a challenge to adjust the electronic structure on two-dimensional iridium dioxide nanosheets (IrO2 NS), which have the advantages of high atom utilization. Here, we regulate the surface properties of IrO2 NS through sulfonated carbon dots (SCDs) to promote the OER catalytic process. The catalyst IrO2 NS/SCDs-2 exhibited excellent catalytic activity with a lower overpotential of 180 mV than IrO2 NS (230 mV) at the current density of 10 mA·cm−2 in a 0.5 M H2SO4 solution. And after 160 h of stability testing, the overpotential of IrO2 NS/SCDs-2 only decreased by 4 mV. Moreover, transient potential scanning test can visually demonstrate that the addition of SCDs improves the conductivity of the catalyst and increases the electron transfer rate. [ABSTRACT FROM AUTHOR]

The production management of lactating sows is a crucial aspect of pig farm operations, as their health directly impacts the farm's production efficiency. The feeding behavior of lactating sows can reflect their health and welfare status, and monitoring this behavior is essential for precise feeding and management. To address the issues of time-consuming and labor-intensive manual inspection of lactating sows' feeding behavior and the reliance on breeders' experience, we propose a method based on the improved YOLO (You Only Look Once) v5s algorithm and image segmentation for detecting the feeding behavior of lactating sows. Based on the YOLOv5s algorithm, the SE (Squeeze-and-Excitation) attention module was added to enhance the algorithm's performance and reduce the probability of incorrect detection. Additionally, the loss function was replaced by WIoU (Weighted Intersection over Union) to accelerate the model's convergence speed and improve detection accuracy. The improved YOLOv5s-C3SE-WIoU model is designed to recognize pre-feeding postures and feed trough conditions by detecting images of lactating sows. Compared to the original YOLOv5s, the improved model achieves an 8.9% increase in mAP@0.5 and a 4.7% increase in mAP@0.5 to 0.95. This improvement satisfies the requirements for excellent detection performance, making it suitable for deployment in large-scale pig farms. From the model detection results, the trough remnant image within the detection rectangle was extracted. This image was further processed using image processing techniques to achieve trough remnant image segmentation and infer the remnant amount. Based on the detection model and residue inference method, video data of lactating sows' feeding behavior were processed to derive the relationship between feeding behavior, standing time, and residue amount. Using a standing duration of 2 s and a leftover-feed proportion threshold of 2% achieves the highest accuracy, enabling the identification of abnormal feeding behavior. We analyzed the pre-feeding postures and residual feed amounts of abnormal and normal groups of lactating sows. Our findings indicated that standing time was significantly lower and residual feed amount was higher in the abnormal groups compared to the normal groups. By combining standing time and residual feed amount information, accurate detection of the feeding status of lactating sows can be realized. This approach facilitates the accurate detection of abnormal feeding behaviors of lactating sows in large-scale pig farm environments. [ABSTRACT FROM AUTHOR]

Background: The risk of low back pain (LBP) increases steeply during adolescence, and adolescents with LBP are more likely to have low back pain in their adult years. This study aimed to investigate the dose-response relationship between daily screen time and the risk of low back pain among children and adolescents. Methods: PubMed, the Cochrane Library, Embase, and Web of Science were searched to collect relevant studies on daily screen time and the risk of low back pain from the establishment of the database up to December 2022. Two investigators independently screened the literature, extracted data, and evaluated the risk of bias in the included studies. Stata16.0 was used to perform a dose-response meta-analysis and the methodological quality evaluation of the included studies. Results: The results of the meta-analysis showed that there is a positive correlation between daily computer time (OR = 1.32, 1.05–1.60), daily mobile phone time (OR = 1.32, 1.00–1.64), daily TV watching (OR = 1.07, 1.04–1.09) and the risk of low back pain, separately. The dose–response meta-analysis showed that there is a linear relationship between daily computer use and low back pain. The risk of low back pain increased by 8.2% for each 1-hour of daily computer use. Conclusions: Screen time is related to the risk of low back pain, and there is a linear relationship between daily computer use and the risk of low back pain. A number of strategic measures should be taken to prevent adolescents from developing severe low back pain.

Density is an important parameter for both geological research and geophysical exploration. However, for model-driven seismic inversion methods, high-fidelity density inversion is challenging due to seismic wave traveltime insensitivity to density and crosstalk that density has with velocity. To circumvent the challenge of density inversion, some inversion methods treat density as a constant value or derive density from velocity through empirical equation. On the other hand, deep learning approaches are completely driven by data and have strong target-oriented characteristics, offering a new way to solve multiparameter coupling problems. Nevertheless, the accuracy of the inversion results of data-driven algorithms is directly related to the amount and diversity of the training data, and thus, they lack the universality of model-driven algorithms. To achieve accurate density inversion, we propose a simultaneous inversion algorithm for velocity and density that combines the advantages of data- and model- driven approaches: A neural network model (U-T), combining the U-net and Transformer architectures, is proposed to construct non-linear mappings between seismic data as inputs and the velocity and density as predictions. Next, the model-driven inversion algorithm uses the U-T prediction as the initial model to obtain the final accurate solution. In the model-driven module, envelope-based sparse constrained deconvolution is used to obtain full-band seismic data, while a variable dominant frequency full waveform inversion algorithm is used to perform multiscale inversion, ultimately leading to accurate inversion results of velocity and density. The performance of the algorithm on the Sigsbee2A and Marmousi models demonstrates its effectiveness. [ABSTRACT FROM AUTHOR]

Zinc oxide (ZnO) is a potential wide band gap semiconductor, but its high photoelectron-hole pairs recombination rate and poor light absorption ability limit its application to photoelectrochemical (PEC) water oxidation. To solve these problems, in this work, a reasonable band structure engineering was carried out on ZnO nanorod arrays (ZnO NRAs) with the co-decoration of nitrogen doped g-C3N4 (NCN) and carbon quantum dot (CQDs) solution. Narrow band gap NCN was loaded onto the surface of ZnO NRAs by a simple spin coating method, and then CQDs were modified on the ZnO/NCN NRAs. The composite photoanode of ZnO/NCN/CQDs NRAs was finally obtained. The PEC performance of the composite photoanode reaches 1.82 times that of the pristine ZnO photoanode, with a photocurrent density of up to 1.20 mA cm−2 at 1.23 V (vs. RHE) bias. The calculated value of ABPE for the composite photoanode was 0.258%, compared with 0.123% for the bare ZnO NRAs. The co-decoration of NCN and CQDs enhanced the charge separation efficiency and expanded the spectral response range, contributing to the improved PEC properties of the composite photoanode. [ABSTRACT FROM AUTHOR]