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The combination of flexible sensors and deep learning has attracted much attention as an efficient method for the recognition of human postures. In this paper, an in situ polymerized MXene/polypyrrole (PPy) composite is dip-coated on a polydimethylsiloxane (PDMS) sponge to fabricate an MXene/PPy@PDMS (MPP) piezoresistive sensor. The sponge sensor achieves ultrahigh sensitivity (6.8925 kPa−1) at 0–15 kPa, a short response/recovery time (100/110 ms), excellent stability (5000 cycles) and wash resistance. The synergistic effect of PPy and MXene improves the performance of the composite materials and facilitates the transfer of electrons, making the MPP sponge at least five times more sensitive than sponges based on each of the individual single materials. The large-area conductive network allows the MPP sensor to maintain excellent electrical performance over a large-scale pressure range. The MPP sensor can detect a variety of human body activity signals, such as radial artery pulse and different joint movements. The detection and analysis of human motion data, which is assisted by convolutional neural network (CNN) deep learning algorithms, enable the recognition and judgment of 16 types of human postures. The MXene/PPy flexible pressure sensor based on a PDMS sponge has broad application prospects in human motion detection, intelligent sensing and wearable devices.

The structure from motion (SfM) method has achieved great success in 3D sparse reconstruction, but it still faces serious challenges in large-scale scenes. Existing hybrid SfM methods usually do not fully consider the compactness between images and the connectivity between subclusters, resulting in a loose spatial distribution of images within subclusters, unbalanced connectivity between subclusters, and poor robustness in the merging stage. In this paper, an efficient and robust hybrid SfM method is proposed. First, the multifactor joint scene partition measure and the preassignment balanced image expansion algorithm among subclusters are constructed, which effectively solves the loose spatial distribution of images in subclusters problem and improves the degree of connection among subclusters. Second, the global GlobalACSfM method is used to complete the local sparse reconstruction of the subclusters under the cluster parallel framework. Then, a decentralized dynamic merging rule considering the connectivity of subclusters is proposed to realize robust merging among subclusters. Finally, public datasets and oblique photography datasets are used for experimental verification. The results show that the method proposed in this paper is superior to the state-of-the-art methods in terms of accuracy and robustness and has good feasibility and advancement prospects.

Mesh simplification is an effective way to solve the contradiction between 3D models and limited transmission bandwidth and smooth model rendering. The existing mesh simplification algorithms usually have problems of texture distortion, deformation of different degrees, and no texture simplification. In this paper, a model simplification algorithm suitable for 3D reconstruction is proposed by taking full advantage of the recovered 3D scene structure and calibrated images. First, the reference 3D model scene is constructed on the basis of the original mesh; second, the images are collected on the basis of the reference 3D model scene; then, the mesh and texture are simplified by using the reference image set combined with the QEM algorithm. Lastly, the 3D model data of a town in Tengzhou are used for experimental verification. The results show that the algorithm proposed in this paper basically has no texture distortion and deformation problems in texture simplification and can effectively reduce the amount of texture data, with good feasibility.

Common methods of texture reconstruction first build a visual list for each triangular face, and then select the best image for each triangular face based on the graph-cut method. These methods have problems such as high memory consumption, and difficulties in large-area texture reconstruction. Hence, this paper proposes a parallel method for texture reconstruction in large-scale 3D automatic modeling. First, the hierarchical relationships between the texture reconstruction are calculated in accordance with the adjacency relationships between partitioning cells. Second, building contours are extracted based on the 3D mesh model, the tiles are divided into two categories (occlusion and non-occlusion), and the incorrect occlusion relationship is restored based on the occluded tiles. Then, the graph-cut algorithm is constructed to select the best-view label. Finally, the jagged labels between adjacent labels are smoothed to alleviate the problem of texture seams. Oblique photography data from an area of 10 km2 in Dongying, Shandong were used for validation. The experimental results reveal the following: (i) concerning reconstruction efficiency, the Waechter method can perform texture reconstruction only in a small area, whereas with the proposed method, the size of the reconstruction area is not restricted. The memory consumption is improved by factors of approximately 2–13. (ii) Concerning reconstruction results, the Waechter method incorrectly reconstructs the textures of partially occluded regions at the tile edges, while the proposed method can reconstruct the textures correctly. (iii) Compared to the Waechter method, the proposed approach has a 30% lower reduction in the number of texture fragments.

With the development of human-computer interaction technology, how to use intelligent, natural and efficient interaction to promote the development of medicine has gradually become a hot topic of research. Mental and nervous system diseases have a great impact on the quality of people’s daily life. The use of human-computer interaction technology to rehabilitate mental and nervous system diseases can improve the treatment effect and reduce the work intensity of doctors, so it has far-reaching clinical significance. This paper first describes the development process of human-computer interaction technology, and then focuses on the application of human-computer interaction technology such as interactive pen, voice interaction, gait/gesture interaction and physiological computing in the rehabilitation treatment of mental and nervous system diseases, which has important practical significance for improving the use of computer technology to improve traditional medical treatment methods.

In recent years, multi-modal flexible tactile sensors have become an important direction in the development of electronic skin because of their excellent sensitivity, flexibility and wearable properties. In this work, a humidity-pressure multi-modal flexible sensor based on polypyrrole (PPy)/Ti 3 C 2 T x sensitive film packaged with porous polydimethylsiloxane (PDMS) is investigated by combining the sensitive structure generation mechanism of in situ polymerization to achieve the simultaneous detection of humidity and pressure, which has a sensitivity of 89,113.4 Ω/% RH in a large humidity range of 0%–97% RH, and response/recovery time of 2.5/1.9 s. The tactile pressure sensing has a high sensitivity, a fast response of 67/52 ms, and a wide detection limit. The device also has excellent performance in terms of stability and repeatability, making it promising for respiratory pattern and motion detection. This work provides a new solution to address the construction of multi-modal tactile sensors with potential applications in the fields of medical health, epidemic prevention.

Tian, Y.; Li, J.; Wang, S.; Ai, B.; Cai, H., and Wen, Z., 2022. Spatio-temporal changes and driving force analysis of wetlands in Jiaozhou Bay. Journal of Coastal Research, 38(2), 328–344. Coconut Creek (Florida), ISSN 0749-0208. Jiaozhou Bay is the largest bay-type coastal wetland with high biodiversity in Shandong Peninsula. In recent years, the wetland of Jiaozhou Bay has been reduced dramatically due to urban expansion. It is significant to study the characteristics and causes of the wetland changes in the past 40 years for the rational exploitation and sustainable development of coastal wetland resources. The types, spatial distribution, and area information of the wetland from 1983 to 2019 are obtained by artificial visual interpretation and supervised classification using ten Landsat remote sensing images, assisted by Google images. The research analyzes the conversion of land use types and the change rule of landscape pattern in the wetland, as well as the driving forces of the wetland evolution and the uncertainty factors affecting the analysis results. The results indicate that the wetland area shows an overall decreasing trend from 1983 to 2019, 77% of which are converted into nonwetland. The wetland area increases initially, followed by a decrease from 1990 to 2000, but then continues to decrease slowly from 2000 to 2019. There are many types of land use changes from coastal tidal flats to other land use types such as construction land or pond, and the landscape pattern has been gradually fragmented. The continuous increase in temperature is the main natural cause for the degradation of the wetland. Population factor is the most important driving force for the change of the wetland. The main uncertain factors affecting the analysis results are the capture time of remote sensing image, tidal level correction, wetland classification system, and method. The results provide a basis for the decision-making of wetland protection. The quantitative data will provide a reference for the construction of a multiyear wetland dataset in Jiaozhou Bay.

Interacting with pedestrians understandably and efficiently is one of the toughest challenges faced by autonomous vehicles (AVs) due to the limitations of current algorithms and external human-machine interfaces (eHMIs). In this paper, we design eHMIs based on gestures inspired by the most popular method of interaction between pedestrians and human drivers. Eight common gestures were selected to convey AVs' yielding or non-yielding intentions at uncontrolled crosswalks from previous literature. Through a VR experiment (N1 = 31) and a following online survey (N2 = 394), we discovered significant differences in the usability of gesture-based eHMIs compared to current eHMIs. Good gesture-based eHMIs increase the efficiency of pedestrian-AV interaction while ensuring safety. Poor gestures, however, cause misinterpretation. The underlying reasons were explored: ambiguity regarding the recipient of the signal and whether the gestures are precise, polite, and familiar to pedestrians. Based on this empirical evidence, we discuss potential opportunities and provide valuable insights into developing comprehensible gesture-based eHMIs in the future to support better interaction between AVs and other road users.

Efficient, durable and inexpensive electrocatalysts that accelerate sluggish oxygen reduction reaction kinetics and achieve high-performance are highly desirable. Here we develop a strategy to fabricate a catalyst comprised of single iron atomic sites supported on a nitrogen, phosphorus and sulfur co-doped hollow carbon polyhedron from a metal-organic framework@polymer composite. The polymer-based coating facilitates the construction of a hollow structure via the Kirkendall effect and electronic modulation of an active metal center by long-range interaction with sulfur and phosphorus. Benefiting from structure functionalities and electronic control of a single-atom iron active center, the catalyst shows a remarkable performance with enhanced kinetics and activity for oxygen reduction in both alkaline and acid media. Moreover, the catalyst shows promise for substitution of expensive platinum to drive the cathodic oxygen reduction reaction in zinc-air batteries and hydrogen-air fuel cells. Development of fuel cells and metal-air batteries is hindered by electrocatalyst performance, which can be enhanced with uniform and atomically dispersed active sites. Here the authors report an iron-based electrocatalyst for oxygen reduction in cathodes for a zinc-air battery and a hydrogen-air fuel cell.

The rational design and construction of hierarchically porous nanostructure for oxygen reduction reaction (ORR) electrocatalysts is crucial to facilitate the exposure of accessible active sites and promote the mass/electron transfer under the gas-solid-liquid triple-phase condition. Herein, an ingenious method through the pyrolysis of creative polyvinylimidazole coordination with Zn/Fe salt precursors is developed to fabricate hierarchically porous Fe-N-doped carbon framework as efficient ORR electrocatalyst. The volatilization of Zn species combined with the nanoscale Kirkendall effect of Fe dopants during the pyrolysis build the hierarchical micro-, meso-, and macroporous nanostructure with a high specific surface area (1,586 m 2 ·g −1 ), which provide sufficient exposed active sites and multiscale mass/charge transport channels. The optimized electrocatalyst exhibits superior ORR activity and robust stability in both alkaline and acidic electrolytes. The Zn-air battery fabricated by such attractive electrocatalyst as air cathode displays a higher peak power density than that of Pt/C-based Zn-air battery, suggesting the great potential of this electrocatalyst for Zn-air batteries.