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Ecological restoration and biodiversity are currently hot issues of global environmental concern. However, knowledge about the specific impacts of restoration duration on understory vegetation diversity remains limited. Therefore, this study comprehensive employed a spatial approach to compare the differences in understory plant diversity and species composition among secondary forests with varying ecological restoration ages (0, 10, 30, 60, and 90-year-old stands) in Huangfu Mountain National Forest Park. This methodology allowed us to clarify the key factors affecting the composition of the understory plant community and investigate the regulatory mechanisms influencing changes in understory plant diversity. The results showed that shrub Shannon’s index value, shrub evenness, herb Shannon’s index value, herb richness, and herb evenness were significantly affected by the years of restoration, with 10 years and 90 years being the highest and 60 years being the lowest. Substrate diversity was the main factor influencing plant diversity in the shrub layer. Overstory richness, soil C/N, soil C, soil N, soil bacterial Observed OTUs, soil bacterial Chao1, soil bacterial Pielou_e, and substrate diversity were the drivers of plant diversity in the herb layer. Overstory evenness had a direct effect (0.256) and an indirect effect (0.284) on herb evenness through light availability and fungal Simpson’s index value. Light availability directly negatively affected herb evenness (−0.360). In addition, 52.6% of the factors affecting the herb evenness index were from the arboreal layer evenness, light availability, and fungal Simpson’s index value. To sum up, moderate disturbance of the understory environment of natural secondary forests can be carried out after 10 years of restoration, which is more conducive to the increase of understory plant diversity. This comprehensive study provides a theoretical basis for formulating ecological restoration measures for secondary forests, particularly in understanding the optimal timing and nature of disturbance in the restoration process, reassuring the audience about the validity and reliability of the findings.

Abstract Background Glycolysis is a pivotal process in metabolic reprogramming of tumorigenesis. Previous research has indicated that lncRNAs might play crucial roles in glycolysis of various tumors. However, the function of lncRNAs in glycolysis of pancreatic cancer has not been fully elucidated. Methods Bio-information analyses were applied to reveal the potential glycolysis-associated lncRNA. RT-PCR and fluorescence in situ hybridization (FISH) assays were applied to detect the expression of antisense RNA1 of DICER1 (DICER1-AS1) in pancreatic cancer tissues and cell lines. Gain- and loss-of-function experiments were performed to evaluate the roles of DICER1-AS1 in glycolysis and tumorigenesis of PC. Mechanistic experiments including luciferase reporter assay, RNA immunoprecipitation (RIP), and chromatin immunoprecipitation (ChIP) were employed to uncover the downstream targets and regulatory mechanism of DICER1-AS1 in glycolysis of PC. Results Bio-information analysis indicated that DICER1-AS1 was downregulated in PC and negatively correlated with glycolytic genes expression. Meanwhile, overexpression of DICER1-AS1 inhibited glycolysis, proliferation, and metastasis of PC cells both in vitro and in vivo. Mechanistically, DICER1-AS1 promoted transcription of its sense gene DICER1 by recruiting transcriptional factor YY1 to the DICER1 promoter. Meanwhile, DICER1 promoted maturation of miR-5586-5p which consequently inhibited glycolytic gene expression including LDHA, HK2, PGK1, and SLC2A1. Notably, enhanced interaction between N6-methyladenosine (m6A) reader YTHDF3 and DICER1-AS1 led to degradation of DICER1-AS1 in response to glucose depletion. Moreover, our data revealed that YTHDF3 was a critical target for miR-5586-5p, by which forming a negative feedback with DICER1-AS1 to regulate glycolysis of PC. Conclusion Our results implicate a negative feedback of m6A reader YTHDF3 and glycolytic lncRNA DICER1-AS1 is involved in glycolysis and tumorigenesis of PC.

Abstract Background Researches indicated the process of Endothelial-Mesenchymal-Transition (EndMT) of vascular endothelial cells (ECs) was critically involved in the progression of tumor. ECs demonstrated functional and phenotypic heterogeneity when located under different microenvironments. The extracellular pH of tumor tissues was acidic compared to that of normal tissues. However, there was still unclear whether the acidic microenvironment affected the EndMT of vascular ECs. Methods Human Umbilical Vein Endothelial Cell (HUVECs) was cultured under the normal or acidic medium to evaluate the alteration of morphology, migration, permeability, and EndMT markers. Microarray assay was adopted to analyze the differential expression of miRNAs in the acidity-treated HUVECs. Gain- and loss- of function experiments were performed to evaluate the functional role of miRNA-548ac on acidity-induced EndMT of HUVECs. Luciferase reporter and Chromatin-immunoprecipitation assays were conducted to assess the downstream pathway of miRNA-548ac in acidity-induced EndMT of HUVECs. Results Our results showed that HUVECs demonstrated mesenchymal transition under acidic conditions with the increase of migration, permeability, and expression of α-SMA and Vimentin, but the expression of vascular endothelial cadherin (VE-cadherin) and CD31 were reduced. In addition, the acidity-treated HUVECs remarkably facilitated the transmigration of pancreatic cancer cells. The expression of miRNA-548ac was significantly decreased in the acidity-treated HUVECs. Moreover, overexpression of miR-548ac inhibited the EndMT of HUVECs and consequently impeded the transmigration of pancreatic cancer cells. The miR-548ac inhibited the expression of YB-1 by binding to the 3’UTR of its mRNA, and YB-1 promoted the translation of Snail which was a critical regulator of EndMT. What’s more, Snail transcriptionally inhibited the expression of miR-548ac through binding to the promoter of its host gene. Conclusions Our data implicated that the acidic microenvironment promoted the EndMT of HUVECs by the miR-548ac/YB-1/Snail axis, which could contribute to the metastasis of pancreatic cancer.

Abstract The majority of long non-coding RNAs (lncRNAs) have been discovered to be overexpressed in pancreatic cancer (PC) and served as promoters in the tumorigenesis of PC, while the inhibitory functions of lncRNAs in the development of PC have not been fully elucidated yet. LncRNA microarray was adopted to analyze the differential expression of lncRNAs in PC tissues and that in normal peritumoral (NP) tissues. Functional role of lncRNA BM466146.1 on PC was evaluated by gain- and loss-of-function experiments in vivo and in vitro. RNA pull-down, RNA immunoprecipitation, luciferase reporter, and Chromatin-immunoprecipitation assays were performed to assess the mechanism of ZNFTR, respectively. The correlation between the expression of ZNFTR and various clinicopathological characteristics was accessed in PC specimens. This study displayed lncRNA BM466146.1 was downregulated in PC tissues and functioned as a suppressor through regulating the expression of adjacent gene Zinc finger protein 24 (ZNF24), which was assigned as ZNFTR. Mechanistically, ZNFTR interacted with activating transcription factor 3 (ATF3) and sequestered ATF3 away from the ZNF24 promoter, which consequently increased the expression of ZNF24. Further, ZNF24 inhibited the proliferative, metastatic, and pro-angiogenic abilities of PC cells by suppressing transcription of vascular endothelial growth factor A (VEGFA). Therefore, the downregulation of ZNFTR in PC led to the decreased expression of ZNF24, which further resulted in the upregulation of VEGFA to facilitate the development of PC. Meanwhile, ZNFTR was transcriptionally inhibited by the HIF-1α/HDAC1 complex-mediated deacetylation. Clinical results further demonstrated that the low expression of ZNFTR was associated with poor overall survival time. Taken together, our results implicated that ZNFTR was a hypoxia-responsive lncRNA, and functioned as an inhibitor by modulating ATF3/ZNF24/VEGFA pathway in PC.

Soft robots have infinite degrees of freedom (DOFs), extremely strong environmental adaptability, and better human-computer interaction. A soft grasping manipulator can grasp objects in a diversified manner, which is an important application and research direction in terms of soft robots. A pneumatic soft actuator structure was designed on the basis of in-depth analysis of the motion mechanism of a soft robot. A kinematic model for the soft actuator and a model for bending deformation of air cavities were established separately to determine the relationship between the bending angle and air pressure. A novel pneumatic soft manipulator with adjustable grasping diameter was designed by utilizing pneumatic soft actuators to realize adaptive grasping of objects with different diameters (within 200 mm). Finally, a physical manipulator was manufactured and the relationship between the bending angle of the soft grasping robot and pressure intensity was experimentally validated. Moreover, tests were conducted to assess the adaptability of the soft manipulator when grasping objects with different diameters, masses, and shapes in order to verify the effectiveness of the soft manipulator.

Linear plasma devices have been increasingly applied in investigating plasma–surface interaction (PSI) processes and divertor/scraped-off-layer (D/SOL) physics because of their economy, flexibility, and expandability. However, only a few existing linear plasma devices are able to obtain high heat and particle fluxes. In this work, we report a compact superconducting linear device, with its scientific goals and specific design methods, at Harbin Institute of Technology (HIT), HIT-PSI, capable of implementing an extreme plasma environment with beams of a long discharge pulse, as well as high heat and particle fluxes in the future fusion reactor regime of ITER/CFETR-like parameters. A five-coil integrated superconducting magnet is designed to generate a >2.0 Tesla steady-state magnetic field for confining a long pulse plasma beam with a density of >1020 m−3 produced by a cascaded arc plasma source. With a pump set of 2500 L/s and a water-cooled target system with bias voltage, it is expected to obtain high-density and low-temperature plasma beams with a heat flux of over 10 MW/m2. Subsystems of the platform, including the plasma source, superconducting magnets, vacuum system, and target holder system, are described in detail. In addition, the function and performance of the platform are numerically simulated and represented by SOLPS-ITER code to predict the laboratory simulation results.

A fully superconducting wind generator employs superconductors in stator and rotor to enable high torque density and low weight, that is, enable an ultra-light electric machine for wind application. However, the level of the AC loss of the stator armature coils is a critical issue, which lacks investigations in the design of the fully superconducting generators. In this paper, an in-house model was developed to analyze the potential of a fully superconducting generator by integrating the electromagnetic design with the AC loss estimation. The electromagnetic model was made through analytical equations, which take into consideration the geometry, the magnetic properties of iron, and the nonlinear E–J constitutive law of superconductors. Since the permeability of iron materials and the critical current of the superconductors depend on the magnetic field, an iteration process was proposed to find their operating points for every electromagnetic design. The AC loss estimation was carried out through finite element software based on the T–A formulation of Maxwell’s equations instead of analytical equations, due to the complexity of magnetic fields, currents and rotation. The results demonstrate that the design approach is viable and efficient, and is therefore useful for the preliminary design of the generator. In addition, it is found that smaller tape width, larger distance between the superconducting coils in the same slot, smaller coil number in one slot and lower working temperature can reduce the AC loss of the stator coils, but the reduction of the AC loss needs careful design to achieve an optimum solution.

Vehicle pose estimation is essential in autonomous vehicle (AV) perception technology. However, due to the different density distributions of the point cloud, it is challenging to achieve sensitive direction extraction based on 3D LiDAR by using the existing pose estimation methods. In this paper, an optimal vehicle pose estimation network based on time series and spatial tightness (TS-OVPE) is proposed. This network uses five pose estimation algorithms proposed as candidate solutions to select each obstacle vehicle’s optimal pose estimation result. Among these pose estimation algorithms, we first propose the Basic Line algorithm, which uses the road direction as the prior knowledge. Secondly, we propose improving principal component analysis based on point cloud distribution to conduct rotating principal component analysis (RPCA) and diagonal principal component analysis (DPCA) algorithms. Finally, we propose two global algorithms independent of the prior direction. We provided four evaluation indexes to transform each algorithm into a unified dimension. These evaluation indexes’ results were input into the ensemble learning network to obtain the optimal pose estimation results from the five proposed algorithms. The spatial dimension evaluation indexes reflected the tightness of the bounding box and the time dimension evaluation index reflected the coherence of the direction estimation. Since the network was indirectly trained through the evaluation index, it could be directly used on untrained LiDAR and showed a good pose estimation performance. Our approach was verified on the SemanticKITTI dataset and our urban environment dataset. Compared with the two mainstream algorithms, the polygon intersection over union (P-IoU) average increased by about 5.25% and 9.67%, the average heading error decreased by about 29.49% and 44.11%, and the average speed direction error decreased by about 3.85% and 46.70%. The experiment results showed that the ensemble learning network could effectively select the optimal pose estimation from the five abovementioned algorithms, making pose estimation more accurate.

Bending is an important procedure for processing sheet metals, while it is a key link in the realization of automatic processing of sheet metal. To improve the efficiency and accuracy of bending processing, this article proposed a structure model and a prototype of a six-axis Cartesian coordinate robot for sheet metal bending to replace workers completing automatic bending processes. Based on the analysis of overall structure schemes of the robot, kinematic simulation is conducted by using the automatic dynamic analysis of mechanical system (ADAMS). Furthermore, the dynamic performance of the structural model of the robot for sheet metal bending is analysed and design optimization is performed. A prototype of the robot based on the optimal structural model of six-axis Cartesian coordinate robot for sheet metal bending is made. Finally, under the work conditions, the efficiencies and accuracies of sheet metal bending by a worker and the robot are compared and tested. The structural model of six-axis Cartesian coordinate robot for sheet metal bending presented in this article is found to be applicable to sheet metal bending robot and improves the stability of sheet metal bending machine. The laboratory testing and experimental results verified the feasibility of the proposed robot.

The perception system has become a topic of great importance for autonomous vehicles, as high accuracy and real-time performance can ensure safety in complex urban scenarios. Clustering is a fundamental step for parsing point cloud due to the extensive input data (over 100,000 points) of a wide variety of complex objects. It is still challenging to achieve high precision real-time performance with limited vehicle-mounted computing resources, which need to balance the accuracy and processing time. We propose a method based on a Two-Layer-Graph (TLG) structure, which can be applied in a real autonomous vehicle under urban scenarios. TLG can describe the point clouds hierarchically, we use a range graph to represent point clouds and a set graph for point cloud sets, which reduce both processing time and memory consumption. In the range graph, Euclidean distance and the angle of the sensor position with two adjacent vectors (calculated from continuing points to different direction) are used as the segmentation standard, which use the local concave features to distinguish different objects close to each other. In the set graph, we use the start and end position to express the whole set of continuous points concisely, and an improved Breadth-First-Search (BFS) algorithm is designed to update categories of point cloud sets between different channels. This method is evaluated on real vehicles and major datasets. The results show that TLG succeeds in providing a real-time performance (less than 20 ms per frame), and a high segmentation accuracy rate (93.64%) for traffic objects in the road of urban scenarios.

Cable is one of the most important parts on cable-stayed bridges. Its safety is very important. The aim of this study is to design an all-wheel-drive climbing robot based on safety recovery mechanism model for automatic inspection of bridge cables. For this purpose, a model of a three-wheel-drive climbing robot with high-altitude safety recovery mechanism is constructed and the basic performances such as climbing ability and anti-skidding properties are analyzed. Secondly, by employing the finite element method, natural frequency of the robot is calculated and that of a cable with concentrated masses is obtained through use of the Rayleigh quotient. Based on the mentioned quantities, the dynamic characteristics of the robot⁻cable system are further analyzed. In order to verify the climbing ability of the designed robot, a prototype of the robot is made, a robot testing platform is established and the climbing & loading experiments of the robot are carried out. The experiment results illustrated that the robot can carry a payload of 10 kg and safely return along the cable under the influences of inertial force.

Climbing robots have been widely used to inspect smooth walls. However, a good adsorption method has not been found for the inspection of a cliff surface and a dusty high-altitude surface with small vibration, both of which are made of coarse concrete, square brick, or rock. In this article, first, we analyzed the bionic structure of the cockroach legs and observed their morphological characteristics of the spiny claws on these legs. We also studied the interaction theory of the bionic claw with the bulges on the rough wall surfaces and deduced the mechanical criteria of the claws grabbing steadily these bulges. Then, an initial mechanical structure of a wall-climbing robot was proposed based on a grasping claw and a climbing model. Furthermore, a mathematical model was established to reflect the relationship between sharp hooks and bulges on the rough wall. Finally, we performed several laboratory experiments to verify the grasping stability.

Feature recognition and fault diagnosis plays an important role in equipment safety and stable operation of rotating machinery. In order to cope with the complexity problem of the vibration signal of rotating machinery, a feature fusion model based on information entropy and probabilistic neural network is proposed in this paper. The new method first uses information entropy theory to extract three kinds of characteristics entropy in vibration signals, namely, singular spectrum entropy, power spectrum entropy, and approximate entropy. Then the feature fusion model is constructed to classify and diagnose the fault signals. The proposed approach can combine comprehensive information from different aspects and is more sensitive to the fault features. The experimental results on simulated fault signals verified better performances of our proposed approach. In real two-span rotor data, the fault detection accuracy of the new method is more than 10% higher compared with the methods using three kinds of information entropy separately. The new approach is proved to be an effective fault recognition method for rotating machinery.

Aiming at the inspection of rough stone and concrete wall surfaces, a grasping module of cross-arranged claw is designed. It can attach onto rough wall surfaces by hooking or grasping walls. First, based on the interaction mechanism of hooks and rough wall surfaces, the hook structures in claw tips are developed. Then, the size of the hook tip is calculated and the failure mode is analyzed. The effectiveness and reliability of the mechanism are verified through simulation and finite element analysis. Afterwards, the prototype of the grasping module of claw is established to carry out grasping experiment on vibrating walls. Finally, the experimental results demonstrate that the proposed cross-arranged claw is able to stably grasp static wall surfaces and perform well in grasping vibrating walls, with certain anti-rollover capability. This research lays a foundation for future researches on wall climbing robots with vibrating rough wall surfaces.

Wall-climbing robots have extensive applications, however, no effective adhesion system has been designed for robots deployed in high-altitude, rough concrete buildings that are subjected to large wind loads and vibrations. This paper proposes a new suction method based on a mechanism utilizing hook-like claws and presents the design of a robot system for inspecting rough concrete walls. We present a method for describing the degree of concrete surface roughness. To study the stress imposed on the hook-like claws, we propose two types of mechanical models for the interactions between sharp claws and microprotuberances. The design method for the tips of the sharp claws is then established. Finally, an 8-foot wall-climbing robot based on a mechanism utilizing hook-like claws is designed and laboratory experiments on a man-made concrete wall are conducted. The results indicate that the low-cost system endows the robot with enhanced climbing stability and satisfies the inspection requirements for tower constructed by water brush stone or bricks.