Study on technical efficiency of traditional Chinese medicine industry of the Belt and Road Initiative based on environmental complexity

Background:Based on the principle of“joint consultation,co-building,and sharing”,with the aim of creating an energetic new model of economic growth,the Belt and Road Initiative achieves connectivity and mutual benefit.However,the degree of coordinated development of innovation subjects directly affects the improvement and optimization of the Belt and Road Initiative’s strategy implementation environment,and technology efficiency needs improvement.Methods:In this study,a data envelopment analysis model based on the environmental noise elimination algorithm(traditional Chinese medicine-Belt and Road Initiative)was used to identify the technical efficiency of the traditional Chinese medicine industry of Belt and Road Initiative by eliminating the impact of environmental factors.Results:Pure technical ineffectiveness,regional impact,and environmental factors represented by policy responses have a non-linear impact on technical efficiency.The overall technical efficiency of the traditional Chinese medicine industry in the countries along the route shows a trend of annual increases,technical efficiency and scale efficiency are relatively high,and operational efficiency is influenced by the degree of response of the countries along the route and its supporting policies.Conclusion:Pure technical inefficiency is the main factor affecting total technical loss.Geographical advantage is the key factor in operational efficiency.External environmental factors have a significant influence on the operational efficiency of the traditional Chinese medicine industry.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

Path Planning Method Based on D^(*) lite Algorithm for Unmanned Surface Vehicles in Complex Environments

In recent decades,path planning for unmanned surface vehicles(USVs)in complex environments,such as harbours and coastlines,has become an important concern.The existing algorithms for real-time path planning for USVs are either too slow at replanning or unreliable in changing environments with multiple dynamic obstacles.In this study,we developed a novel path planning method based on the D^(*) lite algorithm for real-time path planning of USVs in complex environments.The proposed method has the following advantages:(1)the computational time for replanning is reduced significantly owing to the use of an incremental algorithm and a new method for modelling dynamic obstacles;(2)a constrained artificial potential field method is employed to enhance the safety of the planned paths;and(3)the method is practical in terms of vehicle performance.The performance of the proposed method was evaluated through simulations and compared with those of existing algorithms.The simulation results confirmed the efficiency of the method for real-time path planning of USVs in complex environments.

Dynamic analysis of wind turbine tower structures in complex ocean environment

Studying and analyzing the dynamic behavior of offshore wind turbines are of great importance to ensure the safety and improve the efficiency of such expensive equipments.In this work,a tapered beam model is proposed to investigate the dynamic response of an offshore wind turbine tower on the monopile foundation assembled with rotating blades in the complex ocean environment.Several environment factors like wind,wave,current,and soil resistance are taken into account.The proposed model is ana-lytically solved with the Galerkin method.Based on the numerical results,the effects of various structure parameters including the taper angle,the height and thickness of the tower,the depth,and the diameter and the cement filler of the monopile on the funda-mental natural frequency of the wind turbine tower system are investigated in detail.It is found that the fundamental natural frequency decreases with the increase in the taper angle and the height and thickness of the tower,and increases with the increase in the diameter of the monopile.Moreover,filling cement into the monopile can effectively im-prove the fundamental natural frequency of the wind turbine tower system,but there is a critical value of the amount of cement maximizing the property of the monopile.This research may be helpful in the design and safety evaluation of offshore wind turbines.

Method for the fruit tree recognition and navigation in complex environment of an agricultural robot

To realize the visual navigation of agricultural robots in the complex environment of orchards,this study proposed a method for fruit tree recognition and navigation based on YOLOv5.The YOLOv5s model was selected and trained to identify the trunks of the left and right rows of fruit trees;the quadratic curve was fitted to the bottom center of the fruit tree recognition box,and the identified fruit trees were divided into left and right columns by using the extreme value point of the quadratic curve to obtain the left and right rows of fruit trees;the straight-line equation of the left and right fruit tree rows was further solved,the median line of the two straight lines was taken as the expected navigation path of the robot,and the path tracing navigation experiment was carried out by using the improved LQR control algorithm.The experimental results show that under the guidance of the machine vision system and guided by the improved LQR control algorithm,the lateral error and heading error can converge quickly to the desired navigation path in the four initial states of[0 m,−0.34 rad],[0.10 m,0.34 rad],[0.15 m,0 rad]and[0.20 m,−0.34 rad].When the initial speed was 0.5 m/s,the average lateral error was 0.059 m and the average heading error was 0.2787 rad for the navigation trials in the four different initial states.Its average driving was 5.3 m into the steady state,the average value of steady state lateral error was 0.0102 m,the average value of steady state heading error was 0.0253 rad,and the average relative error of the robot driving along the desired navigation path was 4.6%.The results indicate that the navigation algorithm proposed in this study has good robustness,meets the operational requirements of robot autonomous navigation in orchard environment,and improves the reliability of robot driving in orchard.

Switching variability index based multiple strategy CFAR detector

A switching variability index （SVl） constant false alarm rate （CFAR） detector is proposed for improving the detection performance of VI-CFAR detectors in multiple targets backgrounds. When the presence of non-homogeneity in CFAR reference windows is indicated by a VI-CFAR detector, a switching- CFAR detector is introduced to optimize the performance of the VI-CFAR detector in homogeneous, multiple targets and clutter edge backgrounds. The structure and parameters selection method of the SVI-CFAR detector is presented. Comparisons with classic CFAR detectors and recently proposed detectors are also given. Theoretical analysis and simulation results show that SVICFAR detector maintains the good performance of the VI-CFAR detector in homogeneous and clutter edge backgrounds, while greatly improving the capacity of anti-multi targets.

An improved Gmapping algorithm based map construction method for indoor mobile robot

With the rapid development in the service,medical,logistics and other industries,and the increasing demand for unmanned mobile devices,mobile robots with the ability of independent mapping,localization and navigation capabilities have become one of the research hotspots.An accurate map construction is a prerequisite for a mobile robot to achieve autonomous localization and navigation.However,the problems of blurring and missing the borders of obstacles and map boundaries are often faced in the Gmapping algorithm when constructing maps in complex indoor environments.In this pursuit,the present work proposes the development of an improved Gmapping algorithm based on the sparse pose adjustment(SPA)optimizations.The improved Gmapping algorithm is then applied to construct the map of a mobile robot based on single-line Lidar.Experiments show that the improved algorithm could build a more accurate and complete map,reduce the number of particles required for Gmapping,and lower the hardware requirements of the platform,thereby saving and minimizing the computing resources.

Intelligent virtualization of crane lifting using laser scanning technology

Background This paper presents an intelligent path planner for lifting tasks by tower cranes in highly complex environments,such as old industrial plants that were built many decades ago and sites used as tentative storage spaces.Generally,these environments do not have workable digital models and 3 D representations are impractical.Methods The current investigation introduces the use of cutting edge laser scanning technology to convert real environments into virtualized versions of the construction sites or plants in the form of point clouds.The challenge is in dealing with the large point cloud datasets from the multiple scans needed to produce a complete virtualized model.The tower crane is also virtualized for the purpose of path planning.A parallelized genetic algorithm is employed to achieve intelligent path planning for the lifting task performed by tower cranes in complicated environments taking advantage of graphics processing unit technology,which has high computing performance yet low cost.Results Optimal lifting paths are generate d in several seconds.

Path Planning in Complex 3D Environments Using a Probabilistic Roadmap Method

This paper presents a 3D path planning algorithm for an unmanned aerial vehicle （UAV） in complex environments. In this algorithm, the environments are divided into voxels by octree algorithm. In order to satisfy the safety requirement of the UAV, free space is represented by free voxels, which have enough space margin for the UAV to pass through. A bounding box array is created in the whole 3D space to evaluate the free voxel connectivity. The probabilistic roadmap method （PRM） is improved by random sampling in the bounding box array to ensure a more efficient distribution of roadmap nodes in 3D space. According to the connectivity evaluation, the roadmap is used to plan a feasible path by using A＊ algorithm. Experimental results indicate that the proposed algorithm is valid in complex 3D environments.

COMPARISONS OF RAY-TRACING AND PARABOLIC EQUATION METHODS FOR THE LARGE-SCALE COMPLEX ELECTROMAGNETIC ENVIRONMENT SIMULATIONS

It is important for the wireless communication field to conduct research on large-scale complex electromagnetic environment(CEME)simulation.There exist many models for computing CEME simulation,including empirical models,half-empirical or halfdeterministic models and deterministic models.Most of these models cannot obtain satisfactory results due to the limitation of the capacity of computers.The ray tracing(RT)and parabolic equation(PE)methods are very suitable for large-scale CEME simulation.Based on the introduction of RT and PE,qualitative comparisons of the two methods are analyzed in view of algorithm theory,the category of the model,solution to the model and the application field,and then four specific indices are focused on to analyze the computational complexity,accuracy,speed and parallelism in details.The numerical experiments are presented by the three-dimensional(3D)RT method employing the software of Wireless InSite(WI)and a quasi-3DPE method using the sliced method.Although both RT and PE methods can achieve high speedup using coarse-grained parallel computing,the experimental results indicate that the PE method can obtain a higher speed than the RT method,and the two methods can acquire an approximate precision.A hybrid procedure using both RT and PE methods can obtain a better result for solving CEME problems.

Precision Machining Equipment Fault Diagnosis Based on CWT and Improved ResNeXt

A fault diagnosis method based on continuous wavelet transform and improved multi-dimensional residual network was proposed to solve the problem that the working environment of precision machining equipment is very complicated,and the fault characteristic signal is weak and hard to extract.Firstly,the best wavelet base Cmor 3-3 is selected by comparing 6 different wavelet base types.Secondly,continuous wavelet transform(CWT)is applied to the acquired original vibration signal to generate the feature map and process the gray level.Finally,the improved ResNeXt network is used to diagnose faults in precision machining equipment.The experimental results show that the proposed CWT and the improved ResNeXt algorithm have high accuracy in identifying precision machining equipment faults in complex environments,with an average accuracy of 99.4%。

The mechanism of environmental D-xylose perception by the bacterial membrane two-component complex XylFII-LytS

D-xylose,the main building block of plant biomass,is a pentose sugar that can be used by bacteria as a carbon source for bio-based fuel and chemical production through fermentation.In bacteria,the first step for D-xylose metabolism is signal perception at the membrane.Scientists previously identified a threecomponent system in Firmicutes bacteria comprising a membrane-associated sensor protein(XylFII)。

Target localization method of non-cooperative spacecraft on on-orbit service

With the explosion of the number of meteoroid/orbital debris in terrestrial space in recent years, the detection environment of spacecraft becomes more complex. This phenomenon causes most current detection methods based on machine learning intractable to break through the two difficulties of solving scale transformation problem of the targets in image and accelerating detection rate of high-resolution images. To overcome the two challenges, we propose a novel noncooperative target detection method using the framework of deep convolutional neural network.Firstly, a specific spacecraft simulation dataset using over one thousand images to train and test our detection model is built. The deep separable convolution structure is applied and combined with the residual network module to improve the network’s backbone. To count the different shapes of the spacecrafts in the dataset, a particular prior-box generation method based on K-means cluster algorithm is designed for each detection head with different scales. Finally, a comprehensive loss function is presented considering category confidence, box parameters, as well as box confidence. The experimental results verify that the proposed method has strong robustness against varying degrees of luminance change, and can suppress the interference caused by Gaussian noise and background complexity. The mean accuracy precision of our proposed method reaches 93.28%, and the global loss value is 13.252. The comparative experiment results show that under the same epoch and batchsize, the speed of our method is compressed by about 20% in comparison of YOLOv3, the detection accuracy is increased by about 12%, and the size of the model is reduced by nearly 50%.

A resilient adjustment method to weigh pseudorange observation in precise point positioning

The accurate weighting of pseudorange observations is important to improve the convergence time and positioning quality of Precise Point Positioning(PPP).Currently,the weight of a pseudorange observation is mainly determined with empirical stochastic models.However,in a complex environment,due to the inability to adapt for the dynamic changes of the user environment,the empirical stochastic models usually cannot reflect the real error level of pseudorange observations.To address this problem,a resilient adjustment method to weigh pseudorange observations is proposed,which constructs the real-time estimation and inflation model for the variance of pseudorange multipath error and measurement noise to replace the empirical stochastic model to determine the weights of pseudorange observations.A set of static and dynamic Global Positioning System(GPS)test data are used to verify the effectiveness of the proposed method.The test results indicate that the proposed real-time estimation model can provide a better representation of the pseudorange accuracy,and the positioning performance of PPP using the real-time estimation model is better than that with the empirical stochastic model.Compared with the optimal empirical stochastic model,the positioning accuracy of PPP with the real-time estimation model is improved by at least 20%,and the convergence time is reduced by at least 50%.

Scientific problems and research proposals for Sichuan–Tibet railway tunnel construction

The Sichuan–Tibet railway construction has received significant attention from both China and abroad.The new section from Ya’an to Nyingchi is about 1018.6 km long,including 69 tunnels with a total length of 841.7 km.The longest tunnel(Yigong tunnel)is 42.4 km long.The successful construction of the Sichuan–Tibet railway depends largely on tunnel construction.Due to the complex conditions,tunnel construction for the Sichuan–Tibet railway poses great challenges to the research community.This paper aims to illustrate the necessity of interdisciplinary and joint research to solve problems associated with tunnel construction that will be encountered in the immediate future and facilitate the communication and exchange of ideas between disciplines.To this end,based on a systematic analysis of the available data related to Sichuan–Tibet railway tunnels,six major aspects of the geology,environment,and engineering conditions are identified.Next,based on the engineering responses from the Sichuan–Tibet railway tunnels,potential technical problems and risk characteristics are predicted and evaluated.Further,six key scientific issues are identified and discussed based on a coupled multi-layer analysis of essential tunnel engineering issues.Finally,research directions,technical ideas,and research methods that should be carried out for this project are proposed.

SERS-encoded nanocomposites for dual pathogen bioassay

Surface-enhanced Raman spectroscopy(SERS)has been successfully applied to detect various biomolecules,but it is still in challenge to assay living cells or bacteria sensitively,selectively and quantitatively in complex environments.In this paper,4-ATP and DTNB are assembled on Ag nanoparticle(NP)-decorated poly(styrene-co-acrylic acid)(PSA)nanospheres and then sealed by silica shells to form sensitive SERS labels based on the localized surface plasmon resonance of Ag NPs and large light scattering cross-sections of PSA nanospheres.They are further developed as encoding tags for dual detection of S.aureus and E.coli after assembling corresponding aptamers,which demonstrate ultralow detection limits of 8 cell L-1 for S.aureus and 2 cell L-1 for E.coli.Such a bioassay indicates a point-of-care strategy of ultrasensitively biomedical detections by encoding specific SERS tags.