Passenger Vehicle Registrations Rallied in February——registrations reflect auto market tendency

Passenger vehicles (including vans) registrations year-on-year increased 42.2% in Feb. 2007, reaching 408,570 units. Due to the reason there was a larger sales period before the past Chinese New Year in late of February, thus Feb. 2007 made a rather different tendency than previous years. The registrations exceeded more than 0.2 million compared with the

Nonlinear Registration of Brain Magnetic Resonance Images with Cross Constraints of Intensity and Structure

Many deep learning-based registration methods rely on a single-stream encoder-decoder network for computing deformation fields between 3D volumes.However,these methods often lack constraint information and overlook semantic consistency,limiting their performance.To address these issues,we present a novel approach for medical image registration called theDual-VoxelMorph,featuring a dual-channel cross-constraint network.This innovative network utilizes both intensity and segmentation images,which share identical semantic information and feature representations.Two encoder-decoder structures calculate deformation fields for intensity and segmentation images,as generated by the dual-channel cross-constraint network.This design facilitates bidirectional communication between grayscale and segmentation information,enabling the model to better learn the corresponding grayscale and segmentation details of the same anatomical structures.To ensure semantic and directional consistency,we introduce constraints and apply the cosine similarity function to enhance semantic consistency.Evaluation on four public datasets demonstrates superior performance compared to the baselinemethod,achieving Dice scores of 79.9%,64.5%,69.9%,and 63.5%for OASIS-1,OASIS-3,LPBA40,and ADNI,respectively.

The Making of a Health Profession:How Chinese Medicine Became a Nationally Registered Allied Health Profession in Australia

In 2012, traditional Chinese medicine(TCM) practitioners in Australia became nationally-registered allied health professionals in three categories: acupuncturist, Chinese herbal medicine practitioner, and Chinese herbal medicine dispenser. Australia was the first Western country to introduce national registration for Chinese medicine, followed by Portugal and, recently, New Zealand. The practice of TCM in Australia can be traced back to the beginning of Chinese immigration to Australia during the Victorian Gold Rush which began in the 1850s. The process which led to national registration commenced in the early 1970s with the establishment of the first acupuncture courses. Decades of gradual development of courses from unaccredited part-time diplomas to accredited bachelor degrees, and the development, by the profession, of national consensus on educational standards were essential elements in the process which led to registration. Professional associations, such as the Australian Acupuncture and Chinese Medicine Association Ltd.(AACMA), also developed a framework of professional selfregulation, including Codes of Ethics, Codes of Conduct, ongoing professional development requirements and Infection Control Guidelines, and provided leadership in the development of the profession. After decades of tribalism and division within the TCM profession, the National Academic Standards Committee brought almost all stakeholders together to reach a consensus on the Australian Guidelines for Traditional Chinese Medicine Education which were published by AACMA in 2001. Professional associations also collaborated on joint submissions to the government in support of registration, which was introduced first in the state of Victoria in 2000, and subsequently became national in 2012. Despite national registration, some barriers still remain, and professional associations continue to lobby the federal government for inclusion in Medicare, chronic disease management scheme, and Veterans Affairs.

Research on Detection Technology of Micro-Components on Circuit Board Based on Digital Image Processing

Aiming at the stability of the circuit board image in the acquisition process,this paper realizes the accurate registration of the image to be registered and the standard image based on the SIFT feature operator and RANSAC algorithm.The device detection model and data set are established based on Faster RCNN.Finally,the number of training was continuously optimized,and when the loss function of Faster RCNN converged,the identification result of the device was obtained.

Analysis of the Current Situation of Drug Clinical Trial Institutions in Shaanxi Province

To understand the current situation of institutional registration in Shaanxi Province after the implementation ofregistration system management in drug clinical trial institutions.Relevant information was collected on the“Announcement on the Accreditation of Drug Clinical Trial Institutions”issued by the National Medical Products Administration from 2005 to August 2022,the record management information system of drug and medical device clinical trial institutions,and the drug clinical trial registration and information publicity platform.A retrospective analysis was carried out in terms of institutional development,regional distribution,registered majors,principal investigators,and the number of drug clinical trials.After the implementation of institution registration,the number of drug clinical trial institutions in Shaanxi Province increased by 47.4%,884 principal investigators were registered,the number of registered majors expanded from 58 qualified to 117,and the professional scope increased by 50.4%.The policy of institution registration is conducive to promoting the rational use of medical resources and the development of drug clinical trial institutions and improving the healthy development of the pharmaceutical industry in Shaanxi Province.

Model reconstruction for worn blades based on hybrid surface registrations

Model reconstruction is crucial in blade repair because it directly determines the shape precision and finish of a repaired surface.However,owing to insufficient surface data pertaining to defective regions and the unique deformation caused by harsh environments,modeling a worn blade remains difficult.Hence,a model reconstruction method for worn blades is developed in this study.Unlike conventional methods of constructing and interpolating sectional curves,the proposed method focuses on modifying a nominal computer aided design(CAD)model to reconstruct the worn blade.Through weighted rigid registration and constraint-based non-rigid registration,the design surface extracted from the nominal CAD model can be deformed to align with the surface data of the worn blade without a significant loss of its initial shape.Verification results show that the deformed design surface exhibits sufficient smoothness and accuracy for guiding tool path generation in the subsequent blade repair.

老年患者门诊预约挂号的影响因素:基于安德森健康行为模型的实证研究

目的基于安德森健康行为模型,研究老年患者门诊预约挂号的影响因素,为优化门诊预约挂号服务、推进老年患者预约挂号提供参考。方法以安德森健康行为模型为理论框架设计问卷,采用方便抽样法向上海某三级甲等综合医院门诊老年患者发放调查问卷。将各维度因素纳入二分类logistic回归模型,采用单因素分析和logistic回归分析探讨老年患者门诊预约挂号的影响因素。结果共回收282份有效问卷,预约挂号187人,占66.31%。曾使用过1~2种预约挂号服务、存在社会支持、距离医院路程80 min以上、收入为>5000~7500元、就诊科室预约率25.00%以上、有预约挂号使用意愿的老年患者更倾向于预约挂号。患慢性病数量在5种及以上的老年患者更不倾向于预约挂号。结论老年患者门诊预约需求较大且受多种因素影响。须关注老年人预约挂号“痛点”,关怀老年群体,共建支持网络,加强有效宣传,优化医疗资源配置,激发使用意愿。

Registrations for Chemicals Exported to China

2.5.1Purpose and scope of the registra-tion for hazardous chemicals Purpose of the registration:establish a dy-namic administration database of

A review of laser scanning for geological and geotechnical applications in underground mining

Laser scanning can provide timely assessments of mine sites despite adverse challenges in the operational environment.Although there are several published articles on laser scanning,there is a need to review them in the context of underground mining applications.To this end,a holistic review of laser scanning is presented including progress in 3D scanning systems,data capture/processing techniques and primary applications in underground mines.Laser scanning technology has advanced significantly in terms of mobility and mapping,but there are constraints in coherent and consistent data collection at certain mines due to feature deficiency,dynamics,and environmental influences such as dust and water.Studies suggest that laser scanning has matured over the years for change detection,clearance measurements and structure mapping applications.However,there is scope for improvements in lithology identification,surface parameter measurements,logistic tracking and autonomous navigation.Laser scanning has the potential to provide real-time solutions but the lack of infrastructure in underground mines for data transfer,geodetic networking and processing capacity remain limiting factors.Nevertheless,laser scanners are becoming an integral part of mine automation thanks to their affordability,accuracy and mobility,which should support their widespread usage in years to come.

Disordered Multi-view Registration Method Based on the Soft Trimmed Deep Network

Compared with the pair-wise registration of point clouds,multi-view point cloud registration is much less studied.In this dissertation,a disordered multi-view point cloud registration method based on the soft trimmed deep network is proposed.In this method,firstly,the expression ability of feature extraction module is improved and the registration accuracy is increased by enhancing feature extraction network with the point pair feature.Secondly,neighborhood and angle similarities are used to measure the consistency of candidate points to surrounding neighborhoods.By combining distance consistency and high dimensional feature consistency,our network introduces the confidence estimation module of registration,so the point cloud trimmed problem can be converted to candidate for the degree of confidence estimation problem,achieving the pair-wise registration of partially overlapping point clouds.Thirdly,the results from pair-wise registration are fed into the model fusion to achieve the rough registration of multi-view point clouds.Finally,the hierarchical clustering is used to iteratively optimize the clustering center model by gradually increasing the number of clustering categories and performing clustering and registration alternately.This method achieves rough point cloud registration quickly in the early stage,improves the accuracy of multi-view point cloud registration in the later stage,and makes full use of global information to achieve robust and accurate multi-view registration without initial value.

Real-time continuous image guidance for endoscopic retrograde cholangiopancreatography based on 3D/2D registration and respiratory compensation

BACKGROUND It has been confirmed that three-dimensional(3D)imaging allows easier identification of bile duct anatomy and intraoperative guidance of endoscopic retrograde cholangiopancreatography(ERCP),which reduces the radiation dose and procedure time with improved safety.However,current 3D biliary imaging does not have good real-time fusion with intraoperative imaging,a process meant to overcome the influence of intraoperative respiratory motion and guide navigation.The present study explored the feasibility of real-time continuous image-guided ERCP.AIM To explore the feasibility of real-time continuous image-guided ERCP.METHODS We selected 23D-printed abdominal biliary tract models with different structures to simulate different patients.The ERCP environment was simulated for the biliary phantom experiment to create a navigation system,which was further tested in patients.In addition,based on the estimation of the patient’s respiratory motion,preoperative 3D biliary imaging from computed tomography of 18 patients with cholelithiasis was registered and fused in real-time with 2D fluoroscopic sequence generated by the C-arm unit during ERCP.RESULTS Continuous image-guided ERCP was applied in the biliary phantom with a registration error of 0.46 mm±0.13 mm and a tracking error of 0.64 mm±0.24mm.After estimating the respiratory motion,3D/2D registration accurately transformed preoperative 3D biliary images to each image in the X-ray image sequence in real-time in 18 patients,with an average fusion rate of 88%.CONCLUSION Continuous image-guided ERCP may be an effective approach to assist the operator and reduce the use of X-ray and contrast agents.

Fast Estimation of Loader’s Shovel Load Volume by 3D Reconstruction of Material Piles

Fast and accurate measurement of the volume of earthmoving materials is of great signifcance for the real-time evaluation of loader operation efciency and the realization of autonomous operation. Existing methods for volume measurement, such as total station-based methods, cannot measure the volume in real time, while the bucket-based method also has the disadvantage of poor universality. In this study, a fast estimation method for a loader’s shovel load volume by 3D reconstruction of material piles is proposed. First, a dense stereo matching method (QORB–MAPM) was proposed by integrating the improved quadtree ORB algorithm (QORB) and the maximum a posteriori probability model (MAPM), which achieves fast matching of feature points and dense 3D reconstruction of material piles. Second, the 3D point cloud model of the material piles before and after shoveling was registered and segmented to obtain the 3D point cloud model of the shoveling area, and the Alpha-shape algorithm of Delaunay triangulation was used to estimate the volume of the 3D point cloud model. Finally, a shovel loading volume measurement experiment was conducted under loose-soil working conditions. The results show that the shovel loading volume estimation method (QORB–MAPM VE) proposed in this study has higher estimation accuracy and less calculation time in volume estimation and bucket fll factor estimation, and it has signifcant theoretical research and engineering application value.

Automatic Extraction of the Sparse Prior Correspondences for Non-Rigid Point Cloud Registration

Non-rigid registration of point clouds is still far from stable,especially for the largely deformed one.Sparse initial correspondences are often adopted to facilitate the process.However,there are few studies on how to build them automatically.Therefore,in this paper,we propose a robust method to compute such priors automatically,where a global and local combined strategy is adopted.These priors in different degrees of deformation are obtained by the locally geometrical-consistent point matches from the globally structural-consistent region correspondences.To further utilize the matches,this paper also proposes a novel registration method based on the Coherent Point Drift framework.This method takes both the spatial proximity and local structural consistency of the priors as supervision of the registration process and thus obtains a robust alignment for clouds with significantly different deformations.Qualitative and quantitative experiments demonstrate the advantages of the proposed method.

Natural forest ALS-TLS point cloud data registration without control points

Airborne laser scanning(ALS)and terrestrial laser scanning(TLS)has attracted attention due to their forest parameter investigation and research applications.ALS is limited to obtaining fi ne structure information below the forest canopy due to the occlusion of trees in natural forests.In contrast,TLS is unable to gather fi ne structure information about the upper canopy.To address the problem of incomplete acquisition of natural forest point cloud data by ALS and TLS on a single platform,this study proposes data registration without control points.The ALS and TLS original data were cropped according to sample plot size,and the ALS point cloud data was converted into relative coordinates with the center of the cropped data as the origin.The same feature point pairs of the ALS and TLS point cloud data were then selected to register the point cloud data.The initial registered point cloud data was fi nely and optimally registered via the iterative closest point(ICP)algorithm.The results show that the proposed method achieved highprecision registration of ALS and TLS point cloud data from two natural forest plots of Pinus yunnanensis Franch.and Picea asperata Mast.which included diff erent species and environments.An average registration accuracy of 0.06 m and 0.09 m were obtained for P.yunnanensis and P.asperata,respectively.

Free form deformation and symmetry constraint‐based multimodal brain image registration using generative adversarial nets

Multi‐modal brain image registration has been widely applied to functional localisation,neurosurgery and computational anatomy.The existing registration methods based on the dense deformation fields involve too many parameters,which is not conducive to the exploration of correct spatial correspondence between the float and reference images.Meanwhile,the unidirectional registration may involve the deformation folding,which will result in the change of topology during registration.To address these issues,this work has presented an unsupervised image registration method using the free form deformation(FFD)and the symmetry constraint‐based generative adversarial networks(FSGAN).The FSGAN utilises the principle component analysis network‐based structural representations of the reference and float images as the inputs and uses the generator to learn the FFD model parameters,thereby producing two deformation fields.Meanwhile,the FSGAN uses two discriminators to decide whether the bilateral registration have been realised simultaneously.Besides,the symmetry constraint is utilised to construct the loss function,thereby avoiding the deformation folding.Experiments on BrainWeb,high grade gliomas,IXI and LPBA40 show that compared with state‐of‐the‐art methods,the FSGAN provides superior performance in terms of visual comparisons and such quantitative indexes as dice value,target registration error and computational efficiency.

Application of Opening and Closing Morphology in Deep Learning-Based Brain Image Registration

In order to improve the registration accuracy of brain magnetic resonance images(MRI),some deep learning registration methods use segmentation images for training model.How-ever,the segmentation values are constant for each label,which leads to the gradient variation con-centrating on the boundary.Thus,the dense deformation field(DDF)is gathered on the boundary and there even appears folding phenomenon.In order to fully leverage the label information,the morphological opening and closing information maps are introduced to enlarge the non-zero gradi-ent regions and improve the accuracy of DDF estimation.The opening information maps supervise the registration model to focus on smaller,narrow brain regions.The closing information maps supervise the registration model to pay more attention to the complex boundary region.Then,opening and closing morphology networks(OC_Net)are designed to automatically generate open-ing and closing information maps to realize the end-to-end training process.Finally,a new registra-tion architecture,VM_(seg+oc),is proposed by combining OC_Net and VoxelMorph.Experimental results show that the registration accuracy of VM_(seg+oc) is significantly improved on LPBA40 and OASIS1 datasets.Especially,VM_(seg+oc) can well improve registration accuracy in smaller brain regions and narrow regions.

Improvement of iterative closest point with edges of projected image

Background There are many regularly shaped objects in artificial environments.It is difficult to distinguish the poses of these objects when only geometric information is used.With the development of sensor technologies,inclusion of other information can be used to solve this problem.Methods We propose an algorithm to register point clouds by integrating color information.The key idea of the algorithm is to jointly optimize the dense and edge terms.The dense term was built in a manner similar to that of the iterative closest point algorithm.To build the edge term,we extracted the edges of the images obtained by projecting point clouds.The edge term prevents the point clouds from sliding during registration.We used this loosely coupled method to fuse geometric and color information.Results The results of the experiments showed that the edge image approach improves precision,and the algorithm is robust.

构建中国航天器物权法律制度的功能考察与立法研究

A legal regime for property rights of spacecraft in China is of great significance for expanding new-types of international collaboration and promoting commercial space development in China.China has so far yet to establish the relevant legal regime that governs the property rights of spacecraft,this has therefore resulted in the lack of rule and fundamental law with respect to domestic commercial space development and international space collaboration.In contrast,under legislation,the legal regime of property right registration for commercial spacecraft was introduced by Congress in the United States of America(the U.S.A.)in 1984.This effectively promoted the commercial space development in the U.S.A.Similarly,the Civil Code of the People’s Republic of China(effective as from January 1,2021)has specified a legal regime of property right registration for aircraft and ships thereby having effectuated the flourishing development of China’s aviation and maritime industries.Constructing a legal regime for the property rights of spacecraft in China is meant to include but not be limited to the establishment of registration regime of property rights,a distinct regime of ownership,and a registration regime for mortgage and financial leasing in connection with spacecraft.It is recommended that establishment of the registration and exchange centers for property rights of spacecraft be considered in the cities of Beijing and Shanghai.These would operate as centers managing the property right registration and transaction settlement associated with spacecraft along with their effective payloads so as to create the formation of a transaction mechanism for domestic spacecraft and their effective payload applications,thus providing a legal basis for activities such as financing,investment and transfer of ownership,involving spacecraft developed in China.This would drive China’s aerospace industry to be better integrated with the global aerospace market.Formation of a registration regime for property rights of spacecraft in China is advised to take into account a variety of legislation modus such as mandatory registration,optional registration,a mechanism taking effect as a result of registration or against a third party as a result of registration or with a coverage of certain crucial legislative aspects specifically the registration body,registration subject,registration type,validity of registration,types of right,etc.

人工智能赋能医学影像的现状与前景（英文）

Medical imaging is now being reshaped by artificial intelligence (AI) and progressing rapidly toward future.In this article,we review the recent progress of AI-enabled medical imaging.Firstly,we briefly review the background about AI in its way of evolution.Then,we discuss the recent successes of AI in different medical imaging tasks,especially in image segmentation,registration,detection and recognition.Also,we illustrate several representative applications of AI-enabled medical imaging to show its advantage in real scenario,which includes lung nodule in chest CT,neuroimaging,mammography,and etc.Finally,we report the way of human-machine interaction.We believe that,in the future,AI will not only change the traditional way of medical imaging,but also improve the clinical routines of medical care and enable many aspects of the medical society.