Insulin-induced severe thyrotoxic periodic paralysis:A case report

BACKGROUND Thyrotoxic periodic paralysis(TPP)is an endocrine emergency caused by thyrotoxicosis,manifesting mainly as periodic myasthenia and hypokalemia,and posing a serious threat to the patient's health.Fatigue,strenuous exercise,alcohol abuse,high carbohydrate intake and insulin injections are common triggers of paralysis.This article reports a case of severe TPP induced by insulin injection,elucidates the characteristics and pathogenesis of the disease,analyses the risk factors for triggering TPP,and hopefully provides more clinical data for TPP patients.CASE SUMMARY A 38-year-old Asian man presented to the emergency department with a oneweek history of limb weakness and worsening half-day.His medical history included poorly controlled type 2 diabetes and he had been switched to Aspart50 a week earlier.He was alert and oriented with upper extremity strength grade 3 and lower extremity strength grade 1.Emergency department tests showed hypokalemia of 1.6 mmol/L.The paramedics administered 1.5 g of potassium intravenously,followed by 4.0 g orally.Weakness in the arms and legs improved.He was referred to endocrinology where he was diagnosed with Graves'disease,with suboptimal control and insulin injections possibly causing TPP.We stopped his insulin and he was discharged with a potassium level of 4.0 mmol/L.CONCLUSION Insulin is a trigger for TPP and should be avoided in patients with hyperthyroidism.Early recognition and treatment of TPP is crucial,especially in patients presenting with hypokalemic periodic paralysis.

基于轻量化PointNet网络的林果园喷雾作业靶标实时识别方法

为了进一步提高喷雾机器人靶标检测的精准性、实时性和应用部署的实用性,该研究提出一种基于轻量化PointNet网络的林果园喷雾作业靶标实时识别方法。首先通过区域提取降采样、地面分割和改进DBSCAN聚类等点云预处理方法提取原始点云中的靶标;然后通过移动最小二乘上采样将靶标点云转化为满足点云识别网络输入要求的点云数据;最终通过在PointNet网络中引入残差模块和改进循环剪枝算法轻量化PointNet网络,完成林果树靶标的实时识别。试验结果表明,在ModelNet40数据集上,轻量化PointNet网络可达89.7%的准确率;在实际苗圃环境的试验中,该研究方法对靶标的识别准确率可达92.49%,同时误识率与拒识率分别为13.4%和6.47%,相较PointNet网络识别准确率提升了4.38个百分点,误识率和拒识率分别降低了7.2和4.07个百分点;轻量化PointNet网络识别准确率仅比PointNet++网络低1.14个百分点,误识率和拒识率分别高了0.9和1.12个百分点。但是轻量化PointNet网络的模型参数量较PointNet网络和PointNet++网络的模型参数量显著减少,仅为PointNet网络的11.5%,PointNet++网络的27.02%;运算量相较PointNet网络、PointNet++网络分别减少13.3和76.79个百分点。该研究提出的轻量化PointNet网络具有较高的实时性、精确性和鲁棒性,能够满足林果园喷雾作业的靶标识别需求,可为林果园喷雾作业靶标实时识别提供参考。

SOLUTIONS TO DISCRETE MULTIPARAMETER PERIODIC BOUNDARY VALUE PROBLEMS INVOLVING THE p-LAPLACIAN VIA CRITICAL POINT THEORY

In this paper, we consider the existence of three nontrivial solutions for a discrete non-linear multiparameter periodic problem involving the p-Laplacian. By using the similar method for the Dirichlet boundary value problems in [C. Bonanno and P. Candito, Appl. Anal., 88（4） （2009）, pp. 605-616], we construct two new strong maximum principles and obtain that the boundary value problem has three positive solutions for λ and μ in some suitable intervals. The approaches we use are the critical point theory.

Periodic Points on the Parabolic Domain

The papers proves that there is one and only one rational neutral periodic points on the bound ary of any parabolic domain.

PERIODIC POINTS AND NORMALITY CONCERNING MEROMORPHIC FUNCTIONS WITH MULTIPLICITY

In this article,two results concerning the periodic points and normality of meromorphic functions are obtained:(i)the exact lower bound for the numbers of periodic points of rational functions with multiple fixed points and zeros is proven by let ting R(z)be a nonpolynomial rational function,and if all zeros and poles of R(z)—z are multiple,then Rk(z)has at least k+1 fixed points in the complex plane for each integer k≥2;(ii)a complete solution to the problem of normality of meromorphic functions with periodic points is given by let ting F be a family of meromorphic functions in a domain D,and let ting k≥2 be a positive integer.If,for each f∈F,all zeros and poles of f(z)-z are multiple,and its iteration fk has at most k distinct fixed points in D,then F is normal in D.Examples show that all of the conditions are the best possible.

基于K F-PointNet++的油菜植株点云分割算法

针对传统的点云分割算法精度低、鲁棒性差的问题,提出了基于K近邻算法和特征融合的深度点云网络(point clouds network++based on K-nearest neighbor algorithm and feature fusion,K F-PointNet++)三维点云分割算法。该算法首先采用了K近邻(K-nearest neighbors,K NN)算法对点云进行分组;其次,将点云网络(point clouds network,PointNet)中的局部特征与中心点全局特征进行拼接,增强算法对几何细节和全局上下文信息的捕捉能力,从而提高算法的分割精度和鲁棒性,实现了对油菜点云器官的精准分割。使用自制的油菜点云数据集进行实验,结果表明,K F-PointNet++算法在油菜点云分割中的总体精度(overall accuracy,OA)可达97.1%,平均交并比(mean intersection over union,mIoU)为86.4%。该算法在分割性能方面明显优于PointNet、深度点云网络(PointNet++)和核点卷积(kernal point convolution,KPConv),可以为油菜表型研究提供可靠基础。

Periodic orbits around areostationary points in the Martian gravity field

This study investigates the problem of areostationary orbits around Mars in three-dimensional space. Areostationary orbits are expected to be used to establish a future telecommunication network for the exploration of Mars. However, no artificial satellites have been placed in these orbits thus far. The characteristics of the Martian gravity field are presented, and areostationary points and their linear stability are cal- culated. By taking linearized solutions in the planar case as the initial guesses and utilizing the Levenberg-Marquardt method, families of periodic orbits around areo- stationary points are shown to exist. Short-period orbits and long-period orbits are found around linearly stable areostationary points, but only short-period orbits are found around unstable areostationary points. Vertical periodic orbits around both lin- early stable and unstable areostationary points are also examined. Satellites in these periodic orbits could depart from areostationary points by a few degrees in longitude, which would facilitate observation of the Martian topography. Based on the eigenval- ues of the monodromy matrix, the evolution of the stability index of periodic orbits is determined. Finally, heteroclinic orbits connecting the two unstable areostationary points are found, providing the possibility for orbital transfer with minimal energy consumption.

基于PointNet++的邻域特征增强点云语义分割方法

随着智能驾驶、机器人导航等以点云为基础的应用蓬勃发展,点云语义分割逐渐成为研究热点。然而,现有的点云语义分割方法存在局部特征提取不充分、特征融合不完整的缺陷。针对这些不足,提出了对应的解决方案。对于局部特征提取不充分的现象,通过嵌入邻域点的坐标、方向、距离等相关信息去关联邻域点的显式特征。对于特征融合不完整的现象,提出了一种最大池化与自注意力池化相结合的混合池化方法。网络架构基于PointNet++,并结合提出的局部特征提取和融合方法,在S3DIS数据集上的实验结果表明,与基线方法PointNet++相比,各评价指标都有不同程度的提高,证实了新方法的有效性和优越性。

Second Descendible Self-Mapping with Closed Periodic Points Set

Let and f:Xn→Xn be a continuous map. If f is a second descendible map, then P(f) is closed if and only if one of the following hold: 1);2) For any z ε R (f), there exists a yεw (z,f) ∩ P(f) such that every point of the set orb (y,f) is a isolated point of the set w (z,f);3) For any z ε R(f), the set w (z,f) is finite;4) For any z ε R(f), the set w' (z,f) is finite. The consult give another condition of f with closed periodic set other than [1].

Coseismic site response and slope instability using periodic boundary conditions in the material point method

This paper proposed the explicit generalized-a time scheme and periodic boundary conditions in the material point method(MPM)for the simulation of coseismic site response.The proposed boundary condition uses an intuitive particle-relocation algorithm ensuring material points always remain within the computational mesh.The explicit generalized-a time scheme was implemented in MPM to enable the damping of spurious high frequency oscillations.Firstly,the MPM was verified against finite element method(FEM).Secondly,ability of the MPM in capturing the analytical transfer function was investigated.Thirdly,a symmetric embankment was adopted to investigate the effects of ground motion arias intensity(I_(a)),geometry dimensions,and constitutive models.The results show that the larger the model size,the higher the crest runout and settlement for the same ground motion.When using a Mohr-Coulomb model,the crest runout increases with increasing I_(a).However,if the strain-softening law is activated,the results are less influenced by the ground motion.Finally,the MPM results were compared with the Newmark sliding block solution.The simplified analysis herein highlights the capabilities of MPM to capture the full deformation process for earthquake engineering applications,the importance of geometry characterization,and the selection of appropriate constitutive models when simulating coseismic site response and subsequent large deformations.

基于Point Transformer v2的点云枝叶分离方法研究

准确高效的点云枝叶分离对精确计算森林树木的垂直参数至关重要。然而,当前的研究方法计算成本高,且依赖先验知识导致泛化能力不足。针对以上问题,文章提出利用基于点特征的Transformer网络进行自动化的森林场景三维点云的枝叶分离研究。该方法使用Point Transformer v2网络,首先利用网格编码模块提取可学习的局部结构关系,保留点云的几何拓扑结构;其次使用分组注意力实现多通道联合学习,降低特征的冗余度,提高计算的效率;最后构建了基于点的Transformer网络实现高精度森林树木三维点云语义分割,降低了对于先验知识的需求。使用地基激光扫描仪获取的加拿大和芬兰7个不同树种样地的三维点云数据,进行枝叶分离实验和精度评价。实验结果表明,网络整体精度(OA)为94.42%,mIoU为78.89%,能够适应不同树种、不同点云密度的森林场景的枝叶分离。

基于改进PointNet++的船体分段合拢面构件智能识别算法研究

[目的]三维扫描仪获得的船体分段合拢面点云数据,具有精度高、数据量大的优势,能够很好地反映分段合拢面的建造状况。由于现有的PointNet++网络无法处理大容量点云数据,因此提出一种基于改进PointNet++的船体分段合拢面构件智能识别算法,实现针对大容量船体分段合拢面点云数据构件的智能识别。[方法]基于超体素生长理论对船体分段合拢面点云数据进行分割及简化,构建船体分段合拢面点云数据集,并使用该数据集训练基于深度学习理论改进的PointNet++网络。[结果]网络模型在船体分段合拢面点云数据训练集和测试集上的收敛结果趋于稳定,在测试集上识别准确率达到90.012%。[结论]该方法具有良好的识别能力,能够完成船体分段合拢面构件的智能识别。

Measurements of a periodically driving qubit using a quantum point contact

Using the method developed by Gurvitz [1996 Phys. Rev. B 53 15932], we obtained the Bloch-type rate equations describing the entire system of a periodically driving qubit monitored by a quantum point contact detector. The results demonstrate that the isolated qubit can be kept in its initial state with a large driving frequency due to more difficult electron tunneling in qubit, and this initial state can always be measured at a small measurement-induced decoherence rate during a short time.

基于PointNet++进行附属设施语义分割的隧道收敛变形分析

随着城市轨道交通日趋广泛,隧道结构变形引起的地铁安全事故凸显,亟需对运营期隧道进行变形检测。隧道衬砌作为隧道变形分析的研究对象,衬砌内表面存在的大量附属设施影响隧道收敛变形分析精度。为了提高变形分析精度,解决点云处理环节中存在的自动化程度低的问题,提出基于PointNet++点云语义分割的隧道收敛变形分析方法。首先利用深度学习方法进行点云语义分割,对隧道衬砌附属设施进行自动滤除。然后对隧道衬砌进行断面提取,利用随机抽样一致性算法(Random Sample Consensus,RANSAC)对隧道断面点云进行采样,分析隧道收敛变形程度,从Z+F PROFILER 9012A激光断面扫描仪获取山东省济南市地铁盾构隧道点云实测数据上并进行应用。研究结果表明:所提出的处理方法可以有效地将大规模隧道衬砌与连接紧密的附属设施分离出来,隧道附属设施总体分类精度达到96%,滤波结果较好地保留了隧道衬砌原始形态特征。在对隧道整体和局部收敛变形分析的重复性验证中,测试区间内隧道整体变形精度往返测长半轴平均偏差为1.04 mm,短半轴平均偏差为0.9 mm,测试区间内隧道局部收敛变形往返测标准差最小为0.773 mm,最大为0.938 mm,可以满足隧道收敛变形分析的精度需求。研究结果可以有效提升处理大规模隧道数据的自动化程度,具有良好的有效性与可靠性,对运营期地铁隧道收敛变形检测或监测有较好的实践应用意义。

Detecting Unstable Periodic Orbits in Hyperchaotic Systems Using Subspace Fixed-Point Iteration

We present a numerical method for efficiently detecting unstable periodic orbits(UPO’s)embedded in chaotic attractors of high-dimensional systems.This method,which we refer to as subspace fixed-point iteration, locates fixed points of Poincare maps using a form of fixed-point iteration that splits the phase space into appropriate subspaces.In this paper,among a number of possible implementations,we primarily focus on a subspace method based on the Schmelcher-Diakonos(SD)method that selectively locates UPO’s by varying a stabilizing matrix,and present some applications of the resulting subspace SD method to hyperchaotic attractors where the UPO’s have more than one unstable direction.

Phase diagram,correlations,and quantum critical point in the periodic Anderson model

Periodic Anderson model is one of the most important models in the field of strongly correlated electrons. With the recent developed numerical method density matriX embedding theory, we study the ground state properties of the periodic Anderson model on a two-dimensional square lattice. We systematically investigate the phase diagram away from half filling. We find three different phases in this region, which are distinguished by the local moment and the spin-spin correlation functions. The phase transition between the two antiferromagnetic phases is of first order. It is the so-called Lifshitz transition accompanied by a reconstruction of the Fermi surface. As the filling is close to half filling, there is no difference between the two antiferromagnetic phases. From the results of the spin-spin correlation, we find that the Kondo singlet is formed even in the antiferromagnetic phase.

Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://github.com/yahuiliu99/PointC onT.

Ultrafast dynamics of femtosecond laser-induced high spatial frequency periodic structures on silicon surfaces

Femtosecond laser-induced periodic surface structures(LIPSS)have been extensively studied over the past few decades.In particular,the period and groove width of high-spatial-frequency LIPSS(HSFL)is much smaller than the diffraction limit,making it a useful method for efficient nanomanufacturing.However,compared with the low-spatial-frequency LIPSS(LSFL),the structure size of the HSFL is smaller,and it is more easily submerged.Therefore,the formation mechanism of HSFL is complex and has always been a research hotspot in this field.In this study,regular LSFL with a period of 760 nm was fabricated in advance on a silicon surface with two-beam interference using an 800 nm,50 fs femtosecond laser.The ultrafast dynamics of HSFL formation on the silicon surface of prefabricated LSFL under single femtosecond laser pulse irradiation were observed and analyzed for the first time using collinear pump-probe imaging method.In general,the evolution of the surface structure undergoes five sequential stages:the LSFL begins to split,becomes uniform HSFL,degenerates into an irregular LSFL,undergoes secondary splitting into a weakly uniform HSFL,and evolves into an irregular LSFL or is submerged.The results indicate that the local enhancement of the submerged nanocavity,or the nanoplasma,in the prefabricated LSFL ridge led to the splitting of the LSFL,and the thermodynamic effect drove the homogenization of the splitting LSFL,which evolved into HSFL.

Material point method simulation of hydro-mechanical behaviour in twophase porous geomaterials: A state-of-the-art review

The material point method(MPM)has been gaining increasing popularity as an appropriate approach to the solution of coupled hydro-mechanical problems involving large deformation.In this paper,we survey the current state-of-the-art in the MPM simulation of hydro-mechanical behaviour in two-phase porous geomaterials.The review covers the recent advances and developments in the MPM and their extensions to capture the coupled hydro-mechanical problems involving large deformations.The focus of this review is aiming at providing a clear picture of what has or has not been developed or implemented for simulating two-phase coupled large deformation problems,which will provide some direct reference for both practitioners and researchers.

Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.