Precise Positioning Method for Seafloor Geodetic Stations Based on the Temporal Variation of Sound Speed Structure

At present,GNSS-Acoustic(GNSS-A)combined technology is widely used in positioning for seafloor geodetic stations.Based on Sound Velocity Profiles(SVPs)data,the equal gradient acoustic ray-tracing method is applied in high-precision position inversion.However,because of the discreteness of the SVPs used in the forementioned method,it ignores the continuous variation of sound velocity structure in time domain,which worsens the positioning accuracy.In this study,the time-domain variation of Sound Speed Structure(SSS)has been considered,and the cubic B-spline function is applied to characterize the perturbed sound velocity.Based on the ray-tracing theory,an inversion model of“stepwise iteration&progressive corrections”for both positioning and sound speed information is proposed,which conducts the gradual correction of seafloor geodetic station coordinates and disturbed sound velocity.The practical data was used to test the effectiveness of our method.The results show that the Root Mean Square(RMS)errors of the residual values of the traditional methods without sound velocity correction,based on quadratic polynomial correction and based on cubic B-spline function correction are 1.43 ms,0.44 ms and 0.21 ms,respectively.The inversion model with sound velocity correction can effectively eliminate the systematic error caused by the change of SSS,and significantly improve the positioning accuracy of the seafloor geodetic stations.

Cloud Resource Integrated Prediction Model Based on Variational Modal Decomposition-Permutation Entropy and LSTM

Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource clusters.We proposed an integrated prediction method of stacking container cloud resources based on variational modal decomposition(VMD)-Permutation entropy(PE)and long short-term memory(LSTM)neural network to solve the prediction difficulties caused by the non-stationarity and volatility of resource data.The variational modal decomposition algorithm decomposes the time series data of cloud resources to obtain intrinsic mode function and residual components,which solves the signal decomposition algorithm’s end-effect and modal confusion problems.The permutation entropy is used to evaluate the complexity of the intrinsic mode function,and the reconstruction based on similar entropy and low complexity is used to reduce the difficulty of modeling.Finally,we use the LSTM and stacking fusion models to predict and superimpose;the stacking integration model integrates Gradient boosting regression(GBR),Kernel ridge regression(KRR),and Elastic net regression(ENet)as primary learners,and the secondary learner adopts the kernel ridge regression method with solid generalization ability.The Amazon public data set experiment shows that compared with Holt-winters,LSTM,and Neuralprophet models,we can see that the optimization range of multiple evaluation indicators is 0.338∼1.913,0.057∼0.940,0.000∼0.017 and 1.038∼8.481 in root means square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE)and variance(VAR),showing its stability and better prediction accuracy.

THE POSITION VARIATION OF THE WEST PACIFIC SUBTROPICAL HIGH AND ITS POSSIBLE MECHANISM

Using NCEP/NCAR daily reanalysis data and SCSMEX data, an investigation is carried out of the relationship between the position variation of the west Pacific subtropical high （WPSH） and the apparcnt heating in June 1998 based on the complete vertical vorticlty equation. It is tbund that the non-adiabatic heating plays an important role in the position variation of WPSH. In comparison with climatic mean status, the vertical change of non-adiabatic heating is stronger in the north side of WPSH in June 1998, but weaker in the south side of WPSH. The anomalous non-uniform heating induces anomalous cyclonic vorticity in South China, areas to lhe south of the Yangtze and its mid-lower valleys, but anomalous anticyclonic vorticity in the Indo-China Peninsula and South China Sea areas lead to the more southward position of WPSH than the mean.

Variations in Carbohydrate and Protein Accumulation among Spikelets at Different Positions Within a Panicle During Rice Grain Filling

The accumulation dynamics of kernel components for spikelets at different positions within a rice panicle were investigated during grain filling to understand the physiological reasons for the variation of grain quality.Two rice cultivars,Yangdao 6 （indica） and Yangjing 9538 （japonica）,were field-grown,and the grain filling characters and contents of starch,soluble sugar,and protein of the spikelets at different positions were studied.There were significant differences in matter accumulation among spikelets at different positions during grain filling.The early-flowering spikelets presented dominance over the late-flowering spikelets in initial time and initial rate of accumulation.At the initial and mid filling stages,the contents and the rates of starch and amylose accumulation in spikelets decreased with the flowering sequence,but soluble sugar content （SSC） exhibited the opposite trend.The difference in SSC among the spikelets of Yangjing 9538 was greater than that of Yangdao 6,but amylose content in mature spikelets showed no obvious relationship to their flowering sequence.The crude protein content （CPC） of early-flowering spikelets decreased more rapidly than that of late-flowering ones at the initial filling stage,and CPC in the spikelets on the secondary branch was higher than that on the primary branch,but CPC in early-flowering ones was lower than that in late-flowering across the whole grain filling period.Grain water content （GWC） of early-flowering spikelets decreased more rapidly than that of late-flowering spikelets on the same branch at the initial and mid filling stages,especially for the top grain on each primary branch.The results suggested that poor grain filling of late-flowering spikelets may be attributed to their low biological activity rather than carbohydrate supply limitation.

Estimation of annual variation of water vapor in the Arctic Ocean between 80°–87°N using shipborne GPS data based on kinematic precise point positioning

The measurement of atmospheric water vapor （WV） content and variability is important for meteorological and climatological research. A technique for the remote sensing of atmospheric WV content using ground-based Global Positioning System （GPS） has become available, which can routinely achieve accuracies for integrated WV content of 1-2 kg/m2. Some experimental work has shown that the accuracy of WV measurements from a moving platform is comparable to that of （static） land-based receivers. Extending this technique into the marine environment on a moving platform would be greatly beneficial for many aspects of meteorological research, such as the calibration of satellite data, investigation of the air-sea interface, as well as forecasting and climatological studies. In this study, kinematic precise point positioning has been developed to investigate WV in the Arctic Ocean （80°-87°N） and annual variations are obtained for 2008 and 2012 that are identical to those related to the enhanced greenhouse effect.

Spatio-Temporal Wind Speed Prediction Based on Variational Mode Decomposition

Improving short-term wind speed prediction accuracy and stability remains a challenge for wind forecasting researchers.This paper proposes a new variational mode decomposition(VMD)-attention-based spatio-temporal network(VASTN)method that takes advantage of both temporal and spatial correlations of wind speed.First,VASTN is a hybrid wind speed prediction model that combines VMD,squeeze-and-excitation network(SENet),and attention mechanism(AM)-based bidirectional long short-term memory(BiLSTM).VASTN initially employs VMD to decompose the wind speed matrix into a series of intrinsic mode functions(IMF).Then,to extract the spatial features at the bottom of the model,each IMF employs an improved convolutional neural network algorithm based on channel AM,also known as SENet.Second,it combines BiLSTM and AM at the top layer to extract aggregated spatial features and capture temporal dependencies.Finally,VASTN accumulates the predictions of each IMF to obtain the predicted wind speed.This method employs VMD to reduce the randomness and instability of the original data before employing AM to improve prediction accuracy through mapping weight and parameter learning.Experimental results on real-world data demonstrate VASTN’s superiority over previous related algorithms.

Ultrasonographic identification of lateral femoral cutaneous nerve anatomical variation in persistent meralgia paresthetica:A case report

BACKGROUND Meralgia paresthetica(MP)is an entrapment mononeuropathy of the lateral femoral cutaneous nerve(LFCN).Although structural abnormalities in nerve tissues can be confirmed using ultrasonography,this is not routinely performed.CASE SUMMARY Herein,we present the case of a 52-year-old woman who developed MP after laparoscopic gynecological surgery.The patient was referred to our clinic from an obstetrics and gynecology clinic with symptoms of numbness and a tingling sensation in the left anterolateral thigh,which developed after surgery performed 5 mo earlier.Tests were performed to assess the disease status and determine the underlying causes.Ultrasonographic examination revealed an anatomical variation,where the left LFCN was entrapped within the inguinal ligament.This case suggests that performing ultrasonographic examination before and after surgery in the lithotomy position could help prevent MP.CONCLUSION This case demonstrates the value of ultrasonography in detecting anatomical variation and diagnosing persistent MP.Ultrasonography should be considered an adjunct to electromyography for optimal MP management.Further,this case would help other clinicians determine patient prognosis and decide on targeted treatment strategies.

Short-term prone positioning for severe acute respiratory distress syndrome after cardiopulmonary bypass:A case report and literature review

BACKGROUND Aortic dissection is a complex and dangerous cardiovascular disease,with many complications in the perioperative period,including severe acute respiratory distress syndrome(ARDS),which affects prognosis and increases mortality.Despite the effect of prone positioning(PP)in improving oxygenation in patients with severe ARDS,reports about PP early after cardiac surgery are few and such an opt-ion may be an issue in cardiac surgery patients because of the recent sternotomy.CASE SUMMARY A 40-year-old male patient diagnosed with acute type A aortic dissection on October 22,2021 underwent ascending artery replacement plus total aortic arch replacement plus stent elephant trunk implantation under cardiopulmonary bypass.Unfortunately,he developed ARDS on postoperative day 1.Despite comprehensive treatment with aggressive pulmonary protective ventilation,fluid management with continuous renal replacement therapy,the condition continued to deteriorate and rapidly progressed to severe ARDS with a minimum oxygenation index of 51.We are ready to implement salvage therapy,including PP and extracorporeal membrane oxygenation(ECMO).Due to the large amount of pericardial mediastinal and thoracic drainage after thoracotomy,ECMO may result in massive postoperative bleeding.Prolonged prone ventilation is often inappropriate after thoracotomy.Therefore,we chose short-term PP for<6 h.Finally,the oxygenation index greatly improved and the diffuse exudation in both lungs of the patient was significantly reduced with short-term prone positioning.CONCLUSION Intermittent short-term PP can improve early postoperative severe ARDS after acute aortic dissection.

Robust SLAM localization method based on improved variational Bayesian filtering

Aimed at the problem that the state estimation in the measurement update of the simultaneous localization and mapping(SLAM)method is incorrect or even not convergent because of the non-Gaussian measurement noise,outliers,or unknown and time-varying noise statistical characteristics,a robust SLAM method based on the improved variational Bayesian adaptive Kalman filtering(IVBAKF)is proposed.First,the measurement noise covariance is estimated using the variable Bayesian adaptive filtering algorithm.Then,the estimated covariance matrix is robustly processed through the weight function constructed in the form of a reweighted average.Finally,the system updates are iterated multiple times to further gradually correct the state estimation error.Furthermore,to observe features at different depths,a feature measurement model containing depth parameters is constructed.Experimental results show that when the measurement noise does not obey the Gaussian distribution and there are outliers in the measurement information,compared with the variational Bayesian adaptive SLAM method,the positioning accuracy of the proposed method is improved by 17.23%,20.46%,and 17.76%,which has better applicability and robustness to environmental disturbance.

Hybrid Model for Short-Term Passenger Flow Prediction in Rail Transit

A precise and timely forecast of short-term rail transit passenger flow provides data support for traffic management and operation,assisting rail operators in efficiently allocating resources and timely relieving pressure on passenger safety and operation.First,the passenger flow sequence models in the study are broken down using VMD for noise reduction.The objective environment features are then added to the characteristic factors that affect the passenger flow.The target station serves as an additional spatial feature and is mined concurrently using the KNN algorithm.It is shown that the hybrid model VMD-CLSMT has a higher prediction accuracy,by setting BP,CNN,and LSTM reference experiments.All models’second order prediction effects are superior to their first order effects,showing that the residual network can significantly raise model prediction accuracy.Additionally,it confirms the efficacy of supplementary and objective environmental features.

Short-Term Mindfulness Intervention on Adolescents’ Negative Emotion under Global Pandemic

Objective:In this research,we tried to explore how short-term mindfulness(STM)intervention affects adoles-cents’anxiety,depression,and negative and positive emotion during the COVID-19 pandemic.Design:10 classes were divided into experiment groups(5 classes;n=238)and control(5 classes;n=244)randomly.Hospital Anxi-ety and Depression Scale(HADS)and Positive and Negative Affect Schedule(PANAS)were used to measure par-ticipants’dependent variables.In the experiment group,we conducted STM practice interventions every morning in theirfirst class from March to November 2020.No interventions were conducted in the control group.Methods:Paired-sample t-tests were used to identify if a significant difference exists between every time point of the experimental and control groups.Repeated ANOVA and Growth Mixture Model(GMM)were used to analyze the tendency of positive and negative emotions,anxiety,and depression in the experimental group.Results and Conclusions:(1)With the intervention of STM,there was a significant decrease in negative emotions and an increase in positive emotions in the experimental group,whereas there were non-significant differences in the control group.(2)To explore the heterogeneity trajectories of dependent variables,we built a GMM and found there were two latent growth classes in the trajectories.(3)The results of the models showed their trajec-tories were downward,which meant that the levels of anxiety,depression,and negative emotions of participants decreased during the STM training period.Nonetheless,the score of positive affect showed upward in three loops of intervention,which indicated that the level of the participants’positive affect increased through the STM inter-vention.(4)This research indicated that STM should be given increasing consideration to enhance mental health during the worldwide outbreak of COVID-19.

井下电力电缆故障定位研究

针对传统井下电力电缆故障定位方法依赖主观参数选择和抗噪性能较差,无法满足强噪声背景下井下电力电缆故障精确定位要求的问题,提出了一种基于樽海鞘群算法(SSA)优化变分模态分解(VMD)并结合改进型Teager能量算子(NTEO)的井下电力电缆故障定位方法。针对VMD在信号分解上存在的模态混叠、过分解和欠分解问题,采用SSA以模糊熵为适应度函数对VMD模态数K和惩罚因子α2个参数进行优化,得到更能反映故障特征信息的本征模态函数;采用NTEO对本征模态函数进行首波波头标定,得到首末两端的波头到达时刻,根据双端测距法得出故障位置。采用PSCAD/EMTDC进行井下电力电缆故障仿真,模拟具有强背景噪声的井下故障信号,结果表明:①在理想电流信号中加入9,12 dB噪声后,SSA-VMD的信噪比最低,皮尔逊相关系数最大,说明SSA-VMD在最大程度降噪的同时,能很好地保留信号的特征信息。②在不同过渡电阻下,SSA-VMD-NTEO的定位精度较高。③在不同故障相角下,SSA-VMD-NTEO在采样点上出现不同,但定位位置没有改变,依旧保持较高的定位精度。④在不同故障距离下,SSA-VMD-NTEO均能保证较高的定位精度。⑤在井下较大噪声和10 MHz采样频率下,SSA-VMD-NTEO较小波模极大值和VMD+NTEO 2种方法的定位精度具有明显优势。

一类超线性双调和方程正解的存在性

研究以下双调和方程{Δ^(2)u+cΔu=f(x,u),x∈Ω,Δu=u=0,x∈∂Ω,其中Ω∈R^(N)是有界开集,其边界∂Ω光滑.c∈R,f:Ω×R→R是Caratheodory函数.当非线性项f(x,u)在无穷远处为超线性时,通过山路引理和强极大值原理获得了该方程的正解,所设条件弱于相关文献中的条件,使得非线性项更具一般性,从而推广了相关文献中关于超线性的结果.

南京大气颗粒物化学组分的粒径分布和来源解析

大气颗粒物中化学组分的粒径分布与其来源、形成过程、环境及健康效应密切相关。而过去对不同季节颗粒物的粒径分布特征,以及不同来源对粗、细颗粒物的贡献的研究相对较少。于2022年12月(冬季)和2023年8月(夏季)在南京采集了大气分粒径颗粒物样品,分析了粗颗粒物(PM_(2.1-10))和细颗粒物(PM_(2.1))中碳质组分和主要水溶性无机离子的粒径分布和季节变化,运用正定矩阵因子分解模型(Positive Matrix Factorization,PMF)进行PM_(2.1-10)和PM_(2.1)的源解析。结果表明,在粒径分布特征上,冬夏两季有机碳(OC)、元素碳(EC)、水溶性有机碳(WSOC)的平均浓度呈双峰型分布。SO_(4)^(2-)和NO_(3)^(-)的季节平均浓度均为双峰型分布。NH_(4)^(+)主要分布在细颗粒物中,季节平均浓度呈单峰型分布,冬夏季均在0.43-0.65μm出现峰值。在季节变化上,颗粒物中除Na^(+)和SO_(4)^(2-)外的主要化学组分浓度均在冬季高于夏季。冬夏两季Ca^(2+)、Mg^(2+)主要集中在粗颗粒物中。根据PMF模型解析结果,南京大气颗粒物主要有4类来源贡献,即交通源、二次生成、生物质燃烧和扬尘源。PM_(2.1)主要来自二次生成和生物质燃烧源,冬夏季分别贡献了65.7%和61.0%,其中冬季生物质燃烧和二次硝酸盐的贡献占主导地位,而夏季主要来自二次硫酸盐的贡献。冬季PM_(2.1-10)主要来自交通源(41.8%),夏季则主要来自生物质燃烧和硝酸盐的二次生成贡献(43.9%)。研究探讨了大气颗粒物的化学组分的粒径分布特征、季节差异及来源,可为制定有针对性的大气污染防控措施提供科学依据。

基于多种策略改进的鲸鱼优化算法

针对标准鲸鱼优化算法收敛速度慢、搜索与开发不平衡、种群之间信息交流匮乏、容易陷入局部最优的问题,提出了一种改进算法。首先,采用Tent混沌映射提高初始种群的分布均匀性,并引入非线性收敛因子,提升算法前期的全局搜索和中后期局部开发的能力,协调了搜索与开发的转换机制。然后,将种群的平均位置向量引入随机搜索过程中,有效改善个体与种群之间缺乏信息交流的问题。接着,将自适应惯性权重引入位置更新公式中,以加快算法的收敛速度,提高求解精度。最后,利用柯西算子对陷入局部最优的个体进行变异扰动。通过15个基准测试函数对改进算法进行仿真实验,实验结果表明,改进后的鲸鱼优化算法具有良好的性能,并通过Wilcoxon秩和检验证明了改进算法的有效性。

一类拟线性薛定谔方程解的存在性

在R^(N)上探究形式为-Δu+V(x)u-Δ(u^(2))u=h(u)的拟线性薛定谔方程解的存在性,其中N≥3,V:R^(N)→R,h:R→R。运用变分法和山路引理,证明了当函数V和非线性项h分别满足一些适当条件时,该拟线性薛定谔方程存在一个非平凡正解。

The Existence of Solutions for Kirchhoff-Type Equations with General Singular Terms

We study the existence of solutions for Kirchhoff-type equations.Firstly,we use the Sobolev inequality and the weakly lower semi-continuity of the norm to prove that the corresponding function can reach the global minimum.Then,we use the variational method and some analytical techniques to obtain the existence of the positive solution of the equation whenλis small enough.

无创产前检测在筛查胎儿染色体拷贝数变异中的应用价值分析

目的探讨无创产前检测(NIPT)技术在产前筛查胎儿染色体拷贝数变异(CNVs)中的临床意义及应用价值。方法选择2018年1月至2021年7月在淮安市妇幼保健院进行NIPT检测,结果提示可能为胎儿CNVs的224例孕妇为研究对象;对其中171例孕妇进行了羊水穿刺及进一步的产前诊断[羊水染色体核型分析及染色体微阵列分析(CMA)],获得胎儿羊水样本171例。对比分析羊水染色体核型及CMA结果与NIPT结果,并进行随访。结果根据CNVs不同类型分组,微缺失组占所有CNVs的54.02%(121/224),微重复组占所有CNVs的39.73%(89/224),微缺失合并微重复组占所有CNVs的6.25%(14/224);微缺失组、微重复组、微缺失合并微重复组羊水穿刺阳性预测值分别为56.67%、50.70%、80.00%,三组间阳性预测值比较差异无统计学意义(χ2=3.412,P>0.05)。根据CNVs不同片段分组,>10Mb组占所有CNVs的18.75%(42/224),5~10Mb组占所有CNVs的20.54%(46/224),<5Mb组占所有CNVs的60.71%(136/224);>10Mb组、5~10Mb组、<5Mb组羊水穿刺阳性预测值分别为56.76%、60.00%、53.54%,三组间阳性预测值比较差异无统计学意义(χ2=0.465,P>0.05)。在经过羊水穿刺进行染色体核型分析和CMA确诊的95例胎儿CNVs中,致病性CNVs占33.68%(32/95),可能致病性CNVs占21.05%(20/95),意义不明确CNVs占41.05%(39/95),可能良性CNVs占4.21%(4/95),无良性CNVs病例。对NIPT提示可能为胎儿CNVs的224例孕妇进行电话或者门诊随访,随访率为86.16%(193/224);但接受羊水穿刺的孕妇随访率为100.00%。在致病性CNVs中选择继续妊娠的孕妇共5例,有3例经对父母进行CMA验证发现CNVs来自母源,有2例拒绝验证,5例新生儿中均未见明显异常表型。在可能致病性CNVs中选择继续妊娠的孕妇共10例,有3例经对父母进行CMA验证发现CNVs分别来自父源、母源、新发突变,有7例拒绝验证。结论应用NIPT对胎儿CNVs进行初步筛查有效且可行,对于NIPT提示可能为胎儿CNVs的孕妇,必须建议其进行规范的超声筛查及侵入性产前诊断。

基于改进麻雀算法的无线传感器网络覆盖优化研究

针对基本麻雀搜索算法在无线传感器网络覆盖问题中收敛速度较慢、容易陷入局部最优等问题,提出了一个基于改进麻雀算法的优化方案。首先,利用Sobol序列和无限次折叠的ICMIC混沌映射对种群进行初始化,增加了种群遍历性和多样性,为算法的全局寻优奠定了基础。其次,引入混沌映射因子的正余弦算法策略,增强了探索者探索未知区域的能力,提高了算法的全局搜索性能。再次,利用混合变异策略加快算法收敛速度,并改善算法跳出局部最优的能力。最后,将提出的改进麻雀算法应用到无线传感器网络覆盖优化问题中进行仿真实验。仿真结果表明,提出的改进算法相比基本麻雀算法将网络节点的覆盖率提高了7%,同时增强了网络的整体性能,并具有实用性、稳定性和鲁棒性。