基于PCA-ShapeDTW-QWGRU的分布式光伏集群短期功率预测

针对分布式光伏短期功率预测建立基于主成分分析、改进的动态时间规整算法与量子加权门控循环单元(PCAShapeDTW-QWGRU)的集群功率预测模型。针对集群划分不够精细、光伏电站数据蕴含的信息难以捕捉的问题,提出基于主成分分析结合密度聚类算法(PCA-OPTICS)的集群划分方法;针对目前选取代表电站与集群相似性较低的问题,提出基于改进的动态时间规整算法(ShapeDTW)的代表电站的选取方法,利用ShapeDTW度量相似性距离,选取最小值作为代表电站,并利用基于均方根传播梯度下降法优化的量子加权门控循环单元(RMSprop-QWGRU)模型进行预测;为了解决代表电站与集群功率的变换系数转换差异较大的问题,采用实时变换系数对代表电站进行集群功率值预测计算。实验结果表明,所提方法能有效提升光伏集群功率预测的精度。

A redundant subspace weighting procedure for clock ensemble

A redundant-subspace-weighting(RSW)-based approach is proposed to enhance the frequency stability on a time scale of a clock ensemble.In this method,multiple overlapping subspaces are constructed in the clock ensemble,and the weight of each clock in this ensemble is defined by using the spatial covariance matrix.The superimposition average of covariances in different subspaces reduces the correlations between clocks in the same laboratory to some extent.After optimizing the parameters of this weighting procedure,the frequency stabilities of virtual clock ensembles are significantly improved in most cases.

Curve Classification Based onMean-Variance Feature Weighting and Its Application

The classification of functional data has drawn much attention in recent years.The main challenge is representing infinite-dimensional functional data by finite-dimensional features while utilizing those features to achieve better classification accuracy.In this paper,we propose a mean-variance-based(MV)feature weighting method for classifying functional data or functional curves.In the feature extraction stage,each sample curve is approximated by B-splines to transfer features to the coefficients of the spline basis.After that,a feature weighting approach based on statistical principles is introduced by comprehensively considering the between-class differences and within-class variations of the coefficients.We also introduce a scaling parameter to adjust the gap between the weights of features.The new feature weighting approach can adaptively enhance noteworthy local features while mitigating the impact of confusing features.The algorithms for feature weighted K-nearest neighbor and support vector machine classifiers are both provided.Moreover,the new approach can be well integrated into existing functional data classifiers,such as the generalized functional linear model and functional linear discriminant analysis,resulting in a more accurate classification.The performance of the mean-variance-based classifiers is evaluated by simulation studies and real data.The results show that the newfeatureweighting approach significantly improves the classification accuracy for complex functional data.

Spatial search weighting information contained in cell velocity distribution

Cell migration plays a significant role in physiological and pathological processes.Understanding the characteristics of cell movement is crucial for comprehending biological processes such as cell functionality,cell migration,and cell–cell interactions.One of the fundamental characteristics of cell movement is the specific distribution of cell speed,containing valuable information that still requires comprehensive understanding.This article investigates the distribution of mean velocities along cell trajectories,with a focus on optimizing the efficiency of cell food search in the context of the entire colony.We confirm that the specific velocity distribution in the experiments corresponds to an optimal search efficiency when spatial weighting is considered.The simulation results indicate that the distribution of average velocity does not align with the optimal search efficiency when employing average spatial weighting.However,when considering the distribution of central spatial weighting,the specific velocity distribution in the experiment is shown to correspond to the optimal search efficiency.Our simulations reveal that for any given distribution of average velocity,a specific central spatial weighting can be identified among the possible central spatial weighting that aligns with the optimal search strategy.Additionally,our work presents a method for determining the spatial weights embedded in the velocity distribution of cell movement.Our results have provided new avenues for further investigation of significant topics,such as relationship between cell behavior and environmental conditions throughout their evolutionary history,and how cells achieve collective cooperation through cell-cell communication.

Chemical modification of barite for improving the performance of weighting materials for water-based drilling fluids

With increasing drilling depth and large dosage of weighting materials,drilling fluids with high solid content are characterized by poor stability,high viscosity,large water loss,and thick mud cake,easier leading to reservoir damage and wellbore instability.In this paper,micronized barite(MB)was modified(mMB)by grafting with hydrophilic polymer onto the surface through the free radical polymerization to displace conventional API barite partly.The suspension stability of water-based drilling fluids(WBDFs)weighted with API barite:mMB=2:1 in 600 g was significantly enhanced compared with that with API barite/WBDFs,exhibiting the static sag factor within 0.54 and the whole stability index of 2.The viscosity and yield point reached the minimum,with a reduction of more than 40%compared with API barite only at the same density.Through multi-stage filling and dense accumulation of weighting materials and clays,filtration loss was decreased,mud cake quality was improved,and simultaneously it had great reservoir protection performance,and the permeability recovery rate reached 87%.In addition,it also effectively improved the lubricity of WBDFs.The sticking coefficient of mud cake was reduced by 53.4%,and the friction coefficient was 0.2603.Therefore,mMB can serve as a versatile additive to control the density,rheology,filtration,and stability of WBDFs weighted with API barite,thus regulating comprehensive performance and achieving reservoir protection capacity.This work opened up a new path for the productive drilling of extremely deep and intricate wells by providing an efficient method for managing the performance of high-density WBDFs.

Self-supervised recalibration network for person re-identification

The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have the following two shortcomings:On the one hand,they mostly use global average pooling to generate context descriptors,without highlighting the guiding role of salient information on descriptor generation,resulting in insufficient ability of the final generated attention mask representation;On the other hand,the design of most attention modules is complicated,which greatly increases the computational cost of the model.To solve these problems,this paper proposes an attention module called self-supervised recalibration(SR)block,which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask.In particular,a special"Squeeze-Excitation"(SE)unit is designed in the SR block to further process the generated intermediate masks,both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels.Furthermore,we combine the most commonly used Res Net-50 to construct the instantiation model of the SR block,and verify its effectiveness on multiple Re-ID datasets,especially the mean Average Precision(m AP)on the Occluded-Duke dataset exceeds the state-of-the-art(SOTA)algorithm by 4.49%.

Missing Value Imputation for Radar-Derived Time-Series Tracks of Aerial Targets Based on Improved Self-Attention-Based Network

The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven tasks.However,the majority of imputation research focuses on random missing(RM)that differs significantly from common missing patterns of RTT-AT.The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation.Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss.In this paper,a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.Our model consists of two probabilistic sparse diagonal masking self-attention(PSDMSA)units and a weight fusion unit.It learns missing values by combining the representations outputted by the two units,aiming to minimize the difference between the missing values and their actual values.The PSDMSA units effectively capture temporal dependencies and attribute correlations between time steps,improving imputation quality.The weight fusion unit automatically updates the weights of the output representations from the two units to obtain a more accurate final representation.The experimental results indicate that,despite varying missing rates in the two missing patterns,our model consistently outperforms other methods in imputation performance and exhibits a low frequency of deviations in estimates for specific missing entries.Compared to the state-of-the-art autoregressive deep learning imputation model Bidirectional Recurrent Imputation for Time Series(BRITS),our proposed model reduces mean absolute error(MAE)by 31%~50%.Additionally,the model attains a training speed that is 4 to 8 times faster when compared to both BRITS and a standard Transformer model when trained on the same dataset.Finally,the findings from the ablation experiments demonstrate that the PSDMSA,the weight fusion unit,cascade network design,and imputation loss enhance imputation performance and confirm the efficacy of our design.

Security-Enhanced Directional Modulation Based on Two-Dimensional M-WFRFT

Directional modulation(DM)is one of the most promising secure communication techniques.However,when the eavesdropper is co-located with the legitimate receiver,the conventional DM has the disadvantages of weak anti-scanning capability,anti-deciphering capability,and low secrecy rate.In response to these problems,we propose a twodimensional multi-term weighted fractional Fourier transform aided DM scheme,in which the legitimate receiver and the transmitter use different transform terms and transform orders to encrypt and decrypt the confidential information.In order to further lower the probability of being deciphered by an eavesdropper,we use the subblock partition method to convert the one-dimensional modulated signal vector into a twodimensional signal matrix,increasing the confusion of the useful information.Numerical results demonstrate that the proposed DM scheme not only provides stronger anti-deciphering and anti-scanning capabilities but also improves the secrecy rate performance of the system.

Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is still limited understanding of the peripheral immune inflammato ry response in spinal cord inju ry.In this study.we obtained microRNA expression profiles from the peripheral blood of patients with spinal co rd injury using high-throughput sequencing.We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus(GEO)database(GSE151371).We identified 54 differentially expressed microRNAs and 1656 diffe rentially expressed genes using bioinformatics approaches.Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways,such as neutrophil extracellular trap formation pathway,T cell receptor signaling pathway,and nuclear factor-κB signal pathway,we re abnormally activated or inhibited in spinal cord inju ry patient samples.We applied an integrated strategy that combines weighted gene co-expression network analysis,LASSO logistic regression,and SVM-RFE algorithm and identified three biomarke rs associated with spinal cord injury:ANO10,BST1,and ZFP36L2.We verified the expression levels and diagnostic perfo rmance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve.Quantitative polymerase chain reaction results showed that ANO20 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients.We also constructed a small RNA-mRNA interaction network using Cytoscape.Additionally,we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal co rd injury patients using the CIBERSORT tool.The proportions of naive B cells,plasma cells,monocytes,and neutrophils were increased while the proportions of memory B cells,CD8^(+)T cells,resting natural killer cells,resting dendritic cells,and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects,and ANO10,BST1 and ZFP26L2we re closely related to the proportion of certain immune cell types.The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal co rd inju ry and suggest that ANO10,BST2,and ZFP36L2 are potential biomarkers for spinal cord injury.The study was registe red in the Chinese Clinical Trial Registry(registration No.ChiCTR2200066985,December 12,2022).

Exploration of the efficacy and mechanism of treating head wind disease with the combination change of ginger volatile oil and gingerol by using content-weighted network pharmacology technology

Background:Exploring the efficacy,potential components,and mechanism of the combination of ginger essential oil and gingerols in the treatment of head wind disease based on network pharmacology technology with content weight.Methods:The experimental groups were divided into:0:10,1:4,1:2,1:1,2:1,4:1,10:0.The relative content(Ri)of the chemical constituents of ginger's volatile oil was determined using gas chromatography-mass spectrometry(GC-MS).Additionally,the physicochemical and biological property parameters(LogP,MDCK,PPB,MW)of the components were considered.To assess the quantitative effect of the components,a grading score was performed,and the quantitative effect index(Ki)was calculated.Subsequently,the target effect index(Ti)was calculated by combining the component-target matching score(Fit score).Using these calculations,the target effect score A was determined under the influence of multiple components targeting different targets.Key targets with A≥1000 were identified.To predict the targets related to head wind disease,the Comparative Toxicogenomics Database(https://ctdbase.org/),Gene Cards(https://www.genecards.org/),and Disgenet database(https://www.disgenet.org/)were utilized.The key targets,obtained from different proportions of ginger's volatile oil and gingerol,were intersected with the predicted targets.This facilitated network pharmacological analysis and verification of the efficacy.Results:The content of volatile oil in ginger demonstrated an impact on key targets associated with the volatile oil group.Each specific combination of volatile oil consistently activated distinct pathways,with variations stemming from changes in content.Experimental testing revealed that different combinations of ginger's volatile oil and gingerol effectively alleviated migraine symptoms in rats.Conclusion:Through the application of content-weighted network pharmacology technology and pharmacodynamic verification,it was determined that altering the ratio between ginger's volatile oil and gingerol leads to variations in potential targets and pathways,consequently impacting its efficacy.

Analysis of the Effects of Maternal Body Mass Index and Gestational Weight Gain on Maternal and Neonatal Outcomes in Twin Pregnancy

Objective:To investigate the effects of maternal body mass index(BMI)and gestational weight gain on maternal and neonatal outcomes in twin pregnancies.Methods:Five hundred cases of twin pregnancies were divided into a low body weight group(68 cases),a normal weight group(355 cases),an overweight group(65 cases),and an obesity group(12 cases)according to the World Health Organization(WHO)Body Mass Index(BMI)classification guidelines Results:Comparison of weight gain during different pregnancies revealed that pregnant women were mainly of low weight and average weight.The higher the BMI before pregnancy,the higher the incidence of excessive weight gain during pregnancy.The incidences of gestational diabetes mellitus(GDM)and premature rupture of membranes in women with low weight gain were significantly higher than those in women with average weight gain and high weight gain(P<0.05).The incidences of gestational hypertension,preeclampsia,and anemia in women with high weight gain were significantly higher than those in women with low weight gain and average weight gain(P<0.05).The incidence of neonatal birth weight,fetal distress,and macrosomia in the high weight gain group was significantly higher than those in the low weight gain and average weight gain groups(P<0.05).The birth weight of newborns in low-weight gain mothers was significantly lower than that of normal-weight gain mothers(P<0.05).Conclusion:Poor maternal and infant outcomes were common in women with insufficient or excessive weight gain during pregnancy.Therefore,for women with twin pregnancies,weight management is crucial to ensure maternal and infant health.

基于GTWR模型的济南都市圈生态系统服务价值对城市扩张时空响应

【目的】随着我国城镇化发展进入到以中心城市引领都市圈、城市群的发展阶段,如何促进都市圈城镇化与生态环境协调发展成为高质量城镇发展的重要议题。生态系统服务价值对城市扩张的时空响应研究有助于把脉城市发展与生态系统服务的时空演进特征,推动城市与生态系统的协同发展,助力可持续规划以及建设策略的拟定与实施。【方法】本文以济南都市圈为研究对象,基于城镇扩展指数的计算,定量描述各城市扩张的时空特征。采用生态系统服务当量因子法,从多个角度刻画研究区生态系统服务的时空分异特征,并分析生态系统权衡与协同效应。在此基础上,运用时空地理加权回归(GTWR)模型,探究城市扩张对生态系统服务功能变化的驱动方向与驱动强度。【结果】(1)1980—2020年间济南都市圈内城市扩张显著,具有时序阶段性与区域分异性两方面特征,城市空间扩展速率与强度由高到低依次为小城市、大城市、特大城市、中等城市。(2)都市圈内整体生态服务价值量呈逐年下降趋势,黄河干流、东平湖及周边区域与鲁中山区等地区是重要的生态系统服务价值高值聚集区,都市圈内协同关系占比略低于权衡关系,其中特大城市协同关系占比最高。(3)济南都市圈内城市扩张对生态系统服务价值整体具有负面影响,随着时间的推进,影响强度有所下降。城市扩张对各亚类生态系统服务功能的影响作用具有显著差异,对供给服务与支持服务价值量变化具有负面影响,其中对供给服务变化的驱动强度不断增强,对调节服务价值量变化具有正向作用且影响力整体变化不大,对文化服务价值量变化的影响具有两面性,在不同地区的驱动方向与强度差异性较大。【结论】本研究明确了研究期限内济南都市圈中不同等级城市空间扩展的时空分异规律以及生态系统服务逐渐劣化的发展状态,所构建的GTWR模型在空间层面上量化了城市扩张对生态系统服务总量及各亚类变化量的不同驱动特征与驱动强度,研究成果可为都市圈高质量可持续发展提供决策依据。

Twisted Yangian Y(sp_(2))的单权模

讨论了Twisted Yangian Y(sp_(N))的权模问题,构造了Y(sp_(2))的一类无限维单权模,分类了具有一个一维权空间的Y(sp_(2))单权模.

基于GTWR的站域建成环境对城市轨道交通客流量的时空影响

轨道交通客流量影响因素是轨道交通方面研究的一个关注点,不同站点客流量的时空非平稳性被认为与站域建成环境有关。通过构建时空地理加权(geographically and temporally weighted regression,GTWR)模型,揭示了土地多样性、密度、站点属性3个方面因素在时间和空间维度上对天津市轨道交通客流量的影响。结果表明:相较于传统的地理加权(geographically weighted regression,GWR)模型和最小二乘法(ordinary least squares,OLS)模型,GTWR具有更好的拟合优度;公交站点密度对轨道交通客流产生促进作用,尤其在工作日的早晚高峰时段和中心城区位置;市中心的商业设施在工作日晚高峰吸引更多的地铁乘客,而在近郊区它们在早高峰吸引更多的地铁乘客;人口密度促进轨道交通的客流量;充足的停车场设施数量可以吸引更多的轨道交通乘客。

Relationship between water inrush from coal seam floors and main roof weighting

A water-resistant key strata model of a goaf floor prior to main roof weighting was developed to explore the relationship between water inrush from the floor and main roof weighting. The stress distribution,broken characteristics, and the risk area for water inrush of the water-resistant key strata were analysed using elastic thin plate theory. The formula of the maximum water pressure tolerated by the waterresistant key strata was deduced. The effects of the caved load of the goaf, the goaf size prior to main roof weighting, the advancing distance of the workface or weighting step, and the thickness of the waterresistant key strata on the breaking and instability of the water-resistant key strata were analysed.The results indicate that the water inrush from the floor can be predicted and prevented by controlling the initial or periodic weighting step with measures such as artificial forced caving, thus achieving safe mining conditions above confined aquifers. The findings provide an important theoretical basis for determining water inrush from the floor when mining above confined aquifers.

深度学习模型在儿童TW3法骨龄评估的临床效能分析

目的探讨深度学习模型在儿童应用TW3法进行骨龄评估的临床效能。方法回顾性收集180例儿童左手X线片。评估基于Tanner-Whitehouse III(TW3)法的掌指骨和腕骨骨龄。以3位高年资主任医师的评估均值为金标准组,计算并比较深度学习模型(模型组)及2位低年资医师(医师组,分别记作医师1、医师2)与金标准组评估时间差异,掌指骨骨龄、腕骨骨龄的均方误差(MSE)及平均绝对误差(MAE);采用组内相关系数(ICC)分析模型组和医师组与金标准组结果一致性。结果骨龄评估所用时间模型组明显少于医师组(P<0.05)。掌指骨骨龄评估,模型组和医师1、医师2与金标准组相比MSE、MAE差异有统计学意义(P<0.05)。腕骨骨龄评估,模型组和医师1、医师2与金标准组相比MSE、MAE差异有统计学意义(P<0.05)。掌指骨骨龄评估,模型组与金标准组ICC为0.988,医师1、医师2与金标准组的ICC分别为0.986、0.977。腕骨骨龄评估,模型组与金标准组ICC为0.971,医师1、医师2与金标准组ICC分别为0.970、0.953。结论对于儿童,应用TW3法深度学习模型在评估掌指骨和腕骨骨龄有临床价值。

Application of Artificial Neural Network, Kriging, and Inverse Distance Weighting Models for Estimation of Scour Depth around Bridge Pier with Bed Sill

This paper outlines the application of the multi-layer perceptron artificial neural network (ANN), ordinary kriging (OK), and inverse distance weighting (IDW) models in the estimation of local scour depth around bridge piers. As part of this study, bridge piers were installed with bed sills at the bed of an experimental flume. Experimental tests were conducted under different flow conditions and varying distances between bridge pier and bed sill. The ANN, OK and IDW models were applied to the experimental data and it was shown that the artificial neural network model predicts local scour depth more accurately than the kriging and inverse distance weighting models. It was found that the ANN with two hidden layers was the optimum model to predict local scour depth. The results from the sixth test case showed that the ANN with one hidden layer and 17 hidden nodes was the best model to predict local scour depth. Whereas the results from the fifth test case found that the ANN with three hidden layers was the best model to predict local scour depth.

Distributed Weighted Data Aggregation Algorithm in End-to-Edge Communication Networks Based on Multi-armed Bandit

As a combination of edge computing and artificial intelligence,edge intelligence has become a promising technique and provided its users with a series of fast,precise,and customized services.In edge intelligence,when learning agents are deployed on the edge side,the data aggregation from the end side to the designated edge devices is an important research topic.Considering the various importance of end devices,this paper studies the weighted data aggregation problem in a single hop end-to-edge communication network.Firstly,to make sure all the end devices with various weights are fairly treated in data aggregation,a distributed end-to-edge cooperative scheme is proposed.Then,to handle the massive contention on the wireless channel caused by end devices,a multi-armed bandit(MAB)algorithm is designed to help the end devices find their most appropriate update rates.Diffe-rent from the traditional data aggregation works,combining the MAB enables our algorithm a higher efficiency in data aggregation.With a theoretical analysis,we show that the efficiency of our algorithm is asymptotically optimal.Comparative experiments with previous works are also conducted to show the strength of our algorithm.

基于GTWR的广东省乡村聚落规模时空演变研究

乡村聚落在聚落体系中具有重要地位,但长期以来缺乏对乡村聚落规模时空演变内在机理的深入研究,导致乡村聚落的规划、建设和管理缺乏有效的理论指导。本研究基于遥感影像获取1980年、1995年、2005年、2015年4期广东省乡村聚落空间分布数据,以乡村聚落斑块为单元,使用时空地理加权回归模型,揭示乡村聚落规模演变影响因素的时空差异,总结影响机制的阶段性规律。研究结论如下:(1)广东省乡村聚落规模演变呈现出增加–平稳–减少的趋势,35 a间总规模略有减少但区域结构性差异明显。珠三角地区乡村聚落演化与全省趋势一致;粤东地区原有乡村聚落不断扩张导致总规模持续上升,但趋势逐渐减弱;粤西北乡村聚落以部分聚落原地扩张和部分聚落日渐收缩并行为主,新增乡村聚落和乡村聚落城市化幅度均不高。(2)各影响因素对乡村聚落规模演变的作用力存在明显时空差异,地形条件、路网密度、县域人均GDP和基期聚落面积对乡村聚落规模的影响普遍呈现“倒U型”趋势,其它自然环境本底、区位可达性、社会经济因素的驱动方向稳定,但影响力随阶段变化。(3) 35 a间广东省经历了城乡分割、城乡冲击和城乡融合3个发展阶段,各阶段乡村聚落规模演变的动力呈现出显著差别,在城乡分割阶段,乡村聚落发展存在明显的路径依赖;而在城乡冲击阶段,社会经济发展水平高、自然条件优越、交通基础设施建设完备的聚落更易发生城市化;在城乡融合阶段,自然环境本底的影响逐渐减弱,而区位可达性的重要性逐渐凸显。

Assessment of hindlimb motor recovery affer severe thoracic spinal cord injury in rats: classification of CatWalk XT■ gait analysis parameters

Assessment of locomotion recovery in preclinical studies of experimental spinal cord injury remains challenging. We studied the CatWalk XT■gait analysis for evaluating hindlimb functional recovery in a widely used and clinically relevant thoracic contusion/compression spinal cord injury model in rats. Rats were randomly assigned to either a T9 spinal cord injury or sham laminectomy. Locomotion recovery was assessed using the Basso, Beattie, and Bresnahan open field rating scale and the CatWalk XT■gait analysis. To determine the potential bias from weight changes, corrected hindlimb(H) values(divided by the unaffected forelimb(F) values) were calculated. Six weeks after injury, cyst formation, astrogliosis, and the deposition of chondroitin sulfate glycosaminoglycans were assessed by immunohistochemistry staining. Compared with the baseline, a significant spontaneous recovery could be observed in the CatWalk XT■parameters max intensity, mean intensity, max intensity at%, and max contact mean intensity from 4 weeks after injury onwards. Of note, corrected values(H/F) of CatWalk XT■parameters showed a significantly less vulnerability to the weight changes than absolute values, specifically in static parameters. The corrected CatWalk XT■parameters were positively correlated with the Basso, Beattie, and Bresnahan rating scale scores, cyst formation, the immunointensity of astrogliosis and chondroitin sulfate glycosaminoglycan deposition. The CatWalk XT■gait analysis and especially its static parameters, therefore, seem to be highly useful in assessing spontaneous recovery of hindlimb function after severe thoracic spinal cord injury. Because many CatWalk XT■parameters of the hindlimbs seem to be affected by body weight changes, using their corrected values might be a valuable option to improve this dependency.