Unstable evolution of railway slope under the rainfall-vibration joint action

Understanding the unstable evolution of railway slopes is the premise for preventing slope failure and ensuring the safe operation of trains.However,as two major factors affecting the stability of railway slopes,few scholars have explored the unstable evolution of railway slopes under the joint action of rainfall-vibration.Based on the model test of sandy soil slope,the unstable evolution process of slope under locomotive vibration,rainfall,and rainfall-vibration joint action conditions was simulated in this paper.By comparing and analyzing the variation trends of soil pressure and water content of slope under these conditions,the change laws of pore pressure under the influence of vibration and rainfall were explored.The main control factors affecting the stability of slope structure under the joint action conditions were further defined.Combined with the slope failure phenomena under these three conditions,the causes of slope instability resulting from each leading factor were clarified.Finally,according to the above conclusions,the unstable evolution of the slope under the rainfall-vibration joint action was determined.The test results show that the unstable evolution process of sandy soil slope,under the rainfall-vibration joint action,can be divided into:rainfall erosion cracking,vibration promotion penetrating,and slope instability sliding three stages.In the process of slope unstable evolution,rainfall and vibration play the roles of inducing and promoting slide respectively.In addition,the deep cracks,which are the premise for the formation of the sliding surface,and the violent irregular fluctuation of soil pressure,which reflects the near penetration of the sliding surface,constitute the instability characteristics of the railway slope together.This paper reveals the unstable evolution of sandy soil slopes under the joint action of rainfall-vibration,hoping to provide the theoretical basis for the early warning and prevention technology of railway slopes.

Impact of Earthquake Action on the Design and Sizing of Jointed Masonry Structures in South Kivu, DRC

This article deals with the investigation of the effects of seismic impacts on the design and dimensioning of structures in South Kivu. The starting point is the observation of an ambivalence that can be observed in the province, namely the non-consideration of seismic action in the study of structures by both professionals and researchers. The main objective of the study is to show the importance of dynamic analysis of structures in South Kivu. It adopts a meta-analytical approach referring to previous researches on South Kivu and proposes an efficient and optimal method. To arrive at the results, we use Eurocode 7 and 8. In addition, we conducted static analysis using the Coulomb method and dynamic analysis using the Mononobe-Okabe method and compared the results. At Nyabibwe, the results showed that we have a deviation of 24.47% for slip stability, 12.038% for overturning stability and 9.677% for stability against punching through a weight wall.

Industrial Fusion Cascade Detection of Solder Joint

With the remarkable advancements in machine vision research and its ever-expanding applications,scholars have increasingly focused on harnessing various vision methodologies within the industrial realm.Specifically,detecting vehicle floor welding points poses unique challenges,including high operational costs and limited portability in practical settings.To address these challenges,this paper innovatively integrates template matching and the Faster RCNN algorithm,presenting an industrial fusion cascaded solder joint detection algorithm that seamlessly blends template matching with deep learning techniques.This algorithm meticulously weights and fuses the optimized features of both methodologies,enhancing the overall detection capabilities.Furthermore,it introduces an optimized multi-scale and multi-template matching approach,leveraging a diverse array of templates and image pyramid algorithms to bolster the accuracy and resilience of object detection.By integrating deep learning algorithms with this multi-scale and multi-template matching strategy,the cascaded target matching algorithm effectively accurately identifies solder joint types and positions.A comprehensive welding point dataset,labeled by experts specifically for vehicle detection,was constructed based on images from authentic industrial environments to validate the algorithm’s performance.Experiments demonstrate the algorithm’s compelling performance in industrial scenarios,outperforming the single-template matching algorithm by 21.3%,the multi-scale and multitemplate matching algorithm by 3.4%,the Faster RCNN algorithm by 19.7%,and the YOLOv9 algorithm by 17.3%in terms of solder joint detection accuracy.This optimized algorithm exhibits remarkable robustness and portability,ideally suited for detecting solder joints across diverse vehicle workpieces.Notably,this study’s dataset and feature fusion approach can be a valuable resource for other algorithms seeking to enhance their solder joint detection capabilities.This work thus not only presents a novel and effective solution for industrial solder joint detection but lays the groundwork for future advancements in this critical area.

Improving Badminton Action Recognition Using Spatio-Temporal Analysis and a Weighted Ensemble Learning Model

Incredible progress has been made in human action recognition(HAR),significantly impacting computer vision applications in sports analytics.However,identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion patterns.Deep learning techniques like convolutional neural networks(CNNs),long short-term memory(LSTM),and graph convolutional networks(GCNs)improve recognition in large datasets,while the traditional machine learning methods like SVM(support vector machines),RF(random forest),and LR(logistic regression),combined with handcrafted features and ensemble approaches,perform well but struggle with the complexity of fast-paced sports like badminton.We proposed an ensemble learning model combining support vector machines(SVM),logistic regression(LR),random forest(RF),and adaptive boosting(AdaBoost)for badminton action recognition.The data in this study consist of video recordings of badminton stroke techniques,which have been extracted into spatiotemporal data.The three-dimensional distance between each skeleton point and the right hip represents the spatial features.The temporal features are the results of Fast Dynamic Time Warping(FDTW)calculations applied to 15 frames of each video sequence.The weighted ensemble model employs soft voting classifiers from SVM,LR,RF,and AdaBoost to enhance the accuracy of badminton action recognition.The E2 ensemble model,which combines SVM,LR,and AdaBoost,achieves the highest accuracy of 95.38%.

Joint action of phoxim and fenvalerate on reproduction in male rats

Aim: To evaluate the joint action of phoxim and fenvalerate on the reproductive function in male Sprague-Dawley rats. Methods: The 2×2 factorial analysis experiment was used in the study. The pesticides were orally given at daily doses of phoxim (Pho) 8.2 nig/kg, fenvalerate (Fen) 3.3 mg/kg and Pho 8.2+Fen 3,3 mg/kg (Pho:Fen = 5:2), 5 days a week for 60 days. Sperm motility was measured with computer-assisted sperm motility analysis (CASA) and daily sperm production estimated. Immunoenzymatic method and electron microscopy were used to evaluate the serum testosterone and the testicular morphology, respectively. Results: There were significant decreases in sperm motility parameters in the treated animals, including straight line velocity (VSL), beat cross frequency (BCF), linearity (LIN) and straightness (STR). After treated with Fen, significant decreases in VSL, LIN and STR were demonstrated. Significant decreases of daily sperm production were seen in animals treated with Pho and Pho+Fen in comparison with the controls. Serum testosterone levels were not significantly changed in the treated groups. Factorial ANOVA showed that no significant interactions were noted between Pho and Fen in sperm motility, sperm production and serum testosterone. Both the single and mixed pesticides caused various degrees of testicular lesions, involving vacuolation of endoplasmic reticulum and necrosis of Sertoli cells. Conclusion: The pesticides may cause sperm motility changes and testicular lesions in male rats. The action of Pho and Fen may be additive.

Evaluation on the Joint Action Between Chlorsulfuron and Haloxyfop-R by Bioassay

The joint action between chlorsulfuron and haloxyfop R was evaluated by bioassay with wheat and corn,respectivly.The dose response curve derived from wheat bioassay showed that the inhibition of haloxyfop R to wheat root growth wasn't affected by the increasing rate of chlorsulfuron.It indicated that chlorsulfuron had no antagonism to haloxyfop R.Meanwhile,the variation analysis of corn bioassay indicated that these two herbicides had joint action on inhibition to corn primary root growth.The joint action was evaluated as additive action by using Isobole Method.So chlorsulfuron and haloxyfop R could be used as tank mixture.

Study on the Joint Meeting Mechanism for the National Human Rights Action Plan

The joint meeting mechanism for the National Human Rights Action Plan(NHRAP)is a multi-faceted mechanism which unifies political,legislative,administrative,judicial and social sectors.It undertakes the responsibility of formulating,implementing,supervising and evaluating the NHRAP,promotes mutual learning,exchange and competition among member units in the protection of human rights.Over the ten years since it was established,it has proved to be a successful innovative mechanism for protecting human rights that is in line with China’s national conditions.In order to improve the operation of the joint meeting mechanism the NHRAP,it is necessary to establish a more effective coordination mechanism within the joint meeting mechanism,and to subdivide the monitoring and evaluation functions into three levels.

Feature Fusion-Based Deep Learning Network to Recognize Table Tennis Actions

A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study.The wearable device consisted of a six-axis sensor,Raspberry Pi 3,and a power bank.Multiple kernel sizes were used in convolutional neural network(CNN)to evaluate their performance for extracting features.Moreover,a multiscale CNN with two kernel sizes was used to perform feature fusion at different scales in a concatenated manner.The CNN achieved recognition of the four table tennis strokes.Experimental data were obtained from20 research participants who wore sensors on the back of their hands while performing the four table tennis strokes in a laboratory environment.The data were collected to verify the performance of the proposed models for wearable devices.Finally,the sensor and multi-scale CNN designed in this study achieved accuracy and F1 scores of 99.58%and 99.16%,respectively,for the four strokes.The accuracy for five-fold cross validation was 99.87%.This result also shows that the multi-scale convolutional neural network has better robustness after fivefold cross validation.

Programme Launch:Action on Silk Road People-to-People Connectivity

Delegates from home and abroad jointly launched the Action on Silk Road People-to-People Connectivity at the forum.Initiated by China NGO Network for International Exchanges(CNIE),the Action is an updated version of the Silk Road Community Building Initiative for the coming five years.Within the framework of the Action,by pooling resources from various parties,CNIE will continue to cultivate a series of flagship projects on livelihood cooperation and people-to-people exchanges while ensuring the benefit-to-all and inclusiveness of exchanges and cooperation in various fields,so as to enhance people-to-people connectivity in the Belt and Road partner countries.

创建行动导向下美丽乡村联创联建的规划设计方法探索

伴随着浙江省乡村发展愿景从“千万工程”到“美丽乡村”再到共同富裕的纵深推进,以单个行政村为单元的美丽乡村创建模式已经难以适应新的发展诉求。基于自然资源禀赋或基础设施地理空间连续性的美丽乡村联创联建工作,是破解各行政村独立发展的多重瓶颈的创新性尝试。针对具有明确的目标、资金、时间约束的美丽乡村创建行动,基于“产业-空间协同发展”的总体思路,试图探讨一种面向美丽乡村联创联建的规划设计方法,即“二阶三心法”。这一方法从乡村地区的存量资源切入,旨在系统性、创造性地回应创建行动目标,开展面向产业-空间复合型目标的空间规划干预:一是基于“土地矛盾最小化原则”进行空间节点设计,二是基于“空间投入-融合效益最大化原则”形成特色主线,三是基于“优先塑造-示范带动原则”打造示范样板。进一步的,结合湖州市道场乡菰城片区规划设计工作,探讨了该方法在产业策划和空间设计层面落地应用的关键问题和解决策略,为中国先发地区美丽乡村联创联建的规划设计工作提供经验借鉴。

多传感约束下三维运动图像连带动作高质量仿真

因非刚体三维运动图像在成像过程中,传感器区域的随机性对动作限制大、操作复杂,导致动作虚拟精度较低、图像质量差。为此,提出了多传感约束下三维运动图像连带动作高质量仿真。根据多传感器受重力加速度以及地磁场的作用,求解传感器从原始状态到运动当前状态的角度变化。根据变化数值对图像进行去噪,提取连带动作特征。在Vicon系统中计算连带动作空间位置,捕捉连带动作变换轨迹,将运动轨迹结果输入PAR系统,完成连带动作虚拟成像。仿真结果表明:该方法图像去噪效果较好,传感器响应时间始终低于0.52 s,传感器响应速度较快,连带动作轨迹误差始终低于0.045,动作虚拟仿真精度较高,能够有效提高三维运动图像质量。

随机风-浪联合传播数值模拟及分析

海洋风、浪往往同时传播,同步传播过程中风、浪之间具有显著的相互耦合作用。针对海洋脉动风和随机波浪同步传播的数值仿真需求,基于程序开发平台MATLAB和计算流体动力学(computational fluid dynamics,CFD)软件平台Fluent,提出了一种随机风场和随机波浪联合传播数值模拟方法,基于该数值方法构建了随机风-浪联合传播数值水槽,并对该数值水槽单独随机风、单独随机波浪以及随机风-浪联合传播的数值模拟效果进行了试算和验证分析。结果显示该水槽数值模拟脉动风速与对应理论计算风速相吻合,水槽数值模拟随机波浪与对应理论计算随机波浪相吻合,水槽风-浪联合数值模拟显示了风、浪之间显著耦合作用效应。建立的随机风-浪联合数值水槽在随机风和随机波浪同步传播的数值仿真上具备可靠性及可应用性。研究内容为海上风、浪环境要素耦合效应研究及海洋工程结构风-浪联合作用相关研究提供了平台基础。

限制肯定说背景下不作为共同正犯的成立条件

不作为共同正犯具有正犯和共犯的二重性,其正犯性只能从不作为理论中获得,其共犯性也只能从共同犯罪理论中获得。在限制肯定说框架下,不作为共同正犯的成立条件主要包括以下三个方面,一是以互相协力加功的不作为共同实行事实为客观要件。在进行客观归责判断时,通常以假定的因果流程来认定具体案件的结果归责,并且进行违法关联性的审查。二是以具有双重性的共同实行故意为主观要件。在进行主观归责判断时,应以道义责任为根据,以规范责任论为框架来探讨责任要素及其结构。三是以作为义务之间具备等同性和相互性特征,以及各不作为实行行为直接地、共同地引起法益侵害的因果关系为限制条件。

面向肩关节康复的气压驱动外骨骼机器人设计与康复动作规划研究

基于肩关节损伤病理进行分析,针对肩关节垂直轴与冠状轴的运动,设计了面向肩关节康复的气压驱动外骨骼机器人。结合肩关节损伤康复训练临床治疗方法与人体工程学原理,确定机器人肩关节的结构与运动范围;设计了气动摆动缸驱动系统,可提供机器人关节的反向驱动特性,提高康复训练的被动柔顺性。建立外骨骼康复训练机器人运动学模型,仿真得到机器人的工作空间,并与人体肩关节运动空间进行了对比。结果表明,设计的机器人工作空间能满足人体肩关节康复训练的需求。设计了康复训练机器人关节的独立与复合动作,可完成肩关节内旋/外旋、屈曲/伸展、够取运动等多种康复训练作业治疗。经试验验证,在样机按规划动作的运动轨迹运行中,气动摆动缸驱动的机器人关节运动平稳,轨迹跟踪误差满足康复训练要求,验证了外骨骼康复机器人结构设计的合理性和康复动作规划的可行性。

不同意图场景眼动注视模式机器学习算法识别孤独症谱系障碍的研究

目的旨在探究孤独症谱系障碍(autism spectrum disorder,ASD)和正常发育(typically developing,TD)儿童在观看不同意图场景视频时的视觉感知差异,并通过机器学习算法,探索客观区分ASD和TD儿童的可行性。方法共纳入58名ASD儿童和50名TD儿童,观看包含联合意图和非联合意图的视频,使用儿童观看视频时在不同注视兴趣区的注视时长占比和注视次数占比作为原始特征指标,输入到分类器中构建分类模型,分析比较不同分类模型的分类准确率、灵敏度和特异度等指标。结果使用支持向量机、线性判别算法、决策树、随机森林和邻近算法(K=1、3、5、7)8种常见的分类器基于原始特征指标进行分类时,最高分类精度为81.90%。为进一步提高分类精度,采用特征重构方式,运用决策树分类器进行分类,分类模型的准确度高达91.43%,特异度为89.80%,灵敏度为92.86%,受试者操作特征曲线的曲线下面积为0.909(P<0.001)。结论采用眼动追踪数据构建的机器学习模型可精确地将ASD儿童与TD儿童区分开来,为开发快速客观筛查ASD的辅助工具提供科学依据。

基于改进双流视觉Transformer的行为识别模型

针对现有行为识别方法中抗背景干扰能力差和准确率低等问题,提出了一种改进的双流视觉Transformer行为识别模型。该模型采用分段采样的方法来增加模型对长时序列数据的处理能力;在网络头部嵌入无参数的注意力模块,在降低动作背景干扰的同时,增强了模型的特征表示能力;在网络尾部嵌入时间注意力模块,通过融合时域高语义信息来充分提取时序特征。文中提出了一种新的联合损失函数,旨在增大类间差异并减少类内差异;采用决策融合层以充分利用光流与RGB流特征。针对上述改进模型,在基准数据集UCF101和HMDB51上进行消融及对比实验,消融实验结果验证了所提方法的有效性,对比实验结果表明,所提方法相比时间分段网络在两个数据集上的准确率分别提高了3.48%和7.76%,优于目前的主流算法,具有较好的识别效果。

基于有限元法的谐波作用对电缆及其接头的影响研究

现有关于电缆及其接头温度的研究大多按照基波情况考虑,大量非线性负载的使用使输电线路中的谐波含量显著增加,谐波对电缆及其接头附加损耗与温升的影响不容忽视。文中以64/110 kV单芯电缆及预制式电缆接头为例,建立了电缆及其接头的有限元仿真模型,基于电磁-热耦合法,分析了谐波含量与谐波次数对电缆及其接头的影响,并引入等效电流因数,实现多次谐波共存环境下对电缆发热损耗及温度的快速计算。仿真结果表明:谐波含量与谐波次数均会影响电缆的损耗及温度,谐波可能导致电缆接头在压接钳处形成过热点,加速绝缘老化;引入等效电流因数法在估算多次谐波共存环境下的电缆温度具有较高精度。该研究对谐波环境下电缆及其接头的设计、运行温度预测及热老化寿命评估提供了依据。

基于破片随机分布的夹层板受爆响应数值研究

为研究空爆载荷下冲击波与随机分布破片联合作用下夹层板毁伤特性问题,基于模拟的随机破片场实现了冲击波与破片联合作用下的夹层板结构响应分析。使用LS-DYNA有限元软件,以折叠式金属夹层板为研究对象,根据炸药药量及外壳厚度生成面积分布满足概率函数要求的破片场,在验证仿真方法有效性的前提下,开展系列仿真分析金属夹层板结构损伤情况。结果表明,在同密集度不同分布随机破片工况下,夹层板损伤模式相近。不同密集度情况下,随着破片密集度的上升,夹层板整体破口面积减小,吸能效果下降。该研究成果对船舶结构抗爆设计及相关规范的制定具有参考意义。

基于激光表面微纳结构的胶接性能研究进展

胶接具有独特的性能而被广泛关注,但在实际应用中往往由于界面结合强度不足而导致接头在服役过程中过早失效。激光表面处理是一种能够有效提高胶接接头表面性能的方法,通过激光表面处理会使黏接材料表面形成不同的表面微纳结构,从而改善接头的性能。首先介绍了激光表面处理原理及激光器分类,对比了不同激光器的优缺点及加工质量特性,分析了激光加工参数对接头强度的影响。其次从激光加工不同表面微纳结构入手,阐述了不同表面微纳结构的加工方式,分析了不同表面微纳结构对不同材料接头强度的影响。此外,针对不同的表面微纳结构,其结构参数有所不同,导致接头表面化学成分和润湿性能也有所不同,使得接头强度有所差异,在此基础上分析了微纳结构参数对接头强度的影响及原因。同时,胶层间的作用机制与接头强度具有密不可分的联系,研究表明,不同的表面微纳结构对胶层的作用机制具有明显的不同,归纳总结发现,经激光表面处理形成表面微纳结构后接头表面粗糙度、化学性质、润湿性能及胶层间裂纹的扩展对接头强度的提升具有一定作用。最后,对激光表面处理微纳结构的研究进行了展望。

Mechanical response of a tunnel subjected to strike-slip faulting processes,based on a multi-scale modeling method

The stick-slip action of strike-slip faults poses a significant threat to the safety and stability of underground structures.In this study,the north-east area of the Longmenshan fault,Sichuan,provides the geological background;the rheological characteristics of the crustal lithosphere and the nonlinear interactions between plates are described by Burger’s viscoelastic constitutive model and the friction constitutive model,respectively.A large-scale global numerical model for plate squeezing analysis is established,and the seemingly periodic stick-slip action of faults at different crust depths is simulated.For a second model at a smaller scale,a local finite element model(sub-model),the time history of displacement at a ground level location on the Longmenshan fault plane in a stick-slip action is considered as the displacement loading.The integration of these models,creating a multi-scale modeling method,is used to evaluate the crack propagation and mechanical response of a tunnel subjected to strike-slip faulting.The determinations of the recurrence interval of stick-slip action and the cracking characteristics of the tunnel are in substantial agreement with the previous field investigation and experimental results,validating the multi-scale modeling method.It can be concluded that,regardless of stratum stiffness,initial cracks first occur at the inverted arch of the tunnel in the footwall,on the squeezed side under strike-slip faulting.The smaller the stratum stiffness is,the smaller the included angle between the crack expansion and longitudinal direction of the tunnel,and the more extensive the crack expansion range.For the tunnel in a high stiffness stratum,both shear and bending failures occur on the lining under strike-slip faulting,while for that in the low stiffness stratum,only bending failure occurs on the lining.