Measured dynamic load distribution within the in situ axlebox bearing of high-speed trains under polygonal wheel–rail excitation

The dynamic load distribution within in-service axlebox bearings of high-speed trains is crucial for the fatigue reliability assessment and forward design of axlebox bearings. This paper presents an in situ measurement of the dynamic load distribution in the four rows of two axlebox bearings on a bogie wheelset of a high-speed train under polygonal wheel–rail excitation. The measurement employed an improved strain-based method to measure the dynamic radial load distribution of roller bearings. The four rows of two axlebox bearings on a wheelset exhibited different ranges of loaded zones and different means of distributed loads. Besides, the mean value and standard deviation of measured roller–raceway contact loads showed non-monotonic variations with the frequency of wheel–rail excitation. The fatigue life of the four bearing rows under polygonal wheel–rail excitation was quantitatively predicted by compiling the measured roller–raceway contact load spectra of the most loaded position and considering the load spectra as input.

我国FISIM使用分摊方法优化及其产生的影响

间接测算的金融中介服务(FISIM)使用分摊的目的是客观反映非金融机构部门对金融机构部门的依赖关系,以便更好地监测宏观经济。然而,目前我国的FISIM使用分摊方法无法完全准确、真实地反映非金融机构部门对金融中介服务的实际使用状况。本文系统梳理FISIM使用分摊理论的发展历程,深入剖析我国FISIM使用分摊存在的问题,采用参考利率法对我国FISIM使用分摊进行优化,对比分析参考利率法与指标法在机构部门间分摊FISIM的差异,进一步考察采用参考利率法分摊FISIM对国内生产总值和国民总收入的影响。研究发现,我国目前采用指标法分摊FISIM,不但会改变各非金融机构部门的中间消耗或最终消费,还会高估国内生产总值和国民总收入。与指标法相比,参考利率法是一种更精确、更接近实际的FISIM分摊方法,在基础数据允许的条件下,应尽量选择参考利率法分摊FISIM。

高精度NiCr/NiSi薄膜热电偶高温测试系统

针对航空发动机涡轮叶片和燃烧室内壁等高温恶劣环境下对温度测量的迫切需求,提出了一种基于薄膜热电偶的高精度测温方法。采用磁控溅射法在SiC衬底上制备了NiCr/NiSi薄膜热电偶。完成了薄膜热电偶的设计与制备,并设计了一套高精度温度数据采集系统,主控芯片采用STM32,数据采集芯片采用16位高精度芯片ADS1118,实现了对高温恶劣环境下温度数据的实时采集。结果表明:设计制备的NiCr/NiSi薄膜热电偶能够实现-40~1000℃温度区间稳定测温,高精度测温系统与标准温度测量系统相比误差优于±0.4%,为高温恶劣环境下温度测量提供了有效且可靠的解决方法。

基于WSN和TJ_UWIS的地下结构变形动态监测平台与教学应用

为实现复杂条件下地下结构变形的动态监测,提高学生对无线监测系统的认知,设计了基于WSN(wireless sensor network)和TJ_UWIS倾角传感支点的无线监测实验平台,并应用于实际教学中。该实验平台测量精度高,可以通过TJ_UWIS倾角传感支点测得地下结构的X/Y/Z三向的倾角变形值,从而反映结构的实际变形状况;最大监测频率可设置为1 min/次,可实现结构变形的精准动态监测。该实验平台的应用,有助于提高学生对无线传感监测系统原理与系统组成的认识,掌握具体应用方法,锻炼学生的专业素养和创新思维。

基于CRITIC-TOPSIS的中国区域碳减排能力评价及空间分析

气候变化是全人类的共同挑战,中国在气候治理行动中,期望在最短时间内完成碳排放强度全球最大降幅。实现碳达峰碳中和,需统筹处理好整体和局部的关系,有效评价区域碳减排能力。基于CRITIC-TOPSIS法对2015—2019年中国区域碳减排能力进行测度评价,结合GIS技术对碳减排能力空间特征开展分析。结果表明:指标权重随社会变化不断发生动态变化;碳减排能力区域差异显著,呈现明显的东部高,中西部低的现象;区域碳减排能力发展水平空间自相关性显著,其中高值聚类显著性更强,空间分布出现“俱乐部趋同”现象。最后,对目前区域碳减排能力存在的问题提出了相应的对策建议。

超效率SBM-Malmquist模型在燃气管道完整性管理效能评价中的应用

为解决燃气管道完整性管理效能评价时,无法准确对比各评价单元完整性管理水平的差异,以及无法动态测算同一评价单元完整性管理效能的问题,提出了一种基于超效率松弛值测算(Slacks-Based Measure, SBM)的模型和Malmquist模型相结合的方法,对管道完整性管理效能评价进行研究。首先,综合考虑完整性管理实施过程、效果与效益,并结合专家意见和相关规范标准,构建效能评价指标体系,该指标体系由3项一级指标和14项二级指标进行表征。其次,采用考虑非期望产出的超效率SBM模型解决各评价单元的比较与排序问题,再将Malmquist模型与超效率SBM模型相结合,测算各评价单元效能的动态变化趋势。最后,针对5家燃气公司在2019—2022年的完整性管理效能,采用所提出的效能评价模型开展评价。结果表明,超效率SBM模型和Malmquist模型相结合是实现管道完整性管理效能客观定量评价的有效方法,通过横向对比和动态测度发现短板并提出针对性的改进意见,其分析结果与实际工程相吻合,证明该方法能够有效评估完整性管理效能,为管道完整性管理提供了一种评价思路。

我国养老机构服务效率测度及提升路径研究——基于三阶段DEA和Malmquist指数的实证分析

科学评估养老机构服务效率是优化养老资源配置和完善养老服务体系建设的基础。以我国29个省(区、市)为研究对象,基于2018—2022年《中国民政统计年鉴》和《中国统计年鉴》的数据,运用三阶段DEA模型和Malmquist指数模型,对养老机构服务的静态与动态效率测度进行分析。研究表明,养老机构服务效率逐年提升,但仍有改善空间。综合技术效率均值由0.794下降到0.726,受到外部环境因素的影响;技术进步是制约养老机构服务效率的主要原因。因此,要调整投入生产规模,重视环境因素影响,实施差异化的效率提升策略,以技术进步为发展重心,进而提高养老机构服务效率。

Unicompartmental knee arthroplasty vs high tibial osteotomy for knee osteoarthritis:A comparison of clinical and radiological outcomes

BACKGROUND Unicompartmental knee arthroplasty(UKA)and high tibial osteotomy(HTO)are well-established operative interventions in the treatment of knee osteoarthritis.However,which intervention is more beneficial to patients with knee osteoarthritis remains unknown and a topic of much debate.Simultaneously,there is a paucity of research assessing the relationship between radiographic parameters of knee joint alignment and patient-reported clinical outcomes,preoperatively and following HTO or UKA.AIM To compare UKAs and HTOs as interventions for medial-compartment knee osteoarthritis:Examining differences in clinical outcome and investigating the relationship of joint alignment with respect to this.METHODS This longitudinal observational study assessed a total of 42 patients that had undergone UKA(n=23)and HTO(n=19)to treat medial compartment knee osteoarthritis.Patient-reported outcome measures(PROMs)were collected to evaluate clinical outcome.These included two disease-specific(Knee Injury and Osteoarthritis Outcome Score,Oxford Knee Score)and two generic(EQ-5D-5L,Short Form-12)PROMs.The radiographic parameters of knee alignment assessed were the:Hip-knee-ankle angle,mechanical axis deviation and angle of Mikulicz line.RESULTS Statistical analyses demonstrated significant(P<0.001),preoperative to postoperative,improvements in the PROM scores of both groups.There were,however,no significant inter-group differences in the postoperative PROM scores of the UKA and HTO group.Several significant correlations associated a more distolaterally angled Mikulicz line with worse knee function and overall health preoperatively(P<0.05).Postoperatively,two clusters of significant correlations were observed between the disease-specific PROM scores and knee joint alignment parameters(hip-knee-ankle angle,mechanical axis deviation)within the HTO group;yet no such associations were observed within the UKA group.CONCLUSION UKAs and HTOs are both efficacious operations that provide a comparable degree of clinical benefit to patients with medial compartment knee osteoarthritis.Clinical outcome has a limited association with radiographic parameters of knee joint alignment postoperatively;however,a more distolaterally angled Mikulicz line appears associated with worse knee function/health-related quality of life preoperatively.

基于VisuShrink小波阈值变换的甲烷检测降噪技术

在利用可调谐半导体激光吸收光谱技术探测甲烷气体泄漏时,复杂的环境光干扰会降低浓度检测信号的信噪比使波形失真。为了抑制噪声,增强电信号与浓度值的线性相关度,提出一种以VisuShrink方法改善小波阈值函数的小波阈值去噪算法。该算法针对小波阈值去噪算法的软硬度不能平衡信号,存在失真的问题,利用统计方法对阈值进行计算,弱化间断点阈值跳变影响。通过仿真计算对比未加噪声前的信号与滤波后的信号,提出算法的信噪比为28.405 30,优于四阶巴特沃斯滤波器。VisuShrink方法在SNR和NCC参数上性能远优于巴特沃斯滤波器,与软阈值函数相近,在MSE均方误差分析上远小于软阈值精度,与巴特沃斯相近,具有较好的平滑特性。实际搭建检测平台对多个浓度样本采样进行多次采样,采用VisuShrink方法设计的滤波器将系统误差精度从4.14%提升至2.472%,算法可有效用于甲烷检测系统提高光谱数据的平滑性和准确性。

Bunch-length measurement at a bunch-by-bunch rate based on time–frequency-domain joint analysis techniques and its application

This paper presents a new technique for measuring the bunch length of a high-energy electron beam at a bunch-by-bunch rate in storage rings.This technique uses the time–frequency-domain joint analysis of the bunch signal to obtain bunch-by-bunch and turn-by-turn longitudinal parameters,such as bunch length and synchronous phase.The bunch signal is obtained using a button electrode with a bandwidth of several gigahertz.The data acquisition device was a high-speed digital oscilloscope with a sampling rate of more than 10 GS/s,and the single-shot sampling data buffer covered thousands of turns.The bunch-length and synchronous phase information were extracted via offline calculations using Python scripts.The calibration coefficient of the system was determined using a commercial streak camera.Moreover,this technique was tested on two different storage rings and successfully captured various longitudinal transient processes during the harmonic cavity debugging process at the Shanghai Synchrotron Radiation Facility(SSRF),and longitudinal instabilities were observed during the single-bunch accumulation process at Hefei Light Source(HLS).For Gaussian-distribution bunches,the uncertainty of the bunch phase obtained using this technique was better than 0.2 ps,and the bunch-length uncertainty was better than 1 ps.The dynamic range exceeded 10 ms.This technology is a powerful and versatile beam diagnostic tool that can be conveniently deployed in high-energy electron storage rings.

Spatiotemporal characteristics of cultivated land use eco-efficiency and its influencing factors in China from 2000 to 2020

Improving cultivated land use eco-efficiency(CLUE)can effectively promote agricultural sustainability,particularly in developing countries where CLUE is generally low.This study used provincial-level data from China to evaluate the spatiotemporal evolution of CLUE from 2000 to 2020 and identified the influencing factors of CLUE by using a panel Tobit model.In addition,given the undesirable outputs of agricultural production,we incorporated carbon emissions and nonpoint source pollution into the global benchmark-undesirable output-super efficiency-slacks-based measure(GB-US-SBM)model,which combines global benchmark technology,undesirable output,super efficiency,and slacks-based measure.The results indicated that there was an upward trend in CLUE in China from 2000 to 2020,with an increase rate of 2.62%.The temporal evolution of CLUE in China could be classified into three distinct stages:a period of fluctuating decrease(2000-2007),a phase of gradual increase(2008-2014),and a period of rapid growth(2015-2020).The major grain-producing areas(MPAs)had a lower CLUE than their counterparts,namely,non-major grain-production areas(non-MPAs).The spatial agglomeration effect followed a northeast-southwest strip distribution;and the movement path of barycentre revealed a"P"shape,with Luoyang City,Henan Province,as the centre.In terms of influencing factors of CLUE,investment in science and technology played the most vital role in improving CLUE,while irrigation index had the most negative effect.It should be noted that these two influencing factors had different impacts on MPAs and non-MPAs.Therefore,relevant departments should formulate policies to enhance the level of science and technology,improve irrigation condition,and promote sustainable utilization of cultivated land.

Enhancing Energy Efficiency with a Dynamic Trust Measurement Scheme in Power Distribution Network

The application of Intelligent Internet of Things(IIoT)in constructing distribution station areas strongly supports platform transformation,upgrade,and intelligent integration.The sensing layer of IIoT comprises the edge convergence layer and the end sensing layer,with the former using intelligent fusion terminals for real-time data collection and processing.However,the influx of multiple low-voltage in the smart grid raises higher demands for the performance,energy efficiency,and response speed of the substation fusion terminals.Simultaneously,it brings significant security risks to the entire distribution substation,posing a major challenge to the smart grid.In response to these challenges,a proposed dynamic and energy-efficient trust measurement scheme for smart grids aims to address these issues.The scheme begins by establishing a hierarchical trust measurement model,elucidating the trust relationships among smart IoT terminals.It then incorporates multidimensional measurement factors,encompassing static environmental factors,dynamic behaviors,and energy states.This comprehensive approach reduces the impact of subjective factors on trust measurements.Additionally,the scheme incorporates a detection process designed for identifying malicious low-voltage end sensing units,ensuring the prompt identification and elimination of any malicious terminals.This,in turn,enhances the security and reliability of the smart grid environment.The effectiveness of the proposed scheme in pinpointing malicious nodes has been demonstrated through simulation experiments.Notably,the scheme outperforms established trust metric models in terms of energy efficiency,showcasing its significant contribution to the field.

Advancing Malaria Prediction in Uganda through AI and Geospatial Analysis Models

The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R2 of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.

A Portfolio Selection Method Based on Pattern Matching with Dual Information of Direction and Distance

Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.

FPGA and computer-vision-based atom tracking technology for scanning probe microscopy

Atom tracking technology enhanced with innovative algorithms has been implemented in this study,utilizing a comprehensive suite of controllers and software independently developed domestically.Leveraging an on-board field-programmable gate array(FPGA)with a core frequency of 100 MHz,our system facilitates reading and writing operations across 16 channels,performing discrete incremental proportional-integral-derivative(PID)calculations within 3.4 microseconds.Building upon this foundation,gradient and extremum algorithms are further integrated,incorporating circular and spiral scanning modes with a horizontal movement accuracy of 0.38 pm.This integration enhances the real-time performance and significantly increases the accuracy of atom tracking.Atom tracking achieves an equivalent precision of at least 142 pm on a highly oriented pyrolytic graphite(HOPG)surface under room temperature atmospheric conditions.Through applying computer vision and image processing algorithms,atom tracking can be used when scanning a large area.The techniques primarily consist of two algorithms:the region of interest(ROI)-based feature matching algorithm,which achieves 97.92%accuracy,and the feature description-based matching algorithm,with an impressive 99.99%accuracy.Both implementation approaches have been tested for scanner drift measurements,and these technologies are scalable and applicable in various domains of scanning probe microscopy with broad application prospects in the field of nanoengineering.

Sharing quantum nonlocality in the noisy scenario

It was showed in [Phys. Rev. Lett. 125 090401(2020)] that there exist unbounded number of independent Bobs who can share quantum nonlocality with a single Alice by performing sequentially measurements on the Bob's half of the maximally entangled pure two-qubit state. However, from practical perspectives, errors in entanglement generation and noises in quantum measurements will result in the decay of nonlocality in the scenario. In this paper, we analyze the persistency and termination of sharing nonlocality in the noisy scenario. We first obtain the two sufficient conditions under which there exist n independent Bobs who can share nonlocality with a single Alice under noisy measurements and the noisy initial two qubit entangled state. Analyzing the two conditions, we find that the influences on persistency under different kinds of noises can cancel each other out. Furthermore, we describe the change patterns of the maximal nonlocality-sharing number under the influence of different noises. Finally, we extend our investigation to the case of arbitrary finite-dimensional systems.

Complexity Considerations in the Heisenberg Uncertainty Principle

This work introduces a modification to the Heisenberg Uncertainty Principle (HUP) by incorporating quantum complexity, including potential nonlinear effects. Our theoretical framework extends the traditional HUP to consider the complexity of quantum states, offering a more nuanced understanding of measurement precision. By adding a complexity term to the uncertainty relation, we explore nonlinear modifications such as polynomial, exponential, and logarithmic functions. Rigorous mathematical derivations demonstrate the consistency of the modified principle with classical quantum mechanics and quantum information theory. We investigate the implications of this modified HUP for various aspects of quantum mechanics, including quantum metrology, quantum algorithms, quantum error correction, and quantum chaos. Additionally, we propose experimental protocols to test the validity of the modified HUP, evaluating their feasibility with current and near-term quantum technologies. This work highlights the importance of quantum complexity in quantum mechanics and provides a refined perspective on the interplay between complexity, entanglement, and uncertainty in quantum systems. The modified HUP has the potential to stimulate interdisciplinary research at the intersection of quantum physics, information theory, and complexity theory, with significant implications for the development of quantum technologies and the understanding of the quantum-to-classical transition.

Measurement and Analysis of Vibration Effect of Free-falling Corner Cube Driving Mechanism in Free Fall Absolute Gravimeter

The vibration interference of the reference corner cube runs through the free flight process of the free-falling corner cube,which is superimposed on the whole laser interference fringes.Thus,it is necessary to solve the interference fringes with the entire fringe to analyze the quantitative influence of vibration on gravity measurements.

A new centrality measure based on neighbor loop structure for network dismantling

Nearly all real-world networks are complex networks and usually are in danger of collapse.Therefore,it is crucial to exploit and understand the mechanisms of network attacks and provide better protection for network functionalities.Network dismantling aims to find the smallest set of nodes such that after their removal the network is broken into connected components of sub-extensive size.To overcome the limitations and drawbacks of existing network dismantling methods,this paper focuses on network dismantling problem and proposes a neighbor-loop structure based centrality metric,NL,which achieves a balance between computational efficiency and evaluation accuracy.In addition,we design a novel method combining NL-based nodes-removing,greedy tree-breaking and reinsertion.Moreover,we compare five baseline methods with our algorithm on ten widely used real-world networks and three types of model networks including Erd€os-Renyi random networks,Watts-Strogatz smallworld networks and Barabasi-Albert scale-free networks with different network generation parameters.Experimental results demonstrate that our proposed method outperforms most peer methods by obtaining a minimal set of targeted attack nodes.Furthermore,the insights gained from this study may be of assistance to future practical research into real-world networks.

MULTIPLE INTERSECTIONS OF SPACE-TIME ANISOTROPIC GAUSSIAN FIELDS

Let X={X(t)∈R^(d),t∈R^(N)}be a centered space-time anisotropic Gaussian field with indices H=(H_(1),…,H_(N))∈(0,1)~N,where the components X_(i)(i=1,…,d)of X are independent,and the canonical metric√(E(X_(i)(t)-X_(i)(s))^(2))^(1/2)(i=1,…,d)is commensurate with■for s=(s_(1),…,s_(N)),t=(t_(1),…,t_(N))∈R~N,α_(i)∈(0,1],and with the continuous functionγ(·)satisfying certain conditions.First,the upper and lower bounds of the hitting probabilities of X can be derived from the corresponding generalized Hausdorff measure and capacity,which are based on the kernel functions depending explicitly onγ(·).Furthermore,the multiple intersections of the sample paths of two independent centered space-time anisotropic Gaussian fields with different distributions are considered.Our results extend the corresponding results for anisotropic Gaussian fields to a large class of space-time anisotropic Gaussian fields.