Lubrication performance of graphene in the sliding electrical contact interface

Electrical contact materials are increasingly widely used,but the existing electric contact lubricants still have lots of room for improvement,such as anti-wear performance and lubrication life.Due to the excellent electrical and lubrication properties,graphene shows great potential in lubricating the sliding electrical contact interface,but there is a lack of relevant research.Some researchers have studied the lubrication performance of graphene between the gold-coated/TiN-coated friction pair at an ultra-low current.However,the lubrication performance of graphene on more widely used electrical contact materials such as copper and its alloys under larger and more commonly used current or voltage conditions has not been reported.In this paper,we study the lubrication performance of graphene in the copper and its alloys sliding electrical contact interface under usual parameters,which is explored through four aspects:different substrates-copper and brass,different test methods-constant voltage and constant current,different normal loads and durability test.The experiments demonstrate that graphene can significantly reduce the friction and wear on brass and copper under the above test methods and parameters,with low contact resistance at the same time.Our work is expected to provide a new lubricant for electrical contact materials and contribute to enriching the tribological theory of graphene.

EG-STC: An Efficient Secure Two-Party Computation Scheme Based on Embedded GPU for Artificial Intelligence Systems

This paper presents a comprehensive exploration into the integration of Internet of Things(IoT),big data analysis,cloud computing,and Artificial Intelligence(AI),which has led to an unprecedented era of connectivity.We delve into the emerging trend of machine learning on embedded devices,enabling tasks in resource-limited environ-ments.However,the widespread adoption of machine learning raises significant privacy concerns,necessitating the development of privacy-preserving techniques.One such technique,secure multi-party computation(MPC),allows collaborative computations without exposing private inputs.Despite its potential,complex protocols and communication interactions hinder performance,especially on resource-constrained devices.Efforts to enhance efficiency have been made,but scalability remains a challenge.Given the success of GPUs in deep learning,lever-aging embedded GPUs,such as those offered by NVIDIA,emerges as a promising solution.Therefore,we propose an Embedded GPU-based Secure Two-party Computation(EG-STC)framework for Artificial Intelligence(AI)systems.To the best of our knowledge,this work represents the first endeavor to fully implement machine learning model training based on secure two-party computing on the Embedded GPU platform.Our experimental results demonstrate the effectiveness of EG-STC.On an embedded GPU with a power draw of 5 W,our implementation achieved a secure two-party matrix multiplication throughput of 5881.5 kilo-operations per millisecond(kops/ms),with an energy efficiency ratio of 1176.3 kops/ms/W.Furthermore,leveraging our EG-STC framework,we achieved an overall time acceleration ratio of 5–6 times compared to solutions running on server-grade CPUs.Our solution also exhibited a reduced runtime,requiring only 60%to 70%of the runtime of previously best-known methods on the same platform.In summary,our research contributes to the advancement of secure and efficient machine learning implementations on resource-constrained embedded devices,paving the way for broader adoption of AI technologies in various applications.

从“非典”和新冠肺炎疫情看如何做好疫情防控档案管理

做好疫情防控档案的收集、整理和利用工作对于提升我国的疫情防控水平具有重要的意义。因此,本文结合非典和新冠肺炎疫情,分析如何做好疫情防控档案管理。本文首先对疫情防控档案进行了定义,然后分析了疫情防控档案的特点和作用,接着对疫情防控资料的归档范围进行了明确,最后对疫情防控档案的管理明确了要求。

情感治理与湾区融合——广州港澳创新创业基地案例

在粤港澳大湾区建设的背景下,如何使港澳青年融入国家发展大局成为备受关注的议题。港澳创新创业基地作为赴内地创业的港澳青年的活动载体,其被赋予着推动湾区融合的重任。本研究采用半结构式访谈、实地考察与网络文本分析的方法,以广州市港澳双创基地为研究对象,基于情感地理学理论,探讨港澳双创基地中的情感空间的构建过程,及其在湾区治理中的作用机制。研究发现:不同主体在港澳双创基地发展的过程中邂逅。通过基地物质景观的营造和各主体间的良好互动,基地被构建成一个积极正向的情感空间。伴随着港澳创业者在内地的流动、实践,情感空间实现尺度跃升。港澳创业者在内地积极的情感体验一方面增强其社会参与的热情,另一方面通过其在港澳本地的社会实践传递到当地,吸引更多港澳本地居民融入湾区发展,以一种柔性的、非正式的方式发挥着情感的力量。然而港澳创业者对内地的情感具有多元性,其对内地的归属感与其社会参与之间的关系也较为复杂,在内地的港澳创业者仍处于情感融入的状态;当前尚缺少关于如何发挥在内地的港澳创业者对港澳本地的辐射带动作用的顶层设计。上述结论丰富了湾区治理研究视角和尺度,揭示了当前湾区融合中存在的问题,也为湾区情感治理实践提供了有效思路。

Plasma microRNA-15a/16-1-based machine learning for early detection of hepatitis B virus-related hepatocellular carcinoma

Background and aims:Hepatocellular carcinoma(HCC),which is prevalent worldwide and has a high mortality rate,needs to be effectively diagnosed.We aimed to evaluate the significance of plasma microRNA-15a/16-1(miR-15a/16)as a biomarker of hepatitis B virus-related HCC(HBV-HCC)using the machine learning model.This study was the first large-scale investigation of these two miRNAs in HCC plasma samples.Methods:Using quantitative polymerase chain reaction,we measured the plasma miR-15a/16 levels in a total of 766 participants,including 74 healthy controls,335 with chronic hepatitis B(CHB),47 with compensated liver cirrhosis,and 310 with HBV-HCC.The diagnostic performance of miR-15a/16 was examined using a machine learning model and compared with that of alpha-fetoprotein(AFP).Lastly,to validate the diagnostic efficiency of miR-15a/16,we performed pseudotemporal sorting of the samples to simulate progression from CHB to HCC.Results:Plasma miR-15a/16 was significantly decreased in HCC than in all control groups(P<0.05 for all).In the training cohort,the area under the receiver operating characteristic curve(AUC),sensitivity,and average precision(AP)for the detection of HCC were higher for miR-15a(AUC=0.80,67.3%,AP=0.80)and miR-16(AUC=0.83,79.0%,AP=0.83)than for AFP(AUC=0.74,61.7%,AP=0.72).Combining miR-15a/16 with AFP increased the AUC to 0.86(sensitivity 85.9%)and the AP to 0.85 and was significantly superior to the other markers in this study(P<0.05 for all),as further demonstrated by the detection error tradeoff curves.Moreover,miR-15a/16 impressively showed potent diagnostic power in early-stage,small-tumor,and AFP-negative HCC.A validation cohort confirmed these results.Lastly,the simulated follow-up of patients further validated the diagnostic efficiency of miR-15a/16.Conclusions:We developed and validated a plasma miR-15a/16-based machine learning model,which exhibited better diagnostic performance for the early diagnosis of HCC compared to that of AFP.

基于具有动作自寻优能力的深度强化学习的智能发电控制

新能源以及分布式能源大规模并网所带来的随机扰动问题,影响电网安全和经济运行.本文提出一种具有动作自寻优能力的多智能体深度强化学习算法,即DDRQN-AD算法.所提算法可有效获取电网最优协调控制,从而解决传统集中式自动发电控制难以解决的新能源以及分布式能源大规模并网所带来的强随机扰动,使新能源得到最大限度的开发利用.通过对两区域微电网负荷频率控制电力系统模型以及广东电网模型进行仿真,结果显示DDRQN-AD与已有的多种智能算法相比,具有更强的鲁棒性及学习能力,可减少碳排放,提高新能源利用率.