Data-enhanced revealing of trends in Geoscience

Purpose:This article presents an in-depth analysis of global research trends in Geosciences from 2014 to 2023.By integrating bibliometric analysis with expert insights from the Deep-time Digital Earth(DDE)initiative,this article identifies key emerging themes shaping the landscape of Earth Sciences①.Design/methodology/approach:The identification process involved a meticulous analysis of over 400,000 papers from 466 Geosciences journals and approximately 5,800 papers from 93 interdisciplinary journals sourced from the Web of Science and Dimensions database.To map relationships between articles,citation networks were constructed,and spectral clustering algorithms were then employed to identify groups of related research,resulting in 407 clusters.Relevant research terms were extracted using the Log-Likelihood Ratio(LLR)algorithm,followed by statistical analyses on the volume of papers,average publication year,and average citation count within each cluster.Additionally,expert knowledge from DDE Scientific Committee was utilized to select top 30 trends based on their representation,relevance,and impact within Geosciences,and finalize naming of these top trends with consideration of the content and implications of the associated research.This comprehensive approach in systematically delineating and characterizing the trends in a way which is understandable to geoscientists.Findings:Thirty significant trends were identified in the field of Geosciences,spanning five domains:deep space,deep time,deep Earth,habitable Earth,and big data.These topics reflect the latest trends and advancements in Geosciences and have the potential to address real-world problems that are closely related to society,science,and technology.Research limitations:The analyzed data of this study only contain those were included in the Web of Science.Practical implications:This study will strongly support the organizations and individual scientists to understand the modern frontier of earth science,especially on solid earth.The organizations such as the surveys or natural science fund could map out areas for future exploration and analyze the hot topics reference to this study.Originality/value:This paper integrates bibliometric analysis with expert insights to highlight the most significant trends on earth science and reach the individual scientist and public by global voting.

药物化学中“雨课堂”与课程思政结合的教学实践

药物化学是药学专业学生最早接触的专业核心课程。课程内容多,涉及知识面广,授课与学习难度大。虽然丰富的教学内容给课程思政提供了素材与切入点,但是传统的课堂授课方式时间有限,难以很好地将课程思政与教学内容相结合。笔者将课程思政与混合式教学结合,将"思政元素"融入"雨课堂"课件库的建设并在教学中加以实践,以期实现专业课程教育与思政教育同向同行,力争达到全程育人、全方位育人的教学效果。

Analyzing and De-Anonymizing Bitcoin Networks:An IP Matching Method with Clustering and Heuristics

The anonymity and de-anonymity of blockchain and Bitcoin have always been a hot topic in blockchain related research.Since Bitcoin was created by Nakamoto in 2009,it has,to some extent,deviated from its currency attribute as a trading medium but instead turned into an object for financial investment and operations.In this paper,the power-law distribution that the Bitcoin network obeys is given with mathematical proof,while traditional deanonymous methods such as clustering fail to satisfy it.Therefore,considering the profit-oriented characteristics of Bitcoin traders in such occasion,we put forward a de-anonymous heuristic approach that recognizes and analyzes the behavioral patterns of financial High-Frequency Transactions(HFT),with realtime exchange rate of Bitcoin involved.With heuristic approach used for de-anonymity,algorithm that deals with the adjacency matrix and transition probability matrix are also put forward,which then makes it possible to apply clustering to the IP matching method.Basing on the heuristic approach and additional algorithm for clustering,finally we established the de-anonymous method that matches the activity information of the IP with the transaction records in blockchain.Experiments on IP matching method are applied to the actual data.It turns out that similar behavioral pattern between IP and transaction records are shown,which indicates the superiority of IP matching method.

Vertical Water-Entry of Bullet-Shaped Projectiles

The vertical water-entry behavior of bullet-shaped projectiles was experimentally and theoretically studied. Particular attention was given to characterizing projectile dynamics, the resultant evolution of air cavity and particularly surface closure before deep closure in the moderate speed. We developed equations for the projectile motion with significant and negligible gravitational effects. Based on the solution to the Rayleigh-Besant problem, a theoretical model was developed to describe the evolution of the cavity shape, including the time evolution of the cavity on fixed locations and its location evolutions at fixed times. The gravitation effects during the initial stage of the impact of the projectile on the water can be ignored, but those during the later stage should be considered, many literatures do not have the report of this aspect. The theoretical predictions were consistent with the experimental observations. The evolution of air cavity had a significant effect on ballistic stability.

EvolveKG:a general framework to learn evolving knowledge graphs

A great many practical applications have observed knowledge evolution,i.e.,continuous born of new knowledge,with its formation influenced by the structure of historical knowledge.This observation gives rise to evolving knowledge graphs whose structure temporally grows over time.However,both the modal characterization and the algorithmic implementation of evolving knowledge graphs remain unexplored.To this end,we propose EvolveKG–a general framework that enables algorithms in the static knowledge graphs to learn the evolving ones.EvolveKG quantifies the influence of a historical fact on a current one,called the effectiveness of the fact,and makes knowledge prediction by leveraging all the cross-time knowledge interaction.The novelty of EvolveKG lies in Derivative Graph–a weighted snapshot of evolution at a certain time.Particularly,each weight quantifies knowledge effectiveness through a temporarily decaying function of consistency and attenuation,two proposed factors depicting whether or not the effectiveness of a fact fades away with time.Besides,considering both knowledge creation and loss,we obtain higher prediction accuracy when the effectiveness of all the facts increases with time or remains unchanged.Under four real datasets,the superiority of EvolveKG is confirmed in prediction accuracy.

VSAN:A new visualization method for super-large-scale academic networks

As a carrier of knowledge,papers have been a popular choice since ancient times for documenting everything from major historical events to breakthroughs in science and technology.With the booming development of science and technology,the number of papers has been growing exponentially.Just like the fact that Internet of Things(IoT)allows the world to be connected in a flatter way,how will the network formed by massive academic papers look like?Most existing visualization methods can only handle up to hundreds of thousands of node size,which is much smaller than that of academic networks which are usually composed of millions or even more nodes.In this paper,we are thus motivated to break this scale limit and design a new visualization method particularly for super-large-scale academic networks(VSAN).Nodes can represent papers or authors while the edges means the relation(e.g.,citation,coauthorship)between them.In order to comprehensively improve the visualization effect,three levels of optimization are taken into account in the whole design of VSAN in a progressive manner,i.e.,bearing scale,loading speed,and effect of layout details.Our main contributions are two folded:(1)We design an equivalent segmentation layout method that goes beyond the limit encountered by state-of-the-arts,thus ensuring the possibility of visually revealing the correlations of larger-scale academic entities.(2)We further propose a hierarchical slice loading approach that enables users to observe the visualized graphs of the academic network at both macroscopic and microscopic levels,with the ability to quickly zoom between different levels.In addition,we propose a“jumping between nebula graphs”method that connects the static pages of many academic graphs and helps users to form a more systematic and comprehensive understanding of various academic networks.Applying our methods to three academic paper citation datasets in the AceMap database confirms the visualization scalability of VSAN in the sense that it can visualize academic networks with more than 4 million nodes.The super-large-scale visualization not only allows a galaxy-like scholarly picture unfolding that were never discovered previously,but also returns some interesting observations that may drive extra attention from scientists.

一种新型群智感知系统架构模型和实现方法

物联网移动群智感知模式本质是汇聚大规模普通移动个体的智能感知能力,对开放、动态、复杂物理环境进行感知,通过感知大数据的智能分析,对感知群体引导和反馈,使其持续涌现群体智能并辅助综合决策.目前的群智感知系统仍然存在个体感知欠智能、群体目标少引导、群智过程弱调控三方面的局限性.本文首先讨论了群智感知研究现状和面临的挑战;结合人机物融合、云边端协同、感算控闭环3个核心发展趋势,提出一种智能演进与反馈引导结合的新型群智感知系统架构模型Evo-CPS,并研究了该模型的实现方法;然后结合智慧出行应用场景,将所提出的理论方法进行应用验证;最后,总结全文并展望新一代群智感知研究领域的发展方向.

Knowledge System,Ontology,and Knowledge Graph of the Deep-Time Digital Earth(DDE): Progress and Perspective

THE USE OF KNOWLEDGE GRAPH IN NATURAL SCIENCE Knowledge graph is a field of Artificial Intelligence(AI)that aims to represent knowledge in the form of graphs,consisting of nodes and edges which represent entities and relationships between nodes respectively(Aidan et al.,2022).Although the knowledge graph was popularized recently due to use of this idea in Google’s search engine in 2012(Amit,2012),its root can be traced back to the emergence of the Semantic Web as well as earlier works in ontology(Aggarwal,2021).

An adaptive representation model for geoscience knowledge graphs considering complex spatiotemporal features and relationships

Geoscience knowledge graph(GKG)can organize various geoscience knowledge into a machine understandable and computable semantic network and is an effective way to organize geoscience knowledge and provide knowledge-related services.As a result,it has gained significant attention and become a frontier in geoscience.Geoscience knowledge is derived from many disciplines and has complex spatiotemporal features and relationships of multiple scales,granularities,and dimensions.Therefore,establishing a GKG representation model conforming to the characteristics of geoscience knowledge is the basis and premise for the construction and application of GKG.However,existing knowledge graph representation models leverage fixed tuples that are limited in fully representing complex spatiotemporal features and relationships.To address this issue,this paper first systematically analyzes the categorization and spatiotemporal features and relationships of geoscience knowledge.On this basis,an adaptive representation model for GKG is proposed by considering the complex spatiotemporal features and relationships.Under the constraint of a unified spatiotemporal ontology,this model adopts different tuples to adaptively represent different types of geoscience knowledge according to their spatiotemporal correlation.This model can efficiently represent geoscience knowledge,thereby avoiding the isolation of the spatiotemporal feature representation and improving the accuracy and efficiency of geoscience knowledge retrieval.It can further enable the alignment,transformation,computation,and reasoning of spatiotemporal information through a spatiotemporal ontology.

Geoscience knowledge graph in the big data era

Since the beginning of the 21 st century,the geoscience research has been entering a significant transitional period with the establishment of a new knowledge system as the core and with the drive of big data as the means.It is a revolutionary leap in the research of geoscience knowledge discovery from the traditional encyclopedic discipline knowledge system to the computer-understandable and operable knowledge graph.Based on adopting the graph pattern of general knowledge representation,the geoscience knowledge graph expands the unique spatiotemporal features to the Geoscience knowledge,and integrates geoscience knowledge elements,such as map,text,and number,to establish an all-domain geoscience knowledge representation model.A federated,crowd intelligence-based collaborative method of constructing the geoscience knowledge graph is developed here,which realizes the construction of high-quality professional knowledge graph in collaboration with global geo-scientists.We also develop a method for constructing a dynamic knowledge graph of multi-modal geoscience data based on in-depth text analysis,which extracts geoscience knowledge from massive geoscience literature to construct the latest and most complete dynamic geoscience knowledge graph.A comprehensive and systematic geoscience knowledge graph can not only deepen the existing geoscience big data analysis,but also advance the construction of the high-precision geological time scale driven by big data,the compilation of intelligent maps driven by rules and data,and the geoscience knowledge evolution and reasoning analysis,among others.It will further expand the new directions of geoscience research driven by both data and knowledge,break new ground where geoscience,information science,and data science converge,realize the original innovation of the geoscience research and achieve major theoretical breakthroughs in the spatiotemporal big data research.

Laser-induced liquid tin discharge plasma and its EUV spectra

Laser-induced discharge plasmas(LDPs) have the potential to be inspection and metrology sources in extreme ultraviolet(EUV) lithography. An LDP EUV source was developed to avoid tin electrode erosion in which a tin pool was used as a cathode. A CO2 pulse laser was focused on the liquid tin target surface, and then a breakdown occurred in a very short time. The voltage-current characteristics of the discharge oscillated, lasting for several microseconds, and an RLC fitting model was used to obtain the inductance and resistance. An intensified chargecoupled device(ICCD) camera was used to investigate the dynamics of LDP, which can explain the formation of a discharge channel. The EUV spectra of laser-induced liquid tin discharge plasma were detected by a grazing incident ultraviolet spectrometer, compared with a laser-produced tin droplet plasma EUV spectrum. To explain the EUV spectrum difference of laser-induced liquid tin discharge plasma and laser-produced tin droplet plasma,the collision radiation(CR) model combined with COWAN code was used to fit the experimental EUV spectrum, which can estimate the electron temperature and density of the plasma.

Improving accuracy of automatic optical inspection with machine learning

Electronic devices require the printed circuit board(PCB)to support the whole structure,but the assembly of PCBs suffers from welding problem of the electronic components such as surface mounted devices(SMDs)resistors.The automated optical inspection(AOI)machine,widely used in industrial production,can take the image of PCBs and examine the welding issue.However,the AOI machine could commit false negative errors and dedicated technicians have to be employed to pick out those misjudged PCBs.This paper proposes a machine learning based method to improve the accuracy of AOI.In particular,we propose an adjacent pixel RGB value based method to pre-process the image from the AOI machine and build a customized deep learning model to classify the image.We present a practical scheme including two machine learning procedures to mitigate AOI errors.We conduct experiments with the real dataset from a production line for three months,the experimental results show that our method can reduce the rate of misjudgment from 0.3%–0.5%to 0.02%–0.03%,which is meaningful for thousands of PCBs each containing thousands of electronic components in practice.

Characteristics of laser induced discharge tin plasma and its extreme ultraviolet radiation

In this paper,a CO_(2) laser induced discharge plasma extreme ultraviolet(EUV)source experimental device was established.The optical emission spectroscopy was used to diagnose the characteristics of the plasma,and the evolution of electron temperature and electron density with time was obtained.The influence of discharge voltage on plasma parameters was analyzed and discussed.The EUV radiation characteristics of the plasma were investigated by self-made grazing incidence EUV spectrometer.The EUV radiation intensity and conversion efficiency were discussed.

Segmented pulsed discharge for metastable argon lasing medium

Direct current pulsed discharge is a promising route for producing high-density metastable particles required for optically pumped rare gas lasers(OPRGLs).Such metastable densities are easily realized in small discharge volumes at near atmospheric pressures,but problems appear when one is trying to achieve a large volume of plasma for high-power output.In this work,we examined the volume scalability of high-density metastable argon atoms by segmented discharge configuration.Two discharge zones attached with peaking capacitors were connected parallelly by thin wires,through which the peaking capacitors were charged and of which the inductance functioned as ballasting impendence to prevent discharging in only one zone.A uniform and dense plasma with the peak value of the number densities of Ar(1s^(5))on the order of 10^(13)cm^(-3)was readily achieved.The results demonstrated the feasibility of using segmented discharge for 0PRGL development.