为全面了解土壤胶体影响重金属行为方向的研究现状和前沿动态,基于Web of Science(WoS)核心合集数据库,利用WoS自带分析工具、HistCite引文图谱分析软件、VOSviewer和Citespace可视化分析软件对1990—2021年间土壤胶体影响重金属行为的...为全面了解土壤胶体影响重金属行为方向的研究现状和前沿动态,基于Web of Science(WoS)核心合集数据库,利用WoS自带分析工具、HistCite引文图谱分析软件、VOSviewer和Citespace可视化分析软件对1990—2021年间土壤胶体影响重金属行为的文献进行了计量分析。结果表明,在世界范围内该方向的发文量逐年稳步增长,我国相关研究起步较晚,但近些年呈现迅猛发展的势头。目前土壤胶体影响重金属行为研究发文量最多的国家和研究机构分别是美国和中国科学院,发文量最高的期刊为Environmental Science&Technology,主要研究学科为环境科学与生态学的交叉学科。关键词聚类分析显示“土壤胶体颗粒粒径分级与重金属的形态分布”、“土壤胶体的释放、沉积及对重金属的吸附作用”和“土壤胶体颗粒的迁移机制与迁移模型研究”为主要的研究主题,人工纳米颗粒在土壤中的行为、迁移转化以及生物有效性是现阶段的研究热点。利用场流分离技术结合单粒子电感耦合等离子体质谱等技术,探讨土壤胶体与人工纳米颗粒之间发生的复杂相互作用及其对人工纳米颗粒迁移归趋与环境命运的影响,是未来的主要研究方向。展开更多
为了解淀粉多酚相互作用的研究现状和发展趋势,采用文献计量学方法,以Web of Science核心合集数据库出版日期为2010-01-01至2024-04-18的论文为研究对象,进行可视化分析,为相关学科的研究提供参考。结果显示:淀粉多酚相互作用研究领域...为了解淀粉多酚相互作用的研究现状和发展趋势,采用文献计量学方法,以Web of Science核心合集数据库出版日期为2010-01-01至2024-04-18的论文为研究对象,进行可视化分析,为相关学科的研究提供参考。结果显示:淀粉多酚相互作用研究领域的年发文量总体呈上升趋势;中国研究机构在该领域发文数量优势明显,但在影响力方面与新西兰、澳大利亚和西班牙等国的研究机构相比还有一定差距;该领域研究热点主要集中在食品科学技术与化学学科方向,但同时也展示出多学科交叉融合的发展趋势。消化、特性、相互作用、生物利用度、抗菌活性、分子对接、复合物、纳米颗粒、活性膜等方面是该领域的研究前沿。展开更多
Purpose:The goal of this study is to analyze the relationship between funded and unfunded papers and their citations in both basic and applied sciences.Design/methodology/approach:A power law model analyzes the relati...Purpose:The goal of this study is to analyze the relationship between funded and unfunded papers and their citations in both basic and applied sciences.Design/methodology/approach:A power law model analyzes the relationship between research funding and citations of papers using 831,337 documents recorded in the Web of Science database.Findings:The original results reveal general characteristics of the diffusion of science in research fields:a)Funded articles receive higher citations compared to unfunded papers in journals;b)Funded articles exhibit a super-linear growth in citations,surpassing the increase seen in unfunded articles.This finding reveals a higher diffusion of scientific knowledge in funded articles.Moreover,c)funded articles in both basic and applied sciences demonstrate a similar expected change in citations,equivalent to about 1.23%,when the number of funded papers increases by 1%in journals.This result suggests,for the first time,that funding effect of scientific research is an invariant driver,irrespective of the nature of the basic or applied sciences.Originality/value:This evidence suggests empirical laws of funding for scientific citations that explain the importance of robust funding mechanisms for achieving impactful research outcomes in science and society.These findings here also highlight that funding for scientific research is a critical driving force in supporting citations and the dissemination of scientific knowledge in recorded documents in both basic and applied sciences.Practical implications:This comprehensive result provides a holistic view of the relationship between funding and citation performance in science to guide policymakers and R&D managers with science policies by directing funding to research in promoting the scientific development and higher diffusion of results for the progress of human society.展开更多
A large number of nanopores and complex fracture structures in shale reservoirs results in multi-scale flow of oil. With the development of shale oil reservoirs, the permeability of multi-scale media undergoes changes...A large number of nanopores and complex fracture structures in shale reservoirs results in multi-scale flow of oil. With the development of shale oil reservoirs, the permeability of multi-scale media undergoes changes due to stress sensitivity, which plays a crucial role in controlling pressure propagation and oil flow. This paper proposes a multi-scale coupled flow mathematical model of matrix nanopores, induced fractures, and hydraulic fractures. In this model, the micro-scale effects of shale oil flow in fractal nanopores, fractal induced fracture network, and stress sensitivity of multi-scale media are considered. We solved the model iteratively using Pedrosa transform, semi-analytic Segmented Bessel function, Laplace transform. The results of this model exhibit good agreement with the numerical solution and field production data, confirming the high accuracy of the model. As well, the influence of stress sensitivity on permeability, pressure and production is analyzed. It is shown that the permeability and production decrease significantly when induced fractures are weakly supported. Closed induced fractures can inhibit interporosity flow in the stimulated reservoir volume (SRV). It has been shown in sensitivity analysis that hydraulic fractures are beneficial to early production, and induced fractures in SRV are beneficial to middle production. The model can characterize multi-scale flow characteristics of shale oil, providing theoretical guidance for rapid productivity evaluation.展开更多
Since 2011,the Chinese Academy of Sciences(CAS)has implemented the Strategic Priority Program on Space Science(SPP).A series of scientific satellites have been developed and launched,such as Dark Matter Particle Explo...Since 2011,the Chinese Academy of Sciences(CAS)has implemented the Strategic Priority Program on Space Science(SPP).A series of scientific satellites have been developed and launched,such as Dark Matter Particle Explorer(DAMPE),Quantum Experiments at Space Scale(QUESS),Advanced Space-based Solar Observatory(ASO-S),Einstein Probe(EP),and significant scientific outcomes have been achieved.In order to plan the future space science missions in China,CAS has organized the Chinese space science community to conduct medium and long-term development strategy studies,and summarized the major scientific frontiers of space science as“One Black,Two Dark,Three Origins and Five Characterizations”.Five main scientific themes have been identified for China’s future breakthroughs,including the Extreme Universe,Space-Time Ripples,the Panoramic View of the Sun and Earth,the Habitable Planets,and Biological&Physical Science in Space.Space science satellite missions to be implemented before 2030 are proposed accordingly.展开更多
沉香是我国传统名贵南药,广泛应用于医药、香水、香料、宗教等方面,现已形成了工业、文化产业和农业三大类产业,市场价值极高。本研究在中国知网、维普网和万方数据库中以“沉香结香”为主题词检索,导出1978—2022年发表的文章,利用Cite...沉香是我国传统名贵南药,广泛应用于医药、香水、香料、宗教等方面,现已形成了工业、文化产业和农业三大类产业,市场价值极高。本研究在中国知网、维普网和万方数据库中以“沉香结香”为主题词检索,导出1978—2022年发表的文章,利用CiteSpace软件进行可视化分析;在Web of Science以“agarwood production method”为检索词,可视化分析1978—2022年发表的外文文献。结果表明,国内沉香结香的研究一共分为3个阶段,1978—2007年为萌芽阶段,仅有零星文章产出;2008—2017年为快速发展阶段,大量文章产出并在2017年达到顶峰(41篇);2018—2022年为稳定阶段,持续产出文章并维持在年均20篇以上;有关沉香结香机理的研究发文量前10的期刊中,90%等级均在科技核心以上,排在前10的研究结构均处于沉香产业发达地区。国外沉香结香的研究起源于2008年,整体发文量趋势逐年增加;被引量前10的期刊均为SCI;研究量前10的国家均为GDP前列的国家。有关沉香自然结香和人工结香差异分析的研究成为当前研究热点,未来如何利用2种不同沉香结香方式的成分差异发展沉香资源,将会是新的研究趋势。本研究结果将为科研人员研究提供进一步参考方向,也为生产种植者提供沉香结香参考。展开更多
在AI for Science时代,电池设计自动化智能研发(battery design automation,BDA)平台通过整合先进的人工智能技术,为电池研发领域带来了革命性进展。BDA平台覆盖了文献调研、实验设计、合成制备、表征测试和分析优化这五个电池研发的关...在AI for Science时代,电池设计自动化智能研发(battery design automation,BDA)平台通过整合先进的人工智能技术,为电池研发领域带来了革命性进展。BDA平台覆盖了文献调研、实验设计、合成制备、表征测试和分析优化这五个电池研发的关键环节,利用机器学习、多尺度建模、预训练模型等先进算法,结合软件工程开发用户交互友好的工具,加速从理论设计到实验验证的整个电池研发周期。通过自动化的实验设计、合成制备、表征测试和性能优化,BDA平台不仅提升了研发效率,还提高了电池设计的精确度和可靠性,推动了电池技术向更高能量密度、更长循环寿命和更低成本的方向发展。展开更多
Purpose:Interdisciplinary fields have become the driving force of modern science and a significant source of scientific innovation.However,there is still a paucity of analysis about the essential characteristics of di...Purpose:Interdisciplinary fields have become the driving force of modern science and a significant source of scientific innovation.However,there is still a paucity of analysis about the essential characteristics of disciplines’cross-disciplinary impact.Design/methodology/approach:In this study,we define cross-disciplinary impact on one discipline as its impact to other disciplines,and refer to a three-dimensional framework of variety-balance-disparity to characterize the structure of cross-disciplinary impact.The variety of cross-disciplinary impact of the discipline was defined as the proportion of the high cross-disciplinary impact publications,and the balance and disparity of cross-disciplinary impact were measured as well.To demonstrate the cross-disciplinary impact of the disciplines in science,we chose Microsoft Academic Graph(MAG)as the data source,and investigated the relationship between disciplines’cross-disciplinary impact and their positions in the Hierarchy of Science(HOS).Findings:Analytical results show that there is a significant correlation between the ranking of cross-disciplinary impact and the HOS structure,and that the discipline exerts a greater cross-disciplinary impact on its neighboring disciplines.Several bibliometric features that measure the hardness of a discipline,including the number of references,the number of cited disciplines,the citation distribution,and the Price index have a significant positive effect on the variety of cross-disciplinary impact.The number of references,the number of cited disciplines,and the citation distribution have significant positive and negative effects on balance and disparity,respectively.It is concluded that the less hard the discipline,the greater the cross-disciplinary impact,the higher balance and the lower disparity of cross-disciplinary impact.Research limitations:In the empirical analysis of HOS,we only included five broad disciplines.This study also has some biases caused by the data source and applied regression models.Practical implications:This study contributes to the formulation of discipline-specific policies and promotes the growth of interdisciplinary research,as well as offering fresh insights for predicting the cross-disciplinary impact of disciplines.Originality/value:This study provides a new perspective to properly understand the mechanisms of cross-disciplinary impact and disciplinary integration.展开更多
Rock fracture mechanisms can be inferred from moment tensors(MT)inverted from microseismic events.However,MT can only be inverted for events whose waveforms are acquired across a network of sensors.This is limiting fo...Rock fracture mechanisms can be inferred from moment tensors(MT)inverted from microseismic events.However,MT can only be inverted for events whose waveforms are acquired across a network of sensors.This is limiting for underground mines where the microseismic stations often lack azimuthal coverage.Thus,there is a need for a method to invert fracture mechanisms using waveforms acquired by a sparse microseismic network.Here,we present a novel,multi-scale framework to classify whether a rock crack contracts or dilates based on a single waveform.The framework consists of a deep learning model that is initially trained on 2400000+manually labelled field-scale seismic and microseismic waveforms acquired across 692 stations.Transfer learning is then applied to fine-tune the model on 300000+MT-labelled labscale acoustic emission waveforms from 39 individual experiments instrumented with different sensor layouts,loading,and rock types in training.The optimal model achieves over 86%F-score on unseen waveforms at both the lab-and field-scale.This model outperforms existing empirical methods in classification of rock fracture mechanisms monitored by a sparse microseismic network.This facilitates rapid assessment of,and early warning against,various rock engineering hazard such as induced earthquakes and rock bursts.展开更多
As a key material for lithium metal batteries(LMBs),lithium metal is one of the most promising anode materials to break the bottleneck of battery energy density and a commonly used active material for reference electr...As a key material for lithium metal batteries(LMBs),lithium metal is one of the most promising anode materials to break the bottleneck of battery energy density and a commonly used active material for reference electrodes.Although lithium anodes are regarded as the holy grail of lithium batteries,decades of exploration have not led to the successful commercialization of LMBs,due mainly to the challenges related to the inherent properties of lithium metal.To pave the way for further investigation,herein,a comprehensive review focusing on the fundamental science of lithium are provided.Firstly,the natures of lithium atoms and their isotopes,lithium clusters and lithium crystals are revisited,especially their structural and energetic properties.Subsequently,the electrochemical properties of lithium metal are reviewed.Numerous important concepts and scientific questions,including the electronic structure of lithium,influence of high pressure and low temperature on the properties of lithium,factors influencing lithium deposition,generation of lithium dendrites,and electrode potential of lithium in different electrolytes,are explained and analyzed in detail.Approaches to improve the performance of lithium anodes and thoughtfulness about the electrode potential in lithium battery research are proposed.展开更多
Chinese Space Station(CSS)has been fully deployed by the end of 2022,and the facility has entered into the application and development phase.It has conducted scientific research projects in various fields,such as spac...Chinese Space Station(CSS)has been fully deployed by the end of 2022,and the facility has entered into the application and development phase.It has conducted scientific research projects in various fields,such as space life science and biotechnology,space materials science,microgravity fundamental physics,fluid physics,combustion science,space new technologies,and applications.In this review,we introduce the progress of CSS development and provide an overview of the research conducted in Chinese Space Station and the recent scientific findings in several typical research fields.Such compelling findings mainly concern the rapid solidification of ultra-high temperature alloy melts,dynamics of fluid transport in space,gravity scaling law of boiling heat transfer,vibration fluidization phenomenon of particulate matter,cold atom interferometer technology under high microgravity and related equivalence principle testing,the full life cycle of rice under microgravity and so forth.Furthermore,the planned scientific research and corresponding prospects of Chinese space station in the next few years are presented.展开更多
The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classificatio...The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.展开更多
Leveraging big data analytics and advanced algorithms to accelerate and optimize the process of molecular and materials design, synthesis, and application has revolutionized the field of molecular and materials scienc...Leveraging big data analytics and advanced algorithms to accelerate and optimize the process of molecular and materials design, synthesis, and application has revolutionized the field of molecular and materials science, allowing researchers to gain a deeper understanding of material properties and behaviors,leading to the development of new materials that are more efficient and reliable. However, the difficulty in constructing large-scale datasets of new molecules/materials due to the high cost of data acquisition and annotation limits the development of conventional machine learning(ML) approaches. Knowledgereused transfer learning(TL) methods are expected to break this dilemma. The application of TL lowers the data requirements for model training, which makes TL stand out in researches addressing data quality issues. In this review, we summarize recent progress in TL related to molecular and materials. We focus on the application of TL methods for the discovery of advanced molecules/materials, particularly, the construction of TL frameworks for different systems, and how TL can enhance the performance of models. In addition, the challenges of TL are also discussed.展开更多
Purpose:Accurately assigning the document type of review articles in citation index databases like Web of Science(WoS)and Scopus is important.This study aims to investigate the document type assignation of review arti...Purpose:Accurately assigning the document type of review articles in citation index databases like Web of Science(WoS)and Scopus is important.This study aims to investigate the document type assignation of review articles in Web of Science,Scopus and Publisher’s websites on a large scale.Design/methodology/approach:27,616 papers from 160 journals from 10 review journal series indexed in SCI are analyzed.The document types of these papers labeled on journals’websites,and assigned by WoS and Scopus are retrieved and compared to determine the assigning accuracy and identify the possible reasons for wrongly assigning.For the document type labeled on the website,we further differentiate them into explicit review and implicit review based on whether the website directly indicates it is a review or not.Findings:Overall,WoS and Scopus performed similarly,with an average precision of about 99% and recall of about 80%.However,there were some differences between WoS and Scopus across different journal series and within the same journal series.The assigning accuracy of WoS and Scopus for implicit reviews dropped significantly,especially for Scopus.Research limitations:The document types we used as the gold standard were based on the journal websites’labeling which were not manually validated one by one.We only studied the labeling performance for review articles published during 2017-2018 in review journals.Whether this conclusion can be extended to review articles published in non-review journals and most current situation is not very clear.Practical implications:This study provides a reference for the accuracy of document type assigning of review articles in WoS and Scopus,and the identified pattern for assigning implicit reviews may be helpful to better labeling on websites,WoS and Scopus.Originality/value:This study investigated the assigning accuracy of document type of reviews and identified the some patterns of wrong assignments.展开更多
In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of ea...In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.展开更多
Named entity recognition(NER)is an important part in knowledge extraction and one of the main tasks in constructing knowledge graphs.In today’s Chinese named entity recognition(CNER)task,the BERT-BiLSTM-CRF model is ...Named entity recognition(NER)is an important part in knowledge extraction and one of the main tasks in constructing knowledge graphs.In today’s Chinese named entity recognition(CNER)task,the BERT-BiLSTM-CRF model is widely used and often yields notable results.However,recognizing each entity with high accuracy remains challenging.Many entities do not appear as single words but as part of complex phrases,making it difficult to achieve accurate recognition using word embedding information alone because the intricate lexical structure often impacts the performance.To address this issue,we propose an improved Bidirectional Encoder Representations from Transformers(BERT)character word conditional random field(CRF)(BCWC)model.It incorporates a pre-trained word embedding model using the skip-gram with negative sampling(SGNS)method,alongside traditional BERT embeddings.By comparing datasets with different word segmentation tools,we obtain enhanced word embedding features for segmented data.These features are then processed using the multi-scale convolution and iterated dilated convolutional neural networks(IDCNNs)with varying expansion rates to capture features at multiple scales and extract diverse contextual information.Additionally,a multi-attention mechanism is employed to fuse word and character embeddings.Finally,CRFs are applied to learn sequence constraints and optimize entity label annotations.A series of experiments are conducted on three public datasets,demonstrating that the proposed method outperforms the recent advanced baselines.BCWC is capable to address the challenge of recognizing complex entities by combining character-level and word-level embedding information,thereby improving the accuracy of CNER.Such a model is potential to the applications of more precise knowledge extraction such as knowledge graph construction and information retrieval,particularly in domain-specific natural language processing tasks that require high entity recognition precision.展开更多
目的分析近年来全球小于胎龄儿(small for gestational age infant,SGA)的研究现状、热点及前沿,梳理该领域发展脉络并预测未来发展趋势,为SGA研究及临床指导提供参考。方法基于Web of Science(WOS)核心合集数据库,选择科学引文索引扩展...目的分析近年来全球小于胎龄儿(small for gestational age infant,SGA)的研究现状、热点及前沿,梳理该领域发展脉络并预测未来发展趋势,为SGA研究及临床指导提供参考。方法基于Web of Science(WOS)核心合集数据库,选择科学引文索引扩展(Science Citation Index Expanded,SCI-EXPANDED)及社会科学引文索引(Social Sciences Citation Index,SSCI),检索式为:(TS=(“small for gestational age children”or“small for gestational age infant”or“small for gestational age”)AND LA=(English)AND DT=(Article OR Review Article)),时间跨度为2012年1月1日至2022年8月16日。运用CiteSpace,v.6.1.R3.64-bit绘制国家、机构、作者及关键词共现图,关键词聚类图及突现图,探究全球SGA研究领域主题演化及热点。结果共纳入6524篇文献,近年来,全球SGA研究文献年度发文量呈波动性上升趋势;综合中介中心性(0.15)和发文量(1859篇)分析,美国在该研究领域占主导优势;高产机构为卡罗林斯学院(Karolinska Institute,瑞典),以226篇文献稳居榜首;高产作者为NICOLAIDES K团队,主要研究方向为探索SGA的危险因素、SGA的早期有效筛查方法及如何降低SGA的患病率。关键词突现图发现,近3年的研究热点主要集中于流行病学研究和基因表达对妊娠结局影响的研究。结论进一步开展临床多中心的SGA分子流行病学和基础研究,揭示SGA患病危险因素,以提高SGA三级预防及治疗效果,仍是产科和儿科医务人员共同努力的研究方向。展开更多
力学是一门重要的基础学科,也是工程科学的先导和基础。科技期刊是科研成果的载体和服务科研的平台。本文以《Journal of Ocean Engineering and Science》为例,通过分析该期刊刊文的统计信息及与力学相关的文章,揭示了在海洋工程科学...力学是一门重要的基础学科,也是工程科学的先导和基础。科技期刊是科研成果的载体和服务科研的平台。本文以《Journal of Ocean Engineering and Science》为例,通过分析该期刊刊文的统计信息及与力学相关的文章,揭示了在海洋工程科学研究中涉及的基础学科主要是力学和数学以及两学科的重要性,探讨了力学等基础学科对工程科学的科研与教学方面的推动作用,进而结合该期刊刊文中有关力学问题的研究,讨论并提出了科技期刊促进科研与教学的建议。展开更多
为深入了解土壤环境中多环芳烃研究的全球状况和前沿动态,客观反映相关国家、机构和个人在该领域的科学能力和影响,采用ISI Web of Knowledge的Web of Science引文数据库,对1900—2014年间该库收录的土壤环境中多环芳烃研究领域的相关...为深入了解土壤环境中多环芳烃研究的全球状况和前沿动态,客观反映相关国家、机构和个人在该领域的科学能力和影响,采用ISI Web of Knowledge的Web of Science引文数据库,对1900—2014年间该库收录的土壤环境中多环芳烃研究领域的相关文献进行了计量分析。结果表明,该领域全球发文量总体呈持续快速上升趋势,美国的发文量、总被引频次和H指数均居榜首,中国的发文量和总被引频次居次席,但篇均被引频次明显偏低;中国科学院和英国兰卡斯特大学的发文量和H指数居研究机构的前两位,篇均被引频次排名最高的研究机构是美国马萨诸塞大学;英国兰卡斯特大学的Jones K C等两位学者发文量和H指数最高,北达克科特大学Hawthorne S B的篇均被引频次最高,国内学者中北京大学的陶澍和浙江大学的朱利中最有影响力;该领域的主要期刊有Environmental Science&Technology、Chemosphere和Environmental Pollution等;该领域的研究热点多集中于土壤环境中多环芳烃的降解及生物修复、多环芳烃在各介质中的溶解与吸附、以及多环芳烃的源解析等方向,"中国"相关研究在近5年中占重要地位。展开更多
文摘为全面了解土壤胶体影响重金属行为方向的研究现状和前沿动态,基于Web of Science(WoS)核心合集数据库,利用WoS自带分析工具、HistCite引文图谱分析软件、VOSviewer和Citespace可视化分析软件对1990—2021年间土壤胶体影响重金属行为的文献进行了计量分析。结果表明,在世界范围内该方向的发文量逐年稳步增长,我国相关研究起步较晚,但近些年呈现迅猛发展的势头。目前土壤胶体影响重金属行为研究发文量最多的国家和研究机构分别是美国和中国科学院,发文量最高的期刊为Environmental Science&Technology,主要研究学科为环境科学与生态学的交叉学科。关键词聚类分析显示“土壤胶体颗粒粒径分级与重金属的形态分布”、“土壤胶体的释放、沉积及对重金属的吸附作用”和“土壤胶体颗粒的迁移机制与迁移模型研究”为主要的研究主题,人工纳米颗粒在土壤中的行为、迁移转化以及生物有效性是现阶段的研究热点。利用场流分离技术结合单粒子电感耦合等离子体质谱等技术,探讨土壤胶体与人工纳米颗粒之间发生的复杂相互作用及其对人工纳米颗粒迁移归趋与环境命运的影响,是未来的主要研究方向。
文摘为了解淀粉多酚相互作用的研究现状和发展趋势,采用文献计量学方法,以Web of Science核心合集数据库出版日期为2010-01-01至2024-04-18的论文为研究对象,进行可视化分析,为相关学科的研究提供参考。结果显示:淀粉多酚相互作用研究领域的年发文量总体呈上升趋势;中国研究机构在该领域发文数量优势明显,但在影响力方面与新西兰、澳大利亚和西班牙等国的研究机构相比还有一定差距;该领域研究热点主要集中在食品科学技术与化学学科方向,但同时也展示出多学科交叉融合的发展趋势。消化、特性、相互作用、生物利用度、抗菌活性、分子对接、复合物、纳米颗粒、活性膜等方面是该领域的研究前沿。
文摘Purpose:The goal of this study is to analyze the relationship between funded and unfunded papers and their citations in both basic and applied sciences.Design/methodology/approach:A power law model analyzes the relationship between research funding and citations of papers using 831,337 documents recorded in the Web of Science database.Findings:The original results reveal general characteristics of the diffusion of science in research fields:a)Funded articles receive higher citations compared to unfunded papers in journals;b)Funded articles exhibit a super-linear growth in citations,surpassing the increase seen in unfunded articles.This finding reveals a higher diffusion of scientific knowledge in funded articles.Moreover,c)funded articles in both basic and applied sciences demonstrate a similar expected change in citations,equivalent to about 1.23%,when the number of funded papers increases by 1%in journals.This result suggests,for the first time,that funding effect of scientific research is an invariant driver,irrespective of the nature of the basic or applied sciences.Originality/value:This evidence suggests empirical laws of funding for scientific citations that explain the importance of robust funding mechanisms for achieving impactful research outcomes in science and society.These findings here also highlight that funding for scientific research is a critical driving force in supporting citations and the dissemination of scientific knowledge in recorded documents in both basic and applied sciences.Practical implications:This comprehensive result provides a holistic view of the relationship between funding and citation performance in science to guide policymakers and R&D managers with science policies by directing funding to research in promoting the scientific development and higher diffusion of results for the progress of human society.
基金This study was supported by the National Natural Science Foundation of China(U22B2075,52274056,51974356).
文摘A large number of nanopores and complex fracture structures in shale reservoirs results in multi-scale flow of oil. With the development of shale oil reservoirs, the permeability of multi-scale media undergoes changes due to stress sensitivity, which plays a crucial role in controlling pressure propagation and oil flow. This paper proposes a multi-scale coupled flow mathematical model of matrix nanopores, induced fractures, and hydraulic fractures. In this model, the micro-scale effects of shale oil flow in fractal nanopores, fractal induced fracture network, and stress sensitivity of multi-scale media are considered. We solved the model iteratively using Pedrosa transform, semi-analytic Segmented Bessel function, Laplace transform. The results of this model exhibit good agreement with the numerical solution and field production data, confirming the high accuracy of the model. As well, the influence of stress sensitivity on permeability, pressure and production is analyzed. It is shown that the permeability and production decrease significantly when induced fractures are weakly supported. Closed induced fractures can inhibit interporosity flow in the stimulated reservoir volume (SRV). It has been shown in sensitivity analysis that hydraulic fractures are beneficial to early production, and induced fractures in SRV are beneficial to middle production. The model can characterize multi-scale flow characteristics of shale oil, providing theoretical guidance for rapid productivity evaluation.
基金Supported by Consultation and Evaluation Program on Academic Divisions of the Chinese Academy of Sciences(2022-DX02-B-007)。
文摘Since 2011,the Chinese Academy of Sciences(CAS)has implemented the Strategic Priority Program on Space Science(SPP).A series of scientific satellites have been developed and launched,such as Dark Matter Particle Explorer(DAMPE),Quantum Experiments at Space Scale(QUESS),Advanced Space-based Solar Observatory(ASO-S),Einstein Probe(EP),and significant scientific outcomes have been achieved.In order to plan the future space science missions in China,CAS has organized the Chinese space science community to conduct medium and long-term development strategy studies,and summarized the major scientific frontiers of space science as“One Black,Two Dark,Three Origins and Five Characterizations”.Five main scientific themes have been identified for China’s future breakthroughs,including the Extreme Universe,Space-Time Ripples,the Panoramic View of the Sun and Earth,the Habitable Planets,and Biological&Physical Science in Space.Space science satellite missions to be implemented before 2030 are proposed accordingly.
文摘沉香是我国传统名贵南药,广泛应用于医药、香水、香料、宗教等方面,现已形成了工业、文化产业和农业三大类产业,市场价值极高。本研究在中国知网、维普网和万方数据库中以“沉香结香”为主题词检索,导出1978—2022年发表的文章,利用CiteSpace软件进行可视化分析;在Web of Science以“agarwood production method”为检索词,可视化分析1978—2022年发表的外文文献。结果表明,国内沉香结香的研究一共分为3个阶段,1978—2007年为萌芽阶段,仅有零星文章产出;2008—2017年为快速发展阶段,大量文章产出并在2017年达到顶峰(41篇);2018—2022年为稳定阶段,持续产出文章并维持在年均20篇以上;有关沉香结香机理的研究发文量前10的期刊中,90%等级均在科技核心以上,排在前10的研究结构均处于沉香产业发达地区。国外沉香结香的研究起源于2008年,整体发文量趋势逐年增加;被引量前10的期刊均为SCI;研究量前10的国家均为GDP前列的国家。有关沉香自然结香和人工结香差异分析的研究成为当前研究热点,未来如何利用2种不同沉香结香方式的成分差异发展沉香资源,将会是新的研究趋势。本研究结果将为科研人员研究提供进一步参考方向,也为生产种植者提供沉香结香参考。
文摘在AI for Science时代,电池设计自动化智能研发(battery design automation,BDA)平台通过整合先进的人工智能技术,为电池研发领域带来了革命性进展。BDA平台覆盖了文献调研、实验设计、合成制备、表征测试和分析优化这五个电池研发的关键环节,利用机器学习、多尺度建模、预训练模型等先进算法,结合软件工程开发用户交互友好的工具,加速从理论设计到实验验证的整个电池研发周期。通过自动化的实验设计、合成制备、表征测试和性能优化,BDA平台不仅提升了研发效率,还提高了电池设计的精确度和可靠性,推动了电池技术向更高能量密度、更长循环寿命和更低成本的方向发展。
基金funded by the National Natural Science Foundation of China(NSFC)Grant Nos.71921002 and 72174154.
文摘Purpose:Interdisciplinary fields have become the driving force of modern science and a significant source of scientific innovation.However,there is still a paucity of analysis about the essential characteristics of disciplines’cross-disciplinary impact.Design/methodology/approach:In this study,we define cross-disciplinary impact on one discipline as its impact to other disciplines,and refer to a three-dimensional framework of variety-balance-disparity to characterize the structure of cross-disciplinary impact.The variety of cross-disciplinary impact of the discipline was defined as the proportion of the high cross-disciplinary impact publications,and the balance and disparity of cross-disciplinary impact were measured as well.To demonstrate the cross-disciplinary impact of the disciplines in science,we chose Microsoft Academic Graph(MAG)as the data source,and investigated the relationship between disciplines’cross-disciplinary impact and their positions in the Hierarchy of Science(HOS).Findings:Analytical results show that there is a significant correlation between the ranking of cross-disciplinary impact and the HOS structure,and that the discipline exerts a greater cross-disciplinary impact on its neighboring disciplines.Several bibliometric features that measure the hardness of a discipline,including the number of references,the number of cited disciplines,the citation distribution,and the Price index have a significant positive effect on the variety of cross-disciplinary impact.The number of references,the number of cited disciplines,and the citation distribution have significant positive and negative effects on balance and disparity,respectively.It is concluded that the less hard the discipline,the greater the cross-disciplinary impact,the higher balance and the lower disparity of cross-disciplinary impact.Research limitations:In the empirical analysis of HOS,we only included five broad disciplines.This study also has some biases caused by the data source and applied regression models.Practical implications:This study contributes to the formulation of discipline-specific policies and promotes the growth of interdisciplinary research,as well as offering fresh insights for predicting the cross-disciplinary impact of disciplines.Originality/value:This study provides a new perspective to properly understand the mechanisms of cross-disciplinary impact and disciplinary integration.
基金supported by Western Research Interdisciplinary Initiative R6259A03.
文摘Rock fracture mechanisms can be inferred from moment tensors(MT)inverted from microseismic events.However,MT can only be inverted for events whose waveforms are acquired across a network of sensors.This is limiting for underground mines where the microseismic stations often lack azimuthal coverage.Thus,there is a need for a method to invert fracture mechanisms using waveforms acquired by a sparse microseismic network.Here,we present a novel,multi-scale framework to classify whether a rock crack contracts or dilates based on a single waveform.The framework consists of a deep learning model that is initially trained on 2400000+manually labelled field-scale seismic and microseismic waveforms acquired across 692 stations.Transfer learning is then applied to fine-tune the model on 300000+MT-labelled labscale acoustic emission waveforms from 39 individual experiments instrumented with different sensor layouts,loading,and rock types in training.The optimal model achieves over 86%F-score on unseen waveforms at both the lab-and field-scale.This model outperforms existing empirical methods in classification of rock fracture mechanisms monitored by a sparse microseismic network.This facilitates rapid assessment of,and early warning against,various rock engineering hazard such as induced earthquakes and rock bursts.
基金gratitude to the National Natural Science Foundation of China(No.22279070,U21A20170,22279071 and 52206263)the Ministry of Science and Technology of China(No.2019YFA0705703 and 2019YFE0100200)The authors thank Joint Work Plan for Research Projects under the Clean Vehicles Consortium at U.S.and China-Clean Energy Research Center(CERCCVC2.0,2016-2020)。
文摘As a key material for lithium metal batteries(LMBs),lithium metal is one of the most promising anode materials to break the bottleneck of battery energy density and a commonly used active material for reference electrodes.Although lithium anodes are regarded as the holy grail of lithium batteries,decades of exploration have not led to the successful commercialization of LMBs,due mainly to the challenges related to the inherent properties of lithium metal.To pave the way for further investigation,herein,a comprehensive review focusing on the fundamental science of lithium are provided.Firstly,the natures of lithium atoms and their isotopes,lithium clusters and lithium crystals are revisited,especially their structural and energetic properties.Subsequently,the electrochemical properties of lithium metal are reviewed.Numerous important concepts and scientific questions,including the electronic structure of lithium,influence of high pressure and low temperature on the properties of lithium,factors influencing lithium deposition,generation of lithium dendrites,and electrode potential of lithium in different electrolytes,are explained and analyzed in detail.Approaches to improve the performance of lithium anodes and thoughtfulness about the electrode potential in lithium battery research are proposed.
文摘Chinese Space Station(CSS)has been fully deployed by the end of 2022,and the facility has entered into the application and development phase.It has conducted scientific research projects in various fields,such as space life science and biotechnology,space materials science,microgravity fundamental physics,fluid physics,combustion science,space new technologies,and applications.In this review,we introduce the progress of CSS development and provide an overview of the research conducted in Chinese Space Station and the recent scientific findings in several typical research fields.Such compelling findings mainly concern the rapid solidification of ultra-high temperature alloy melts,dynamics of fluid transport in space,gravity scaling law of boiling heat transfer,vibration fluidization phenomenon of particulate matter,cold atom interferometer technology under high microgravity and related equivalence principle testing,the full life cycle of rice under microgravity and so forth.Furthermore,the planned scientific research and corresponding prospects of Chinese space station in the next few years are presented.
基金funded by the Informatization Plan of Chinese Academy of Sciences(Grant No.CASWX2021SF-0102)the National Key R&D Program of China(Grant Nos.2022YFA1603903,2022YFA1403800,and 2021YFA0718700)+1 种基金the National Natural Science Foundation of China(Grant Nos.11925408,11921004,and 12188101)the Chinese Academy of Sciences(Grant No.XDB33000000)。
文摘The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.
基金National Key R&D Program of China (No. 2021YFC2100100)Shanghai Science and Technology Project (No. 21JC1403400, 23JC1402300)。
文摘Leveraging big data analytics and advanced algorithms to accelerate and optimize the process of molecular and materials design, synthesis, and application has revolutionized the field of molecular and materials science, allowing researchers to gain a deeper understanding of material properties and behaviors,leading to the development of new materials that are more efficient and reliable. However, the difficulty in constructing large-scale datasets of new molecules/materials due to the high cost of data acquisition and annotation limits the development of conventional machine learning(ML) approaches. Knowledgereused transfer learning(TL) methods are expected to break this dilemma. The application of TL lowers the data requirements for model training, which makes TL stand out in researches addressing data quality issues. In this review, we summarize recent progress in TL related to molecular and materials. We focus on the application of TL methods for the discovery of advanced molecules/materials, particularly, the construction of TL frameworks for different systems, and how TL can enhance the performance of models. In addition, the challenges of TL are also discussed.
文摘Purpose:Accurately assigning the document type of review articles in citation index databases like Web of Science(WoS)and Scopus is important.This study aims to investigate the document type assignation of review articles in Web of Science,Scopus and Publisher’s websites on a large scale.Design/methodology/approach:27,616 papers from 160 journals from 10 review journal series indexed in SCI are analyzed.The document types of these papers labeled on journals’websites,and assigned by WoS and Scopus are retrieved and compared to determine the assigning accuracy and identify the possible reasons for wrongly assigning.For the document type labeled on the website,we further differentiate them into explicit review and implicit review based on whether the website directly indicates it is a review or not.Findings:Overall,WoS and Scopus performed similarly,with an average precision of about 99% and recall of about 80%.However,there were some differences between WoS and Scopus across different journal series and within the same journal series.The assigning accuracy of WoS and Scopus for implicit reviews dropped significantly,especially for Scopus.Research limitations:The document types we used as the gold standard were based on the journal websites’labeling which were not manually validated one by one.We only studied the labeling performance for review articles published during 2017-2018 in review journals.Whether this conclusion can be extended to review articles published in non-review journals and most current situation is not very clear.Practical implications:This study provides a reference for the accuracy of document type assigning of review articles in WoS and Scopus,and the identified pattern for assigning implicit reviews may be helpful to better labeling on websites,WoS and Scopus.Originality/value:This study investigated the assigning accuracy of document type of reviews and identified the some patterns of wrong assignments.
文摘In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.
基金supported by the International Research Center of Big Data for Sustainable Development Goals under Grant No.CBAS2022GSP05the Open Fund of State Key Laboratory of Remote Sensing Science under Grant No.6142A01210404the Hubei Key Laboratory of Intelligent Geo-Information Processing under Grant No.KLIGIP-2022-B03.
文摘Named entity recognition(NER)is an important part in knowledge extraction and one of the main tasks in constructing knowledge graphs.In today’s Chinese named entity recognition(CNER)task,the BERT-BiLSTM-CRF model is widely used and often yields notable results.However,recognizing each entity with high accuracy remains challenging.Many entities do not appear as single words but as part of complex phrases,making it difficult to achieve accurate recognition using word embedding information alone because the intricate lexical structure often impacts the performance.To address this issue,we propose an improved Bidirectional Encoder Representations from Transformers(BERT)character word conditional random field(CRF)(BCWC)model.It incorporates a pre-trained word embedding model using the skip-gram with negative sampling(SGNS)method,alongside traditional BERT embeddings.By comparing datasets with different word segmentation tools,we obtain enhanced word embedding features for segmented data.These features are then processed using the multi-scale convolution and iterated dilated convolutional neural networks(IDCNNs)with varying expansion rates to capture features at multiple scales and extract diverse contextual information.Additionally,a multi-attention mechanism is employed to fuse word and character embeddings.Finally,CRFs are applied to learn sequence constraints and optimize entity label annotations.A series of experiments are conducted on three public datasets,demonstrating that the proposed method outperforms the recent advanced baselines.BCWC is capable to address the challenge of recognizing complex entities by combining character-level and word-level embedding information,thereby improving the accuracy of CNER.Such a model is potential to the applications of more precise knowledge extraction such as knowledge graph construction and information retrieval,particularly in domain-specific natural language processing tasks that require high entity recognition precision.
文摘目的分析近年来全球小于胎龄儿(small for gestational age infant,SGA)的研究现状、热点及前沿,梳理该领域发展脉络并预测未来发展趋势,为SGA研究及临床指导提供参考。方法基于Web of Science(WOS)核心合集数据库,选择科学引文索引扩展(Science Citation Index Expanded,SCI-EXPANDED)及社会科学引文索引(Social Sciences Citation Index,SSCI),检索式为:(TS=(“small for gestational age children”or“small for gestational age infant”or“small for gestational age”)AND LA=(English)AND DT=(Article OR Review Article)),时间跨度为2012年1月1日至2022年8月16日。运用CiteSpace,v.6.1.R3.64-bit绘制国家、机构、作者及关键词共现图,关键词聚类图及突现图,探究全球SGA研究领域主题演化及热点。结果共纳入6524篇文献,近年来,全球SGA研究文献年度发文量呈波动性上升趋势;综合中介中心性(0.15)和发文量(1859篇)分析,美国在该研究领域占主导优势;高产机构为卡罗林斯学院(Karolinska Institute,瑞典),以226篇文献稳居榜首;高产作者为NICOLAIDES K团队,主要研究方向为探索SGA的危险因素、SGA的早期有效筛查方法及如何降低SGA的患病率。关键词突现图发现,近3年的研究热点主要集中于流行病学研究和基因表达对妊娠结局影响的研究。结论进一步开展临床多中心的SGA分子流行病学和基础研究,揭示SGA患病危险因素,以提高SGA三级预防及治疗效果,仍是产科和儿科医务人员共同努力的研究方向。
文摘力学是一门重要的基础学科,也是工程科学的先导和基础。科技期刊是科研成果的载体和服务科研的平台。本文以《Journal of Ocean Engineering and Science》为例,通过分析该期刊刊文的统计信息及与力学相关的文章,揭示了在海洋工程科学研究中涉及的基础学科主要是力学和数学以及两学科的重要性,探讨了力学等基础学科对工程科学的科研与教学方面的推动作用,进而结合该期刊刊文中有关力学问题的研究,讨论并提出了科技期刊促进科研与教学的建议。
文摘为深入了解土壤环境中多环芳烃研究的全球状况和前沿动态,客观反映相关国家、机构和个人在该领域的科学能力和影响,采用ISI Web of Knowledge的Web of Science引文数据库,对1900—2014年间该库收录的土壤环境中多环芳烃研究领域的相关文献进行了计量分析。结果表明,该领域全球发文量总体呈持续快速上升趋势,美国的发文量、总被引频次和H指数均居榜首,中国的发文量和总被引频次居次席,但篇均被引频次明显偏低;中国科学院和英国兰卡斯特大学的发文量和H指数居研究机构的前两位,篇均被引频次排名最高的研究机构是美国马萨诸塞大学;英国兰卡斯特大学的Jones K C等两位学者发文量和H指数最高,北达克科特大学Hawthorne S B的篇均被引频次最高,国内学者中北京大学的陶澍和浙江大学的朱利中最有影响力;该领域的主要期刊有Environmental Science&Technology、Chemosphere和Environmental Pollution等;该领域的研究热点多集中于土壤环境中多环芳烃的降解及生物修复、多环芳烃在各介质中的溶解与吸附、以及多环芳烃的源解析等方向,"中国"相关研究在近5年中占重要地位。
