目的:对比三维多回波恢复梯度回波(3D MERGE)、三维可变反转角快速自旋回波(3D SPACE STIR)序列在腰椎间盘突出症(LDH)检查中的应用效果。方法:选择2020年1月~2022年11月收治的135例LDH患者,回顾性分析患者临床和磁共振成像(MRI)资料,...目的:对比三维多回波恢复梯度回波(3D MERGE)、三维可变反转角快速自旋回波(3D SPACE STIR)序列在腰椎间盘突出症(LDH)检查中的应用效果。方法:选择2020年1月~2022年11月收治的135例LDH患者,回顾性分析患者临床和磁共振成像(MRI)资料,所有患者均接受常规MRI扫描及3D MERGE、3D SPACE STIR序列扫描,对比3D MERGE、3D SPACE STIR序列测量神经根直径的一致性,评价两种序列的图像质量参数[信噪比(SNR)、对比噪声比(CNR)]、图像清晰度评分。结果:3D MERGE和3D SPACE STIR序列测量的L3~S1神经根直径比较差异无统计学意义(P>0.05),且两组序列测量的L3、L4、L5和S1直径均显示出较高相关性(r=0.957,0.986,0.975,0.972,P<0.05);3D MERGE序列的SNR及CNR均高于3D SPACE STIR序列,神经根显示分级、图像清晰度评分优于3D SPACE STIR序列,差异有统计学意义(P<0.05)。结论:3D MERGE、3D SPACE STIR序列在LDH神经根直径测量中具有极高一致性,3D MERGE序列较3D SPACE STIR序列能够更清晰显示神经跟的解剖形态,图像质量更好。展开更多
目的梳理国内多发伤急救相关研究文献,分析研究现状、热点和趋势,为我国多发伤急救研究提供借鉴和指导。方法检索中国知网数据库中2011—2021年关于多发伤急救的相关文献,使用Cite Space 6.1.R3可视化软件对该领域的年发文量、机构、作...目的梳理国内多发伤急救相关研究文献,分析研究现状、热点和趋势,为我国多发伤急救研究提供借鉴和指导。方法检索中国知网数据库中2011—2021年关于多发伤急救的相关文献,使用Cite Space 6.1.R3可视化软件对该领域的年发文量、机构、作者、关键词进行分析。结果最终纳入多发伤急救研究文献2519篇,整体发文数量较平稳,以2016年为小高峰;发文量最高的机构是华中科技大学附属同济医院。多发伤急救研究热点包括院前急救、并发症护理、风险因素分析和预后效果评估,研究前沿包括不同多发伤人群的诊断、治疗、手术和护理体会等方面。结论本文通过可视化分析国内多发伤急救研究的热点及趋势,指明了多发伤目前研究存在的问题和未来研究发展的方向,为进一步完善多发伤急救卫生服务和管理体系提供指导。展开更多
高质量教师是高质量教育发展的中坚力量。教师信念作为教师专业素养构成的关键要素,对促进教师专业发展、提升教师质量具有重要作用与影响。为借鉴国际体育教师信念研究的成果与经验,促进国内对体育教师信念的研究,研究利用CiteSpace软...高质量教师是高质量教育发展的中坚力量。教师信念作为教师专业素养构成的关键要素,对促进教师专业发展、提升教师质量具有重要作用与影响。为借鉴国际体育教师信念研究的成果与经验,促进国内对体育教师信念的研究,研究利用CiteSpace软件,对Web of Science核心合集数据库中1960—2022年的英文文献进行可视化研究。发现:体育教师信念研究高潮出现于2021年,载文数量最多的期刊是Journal of Teaching in Physical Education;研究中心度最高的国家是美国,核心圈层的代表学者是Richards KAR、Kulinna PH和Curtner-smith MD等人;研究热点趋势集中于体力活动促进、职业社会化、批判性教学法、职前体育教师、专业发展等方面。启示:国内未来研究应重点关注体育教师信念对课程改革的影响以及促进职前、职后阶段体育教师信念的发展。展开更多
近年来,我国生物产业快速发展,且发酵对身体和胃肠道健康具有很多益处,有关微生物发酵的研究逐渐增多。为探讨发酵领域近5年的研究现状与发展前景,对中国知网和Web of Science数据库中2019—2023年发表的文献进行检索并筛选,使用Cite Sp...近年来,我国生物产业快速发展,且发酵对身体和胃肠道健康具有很多益处,有关微生物发酵的研究逐渐增多。为探讨发酵领域近5年的研究现状与发展前景,对中国知网和Web of Science数据库中2019—2023年发表的文献进行检索并筛选,使用Cite Space软件对文章发文量、研究团队、研究热点进行图谱分析。结果表明,近5年国内发酵领域发文量偏少,年发文量平稳上升;国外发文量较多,年发文量超过100篇,2023年发文量达到200篇;国内外研究群体主要是中医药高校和研究机构;国内外研究热点集中在抗氧化和肠道菌群,国内研究重点为发酵技术,而国外研究范围较广,重点为益生菌的研究;国内外学者间合作较为密切,但研究机构间尚未形成紧密的合作关系。高校和研究机构应突破地区间的限制,促进发酵研究领域的融合。肠道菌群是近5年发酵的研究热点,也是未来发酵研究深入的对象。展开更多
为了解国内外近10年护理不良事件的研究热点与前沿,为医院管理和临床护士提供参考,保障患者安全,通过检索中国期刊全文数据库(CNKI)和Web of Science(WOS)数据库,检索时限为2012年1月—2022年8月,导入CiteSpace进行可视化分析。研究显示...为了解国内外近10年护理不良事件的研究热点与前沿,为医院管理和临床护士提供参考,保障患者安全,通过检索中国期刊全文数据库(CNKI)和Web of Science(WOS)数据库,检索时限为2012年1月—2022年8月,导入CiteSpace进行可视化分析。研究显示,从发文量看,国内外学者对护理不良事件的关注度整体较高,可视化分析结果并未发现作者之间、机构之间的联系。未来减少护理不良事件的方法一定会越来越紧贴科学技术,越来越多的新型信息化平台、App等会用于医疗行业,降低护理不良事件预防的难度,但护理人员仍要重视护理安全。展开更多
This study introduces a pre-orthogonal adaptive Fourier decomposition(POAFD)to obtain approximations and numerical solutions to the fractional Laplacian initial value problem and the extension problem of Caffarelli an...This study introduces a pre-orthogonal adaptive Fourier decomposition(POAFD)to obtain approximations and numerical solutions to the fractional Laplacian initial value problem and the extension problem of Caffarelli and Silvestre(generalized Poisson equation).As a first step,the method expands the initial data function into a sparse series of the fundamental solutions with fast convergence,and,as a second step,makes use of the semigroup or the reproducing kernel property of each of the expanding entries.Experiments show the effectiveness and efficiency of the proposed series solutions.展开更多
Deep learning has been a catalyst for a transformative revo-lution in machine learning and computer vision in the past decade.Within these research domains,methods grounded in deep learning have exhibited exceptional ...Deep learning has been a catalyst for a transformative revo-lution in machine learning and computer vision in the past decade.Within these research domains,methods grounded in deep learning have exhibited exceptional performance across a spectrum of tasks.The success of deep learning methods can be attributed to their capability to derive potent representations from data,integral for a myriad of downstream applications.These representations encapsulate the intrinsic structure,fea-tures,or latent variables characterising the underlying statistics of visual data.Despite these achievements,the challenge per-sists in effectively conducting representation learning of visual data with deep models,particularly when confronted with vast and noisy datasets.This special issue is a dedicated platform for researchers worldwide to disseminate their latest,high-quality articles,aiming to enhance readers'comprehension of the principles,limitations,and diverse applications of repre-sentation learning in computer vision.展开更多
As the development of 5G technology and new media platform,UGC and the participation culture appears.Short video platform such as Kuaishou has the characterist of low production threshold,easy to use,attracting a grea...As the development of 5G technology and new media platform,UGC and the participation culture appears.Short video platform such as Kuaishou has the characterist of low production threshold,easy to use,attracting a great number of users.A great number of internet celebries appears including rural wanghong.In the traditional media era,the communication mode is one way,farmers,especially rural women,are always on the margins of mainstream media discourse.However,the appearance of short video platform gives them right of spearking,in the space conducted by short video platform contains complex Ideology and social structure which also restrict the online space practice of users.Thus,rural female users’online practice is a kind of dynamic balance following the spatial discipline.This study will analyze the support and constraints of rural female wanghong’s online representation,figure out the relationship of online representation and their traditional rural culture.It provides theoretical suggestions for harmonious coexistence among rural women,social media platforms,the state and society.展开更多
Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurr...Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.展开更多
In this article,we investigate the(big) Hankel operator H_(f) on the Hardy spaces of bounded strongly pseudoconvex domains Ω in C^(n).We observe that H_(f ) is bounded on H~p(Ω)(1 <p <∞) if f belongs to BMO a...In this article,we investigate the(big) Hankel operator H_(f) on the Hardy spaces of bounded strongly pseudoconvex domains Ω in C^(n).We observe that H_(f ) is bounded on H~p(Ω)(1 <p <∞) if f belongs to BMO and we obtain some characterizations for Hf on H^(2)(Ω) of other pseudoconvex domains.In these arguments,Amar's L^(p)-estimations and Berndtsson's L^(2)-estimations for solutions of the ■_(b)-equation play a crucial role.In addition,we solve Gleason's problem for Hardy spaces H^(p)(Ω)(1 ≤p≤∞) of bounded strongly pseudoconvex domains.展开更多
3D printing stands at the forefront of transforming space exploration,offering unprecedented on-demand and rapid manufacturing capabilities.It adeptly addresses challenges such as mass reduction,intricate component fa...3D printing stands at the forefront of transforming space exploration,offering unprecedented on-demand and rapid manufacturing capabilities.It adeptly addresses challenges such as mass reduction,intricate component fabrication,and resource constraints.Despite the obstacles posed by microgravity and extreme environments,continual advancements underscore the pivotal role of 3D printing in aerospace science.Beyond its primary function of producing space structures,3D printing contributes significantly to progress in electronics,biomedicine,and resource optimization.This perspective delves into the technological advantages,environmental challenges,development status,and opportunities of 3D printing in space.Envisioning its crucial impact,we anticipate that 3D printing will unlock innovative solutions,reshape manufacturing practices,and foster self-sufficiency in future space endeavors.展开更多
Space metallurgy is an interdisciplinary field that combines planetary space science and metallurgical engineering.It involves systematic and theoretical engineering technology for utilizing planetary resources in sit...Space metallurgy is an interdisciplinary field that combines planetary space science and metallurgical engineering.It involves systematic and theoretical engineering technology for utilizing planetary resources in situ.However,space metallurgy on the Moon is challenging because the lunar surface has experienced space weathering due to the lack of atmosphere and magnetic field,making the mi-crostructure of lunar soil differ from that of minerals on the Earth.In this study,scanning electron microscopy and transmission electron microscopy analyses were performed on Chang’e-5 powder lunar soil samples.The microstructural characteristics of the lunar soil may drastically change its metallurgical performance.The main special structure of lunar soil minerals include the nanophase iron formed by the impact of micrometeorites,the amorphous layer caused by solar wind injection,and radiation tracks modified by high-energy particle rays inside mineral crystals.The nanophase iron presents a wide distribution,which may have a great impact on the electromagnetic prop-erties of lunar soil.Hydrogen ions injected by solar wind may promote the hydrogen reduction process.The widely distributed amorph-ous layer and impact glass can promote the melting and diffusion process of lunar soil.Therefore,although high-energy events on the lun-ar surface transform the lunar soil,they also increase the chemical activity of the lunar soil.This is a property that earth samples and tradi-tional simulated lunar soil lack.The application of space metallurgy requires comprehensive consideration of the unique physical and chemical properties of lunar soil.展开更多
Edge devices,due to their limited computational and storage resources,often require the use of compilers for program optimization.Therefore,ensuring the security and reliability of these compilers is of paramount impo...Edge devices,due to their limited computational and storage resources,often require the use of compilers for program optimization.Therefore,ensuring the security and reliability of these compilers is of paramount importance in the emerging field of edge AI.One widely used testing method for this purpose is fuzz testing,which detects bugs by inputting random test cases into the target program.However,this process consumes significant time and resources.To improve the efficiency of compiler fuzz testing,it is common practice to utilize test case prioritization techniques.Some researchers use machine learning to predict the code coverage of test cases,aiming to maximize the test capability for the target compiler by increasing the overall predicted coverage of the test cases.Nevertheless,these methods can only forecast the code coverage of the compiler at a specific optimization level,potentially missing many optimization-related bugs.In this paper,we introduce C-CORE(short for Clustering by Code Representation),the first framework to prioritize test cases according to their code representations,which are derived directly from the source codes.This approach avoids being limited to specific compiler states and extends to a broader range of compiler bugs.Specifically,we first train a scaled pre-trained programming language model to capture as many common features as possible from the test cases generated by a fuzzer.Using this pre-trained model,we then train two downstream models:one for predicting the likelihood of triggering a bug and another for identifying code representations associated with bugs.Subsequently,we cluster the test cases according to their code representations and select the highest-scoring test case from each cluster as the high-quality test case.This reduction in redundant testing cases leads to time savings.Comprehensive evaluation results reveal that code representations are better at distinguishing test capabilities,and C-CORE significantly enhances testing efficiency.Across four datasets,C-CORE increases the average of the percentage of faults detected(APFD)value by 0.16 to 0.31 and reduces test time by over 50% in 46% of cases.When compared to the best results from approaches using predicted code coverage,C-CORE improves the APFD value by 1.1% to 12.3% and achieves an overall time-saving of 159.1%.展开更多
Sparse representation is an effective data classification algorithm that depends on the known training samples to categorise the test sample.It has been widely used in various image classification tasks.Sparseness in ...Sparse representation is an effective data classification algorithm that depends on the known training samples to categorise the test sample.It has been widely used in various image classification tasks.Sparseness in sparse representation means that only a few of instances selected from all training samples can effectively convey the essential class-specific information of the test sample,which is very important for classification.For deformable images such as human faces,pixels at the same location of different images of the same subject usually have different intensities.Therefore,extracting features and correctly classifying such deformable objects is very hard.Moreover,the lighting,attitude and occlusion cause more difficulty.Considering the problems and challenges listed above,a novel image representation and classification algorithm is proposed.First,the authors’algorithm generates virtual samples by a non-linear variation method.This method can effectively extract the low-frequency information of space-domain features of the original image,which is very useful for representing deformable objects.The combination of the original and virtual samples is more beneficial to improve the clas-sification performance and robustness of the algorithm.Thereby,the authors’algorithm calculates the expression coefficients of the original and virtual samples separately using the sparse representation principle and obtains the final score by a designed efficient score fusion scheme.The weighting coefficients in the score fusion scheme are set entirely automatically.Finally,the algorithm classifies the samples based on the final scores.The experimental results show that our method performs better classification than conventional sparse representation algorithms.展开更多
Assume that L is a non-negative self-adjoint operator on L^(2)(ℝ^(n))with its heat kernels satisfying the so-called Gaussian upper bound estimate and that X is a ball quasi-Banach function space onℝ^(n) satisfying som...Assume that L is a non-negative self-adjoint operator on L^(2)(ℝ^(n))with its heat kernels satisfying the so-called Gaussian upper bound estimate and that X is a ball quasi-Banach function space onℝ^(n) satisfying some mild assumptions.Let HX,L(ℝ^(n))be the Hardy space associated with both X and L,which is defined by the Lusin area function related to the semigroup generated by L.In this article,the authors establish various maximal function characterizations of the Hardy space HX,L(ℝ^(n))and then apply these characterizations to obtain the solvability of the related Cauchy problem.These results have a wide range of generality and,in particular,the specific spaces X to which these results can be applied include the weighted space,the variable space,the mixed-norm space,the Orlicz space,the Orlicz-slice space,and the Morrey space.Moreover,the obtained maximal function characterizations of the mixed-norm Hardy space,the Orlicz-slice Hardy space,and the Morrey-Hardy space associated with L are completely new.展开更多
Prior studies have demonstrated that deep learning-based approaches can enhance the performance of source code vulnerability detection by training neural networks to learn vulnerability patterns in code representation...Prior studies have demonstrated that deep learning-based approaches can enhance the performance of source code vulnerability detection by training neural networks to learn vulnerability patterns in code representations.However,due to limitations in code representation and neural network design,the validity and practicality of the model still need to be improved.Additionally,due to differences in programming languages,most methods lack cross-language detection generality.To address these issues,in this paper,we analyze the shortcomings of previous code representations and neural networks.We propose a novel hierarchical code representation that combines Concrete Syntax Trees(CST)with Program Dependence Graphs(PDG).Furthermore,we introduce a Tree-Graph-Gated-Attention(TGGA)network based on gated recurrent units and attention mechanisms to build a Hierarchical Code Representation learning-based Vulnerability Detection(HCRVD)system.This system enables cross-language vulnerability detection at the function-level.The experiments show that HCRVD surpasses many competitors in vulnerability detection capabilities.It benefits from the hierarchical code representation learning method,and outperforms baseline in cross-language vulnerability detection by 9.772%and 11.819%in the C/C++and Java datasets,respectively.Moreover,HCRVD has certain ability to detect vulnerabilities in unknown programming languages and is useful in real open-source projects.HCRVD shows good validity,generality and practicality.展开更多
文摘目的:对比三维多回波恢复梯度回波(3D MERGE)、三维可变反转角快速自旋回波(3D SPACE STIR)序列在腰椎间盘突出症(LDH)检查中的应用效果。方法:选择2020年1月~2022年11月收治的135例LDH患者,回顾性分析患者临床和磁共振成像(MRI)资料,所有患者均接受常规MRI扫描及3D MERGE、3D SPACE STIR序列扫描,对比3D MERGE、3D SPACE STIR序列测量神经根直径的一致性,评价两种序列的图像质量参数[信噪比(SNR)、对比噪声比(CNR)]、图像清晰度评分。结果:3D MERGE和3D SPACE STIR序列测量的L3~S1神经根直径比较差异无统计学意义(P>0.05),且两组序列测量的L3、L4、L5和S1直径均显示出较高相关性(r=0.957,0.986,0.975,0.972,P<0.05);3D MERGE序列的SNR及CNR均高于3D SPACE STIR序列,神经根显示分级、图像清晰度评分优于3D SPACE STIR序列,差异有统计学意义(P<0.05)。结论:3D MERGE、3D SPACE STIR序列在LDH神经根直径测量中具有极高一致性,3D MERGE序列较3D SPACE STIR序列能够更清晰显示神经跟的解剖形态,图像质量更好。
文摘目的梳理国内多发伤急救相关研究文献,分析研究现状、热点和趋势,为我国多发伤急救研究提供借鉴和指导。方法检索中国知网数据库中2011—2021年关于多发伤急救的相关文献,使用Cite Space 6.1.R3可视化软件对该领域的年发文量、机构、作者、关键词进行分析。结果最终纳入多发伤急救研究文献2519篇,整体发文数量较平稳,以2016年为小高峰;发文量最高的机构是华中科技大学附属同济医院。多发伤急救研究热点包括院前急救、并发症护理、风险因素分析和预后效果评估,研究前沿包括不同多发伤人群的诊断、治疗、手术和护理体会等方面。结论本文通过可视化分析国内多发伤急救研究的热点及趋势,指明了多发伤目前研究存在的问题和未来研究发展的方向,为进一步完善多发伤急救卫生服务和管理体系提供指导。
文摘高质量教师是高质量教育发展的中坚力量。教师信念作为教师专业素养构成的关键要素,对促进教师专业发展、提升教师质量具有重要作用与影响。为借鉴国际体育教师信念研究的成果与经验,促进国内对体育教师信念的研究,研究利用CiteSpace软件,对Web of Science核心合集数据库中1960—2022年的英文文献进行可视化研究。发现:体育教师信念研究高潮出现于2021年,载文数量最多的期刊是Journal of Teaching in Physical Education;研究中心度最高的国家是美国,核心圈层的代表学者是Richards KAR、Kulinna PH和Curtner-smith MD等人;研究热点趋势集中于体力活动促进、职业社会化、批判性教学法、职前体育教师、专业发展等方面。启示:国内未来研究应重点关注体育教师信念对课程改革的影响以及促进职前、职后阶段体育教师信念的发展。
文摘近年来,我国生物产业快速发展,且发酵对身体和胃肠道健康具有很多益处,有关微生物发酵的研究逐渐增多。为探讨发酵领域近5年的研究现状与发展前景,对中国知网和Web of Science数据库中2019—2023年发表的文献进行检索并筛选,使用Cite Space软件对文章发文量、研究团队、研究热点进行图谱分析。结果表明,近5年国内发酵领域发文量偏少,年发文量平稳上升;国外发文量较多,年发文量超过100篇,2023年发文量达到200篇;国内外研究群体主要是中医药高校和研究机构;国内外研究热点集中在抗氧化和肠道菌群,国内研究重点为发酵技术,而国外研究范围较广,重点为益生菌的研究;国内外学者间合作较为密切,但研究机构间尚未形成紧密的合作关系。高校和研究机构应突破地区间的限制,促进发酵研究领域的融合。肠道菌群是近5年发酵的研究热点,也是未来发酵研究深入的对象。
文摘为了解国内外近10年护理不良事件的研究热点与前沿,为医院管理和临床护士提供参考,保障患者安全,通过检索中国期刊全文数据库(CNKI)和Web of Science(WOS)数据库,检索时限为2012年1月—2022年8月,导入CiteSpace进行可视化分析。研究显示,从发文量看,国内外学者对护理不良事件的关注度整体较高,可视化分析结果并未发现作者之间、机构之间的联系。未来减少护理不良事件的方法一定会越来越紧贴科学技术,越来越多的新型信息化平台、App等会用于医疗行业,降低护理不良事件预防的难度,但护理人员仍要重视护理安全。
基金supported by the Science and Technology Development Fund of Macao SAR(FDCT0128/2022/A,0020/2023/RIB1,0111/2023/AFJ,005/2022/ALC)the Shandong Natural Science Foundation of China(ZR2020MA004)+2 种基金the National Natural Science Foundation of China(12071272)the MYRG 2018-00168-FSTZhejiang Provincial Natural Science Foundation of China(LQ23A010014).
文摘This study introduces a pre-orthogonal adaptive Fourier decomposition(POAFD)to obtain approximations and numerical solutions to the fractional Laplacian initial value problem and the extension problem of Caffarelli and Silvestre(generalized Poisson equation).As a first step,the method expands the initial data function into a sparse series of the fundamental solutions with fast convergence,and,as a second step,makes use of the semigroup or the reproducing kernel property of each of the expanding entries.Experiments show the effectiveness and efficiency of the proposed series solutions.
文摘Deep learning has been a catalyst for a transformative revo-lution in machine learning and computer vision in the past decade.Within these research domains,methods grounded in deep learning have exhibited exceptional performance across a spectrum of tasks.The success of deep learning methods can be attributed to their capability to derive potent representations from data,integral for a myriad of downstream applications.These representations encapsulate the intrinsic structure,fea-tures,or latent variables characterising the underlying statistics of visual data.Despite these achievements,the challenge per-sists in effectively conducting representation learning of visual data with deep models,particularly when confronted with vast and noisy datasets.This special issue is a dedicated platform for researchers worldwide to disseminate their latest,high-quality articles,aiming to enhance readers'comprehension of the principles,limitations,and diverse applications of repre-sentation learning in computer vision.
文摘As the development of 5G technology and new media platform,UGC and the participation culture appears.Short video platform such as Kuaishou has the characterist of low production threshold,easy to use,attracting a great number of users.A great number of internet celebries appears including rural wanghong.In the traditional media era,the communication mode is one way,farmers,especially rural women,are always on the margins of mainstream media discourse.However,the appearance of short video platform gives them right of spearking,in the space conducted by short video platform contains complex Ideology and social structure which also restrict the online space practice of users.Thus,rural female users’online practice is a kind of dynamic balance following the spatial discipline.This study will analyze the support and constraints of rural female wanghong’s online representation,figure out the relationship of online representation and their traditional rural culture.It provides theoretical suggestions for harmonious coexistence among rural women,social media platforms,the state and society.
基金the National Natural Science Founda-tion of China(62062062)hosted by Gulila Altenbek.
文摘Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.
基金supported by the National Natural Science Foundation of China(12271101)。
文摘In this article,we investigate the(big) Hankel operator H_(f) on the Hardy spaces of bounded strongly pseudoconvex domains Ω in C^(n).We observe that H_(f ) is bounded on H~p(Ω)(1 <p <∞) if f belongs to BMO and we obtain some characterizations for Hf on H^(2)(Ω) of other pseudoconvex domains.In these arguments,Amar's L^(p)-estimations and Berndtsson's L^(2)-estimations for solutions of the ■_(b)-equation play a crucial role.In addition,we solve Gleason's problem for Hardy spaces H^(p)(Ω)(1 ≤p≤∞) of bounded strongly pseudoconvex domains.
基金supported by the National Natural Science Foundation of China(52125501 and 52205317)the Key Research Project of Shaanxi Province(2021LLRH-08)+4 种基金the Program for Innovation Team of Shaanxi Province(2023-CX-TD-17)the Natural Science Basis Research Plan in Shaanxi Province of China(2022JQ-523)the High-Level Talent Recruitment Program of Shaanxi Provincethe Fundamental Research Funds for the Central UniversitiesChina Postdoctoral Science Foundation。
文摘3D printing stands at the forefront of transforming space exploration,offering unprecedented on-demand and rapid manufacturing capabilities.It adeptly addresses challenges such as mass reduction,intricate component fabrication,and resource constraints.Despite the obstacles posed by microgravity and extreme environments,continual advancements underscore the pivotal role of 3D printing in aerospace science.Beyond its primary function of producing space structures,3D printing contributes significantly to progress in electronics,biomedicine,and resource optimization.This perspective delves into the technological advantages,environmental challenges,development status,and opportunities of 3D printing in space.Envisioning its crucial impact,we anticipate that 3D printing will unlock innovative solutions,reshape manufacturing practices,and foster self-sufficiency in future space endeavors.
基金CNSA for providing access to the lunar sample CE5C0200YJFM00302funding support from the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDB 41000000)+5 种基金the National Natural Science Foundation of China (Nos. 42273042 and 41931077)the Youth Innovation Promotion Association Chinese Academy of Sciences (No. 2020395)Key Research Program of Frontier Sciences, Chinese Academy of Sciences (Nos. ZDBS-SSW-JSC00710 and QYZDY-SSW-DQC028)the Young and Middleaged Academic Technology Leader Reserve Talent Project of Yunnan Province (No. 2018HB009)the Science Fund for Outstanding Youth of Yunnan Province (No. 202101 AV070007)the "From 0 to 1" Original Exploration Cultivation Project, Institute of Geochemistry, Chinese Academy of Sciences (No. DHSZZ2023-3)
文摘Space metallurgy is an interdisciplinary field that combines planetary space science and metallurgical engineering.It involves systematic and theoretical engineering technology for utilizing planetary resources in situ.However,space metallurgy on the Moon is challenging because the lunar surface has experienced space weathering due to the lack of atmosphere and magnetic field,making the mi-crostructure of lunar soil differ from that of minerals on the Earth.In this study,scanning electron microscopy and transmission electron microscopy analyses were performed on Chang’e-5 powder lunar soil samples.The microstructural characteristics of the lunar soil may drastically change its metallurgical performance.The main special structure of lunar soil minerals include the nanophase iron formed by the impact of micrometeorites,the amorphous layer caused by solar wind injection,and radiation tracks modified by high-energy particle rays inside mineral crystals.The nanophase iron presents a wide distribution,which may have a great impact on the electromagnetic prop-erties of lunar soil.Hydrogen ions injected by solar wind may promote the hydrogen reduction process.The widely distributed amorph-ous layer and impact glass can promote the melting and diffusion process of lunar soil.Therefore,although high-energy events on the lun-ar surface transform the lunar soil,they also increase the chemical activity of the lunar soil.This is a property that earth samples and tradi-tional simulated lunar soil lack.The application of space metallurgy requires comprehensive consideration of the unique physical and chemical properties of lunar soil.
文摘Edge devices,due to their limited computational and storage resources,often require the use of compilers for program optimization.Therefore,ensuring the security and reliability of these compilers is of paramount importance in the emerging field of edge AI.One widely used testing method for this purpose is fuzz testing,which detects bugs by inputting random test cases into the target program.However,this process consumes significant time and resources.To improve the efficiency of compiler fuzz testing,it is common practice to utilize test case prioritization techniques.Some researchers use machine learning to predict the code coverage of test cases,aiming to maximize the test capability for the target compiler by increasing the overall predicted coverage of the test cases.Nevertheless,these methods can only forecast the code coverage of the compiler at a specific optimization level,potentially missing many optimization-related bugs.In this paper,we introduce C-CORE(short for Clustering by Code Representation),the first framework to prioritize test cases according to their code representations,which are derived directly from the source codes.This approach avoids being limited to specific compiler states and extends to a broader range of compiler bugs.Specifically,we first train a scaled pre-trained programming language model to capture as many common features as possible from the test cases generated by a fuzzer.Using this pre-trained model,we then train two downstream models:one for predicting the likelihood of triggering a bug and another for identifying code representations associated with bugs.Subsequently,we cluster the test cases according to their code representations and select the highest-scoring test case from each cluster as the high-quality test case.This reduction in redundant testing cases leads to time savings.Comprehensive evaluation results reveal that code representations are better at distinguishing test capabilities,and C-CORE significantly enhances testing efficiency.Across four datasets,C-CORE increases the average of the percentage of faults detected(APFD)value by 0.16 to 0.31 and reduces test time by over 50% in 46% of cases.When compared to the best results from approaches using predicted code coverage,C-CORE improves the APFD value by 1.1% to 12.3% and achieves an overall time-saving of 159.1%.
文摘Sparse representation is an effective data classification algorithm that depends on the known training samples to categorise the test sample.It has been widely used in various image classification tasks.Sparseness in sparse representation means that only a few of instances selected from all training samples can effectively convey the essential class-specific information of the test sample,which is very important for classification.For deformable images such as human faces,pixels at the same location of different images of the same subject usually have different intensities.Therefore,extracting features and correctly classifying such deformable objects is very hard.Moreover,the lighting,attitude and occlusion cause more difficulty.Considering the problems and challenges listed above,a novel image representation and classification algorithm is proposed.First,the authors’algorithm generates virtual samples by a non-linear variation method.This method can effectively extract the low-frequency information of space-domain features of the original image,which is very useful for representing deformable objects.The combination of the original and virtual samples is more beneficial to improve the clas-sification performance and robustness of the algorithm.Thereby,the authors’algorithm calculates the expression coefficients of the original and virtual samples separately using the sparse representation principle and obtains the final score by a designed efficient score fusion scheme.The weighting coefficients in the score fusion scheme are set entirely automatically.Finally,the algorithm classifies the samples based on the final scores.The experimental results show that our method performs better classification than conventional sparse representation algorithms.
基金supported by the National Key Research and Development Program of China(2020YFA0712900)the National Natural Science Foundation of China(12371093,12071197,12122102 and 12071431)+2 种基金the Key Project of Gansu Provincial National Science Foundation(23JRRA1022)the Fundamental Research Funds for the Central Universities(2233300008 and lzujbky-2021-ey18)the Innovative Groups of Basic Research in Gansu Province(22JR5RA391).
文摘Assume that L is a non-negative self-adjoint operator on L^(2)(ℝ^(n))with its heat kernels satisfying the so-called Gaussian upper bound estimate and that X is a ball quasi-Banach function space onℝ^(n) satisfying some mild assumptions.Let HX,L(ℝ^(n))be the Hardy space associated with both X and L,which is defined by the Lusin area function related to the semigroup generated by L.In this article,the authors establish various maximal function characterizations of the Hardy space HX,L(ℝ^(n))and then apply these characterizations to obtain the solvability of the related Cauchy problem.These results have a wide range of generality and,in particular,the specific spaces X to which these results can be applied include the weighted space,the variable space,the mixed-norm space,the Orlicz space,the Orlicz-slice space,and the Morrey space.Moreover,the obtained maximal function characterizations of the mixed-norm Hardy space,the Orlicz-slice Hardy space,and the Morrey-Hardy space associated with L are completely new.
基金funded by the Major Science and Technology Projects in Henan Province,China,Grant No.221100210600.
文摘Prior studies have demonstrated that deep learning-based approaches can enhance the performance of source code vulnerability detection by training neural networks to learn vulnerability patterns in code representations.However,due to limitations in code representation and neural network design,the validity and practicality of the model still need to be improved.Additionally,due to differences in programming languages,most methods lack cross-language detection generality.To address these issues,in this paper,we analyze the shortcomings of previous code representations and neural networks.We propose a novel hierarchical code representation that combines Concrete Syntax Trees(CST)with Program Dependence Graphs(PDG).Furthermore,we introduce a Tree-Graph-Gated-Attention(TGGA)network based on gated recurrent units and attention mechanisms to build a Hierarchical Code Representation learning-based Vulnerability Detection(HCRVD)system.This system enables cross-language vulnerability detection at the function-level.The experiments show that HCRVD surpasses many competitors in vulnerability detection capabilities.It benefits from the hierarchical code representation learning method,and outperforms baseline in cross-language vulnerability detection by 9.772%and 11.819%in the C/C++and Java datasets,respectively.Moreover,HCRVD has certain ability to detect vulnerabilities in unknown programming languages and is useful in real open-source projects.HCRVD shows good validity,generality and practicality.