Tracking direct and indirect impact on technology and policy of transformative research via ego citation network

Purpose:The disseminating of academic knowledge to nonacademic audiences partly relies on the transition of subsequent citing papers.This study aims to investigate direct and indirect impact on technology and policy originating from transformative research based on ego citation network.Design/methodology/approach:Key Nobel Prize-winning publications(NPs)in fields of gene engineering and astrophysics are regarded as a proxy for transformative research.In this contribution,we introduce a network-structural indicator of citing patents to measure technological impact of a target article and use policy citations as a preliminary tool for policy impact.Findings:The results show that the impact on technology and policy of NPs are higher than that of their subsequent citation generations in gene engineering but not in astrophysics.Research limitations:The selection of Nobel Prizes is not balanced and the database used in this study,Dimensions,suffers from incompleteness and inaccuracy of citation links.Practical implications:Our findings provide useful clues to better understand the characteristics of transformative research in technological and policy impact.Originality/value:This study proposes a new framework to explore the direct and indirect impact on technology and policy originating from transformative research.

Cutting-Edge Challenges in Communication Technology and Computer Network Security

The rapid development of communication technology and computer networks has brought a lot of convenience to production and life,but it also increases the security problem.Information security has become one of the severe challenges faced by people in the digital age.Currently,the security problems facing the field of communication technology and computer networks in China mainly include the evolution of offensive technology,the risk of large-scale data transmission,the potential vulnerabilities introduced by emerging technology,and the dilemma of user identity verification.This paper analyzes the frontier challenges of communication technology and computer network security,and puts forward corresponding solutions,hoping to provide ideas for coping with the security challenges of communication technology and computer networks.

Exploration of Industrial Internet Security Technology and Application from the Perspective of Generative Artificial Intelligence

In recent years,artificial intelligence technology has developed rapidly around the world is widely used in various fields,and plays an important role.The integration of industrial Internet security with new technologies such as big models and generative artificial intelligence has become a hot research issue.In this regard,this paper briefly analyzes the industrial Internet security technology and application from the perspective of generative artificial intelligence,hoping to provide some valuable reference and reference for readers.

Computer Network and Database Security Technology Optimization

With the continuous development of computer network technology, its applications in daily life and work have become increasingly widespread, greatly improving efficiency. However, certain security risks remain. To ensure the security of computer networks and databases, it is essential to enhance the security of both through optimization of technology. This includes improving management practices, optimizing data processing methods, and establishing comprehensive laws and regulations. This paper analyzes the current security risks in computer networks and databases and proposes corresponding solutions, offering reference points for relevant personnel.

面向遥感图像场景分类的LAG-MANet模型

遥感图像分类过程中,局部信息与全局信息至关重要。目前,遥感图像分类的方法主要包括卷积神经网络(CNN)及Transformer。CNN在局部信息提取方面具有优势,但在全局信息提取方面有一定的局限性。相比之下,Transformer在全局信息提取方面表现出色,但计算复杂度高。为提高遥感图像场景分类性能,降低复杂度,设计了LAG-MANet纯卷积网络。该网络既关注局部特征,又关注全局特征,并且考虑了多尺度特征。输入图像被预处理后,首先采用多分支扩张卷积模块(MBDConv)提取多尺度特征;然后依次进入网络的4个阶段,在每个阶段采用并行双域特征融合模块(P2DF)分支路提取局部、全局特征并进行融合;最后先经过全局平均池化、再经过全连接层输出分类标签。LAG-MANet在WHU-RS19数据集、SIRI-WHU数据集及RSSCN7数据集上的分类准确率分别为97.76%、97.04%、97.18%。试验结果表明,在3个具有挑战性的公开遥感数据集上,LAG-MANet更具有优越性。

基于OneNET云平台的智慧消防远程监控系统的设计

针对传统消防监控系统存在开发成本高、误警率高、实时监控不便的问题,提出一种基于物联网云平台的智慧消防远程监控系统。采用STM32单片机作为中枢控制芯片,经多传感器采集温度、湿度、烟雾、火焰等环境数据,通过窄带物联网(NB-IoT,Narrow Band Internet of Things)上传至OneNET云平台。经数据分析后以可视化方式呈现,对异常数据触发报警实时响应。通过手机APP实现数据实时监测及一键处置。经测试,监控系统报警准确率高于97.2%,数据延迟低于50 ms,表明该系统能够实现消防火警的无线远程监控,并做出快速反应,满足中小微企业和普通家庭用户的消防监控需要。

基于改进HRNet的遥感影像冬小麦语义分割方法

冬小麦在影像中呈现田块碎小且分布零散等空间特征,同时影像包含的复杂地物对冬小麦识别造成干扰,易出现识别精度低且边界分割模糊等问题。为及时准确获取大范围冬小麦空间分布信息,该研究以高分二号卫星影像作为数据源,提出一种CAHRNet(change attention high-resolution Net)语义分割模型。采用HRNet(high-resolution Net)替换ResNet作为模型的主干网络,网络的并行交互方式易获取高分辨率的特征信息;联合OCR(object-contextual representations)模块聚合上下文信息,以增强像素点与目标对象区域的关联性;3)引入坐标注意力(coordinate attention)机制,使网络模型充分利用有效的空间位置信息,以保留分割区域的边缘细节,提高对分布零散、形状多变的冬小麦田块的特征提取能力。试验结果表明,在自制的高分辨率遥感数据集上,CAHRNet模型的平均交并比(mean intersection over union,MIoU)和像素准确率(pixel accuracy, PA)分别达到81.72%和97.08%,MIoU相较U-Net、DeepLabv3+分别提高了9.09、2.44个百分点;PA相较U-Net、DeepLabv3+分别提高6.80、1.59个百分点,说明CAHRNet模型具有较高的分割识别精度,可为进一步准确获取冬小麦作物分布信息提供技术支撑。

NHNet——新型层次化遥感图像语义分割网络

深度学习分割方法是遥感图像分割领域的热点之一,主流的深度学习方法有卷积神经网络、transformer神经网络及两者的结合。特征提取是图像分割的重要环节,除了用卷积等方式提取特征,最近的研究聚焦于一些新的特征提取范式,如图卷积、小波变换等。本文利用聚类算法的区域构建属性,将改进的聚类算法用于骨干特征提取模块,同时使用卷积和视觉transformer作为辅助模块,以获取更丰富的特征表述;在模块基础上,提出了一种新型层次化遥感图像语义分割网络(NHNet);评估了NHNet语义分割的性能,并在LoveDA遥感数据集上与其他方法进行比较。结果表明,基于多特征提取的NHNet获得了竞争性的性能表现,平均交并比为49.64%,F_(1)分数为65.7%。同时,消融实验证明辅助模块提高了聚类算法分割的精确性,给NHNet分别提升了1.03%和2.41%的平均交并比。

基于ViT-D-UNet的双分支遥感云影检测网络

云及其阴影的有效分割是遥感图像处理领域中重要的问题,它对于地表特征提取、气候检测、大气校正等有很大帮助.然而云和云影遥感图像特征复杂,云分布多样不规则,且边界信息模糊易受背景干扰等特点,导致其特征难以准确提取,也少有专门为其设计的网络.针对以上问题,本文提出一种ViT(vision Transformer)和D-UNet双路网络.本文网络分为两个分支:一路是基于卷积的局部特征提取模块,在D-UNet的膨胀卷积模块基础上,引入深度可分离卷积,提取多尺度特征的同时,减少参数;另一路通过ViT在全局上理解上下文语义,加深对整体特征提取.两支路间存在信息交互,完善提取的特征信息.最后通过独特设计的融合特征解码器,进行上采样,减少信息丢失.模型在自建的云和云影数据集以及HRC_WHU公开数据集上取得优越的性能,在MIoU指标上分别领先次优模型0.52%和0.44%,达到了92.05%和85.37%.

基于HRNet和自注意力机制的多源遥感影像水稻提取

为了提高协同光学和雷达影像提取水稻的精度,该研究通过改进深度学习网络HRNet,提出一种多级特征融合的框架方法,改进后的MSATT-HRNet模型综合利用欧空局哥白尼项目哨兵1号(Sentinel-1)的双极化合成孔径雷达(synthetic aperture radar,SAR)影像和哨兵2号(Sentinel-2)多光谱光学影像,提取了湖南省长沙市望城区水稻种植区域。针对HRNet网络的改进主要包括两部分:1)设计了通道注意力机制与最大池化组成的卷积组用于提取SAR特征,同时将自注意力模块嵌入HRNet基础特征提取模块中用于提取多光谱光学影像的特征;2)为了探索双模态特征之间的内在互补关系,设计了由通道注意力与空间注意力组成的特征融合模块。研究针对改进模型进行了消融试验,并将MSATT-HRNet与其他常用深度学习方法(MCANet、Deeplabv3、Unet)进行了比较。结果表明,该研究提出的多源数据融合方法能够利用不同数据源的互补优势。水稻种植区域提取结果的总体精度、Kappa系数分别达到97.04%和0.961,与MCANet、Deeplabv3、Unet相比,总体精度分别提高6.90、2.67和2.98个百分点,Kappa系数分别提高0.055、0.025和0.030。证实了该方法可以有效提高水稻的判别精度。研究通过深度学习技术与遥感影像的耦合,为南方多云雨地区水稻制图提供了一种可行的选择。

Water source identification in mines combining LIF technology and ResNet

The problem of mine water source has always been an important hidden danger in mine safety production.The water source under the mine working face may lead to geological disasters,such as mine collapse and water disaster.The research background of mine water source identification involves many fields such as mining production,environmental protection,resource utilization and technological progress.It is a comprehensive and interdisciplinary subject,which helps to improve the safety and sustainability of mine production.Therefore,timely and accurate identification and control of mine water source is very important to ensure mine production safety.Laser-Induced Fluorescence(LIF)technology,characterized by high sensitivity,specificity,and spatial resolution,overcomes the time-consuming nature of traditional chemical methods.In this experiment,sandstone water and old air water were collected from the Huainan mining area as original samples.Five types of mixed water samples were prepared by varying their proportions,in addition to the two original water samples,resulting in a total of seven different water samples for testing.Four preprocessing methods,namely,MinMaxScaler,StandardScaler,Standard Normal Variate(SNV)transformation,and Centering Transformation(CT),were applied to preprocess the original spectral data to reduce noise and interference.CT was determined as the optimal preprocessing method based on class discrimination,data distribution,and data range.To maintain the original data features while reducing the data dimension,including the original spectral data,five sets of data were subjected to Principal Component Analysis(PCA)and Linear Discriminant Analysis(LDA)dimensionality reduction.Through comparing the clustering effect and Fisher's ratio of the first three dimensions,PCA was identified as the optimal dimensionality reduction method.Finally,two neural network models,CT+PCA+CNN and CT+PCA+ResNet,were constructed by combining Convolutional Neural Networks(CNN)and Residual Neural Networks(ResNet),respectively.When selecting the neural network models,the training time,number of iterative parameters,accuracy,and cross-entropy loss function in the classification problem were compared to determine the model best suited for water source data.The results indicated that CT+PCA+ResNet was the optimal approach for water source identification in this study.

基于改进U-Net模型的遥感影像道路提取方法研究

针对高分辨率遥感影像背景信息复杂,道路提取难度大,自动化程度低等问题,论文提出了一种改进的U-Net的道路提取方法。首先,编码器使用VGG16网络结构替代原始U-Net编码器结构;然后,在每个编码器和解码器块后加入特征压缩激活模块(SENet)增强网络特征学习能力;最后,使用Dice损失函数和二分类交叉熵损失函数复合的损失函数进行训练,减轻了道路提取任务中的样本不平衡问题。在Massachusetts Road数据集上的结果表明,改进后的算法对道路提取结果得到了有效的提升。所提方法在测试集上的精确度、召回率、F1-score和mIoU评价指标分别达到82.5%、77.8%、80.0%及82.1%,在测试影像中对错综交叉的道路具有更好的识别效果。

美国国家教育技术计划的政策主题、目标与工具变迁--基于NETP 1996-2024年的文本研究

教育数字化转型是推动未来教育发展与变革的关键。美国通过制定并调整教育技术政策以培养具有技术素养的创新人才提升国家竞争力。文章基于政策文本分析视角,构建了包括政策主题、政策目标与政策内容三个维度的分析框架,运用政策文献量化分析法对美国联邦教育部教育技术规划办公室于1996-2024年发布的7份关于教育技术与教育数字化发展的战略规划性文件进行整体分析,研究结论如下:在政策主题维度,政策主题的针对性与可操作性逐渐增强;政策目标维度,政策目标的内涵与协同性不断丰富与提升;政策工具维度,供给型、环境型和需求型政策工具使用较为均衡,在不同教育技术内容要素中的政策工具使用各有偏好与侧重。美国教育技术政策的变迁动向与行动逻辑为我国数字教育政策发展提供借鉴参考。

基于DFECANet的遥感图像飞机目标检测方法

针对现有的遥感图像目标检测方法中对小尺寸飞机目标的检测精度不高、特征信息传递不准确、信息交互不充分等问题,提出了一种基于可辨别特征提取和上下文感知的遥感图像飞机目标检测方法。设计了以可辨别特征提取模块为主体的主干网络,用以加强对多尺度飞机目标的特征提取;引入自适应特征增强模块,选择性关注小目标、优化特征信息的传递与信息交互;并设计了特征融合上采样模块对特征图进行上采样操作,用以提升高层语义信息的准确性。在DOTAv1数据集上的检测精度达到了95.2%,相较于YOLOv5s、SCRDet、ASSD等主流算法,飞机目标的检测精度提高了3.7%~18%。此外,该方法的检测速度以及模型参数量分别为147 fps和13.4 M,相较于当前主流算法具备较强的竞争力,满足在遥感背景下对飞机目标的实时检测需求。

Advancing approach and toolbox in optimization of chloroplast genetic transformation technology

Chloroplast is a discrete,highly structured,and semi-autonomous cellular organelle.The small genome of chloroplast makes it an up-and-coming platform for synthetic biology.As a special means of synthetic biology,chloroplast genetic engineering shows excellent potential in reconstructing various sophisticated metabolic pathways within the plants for specific purposes,such as improving crop photosynthetic capacity,enhancing plant stress resistance,and synthesizing new drugs and vaccines.However,many plant species exhibit limited efficiency or inability in chloroplast genetic transformation.Hence,new transformation technologies and tools are being constantly developed.In order to further expand and facilitate the application of chloroplast genetic engineering,this review summarizes the new technologies in chloroplast genetic transformation in recent years and discusses the choice of appropriate synthetic biological elements for the construction of efficient chloroplast transformation vectors.

A frequency servo SoC with output power stabilization loop technology for miniaturized atomic clocks

A frequency servo system-on-chip(FS-SoC)featuring output power stabilization technology is introduced in this study for high-precision and miniaturized cesium(Cs)atomic clocks.The proposed power stabilization loop(PSL)technique,incorporating an off-chip power detector(PD),ensures that the output power of the FS-SoC remains stable,mitigating the impact of power fluctuations on the atomic clock's stability.Additionally,a one-pulse-per-second(1PPS)is employed to syn-chronize the clock with GPS.Fabricated using 65 nm CMOS technology,the measured phase noise of the FS-SoC stands at-69.5 dBc/Hz@100 Hz offset and-83.9 dBc/Hz@1 kHz offset,accompanied by a power dissipation of 19.7 mW.The Cs atomic clock employing the proposed FS-SoC and PSL obtains an Allan deviation of 1.7×10^(-11) with 1-s averaging time.

Metal–Organic Gel Leading to Customized Magnetic‑Coupling Engineering in Carbon Aerogels for Excellent Radar Stealth and Thermal Insulation Performances

Metal–organic gel(MOG)derived composites are promising multi-functional materials due to their alterable composition,identifiable chemical homogeneity,tunable shape,and porous structure.Herein,stable metal–organic hydrogels are prepared by regulating the complexation effect,solution polarity and curing speed.Meanwhile,collagen peptide is used to facilitate the fabrication of a porous aerogel with excellent physical properties as well as the homogeneous dispersion of magnetic particles during calcination.Subsequently,two kinds of heterometallic magnetic coupling systems are obtained through the application of Kirkendall effect.FeCo/nitrogen-doped carbon(NC)aerogel demonstrates an ultra-strong microwave absorption of−85 dB at an ultra-low loading of 5%.After reducing the time taken by atom shifting,a FeCo/Fe3O4/NC aerogel containing virus-shaped particles is obtained,which achieves an ultra-broad absorption of 7.44 GHz at an ultra-thin thickness of 1.59 mm due to the coupling effect offered by dual-soft-magnetic particles.Furthermore,both aerogels show excellent thermal insulation property,and their outstanding radar stealth performances in J-20 aircraft are confirmed by computer simulation technology.The formation mechanism of MOG is also discussed along with the thermal insulation and electromagnetic wave absorption mechanism of the aerogels,which will enable the development and application of novel and lightweight stealth coatings.

Development and technology status of energy storage in depleted gas reservoirs

Utilizing energy storage in depleted oil and gas reservoirs can improve productivity while reducing power costs and is one of the best ways to achieve synergistic development of"Carbon Peak–Carbon Neutral"and"Underground Resource Utiliza-tion".Starting from the development of Compressed Air Energy Storage(CAES)technology,the site selection of CAES in depleted gas and oil reservoirs,the evolution mechanism of reservoir dynamic sealing,and the high-flow CAES and injection technology are summarized.It focuses on analyzing the characteristics,key equipment,reservoir construction,application scenarios and cost analysis of CAES projects,and sorting out the technical key points and existing difficulties.The devel-opment trend of CAES technology is proposed,and the future development path is scrutinized to provide reference for the research of CAES projects in depleted oil and gas reservoirs.

Sodium Nitrate Passivation as a Novel Insulation Technology for Soft Magnetic Composites

Sodium nitrate passivation has been developed as a new insulation technology for the production of FeSiAl soft magnetic composites (SMCs). In this work, the evolution of coating layers grown at different pH values is investigated involving analyses on their composition and microstructure. An insulation coating obtained using an acidic NaNO_(3) solution is found to contain Fe2O_(3), SiO_(2), Al2O_(3), and AlO(OH). The Fe2O_(3) transforms into Fe3O4 with weakened oxidizability of the NO_(3)– at an elevated pH, whereas an alkaline NaNO_(3) solution leads to the production of Al2O_(3), AlO(OH), and SiO_(2). Such growth is explained from both thermodynamic and kinetic perspectives and is correlated to the soft magnetic properties of the FeSiAl SMCs. Under tuned passivation conditions, optimal performance with an effective permeability of 97.2 and a core loss of 296.4 mW∙cm−3 is achieved at 50 kHz and 100 mT.

基于One NET云平台和北斗GPS双模定位的智慧停车系统

本文指出目前智慧停车面临三大技术难题:一是如何实现高精度停车位置定位;二是如何准确实现车位空闲状态检测;三是构建互联互通的停车场停车信息网络系统并对接到云平台,实现整个城市停车信息的大数据共享。为此,本文设计了一款基于OneNET云平台和北斗GPS双模定位的智慧停车系统,从理论上可解决上述3个关键问题。系统经过测试运行正常,数据收发正常,各传感器和北斗GPS定位数据准确,云平台数据更新保持在1~2 s之间。表明该系统具有车位状态监测准确、位置定位可精确到分米级别、数据上传到云平台存储和显示快速及时的特点,能够提高停车场的利用率和周转率,方便车主出行和生活。