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.

Combined Economic and Emission Power Dispatch Control Using Substantial Augmented Transformative Algorithm

The purpose of the Combined Economic Emission Dispatch(CEED)of electric power is to offer the most exceptional schedule for production units,which must run with both low fuel costs and emission levels concurrently,thereby meeting the lack of system equality and inequality constraints.Economic and emissions dispatching has become a primary and significant concern in power system networks.Consequences of using non-renewable fuels as input to exhaust power systems with toxic gas emissions and depleted resources for future generations.The optimal power allocation to generators serves as a solution to this problem.Emission dispatch reduces emissions while ignoring economic considerations.A collective strategy known as Combined Economic and Emission Dispatch is utilized to resolve the above-mentioned problems and investigate the trade-off relationship between fuel cost and emissions.Consequently,this work manages the Substantial Augmented Transformative Algorithm(SATA)to take care of the Combined Economic Emission Dispatch Problem(CEEDP)of warm units while fulfilling imperatives,for example,confines on generator limit,diminish the fuel cost,lessen the emission and decrease the force misfortune.SATA is a stochastic streamlining process that relies upon the development and knowledge of swarms.The goal is to minimize the total fuel cost of fossil-based thermal power generation units that generate and cause environmental pollution.The algorithm searches for solutions in the search space from the smallest to the largest in the case of forwarding search.The simulation of the proposed system is developed using MATLAB Simulink software.Simulation results show the effectiveness and practicability of this method in terms of economic and emission dispatching issues.The performance of the proposed system is compared with existing Artificial Bee Colony-Particle Swarm Optimization(ABC-PSO),Simulated Annealing(SA),and Differential Evolution(DE)methods.The fuel cost and gas emission of the proposed system are 128904$/hr and 138094.4652$/hr.

The Economic Context of Our Time:A Diagnosis in the Light of Transformative Ethics

The following contribution is the attempt to touch upon some fundamental questions for a reliable ethical diagnosis of what can be called the economic context of our epoch.For this purpose,we will consider a well-known concept within the medieval economics in light of its modern reappropriation against the backdrop of transformative ethics:the meant concept is usury;the meant reappropriation is that of Ezra Pound.For reasons of space,the single steps will merely hint at ethical questions that require a much more careful investigation.Within these limits,the essay will therefore analyze certain traits of our current economic context while reconsidering its ethical dimension in the light of the concept of usury.The framework within which this analysis will be conducted is that of transformative ethics.

Transformative learning in nursing education: A concept analysis

Objective:There is vast literature on transformative learning,which is an important aspect of nursing education,but its meaning remains unclear.It is therefore important to clarify the meaning of transformative learning,identify its attributes,antecedents and consequences to increase its use in nursing education,practice and research.Methods:Walker and Avant's method was used,and the process provided a structured way to analyse the concept of'transfonnative leaming'.Nursing education dictionaries,encyclopaedias,conference papers,research articles,dissertations,theses,journal articles,thesauri and relevant books through the database library and intemet searches were reviewed.One hundred and two literature sources were reviewed,and data saturation was reached.Results:The results of the concept analysis of transformative learning within the context of nursing education identified three categories,namely,1)Antecedents as cognitive and affective perspective,democratic education principles and inspiration;2)Process through three phases,namely i)awareness through self-reflection,ii)the meaningful interactive,integrative and democratic construction process,and iii)metacognitive reasoning abilities;and 3)Outcomes.A theoretical definition of transformative learning was formulated.Theoretical validity was ensured.Conclusion:The results of the concept analysis of transformative learning were used to describe a model to facilitate transformative learning within the context of nursing education.

A model to facilitate transformative learning in nursing education

Objective:Transformative learning is a learner-centered process of learning.Learners are actively engaged through critical reflection and discourse to question assumptions and expectations.The purpose of this article is to describe a model to facilitate transformative learning in nursing education.Methods:A qualitative,exploratory,descriptive and contextual design for theory generation was selected in this study to describe a model to facilitate transformative learning in nursing education.Concept analysis of transformative learning was done in the first stage of the main study using Walker and Avant's eight step approach to clarify the conceptual identification and meaning.The results of concept analysis guided data collection in the second stage.Eleven individual agenda semi-structured interviews were conducted with nurse educators to explore and describe their perceptions regarding how transformative learning can be facilitated in nursing education.Matrix building approach was used to analyse the collected data.The third stage constituted the conceptualisation of findings from the second stage using relevant literature within the elements of practice theory.The fourth stage focused on the description and evaluation of a model to facilitate transformative learning in nursing education.Findings:Four themes and nine sub-themes emerged and were conceptualised within the six elements of practice theory namely the context,agent,recipient,dynamic,process and procedure and outcome.Conclusion:The relation statements provided the basis for model description.Reliable method was used to describe and evaluate the model.The refinement of the model by experts in model development andqualitative research was made.

Transformative Learning in Language Arts as a Method of Guidance and Counseling

Our voluntary project ran in different groups of adults （healthy, depressed, schizophrenic, and clients with anxiety disorders）. The study is based on analysis of personal counseling experience, where literature was used as a tool to disclose client＇s personal meanings. During our sessions, clients were observed phenomenologically, and their speach was written down. Reflecting on the qualitative data of transformative learning in language arts, we developed techniques for facilitation and disclosure of personal meanings. Grounded theory was used for data generalization： Personal educational insights and its comparison with other researchers＇ theoretical insights were the basis to develop the methodical system for psychological guidance and counseling and to explain it. We revealed that focus on personally important meanings during discussion after literature reading has influence on the mental state of personality and deepens the interactions with the literature art, so we suggest a spiral model to explain the methodical system of our method. Transformative learning in language arts as a method of guidance and counseling can be understood as spiritual motion by spirale. It has three levels： （1） art level （interaction with the literature）; （2） psychology level （counseling）; and （3） art level （interaction with the same peace of literature as in the beginning of the session, poetic summary of the session）. Levels （1） and （3） mean interaction with literature art, but at the third level, this interaction has new quality, because personality has better perception of Self and more ability to connect personal meanings and literature wisdom. At the first level, we have direction from literature to Personality. At the third level, we have direction from the Self （Speaking from Within） to literature. Disclosure of personally important meanings is a key to self-understanding and poetical thinking; our developed methodical system reduces emotional tension and strengthens interconnectedness between inner and outer world and improves poetical understanding. In this paper, the methodical system of transformative learning and guidance is discussed.

Transformative Presidents： A Review of Definitions, Focused on the Reagan Presidency

The most significant variable in ranking a President as transformative is the nature of his legacy. Reagan＇s legacy resulted from the Tea Party＇s adoption of Reagan＇s conservative policy proposals, labeled the Reagan Revolution, or as Reagan defined in his farewell address, the ＂... Great Rediscovery, a recovery of our values and common sense＂ （Skowronek, 1967, p. 95）. Transformative presidents create changes in the American Political System. Stephen Skowronek has defined five transformational presidents whose administration transformed the American political system： Jefferson, Jackson, Lincoln, Franklin D. Roosevelt, and Reagan （Skowronek, 1967, p. 95）. Further, a comparison of the Reagan and Obama presidencies is included to explain why a President may be defined as being or not transformative. Each of these transformation repudiated their predecessor＇s policies. The Reagan Revolution enabled Reagan to become transformative by repudiating Carter＇s New Deal liberal and progressive policies. Presidential scholars who deny the ability of presidents to become transformative do so based on their research that presidents lack the power to persuade Congress or change public opinion. Skowronek utilized Neustadt＇s theory of ＂periodization＂ to explain how the five transformational presidents each left through their ＂legacy＂, presidents who followed their policies. Reagan＇s followers included： George Bush, Clinton＇s centrist policies adhered to Reagan＇s, and George W. Bush. Obama was elected by repudiating his predecessor＇s policies. Whether Obama was transformative has yet to be determined.

Strategies on Promoting Transformative Learning of College English Teachers

Based on the previous case study in promoting transformative learning of college English teachers,who participated in a three-month online training courses,the article made a further research and concluded four strategies on promoting transformative learning of college English Teachers.

Transformative Learning:Intercultural Adaptation of Chinese Teachers at the Confucius Institute in Spain

In recent years there has been a proliferation of studies demonstrating the value of teaching abroad as much for its benefits for the training and professional development of these teachers,as for its impact and beneficial effects on students.This article uses transformative learning as a theoretical framework to interpret the achievements associated with the experience of teaching abroad,and to identify and analyze different motivational factors,adjustments,changes,challenges,and perspectives of Chinese teachers linked to a Confucius Institute in Spain,through a qualitative analysis of narratives elicited through in-depth interviews and focus groups.It highlights the role and potential of the transnational,intercultural experience of these teachers as authentic actors in the part played by the Confucius Institutes in language teaching and the promotion of Chinese culture,indicating many issues including language difficulties,professional adjustment,ideas about education,beliefs of teachers and the management of the program.

Can University Be a Transformative Environment？ Fostering Critical Reflection Through Art in Higher Education＊

The uncertainty of postmodern world is a common belief. This uncertainty influences citizens in all aspects of their lives （personal, professional, social, etc.）. The question arises intensively： How can people be prepared for this continuous changing？ One of the suggestions presented in current literature is the development of critical reflection Educational environment can play a major role in this domain. The need for the development of critical reflection is especially noted in the higher education framework. Universities can become a ＂holding＂ environment for transformation. This study underlines the importance of preparing young students as future professionals and citizens, by developing their critical reflection. A practical method is discussed and concerns the use and benefits of the aesthetic experience in a transformative learning procedure. The research presented here is based on the application of this method and aims to the reinforcement of specific dimensions of adulthood in a university environment. The application was carried out on students of the department of educational studies at a Greek regional university. One of the topics analyzed through art was the notion of ＂useful knowledge＂ and was part of a wider range of applications that concern the process of learning

Empowering higher order thinking via language and transformative learning

This paper examines how thinking can be shaped via language and transformative learning. It examines four aspects of higher order thinking viz metacognition, locus of control, self-efficacy and intrinsic values. The study involves a teaching intervention to provide empirical evidence to show that higher order thinking is improved when it is in the learning environment： （1） Provides opportunities for negotiation, shared control, critical voice, personal relevance and uncertainty; and （2） encourage language use in interacting, communicating and constructing meanings. The sample consisted of 77 accounting college students in Malaysia, randomly grouped into the traditional lecture style classroom and the transformative classroom. Four self-report questionnaires were used to measure higher order thinking and to assess whether students＇ perceptions of their learning environment. Split Plot Analysis of Variance （SPANOVA） and t-tests were used to test 3 hypotheses formulated for the study. The results show that when learning environment simulate the true arena for transformative learning and provides ample opportunities to use language, higher order thinking can be empowered.

Phronesis and Transformative Learning： A Joint Challenge for Moral Philosophy and Educational Theory

Strengthening moral learning may become available to us by bringing phronesis and transformative learning in a common theoretical space. For both Aristotle and Mezirow, the exercise of morality, or rising to the standard of moral choice, decision, and action, is not the result of an intuitive achievement or a sudden understanding of a morally demanding situation but a lifelong affair. Our strategy here addresses three aims： Firstly, to invoke and reclaim the endemic bond between education in the broader sense of paideia and the significant role that reeds to be re-ascribed to moral education. This allows a turn towards qualitative features and makes room for an inclusion of moral education, or values education, within education. Secondly, to portray the exercise of autonomy, choice, and judgment as a result of paideutic development; both theories share the assumption that moral learning rests on constant reflection upon past experiences and the zetesis of future goals. Thirdly, to focus on the way one reclaims the right to exercise judgment, whenever this is required. A joint study of the two theories may enlighten the content of this lifelong reflective procedure.

基于RoBERTa和图增强Transformer的序列推荐方法

自推荐系统出现以来,有限的数据信息就一直制约着推荐算法的进一步发展。为降低数据稀疏性的影响,增强非评分数据的利用率,基于神经网络的文本推荐模型相继被提出,但主流的卷积或循环神经网络在文本语义理解和长距离关系捕捉方面存在明显劣势。为了更好地挖掘用户与商品之间的深层潜在特征,进一步提高推荐质量,提出一种基于Ro BERTa和图增强Transformer的序列推荐(RGT)模型。引入评论文本数据,首先利用预训练的Ro BERTa模型捕获评论文本中的字词语义特征,初步建模用户的个性化兴趣,然后根据用户与商品的历史交互信息,构建具有时序特性的商品关联图注意力机制网络模型,通过图增强Transformer的方法将图模型学习到的各个商品的特征表示以序列的形式输入Transformer编码层,最后将得到的输出向量与之前捕获的语义表征以及计算得到的商品关联图的全图表征输入全连接层,以捕获用户全局的兴趣偏好,实现用户对商品的预测评分。在3组真实亚马逊公开数据集上的实验结果表明,与Deep FM、Conv MF等经典文本推荐模型相比,RGT模型在均方根误差(RMSE)和平均绝对误差(MAE)2种指标上有显著提升,相较于最优对比模型最高分别提升4.7%和5.3%。

基于Transformer和自适应特征融合的矿井低照度图像亮度提升和细节增强方法

高质量矿井影像为矿山安全生产提供保障,也有利于提高后续图像分析技术的性能。矿井影像受低照度环境的影响,易出现亮度低,照度不均,颜色失真,细节信息丢失严重等问题。针对上述问题,提出一种基于Transformer和自适应特征融合的矿井低照度图像亮度提升和细节增强方法。基于生成对抗思想搭建生成对抗式主体模型框架,使用目标图像域而非单一参考图像驱动判别器监督生成器的训练,实现对低照度图像的充分增强;基于特征表示学习理论搭建特征编码器,将图像解耦为亮度分量和反射分量,避免图像增强过程中亮度与颜色特征相互影响从而导致颜色失真问题;设计CEM-Transformer Encoder通过捕获全局上下文关系和提取局部区域特征,能够充分提升整体图像亮度并消除局部区域照度不均;在反射分量增强过程中,使用结合CEM-Cross-Transformer Encoder的跳跃连接将低级特征与深层网络处特征进行自适应融合,能够有效避免细节特征丢失,并在编码网络中添加ECA-Net,提高浅层网络的特征提取效率。制作矿井低照度图像数据集为矿井低照度图像增强任务提供数据资源。试验显示,在矿井低照度图像数据集和公共数据集中,与5种先进的低照度图像增强算法相比,该算法增强图像的质量指标PSNR、SSIM、VIF平均提高了16.564%,10.998%,16.226%和14.438%,10.888%,14.948%,证明该算法能够有效提升整体图像亮度,消除照度不均,避免颜色失真和细节丢失,实现矿井低照度图像增强。

基于Depth-wise卷积和视觉Transformer的图像分类模型

图像分类作为一种常见的视觉识别任务,有着广阔的应用场景。在处理图像分类问题时,传统的方法通常使用卷积神经网络,然而,卷积网络的感受野有限,难以建模图像的全局关系表示,导致分类精度低,难以处理复杂多样的图像数据。为了对全局关系进行建模,一些研究者将Transformer应用于图像分类任务,但为了满足Transformer的序列化和并行化要求,需要将图像分割成大小相等、互不重叠的图像块,破坏了相邻图像数据块之间的局部信息。此外,由于Transformer具有较少的先验知识,模型往往需要在大规模数据集上进行预训练,因此计算复杂度较高。为了同时建模图像相邻块之间的局部信息并充分利用图像的全局信息,提出了一种基于Depth-wise卷积的视觉Transformer(Efficient Pyramid Vision Transformer,EPVT)模型。EPVT模型可以实现以较低的计算成本提取相邻图像块之间的局部和全局信息。EPVT模型主要包含3个关键组件:局部感知模块(Local Perceptron Module,LPM)、空间信息融合模块(Spatial Information Fusion,SIF)和“+卷积前馈神经网络(Convolution Feed-forward Network,CFFN)。LPM模块用于捕获图像的局部相关性;SIF模块用于融合相邻图像块之间的局部信息,并利用不同图像块之间的远距离依赖关系,提升模型的特征表达能力,使模型学习到输出特征在不同维度下的语义信息;CFFN模块用于编码位置信息和重塑张量。在图像分类数据集ImageNet-1K上,所提模型优于现有的同等规模的视觉Transformer分类模型,取得了82.6%的分类准确度,证明了该模型在大规模数据集上具有竞争力。

基于残差U-Net和自注意力Transformer编码器的磁场预测方法

利用有限元方法对几何结构复杂的电机和变压器进行磁场分析,存在仿真时间长且无法复用的问题。因此,该文提出一种基于残差U-Net和自注意力Transformer编码器的磁场预测方法。首先建立永磁同步电机(PMSM)和非晶合金变压器(AMT)有限元模型,得到深度学习训练所需的数据集;然后将Transformer模块与U-Net模型结合,并引入短残差机制建立ResUnet-Transformer模型,通过预测图像的像素实现磁场预测;最后通过Targeted Dropout算法和动态学习率调整策略对模型进行优化,解决拟合问题并提高预测精度。计算实例证明,ResUnet-Transformer模型在PMSM和AMT数据集上测试集的平均绝对百分比误差(MAPE)均小于1%,且仅需500组样本。该文提出的磁场预测方法能减少实际工况和多工况下精细模拟和拓扑优化的时间和资源消耗,亦是虚拟传感器乃至数字孪生的关键实现方法之一。

CNN-Transformer特征融合多目标跟踪算法

在卷积神经网络(CNN)中,卷积运算能高效地提取目标的局部特征,却难以捕获全局表示;而在视觉Transformer中,注意力机制可以捕获长距离的特征依赖,但会忽略局部特征细节。针对以上问题,提出一种基于CNN-Transformer双分支主干网络进行特征提取和融合的多目标跟踪算法CTMOT(CNN-transformer multi-object tracking)。使用基于CNN和Transformer双分支并行的主干网络分别提取图像的局部和全局特征。使用双向桥接模块(two-way braidge module,TBM)对两种特征进行充分融合。将融合后的特征输入两组并行的解码器进行处理。将解码器输出的检测框和跟踪框进行匹配,完成多目标跟踪任务。在多目标跟踪数据集MOT17、MOT20、KITTI以及UADETRAC上进行评估,CTMOT算法的MOTP和IDs指标在四个数据集上均达到了SOTA效果,MOTA指标分别达到了76.4%、66.3%、92.36%和88.57%,在MOT数据集上与SOTA方法效果相当,在KITTI数据集上达到SOTA效果。由于同时完成目标检测和关联,能够端到端进行目标跟踪,跟踪速度可达35 FPS,表明CTMOT算法在跟踪的实时性和准确性上达到了较好的平衡,具有较大潜力。

基于多尺度注意力特征融合的场景文本检测

针对目前文本检测中小尺度文本和长文本检测精度低的问题,提出了一种基于多尺度注意力特征融合的场景文本检测算法。该方法以Mask R-CNN为基线模型,引入Swin_Transformer作为骨干网络提取底层特征。在特征金字塔(feature pyramid networks,FPN)中,通过将多尺度注意力热图与底层特征通过横向连接相融合,使检测器的不同层级专注于特定尺度的目标,并利用相邻层注意力热图之间的关系实现了FPN结构中的纵向特征共享,避免了不同层之间梯度计算的不一致性问题。实验结果表明:在ICDAR2015数据集上,该方法的准确率、召回率和F值分别达到了88.3%、83.07%和85.61%,在CTW1500和Total-Text弯曲文本数据集上相较现有方法均有良好表现。

融合卷积注意力和Transformer架构的行人重识别方法

行人重识别技术是智能安防系统中的重要方法之一,为构建一个适用各种复杂场景的行人重识别模型,基于现有的卷积神经网络和Transformer模型,提出一种融合卷积注意力和Transformer(FCAT)架构的方法,以增强Transformer对局部细节信息的关注。所提方法主要将卷积空间注意力和通道注意力嵌入Transformer架构中,分别加强对图像中重要区域的关注和对重要通道特征的关注,以进一步提高Transformer架构对局部细节特征的提取能力。在3个公开行人重识别数据集上的对比消融实验证明,所提方法在非遮挡数据集上取得了与现有方法相当的结果,在遮挡数据集上的性能得到显著提升。所提方法更加轻量化,在不增加额外计算量和模型参数的情况下,推理速度得到了提升。

基于Transformer和动态3D卷积的多源遥感图像分类

多源遥感数据具有互补性和协同性,近年来,基于深度学习的方法已经在多源遥感图像分类中取得了一定进展,但当前方法仍面临关键难题,如多源遥感图像特征表达不一致,融合困难,基于静态推理范式的神经网络缺乏对不同类别地物的适应性。为解决上述问题,提出了基于跨模态Transformer和多尺度动态3D卷积的多源遥感图像分类模型。为提高多源特征表达的一致性,设计了基于Transformer的融合模块,借助其强大的注意力建模能力挖掘高光谱和LiDAR数据特征之间的相互作用;为提高特征提取方法对不同地物类别的适应性,设计了多尺度动态3D卷积模块,将输入特征的多尺度信息融入卷积核的调制,提高卷积操作对不同地物的适应性。采用多源遥感数据集Houston和Trento对所提方法进行验证,实验结果表明:所提方法在Houston和Trento数据集上总体准确率分别达到94.60%和98.21%,相比MGA-MFN等主流方法,总体准确率分别至少提升0.97%和0.25%,验证了所提方法可有效提升多源遥感图像分类的准确率。