Soft Colour Interactions and the Diffractive Structure Function

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两性互动下的解构与重构——论爱丽丝·沃克《紫颜色》中的黑人男性气概

91隋红升、陈吉：两性互动下的解构与重构两性互动下的解构与重构*——论爱丽丝沃克《紫颜色》中的黑人男性气概隋红升　陈　吉内容提要：笔者通过对爱丽丝沃克的《紫颜色》中两性互动关系的分析，阐述传统黑人男性气概在女性反抗以及主体意识觉醒过程中逐步被解构以及新型男性气概在相互理解与尊重的两性互动中得以建构的过程，由此管窥两性互动对黑人男性气概建构的重要意义。

关键词：《紫颜色》　男性气概　解构与重构　两性互动作者简介：隋红升，浙江大学外国语言文化与国际交流学院，主要研究美国文学、性别诗学。

陈吉，浙江大学外国语言文化与国际交流学院，主要研究美国文学。

Title:Destruction and Reconstruction under Gender Interactions:On the Black Masculinity in Alice Walker's The Color Purple Abstract:By exploring the gender interactions among the main characters in Alice Walker's The Color Purple,the paper is to analyze the procedures of destruction of the traditional masculinity with the black women's protest and the awakening of their subjectivity and the construction of new masculinity in gender interaction characterized by mutual understanding and respect, thus probing the profound signi cance of gender interaction to the construction of black masculinity.Key wor ds:The Color Pur ple　masculinity　destruction and construction　gender interactionsAuthor s:Sui Hongsheng,is from the School of International Studies,Zhejiang University,specializing in American literature and gender studies.Chen Ji,is from the School of International Studies,Zhejiang University,specializing in American literature.就美国黑人作家爱丽丝沃克的代表作《紫颜色》的研究现状而言，已有的研究主要侧重于该作中黑人女性的成长及其主体意识的觉醒，比如唐红梅女士认为该作凸显了“黑人女性的自我不断拓展”（唐红梅211）。

The Interaction Between Light and Matter

The Interaction Between Light andMatter光和物质的相互作用光和物质的相互作用是自然界中的一个重要现象，有着广泛而深刻的影响。

光是电磁波，而物质则是由原子、离子或分子组成的。

当光与物质相互作用时，就会发生很多有趣的现象，如折射、反射、散射、吸收和发射等。

这些现象既有理论上的解释，也可以应用到很多领域中。

折射和反射折射和反射是光线与界面相互作用时的两个主要现象。

当光线从一种介质射向另一种介质时，会改变传播方向，这种现象称为折射。

折射的角度与入射角度之比称为折射率，其大小取决于两种介质的物理性质。

反射是指光线遇到平滑的表面后，从表面反弹回来的现象。

反射的角度等于入射角度，这是由光的传播规律所决定的。

反射还有很多应用，如光学反射镜、光学望远镜等。

散射和吸收散射是指光线穿过介质时发生方向改变的现象，通常由于介质中存在的微观杂质、气体或液滴等而产生。

散射还可以发生在分子或原子的表面上，这种现象被称为拉曼散射。

散射还有很多应用，如激光散射测量、光学成像等。

吸收是指介质对光线的一部分或全部能量吸收的现象。

当光线被吸收后，其能量可以被转化成热能或化学能，从而对物质的性质和状态产生影响。

吸收还有很多应用，如光能电池、光催化等。

发射和激发发射是指当物质受到能量激发后，会放出能量的一部分或全部，这种现象称为发射。

发射可以是自发的，也可以是受到外部刺激后发生的。

自发发射通常包括热辐射和荧光等现象，而受外部刺激后的发射则包括激光和电致发光等现象。

发射现象在光谱学、光化学和光学传感等领域中有广泛的应用。

激发是指物质受到外部刺激后，能级发生变化，从低能级向高能级跃迁，发生吸收或发射现象。

激发还有很多应用，如荧光定量分析、激光切割等。

总结光和物质的相互作用是自然界中的一种基本现象，涉及到光学、化学、材料科学等多个领域。

了解光和物质的相互作用规律，可以为科学研究和技术开发提供重要的理论支持和实验方法。

揭示真菌色彩显现背后的科学机制

揭示真菌色彩显现背后的科学机制Title: Unraveling the Science Behind Fungal Coloration: A Photographic JourneyIntroductionIn the vast world of biology, fungi play a fascinating yet often overlooked role. One aspect that captivates scientists and nature enthusiasts alike is their ability to produce an astonishing array of colors. From vivid shades of red, blue, and green to subtle hues, the mechanisms behind fungal coloration are intricate and diverse. This article delves into the scientific principles behind these vibrant displays, exploring the roles of pigments, structural components, and environmental factors.1. Pigment-Based ColorationFungi primarily use two types of pigments to display their colors: secondary metabolites and chlorophyll-like compounds. Secondary metabolites, such as melanin and carotenoids, are responsible for the majority of fungal hues. Melanin, for instance, is found in many fungi and gives rise to black, brown, or dark shades. Carotenoids,on the other hand, are responsible for the bright yellows, oranges, and reds seen in mushrooms like chanterelles and morels.Chinese Translation:在生物学的广袤世界中，真菌以其独特的魅力吸引着人们的注意。

手机APP交互设计中动态色彩的视知觉研究_高玉娇

手机APP 交互设计中动态色彩的视知觉研究高玉娇，覃京燕，陶晋（北京科技大学，北京100083）摘要：目的针对色彩心理学在交互设计中的应用规律，从时空维度中的生理、认知和情绪3个方面，对手机APP 动态色彩交互设计进行研究并探讨设计方法。

方法方法首先从色彩的物理及生理感知，剖析流动色彩的设计因素，然后从心理学研究色彩对情绪和认知的影响因素，最后从用户的交互操作情境、人对动态色彩心理认知及手机平台的特质等多角度出发，通过案例分析，对比研究动态色彩与静态色彩的设计方法差异。

结论结论提出交互设计中动态色彩的4种设计方法，以及符合物理运动规律及匹配使用者心理认知模型的动态色彩的交互设计细则。

关键词：动态色彩；交互设计；心理认知；色彩设计中图分类号：TB472文献标识码：A文章编号：1001-3563（2016）08-0134-04Visual Perception of Dynamic Color in Mobile APP Interactive DesignGAO Yu-jiao ，QIN Jing-yan ，TAO Jin（University of Science &Technology Beijing ，Beijing 100083，China ）ABSTRACT ：According to the application rules of color psychology in interaction design ，from three aspects of physiology ，cognitive processing and emotion comprehension in time dimension ，it studies and analyzes the interactive design of mobile phone dynamic color.First of all ，from the physical physiological perception of color ，it analyzes the design factors of flow color and color ，conducts the research to the emotional and cognitive factors from the psychological point of view ，finally from the user ′s interactive operation situation ，color psychology cognitive of flow and the characteristics of the mobile platform of multi angle ，with outstanding domestic and foreign interface design for mobile phone APP ，the liquidity of color were studied and extraction and analysis of the corresponding design principles.It provides four design principles of the interactive color and guidelines which include that the color of the interactive dynamic effect of design should be consistent with the laws of physics and matching users ′psychological cognitive model.KEY WORDS ：flow color ；interaction design ；cognitive psychology ；color design收稿日期：2016-01-09基金项目：国家自然科学基金资助项目（71173012）；北京科技大学本科教改重点项目（JG2013Z05）作者简介：高玉娇（1991—），女，河北人，北京科技大学硕士生，主攻界面设计、视觉传达设计。

蛋白-蛋白互作可视化

蛋白-蛋白互作可视化Protein-protein interactions play a crucial role in various biological processes, and understanding these interactions is essential for deciphering the complex mechanisms underlying cellular functions. One effective way to gain insights into protein-protein interactions is through visualization techniques. In this article, we will explore the importance of protein-protein interaction visualization, the challenges associated with it, and the various approaches used to tackle these challenges.Visualizing protein-protein interactions provides a means to comprehend the complex networks formed by interacting proteins. It allows researchers to identify key interaction partners and understand the dynamics of these interactions in a spatial and temporal context. By visualizing protein-protein interactions, researchers can gain a holistic view of the interplay between proteins, which can lead to the discovery of novel therapeutic targets and the development of more effective drugs.However, visualizing protein-protein interactions isnot without its challenges. One major challenge is the vast amount of data generated from experimental techniques such as yeast two-hybrid assays or mass spectrometry. These techniques can identify hundreds or even thousands of potential protein-protein interactions, making it difficult to represent the data in a meaningful and interpretable way. Therefore, efficient data processing and visualizationtools are needed to extract relevant information andpresent it in a concise and intuitive manner.Another challenge is the dynamic nature of protein-protein interactions. Proteins can interact with different partners under different conditions, and these interactions can change over time. Visualizing these dynamicinteractions requires the ability to represent temporal and spatial information accurately. Additionally, the visualization should be interactive, allowing researchersto explore the data and manipulate the visualization togain deeper insights.To address these challenges, various approaches have been developed for protein-protein interaction visualization. One common approach is the use of network diagrams, where proteins are represented as nodes, and interactions are represented as edges. This approach allows for a clear representation of the protein-protein interaction network and facilitates the identification of highly connected proteins or protein modules. Network diagrams can also be combined with other visualization techniques, such as heatmaps or 3D structures, to provide additional information about protein function or structural characteristics.Another approach is the use of molecular visualization tools, such as PyMOL or VMD, which allow for the visualization of protein structures and their interactions. These tools can provide detailed information about the spatial arrangement of interacting proteins and can be used to study the structural basis of protein-protein interactions. However, these tools are often limited to visualizing a small number of proteins at a time and may not be suitable for large-scale interaction networks.In recent years, there has been a growing interest in the development of web-based tools for protein-protein interaction visualization. These tools provide a user-friendly interface that allows researchers to upload their data, visualize the protein-protein interaction network, and perform various analyses. Some of these tools also provide additional features, such as the integration of functional annotations or the ability to compare multiple interaction networks. Web-based tools offer the advantage of accessibility, as they can be accessed from any computer with an internet connection, and they can handle large datasets more efficiently than desktop-based tools.In conclusion, protein-protein interactionvisualization is a powerful tool for understanding the complex networks formed by interacting proteins. It allows researchers to gain insights into the functional and structural aspects of protein-protein interactions and can aid in the discovery of new therapeutic targets. Despite the challenges associated with visualizing protein-protein interactions, various approaches have been developed totackle these challenges, ranging from network diagrams to molecular visualization tools to web-based platforms. These tools continue to evolve, providing researchers with increasingly sophisticated ways to explore and analyze protein-protein interactions.。

Plant-Microbe Interactions in the Rhizosphere

Plant-Microbe Interactions in theRhizospherePlant-microbe interactions in the rhizosphere are a crucial aspect of the soil ecosystem, playing a significant role in plant growth, nutrient uptake, andoverall soil health. The rhizosphere is the narrow region of soil that is directly influenced by the roots of plants, where a complex network of interactions occurs between the plant, soil, and various microorganisms. These interactions can beboth beneficial and detrimental, depending on the specific microorganisms involved and the environmental conditions. Understanding the dynamics of plant-microbe interactions in the rhizosphere is essential for developing sustainableagricultural practices and improving crop productivity. One of the most important aspects of plant-microbe interactions in the rhizosphere is the exchange of nutrients between the plant and the microorganisms. Plants release a variety of compounds, such as sugars, amino acids, and organic acids, into the rhizosphere through their roots. These compounds serve as an energy source for the diverse microbial community in the soil, including bacteria, fungi, and archaea. In return, the microorganisms help the plant acquire essential nutrients, such as nitrogen, phosphorus, and iron, by solubilizing and mineralizing soil nutrients, making them more available for plant uptake. This mutualistic relationship between plants and microorganisms is crucial for the overall health and productivity of the plant.In addition to nutrient exchange, plant-microbe interactions in the rhizosphere also play a vital role in plant defense against pathogens. Certain microorganismsin the rhizosphere, known as plant growth-promoting rhizobacteria (PGPR), have been shown to stimulate plant growth and enhance resistance to diseases. These beneficial microorganisms can directly inhibit the growth of plant pathogens by producing antimicrobial compounds or competing for space and resources in the rhizosphere. Furthermore, PGPR can also induce systemic resistance in plants, activating their defense mechanisms against a wide range of pathogens. Understanding the mechanisms by which PGPR confer disease resistance to plants can have significant implications for reducing the reliance on chemical pesticides in agriculture. However, not all plant-microbe interactions in the rhizosphere arebeneficial. Some microorganisms can have detrimental effects on plant health, causing diseases and reducing crop yields. For example, soil-borne pathogens, such as Fusarium and Phytophthora species, can infect plant roots and cause root rot, leading to stunted growth and wilting of the plant. These pathogenic microorganisms can outcompete beneficial microbes in the rhizosphere, disrupting the delicate balance of the soil ecosystem. Understanding the factors that contribute to the proliferation of pathogenic microorganisms in the rhizosphere is essential for developing effective strategies to manage plant diseases and maintain soil health. Moreover, the composition and diversity of the microbial community in the rhizosphere are influenced by various factors, including soil type, plant species, and environmental conditions. Different plants release different types and amounts of root exudates, which can selectively promote the growth of specific groups of microorganisms in the rhizosphere. Furthermore, the physical and chemical properties of the soil, such as pH, moisture, and organic matter content, can also have a significant impact on the structure and function of the rhizosphere microbial community. Understanding the complex interplay between these factors and their effects on plant-microbe interactions is crucial for optimizing soil management practices and promoting sustainable agriculture. In conclusion, plant-microbe interactions in the rhizosphere are a dynamic and intricate network of relationships that profoundly impact plant growth, nutrient cycling, and soil health. The exchange of nutrients, the promotion of plant defense mechanisms, and the influence of environmental factors all contribute to the complexity of these interactions. By gaining a deeper understanding of the mechanisms underlying plant-microbe interactions in the rhizosphere, we can develop innovative strategies to enhance crop productivity, reduce the reliance on chemical inputs, and promote sustainable agricultural practices. Ultimately, this knowledge can contribute to the development of a more resilient and environmentally friendly agricultural system, benefiting both farmers and the broader ecosystem.。

characteristic of colour题目

characteristic of colour题目Some possible characteristics of color include:1. Hue: The unique quality that distinguishes one color from another. Examples of hues include red, blue, and yellow.2. Saturation: The intensity or purity of a color. A highly saturated color appears vivid and vibrant, while a desaturated color appears dull or muted.3. Brightness: The perceived lightness or darkness of a color. Bright colors have a high brightness value, while dark colors havea low brightness value.4. Value: The relative lightness or darkness of a color. It is determined by the amount of white or black added to a hue. High value colors are light, while low value colors are dark.5. Temperature: The color's perceived warmth or coolness. Warm colors, such as red and yellow, appear to advance and evoke a sense of warmth. Cool colors, such as blue and green, appear to recede and evoke a sense of coolness.6. Contrast: The difference in brightness, hue, or saturation between two or more colors. High contrast colors appear very different from each other, while low contrast colors appear similar.7. Complementary colors: Colors that are opposite each other on the color wheel. When placed together, complementary colors create a high level of contrast and can create a visually pleasingcolor scheme.8. Color harmony: A combination of colors that are visually appealing and create a sense of unity. It may be achieved through the use of complementary colors, analogous colors (colors that are adjacent on the color wheel), or through specific color schemes such as monochromatic or triadic.9. Color psychology: The study of how colors can influence human emotions and behavior. Different colors can evoke specific feelings or associations, and are often used in marketing and design to create desired effects.10. Cultural symbolism: Colors can have different meanings and associations across different cultures and societies. For example, red may symbolize passion and luck in one culture, while it may represent mourning or danger in another.。

色彩分层实验作文

色彩分层实验作文Color layering is an experimental technique that involves using multiple layers of paint to create a sense of depth and dimension in an artwork.色彩分层是一种实验性技术，涉及使用多层涂料在艺术作品中创造出一种深度和立体感。

This technique is often used in both traditional and digital art, and it can produce stunning and vibrant results when executed effectively.这种技术通常在传统和数字艺术中使用，当有效执行时，可以产生令人惊叹和充满活力的效果。

By layering different colors on top of each other, artists can create a sense of depth and movement within their work.通过将不同颜色叠加在一起，艺术家可以在作品中创造出一种深度和运动的感觉。

The process allows for a high degree of control over the final appearance of the artwork, as artists can add and adjust layers to achieve the desired effect.此过程允许对艺术作品的最终外观具有高度控制权，因为艺术家可以添加和调整层以达到期望的效果。

Color layering is also a versatile technique, as artists can experiment with different combinations of colors, opacities, and textures to achieve various visual effects.色彩分层也是一种多才多艺的技术，因为艺术家可以尝试不同颜色、不透明度和纹理的组合，以达到各种视觉效果。

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a r X i v :h e p -p h /9602227v 1 6 F e b 1996DESY 96-019ISSN 0418-9833February 1996Soft Colour Interactions and the Diﬀractive Structure Function 1A.Edin a ,G.Ingelman a,b ,J.Rathsman aa Dept.of Radiation Sciences,Uppsala University,Box 535,S-75121Uppsala,Sweden b Deutsches Elektronen-Synchrotron DESY,Notkestrasse 85,D-22603Hamburg,GermanyAbstract:We discuss our model for soft colour interactions and present results on the diﬀractive structure function F D 2(β,x I P ,Q 2)and inclusive transverse energy ﬂows,which agree with available HERA data.The observation of rapidity gap events at HERA [1,2]has led to a renewed interest in diﬀractive phenomena and how they can be understood within QCD.In [3]we presented a new model for diﬀractive hard scattering based on a mechanism for soft colour interactions (SCI).The starting point is the normal DIS parton level interactions,with perturbative QCD corrections based on standard ﬁrst order matrix elements and conventional leading log parton showers for higher order parton emission.This gives a state of colour-ordered partons that will have further non-perturbative interactions to produce the ﬁnal hadron state.The novel feature in our model is the introduction [3]of random soft colour interactions corresponding to non-perturbative gluon exchange between these partons.This changes the colour structure and thereby aﬀects the hadronic ﬁnal state when a conventional hadronisation model such as the Lund string model [4]is applied.Rapidity gaps may then arise when a gap at the parton level is not spanned by a string.In particular,when the hard process starts with a gluon from the proton,leaving a colour octet remnant and a colour octet hard scattering system,a soft colour exchange between the two octet systems can give two colour singlets separated in rapidity [3].Large forward rapidity gaps are here favoured by the large momentum of the remnant,in particular at small-x .Our model [5]is implemented in the Monte Carlo (MC)Lepto 6.4producing complete events.To compare our model with experimental data on rapidity gap events we consider the diﬀrac-tive structure function F D 2(β,x I P,Q 2)deﬁned by [6]dσDβQ 4 1−y +y 21Presented by G.Ingelman at Workshop on ‘HERA Physics –Proton,Photon and Pomeron Structure’,Durham,September 1995,to appear in the proceedings.result a=1.2±0.1[2]and ZEUS results a=1.3±0.1[1]and a=1.5±0.1[7]using diﬀerent methods.However the result obtained in the model depends signiﬁcantly on which x I P-bins that are included in theﬁtting procedure.This originates from the model not quite giving a straight line in this plot,but having a tendency for a curvature with smaller slope at small x I P and larger slope at large x I P.With improved experimental data this aspect of the model can be tested.Figure1:The diﬀractive structure function F D2(β,x I P,Q2)from our soft colour interaction model(histograms)compared to the H1data points[2].Our model is meant to be applicable for DIS in general and should therefore also be compared with normal DIS events.The observed large forward transverse energyﬂow in an inclusive event sample requires a substantial energy and particle production and is thereby‘orthogonal’to forward rapidity gaps.In Fig.2we compare our model result with the H1data[8].The agreement is quite good except for the smallest x-values where the model is below the data in the central region of the hadronic cms.The possibility to obtain rapidity gaps depends on to which extent there are gaps at the parton level,i.e.on the amount of parton emission in the forward region.In our model we use theﬁrst order QCD matrix elements for the primary emission and therefore the treatment of their soft and collinear divergences is of importance.We have replaced the conventional requirement m2ij>y cut W2on any pair ij of partons with an advantageous‘mixed’scheme using cuts inˆs=(p1+p2)2and z=p1P/qP which handles initial state collinear singularities better [5].Numerically we useˆs min=1GeV2and z min=0.01.Figure2:Transverse energyﬂow versus pseudorapidityη∗in the hadronic cms from our model (histograms)compared to H1data points[8].Higher order parton emissions are taken into account approximately through parton showers developing from the incoming and scattered parton.The initial state shower is most important for the forward rapidity region.In our model we are using the conventional GLAP evolution scheme[10]summing leading log Q2terms.Terms with log(1/x)are neglected in GLAP,but will become important at small x and should then be resummed as in the BFKL equation[11]. Therefore GLAP evolution should no longer be valid at some small x.Recent studies[12]based on the CCFM equation[13]which sums both leading log Q2and log(1/x)terms suggests that this happens only at very small x,below the region10−4<∼x<∼10−2where the rapidity gap eventsare observed.Thus,there is no strong reason that the GLAP formalism should not be applicable for our purposes.One may worry that unsuppressed parton emission will destroy the gaps[9].The cut-oﬀin parton virtuality,deﬁning the borderline to the non-perturbative region,is a regulator of this. We have previously[3]used the value2GeV,but in our improved model[5]returned to1GeV, which has been standard and conforms with e.g.LEP analyses.Thus,our model works with a conventional unsuppressed GLAP parton shower.Of importance for the gap rate is also theﬂuctuations in the initial parton emission[3]. Although,one may expect that a GLAP parton shower gives a fair mean description of events, there is no guarantee that it accounts properly forﬂrgerﬂuctuations of the number of emitted gluons would increase the rate of gap events and also increase the inclusive forward energyﬂow due to‘downwards’and‘upwards’ﬂuctuations,respectively.In summary,our model based on non-perturbative soft colour interactions can explain im-portant features of both rapidity gap events and normal DIS interactions at HERA. References[1]ZEUS Coll.,Phys.Lett.B315(1993)481;Z.Phys.C68(1995)569[2]H1Collaboration,Nucl.Phys.B429(1994)477;Phys.Lett.B348(1995)681[3]A.Edin,G.Ingelman,J.Rathsman,DESY95-145to appear in proc.‘DIS and QCD’,Paris,April1995;DESY95-163to appear in Phys.Lett.B[4]B.Andersson et al.,Phys.Rep.97(1983)31[5]A.Edin,G.Ingelman,J.Rathsman,in preparation[6]G.Ingelman,K.Prytz,Z.Physik C58(1993)285[7]ZEUS Coll.,Contribution to the Beijing Conference,August1995[8]H1Collaboration,Phys.Lett.B356(1995)118[9]L.L¨o nnblad,these procedings,hep-ph/9512373[10]V.N.Gribov,I.N.Lipatov,Sov.J.Nucl.Phys.15(1972)438G.Altarelli,G.Parisi,Nucl.Phys.B126(1977)298[11]E.A.Kuraev,L.N.Lipatov,V.S.Fadin Sov.Phys.JETP45(1977)199Ya.Ya.Balitsky,L.N.Lipatov,Sov.J.Nucl.Phys.28(1978)822[12]B.Andersson et al.,Lund preprint LU TP95-34[13]M.Ciafaloni,Nucl.Phys.B296(1988)49S.Catani,F.Fiorani,G.Marchesini,Phys.Lett.B234(1990)339;Nucl.Phys.B336(1990)18。