SPB Academic Publishing bv, The Hague Macroclimate, microclimate and dune formation along t

附件四：
1.北美：Harvard University Press（哈佛大学出版社），
Yale University Press（耶鲁大学出版社），
Princeton University Press（普林斯顿大学出版社），
Stanford University Press（斯坦福大学出版社），
University of Chicago Press（芝加哥大学出版社），
Columbia University Press（哥伦比亚大学出版社）， MIT Press（麻省理工学院出版社），University of California Press（加州大学出版社），Cornell University Press（康奈尔大学出版社），
Johns Hopkins University Press（约翰·霍普金斯大学出版
社）， Duke University Press（杜克大学出版社），
University of Toronto Press（多伦多大学出版社），Legas Press（莱格斯出版社）。

2.欧洲：Oxford University Press（牛津大学出版社），Cambridge University Press（剑桥大学出版社），Routledge（路特利支出版社），Blackwell（布莱克威尔出版社），Sage（赛奇出版社），Mouton de Gruyter（莫顿·德·格鲁特出版社），John Benjamins （约翰·本杰明出版公司），Springer（斯普林格出版社），Brill（布里尔出版社）。

质疑学术权威的英语作文As an AI language model, I do not possess personal emotions or opinions. However, I can provide a response to the topic based on commonly expressed perspectives.There is a growing trend of skepticism towards academic authority and expertise in various fields. This phenomenon has been fueled by the rise of alternative sources of information, the spread of misinformation, and the increasing accessibility of knowledge through the internet. While it is healthy to question and critically evaluate established knowledge, the outright dismissal of academic authority can have detrimental effects on the pursuit of truth and the advancement of human knowledge.One of the main reasons for questioning academic authority is the perception of bias and conflicts of interest within the academic community. Critics argue that academic research and knowledge production are often influenced by funding sources, institutional affiliations,and personal agendas. This perceived lack of objectivity has led to doubts about the credibility and reliability of academic findings. In some cases, high-profile scandals involving academic misconduct have further eroded public trust in the integrity of the academic establishment.Moreover, the exclusivity and elitism associated with academic institutions have contributed to the skepticism towards academic authority. The traditional hierarchical structure of academia, with its emphasis on credentials, tenure, and peer review, has been criticized for creating barriers to entry and perpetuating a culture ofintellectual elitism. This has led to the perception that academic experts are out of touch with the concerns and perspectives of the general public, leading to a disconnect between academic knowledge and real-world issues.In addition, the democratization of information through the internet has empowered individuals to challenge established academic narratives. With the proliferation of online platforms and social media, anyone can present themselves as an authority on a given topic, regardless oftheir qualifications or expertise. This has led to the spread of misinformation and the blurring of lines between credible academic research and unverified claims, further eroding public trust in academic authority.However, it is important to recognize the value of academic expertise and authority in advancing human knowledge and understanding. Academic institutions serve as the foundation for rigorous research, critical inquiry, and the peer review process, which are essential for ensuring the quality and reliability of knowledge production. While it is crucial to question and scrutinize academic findings, it is equally important to acknowledge the expertise and dedication of scholars who have devoted their careers to the pursuit of knowledge.Furthermore, the peer review process, which is a cornerstone of academic research, serves as a mechanism for quality control and validation of scholarly work. While it is not without its flaws, peer review helps to ensure that academic research meets rigorous standards of evidence and methodology, contributing to the overall credibility ofacademic authority.In conclusion, while it is natural to question and challenge established authority, it is important to recognize the value of academic expertise in advancing human knowledge and understanding. The skepticism towards academic authority should not lead to the wholesale rejection of scholarly research and expertise, but rather to a critical evaluation of knowledge claims and a commitment to upholding the integrity of academic inquiry. By fostering a culture of open dialogue, transparency, and accountability, we can work towards restoring public trust in academic authority and promoting the pursuit of truth and knowledge.。

bib引用参考书籍Bibliographies can be a tricky part of academic writing, but they are essential for providing credibility and evidence for your arguments. When citing books in your bibliography, it's important to follow the correct format depending on the citation style required by your professor or publication. This not only ensures that your sources are properly credited, but also allows readers to easily locate the books you have referenced.在学术写作中，参考书目可能会让人感到棘手，但它们对于为你的论点提供可信度和证据至关重要。

在参考书目中引用书籍时，根据你的教授或出版物要求的引用样式，采用正确的格式是非常重要的。

这不仅确保你的来源得到正确的证书，还让读者能够轻松地找到你引用的书籍。

When citing a book in your bibliography, make sure to include all the necessary information such as the author, title, publication date, and publisher. The format of the citation may vary depending on the citation style you are using, whether it's APA, MLA, Chicago, or others. It's also important to include the page numbers of thespecific information you are referencing, especially when quoting directly from the book.在参考书目中引用书籍时，确保包含所有必要信息，比如作者、书名、出版日期和出版商等。

Landscape Ecology vol. 2 no. 2 pp 111-133 (1989)SPB Academic Publishing, The Hague A review of models of landscape changeWilliam L. BakerDepartment of Geography, University of Kansas, Lawrence, Kansas 66045Keywords: Models, landscape change, reviewAbstractModels of landscape change may serve a variety of purposes, from exploring the interaction of natural processes to evaluating proposed management treatments. These models can be categorized as either whole11240Earranged ‘landscape elements’114distribution of land area among states in distribu-tional landscape models, and a complete raster of grid cell values in a spatial landscape model. These initial configurations may be derived from a variety of sources, including published land use data, per-manent plots or monitoring data, oron a fixedland area, birth and death functions are often ab-sent. The change function modifies the output of a whole landscape model, changes the distribution of land area in a distributional landscape model, and alters the subarea values in a spatial landscape model. The change function may be as simple as a single linear differential or difference equation, but could also be a set of complex nonlinear equations with interactions.Outputs from whole landscape models are noth-ing more than a value for each variable. Output from distributional landscape models may include summary values for variables, as in whole land-scape models, but the more important output is a univariate or .multivariate distribution of land area.Spatial landscape models can output whole land-scape summary statistics, distributional landscape data, or individual subarea values. One can ag-gregate data, in other words, in more detailed models, and produce several kinds of output. Spa-tial models, in this sense, are the most flexible.Whole landscape modelsWhole landscape models focus on the value of a variable or several variables in a particular land area as a whole. Values of variables can be output directly (continuous state space) or can be classified into states (discrete state space). The time dimen-sion in these models can be formulated using con-tinuous or discrete mathematics. The basic dif-ferential equation, in the case of continuousmathematics, iswhere X is some landscape variable of interest, f(X)is some function of X, and t is time. The basic difference equation, using discrete mathematics, isX115may also be constrained or determined by the data source. Only a certain number and kind of states,for example, can currently be distinguished in remotely-rensed data (Hallet al.though these methods do not objectively determine the number of states. Vandermeer (1978)suggested that the optimum number of states can be determined by a method that minimizes the trade-off between a ‘sample error’, that increases as the narrowness of states (and the number of states) in-creases, and an ‘error of distribution’, that in-creases as the narrowness of states (and the number of states) decreases. As the number of states in-creases, the computational burden and data re-quirements for these models increase exponentially.Both continuous and discrete mathematics have been used for the time dimension in these models,but there may be little difference in the utility of these two approaches. For example, the average response of a stationary Markov chain can be matched by a corresponding linear, constant-coef-ficient differential equation (Shugart et al. 1973).Differences in the development of the supporting literature may influence the choice between these two approaches, but differential equations may no longer be a better framework for the use of non-stationary transitions (Johnson and Sharpe=at(3)where N(a, t) is a function describing the age dis-tribution of individuals (or land area, in a land-scape model),Von Foerster equation is quite general, and doesnot, for example, require constant coefficients. The1979; Nisbet and Gur-ney 1982; Gyllenberg 1984).Continuous state space differential equation models may also be multivariate. Multivariate ver-sions were developed for modelling joint age-size distributions of animal populations (e.g., Sinko and Streifer 1967, 1969; Streifer 1974; Oster andTakahashi 1974). The equation in this case isatis the death rate foranimals of age a and mass m at time t.The multivariate version of these models can a be applied to landscape change. The model in case describes the changing age-size distribution patches, where the patches in the original116tion (Levin and Paine 1974; Levin 1976) may be of any size, including those on the landscape scale.The equation, then, is identical to equation 4, but N(t,a,m) is the age-size distribution of patches at time t, g(t,a,m) is the average rate of growth for apatch of age a and size m at time t, whilein intertidal mussel beds along the Washingtoncoast (Paine and Levinr117coefficients and initial conditions, and model valid-ity were challenged (Hahn and Learyson 1977), but Johnson also simulated the effects of harvesting and natural disturbances (e.g., wind, in-sects). Johnson’s model has since been modified and coupled directly with a model of gypsy moth in-festation (Byrne et al. 1987). All three modelling ef-forts required substantial estimation, as direct esti-mates of transfer coefficients were unavailable.Moreover, these models did not include the effect of exogenous variables, though such variables could be incorporated in the equations. Nonethe-less, these models represent a potentially useful ap-proach to modelling landscapescale change in a non-spatial format.Difference equation modelsThere are no landscape models that use difference equations and a continuous state space. All differ-ence equation, distributional models, using discrete state spaces, can be expressed in their simplestform, in matrix notation, as:where . . . n,),whose elements are the fraction of land area in each of m states at time t, and P is an m X m matrix,whose elements,als tim118populations (Usher and Parrchanges in diameter distributions offorest trees (e.g., Roberts and Hruska and migration of people (e.g., Brown 1970). But,although the methods employed in these studies are comparable, more relevant to landscape modelling are applications in modelling changes in vegetation types and land use.AuthorStates LocationLand useDrewett 1969Bourne 1971Bell 1974Bell and Hinojosa 1977Robinson 197810 levels of urbanization Reading 10 levels of urban land use Toronto 6 land-use types San Juan Is.6 land-use types San Juan Is.Markov chains have been used to model changes in vegetation types on a variety of spatial scales.Changes on small areas of less than a few hectares (Austin 1980; Austin and Belbin 1981; Kachi et al.1986) or on a single small plot (Lough et al. 1987)have been modelled. There are also many studies of changes in vegetation on areas of less than a few hundred hectares, based upon changes in scattered plots or transects within the area (e.g., Williams et al.1969; Stephens and Waggoner 1970;119 fining the state space so that the new states are de-fined by both present and preceding states (Massyet al. 1970). A second-order model would thus in-cludeorder model. Estimating the transitions for such amodel would require substantial data, derived fromobservations during at least two time intervals fol-lowing the initial observation.The contribution of exogenous or endogenousvariables to the transitions, and thus nonstationarytransitions, can be120landscape feature (or pixel) has been in state i. Such phenomena are thus non-Markovian, and Markov chain models may be inappropriate. Examples of such phenomena might include forests whose fireprobability increases with time since fire and urban structures more likely to be either torn down or, al-ternatively, preserved as historical features, as their age increases.The effect of this varying ‘duration-of-stay’ or ‘sojourn time’ can be dealt with in two ways. The first way is to redefine the state space so that each previous “state includes several duration classes (e.g., McGinnis 1968). The resulting model may then be Markovian.‘The second way (Ginsberg 1971, 1972a) is to use a semi-Markov model. In such a model movements among states are govern-ed by a constant-transition Markov chain, with transition matrix P = ) , while sojourn times have distribution which depend only on i and j. In a Markov chain the sojourn times are constant, while in the semi-Markov model the distribution is, then, a matrix of transition distributions for the semi-Markov process. Transition probabilities for the process and the expected distribution among states at any time t can be derived from the Q matrix (Ginsberg 1971; see Gilbert 1972 for a workedexample). TheMarkov models to landscape change is a model of the effects of fires on the Tasmanian landscape (Henderson and Wilkins 1975; Wilkins 1977). These authors experimented with two Gamma dis-tributions forand derivatives of such models have been used in landscape modelling. These deter-ministic projection models again have the general form of equation 6, but the P matrix may be dif-ferent from that used in Markov chain models. Researchers in many fields have preferred deter-ministic projection models over stochastic models, such as Markov chains, in part because the transi-tion rates beingbut are simpler conceptu-ally than are corresponding stochastic models. In a utilitarian sense, there may be little real difference in projection results using the two approaches, as nearly equivalent models can be, and have been de-veloped. This is particularly true, since many of the limitations thought to be inherent in the Markov chain framework have been overcome, as was dis-cussed earlier in the paper. The choice between the two modelling frameworks appears to be based in part on the tradition in a particular discipline. The Leslie model and its derivatives, as well as more generic projection models, have long been used to model plant and animal, as well as human population dynamics. While the Leslie models have explicit birth functions designed for biological populations, it is a simple matter to adapt exten-sions of these models and other projection models for landscape use. Developments in deterministic projection modelling are too extensive to review in detail here (see Rees and Wilson 1977; Usher 1972, 1981; Rees 1986). But, there are few limits in thesemodels in incorporating higher-order effects (Leslienonstationary transitions and effects ofet al.spatial effects (Rees and Wilson121 (e.g., Ek 1974). As an example of how extensivethese models can become, the Census Bureau usesa model with ‘142 economic equations for 51 geo-graphic units, or over 7000 endogenous equationswith over 10,000 endogenous and exogenous varia-bles, . . .26,000 migration flows, and . . . over 500age-sex groups’ (Long and122with each123Fig.2. Example of a mosaic model for a 3 x3 grid. Change in each cell isis a column vector,. .of m states at time t in the grid cell inrow i and column j, and Pij is an m X m matrix, whose elements,1987). The neutral modelsare randomly-generated mosaics of occupied or not-occupied cells, whose generation and analysis is based upon percolation theory (Stauffer 1985). The random patterns can be used as ‘null’ models to test hypotheses about landscape pattern, but Turner et al. (1988) also used them to simulate the effects of landscape pattern on the spread of disturbance.Distributional mosaic models:In these models each cell has a distributional land-scape submodel, which can be univariate orm m m t1241985; Rees and Wilson 1977; Woods and Rees 1986; Long andComparable, but much less complex spatial projec-tion models have been applied in modelling other animal and plant populations (e.g., Usher and Wil-liamson 1970; Cuff and 1980; Hobbs and Hobbs 1987) at small spatial scales. As will be dis-cussed below, these spatial population models can also be applied at the landscape scale.Building on multi-regional population models there is also a very extensive spatial modelling liter-ature relating to urban systems (reviewed bythese models now typi-cally include submodels for population growth, residential location, workplace location, the de-velopment of infrastructure and transport systems, job location, location of services, and economic de-velopment (e.g.,, Wilson 1987). Models such as these, modified for rural settings, could help in un-derstanding how landscape structure develops in human-modified landscapes.A major impetus for the development of land-scape-level models of natural systems came from the realization that spatial environmental hetero-geneity results in spatial variation in the population dynamics of plants and animals (e.g., Smith 1980). Some authors (Shugart and Noble 1981; Dale and Gardnerto simulate landscape-level spatial variation in forest growth and disturbance effects. But, the JABOWA model and its derivatives were designed for small plots (typically 0.1 ha), and the landscape-level simulations involve simply altering the boundary conditions to replicate spatial variation in the en-vironment at distinct locations over a region.125water volume, salinity, and sediments were used topredict changes in marsh habitats. Sklar and1979b; Kessell and Cattelino1978; Potter et al. 1979; Potter and Kessell 1980;Kessell et al. 1984). These models have been applied in the coniferous forests of Glacier National Park,Montana (Kesselll976, 1977,and in a variety of ecosystems in Australian parks and nature reserves (Kessell and Good 1982;Kessell et al. 1984). A very general version of themodels, called has also been developed and applied (Potter et al. 1979; Potter and Kessell 1980).All of Kessell et al.‘s models operate from a(GIS). Vegetation and fuels data are estimated for each grid-cell from its environmental location by using gradient models that relate species composi-tion and fuel levels to environment location. A major strength of Kessell et al. ‘s models is their ex-plicit modelling of fire behavior and post-fire suc-cession on the landscape in relation to the landscapedata in theand it has been linkedexplicitly, in some cases, with a weather model (e.g., Kessell 1979a). The post-fire succession models have been deterministic models, based on habitat types (Kessell 1979a) or life-history traits of each species (e.g., Cattelino et al. 1979). Separate models have also been developed, on a more limited basis, for predicting the response of large and small mammals to changes in landscape structure (Kessell 1979a; Potter and Kessell 1980; Kessell et al. 1984).In some senses the strength of Kesselletthe ef-fects of other kinds of disturbances (e.g., insect at-tacks), and species-specific response to climate change. Certainly, there are an almost unlimited number of other subroutines that would be desir-able for specific modelling purposes, but, inevi-tably, models of this complexity are limited by fis-cal constraints, computer capabilities, available data, and scientific knowledge. Kesselletaverage (STARIMA) models (e.g., Pfeifer and Deutsch 1980). Such models have been applied in hydrologic forecasting (Deutsch and Ramosa number of models have been developed that focus on the response of individual organisms to the spatial configuration, character,and density of neighbors. Such models, using a grid-cell or vector data base of mapped organism locations, often use spatial-influence, or compe-tition-indices to quantify neighborhood effects126also include dispersal functions (e.g., Weiner and Conte 1981) and a mechanism for lateral growth (e.g., Tongeren and Prentice 1986). Models of thistype have been developed for trees (e.g.,annual plants (Weiner and Conte 1981;Pacala and Silanderand sessile marine organisms(Maguire and Porter 1977; Karlson.1981; Eston et al. 1986).Such individual organism models are not land-scape models, but they may have some relevance to modelling landscape change. First, it is possible that analogous individual landscape ‘element’models could be developed, particularly in land-scapes where ecosystem-to-ecosystem interactions are significant, or disturbance patches are themajor landscape elements 1986). Although individual organism models have focused on the growth of the organism, individual landscape elements, which are landscape analogs of individual organisms, may not grow, or their growth may be of less interest than changes in other properties, such as their composition, age, or physi-cal characteristics. It is unclear, however, just how such element models might be constructed and whether they would have advantages over mosaic models. In contrast, in landscapes with disturbance patches, individual patch models can be developed that describe the birth, growth, and mortality of patches. Such disturbance-patch models have beendeveloped for fires in urban landscapes and for the oak-wilt diseasein midwestern forests (Menges and Loucks 1984).Second, the overall character of ecosystems, which constitute most landscape elements in natural land-scapes, is determined in part byand Sivec 1973). As another example, the spread of patch-creating insect attacks and diseases may de-pend upon tree-to-tree spatial relations that can bedividual organism or individual patch models may be appropriate as submodels in a mosaic model.Obviously, such models may require immense quantities of data and substantial computer re-sources, and could, as a result, be impractical for many landscapes.DiscussionThere is no perfect landscape change model, but models have been developed to serve a variety of purposes. Whole landscape models, which focus on aggregate phenomena of the landscape as a whole,have not been developed, perhaps because distribu-tional and spatial data are usually desired. Certain-ly the most widely used models of landscape change have been distributional models, which are models whose output is the percentage of land area in a set of classes or element types. Distributional models are popular largely because of their simplicity and utility, in addition to a well-established history of use. Spatial models, models that focus on the loca-tion and configuration of landscape elements, have not been widely developed and used, in spite of the necessity of spatial data for answering many land-scape ecological questions. This lack of devel-opment is probably because the data and com-putational requirements have, in the past, been prohibitive.Data and computational limits are becoming less significant, at least for some purposes, due to ad-vances in remote sensing for change detection (e.g.,Price 1986) and in the incorporation of sensed data and auxiliary data into geographical in-formation systems (Burrough 1986). But, although there are substantial data on how much and what kind of landscape change has occurred, remote sensing change-detection studies seldom include ex-plicit modelling of change processes. Similarly, formodelling important landscape127derived (perhaps from historical sequences of aerialphotographs) rates of landscape change may helpto refine our understanding of the causes of land-scape change (e.g., Alig 1986). This may be particu-larly true if such studies employ carefully designed‘natural experiments’ (Diamond 1986) to limit pos-sible outcomes. Nonetheless, multivariate analysisis no panacea, and has its own limits (e.g., Green1979). Modelling itself is another route to identifi-cation of key variables controlling landscapeprocesses.Second, modelling of individual ,128have limited value for some kinds of studies. I have argued in this paper that models of landscape change may be the only means we have to under-stand some landscape processes, but useful models cannot be developed without appropriate empirical data. An important prerequisite to developing use-ful models of landscape change is that landscape processes be perpetuated in some of the remaining relatively unaltered landscapes, and that these pro-cesses be studied over time. AcknowledgmentsThis work was supported in part by a grant from the National Science Foundation (SES-8601079). I thank the Department of Geography at the Univer-sity of Hawaii at Manoa for providing office space and assistance during the writing. Some of the ideas in this paper were formulated while I was a research assistant with Robert K. Peet at the University of North Carolina.ReferencesAcevedo, M.F. 1981. Electrical network simulation of tropical forests successional dynamics. In: Progress in Ecological En-gineering and Management by Mathematical Modelling, pp. 883-892. Edited by D.M. Dubois. Editions cebedoc, Liege. Alemdag, I.S. 1978. Evaluation of some competition indexes for the prediction of diameter increment in planted white spruce.Can. For. Serv., For. Manage. Inst. Inform. Rep.Range Exp. Sta., Fort Collins,AnAuAu129Envir. Manage. 3:4:157-185.Craig, R.G. and Labovitz, M.L. 1980. Sources of variation inAnn Arbor.Cuff, W.H. and Hardman, J.M. 1980. A development of the Leslie matrix formulation for restructuring and extending anecosystem model: The infestation of stored wheat by Ecol. Model. 9: 281-305.Dale, V.H. and Gardner, R.H. 1987. Assessing regional impacts of growth declines using a forest succession model. J. Envir.Manage. 24: 83-93.DeAngelis, D.L.andR.V. 1985. Ecological modelling and disturbance evaluation.Ecol. Model. 29: 399-419.Debussche, M.,Ecosystems 3: 81-92.Deutsch, S.J. and Ramos, J.A. 1986. Space-time modeling of vector hydrologic sequences. Water Resources Bull. 22:967-981.Diamond, J. 1986. Overview: Laboratory experiments, field ex-periments, and natural experiments.Row, New York.Dodson, J.R., Greenwood, P.W. and Jones, R.L. 1986. Holo-cene forest and wetland vegetation dynamics at Barrington Tops, New South Wales. J. Biogeogr. 13: 561-585.Doubleday, W.G. 1975. Harvesting in matrix population models. Biometrics 31: 189-200.Drewett, J.R. 1969. A stochastic model of the land conversion process. Regional Studies 3: 269-280.Edelstein-Keshet, L. 1988. Mathematical Models in Biology.Random House, New York.Ek, A.R. 1974. Nonlinear models for stand table projection in northern hardwood stands. Can. J. For. Res. 4: 23-27.Emanuel, W.R., Shugart, H.H. and Stevenson, M.P. 1985. Cli-matic change and the broad-scale distribution of terrestrial ecosystem complexes. Climatic Change 7: 29-43.Eston, V.R., Calves, A., Jacobi, C.M., Langevin, R. and Tana-ka, N.I. 1986. (Cirripedia) andBrachidontesogical Systems: State-of-the-Art in Ecological Modelling. pp.61-67. Edited by W.K. Lauenroth, G.V. Skogerboe and M. Flug. Elsevier, New York.Fahrig, L. and Merriam, G. 1985. Habitat patch connectivity and population survival. Ecology 66: 1762-1768.Feller, W. 1968. An Introduction to Probability Theory and its Applications. Vol. 1. 3rd Ed. Wiley, New York.Finn, J.T. 1985. Analysis of land use change statistics throughthe use of Markov chains.R.T.T. andR.T.T. 1987. Creating landscapepatterns by forest cutting: Ecological consequences and prin-ciples. Landscape Ecology 1: 18.Freedman, H.I. 1980. Deterministic Mathematical Models in Population Biology. Marcel Dekker, New York.Gardner, R.H., Milne, B.T., Turner, M.G. andVegetatio 52: 151-159.Gilbert, G. 1972. Two Markov models of neighborhood housing turnover. Envir. and Planning A 4: 133-146.Ginsberg, R.B. 1971. Semi-Markov processes and mobility. J.Math.Hydractinia echinata.Ecol. Model. 13: 29-47.Kemeny, J.G. and Snell, J.L. 1960. Finite Markov Chains. D. Van Nostrand, Princeton, New Jersey.Kessell, S.R. 1976. Gradient modeling: A new approach to fire modeling and wilderness resource management. Envir. Manage. 1: 39-48.Kessell, S.R. 1977. Gradient modeling: A new approach to fire modeling and resource management. In: Ecosystem Modeling in Theory and Practice: An Introduction with Case Histories. pp. 575-605. Edited by C.A.S. Hall and J.W. Day, Jr. Wiley, New York.Kessell, S.R. 1979a. Gradient Modeling: Resource and Fire Management. Springer-Verlag, New York.Kessell, S.R. 1979b. Phytosociological inference and resource management. Envir. Manage. 3: 29-40.Kessell, S.R. and Cattelino, P.J. 1978. Evaluation of a fire be-havior information integration system for southern Califor-nia chaparral wildlands. Envir. Manage. 2: 135-159. Kessell, S.R. and Good, R.B. 1982. PREPLAN (Pristine En-vironment Planning Language and Simulator) user’s guide for Kosciusko National Park. National Parks and WildlifeService of New South Wales131 Markovian model. Vegetatio 71: 129-138.Lowry, 1.9. 1964. A Model ofH.E., Jr. and Sivec, N. 1959. The present compositionof a former oak-chestnut forest in the Allegheny Mountainsof western Pennsylvania. Ecology 54: 915-918.Maguire, L.A. and Porter, J.W. 1977. A spatial model ofgrowth and competition strategies in coral communities.Ecol. Model. 3: 249-271.132ing forest succession over large regions. For. Sci. 19: 203-212.Shugart, H.H., Jr., Crow, T.R. and Hett, J.M. 1974. Reply to Jerold T. Hahn and Rolfe A. Leary on forest succession models. For. Sci. 20: 213.Shugart, H.H., Jr. and Noble, I.R. 1981. A computer model of succession and fire response of the high-altitude Eucalyptus forest of the Brindabella Range, Australian Capital Territory. Austr. J. Ecol. 6: 149-164.Shugart, H.H., Jr. and Seagle, S.W. 1985. Modeling forest landscapes and the role of disturbance in ecosystems and com-munities.and P.S. White. Academic Press, New York.Shugart, H.H., Jr. and West, D.C. 1977. Development of an Appalachian deciduous forest succession model and its appli-cation to assessment of the impact of the chestnut blight. J. Envir. Manage. 5: 161-179.Shugart, H.H., Jr. and West, D.C. 1980. Forest succession models. Bioscience 30: 308-313.Simmons, A.J. and Bengtsson, L. 1984. Atmospheric general circulation models: their design and use for climate studies.Ecology 50: 608-615. Sklar, F.H. and Costanza, R. 1986. A spatial simulation of ecosystem succession in a Louisiana coastal landscape.M. 1974. Competition and regional coexistence. Ecol-ogy 5.5: 128-134.Slobodkin, L.B. 1953. An algebra of population growth. Ecol-ogy 34: 513-519.Smith, O.L. 1980. The influence of environmental gradients on ecosystem stability. Am. Natur. 116: l-24.dependent growth model of a perennial herb,cessional data. J. Ecol. 57: 515-536.Wilson, A.G. 1974. Urban and Regional Models in Geographyand Planning. Wiley, Chichester, U.K.Wilson, A.G. 1987. Transport, location and spatial systems:Planning with spatial interaction models.R. 1987. A transition matrixmodel of seasonal changes in mite populations. Ecol. Model.37: 167-189.。

质疑学术权威的英语作文Questioning academic authority is a natural and healthy part of intellectual inquiry. 质疑学术权威是智识探究中自然而健康的一部分。

Academic authority is often seen as the pinnacle of knowledge and expertise in a particular field. 学术权威通常被视为某一领域知识和专业技能的最高水平。

However, blindly accepting academic authority without question can lead to stagnation and limited intellectual growth. 但是，盲目地接受学术权威而不加质疑可能导致停滞和知识增长受限。

It is important to remember that academic authority is not infallible and can be subject to bias, error, and manipulation. 需要记住学术权威并非绝对可靠，可能受到偏见、错误和操纵的影响。

Questioning academic authority can lead to new insights, innovative approaches, and a deeper understanding of complex issues. 质疑学术权威有助于带来新的见解、创新方法和对复杂问题的深入理解。

Everyone should feel empowered to challenge academic authority and contribute to the ongoing pursuit of knowledge. 每个人都应当有力量挑战学术权威，为知识的持续追求做出贡献。

中南大学商学院学术期刊分级方案中文期刊一、I级中文期刊（4本）管理科学学报、经济研究、管理世界、中国社会科学（中英文版）二、II级中文期刊（17本）管理科学与工程：系统工程理论与实践、中国管理科学、系统工程学报、管理工程学报工商管理：会计研究、南开管理评论、科研管理、中国软科学、心理学报应用经济学：经济学（季刊）、世界经济、金融研究、中国工业经济理论经济学：经济科学、财经研究综合：新华文摘（全文转载）、中国社会科学文摘（全文转载）三、III级中文期刊（20本）管理科学与工程：运筹与管理、管理科学、控制与决策、系统工程与电子技术、系统工程工商管理：科学学研究、管理评论、管理学报、研究与发展管理、营销科学学报、心理科学、审计研究应用经济学：经济学动态、数量经济与技术经济、国际贸易问题、产业经济研究、资源科学理论经济学：财贸经济、世界经济文汇、中国农村经济四、IV级中文期刊1、CSSCI、CSCD、EI 收录期刊（III级及以上除外，收录期刊根据最新目录适时调整，包括论文见刊时上一年度或发表论文当年来源期刊）。

备注：教育部《高校智库专刊》、教育部社科委《专家建议》、中宣部社科规划办《成果要报》、人民日报（理论版）、光明日报（理论版）、经济日报（理论版）视同为II级中文期刊。

2、商学院承办的《财务与金融》杂志。

英文期刊期刊分级原则：以英国商学院协会（the Association of Business School, 简称ABS）出版的高质量学术期刊指南（ABS Academic Journal Quality Guide 2015版本）为基础，参考美国商学院前100名研究能力评估的24种顶级期刊（由美国得克萨斯大学达拉斯分校发布，简称UT/DALLAS 24种）和英国金融时报（Financial Times）界定的45种管理类一流学术期刊（简称FT/45），同时考虑领域交叉，将汤森路透JCR分区的一区和二区相关国际期刊也一并进行界定。

纸张A4，页边距上下左右分别为2.5 cm ，正文单栏排版，不低于10页Title Sample (Title Case ，居中，Time New Roman, 字号 14磅, 粗体, 固定值16磅, 段后1行)First Author 1, Second Author 1*, Third Author 2 (姓名用全称，名前姓后，居中，Time New Roman, 字号10磅, 固定值12磅，段后1行。

通讯作者标*，如果作者来自不同单位，用1, 2, 3区分)1Departments of xxxx, University of xxxxx, City Post code, Country (两端对齐，Times New Roman, 字号 10磅,斜体, 固定值12磅)2Departments of xxxx, University of xxxxx, City Post code, Country (两端对齐，Times New Roman, 字号 10磅,斜体, 固定值12磅，段后1行)Email: xxxx (全部作者的邮箱)TEL:通讯作者的联系电话Abstract: Our purpose was to evaluate the usefulness of the germination vs. the X-ray test in deter- mining the initial viability of seeds of eight wild species .. (150-250个英文单词，两端对齐，Times New Roman, 字号10磅, 固定值12磅)Keywords: K eywords 1; Keywords 2; Keywords 3;….. (两端对齐，Times New Roman, 字号10磅, 固定值12磅, 段后1行。

每个关键词只第一个字母大写，关键词之间用英文;隔开)要求一级标题要分别写为,1. Introduction;2. Material and Methods;3. Results;4. Discussion;5. Conclusions;Acknowledgements; References1. Heading 1 (一级标题，Title Case ，两端对齐，Time New Roman ，字号10磅，粗体，固定值12磅，段后1行)Paragraph (正文段落，两端对齐，首行缩进1.5字符， Times New Roman, 字号10磅, 固定值12磅)1.1. Heading 2 (二级标题，Title Case ，两端对齐，Time New Roman ，字号10磅，固定值12磅，段后1行)1.1.1. Heading 3 (三级标题，Title Case ，两端对齐， Time New Roman ，字号10磅，斜体，固定值12磅，段后0.5行)XXFigure 1. Final germination percentages (mean ± se) of seeds of Salvia spinose.（图题，居中，Times New Roman, 字号9磅, 固定值12磅，段前6磅，段后12磅,以英文.结束。

您的⼤英百科学术版Bri t annica Academic ⼊⻔指南。

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欢迎academic .eb .com欢迎订阅⼤英百科学术版Bri t annica Academic⼤英百科学术版为进⾏全⾯的跨学科研究提供了所有必要条件。

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⾸⻚搜索栏⽬在搜索栏⽬在输⼊关键词进⾏搜索。

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哈佛通识教育红皮书英文The Harvard Red Book of General EducationChapter 1: IntroductionThis chapter provides an overview of the purpose and goals of Harvard's General Education program, including the development of critical thinking skills and a broad understanding of different academic disciplines.Chapter 2: Aesthetic and Interpretive UnderstandingThis chapter explores the role of the arts in society and the ways in which they can be interpreted and understood. It includes discussions of literature, music, visual arts, and theater.Chapter 3: Culture and BeliefThis chapter examines the ways in which culture and belief systems shape human experience and behavior. It includes discussions of religion, philosophy, and anthropology.Chapter 4: Empirical and Mathematical ReasoningThis chapter focuses on the use of empirical and mathematical methodsto understand the natural world. It includes discussions of physics, biology, chemistry, and mathematics.Chapter 5: Ethical Reasoning and ActionThis chapter explores the ways in which individuals and societies make ethical decisions and take action based on those decisions. It includes discussions of ethics, political philosophy, and social justice.Chapter 6: Science of Living SystemsThis chapter examines the ways in which living systems function and interact with their environments. It includes discussions of neuroscience, psychology, and evolutionary biology.Chapter 7: Societies of the WorldThis chapter explores the diversity of human societies and the ways in which they are shaped by historical, cultural, and political factors. It includes discussions of anthropology, sociology, and history.Chapter 8: United States in the WorldThis chapter examines the role of the United States in global politics and economics. It includes discussions of international relations, economics, and history.Chapter 9: Engaging with DifferenceThis chapter focuses on the importance of understanding and engaging with difference in a diverse and interconnected world. It includes discussions of race, ethnicity, gender, and sexuality.Chapter 10: Expository WritingThis chapter provides guidance on developing effective writing skills, including the ability to analyze and communicate complex ideas. It includes discussions of rhetorical strategies, argumentation, and research methods.。

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Mario Vargas Llosa y José María Arguedas, La novela/La novela y el problema de la expresión literaria en el Perú, América Nueva, Buenos Aires, pp. 9-50, cited in N. Klahn & W. Corral (1991), pp. 341-359.Weisberg, R.W. 1993. Creativity: beyond the myth of genius. W. H. Freeman and Company.Wellek, R. and Warren, A. 1970. The Theory of Literature (3rd edition). New York: Harvest.Wilensky, R. 1983. ‘Story grammar versus story points.’ The Behavioral and Brain Sciences, 6, pp. 579-623.Appendix ASyntax of Primitive Actions.This appendix gives a formal description of the syntax of a language called Primitive Actions Description (PAD). The specification of PAD’s syntax is done in a story-grammar style where the following symbols are used:‘/’ represents the logical operator OR.‘<>‘ indicates that an element is a non-terminal symbol.’’’’ indicates that an element is a terminal symbol.‘{}’ indicates that an element is optional.‘,’ indicates that the following element must be defined in the next line of the file.’=‘ indicates the beginning of an expression.‘.’ indicates the end of an expression.’()’ used to group elements to maintain the clarity of the definition.A non-terminal symbol is an element that can be divided into sub-elements. A terminal symbol is an element that cannot be further divided. Any element on the left side of the symbol ‘=‘ is a non-terminal symbol. An expression defines the set of sub-elements given on the right-hand side, which form a non-terminal symbol. In this way, an element on the left side of the symbol ‘=‘ can be sub-divided in the elements included in the expression on the right side of the symbol ‘=‘.Some of the elements in the Primitive Actions require to be defined on different lines. The symbol ‘,’ is used to indicate that the next element must be defined in the following line of the text file. When elements are not separated by the symbol ‘,’ they can be defined in one line, but there must be at least one space between them.For example:Definition of Primitive Actions = <Primitive Action>,(<Definition of Primitive Actions>/ ‘END’).must be interpreted as follows: The non-terminal symbol Definition of Primitive Actions can be sub-divided in the following elements: the non-terminal symbol <Primitive Action>, and (in the next line) either the non-terminal symbol <Definition of Primitive Actions> or the terminal symbol ‘END’.A. Syntax to define Primitive Actions.Definition of Primitive Actions = <Primitive Action>,(<Definition of Primitive Actions>/ ‘END’).Primitive Action = <Description of the Action>, {<Description Preconditions>},{<Description Postconditions>}, {<Description Text>}.Description of an Action = ‘ACT’, <String > <Number of Characters>.Number of Characters = ‘1’/’2’/’3’.Description of Preconditions = ‘PRE’, <List of Preconditions>.List of Preconditions = <Precondition>, {<List of Preconditions>}.Precondition = <Pre-Emotional Link>/<Pre-Tension>.Pre-Emotional Link = ‘E’ <Character> <Character> <Intensity of EL> (<Type of Intensity>/’*’). Pre-Tension = ‘T’ <Tension> <Character> {(<Character>/’*’) {‘+’} }.Description Postconditions = {<Description Post-Emotional Link and Position>},{<Description Post-Tension>}.Description Post-Emotional Link and Position = ‘POS’, <List of Post-Emotional Link and Position>. List of Post-Emotional Link and Position = (<List of Post-Emotional Link>/<List of Position>),{<List of Post-Emotional Link and Position>}. Description Text = ‘TEX’, <String_Text>, {<String_Text>}.String_Text = (‘@A’ / ‘@B’ / ‘@C’ / <Symbol>) {<String_Text>}.List of Post-Emotional Link = <Post-Emotional Link>, {<List of Post-Emotional Link>}.Post-Emotional Link = ‘E’ (<Character>/<Linked Character>) <Character> (<Intensity of EL>/’%’) <Type of Intensity>.Linked Character = ‘La’/’Lb’/’Lc’.List of Position = <Position>, {List of Position}.Position = ‘P’ <Character> (<Number Position>/’b_pos’).Number Position = ‘1’/.../’9’.Description Pos Tension = ‘TEN’, <List of Pos Tension>.List of Pos Tension = <Pos Tension>, {<List of Pos Tension>}.Pos Tension = ‘T’ <Tension> <Character> {<Character> {‘+’} }.Description Pos Position = ‘Tension = ‘Ad’/’Lr’/’Hr’/’Pr’/’Ln’/’Hn’/’Pf’.Intensity of EL = ‘-3’/.../’+3’.Type of Intensity = ‘1’/’2’/’3’.Character = ‘A’/’B’/’C’/’a’/’b’/’c’.String = <Symbol> {<String >}.Symbol = ‘a’/.../’z’/’A’/.../’Z’/’1’../’9’/’_’.Appendix BSyntax Previous Stories.Following the same notation used in Appendix A, this appendix describes the syntax of a language called Definition of Previous Stories (DPS).Previous Stories = <Story>, (<Previous Stories>/’END’).Story = ‘STO’, {Definition of Scenery}, <Sequence of Actions>.Definition of Scenery = ‘SCENERY’ <Location>.Location = ‘Texcoco_Lake’/’Popocatepetl_Volcano’/Tlatelolco_Market’/’Palace’/’Tenochtitlan_City’/ ‘Temple’/’Jail’/’Chapultepect_Forest’.Sequence of Actions = <Action>, {<Sequence of Actions>}.Action = <Normal Action>/<Compound Action>.Normal Action = <String Character> <String Action> <String Character>.Compound Action = <String Character> (‘WAS_TOLD’/’REALISED’) <Normal Action>.String Character = <String>.String Action = <String>.String = Any character represented in ASCIII.Appendix CPrimitive Actions to Test the PrototypeFor problems of space it is impossible to include all the Primitives Actions used to test MEXICA. However, this appendix includes representative examples. Notice that the Primitive Actions Definition language allows using the semicolon ‘;’ to insert comments in the specification of actions.The syntax to define Primitive Actions specifies that associated texts must be defined in on line. However, sometimes such texts are too long that — in this appendix — it is necessary to use two or more lines. Those parts of the texts that cannot be included in the original first line are marked with the symbol ‘&-’.ACTAttacked 2PREE a b -2 * ; A(-2,*):BPOSE b a -3 1 ; B(-3,1):AE Lb a % 1 ; Lb(%,1):ATENT Lr b a + ; Lr(b):a+TEXT@A thoroughly observed @B. Then, @A took a dagger, jumped towards @B and attacked @B.@A's frame of mind was very volatile and without thinking about it @A charged against @B.ACTCured 2PRET Hr b *POSE b a +3 1E Lb a % 1TENT Hn b aTEXT@A went in search of some medical plants and cured @B. As a result @B was very grateful to @A.@A had heard that the Tepescohuitle was an effective curative plant. So, @A prepared a plasma and applied it to &- @B's wounds. It worked and @B started to recuperate! @B realised that @A's determination had saved @B's &- life.ACTdied_by_injuries 1PRET Hr a *TENT Dead aTEXTThe injuries that @A received were very serious. So, while praying to Mictlantecuhtli (the Lord of the land of the &- dead) @A died.The injuries that @A received were very serious. However, @A knew that when a Mexica dies fighting, the Gods &- protect that soul in order it arrives safely to the other world. So, @A died in peace.discovered_murder_of 2 ; @A discovers that @B killed someone very close to @APREE a b +2 *POSE a b -3 1TEXT@A was kissing @B when suddenly @A recognised @B's tattoo. It was the same as the one used by the fraternity &- which had murdered @A's father some months ago. At once all those terrible memories were present again.@A was happy to have a strong relationship with @B. Suddenly, a farmer arrived and observed @B; therewas &- no doubt, @B was the murder of @A’s brother.ACTHad_an_accident 1TENT Hr a -TEXTTlaloc -the God of the rain- was angry and sent a storm. The heavy rain damaged the old wooden bridge.When &- @A tried to cross the river the bridge collapsed injuring badly @A's head.ACTKidnapped 2POSE b a -3 1E Lb a % 1P a 9 ; A’s position is set to Chapultepec ForestP b 9 ; B’s position is set to Chapultepec ForestTENT Pr b aTEXT@A was an ambitious person and wanted to be rich and powerful. So, @A kidnapped @B and went to &- Chapultepec forest. @A's plan was to ask for an important amount of cacauatl (cacao beans) and quetzalli &- (quetzal) feathers to liberate @B.@A was an ambitious person and wanted power and money in an easy way. So, @A kidnapped @B andwent to &- Chapultepec forest. @A's plan was to ask for an important amount of cacauatl (cacao beans)and quetzalli &- (quetzal) feathers to liberate @B.During the last war @B's father humiliated @A's family. Now, it was time of revenge and @A kidnapped@B. &- They went to the forest where @A tied @B to a huge rock. Exactly at midnight @A would cut@B up.ACTKilled 2PREE a b -3 *POSE b a -3 1E Lb a % 1TENT Dead b aTEXT@A felt a deeply odium for @B. Invoking Huitzilopochtli, God of the war, @A cut @B's jugular. Theblood &- covered the floor.@A threw some dust in @B's face. Then, using a dagger @A perforated @B's chess. Imitating the Sacred&- Ceremony of the Sacrifice, @A took @B's heart with one hand and raised it towards the sun as a signof &- respect to the Gods.@A took a dagger and cut @B’s throat. @B bled to death while Tonatiuh (the God representing the sun) disappeared in the horizon.Rescued 2PRET Pr b *POSE b a +3 1E Lb a % 1TENT Pf b aTEXTThus, while Tlahuizcalpantecuhtli (the God who affected people's fate with his lance) observed, @A cutthe rope &- which bound @B. Finally, @B was free again!@A walked towards @B. Full of admiration for all the braveness that @B had shown in those hard moments @A &- liberated @B!;SPECIAL ACTIONS *****ACTCLASH_EMOTION_1 2 ;the first time it appears in the storyTEXT@A had ambivalent thoughts towards @B. On the one hand @A had strong feelings for @B but on the other &- hand @A abominated what @B did.@A was emotionally tied to @B but @A could not accept @B's behaviour. What did @A must do?ACTCLASH_EMOTION_2 2 ; the second time a Ce appears in a storyTEXT@A was shocked by @B's actions and for some seconds @A did not know what to do.ACTCLASH_EMOTION_3 1 ; When in a Ce both characters are the same.TEXT@A was emotionally devastated and was not sure if what @A did was right. @A was really confused.@A was emotionally devastated and confused, and was not sure if what @A did was right.ACTHealth_Risk_1 2 ; A = hero B = Victim A is aware of Hr(B)TEXT@A knew that @B could die and that @A had to do something about it.ACTLife_Risk_1 2 ; A = hero B = Victim A is aware of Lr(B)TEXT@A knew that the @B's life was in risk and had to do something about it.ACTPr_Free_1 2 ; A = hero B = Victim A is aware of Hr(B)TEXTAlthough it was very dangerous @A decided to do something in order to liberate @B. For some minutes@A &- prayed to Quetzalcoatl -the feathered-snake, the God between the Gods- and asked for wisdomand braveness. &- Now @A was ready to find out its fate.Suddenly, the day turned into night and after seconds the sun shone again. @A was scared. The Shaman &- explained to @A that Tonatiuh (the divinity representing the sun) was demanding @A to rescue @Band &- punish the criminal. Otherwise @A's family would die.ENDAppendix DPrevious Stories.Previous Stories used to generate The princess who cured the jaguar knight .Sto ;1 Eagle_Knight Actor Jaguar_Knight Actor Eagle_Knight Was_In_Love_With Princess Jaguar_Knight Was_In_Love_With Princess Princess Was_In_Love_With Warrior Eagle_Knight Got_Jealous_Of Warrior Eagle_Knight Killed Warrior Princess Attacked Eagle_Knight Eagle_Knight Wounded Princess Jaguar_Knight Attacked Eagle_Knight Jaguar_Knight Fought Eagle_Knight Jaguar_Knight Killed Eagle_Knight Jaguar_Knight Exiled Jaguar_Knight Sto ;2 Prince Went_Texcoco_Lake Prince Had_An_Accident Priest Found_By_Accident Prince Priest Realised Prince Had_An_Accident Priest Cured Prince Prince Went_Palace Fisherman Mugged Priest Prince Realised Fisherman Mugged Priest Prince Looked_For_And_Found Fisherman Prince Made_Prisioner Fisherman Sto ;3 Eagle_Knight Were_In_Love Lady Eagle_Knight Loved Princess Lady Loved Princess Princess Went_Popocatepetl_Volcano Enemy Kidnapped Princess Eagle_Knight Realised Enemy Kidnapped Princess Eagle_Knight Looked_For_And_Found Enemy Eagle_Knight Attacked Enemy Eagle_Knight Fought Enemy Eagle_Knight Killed Enemy Eagle_Knight Rescued Princess Princess Fell_In_Love Eagle_Knight Princess Realised Eagle_Knight Were_In_Love LadyPrincess Looked_For_And_Found Lady Princess Killed LadyEagle_Knight Realised Princess Killed Lady Eagle_Knight Followed Princess Eagle_Knight Killed PrincessEagle_Knight Killed Eagle_Knight Sto ;4 Tlatoani Was_Father_Of Prince Tlatoani Went_Hunting_With Prince Tlatoani Had_An_Accident Prince Did_Not_Cure Tlatoani Prince Went_Tenochtitlan_City Hunter Found_By_Accident Tlatoani Hunter Realised Tlatoani Had_An_Accident Hunter Cured Tlatoani Tlatoani Rewarded Hunter Tlatoani Looked_For_And_Found Prince Tlatoani Exiled Prince Sto ;5 Scenery City Eagle_Knight Realised Eagle_Knight Was_In_Love_With Lady Eagle_Knight Went_Tlatelolco_Market Lady Was_Attracted_To Jaguar_Knight Lady Went_Texcoco_Lake_With Jaguar_Knight Eagle_Knight Followed Lady Eagle_Knight Realised Lady Was_Attracted_To Jaguar_Knight Eagle_Knight Got_Jealous_Of Jaguar_Knight Eagle_Knight Attacked Jaguar_Knight Eagle_Knight Wounded Jaguar_Knight Lady Cured Jaguar_Knight Eagle_Knight Exiled Eagle_Knight Sto ;6 Scenery City Eagle_Knight Realised Eagle_Knight Was_In_Love_With Lady Eagle_Knight Went_Tlatelolco_Market Lady Was_Attracted_To Jaguar_Knight Lady Went_Texcoco_Lake_With Jaguar_Knight Eagle_Knight Followed LadyEagle_Knight Realised Lady Was_Attracted_To Jaguar_Knight Eagle_Knight Got_Jealous_Of Jaguar_Knight Eagle_Knight Attacked Jaguar_Knight Jaguar_Knight Fought Eagle_Knight Jaguar_Knight Killed Eagle_KnightSto ;7Scenery CityPrincess Went_Popocatepetl_Volcano Hunter Kidnapped PrincessFarmer Found_By_Accident HunterFarmer Realised Hunter Kidnapped Princess Hunter Attacked Farmer Farmer Fought Hunter Hunter Wounded Farmer Hunter Ran_AwayPrincess Did_Not_Cure Farmer Princess Went_Tenochtitlan_City Farmer Died_By_InjuriesPrevious Stories used to generate The lovers .STO;1 SCENERY Market START Eagle_Knight was_brother_of Jaguar_Knight Eagle_Knight were_in_love Princess Jaguar_Knight met Princess Jaguar_Knight fell_in_love Princess Jaguar_Knight tried_to_force_kiss Princess Eagle_Knight realised Jaguar_Knight tried_to_force_kiss Princess Eagle_Knight honor_was_damaged_by Jaguar_Knight Eagle_Knight attacked Jaguar_Knight Jaguar_Knight fought Eagle_Knight Jaguar_Knight hurt Eagle_Knight Jaguar_Knight kidnapped Princess Priest cured Eagle_Knight Eagle_Knight Looked_for_and_found Jaguar_Knight Jaguar_Knight attacked Princess Eagle_Knight killed Jaguar_Knight Eagle_Knight rescued Princess Eagle_Knight went_back_home Princess STO;2 START Eagle_Knight were_in_love Lady Eagle_Knight admired_and_respected Princess Princess Went_Popocatepetl_Volcano Enemy kidnapped Princess Eagle_Knight realised Enemy kidnapped Princess Eagle_Knight Looked_for_and_found Enemy Eagle_Knight attacked Enemy Eagle_Knight fought Enemy Eagle_Knight killed Enemy Eagle_Knight Rescued Princess Princess Fell_in_love Eagle_Knight Princess realised Eagle_Knight were_in_love Lady Princess Looked_for_and_found Lady Princess killed Lady Eagle_Knight realised Princess killed Lady Eagle_Knight followed Princess Eagle_Knight killed Princess Eagle_Knight commited_suicideSTO;3STARTPrince relatives_envy TlatoaniTlatoani Went_Hunting_With Prince Tlatoani had_an_accidentPrince Did_Not_Cure TlatoaniPrince Went_Tenochtitlan_CityHunter Found_By_Accident Tlatoani Hunter realised Tlatoani Had_an_accident Hunter cured TlatoaniTlatoani Rewarded HunterTlatoani Looked_for_and_Found Prince Tlatoani hated_and_loved Prince Tlatoani Exiled Prince STO;4SCENERY CitySTARTEagle_Knight Was_in_Love_With LadyLady went_Texcoco_lake_with Jaguar_KnightLady Were_Attracted_to Jaguar_KnightEagle_Knight followed LadyEagle_Knight realised Lady Were_Attracted_to Jaguar_KnightEagle_Knight hated Jaguar_KnightEagle_Knight hated LadyEagle_Knight attacked Jaguar_KnightEagle_Knight fought Jaguar_KnightEagle_Knight wounded Jaguar_KnightLady killed Eagle_KnightLady Did_not_know_to_cure Jaguar_KnightJaguar_Knight died_by_injuriesLady commited_SuicideSTO;5SCENERY CitySTARTPrincess went_Popocatepetl_volcano Hunter Kidnapped PrincessFarmer found_by_accident Hunter Farmer admired_and_respected Princess Farmer realised Hunter Kidnapped Princess Farmer attacked HunterFarmer fought HunterHunter wounded FarmerHunter ran_awayFarmer rescued PrincessPrincess Did_Not_Know_to_cure Farmer Farmer died_by_injuriesPrincess went_Tenochtitlan_city STO;6SCENERY CitySTARTHunter saved_life TlatoaniTlatoani rewarded HunterWarrior realised Tlatoani rewarded HunterWarrior mugged HunterWarrior went_Popocatepetl_volcanoHunter followed WarriorHunter had_an_accidentWarrior prepared_to_Sacrifice HunterTlatoani found_by_accident HunterTlatoani realised Warrior prepared_to_Sacrifice Hunter Tlatoani fought WarriorWarrior ran_awayTlatoani rescued HunterTlatoani cured HunterTlatoani went_Tenochtitlan_city_with Hunter。