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Sample records for adaptive landscape classification

  1. Adaptive Classification of Landscape Process and Function: An Integration of Geoinformatics and Self-Organizing Maps

    Coleman, Andre M.

    2009-07-17

    The advanced geospatial information extraction and analysis capabilities of a Geographic Information System (GISs) and Artificial Neural Networks (ANNs), particularly Self-Organizing Maps (SOMs), provide a topology-preserving means for reducing and understanding complex data relationships in the landscape. The Adaptive Landscape Classification Procedure (ALCP) is presented as an adaptive and evolutionary capability where varying types of data can be assimilated to address different management needs such as hydrologic response, erosion potential, habitat structure, instrumentation placement, and various forecast or what-if scenarios. This paper defines how the evaluation and analysis of spatial and/or temporal patterns in the landscape can provide insight into complex ecological, hydrological, climatic, and other natural and anthropogenic-influenced processes. Establishing relationships among high-dimensional datasets through neurocomputing based pattern recognition methods can help 1) resolve large volumes of data into a structured and meaningful form; 2) provide an approach for inferring landscape processes in areas that have limited data available but exhibit similar landscape characteristics; and 3) discover the value of individual variables or groups of variables that contribute to specific processes in the landscape. Classification of hydrologic patterns in the landscape is demonstrated.

  2. An Adaptive Landscape Classification Procedure using Geoinformatics and Artificial Neural Networks

    Coleman, Andre Michael [Vrije Univ., Amsterdam (Netherlands)

    2008-06-01

    The Adaptive Landscape Classification Procedure (ALCP), which links the advanced geospatial analysis capabilities of Geographic Information Systems (GISs) and Artificial Neural Networks (ANNs) and particularly Self-Organizing Maps (SOMs), is proposed as a method for establishing and reducing complex data relationships. Its adaptive and evolutionary capability is evaluated for situations where varying types of data can be combined to address different prediction and/or management needs such as hydrologic response, water quality, aquatic habitat, groundwater recharge, land use, instrumentation placement, and forecast scenarios. The research presented here documents and presents favorable results of a procedure that aims to be a powerful and flexible spatial data classifier that fuses the strengths of geoinformatics and the intelligence of SOMs to provide data patterns and spatial information for environmental managers and researchers. This research shows how evaluation and analysis of spatial and/or temporal patterns in the landscape can provide insight into complex ecological, hydrological, climatic, and other natural and anthropogenic-influenced processes. Certainly, environmental management and research within heterogeneous watersheds provide challenges for consistent evaluation and understanding of system functions. For instance, watersheds over a range of scales are likely to exhibit varying levels of diversity in their characteristics of climate, hydrology, physiography, ecology, and anthropogenic influence. Furthermore, it has become evident that understanding and analyzing these diverse systems can be difficult not only because of varying natural characteristics, but also because of the availability, quality, and variability of spatial and temporal data. Developments in geospatial technologies, however, are providing a wide range of relevant data, and in many cases, at a high temporal and spatial resolution. Such data resources can take the form of high

  3. Adaptation on Rugged Landscapes

    Daniel A. Levinthal

    1997-01-01

    A simple model is developed to explore the interrelationship between processes of organizational level change and population selection forces. A critical property of the model is that the effect on organizational fitness of the various attributes that constitute an organization's form is interactive. As a result of these interaction effects, the fitness landscape is "rugged." An organization's form at founding has a persistent effect on its future form when there are multiple peaks in the fit...

  4. A Classification of Landscape Services to Support Local Landscape Planning

    2014-03-01

    Full Text Available The ecosystem services approach has been proven successful to measure the contributions of nature and greenery to human well-being. Ecosystems have an effect on quality of life, but landscapes also, as a broader concept, may contribute to people's well-being. The concept of landscape services, compared to ecosystem services, involves the social dimension of landscape and the spatial pattern resulting from both natural and human processes in the provision of benefits for human-well being. Our aim is to develop a classification for landscape services. The proposed typology of services is built on the Common International Classification of Ecosystem Services (CICES and on a critical review of existing literature on human well-being dimensions, existing ecosystem service classifications, and landscape perception. Three themes of landscape services are defined, each divided into several groups: provisioning, regulation and maintenance, cultural and social life fulfillment, with the latter focusing on health, enjoyment, and personal and social fulfillment. A special emphasis is made on cultural services, which are especially important when applied to landscape and which have received less attention.

  5. Landscape structure and the speed of adaptation

    The role of fragmentation in the adaptive process is addressed. We investigate how landscape structure affects the speed of adaptation in a spatially structured population model. As models of fragmented landscapes, here we simulate the percolation maps and the fractal landscapes. In the latter the degree of spatial autocorrelation can be suited. We verified that fragmentation can effectively affect the adaptive process. The examination of the fixation rates and speed of adaptation discloses the dichotomy exhibited by percolation maps and fractal landscapes. In the latter, there is a smooth change in the pace of the adaptation process, as the landscapes become more aggregated higher fixation rates and speed of adaptation are obtained. On the other hand, in random percolation the geometry of the percolating cluster matters. Thus, the scenario depends on whether the system is below or above the percolation threshold. - Highlights: • The role of fragmentation on the adaptive process is addressed. • Our approach makes the linkage between population genetics and landscape ecology. • Fragmentation affects gene flow and thus influences the speed of adaptation. • The level of clumping determines how the speed of adaptation is influenced

  6. Classification algorithms using adaptive partitioning

    Binev, Peter

    2014-12-01

    © 2014 Institute of Mathematical Statistics. Algorithms for binary classification based on adaptive tree partitioning are formulated and analyzed for both their risk performance and their friendliness to numerical implementation. The algorithms can be viewed as generating a set approximation to the Bayes set and thus fall into the general category of set estimators. In contrast with the most studied tree-based algorithms, which utilize piecewise constant approximation on the generated partition [IEEE Trans. Inform. Theory 52 (2006) 1335.1353; Mach. Learn. 66 (2007) 209.242], we consider decorated trees, which allow us to derive higher order methods. Convergence rates for these methods are derived in terms the parameter - of margin conditions and a rate s of best approximation of the Bayes set by decorated adaptive partitions. They can also be expressed in terms of the Besov smoothness β of the regression function that governs its approximability by piecewise polynomials on adaptive partition. The execution of the algorithms does not require knowledge of the smoothness or margin conditions. Besov smoothness conditions are weaker than the commonly used Holder conditions, which govern approximation by nonadaptive partitions, and therefore for a given regression function can result in a higher rate of convergence. This in turn mitigates the compatibility conflict between smoothness and margin parameters.

  7. The sensory ecology of adaptive landscapes.

    Jordan, Lyndon A; Ryan, Michael J

    2015-05-01

    In complex environments, behavioural plasticity depends on the ability of an animal to integrate numerous sensory stimuli. The multidimensionality of factors interacting to shape plastic behaviour means it is difficult for both organisms and researchers to predict what constitutes an adaptive response to a given set of conditions. Although researchers may be able to map the fitness pay-offs of different behavioural strategies in changing environments, there is no guarantee that the study species will be able to perceive these pay-offs. We thus risk a disconnect between our own predictions about adaptive behaviour and what is behaviourally achievable given the umwelt of the animal being studied. This may lead to erroneous conclusions about maladaptive behaviour in circumstances when the behaviour exhibited is the most adaptive possible given sensory limitations. With advances in the computational resources available to behavioural ecologists, we can now measure vast numbers of interactions among behaviours and environments to create adaptive behavioural surfaces. These surfaces have massive heuristic, predictive and analytical potential in understanding adaptive animal behaviour, but researchers using them are destined to fail if they ignore the sensory ecology of the species they study. Here, we advocate the continued use of these approaches while directly linking them to perceptual space to ensure that the topology of the generated adaptive landscape matches the perceptual reality of the animal it intends to study. Doing so will allow predictive models of animal behaviour to reflect the reality faced by the agents on adaptive surfaces, vastly improving our ability to determine what constitutes an adaptive response for the animal in question. PMID:26018831

  8. Seri Landscape Classification and Spatial Reference

    O'Meara, Carolyn

    2010-01-01

    This thesis contributes to the growing field of ethnophysiography, a new subfield of cognitive anthropology that aims to determine the universals and variation in the categorization of landscape objects across cultures. More specifically, this work looks at the case of the Seri people of Sonora, Mexico to investigate the way they categorize…

  9. Landscape Classifications for Landscape Metrics-based Assessment of Urban Heat Island: A Comparative Study

    In recent years, some studies have been carried out on the landscape analysis of urban thermal patterns. With the prevalence of thermal landscape, a key problem has come forth, which is how to classify thermal landscape into thermal patches. Current researches used different methods of thermal landscape classification such as standard deviation method (SD) and R method. To find out the differences, a comparative study was carried out in Xiamen using a 20-year winter time-serial Landsat images. After the retrieval of land surface temperature (LST), the thermal landscape was classified using the two methods separately. Then landscape metrics, 6 at class level and 14 at landscape level, were calculated and analyzed using Fragstats 3.3. We found that: (1) at the class level, all the metrics with SD method were evened and did not show an obvious trend along with the process of urbanization, while the R method could. (2) While at the landscape level, 6 of the 14 metrics remains the similar trends, 5 were different at local turn points of the curve, 3 of them differed completely in the shape of curves. (3) When examined with visual interpretation, SD method tended to exaggerate urban heat island effects than the R method

  10. Hydrologic landscape regionalisation using deductive classification and random forests.

    Stuart C Brown

    Full Text Available Landscape classification and hydrological regionalisation studies are being increasingly used in ecohydrology to aid in the management and research of aquatic resources. We present a methodology for classifying hydrologic landscapes based on spatial environmental variables by employing non-parametric statistics and hybrid image classification. Our approach differed from previous classifications which have required the use of an a priori spatial unit (e.g. a catchment which necessarily results in the loss of variability that is known to exist within those units. The use of a simple statistical approach to identify an appropriate number of classes eliminated the need for large amounts of post-hoc testing with different number of groups, or the selection and justification of an arbitrary number. Using statistical clustering, we identified 23 distinct groups within our training dataset. The use of a hybrid classification employing random forests extended this statistical clustering to an area of approximately 228,000 km2 of south-eastern Australia without the need to rely on catchments, landscape units or stream sections. This extension resulted in a highly accurate regionalisation at both 30-m and 2.5-km resolution, and a less-accurate 10-km classification that would be more appropriate for use at a continental scale. A smaller case study, of an area covering 27,000 km2, demonstrated that the method preserved the intra- and inter-catchment variability that is known to exist in local hydrology, based on previous research. Preliminary analysis linking the regionalisation to streamflow indices is promising suggesting that the method could be used to predict streamflow behaviour in ungauged catchments. Our work therefore simplifies current classification frameworks that are becoming more popular in ecohydrology, while better retaining small-scale variability in hydrology, thus enabling future attempts to explain and visualise broad-scale hydrologic

  11. Blurred Image Classification based on Adaptive Dictionary

    Xiaofei Zhou; Guangling Sun; Jie Yin

    2012-01-01

    Two frameworks for blurred image classification bas ed on adaptive dictionary are proposed. Given a blurred image, instead of image deblurring, the sem antic category of the image is determined by blur insensitive sparse coefficients calculated dependin g on an adaptive dictionary. The dictionary is adap tive to an assumed space invariant Point Spread Function (PSF) estimated from the input blurred image. In o ne of th...

  12. A dynamical theory of speciation on holey adaptive landscapes

    Gavrilets, S

    1998-01-01

    The metaphor of holey adaptive landscapes provides a pictorial representation of the process of speciation as a consequence of genetic divergence. In this metaphor, biological populations diverge along connected clusters of well-fit genotypes in a multidimensional adaptive landscape and become reproductively isolated species when they come to be on opposite sides of a ``hole'' in the adaptive landscape. No crossing of any adaptive valleys is required. I formulate and study a series of simple models describing the dynamics of speciation on holey adaptive landscapes driven by mutation and random genetic drift. Unlike most previous models that concentrate only on some stages of speciation, the models studied here describe the complete process of speciation from initiation until completion. The evolutionary factors included are selection (reproductive isolation), random genetic drift, mutation, recombination, and migration. In these models, pre- and post-mating reproductive isolation is a consequence of cumulativ...

  13. Classification of pasture habitats by Hungarian herders in a steppe landscape (Hungary)

    Molnár Zsolt

    2012-01-01

    Abstract Background Landscape ethnoecology focuses on the ecological features of the landscape, how the landscape is perceived, and used by people who live in it. Though studying folk classifications of species has a long history, the comparative study of habitat classifications is just beginning. I studied the habitat classification of herders in a Hungarian steppe, and compared it to classifications of botanists and laymen. Methods For a quantitative analysis the picture sort method was use...

  14. Adaptation and extinction in experimentally fragmented landscapes.

    Fakheran, Sima; Paul-Victor, Cloé; Heichinger, Christian; Schmid, Bernhard; Grossniklaus, Ueli; Turnbull, Lindsay A

    2010-11-01

    Competition and disturbance are potent ecological forces that shape evolutionary trajectories. These forces typically work in opposition: when disturbance is infrequent, densities are high and competition is intense. In contrast, frequent disturbance creates a low-density environment in which competition is weak and good dispersal essential. We exploited recent advances in genomic research to quantify the response to selection by these powerful ecological forces at the phenotypic and molecular genetic level in experimental landscapes. We grew the annual plant Arabidopsis thaliana in discrete patches embedded in a hostile matrix and varied the number and size of patches and the intensity of disturbance, by creating both static and dynamic landscapes. In static landscapes all patches were undisturbed, whereas in dynamic landscapes all patches were destroyed in each generation, forcing seeds to disperse to new locations. We measured the resulting changes in phenotypic, genetic, and genotypic diversity after five generations of selection. Simulations revealed that the observed loss of genetic diversity dwarfed that expected under drift, with dramatic diversity loss, particularly from dynamic landscapes. In line with ecological theory, static landscapes favored good competitors; however, competitive ability was linked to growth rate and not, as expected, to seed mass. In dynamic landscapes, there was strong selection for increased dispersal ability in the form of increased inflorescence height and reduced seed mass. The most competitive genotypes were almost eliminated from highly disturbed landscapes, raising concern over the impact of increased levels of human-induced disturbance in natural landscapes. PMID:20956303

  15. Mitigation/Adaptation: landscape architecture meets sustainable energy transition

    Stremke, S.

    2009-01-01

    Mitigation of climate change and adaptation to renewable energy sources are among the emerging fields of activity in landscape architecture. If landscape architects recognize the need for sustainable development on the basis of renewable energy sources, then how can we contribute to sustainable and aesthetic transformation of the human environment?

  16. Transcriptome Analysis Reveals Signature of Adaptation to Landscape Fragmentation

    Ikonen, Suvi; Auvinen, Petri; Paulin, Lars; Koskinen, Patrik; Holm, Liisa; Taipale, Minna; Duplouy, Anne; Ruokolainen, Annukka; Saarnio, Suvi; Sirén, Jukka; Kohonen, Jukka; Corander, Jukka; Frilander, Mikko J.; Ahola, Virpi; Hanski, Ilkka

    2014-01-01

    We characterize allelic and gene expression variation between populations of the Glanville fritillary butterfly (Melitaea cinxia) from two fragmented and two continuous landscapes in northern Europe. The populations exhibit significant differences in their life history traits, e.g. butterflies from fragmented landscapes have higher flight metabolic rate and dispersal rate in the field, and higher larval growth rate, than butterflies from continuous landscapes. In fragmented landscapes, local populations are small and have a high risk of local extinction, and hence the long-term persistence at the landscape level is based on frequent re-colonization of vacant habitat patches, which is predicted to select for increased dispersal rate. Using RNA-seq data and a common garden experiment, we found that a large number of genes (1,841) were differentially expressed between the landscape types. Hexamerin genes, the expression of which has previously been shown to have high heritability and which correlate strongly with larval development time in the Glanville fritillary, had higher expression in fragmented than continuous landscapes. Genes that were more highly expressed in butterflies from newly-established than old local populations within a fragmented landscape were also more highly expressed, at the landscape level, in fragmented than continuous landscapes. This result suggests that recurrent extinctions and re-colonizations in fragmented landscapes select a for specific expression profile. Genes that were significantly up-regulated following an experimental flight treatment had higher basal expression in fragmented landscapes, indicating that these butterflies are genetically primed for frequent flight. Active flight causes oxidative stress, but butterflies from fragmented landscapes were more tolerant of hypoxia. We conclude that differences in gene expression between the landscape types reflect genomic adaptations to landscape fragmentation. PMID:24988207

  17. Adaptive walks on correlated fitness landscapes with heterogeneous connectivities

    We propose a model for studying the statistical properties of adaptive walks on correlated fitness landscapes which are established in genotype spaces of complex structure. The fitness distribution on the genotype space follows either the bivariate Gaussian distribution or the bivariate exponential distribution. In both cases the degree of correlation of the fitness landscape can be tuned by using a single parameter. To perform the adaptive walks two distinct rules are applied: the random adaptation walk (RAW) and the gradient adaptation walk (GAW). While for the RAW the mean walk length, L-bar, is a monotonic increasing function of the connectivity of the genotype space, for the GAW L-bar is a one-humped function. The RAW produces longer adaptive walks compared to the GAW, though its performance is slightly poorer and thereby the local maxima reached by the GAW algorithm are usually closer to the global optimum of the fitness landscape

  18. Urban Climate Adaptation in Landscape Architecture Design Studios

    Lenzholzer, S.

    2012-01-01

    The adaptation of cities to existing problems such as urban heat islands and to the expected effects of climate change asks for new focuses in urban design professions. Especially for landscape architects, many new assignments will occur within climate adaptation, because the ‘materials’ they work w

  19. Efficient retrieval of landscape Hessian: Forced optimal covariance adaptive learning

    Shir, Ofer M.; Roslund, Jonathan; Whitley, Darrell; Rabitz, Herschel

    2014-06-01

    Knowledge of the Hessian matrix at the landscape optimum of a controlled physical observable offers valuable information about the system robustness to control noise. The Hessian can also assist in physical landscape characterization, which is of particular interest in quantum system control experiments. The recently developed landscape theoretical analysis motivated the compilation of an automated method to learn the Hessian matrix about the global optimum without derivative measurements from noisy data. The current study introduces the forced optimal covariance adaptive learning (FOCAL) technique for this purpose. FOCAL relies on the covariance matrix adaptation evolution strategy (CMA-ES) that exploits covariance information amongst the control variables by means of principal component analysis. The FOCAL technique is designed to operate with experimental optimization, generally involving continuous high-dimensional search landscapes (≳30) with large Hessian condition numbers (≳104). This paper introduces the theoretical foundations of the inverse relationship between the covariance learned by the evolution strategy and the actual Hessian matrix of the landscape. FOCAL is presented and demonstrated to retrieve the Hessian matrix with high fidelity on both model landscapes and quantum control experiments, which are observed to possess nonseparable, nonquadratic search landscapes. The recovered Hessian forms were corroborated by physical knowledge of the systems. The implications of FOCAL extend beyond the investigated studies to potentially cover other physically motivated multivariate landscapes.

  20. CLASSIFICATION OF HIGHWAY LANDSCAPE ELEMENTS AND THEIR DESIGN

    Vitrinskaya, I.

    2009-01-01

    The constituent elements of highway landscape are determined. Six models of highway landscape are offered. Recommendations concerning effective landscape design for application in highway construction are presented.

  1. CLIMATE CHANGE ADAPTATION IN ACID SULFATE LANDSCAPES

    Chuxia Lin

    2012-01-01

    Full Text Available Oxidation of sulfide minerals produces sulfuric acid and consequently creates Acid Sulfate Landscapes (ASLs, which represent one of the most degraded types of land-surface environments. Although acid sulfate-producing weathering is a naturally occurring process, it is markedly facilitated by human intervention. Mining is by far the dominant anthropogenic cause for the creation of inland acid sulfate footprints while land reclamation in coastal lowlands is the driver for the formation of coastal ASLs. The projected climate change highlights the possibility of an increase in the frequency and severity of extreme weather events such as droughts and heavy rains, which is likely to accelerate the acid generation in some circumstances and increase the frequency and magnitude of acid discharge. Sea level rise as a result of global warming will cause additional problems with the coastal ASLs. This is a review article. The following aspects are covered: (a the overriding biogeochemical processes leading to acid sulfate-producing weathering, (b a brief introduction to the inland acid sulfate landscapes, (c a brief introduction to the coastal acid sulfate landscapes, (d the likely impacts of climate change on ASLs and (e the possible measures to combat climate change-induced environmental degradation in the identified key acid sulfate footprints. The projected climate change is like to significantly affect the acid sulfate landscapes in different ways. Appropriate management strategies and cost-effective technologies need to be developed in order to minimize the climate change-induced ecological degradation.

  2. Adaptation in protein fitness landscapes is facilitated by indirect paths.

    Wu, N.; Dai, L.; Olson, CA; Lloyd-Smith, JO; Sun, R

    2016-01-01

    The structure of fitness landscapes is critical for understanding adaptive protein evolution. Previous empirical studies on fitness landscapes were confined to either the neighborhood around the wild type sequence, involving mostly single and double mutants, or a combinatorially complete subgraph involving only two amino acids at each site. In reality, the dimensionality of protein sequence space is higher (20(L)) and there may be higher-order interactions among more than two sites. Here we e...

  3. CLIMATE CHANGE ADAPTATION IN ACID SULFATE LANDSCAPES

    Chuxia Lin

    2012-01-01

    Oxidation of sulfide minerals produces sulfuric acid and consequently creates Acid Sulfate Landscapes (ASLs), which represent one of the most degraded types of land-surface environments. Although acid sulfate-producing weathering is a naturally occurring process, it is markedly facilitated by human intervention. Mining is by far the dominant anthropogenic cause for the creation of inland acid sulfate footprints while land reclamation in coastal lowlands is the driver for the formation of coas...

  4. Blurred Image Classification Based on Adaptive Dictionary

    Guangling Sun

    2013-02-01

    Full Text Available Two frameworks for blurred image classification bas ed on adaptive dictionary are proposed. Given a blurred image, instead of image deblurring, the sem antic category of the image is determined by blur insensitive sparse coefficients calculated dependin g on an adaptive dictionary. The dictionary is adap tive to an assumed space invariant Point Spread Function (PSF estimated from the input blurred image. In o ne of the proposed two frameworks, the PSF is inferred separately and in the other, the PSF is updated combined with sparse coefficients calculation in an alternative and iterative manner. The experimental results have evaluated three types of blur namely d efocus blur, simple motion blur and camera shake bl ur. The experiment results confirm the effectiveness of the proposed frameworks.

  5. Dynamic LiDAR-NDVI classification of fluvial landscape units

    Ramírez-Núñez, Carolina; Parrot, Jean-François

    2015-04-01

    The lower basin of the Coatzacoalcos River is a wide floodplain in which, during the wet season, local and major flooding are distinguished. Both types of floods, intermittent and regional, are important in terms of resources; the regional flood sediments enrich the soils of the plains and intermittent floods allow obtaining aquatic resources for subsistence during the heatwave. In the floodplain different abandoned meanders and intermittent streams are quickly colonized by aquatic vegetation. However, from the 1990s, the Coatzacoalcos River floodplain has important topographic changes due to mining, road and bridges construction; erosion and sedimentation requires continuous parcel boundaries along with the increasing demand of channel reparation, embankments, levees and bridges associated to tributaries. NDVI data, LiDAR point cloud and various types of flood simulations taking into account the DTM are used to classify the dynamic landscape units. These units are associated to floods in relation with water resources, agriculture and livestock. In the study area, the first returns of the point cloud allow extracting vegetation strata. The last returns correspond to the bare earth surface, especially in this area with few human settlements. The surface that is not covered by trees or by aquatic vegetation, correspond to crops, pastures and bare soils. The classification is obtained by using the NDVI index coupled with vegetation strata and water bodies. The result shows that 47.96% of the area does not present active vegetation and it includes 31.53% of bare soils. Concerning the active vegetation, pastures, bushes and trees represent respectively 25.59%, 11.14% and 13.25%. The remaining 1.25% is distributed between water bodies with aquatic vegetation, trees and shrubs. Dynamic landscape units' classification represents a tool for monitoring water resources in a fluvial plain. This approach can be also applied to forest management, environmental services and

  6. Rapid parapatric speciation on holey adaptive landscapes

    Gavrilets, S; Vose, M D; Gavrilets, Sergey; Li, Hai; Vose, Michael D.

    1998-01-01

    A classical view of speciation is that reproductive isolation arises as a by-product of genetic divergence. Here, individual-based simulations are used to evaluate whether the mechanisms implied by this view may result in rapid speciation if the only source of genetic divergence are mutation and random genetic drift. Distinctive features of the simulations are the consideration of the complete process of speciation (from initiation until completion), and of a large number of loci, which was only one order of magnitude smaller than that of bacteria. It is demonstrated that rapid speciation on the time scale of hundreds of generations is plausible without the need for extreme founder events, complete geographic isolation, the existence of distinct adaptive peaks or selection for local adaptation. The plausibility of speciation is enhanced by population subdivision. Simultaneous emergence of more than two new species from a subdivided population is highly probable. Numerical examples relevant to the theory of ce...

  7. The sensory ecology of adaptive landscapes

    Jordan, Lyndon A.; Ryan, Michael J.

    2015-01-01

    In complex environments, behavioural plasticity depends on the ability of an animal to integrate numerous sensory stimuli. The multidimensionality of factors interacting to shape plastic behaviour means it is difficult for both organisms and researchers to predict what constitutes an adaptive response to a given set of conditions. Although researchers may be able to map the fitness pay-offs of different behavioural strategies in changing environments, there is no guarantee that the study spec...

  8. Adaptive Landscapes of Resistance Genes Change as Antibiotic Concentrations Change.

    Mira, Portia M; Meza, Juan C; Nandipati, Anna; Barlow, Miriam

    2015-10-01

    Most studies on the evolution of antibiotic resistance are focused on selection for resistance at lethal antibiotic concentrations, which has allowed the detection of mutant strains that show strong phenotypic traits. However, solely focusing on lethal concentrations of antibiotics narrowly limits our perspective of antibiotic resistance evolution. New high-resolution competition assays have shown that resistant bacteria are selected at relatively low concentrations of antibiotics. This finding is important because sublethal concentrations of antibiotics are found widely in patients undergoing antibiotic therapies, and in nonmedical conditions such as wastewater treatment plants, and food and water used in agriculture and farming. To understand the impacts of sublethal concentrations on selection, we measured 30 adaptive landscapes for a set of TEM β-lactamases containing all combinations of the four amino acid substitutions that exist in TEM-50 for 15 β-lactam antibiotics at multiple concentrations. We found that there are many evolutionary pathways within this collection of landscapes that lead to nearly every TEM-genotype that we studied. While it is known that the pathways change depending on the type of β-lactam, this study demonstrates that the landscapes including fitness optima also change dramatically as the concentrations of antibiotics change. Based on these results we conclude that the presence of multiple concentrations of β-lactams in an environment result in many different adaptive landscapes through which pathways to nearly every genotype are available. Ultimately this may increase the diversity of genotypes in microbial populations. PMID:26113371

  9. INTERPRETATIONS OF A CULTURAL LANDSCAPE : CASE STUDY IN IMPLEMENTATION OF ADAPTIVE CO- MANAGEMENT IN BALI’S SUBAK CULTURAL LANDSCAPE

    Silfwerbrand, Gabriella

    2012-01-01

    Cultural landscapes are places that have developed distinct characteristics from the interaction of people and nature. Actors with different roles in a cultural landscape will interpret the value of the landscape features differently. By combining these perspectives, or knowledge systems, a more complete interpretation can be included in development of an adaptive and collaborative environmental management systems. The principles of such adaptive co-management have guided a management initiat...

  10. Adaptation in protein fitness landscapes is facilitated by indirect paths

    Wu, Nicholas C; Dai, Lei; Olson, C Anders; Lloyd-Smith, James O; Sun, Ren

    2016-01-01

    The structure of fitness landscapes is critical for understanding adaptive protein evolution. Previous empirical studies on fitness landscapes were confined to either the neighborhood around the wild type sequence, involving mostly single and double mutants, or a combinatorially complete subgraph involving only two amino acids at each site. In reality, the dimensionality of protein sequence space is higher (20L) and there may be higher-order interactions among more than two sites. Here we experimentally characterized the fitness landscape of four sites in protein GB1, containing 204 = 160,000 variants. We found that while reciprocal sign epistasis blocked many direct paths of adaptation, such evolutionary traps could be circumvented by indirect paths through genotype space involving gain and subsequent loss of mutations. These indirect paths alleviate the constraint on adaptive protein evolution, suggesting that the heretofore neglected dimensions of sequence space may change our views on how proteins evolve. DOI: http://dx.doi.org/10.7554/eLife.16965.001 PMID:27391790

  11. Fitness-associated recombination on rugged adaptive landscapes.

    Hadany, L; Beker, T

    2003-09-01

    A negative correlation between fitness and recombination rates seems to exist in various organisms. In this article we suggest that a correlation of that kind may play an important role in the evolution of complex traits. We study the effects of such fitness-associated recombination (FAR) in a simple two-locus deterministic model, as well as in a multi-loci NK rugged adaptive landscape. In both models studied, FAR results in faster adaptation and higher average population fitness, compared with uniform-rate recombination. PMID:14635901

  12. Post-industrial landscape - its identification and classification as contemporary challenges faced by geographic research

    Kolejka, Jaromír

    2010-01-01

    Roč. 14, č. 2 (2010), s. 67-78. ISSN 1842-5135 Institutional research plan: CEZ:AV0Z30860518 Keywords : classification * geographical research * identification method * landscape structure Subject RIV: DE - Earth Magnetism, Geodesy, Geography http://studiacrescent.com/images/02_2010/09_jaromir_kolejka_post_industrial_landscape_its_identification_and_classification_as_contemporary_challenges_faced_by_geographic_.pdf

  13. Mapping agricultural landscapes and characterizing adaptive capacity in Central America

    Holland, M. B.; Imbach, P. A.; Bouroncle, C.; Donatti, C.; Leguia, E.; Martinez, M.; Medellin, C.; Saborio-Rodriguez, M.; Shamer, S.; Zamora, J.

    2013-12-01

    One of the key challenges in developing adaptation strategies for smallholder farmers in developing countries is that of a data-poor environment, where spatially-explicit information about where the most vulnerable smallholder communities are located is lacking. Developing countries tend to lack consistent and reliable maps on agricultural land use, and have limited information available on smallholder adaptive capacity. We developed a novel participatory and expert mapping process to overcome these barriers and develop detailed national-scale maps that allow for a characterization of unique agricultural landscapes based on profiles of adaptive capacity for smallholder agriculture in each area. This research focuses specifically on the Central American nations of Costa Rica, Guatemala, and Honduras, where our focus is on coffee and basic grains as the two main cropping systems. Here we present the methodology and results of a series of in-depth interviews and participatory mapping sessions with experts working within the broader agricultural sector in each country. We held individual interviews and mapping sessions with approximately thirty experts from each country, and used a detailed survey instrument for each mapping session to both spatially identify distinct agricultural landscapes, and to further characterize each area based on specific farm practices and social context. The survey also included a series of questions to help us assess the relative adaptive capacity of smallholder agriculture within each landscape. After all expert mapping sessions were completed in each country we convened an expert group to assist in both validating and refining the set of landscapes already defined. We developed a characterization of adaptive capacity by aggregating indicators into main assets-based criteria (e.g. land tenure, access to credit, access to technical assistance, sustainable farm practices) derived from further expert weighting of indicators through an online

  14. Nonparametric Transient Classification using Adaptive Wavelets

    Varughese, Melvin; von Sachs, Rainer; Stephanou, Michael; Bassett, Bruce

    2015-01-01

    Classifying transients based on the multi band light curves is a challenging but crucial problem in the era of GAIA and LSST since the sheer volume of transients will make spectroscopic classification unfeasible. Here we present a nonparametric classier that uses the transient's light curve measurements to predict its class given training data. It implements two novel components: the first is the use of the BAGIDIS wavelet methodology - a method of characterizing functional data using hierarc...

  15. Classification of adaptive memetic algorithms: a comparative study.

    Ong, Yew-Soon; Lim, Meng-Hiot; Zhu, Ning; Wong, Kok-Wai

    2006-02-01

    Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing self-configuring algorithms that acclimatize to suit the problem in hand. Here, our interests are on a recent breed of hybrid evolutionary algorithms typically known as adaptive memetic algorithms (MAs). One unique feature of adaptive MAs is the choice of local search methods or memes and recent studies have shown that this choice significantly affects the performances of problem searches. In this paper, we present a classification of memes adaptation in adaptive MAs on the basis of the mechanism used and the level of historical knowledge on the memes employed. Then the asymptotic convergence properties of the adaptive MAs considered are analyzed according to the classification. Subsequently, empirical studies on representatives of adaptive MAs for different type-level meme adaptations using continuous benchmark problems indicate that global-level adaptive MAs exhibit better search performances. Finally we conclude with some promising research directions in the area. PMID:16468573

  16. Nonparametric Transient Classification using Adaptive Wavelets

    Varughese, Melvin M; Stephanou, Michael; Bassett, Bruce A

    2015-01-01

    Classifying transients based on multi band light curves is a challenging but crucial problem in the era of GAIA and LSST since the sheer volume of transients will make spectroscopic classification unfeasible. Here we present a nonparametric classifier that uses the transient's light curve measurements to predict its class given training data. It implements two novel components: the first is the use of the BAGIDIS wavelet methodology - a characterization of functional data using hierarchical wavelet coefficients. The second novelty is the introduction of a ranked probability classifier on the wavelet coefficients that handles both the heteroscedasticity of the data in addition to the potential non-representativity of the training set. The ranked classifier is simple and quick to implement while a major advantage of the BAGIDIS wavelets is that they are translation invariant, hence they do not need the light curves to be aligned to extract features. Further, BAGIDIS is nonparametric so it can be used for blind ...

  17. Students Classification With Adaptive Neuro Fuzzy

    Mohammad Saber Iraji

    2012-07-01

    Full Text Available Identifying exceptional students for scholarships is an essential part of the admissions process in undergraduate and postgraduate institutions, and identifying weak students who are likely to fail is also important for allocating limited tutoring resources. In this article, we have tried to design an intelligent system which can separate and classify student according to learning factor and performance. a system is proposed through Lvq networks methods, anfis method to separate these student on learning factor . In our proposed system, adaptive fuzzy neural network(anfis has less error and can be used as an effective alternative system for classifying students

  18. Extreme learning machine and adaptive sparse representation for image classification.

    Cao, Jiuwen; Zhang, Kai; Luo, Minxia; Yin, Chun; Lai, Xiaoping

    2016-09-01

    Recent research has shown the speed advantage of extreme learning machine (ELM) and the accuracy advantage of sparse representation classification (SRC) in the area of image classification. Those two methods, however, have their respective drawbacks, e.g., in general, ELM is known to be less robust to noise while SRC is known to be time-consuming. Consequently, ELM and SRC complement each other in computational complexity and classification accuracy. In order to unify such mutual complementarity and thus further enhance the classification performance, we propose an efficient hybrid classifier to exploit the advantages of ELM and SRC in this paper. More precisely, the proposed classifier consists of two stages: first, an ELM network is trained by supervised learning. Second, a discriminative criterion about the reliability of the obtained ELM output is adopted to decide whether the query image can be correctly classified or not. If the output is reliable, the classification will be performed by ELM; otherwise the query image will be fed to SRC. Meanwhile, in the stage of SRC, a sub-dictionary that is adaptive to the query image instead of the entire dictionary is extracted via the ELM output. The computational burden of SRC thus can be reduced. Extensive experiments on handwritten digit classification, landmark recognition and face recognition demonstrate that the proposed hybrid classifier outperforms ELM and SRC in classification accuracy with outstanding computational efficiency. PMID:27389571

  19. Classification of ecological landscape stability on example of the east-Slovakian districts; 1 : 320 000

    Part of the methodology of the Territorial System of Ecological Stability (TSES) is spatial classification of ecological stability of territory (EST), which assesses the representation and effect of positive, as well as negative factors and properties. The result of this procedures is identification of areas with approximately the same level of stability, which makes possible to assess the hierarchy of landscape use and protection. The EST classification takes into account: · Landscape properties, which characterise EST from the supportive aspect: they are the real elements of the present landscape structure and biotopes with eco-stabilising properties and prerequisites (eco-stabilising landscape elements, which support EST). · Landscape factors relevant from the point of view of the present and proposed legal nature and natural resource protection · they contribute to conservation and development of EST (elements of nature and natural resource protection that protect and develop EST). · Factors, which reduce or disturb EST - loading of the environment caused by stress phenomena. (authors)

  20. Classification of pasture habitats by Hungarian herders in a steppe landscape (Hungary

    Molnár Zsolt

    2012-08-01

    Full Text Available Abstract Background Landscape ethnoecology focuses on the ecological features of the landscape, how the landscape is perceived, and used by people who live in it. Though studying folk classifications of species has a long history, the comparative study of habitat classifications is just beginning. I studied the habitat classification of herders in a Hungarian steppe, and compared it to classifications of botanists and laymen. Methods For a quantitative analysis the picture sort method was used. Twenty-three pictures of 7-11 habitat types were sorted by 25 herders.’Density’ of pictures along the habitat gradient of the Hortobágy salt steppe was set as equal as possible, but pictures differed in their dominant species, wetness, season, etc. Before sorts, herders were asked to describe pictures to assure proper recognition of habitats. Results Herders classified the images into three main groups: (1 fertile habitats at the higher parts of the habitat gradient (partos, lit. on the shore; (2 saline habitats (szík, lit. salt or saline place, and (3 meadows and marshes (lapos, lit. flooded at the lower end of the habitat gradient. Sharpness of delimitation changed along the gradient. Saline habitats were the most isolated from the rest. Botanists identified 6 groups. Laymen grouped habitats in a less coherent way. As opposed to my expectations, botanical classification was not more structured than that done by herders. I expected and found high correspondence between the classifications by herders, botanists and laymen. All tended to recognize similar main groups: wetlands, ”good grass” and dry/saline habitats. Two main factors could have been responsible for similar classifications: salient features correlated (e.g. salinity recognizable by herders and botanists but not by laymen correlated with the density of grasslands or height of vegetation recognizable also for laymen, or the same salient features were used as a basis for sorting

  1. A Classification of Adaptive Feedback in Educational Systems for Programming

    Nguyen-Thinh Le

    2016-01-01

    Over the last three decades, many educational systems for programming have been developed to support learning/teaching programming. In this paper, feedback types that are supported by existing educational systems for programming are classified. In order to be able to provide feedback, educational systems for programming deployed various approaches to analyzing students’ programs. This paper identifies analysis approaches for programs and introduces a classification for adaptive feedback suppo...

  2. Adaptive wavelets for visual object detection and classification

    Aghdasi, Farzin

    1997-10-01

    We investigate the application of adaptive wavelets for the representation and classification of signals in digitized speech and medical images. A class of wavelet basis functions are used to extract features from the regions of interest. These features are then used in an artificial neural network to classify the region are containing the desired object or belonging to the background clutter. The dilation and shift parameters of the wavelet functions are not fixed. These parameters are included in the training scheme. In this way the wavelets are adaptive to the expected shape and size of the signals. The results indicate that adaptive wavelet functions may outperform the classical fixed wavelet analysis in detection of subtle objects.

  3. Implementation of Multispectral Image Classification on a Remote Adaptive Computer

    Figueiredo, Marco A.; Gloster, Clay S.; Stephens, Mark; Graves, Corey A.; Nakkar, Mouna

    1999-01-01

    As the demand for higher performance computers for the processing of remote sensing science algorithms increases, the need to investigate new computing paradigms its justified. Field Programmable Gate Arrays enable the implementation of algorithms at the hardware gate level, leading to orders of m a,gnitude performance increase over microprocessor based systems. The automatic classification of spaceborne multispectral images is an example of a computation intensive application, that, can benefit from implementation on an FPGA - based custom computing machine (adaptive or reconfigurable computer). A probabilistic neural network is used here to classify pixels of of a multispectral LANDSAT-2 image. The implementation described utilizes Java client/server application programs to access the adaptive computer from a remote site. Results verify that a remote hardware version of the algorithm (implemented on an adaptive computer) is significantly faster than a local software version of the same algorithm implemented on a typical general - purpose computer).

  4. Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function.

    Derek G Groenendyk

    of landscape function, suggest that hydrologic-process-based classifications should be incorporated into environmental process models and can be used to define application-specific maps of hydrologic function.

  5. Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function.

    Groenendyk, Derek G; Ferré, Ty P A; Thorp, Kelly R; Rice, Amy K

    2015-01-01

    Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth's surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape

  6. Classification of transient signals using sparse representations over adaptive dictionaries

    Moody, Daniela I.; Brumby, Steven P.; Myers, Kary L.; Pawley, Norma H.

    2011-06-01

    Automatic classification of broadband transient radio frequency (RF) signals is of particular interest in persistent surveillance applications. Because such transients are often acquired in noisy, cluttered environments, and are characterized by complex or unknown analytical models, feature extraction and classification can be difficult. We propose a fast, adaptive classification approach based on non-analytical dictionaries learned from data. Conventional representations using fixed (or analytical) orthogonal dictionaries, e.g., Short Time Fourier and Wavelet Transforms, can be suboptimal for classification of transients, as they provide a rigid tiling of the time-frequency space, and are not specifically designed for a particular signal class. They do not usually lead to sparse decompositions, and require separate feature selection algorithms, creating additional computational overhead. Pursuit-type decompositions over analytical, redundant dictionaries yield sparse representations by design, and work well for target signals in the same function class as the dictionary atoms. The pursuit search however has a high computational cost, and the method can perform poorly in the presence of realistic noise and clutter. Our approach builds on the image analysis work of Mairal et al. (2008) to learn a discriminative dictionary for RF transients directly from data without relying on analytical constraints or additional knowledge about the signal characteristics. We then use a pursuit search over this dictionary to generate sparse classification features. We demonstrate that our learned dictionary is robust to unexpected changes in background content and noise levels. The target classification decision is obtained in almost real-time via a parallel, vectorized implementation.

  7. Domain Adaptation for Opinion Classification: A Self-Training Approach

    Yu, Ning

    2013-03-01

    Full Text Available Domain transfer is a widely recognized problem for machine learning algorithms because models built upon one data domain generally do not perform well in another data domain. This is especially a challenge for tasks such as opinion classification, which often has to deal with insufficient quantities of labeled data. This study investigates the feasibility of self-training in dealing with the domain transfer problem in opinion classification via leveraging labeled data in non-target data domain(s and unlabeled data in the target-domain. Specifically, self-training is evaluated for effectiveness in sparse data situations and feasibility for domain adaptation in opinion classification. Three types of Web content are tested: edited news articles, semi-structured movie reviews, and the informal and unstructured content of the blogosphere. Findings of this study suggest that, when there are limited labeled data, self-training is a promising approach for opinion classification, although the contributions vary across data domains. Significant improvement was demonstrated for the most challenging data domain-the blogosphere-when a domain transfer-based self-training strategy was implemented.

  8. Polytomous Adaptive Classification Testing: Effects of Item Pool Size, Test Termination Criterion, and Number of Cutscores

    Gnambs, Timo; Batinic, Bernad

    2011-01-01

    Computer-adaptive classification tests focus on classifying respondents in different proficiency groups (e.g., for pass/fail decisions). To date, adaptive classification testing has been dominated by research on dichotomous response formats and classifications in two groups. This article extends this line of research to polytomous classification…

  9. Landscape Risk Factors for Lyme Disease in the Eastern Broadleaf Forest Province of the Hudson River Valley and the Effect of Explanatory Data Classification Resolution

    This study assessed how landcover classification affects associations between landscape characteristics and Lyme disease rate. Landscape variables were derived from the National Land Cover Database (NLCD), including native classes (e.g., deciduous forest, developed low intensity)...

  10. Rural landscape and cultural routes: a multicriteria spatial classification method tested on an Italian case study

    Irene Diti

    2015-04-01

    Full Text Available Europe is characterised by a rich net of itineraries that during the Middle Ages were taken by pilgrims head toward the holy places of Christianity. In Italy the main pilgrimage route is the Via Francigena (the road that comes from France, which starts from Canterbury and arrives in Rome, running through Europe for about 1800 km. Municipalities and local associations are focused on purposes and actions aimed at the promotion of those routes, rich in history and spirituality. Also for the European Union the enhancement of those itineraries, nowadays used both by pilgrims and tourists, is crucial, as shown by the various projects aimed at the identification of tools for the development of sustainable cultural tourism. It is important to understand how landscape, that according to the European Landscape Convention reflects the sense of places and represents the image of their history, has evolved along those roads, and to analyse the relationships between the built and natural environments, since they maintain a remarkable symbolic connection between places and peoples over time and history. This study focuses on the Italian section of the Via Francigena that crosses the Emilia-Romagna region, in the province of Piacenza. A land classification method is proposed, with the aim to take into account different indicators: land zoning provided by regional laws, elements of relevant historical and natural value, urban elements, type of agriculture. The analyses are carried out on suitable buffers around the path, thus allowing to create landscape profiles. As nature is a key element for the spirituality character of these pilgrimage routes, the classification process takes into account both protected and other valuable natural elements, besides agricultural activities. The outcomes can be useful to define tools aimed to help pilgrims and tourists to understand the surrounding places along their walk, as well as to lend support to rural and urban planning

  11. Classification of vegetation in an open landscape using full-waveform airborne laser scanner data

    Alexander, Cici; Deák, Balázs; Kania, Adam; Mücke, Werner; Heilmeier, Hermann

    2015-09-01

    Airborne laser scanning (ALS) is increasingly being used for the mapping of vegetation, although the focus so far has been on woody vegetation, and ALS data have only rarely been used for the classification of grassland vegetation. In this study, we classified the vegetation of an open alkali landscape, characterized by two Natura 2000 habitat types: Pannonic salt steppes and salt marshes and Pannonic loess steppic grasslands. We generated 18 variables from an ALS dataset collected in the growing (leaf-on) season. Elevation is a key factor determining the patterns of vegetation types in the landscape, and hence 3 additional variables were based on a digital terrain model (DTM) generated from an ALS dataset collected in the dormant (leaf-off) season. We classified the vegetation into 24 classes based on these 21 variables, at a pixel size of 1 m. Two groups of variables with and without the DTM-based variables were used in a Random Forest classifier, to estimate the influence of elevation, on the accuracy of the classification. The resulting classes at Level 4, based on associations, were aggregated at three levels - Level 3 (11 classes), Level 2 (8 classes) and Level 1 (5 classes) - based on species pool, site conditions and structure, and the accuracies were assessed. The classes were also aggregated based on Natura 2000 habitat types to assess the accuracy of the classification, and its usefulness for the monitoring of habitat quality. The vegetation could be classified into dry grasslands, wetlands, weeds, woody species and man-made features, at Level 1, with an accuracy of 0.79 (Cohen's kappa coefficient, κ). The accuracies at Levels 2-4 and the classification based on the Natura 2000 habitat types were κ: 0.76, 0.61, 0.51 and 0.69, respectively. Levels 1 and 2 provide suitable information for nature conservationists and land managers, while Levels 3 and 4 are especially useful for ecologists, geologists and soil scientists as they provide high resolution

  12. Reciprocal sign epistasis between frequently experimentally evolved adaptive mutations causes a rugged fitness landscape.

    Daniel J Kvitek

    2011-04-01

    Full Text Available The fitness landscape captures the relationship between genotype and evolutionary fitness and is a pervasive metaphor used to describe the possible evolutionary trajectories of adaptation. However, little is known about the actual shape of fitness landscapes, including whether valleys of low fitness create local fitness optima, acting as barriers to adaptive change. Here we provide evidence of a rugged molecular fitness landscape arising during an evolution experiment in an asexual population of Saccharomyces cerevisiae. We identify the mutations that arose during the evolution using whole-genome sequencing and use competitive fitness assays to describe the mutations individually responsible for adaptation. In addition, we find that a fitness valley between two adaptive mutations in the genes MTH1 and HXT6/HXT7 is caused by reciprocal sign epistasis, where the fitness cost of the double mutant prohibits the two mutations from being selected in the same genetic background. The constraint enforced by reciprocal sign epistasis causes the mutations to remain mutually exclusive during the experiment, even though adaptive mutations in these two genes occur several times in independent lineages during the experiment. Our results show that epistasis plays a key role during adaptation and that inter-genic interactions can act as barriers between adaptive solutions. These results also provide a new interpretation on the classic Dobzhansky-Muller model of reproductive isolation and display some surprising parallels with mutations in genes often associated with tumors.

  13. Structured estimation - Sample size reduction for adaptive pattern classification

    Morgera, S.; Cooper, D. B.

    1977-01-01

    The Gaussian two-category classification problem with known category mean value vectors and identical but unknown category covariance matrices is considered. The weight vector depends on the unknown common covariance matrix, so the procedure is to estimate the covariance matrix in order to obtain an estimate of the optimum weight vector. The measure of performance for the adapted classifier is the output signal-to-interference noise ratio (SIR). A simple approximation for the expected SIR is gained by using the general sample covariance matrix estimator; this performance is both signal and true covariance matrix independent. An approximation is also found for the expected SIR obtained by using a Toeplitz form covariance matrix estimator; this performance is found to be dependent on both the signal and the true covariance matrix.

  14. Cherokee Adaptation to the Landscape of the West and Overcoming the Loss of Culturally Significant Plants

    Vick, R. Alfred

    2011-01-01

    Plant species utilized by Cherokees have been documented by several authors. However, many of the traditional uses of plants were lost or forgotten in the generations following the Trail of Tears. The pressures of overcoming the physical and psychological impact of the removal, adapting to a new landscape, rebuilding a government, rebuilding…

  15. Natural selection fails to optimize mutation rates for long-term adaptation on rugged fitness landscapes.

    Clune, Jeff; Misevic, Dusan; Ofria, Charles; Lenski, Richard E; Elena, Santiago F; Sanjuán, Rafael

    2008-01-01

    The rate of mutation is central to evolution. Mutations are required for adaptation, yet most mutations with phenotypic effects are deleterious. As a consequence, the mutation rate that maximizes adaptation will be some intermediate value. Here, we used digital organisms to investigate the ability of natural selection to adjust and optimize mutation rates. We assessed the optimal mutation rate by empirically determining what mutation rate produced the highest rate of adaptation. Then, we allowed mutation rates to evolve, and we evaluated the proximity to the optimum. Although we chose conditions favorable for mutation rate optimization, the evolved rates were invariably far below the optimum across a wide range of experimental parameter settings. We hypothesized that the reason that mutation rates evolved to be suboptimal was the ruggedness of fitness landscapes. To test this hypothesis, we created a simplified landscape without any fitness valleys and found that, in such conditions, populations evolved near-optimal mutation rates. In contrast, when fitness valleys were added to this simple landscape, the ability of evolving populations to find the optimal mutation rate was lost. We conclude that rugged fitness landscapes can prevent the evolution of mutation rates that are optimal for long-term adaptation. This finding has important implications for applied evolutionary research in both biological and computational realms. PMID:18818724

  16. Natural selection fails to optimize mutation rates for long-term adaptation on rugged fitness landscapes.

    Jeff Clune

    Full Text Available The rate of mutation is central to evolution. Mutations are required for adaptation, yet most mutations with phenotypic effects are deleterious. As a consequence, the mutation rate that maximizes adaptation will be some intermediate value. Here, we used digital organisms to investigate the ability of natural selection to adjust and optimize mutation rates. We assessed the optimal mutation rate by empirically determining what mutation rate produced the highest rate of adaptation. Then, we allowed mutation rates to evolve, and we evaluated the proximity to the optimum. Although we chose conditions favorable for mutation rate optimization, the evolved rates were invariably far below the optimum across a wide range of experimental parameter settings. We hypothesized that the reason that mutation rates evolved to be suboptimal was the ruggedness of fitness landscapes. To test this hypothesis, we created a simplified landscape without any fitness valleys and found that, in such conditions, populations evolved near-optimal mutation rates. In contrast, when fitness valleys were added to this simple landscape, the ability of evolving populations to find the optimal mutation rate was lost. We conclude that rugged fitness landscapes can prevent the evolution of mutation rates that are optimal for long-term adaptation. This finding has important implications for applied evolutionary research in both biological and computational realms.

  17. An ecoregional classification for the state of Roraima, Brazil: the importance of landscape in malaria biology

    Maria Goreti Rosa-Freitas

    2007-06-01

    Full Text Available Understanding the different background landscapes in which malaria transmission occurs is fundamental to understanding malaria epidemiology and to designing effective local malaria control programs. Geology, geomorphology, vegetation, climate, land use, and anopheline distribution were used as a basis for an ecological classification of the state of Roraima, Brazil, in the northern Amazon Basin, focused on the natural history of malaria and transmission. We used unsupervised maximum likelihood classification, principal components analysis, and weighted overlay with equal contribution analyses to fine-scale thematic maps that resulted in clustered regions. We used ecological niche modeling techniques to develop a fine-scale picture of malaria vector distributions in the state. Eight ecoregions were identified and malaria-related aspects are discussed based on this classification, including 5 types of dense tropical rain forest and 3 types of savannah. Ecoregions formed by dense tropical rain forest were named as montane (ecoregion I, submontane (II, plateau (III, lowland (IV, and alluvial (V. Ecoregions formed by savannah were divided into steppe (VI, campos de Roraima, savannah (VII, cerrado, and wetland (VIII, campinarana. Such ecoregional mappings are important tools in integrated malaria control programs that aim to identify specific characteristics of malaria transmission, classify transmission risk, and define priority areas and appropriate interventions. For some areas, extension of these approaches to still-finer resolutions will provide an improved picture of malaria transmission patterns.

  18. Multiview Sample Classification Algorithm Based on L1-Graph Domain Adaptation Learning

    Huibin Lu; Zhengping Hu; Hongxiao Gao

    2015-01-01

    In the case of multiview sample classification with different distribution, training and testing samples are from different domains. In order to improve the classification performance, a multiview sample classification algorithm based on L1-Graph domain adaptation learning is presented. First of all, a framework of nonnegative matrix trifactorization based on domain adaptation learning is formed, in which the unchanged information is regarded as the bridge of knowledge transformation from the...

  19. Adaptation and Coevolution on an Emergent Global Competitive Landscape

    Fellman, Philip Vos; Post, Jonathan Vos; Wright, Roxana; Dasari, Usha

    Notions of Darwinian selection have been implicit in economic theory for at least sixty years. Richard Nelson and Sidney Winter have argued that while evolutionary thinking was prevalent in prewar economics, the postwar Neoclassical school became almost entirely preoccupied with equilibrium conditions and their mathematical conditions. One of the problems with the economic interpretation of firm selection through competition has been a weak grasp on an incomplete scientific paradigm. As I.F. Price notes: "The biological metaphor has long lurked in the background of management theory largely because the message of 'survival of the fittest' (usually wrongly attributed to Charles Darwin rather than Herbert Spencer) provides a seemingly natural model for market competition (e.g. Alchian 1950, Merrell 1984, Henderson 1989, Moore 1993), without seriously challenging the underlying paradigms of what an organisation is." [1] In this paper we examine the application of dynamic fitness landscape models to economic theory, particularly the theory of technology substitution, drawing on recent work by Kauffman, Arthur, McKelvey, Nelson and Winter, and Windrum and Birchenhall. In particular we use Professor Post's early work with John Holland on the genetic algorithm to explain some of the key differences between static and dynamic approaches to economic modeling.

  20. [Ecological classification system of forest landscape in eastern mountainous region of Liaoning Province].

    Tang, Li-na; Wang, Qing-li; Dai, Li-min; Shao, Guo-fan

    2008-01-01

    Based on Digital Elevation Models (DEM) and satellite SPOT-5 data, and by using the spatial analysis function in Geographic Information System, a hierachical Ecological Classification System of forest landscape was developed for the eastern mountainous region of Liaoning Province, and the two lowest layers in the hierachical framework, Ecological Land Types (ELTs) and Ecological Land Type Phases (ELTPs), were mapped. The results indicated that there were 5 ELTs and 34 ELTPs. The boundaries of ELTs, which presented the potential vegetation distribution and potential forestry ecosystem productivity, were determined by environmental conditions quantified by DEM. ELTPs were classified by overlaying ELTs with forest vegetation data layers which were obtained from remotely sensed data, forest inventory data, and ground data. The ELTPs represented the divisions of land in terms of both natural and human-induced forest conditions, and therefore, were reliable units for forest inventories and management. ELTPs could function as conventional forest inventory sub-compartments. By this means, forestry departments could adjust forest management planning and forest management measures from the viewpoint of forest landscape scale to realize forest ecosystem management. PMID:18419066

  1. Urban landscape classification using Chinese advanced high-resolution satellite imagery and an object-oriented multi-variable model

    Li-gang MA; Jin-song DENG; Huai YANG; Yang HONG; Ke WANG

    2015-01-01

    The Chinese ZY-1 02C satellite is one of the most advanced high-resolution earth observation systems designed for terrestrial resource monitoring. Its capability for comprehensive landscape classification, especially in urban areas, has been under constant study. In view of the limited spectral resolution of the ZY-1 02C satellite (three bands), and the complexity and hetero-geneity across urban environments, we attempt to test its performance of urban landscape classification by combining a multi-variable model with an object-oriented approach. The multiple variables including spectral reflection, texture, spatial autocorre-lation, impervious surface fraction, vegetation, and geometry indexes were first calculated and selected using forward stepwise linear discriminant analysis and applied in the following object-oriented classification process. Comprehensive accuracy as-sessment which adopts traditional error matrices with stratified random samples and polygon area consistency (PAC) indexes was then conducted to examine the real area agreement between a classified polygon and its references. Results indicated an overall classification accuracy of 92.63%and a kappa statistic of 0.9124. Furthermore, the proposed PAC index showed that more than 82%of all polygons were correctly classified. Misclassification occurred mostly between residential area and barren/farmland. The presented method and the Chinese ZY-1 02C satellite imagery are robust and effective for urban landscape classification.

  2. Adaptation of bird communities to farmland abandonment in a mountain landscape.

    João Lopes Guilherme

    Full Text Available Widespread farmland abandonment has led to significant landscape transformations of many European mountain areas. These semi-natural multi-habitat landscapes are important reservoirs of biodiversity and their abandonment has important conservation implications. In multi-habitat landscapes the adaptation of communities depends on the differential affinity of the species to the available habitats. We use nested species-area relationships (SAR to model species richness patterns of bird communities across scales in a mountain landscape, in NW Portugal. We compare the performance of the classic-SAR and the countryside-SAR (i.e. multi-habitat models at the landscape scale, and compare species similarity decay (SSD at the regional scale. We find a considerable overlap of bird communities in the different land-uses (farmland, shrubland and oak forest at the landscape scale. Analysis of the classic and countryside SAR show that specialist species are strongly related to their favourite habitat. Farmland and shrubland have higher regional SSD compared to oak forests. However, this is due to the opportunistic use of farmlands by generalist birds. Forest specialists display significant regional turnover in oak forest. Overall, the countryside-SAR model had a better fit to the data showing that habitat composition determines species richness across scales. Finally, we use the countryside-SAR model to forecast bird diversity under four scenarios of land-use change. Farmland abandonment scenarios show little impact on bird diversity as the model predicts that the complete loss of farmland is less dramatic, in terms of species diversity loss, than the disappearance of native Galicio-Portuguese oak forest. The affinities of species to non-preferred habitats suggest that bird communities can adapt to land-use changes derived from farmland abandonment. Based on model predictions we argue that rewilding may be a suitable management option for many European mountain

  3. Validating the Danish adaptation of the World Health Organization's International Classification for Patient Safety classification of patient safety incident types

    Mikkelsen, Kim Lyngby; Thommesen, Jacob; Andersen, Henning Boje

    2013-01-01

    Objectives Validation of a Danish patient safety incident classification adapted from the World Health Organizaton's International Classification for Patient Safety (ICPS-WHO). Design Thirty-three hospital safety management experts classified 58 safety incident cases selected to represent all types......; clarity of case descriptions; clarity of the operational definitions of the types and the instructions guiding the coding process; adequacy of the underlying classification scheme. Conclusions The incident types of the ICPS-DK are adequate, exhaustive and well suited for classifying and structuring...

  4. Adaptive Capacity in Tanzanian Maasailand: Changing strategies to cope with drought in fragmented landscapes.

    Goldman, Mara J; Riosmena, Fernando

    2013-06-01

    This study examines the ways in which the adaptive capacity of households to climatic events varies within communities and is mediated by institutional and landscape changes. We present qualitative and quantitative data from two Maasai communities differentially exposed to the devastating drought of 2009 in Northern Tanzania. We show how rangeland fragmentation combined with the decoupling of institutions and landscapes are affecting pastoralists ability to cope with drought. Our data highlight that mobility remains a key coping mechanism for pastoralists to avoid cattle loss during a drought. However, mobility is now happening in new ways that require not only large amounts of money but new forms of knowledge and connections outside of customary reciprocity networks. Those least affected by the drought, in terms of cattle lost, were those with large herds who were able to sell some of their cattle and to pay for private access to pastures outside of Maasai areas. Drawing on an entitlements framework, we argue that the new coping mechanisms are not available to all, could be making some households more vulnerable to climate change, and reduce the adaptive capacity of the overall system as reciprocity networks and customary institutions are weakened. As such, we posit that adaptive capacity to climate change is uneven within and across communities, is scale-dependent, and is intimately tied to institutional and landscape changes. PMID:25400331

  5. Experimental demonstration of an adaptive architecture for direct spectral imaging classification.

    Dunlop-Gray, Matthew; Poon, Phillip K; Golish, Dathon; Vera, Esteban; Gehm, Michael E

    2016-08-01

    Spectral imaging is a powerful tool for providing in situ material classification across a spatial scene. Typically, spectral imaging analyses are interested in classification, though often the classification is performed only after reconstruction of the spectral datacube. We present a computational spectral imaging system, the Adaptive Feature-Specific Spectral Imaging Classifier (AFSSI-C), which yields direct classification across the spatial scene without reconstruction of the source datacube. With a dual disperser architecture and a programmable spatial light modulator, the AFSSI-C measures specific projections of the spectral datacube which are generated by an adaptive Bayesian classification and feature design framework. We experimentally demonstrate multiple order-of-magnitude improvement of classification accuracy in low signal-to-noise (SNR) environments when compared to legacy spectral imaging systems. PMID:27505794

  6. A novel adaptive classification scheme for digital modulations in satellite communication

    Wu Dan; Gu Xuemai; Guo Qing

    2007-01-01

    To make the modulation classification system more suitable for signals in a wide range of signal to noise ratios (SNRs) , a novel adaptive modulation classification scheme is presented in this paper. Different from traditional schemes, the proposed scheme employs a new SNR estimation algorithm for small samples before modulation classification, which makes the modulation classifier work adaptively according to estimated SNRs. Furthermore, it uses three efficient features and support vector machines (SVM) in modulation classification. Computer simulation shows that the scheme can adaptively classify ten digital modulation types (i.e. 2ASK, 4ASK, 2FSK, 4FSK, 2PSK, 4PSK, 16QAM, TFM, π/4QPSK and OQPSK) at SNRS ranging from OdB to 25 dB and success rates are over 95% when SNR is not lower than 3dB. Accuracy, efficiency and simplicity of the proposed scheme are obviously improved, which make it more adaptive to engineering applications.

  7. Tree Species Abundance Predictions in a Tropical Agricultural Landscape with a Supervised Classification Model and Imbalanced Data

    Sarah J. Graves

    2016-02-01

    Full Text Available Mapping species through classification of imaging spectroscopy data is facilitating research to understand tree species distributions at increasingly greater spatial scales. Classification requires a dataset of field observations matched to the image, which will often reflect natural species distributions, resulting in an imbalanced dataset with many samples for common species and few samples for less common species. Despite the high prevalence of imbalanced datasets in multiclass species predictions, the effect on species prediction accuracy and landscape species abundance has not yet been quantified. First, we trained and assessed the accuracy of a support vector machine (SVM model with a highly imbalanced dataset of 20 tropical species and one mixed-species class of 24 species identified in a hyperspectral image mosaic (350–2500 nm of Panamanian farmland and secondary forest fragments. The model, with an overall accuracy of 62% ± 2.3% and F-score of 59% ± 2.7%, was applied to the full image mosaic (23,000 ha at a 2-m resolution to produce a species prediction map, which suggested that this tropical agricultural landscape is more diverse than what has been presented in field-based studies. Second, we quantified the effect of class imbalance on model accuracy. Model assessment showed a trend where species with more samples were consistently over predicted while species with fewer samples were under predicted. Standardizing sample size reduced model accuracy, but also reduced the level of species over- and under-prediction. This study advances operational species mapping of diverse tropical landscapes by detailing the effect of imbalanced data on classification accuracy and providing estimates of tree species abundance in an agricultural landscape. Species maps using data and methods presented here can be used in landscape analyses of species distributions to understand human or environmental effects, in addition to focusing conservation

  8. Can public managers make their welfare organizations adapt to the new performance landscape shaped by the current austerity?

    Aagaard, Peter; Pedersen, John Storm

    2014-01-01

    How has the current austerity changed the public welfare organizations’ performance landscape in modern welfare states? Can public managers make their organizations adapt to the new performance landscape shaped by the austerity? These questions are answered on the basis of the Danish case of the...... provision of the services to the citizens with disabilities and/or social disadvantages. The result has implications, especially for public management in praxis. The case study shows that the managers’ most important managerial tool to make their organizations adapt to the new landscape is the challenging...... and decision-oriented dialogue....

  9. Landscape classification of Huelva (Spain: An objective method of identification and characterization

    Alcántara Manzanares, Jorge

    2015-12-01

    Full Text Available This study sought to classify the landscape of the province of Huelva (Andalusia, Spain and validate the results, using a new application of classical multivariate methods in conjunction with GIS tools. The province was divided into 1 km x 1 km grid squares to which information was associated on four visually-perceivable variables: soil use, plant cover, lithology and relief. Grid cells were then classified using twoway indicator species analysis (TWINSPAN and ordered by detrended correspondence analysis (DCA. Analysis of results yielded 8 major landscape types that were characterized by its indicator variables. This classification was checked by Discriminant Analysis, which yielded an 80% match with the TWINSPAN estimate.Este estudio trata de clasificar el paisaje de la provincia de Huelva (Andalucía, España y validar los resultados, mediante una nueva aplicación de métodos multivariantes clásicos en combinación con herramientas SIG. La provincia se dividió en cuadrículas de 1 km x 1 km a las que se asoció la información relativa a cuatro variables perceptibles visualmente: usos del suelo, coberturas vegetales, litología y relieve. Las cuadrículas se clasificaron utilizando el análisis de especies indicadoras de doble vía (TWINSPAN y se ordenaron mediante el análisis de correspondencia escalado (DCA. El análisis de los resultados dio lugar a 8 tipos de paisaje que se caracterizaron gracias a sus variables indicadoras. Esta clasificación se validó mediante un análisis discriminante, que coincidió en un 80% con la estimación del TWINSPAN. [fr] Cette étude visait à classer le paysage de la province de Huelva (Andalousie, Espagne et de valider les résultats, à l’aide d’une nouvelle application de méthodes multivariées classiques avec des outils SIG. La province a été divisée en 1 km x 1 km carrés de la grille dans laquelle l’information a été associée à quatre variables visuellement perceptibles: l

  10. COMPARE: classification of morphological patterns using adaptive regional elements.

    Fan, Yong; Shen, Dinggang; Gur, Ruben C; Gur, Raquel E; Davatzikos, Christos

    2007-01-01

    This paper presents a method for classification of structural brain magnetic resonance (MR) images, by using a combination of deformation-based morphometry and machine learning methods. A morphological representation of the anatomy of interest is first obtained using a high-dimensional mass-preserving template warping method, which results in tissue density maps that constitute local tissue volumetric measurements. Regions that display strong correlations between tissue volume and classification (clinical) variables are extracted using a watershed segmentation algorithm, taking into account the regional smoothness of the correlation map which is estimated by a cross-validation strategy to achieve robustness to outliers. A volume increment algorithm is then applied to these regions to extract regional volumetric features, from which a feature selection technique using support vector machine (SVM)-based criteria is used to select the most discriminative features, according to their effect on the upper bound of the leave-one-out generalization error. Finally, SVM-based classification is applied using the best set of features, and it is tested using a leave-one-out cross-validation strategy. The results on MR brain images of healthy controls and schizophrenia patients demonstrate not only high classification accuracy (91.8% for female subjects and 90.8% for male subjects), but also good stability with respect to the number of features selected and the size of SVM kernel used. PMID:17243588

  11. Classification in Medical Image Analysis Using Adaptive Metric KNN

    Chen, Chen; Chernoff, Konstantin; Karemore, Gopal Raghunath; Lo, Pechin Chien Pau; Nielsen, Mads; Lauze, Francois Bernard

    2010-01-01

    the assumption that images are drawn from Brownian Image Model (BIM), the normalized metric based on variance of the data, the empirical metric is based on the empirical covariance matrix of the unlabeled data, and an optimized metric obtained by minimizing the classification error. The spectral...

  12. An Unbiased Adaptive Sampling Algorithm for the Exploration of RNA Mutational Landscapes under Evolutionary Pressure

    Waldispühl, Jérôme; Ponty, Yann

    The analysis of the impact of mutations on folding properties of RNAs is essential to decipher principles driving molecular evolution and to design new molecules. We recently introduced an algorithm called RNAmutants which samples RNA sequence-structure maps in polynomial time and space. However, since the mutation probabilities depend of the free energy of the structures, RNAmutants is bias toward G+C-rich regions of the mutational landscape. In this paper we introduce an unbiased adaptive sampling algorithm that enables RNAmutants to sample regions of the mutational landscape poorly covered by previous techniques. We applied the method to sample mutations in complete RNA sequence-structures maps of sizes up to 40 nucleotides. Our results indicate that the G+C-contents has a strong influence on the evolutionary accessible structural ensembles. In particular, we show that low G+C-contents favor the apparition of internal loops, while high G+C-contents reduce the size of the evolutionary accessible mutational landscapes.

  13. Exploring Panarchy in Alpine Grasslands: an Application of Adaptive Cycle Concepts to the Conservation of a Cultural Landscape

    Klaus Hubacek; Alessandro Gretter; Rocco Scolozzi; Ian D. Soane

    2012-01-01

    This paper explores approaches of applying the panarchy perspective to a case study of natural resource management in the cultural landscape of upland alpine pastures in northern Italy. The close interaction within the cultural landscape between alpine pasture ecology and the management regimes offers a strong fit with the concept of social-ecological systems and provides insights to appropriate and adaptive management of sites of conservation interest. We examine the limited literature avail...

  14. HyperSpectral classification with adaptively weighted L1-norm regularization and spatial postprocessing

    Aldea, Victor Stefan; Ahmad, M.O.; Lynch, W. E.

    2014-01-01

    Sparse regression methods have been proven effective in a wide range of signal processing problems such as image compression, speech coding, channel equalization, linear regression and classification. In this paper we develop a new method of hyperspectral image classification based on the sparse unmixing algorithm SUnSAL for which a pixel adaptive L1-norm regularization term is introduced. To further enhance class separability, the algorithm is kernelized using a RBF kernel and the final resu...

  15. A Study of Self—adaptive X/Y Flow Classification Method in LER

    SHAOXu; DINGWei; 等

    2001-01-01

    According to the X/Y flow classification method based on TCP and UDP port,a new method named self-adaptive X/Y flow classification method is proposed in the paper, which can make the curve of the ration of label resource usage more stable than ever so as to improve the performance of both L3 forwarding and L2 label switching of LER in MPLS networks.With the simulation of real Internet data, a satisfactory classification result has been obtained.

  16. A Study of Self-adaptive X/Y Flow Classification Method in LER

    2001-01-01

    According to the X/Y flow classification method based on TCP and UDP port, a new method named self-adaptive X/Y flow classification method is proposed in the paper, which can make the curve of the ratio of label resource usage more stable than ever so as to improve the performance of both L3 forwarding and L2 label switching of LER in MPLS networks. With the simulation of real Internet data, a satisfactory classification result has been obtained.

  17. Non-parametric transient classification using adaptive wavelets

    Varughese, Melvin M.; von Sachs, Rainer; Stephanou, Michael; Bassett, Bruce A.

    2015-11-01

    Classifying transients based on multiband light curves is a challenging but crucial problem in the era of GAIA and Large Synoptic Sky Telescope since the sheer volume of transients will make spectroscopic classification unfeasible. We present a non-parametric classifier that predicts the transient's class given training data. It implements two novel components: the use of the BAGIDIS wavelet methodology - a characterization of functional data using hierarchical wavelet coefficients - as well as the introduction of a ranked probability classifier on the wavelet coefficients that handles both the heteroscedasticity of the data in addition to the potential non-representativity of the training set. The classifier is simple to implement while a major advantage of the BAGIDIS wavelets is that they are translation invariant. Hence, BAGIDIS does not need the light curves to be aligned to extract features. Further, BAGIDIS is non-parametric so it can be used effectively in blind searches for new objects. We demonstrate the effectiveness of our classifier against the Supernova Photometric Classification Challenge to correctly classify supernova light curves as Type Ia or non-Ia. We train our classifier on the spectroscopically confirmed subsample (which is not representative) and show that it works well for supernova with observed light-curve time spans greater than 100 d (roughly 55 per cent of the data set). For such data, we obtain a Ia efficiency of 80.5 per cent and a purity of 82.4 per cent, yielding a highly competitive challenge score of 0.49. This indicates that our `model-blind' approach may be particularly suitable for the general classification of astronomical transients in the era of large synoptic sky surveys.

  18. Real Time Video Quality Representation Classification of Encrypted HTTP Adaptive Video Streaming - the Case of Safari

    Dubin, Ran; Dvir, Amit; Pele, Ofir; Hadar, Ofer; Richman, Itay; Trabelsi, Ofir

    2016-01-01

    The increasing popularity of HTTP adaptive video streaming services has dramatically increased bandwidth requirements on operator networks, which attempt to shape their traffic through Deep Packet Inspection (DPI). However, Google and certain content providers have started to encrypt their video services. As a result, operators often encounter difficulties in shaping their encrypted video traffic via DPI. This highlights the need for new traffic classification methods for encrypted HTTP adapt...

  19. Adaptive frequency estimation by MUSIC (Multiple Signal Classification) method

    Karhunen, Juha; Nieminen, Esko; Joutsensalo, Jyrki

    During the last years, the eigenvector-based method called MUSIC has become very popular in estimating the frequencies of sinusoids in additive white noise. Adaptive realizations of the MUSIC method are studied using simulated data. Several of the adaptive realizations seem to give in practice equally good results as the nonadaptive standard realization. The only exceptions are instantaneous gradient type algorithms that need considerably more samples to achieve a comparable performance. A new method is proposed for constructing initial estimates to the signal subspace. The method improves often dramatically the performance of instantaneous gradient type algorithms. The new signal subspace estimate can also be used to define a frequency estimator directly or to simplify eigenvector computation.

  20. Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm

    E. Parvinnia

    2014-01-01

    Full Text Available Electroencephalogram (EEG signals are often used to diagnose diseases such as seizure, alzheimer, and schizophrenia. One main problem with the recorded EEG samples is that they are not equally reliable due to the artifacts at the time of recording. EEG signal classification algorithms should have a mechanism to handle this issue. It seems that using adaptive classifiers can be useful for the biological signals such as EEG. In this paper, a general adaptive method named weighted distance nearest neighbor (WDNN is applied for EEG signal classification to tackle this problem. This classification algorithm assigns a weight to each training sample to control its influence in classifying test samples. The weights of training samples are used to find the nearest neighbor of an input query pattern. To assess the performance of this scheme, EEG signals of thirteen schizophrenic patients and eighteen normal subjects are analyzed for the classification of these two groups. Several features including, fractal dimension, band power and autoregressive (AR model are extracted from EEG signals. The classification results are evaluated using Leave one (subject out cross validation for reliable estimation. The results indicate that combination of WDNN and selected features can significantly outperform the basic nearest-neighbor and the other methods proposed in the past for the classification of these two groups. Therefore, this method can be a complementary tool for specialists to distinguish schizophrenia disorder.

  1. Analysis of Distributed and Adaptive Genetic Algorithm for Mining Interesting Classification Rules

    YI Yunfei; LIN Fang; QIN Jun

    2008-01-01

    Distributed genetic algorithm can be combined with the adaptive genetic algorithm for mining the interesting and comprehensible classification rules. The paper gives the method to encode for the rules, the fitness function, the selecting, crossover, mutation and migration operator for the DAGA at the same time are designed.

  2. Climate Change Impact Assessment and Adaptation Options in Vulnerable Agro-Landscapes in East-Africa

    Manful, D.; Tscherning, K.; Kersebaum, K.; Dietz, J.; Dietrich, O.; Gomani, C.; Böhm, H.; Büchner, M.; Lischeid, G.,; Ojoyi, M.,

    2009-04-01

    Climate change poses a risk to the livelihoods of large populations in the developing world, especially in Africa. In East Africa, climate change is expected to affect the spatial distribution and quantity of precipitation. The proposed project will assess aspects of climate impacts and adaptation options in Tanzania. The project will attempt to quantify (1) projected impacts including: variability in temperature, rainfall, flooding and drought (2) the affect changes in 1. will have on specific sectors namely agriculture (food security), water resources and ecosystem services. The cumulative effects of diminished surface and ground water flow on agricultural production coupled with increasing demand for food due to increase in human pressure will also be evaluated. Expected outputs of the project include (1) downscaled climate change scenarios for different IPCC emission scenarios (2) model based estimations of climate change impacts on hydrological cycle and assessment of land use options (3) scenarios of sustainable livelihoods and resilient agro-landscapes under climate change (4) assessment of adaptive practices and criteria for best adaptation practices. The presentation will focus on novel approaches that focus on the use of agro-ecosystem models to predict local and regional impacts of climate variability on food with specific needs of the end-user factored into model set-up process. In other words, model configurations adapted to the information needs of a specific end-user or audience are evaluated. The perception of risk within different end-users (small scale farmer versus a regional or state level policy maker) are explicitly taken into consideration with the overarching aim of maximizing the impact of the results obtained from computer-based simulations.

  3. Hierarchic levels of a system classification of radiation-contaminated landscapes

    Five hierarchic levels of the systematic organization of natural landscapes are determined: substantial-phase, soil-profile, biogeocenotic, landscape, and geosystematic. Systems and subsystems of compounds of chemical elements and natural and man-caused factors that characterized properties and mechanisms of ecological self-organization of biogeocenoses are brought into accordance with each level. A scheme of hierarchic subordination of systems, subsystems, and processes is worked out. Leading links of transformation and migration of radionuclides that define the contamination of tropic chains are determined

  4. Fast Model Adaptation for Automated Section Classification in Electronic Medical Records.

    Ni, Jian; Delaney, Brian; Florian, Radu

    2015-01-01

    Medical information extraction is the automatic extraction of structured information from electronic medical records, where such information can be used for improving healthcare processes and medical decision making. In this paper, we study one important medical information extraction task called section classification. The objective of section classification is to automatically identify sections in a medical document and classify them into one of the pre-defined section types. Training section classification models typically requires large amounts of human labeled training data to achieve high accuracy. Annotating institution-specific data, however, can be both expensive and time-consuming; which poses a big hurdle for adapting a section classification model to new medical institutions. In this paper, we apply two advanced machine learning techniques, active learning and distant supervision, to reduce annotation cost and achieve fast model adaptation for automated section classification in electronic medical records. Our experiment results show that active learning reduces the annotation cost and time by more than 50%, and distant supervision can achieve good model accuracy using weakly labeled training data only. PMID:26262005

  5. Multiview Sample Classification Algorithm Based on L1-Graph Domain Adaptation Learning

    Huibin Lu

    2015-01-01

    Full Text Available In the case of multiview sample classification with different distribution, training and testing samples are from different domains. In order to improve the classification performance, a multiview sample classification algorithm based on L1-Graph domain adaptation learning is presented. First of all, a framework of nonnegative matrix trifactorization based on domain adaptation learning is formed, in which the unchanged information is regarded as the bridge of knowledge transformation from the source domain to the target domain; the second step is to construct L1-Graph on the basis of sparse representation, so as to search for the nearest neighbor data with self-adaptation and preserve the samples and the geometric structure; lastly, we integrate two complementary objective functions into the unified optimization issue and use the iterative algorithm to cope with it, and then the estimation of the testing sample classification is completed. Comparative experiments are conducted in USPS-Binary digital database, Three-Domain Object Benchmark database, and ALOI database; the experimental results verify the effectiveness of the proposed algorithm, which improves the recognition accuracy and ensures the robustness of algorithm.

  6. Adaptive road crack detection system by pavement classification.

    Gavilán, Miguel; Balcones, David; Marcos, Oscar; Llorca, David F; Sotelo, Miguel A; Parra, Ignacio; Ocaña, Manuel; Aliseda, Pedro; Yarza, Pedro; Amírola, Alejandro

    2011-01-01

    This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement. PMID:22163717

  7. Adaptive Road Crack Detection System by Pavement Classification

    Alejandro Amírola

    2011-10-01

    Full Text Available This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement.

  8. INTERACTIVE DOMAIN ADAPTION FOR THE CLASSIFICATION OF REMOTE SENSING IMAGES USING ACTIVE LEARNING

    U.Pushpa Lingam

    2015-11-01

    Full Text Available Interactive Domain Adaptation (IDA technique based on active learning for the classification of remote sensing images. Interactive domain adaptation method is used for adapting the supervised classifier trained on a given remote sensing source image to make it suitable for classifying a different but related target image. The two images can be acquired in different locations and at different times. This method iteratively selects the most informative samples of the target image to be labeled and included in the training set and the source image samples are reweighted or removed from the training set on the basis of their disagreement with the target image classification problem. The consistent information available from the source image can be effectively exploited for the classification of the target image and for guiding the selection of new samples to be labeled, whereas the inconsistent information is automatically detected and removed. This approach significantly reduces the number of new labeled samples to be collected from the target image. Experimental results on both a multispectral very high resolution and a hyper spectral data set confirm the effectiveness of the interactive domain adaptation for theclassification of remote sensing using active learning method.

  9. Classification of crops across heterogeneous agricultural landscape in Kenya using AisaEAGLE imaging spectroscopy data

    Piiroinen, Rami; Heiskanen, Janne; Mõttus, Matti; Pellikka, Petri

    2015-07-01

    Land use practices are changing at a fast pace in the tropics. In sub-Saharan Africa forests, woodlands and bushlands are being transformed for agricultural use to produce food for the rapidly growing population. The objective of this study was to assess the prospects of mapping the common agricultural crops in highly heterogeneous study area in south-eastern Kenya using high spatial and spectral resolution AisaEAGLE imaging spectroscopy data. Minimum noise fraction transformation was used to pack the coherent information in smaller set of bands and the data was classified with support vector machine (SVM) algorithm. A total of 35 plant species were mapped in the field and seven most dominant ones were used as classification targets. Five of the targets were agricultural crops. The overall accuracy (OA) for the classification was 90.8%. To assess the possibility of excluding the remaining 28 plant species from the classification results, 10 different probability thresholds (PT) were tried with SVM. The impact of PT was assessed with validation polygons of all 35 mapped plant species. The results showed that while PT was increased more pixels were excluded from non-target polygons than from the polygons of the seven classification targets. This increased the OA and reduced salt-and-pepper effects in the classification results. Very high spatial resolution imagery and pixel-based classification approach worked well with small targets such as maize while there was mixing of classes on the sides of the tree crowns.

  10. Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms

    Mensi, Skander; Naud, Richard; Pozzorini, Christian; Avermann, Michael; Petersen, Carl C. H.; Gerstner, Wulfram

    2012-01-01

    Mensi S, Naud R, Pozzorini C, Avermann M, Petersen CCH, Gerstner W. Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms. J Neurophysiol 107: 1756-1775, 2012. First published December 7, 2011; doi:10.1152/jn.00408.2011.-Cortical information processing originates from the exchange of action potentials between many cell types. To capture the essence of these interactions, it is of critical importance to build mathematical models that ...

  11. Adaptive landscapes and emergent phenotypes: why do cancers have high glycolysis?

    Gillies, Robert J; Gatenby, Robert A

    2007-06-01

    Investigating the causes of increased aerobic glycolysis in tumors (Warburg Effect) has gone in and out of fashion many times since it was first described almost a century ago. The field is currently in ascendance due to two factors. Over a million FDG-PET studies have unequivocally identified increased glucose uptake as a hallmark of metastatic cancer in humans. These observations, combined with new molecular insights with HIF-1alpha and c-myc, have rekindled an interest in this important phenotype. A preponderance of work has been focused on the molecular mechanisms underlying this effect, with the expectation that a mechanistic understanding may lead to novel therapeutic approaches. There is also an implicit assumption that a mechanistic understanding, although fundamentally reductionist, will nonetheless lead to a more profound teleological understanding of the need for altered metabolism in invasive cancers. In this communication, we describe an alternative approach that begins with teleology; i.e. adaptive landscapes and selection pressures that promote emergence of aerobic glycolysis during the somatic evolution of invasive cancer. Mathematical models and empirical observations are used to define the adaptive advantage of aerobic glycolysis that would explain its remarkable prevalence in human cancers. These studies have led to the hypothesis that increased consumption of glucose in metastatic lesions is not used for substantial energy production via Embden-Meyerhoff glycolysis, but rather for production of acid, which gives the cancer cells a competitive advantage for invasion. Alternative hypotheses, wherein the glucose is used for generation of reducing equivalents (NADPH) or anabolic precursors (ribose) are also discussed. PMID:17624581

  12. Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function

    Groenendyk, Derek G.; Ferré, Ty P.A.; Kelly R. Thorp; Rice, Amy K.

    2015-01-01

    Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth’s surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertain...

  13. Wavelength-adaptive dehazing using histogram merging-based classification for UAV images.

    Yoon, Inhye; Jeong, Seokhwa; Jeong, Jaeheon; Seo, Doochun; Paik, Joonki

    2015-01-01

    Since incoming light to an unmanned aerial vehicle (UAV) platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image acquisition model, the proposed dehazing algorithm consists of three steps: (i) image segmentation based on geometric classes; (ii) generation of the context-adaptive transmission map; and (iii) intensity transformation for enhancing a hazy UAV image. The major contribution of the research is a novel hazy UAV image degradation model by considering the wavelength of light sources. In addition, the proposed transmission map provides a theoretical basis to differentiate visually important regions from others based on the turbidity and merged classification results. PMID:25808767

  14. Wavelength-Adaptive Dehazing Using Histogram Merging-Based Classification for UAV Images

    Inhye Yoon

    2015-03-01

    Full Text Available Since incoming light to an unmanned aerial vehicle (UAV platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image acquisition model, the proposed dehazing algorithm consists of three steps: (i image segmentation based on geometric classes; (ii generation of the context-adaptive transmission map; and (iii intensity transformation for enhancing a hazy UAV image. The major contribution of the research is a novel hazy UAV image degradation model by considering the wavelength of light sources. In addition, the proposed transmission map provides a theoretical basis to differentiate visually important regions from others based on the turbidity and merged classification results.

  15. Multiclass Classification by Adaptive Network of Dendritic Neurons with Binary Synapses Using Structural Plasticity.

    Hussain, Shaista; Basu, Arindam

    2016-01-01

    The development of power-efficient neuromorphic devices presents the challenge of designing spike pattern classification algorithms which can be implemented on low-precision hardware and can also achieve state-of-the-art performance. In our pursuit of meeting this challenge, we present a pattern classification model which uses a sparse connection matrix and exploits the mechanism of nonlinear dendritic processing to achieve high classification accuracy. A rate-based structural learning rule for multiclass classification is proposed which modifies a connectivity matrix of binary synaptic connections by choosing the best "k" out of "d" inputs to make connections on every dendritic branch (k classification problem, a two-step solution is proposed. First, an adaptive approach is proposed which scales the relative size of the dendritic trees of neurons for each class. It works by progressively adding dendrites with fixed number of synapses to the network, thereby allocating synaptic resources as per the complexity of the given problem. As a second step, theoretical capacity calculations are used to convert each neuronal dendritic tree to its optimal topology where dendrites of each class are assigned different number of synapses. The performance of the model is evaluated on classification of handwritten digits from the benchmark MNIST dataset and compared with other spike classifiers. We show that our system can achieve classification accuracy within 1 - 2% of other reported spike-based classifiers while using much less synaptic resources (only 7%) compared to that used by other methods. Further, an ensemble classifier created with adaptively learned sizes can attain accuracy of 96.4% which is at par with the best reported performance of spike-based classifiers. Moreover, the proposed method achieves this by using about 20% of the synapses used by other spike algorithms. We also present results of applying our algorithm to classify the MNIST-DVS dataset collected from a

  16. Automatic classification of schizophrenia using resting-state functional language network via an adaptive learning algorithm

    Zhu, Maohu; Jie, Nanfeng; Jiang, Tianzi

    2014-03-01

    A reliable and precise classification of schizophrenia is significant for its diagnosis and treatment of schizophrenia. Functional magnetic resonance imaging (fMRI) is a novel tool increasingly used in schizophrenia research. Recent advances in statistical learning theory have led to applying pattern classification algorithms to access the diagnostic value of functional brain networks, discovered from resting state fMRI data. The aim of this study was to propose an adaptive learning algorithm to distinguish schizophrenia patients from normal controls using resting-state functional language network. Furthermore, here the classification of schizophrenia was regarded as a sample selection problem where a sparse subset of samples was chosen from the labeled training set. Using these selected samples, which we call informative vectors, a classifier for the clinic diagnosis of schizophrenia was established. We experimentally demonstrated that the proposed algorithm incorporating resting-state functional language network achieved 83.6% leaveone- out accuracy on resting-state fMRI data of 27 schizophrenia patients and 28 normal controls. In contrast with KNearest- Neighbor (KNN), Support Vector Machine (SVM) and l1-norm, our method yielded better classification performance. Moreover, our results suggested that a dysfunction of resting-state functional language network plays an important role in the clinic diagnosis of schizophrenia.

  17. Comparative landscape genetics and the adaptive radiation of Darwin's finches: the role of peripheral isolation.

    Petren, K; Grant, P R; Grant, B R; Keller, L F

    2005-09-01

    We use genetic divergence at 16 microsatellite loci to investigate how geographical features of the Galápagos landscape structure island populations of Darwin's finches. We compare the three most genetically divergent groups of Darwin's finches comprising morphologically and ecologically similar allopatric populations: the cactus finches (Geospiza scandens and Geospiza conirostris), the sharp-beaked ground finches (Geospiza difficilis) and the warbler finches (Certhidea olivacea and Certhidea fusca). Evidence of reduced genetic diversity due to drift was limited to warbler finches on small, peripheral islands. Evidence of low levels of recent interisland migration was widespread throughout all three groups. The hypothesis of distance-limited dispersal received the strongest support in cactus and sharp-beaked ground finches as evidenced by patterns of isolation by distance, while warbler finches showed a weaker relationship. Support for the hypothesis that gene flow constrains morphological divergence was only found in one of eight comparisons within these groups. Among warbler finches, genetic divergence was relatively high while phenotypic divergence was low, implicating stabilizing selection rather than constraint due to gene flow. We conclude that the adaptive radiation of Darwin's finches has occurred in the presence of ongoing but low levels of gene flow caused by distance-dependent interisland dispersal. Gene flow does not constrain phenotypic divergence, but may augment genetic variation and facilitate evolution due to natural selection. Both microsatellites and mtDNA agree in that subsets of peripheral populations of two older groups are genetically more similar to other species that underwent dramatic morphological change. The apparent decoupling of morphological and molecular evolution may be accounted for by a modification of Lack's two-stage model of speciation: relative ecological stasis in allopatry followed by secondary contact, ecological

  18. CLASSIFICATIONS OF EEG SIGNALS FOR MENTAL TASKS USING ADAPTIVE RBF NETWORK

    薛建中; 郑崇勋; 闫相国

    2004-01-01

    Objective This paper presents classifications of mental tasks based on EEG signals using an adaptive Radial Basis Function (RBF) network with optimal centers and widths for the Brain-Computer Interface (BCI) schemes. Methods Initial centers and widths of the network are selected by a cluster estimation method based on the distribution of the training set. Using a conjugate gradient descent method, they are optimized during training phase according to a regularized error function considering the influence of their changes to output values. Results The optimizing process improves the performance of RBF network, and its best cognition rate of three task pairs over four subjects achieves 87.0%. Moreover, this network runs fast due to the fewer hidden layer neurons. Conclusion The adaptive RBF network with optimal centers and widths has high recognition rate and runs fast. It may be a promising classifier for on-line BCI scheme.

  19. Human action classification using adaptive key frame interval for feature extraction

    Lertniphonphan, Kanokphan; Aramvith, Supavadee; Chalidabhongse, Thanarat H.

    2016-01-01

    Human action classification based on the adaptive key frame interval (AKFI) feature extraction is presented. Since human movement periods are different, the action intervals that contain the intensive and compact motion information are considered in this work. We specify AKFI by analyzing an amount of motion through time. The key frame is defined to be the local minimum interframe motion, which is computed by using frame differencing between consecutive frames. Once key frames are detected, the features within a segmented period are encoded by adaptive motion history image and key pose history image. The action representation consists of the local orientation histogram of the features during AKFI. The experimental results on Weizmann dataset, KTH dataset, and UT Interaction dataset demonstrate that the features can effectively classify action and can classify irregular cases of walking compared to other well-known algorithms.

  20. Charting the landscape of priority problems in psychiatry, part 1: classification and diagnosis.

    Stephan, Klaas E; Bach, Dominik R; Fletcher, Paul C; Flint, Jonathan; Frank, Michael J; Friston, Karl J; Heinz, Andreas; Huys, Quentin J M; Owen, Michael J; Binder, Elisabeth B; Dayan, Peter; Johnstone, Eve C; Meyer-Lindenberg, Andreas; Montague, P Read; Schnyder, Ulrich; Wang, Xiao-Jing; Breakspear, Michael

    2016-01-01

    Contemporary psychiatry faces major challenges. Its syndrome-based disease classification is not based on mechanisms and does not guide treatment, which largely depends on trial and error. The development of therapies is hindered by ignorance of potential beneficiary patient subgroups. Neuroscientific and genetics research have yet to affect disease definitions or contribute to clinical decision making. In this challenging setting, what should psychiatric research focus on? In two companion papers, we present a list of problems nominated by clinicians and researchers from different disciplines as candidates for future scientific investigation of mental disorders. These problems are loosely grouped into challenges concerning nosology and diagnosis (this Personal View) and problems related to pathogenesis and aetiology (in the companion Personal View). Motivated by successful examples in other disciplines, particularly the list of Hilbert's problems in mathematics, this subjective and eclectic list of priority problems is intended for psychiatric researchers, helping to re-focus existing research and providing perspectives for future psychiatric science. PMID:26573970

  1. Classification

    Clary, Renee; Wandersee, James

    2013-01-01

    In this article, Renee Clary and James Wandersee describe the beginnings of "Classification," which lies at the very heart of science and depends upon pattern recognition. Clary and Wandersee approach patterns by first telling the story of the "Linnaean classification system," introduced by Carl Linnacus (1707-1778), who is…

  2. Using pattern classification to measure adaptation to the orientation of high order aberrations.

    Lucie Sawides

    Full Text Available BACKGROUND: The image formed by the eye's optics is blurred by the ocular aberrations, specific to each eye. Recent studies demonstrated that the eye is adapted to the level of blur produced by the high order aberrations (HOA. We examined whether visual coding is also adapted to the orientation of the natural HOA of the eye. METHODS AND FINDINGS: Judgments of perceived blur were measured in 5 subjects in a psychophysical procedure inspired by the "Classification Images" technique. Subjects were presented 500 pairs of images, artificially blurred with HOA from 100 real eyes (i.e. different orientations, with total blur level adjusted to match the subject's natural blur. Subjects selected the image that appeared best focused in each random pair, in a 6-choice ranked response. Images were presented through Adaptive Optics correction of the subject's aberrations. The images selected as best focused were identified as positive, the other as negative responses. The highest classified positive responses correlated more with the subject's Point Spread Function, PSF, (r = 0.47 on average than the negative (r = 0.34 and the difference was significant for all subjects (p<0.02. Using the orientation of the best fitting ellipse of angularly averaged integrated PSF intensities (weighted by the subject's responses we found that in 4 subjects the positive PSF response was close to the subject's natural PSF orientation (within 21 degrees on average whereas the negative PSF response was almost perpendicularly oriented to the natural PSF (at 76 degrees on average. CONCLUSIONS: The Classification-Images inspired method is very powerful in identifying the internally coded blur of subjects. The consistent bias of the Positive PSFs towards the natural PSF in most subjects indicates that the internal code of blur appears rather specific to each subject's high order aberrations and reveals that the calibration mechanisms for normalizing blur also operate using

  3. Adapted Verbal Feedback, Instructor Interaction and Student Emotions in the Landscape Architecture Studio

    Smith, Carl A.; Boyer, Mark E.

    2015-01-01

    In light of concerns with architectural students' emotional jeopardy during traditional desk and final-jury critiques, the authors pursue alternative approaches intended to provide more supportive and mentoring verbal assessment in landscape architecture studios. In addition to traditional studio-based critiques throughout a semester, we provide…

  4. Adaptive-Neuro Fuzzy Inference System for Human Posture Classification Using a Simplified Shock Graph

    Shahbudin, S.; Hussain, A.; El-Shafie, Ahmed; Tahir, N. M.; Samad, S. A.

    In this paper, a neuro-fuzzy technique known as the Adaptive-Neuro Fuzzy Inference System (ANFIS) has been used to highlight the application of ANFIS to perform human posture classification task using the new simplified shock graph (SSG) representation. Basically, a shock graph is a shape abstraction that decomposed a shape into a set of hierarchically organized primitive parts. The shock graph that represents the silhouette of an object in terms of a set of qualitatively defined parts and organized in a hierarchical, directed acyclic graph is used as a powerful representation of human shape in our work. The SSG feature provides a compact, unique and simple way of representing human shape and has been tested with several classifiers. As such, in this paper we intend to test its efficacy with another classifier, that is, the ANFIS classifier system. The result showed that the proposed ANFIS model can be used in classifying various human postures.

  5. An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects

    Min Se Dong

    2011-06-01

    Full Text Available Abstract Background Numerous studies have been conducted regarding a heartbeat classification algorithm over the past several decades. However, many algorithms have also been studied to acquire robust performance, as biosignals have a large amount of variation among individuals. Various methods have been proposed to reduce the differences coming from personal characteristics, but these expand the differences caused by arrhythmia. Methods In this paper, an arrhythmia classification algorithm using a dedicated wavelet adapted to individual subjects is proposed. We reduced the performance variation using dedicated wavelets, as in the ECG morphologies of the subjects. The proposed algorithm utilizes morphological filtering and a continuous wavelet transform with a dedicated wavelet. A principal component analysis and linear discriminant analysis were utilized to compress the morphological data transformed by the dedicated wavelets. An extreme learning machine was used as a classifier in the proposed algorithm. Results A performance evaluation was conducted with the MIT-BIH arrhythmia database. The results showed a high sensitivity of 97.51%, specificity of 85.07%, accuracy of 97.94%, and a positive predictive value of 97.26%. Conclusions The proposed algorithm achieves better accuracy than other state-of-the-art algorithms with no intrasubject between the training and evaluation datasets. And it significantly reduces the amount of intervention needed by physicians.

  6. A Self-adaptive Threshold Method for Automatic Sleep Stage Classification Using EOG and EMG

    Li Jie

    2015-01-01

    Full Text Available Sleep stages are generally divided into three stages including Wake, REM and NRME. The standard sleep monitoring technology is Polysomnography (PSG. The inconvenience for PSG monitoring limits the usage of PSG in some areas. In this study, we developed a new method to classify sleep stage using electrooculogram (EOG and electromyography (EMG automatically. We extracted right and left EOG features and EMG feature in time domain, and classified them into strong, weak and none types through calculating self-adaptive threshold. Combination of the time features of EOG and EMG signals, we classified sleep stages into Wake, REM and NREM stages. The time domain features utilized in the method were Integrate Value, variance and energy. The experiment of 30 datasets showed a satisfactory result with the accuracy of 82.93% for Wake, NREM and REM stages classification, and the average accuracy of Wake stage classification was 83.29%, 82.11% for NREM stage and 76.73% for REM stage.

  7. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation.

    Fitzpatrick, Matthew C; Keller, Stephen R

    2015-01-01

    Local adaptation is a central feature of most species occupying spatially heterogeneous environments, and may factor critically in responses to environmental change. However, most efforts to model the response of species to climate change ignore intraspecific variation due to local adaptation. Here, we present a new perspective on spatial modelling of organism-environment relationships that combines genomic data and community-level modelling to develop scenarios regarding the geographic distribution of genomic variation in response to environmental change. Rather than modelling species within communities, we use these techniques to model large numbers of loci across genomes. Using balsam poplar (Populus balsamifera) as a case study, we demonstrate how our framework can accommodate nonlinear responses of loci to environmental gradients. We identify a threshold response to temperature in the circadian clock gene GIGANTEA-5 (GI5), suggesting that this gene has experienced strong local adaptation to temperature. We also demonstrate how these methods can map ecological adaptation from genomic data, including the identification of predicted differences in the genetic composition of populations under current and future climates. Community-level modelling of genomic variation represents an important advance in landscape genomics and spatial modelling of biodiversity that moves beyond species-level assessments of climate change vulnerability. PMID:25270536

  8. Natural Selection Fails to Optimize Mutation Rates for Long-Term Adaptation on Rugged Fitness Landscapes

    Clune, Jeff; Misevic, Dusan; Ofria, Charles; Richard E Lenski; Elena, Santiago F.; Sanjuán, Rafael

    2008-01-01

    The rate of mutation is central to evolution. Mutations are required for adaptation, yet most mutations with phenotypic effects are deleterious. As a consequence, the mutation rate that maximizes adaptation will be some intermediate value. Here, we used digital organisms to investigate the ability of natural selection to adjust and optimize mutation rates. We assessed the optimal mutation rate by empirically determining what mutation rate produced the highest rate of adaptation. Then, we allo...

  9. Using ethnographic, landscape history and climate data to identify smallholder adaptation strategies to tidal regime changes in the Amazon Estuary

    Vogt, N. D.; Fernandes, K. D.; Pinedo-Vasquez, M.

    2013-12-01

    Although climate change is predicted to negatively impact production of smallholder farmers in tropical estuaries, how changes in the local climate will impact tidal dynamics specifically relevant to the Amazon River estuarine populations is not clear. We argue that using ethnographic and landscape history data can improve the linkages between climate studies and changes in tidal patterns relevant to local populations. Survey data collected from local elders describe spatial and temporal variations in the local hydro-climatic conditions over recent decades and how farmers are adapting their resource-use patterns to these changes. We also analyze how they adapt resource-use system to unpredictable events. The ethnographic and landscape history information are then used to guide climate studies by identifying how to aggregate climate and tidal data to seasons of production relevant to the study population. Climate studies often aggregate data into astronomical seasons not taking into account local production calendars, which may mask long term trends or patterns of extreme events underway that affect local production. The climate deviations are then correlated to large-scale forcings, such as the El Niño Southern Oscillation (ENSO), to verify whether seasonal climate forecast can be used to predict events to which local populations are most vulnerable. We have applied this approach to identify and analyze extremes changes in the local climate regimens in the Amazon Estuary in both north and south channels using over 40 years of river heightand precipitation data. We present the most significant changes underway, climate drivers of them, and discuss how smallholder farmers are able to adapt to the challenges and opportunities produced by ongoing changes in the local hydro-climatic patterns.

  10. An adaptive threshold based image processing technique for improved glaucoma detection and classification.

    Issac, Ashish; Partha Sarathi, M; Dutta, Malay Kishore

    2015-11-01

    Glaucoma is an optic neuropathy which is one of the main causes of permanent blindness worldwide. This paper presents an automatic image processing based method for detection of glaucoma from the digital fundus images. In this proposed work, the discriminatory parameters of glaucoma infection, such as cup to disc ratio (CDR), neuro retinal rim (NRR) area and blood vessels in different regions of the optic disc has been used as features and fed as inputs to learning algorithms for glaucoma diagnosis. These features which have discriminatory changes with the occurrence of glaucoma are strategically used for training the classifiers to improve the accuracy of identification. The segmentation of optic disc and cup based on adaptive threshold of the pixel intensities lying in the optic nerve head region. Unlike existing methods the proposed algorithm is based on an adaptive threshold that uses local features from the fundus image for segmentation of optic cup and optic disc making it invariant to the quality of the image and noise content which may find wider acceptability. The experimental results indicate that such features are more significant in comparison to the statistical or textural features as considered in existing works. The proposed work achieves an accuracy of 94.11% with a sensitivity of 100%. A comparison of the proposed work with the existing methods indicates that the proposed approach has improved accuracy of classification glaucoma from a digital fundus which may be considered clinically significant. PMID:26321351

  11. Medical image classification using spatial adjacent histogram based on adaptive local binary patterns.

    Liu, Dong; Wang, Shengsheng; Huang, Dezhi; Deng, Gang; Zeng, Fantao; Chen, Huiling

    2016-05-01

    Medical image recognition is an important task in both computer vision and computational biology. In the field of medical image classification, representing an image based on local binary patterns (LBP) descriptor has become popular. However, most existing LBP-based methods encode the binary patterns in a fixed neighborhood radius and ignore the spatial relationships among local patterns. The ignoring of the spatial relationships in the LBP will cause a poor performance in the process of capturing discriminative features for complex samples, such as medical images obtained by microscope. To address this problem, in this paper we propose a novel method to improve local binary patterns by assigning an adaptive neighborhood radius for each pixel. Based on these adaptive local binary patterns, we further propose a spatial adjacent histogram strategy to encode the micro-structures for image representation. An extensive set of evaluations are performed on four medical datasets which show that the proposed method significantly improves standard LBP and compares favorably with several other prevailing approaches. PMID:27058283

  12. Self-adaptive MOEA feature selection for classification of bankruptcy prediction data.

    Gaspar-Cunha, A; Recio, G; Costa, L; Estébanez, C

    2014-01-01

    Bankruptcy prediction is a vast area of finance and accounting whose importance lies in the relevance for creditors and investors in evaluating the likelihood of getting into bankrupt. As companies become complex, they develop sophisticated schemes to hide their real situation. In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved. This paper provides a methodology for feature selection in classification of bankruptcy data sets using an evolutionary multiobjective approach that simultaneously minimise the number of features and maximise the classifier quality measure (e.g., accuracy). The proposed methodology makes use of self-adaptation by applying the feature selection algorithm while simultaneously optimising the parameters of the classifier used. The methodology was applied to four different sets of data. The obtained results showed the utility of using the self-adaptation of the classifier. PMID:24707201

  13. Examining Social Adaptations in a Volatile Landscape in Northern Mongolia via the Agent-Based Model Ger Grouper

    Julia K. Clark

    2015-03-01

    Full Text Available The environment of the mountain-steppe-taiga of northern Mongolia is often characterized as marginal because of the high altitude, highly variable precipitation levels, low winter temperatures, and periodic droughts coupled with severe winter storms (known as dzuds. Despite these conditions, herders have inhabited this landscape for thousands of years, and hunter-gatherer-fishers before that. One way in which the risks associated with such a challenging and variable landscape are mitigated is through social networks and inter-family cooperation. We present an agent-based simulation, Ger Grouper, to examine how households have mitigated these risks through cooperation. The Ger Grouper simulation takes into account locational decisions of households, looks at fission/fusion dynamics of households and how those relate to environmental pressures, and assesses how degrees of relatedness can influence sharing of resources during harsh winters. This model, coupled with the traditional archaeological and ethnographic methods, helps shed light on the links between early Mongolian pastoralist adaptations and the environment. While preliminary results are promising, it is hoped that further development of this model will be able to characterize changing land-use patterns as social and political networks developed.

  14. Adaptable neighbours: movement patterns of GPS-collared leopards in human dominated landscapes in India.

    Morten Odden

    Full Text Available Understanding the nature of the interactions between humans and wildlife is of vital importance for conflict mitigation. We equipped five leopards with GPS-collars in Maharashtra (4 and Himachal Pradesh (1, India, to study movement patterns in human-dominated landscapes outside protected areas. An adult male and an adult female were both translocated 52 km, and exhibited extensive, and directional, post release movements (straight line movements: male = 89 km in 37 days, female = 45 km in 5 months, until they settled in home ranges of 42 km2 (male and 65 km2 (female. The three other leopards, two adult females and a young male were released close to their capture sites and used small home ranges of 8 km2 (male, 11 km2 and 15 km2 (females. Movement patterns were markedly nocturnal, with hourly step lengths averaging 339±9.5 m (SE during night and 60±4.1 m during day, and night locations were significantly closer to human settlements than day locations. However, more nocturnal movements were observed among those three living in the areas with high human population densities. These visited houses regularly at nighttime (20% of locations <25 m from houses, but rarely during day (<1%. One leopard living in a sparsely populated area avoided human settlements both day and night. The small home ranges of the leopards indicate that anthropogenic food resources may be plentiful although wild prey is absent. The study provides clear insights into the ability of leopards to live and move in landscapes that are extremely modified by human activity.

  15. ADAPT: building conceptual models of the physical and biological processes across permafrost landscapes

    Allard, M.; Vincent, W. F.; Lemay, M.

    2012-12-01

    Fundamental and applied permafrost research is called upon in Canada in support of environmental protection, economic development and for contributing to the international efforts in understanding climatic and ecological feedbacks of permafrost thawing under a warming climate. The five year "Arctic Development and Adaptation to Permafrost in Transition" program (ADAPT) funded by NSERC brings together 14 scientists from 10 Canadian universities and involves numerous collaborators from academia, territorial and provincial governments, Inuit communities and industry. The geographical coverage of the program encompasses all of the permafrost regions of Canada. Field research at a series of sites across the country is being coordinated. A common protocol for measuring ground thermal and moisture regime, characterizing terrain conditions (vegetation, topography, surface water regime and soil organic matter contents) is being applied in order to provide inputs for designing a general model to provide an understanding of transfers of energy and matter in permafrost terrain, and the implications for biological and human systems. The ADAPT mission is to produce an 'Integrated Permafrost Systems Science' framework that will be used to help generate sustainable development and adaptation strategies for the North in the context of rapid socio-economic and climate change. ADAPT has three major objectives: to examine how changing precipitation and warming temperatures affect permafrost geosystems and ecosystems, specifically by testing hypotheses concerning the influence of the snowpack, the effects of water as a conveyor of heat, sediments, and carbon in warming permafrost terrain and the processes of permafrost decay; to interact directly with Inuit communities, the public sector and the private sector for development and adaptation to changes in permafrost environments; and to train the new generation of experts and scientists in this critical domain of research in Canada

  16. MRI-based treatment plan simulation and adaptation for ion radiotherapy using a classification-based approach

    In order to benefit from the highly conformal irradiation of tumors in ion radiotherapy, sophisticated treatment planning and simulation are required. The purpose of this study was to investigate the potential of MRI for ion radiotherapy treatment plan simulation and adaptation using a classification-based approach. Firstly, a voxelwise tissue classification was applied to derive pseudo CT numbers from MR images using up to 8 contrasts. Appropriate MR sequences and parameters were evaluated in cross-validation studies of three phantoms. Secondly, ion radiotherapy treatment plans were optimized using both MRI-based pseudo CT and reference CT and recalculated on reference CT. Finally, a target shift was simulated and a treatment plan adapted to the shift was optimized on a pseudo CT and compared to reference CT optimizations without plan adaptation. The derivation of pseudo CT values led to mean absolute errors in the range of 81 - 95 HU. Most significant deviations appeared at borders between air and different tissue classes and originated from partial volume effects. Simulations of ion radiotherapy treatment plans using pseudo CT for optimization revealed only small underdosages in distal regions of a target volume with deviations of the mean dose of PTV between 1.4 - 3.1% compared to reference CT optimizations. A plan adapted to the target volume shift and optimized on the pseudo CT exhibited a comparable target dose coverage as a non-adapted plan optimized on a reference CT. We were able to show that a MRI-based derivation of pseudo CT values using a purely statistical classification approach is feasible although no physical relationship exists. Large errors appeared at compact bone classes and came from an imperfect distinction of bones and other tissue types in MRI. In simulations of treatment plans, it was demonstrated that these deviations are comparable to uncertainties of a target volume shift of 2 mm in two directions indicating that especially

  17. Key landscape ecology metrics for assessing climate change adaptation options: rate of change and patchiness of impacts

    López-Hoffman, Laura; Breshears, David D.; Allen, Craig D.; Miller, Marc L.

    2013-01-01

    Under a changing climate, devising strategies to help stakeholders adapt to alterations to ecosystems and their services is of utmost importance. In western North America, diminished snowpack and river flows are causing relatively gradual, homogeneous (system-wide) changes in ecosystems and services. In addition, increased climate variability is also accelerating the incidence of abrupt and patchy disturbances such as fires, floods and droughts. This paper posits that two key variables often considered in landscape ecology—the rate of change and the degree of patchiness of change—can aid in developing climate change adaptation strategies. We use two examples from the “borderland” region of the southwestern United States and northwestern Mexico. In piñon-juniper woodland die-offs that occurred in the southwestern United States during the 2000s, ecosystem services suddenly crashed in some parts of the system while remaining unaffected in other locations. The precise timing and location of die-offs was uncertain. On the other hand, slower, homogeneous change, such as the expected declines in water supply to the Colorado River delta, will likely impact the entire ecosystem, with ecosystem services everywhere in the delta subject to alteration, and all users likely exposed. The rapidity and spatial heterogeneity of faster, patchy climate change exemplified by tree die-off suggests that decision-makers and local stakeholders would be wise to operate under a Rawlsian “veil of ignorance,” and implement adaptation strategies that allow ecosystem service users to equitably share the risk of sudden loss of ecosystem services before actual ecosystem changes occur. On the other hand, in the case of slower, homogeneous, system-wide impacts to ecosystem services as exemplified by the Colorado River delta, adaptation strategies can be implemented after the changes begin, but will require a fundamental rethinking of how ecosystems and services are used and valued. In

  18. Multiclass Classification by Adaptive Network of Dendritic Neurons with Binary Synapses using Structural Plasticity

    Shaista eHussain; Arindam eBasu

    2016-01-01

    The development of power-efficient neuromorphic devices presents the challenge of designing spike pattern classification algorithms which can be implemented on low-precision hardware and can also achieve state-of-the-art performance. In our pursuit of meeting this challenge, we present a pattern classification model which uses a sparse connection matrix and exploits the mechanism of nonlinear dendritic processing to achieve high classification accuracy. A rate-based structural learning rule f...

  19. Multiclass Classification by Adaptive Network of Dendritic Neurons with Binary Synapses Using Structural Plasticity

    Hussain, Shaista; Basu, Arindam

    2016-01-01

    The development of power-efficient neuromorphic devices presents the challenge of designing spike pattern classification algorithms which can be implemented on low-precision hardware and can also achieve state-of-the-art performance. In our pursuit of meeting this challenge, we present a pattern classification model which uses a sparse connection matrix and exploits the mechanism of nonlinear dendritic processing to achieve high classification accuracy. A rate-based structural learning rule f...

  20. Aesthetic Study of Native Landscape in Landscape Degisn

    郑小伟

    2013-01-01

    As Ji Cheng says in "Yuan Ye": planning should be adapted to local conditions by the square, round, slope and winding. During landscape planning and design, we should make ful use of native landscape as a design element according to local conditions. The paper wil analyze the native landscape elements from an aesthetic point of view through case study of water landscape, plants, topography, heritage sites and so on to explain the aesthetic significance of native landscape in landscape planning.

  1. Adapting Landscape and Place in Transcultural Remakes: The Case of Bron|Broen, The Bridge and The Tunnel

    Isadora García Avis

    2015-12-01

    Full Text Available Although place tends to be overlooked as a narrative component in audiovisual fiction, it is undeniable that landscapes, settings and locations play a defining role in television series. Not only are these forms of place central to reinforcing the genre, themes and plots of the story; they also serve to reflect the characters’ emotions and cultural identities. Therefore, when a scripted format is remade in a foreign country, the narrative dimension of place is one of the elements that need to be relocalised to a new sociocultural environment. This paper aims to examine how the significance of place is adapted in the specific case of transcultural televisual remakes. In order to do so, the study will present a comparative analysis of the Swedish-Danish co-production Bron|Broen (2011- and its two remakes: the American The Bridge (2013-2014 and the Anglo-French The Tunnel (2013-. More specifically, the representations of place in these three series will be studied in relation to other narrative components, such as genre conventions and aesthetics, characters, and dramatic conflict. Ultimately, this paper will prove that, when you take a story built for a specific setting and relocate it elsewhere, that new context informs the architecture of the story itself.

  2. Statistical topography of fitness landscapes

    Franke, Jasper

    2011-01-01

    Fitness landscapes are generalized energy landscapes that play an important conceptual role in evolutionary biology. These landscapes provide a relation between the genetic configuration of an organism and that organism’s adaptive properties. In this work, global topographical features of these fitness landscapes are investigated using theoretical models. The resulting predictions are compared to empirical landscapes. It is shown that these landscapes allow, at least with respe...

  3. Landscape Influences on Fisher Success: Adaptation Strategies in Closed and Open Access Fisheries in Southern Chile

    Tracy Van Holt

    2012-03-01

    Full Text Available Determinants of fisher success in southern Chile's loco (Concholepas concholepas fishery are examined by comparing fisher success in exclusive access territories that vary in relationship to tree-plantation development, which can affect shellfish quality. The relative importance of fishers' experience and capture technology (traditional measures of fisher success are evaluated against environmental and geospatial characteristics. While knowledge and technology explained variation in catches, this did not translate into higher prices or profit. Fishers succeeded (gained higher prices for locos and had higher monthly incomes from their management areas when they harvested shellfish from closed (exclusive nearshore management areas where the environmental condition produced high quality locos regardless of their fishing experience, technology, and the geospatial features of management areas. Experienced fishers who worked in management areas near tree plantations that fail to produce resources of sufficient quality shifted to offshore fisheries where their experience counted. Offshore fishers working in the congrio (Genypterus chilensis fishery likely exposed themselves to more risk and benefited from their experience and available technology; environmental condition and geospatial factors played little role in their success (price. Closed management areas provided resources to harvest, but may reduce a fisher's ability to adapt to environmental change because success depends on environmental factors outside of a fisher's control. Fishers were not financially rewarded for their experience or their technology in the loco fishery.

  4. Land Cover classification and change-detection analysis using multi-temporal remote sensed imagery and landscape metrics

    Carmelo Riccardo Fichera

    2012-03-01

    Full Text Available Remote Sensing (RS data and techniques, in combination with GIS and landscape metrics, are fundamental to analyse and characterise Land Cover (LC and its changes. The case study here described, has been conducted in the area of Avellino (Southern Italy. To characterise the dynamics of changes during a fifty year period (1954÷2004, a multi-temporal set of images has been processed: aerial photos (1954, and Landsat scenes (MSS 1975, TM 1985 and 1993, ETM+ 2004. LC pattern and its changes are linked to both natural and social processes whose driving role has been clearly demonstrated in the case study: after the disastrous Irpinia earthquake (1980, specific zoning laws and urban plans have significantly addressed landscape changes.

  5. The population genomic landscape of human genetic structure, admixture history and local adaptation in Peninsular Malaysia.

    Deng, Lian; Hoh, Boon Peng; Lu, Dongsheng; Fu, Ruiqing; Phipps, Maude E; Li, Shilin; Nur-Shafawati, Ab Rajab; Hatin, Wan Isa; Ismail, Endom; Mokhtar, Siti Shuhada; Jin, Li; Zilfalil, Bin Alwi; Marshall, Christian R; Scherer, Stephen W; Al-Mulla, Fahd; Xu, Shuhua

    2014-09-01

    Peninsular Malaysia is a strategic region which might have played an important role in the initial peopling and subsequent human migrations in Asia. However, the genetic diversity and history of human populations--especially indigenous populations--inhabiting this area remain poorly understood. Here, we conducted a genome-wide study using over 900,000 single nucleotide polymorphisms (SNPs) in four major Malaysian ethnic groups (MEGs; Malay, Proto-Malay, Senoi and Negrito), and made comparisons of 17 world-wide populations. Our data revealed that Peninsular Malaysia has greater genetic diversity corresponding to its role as a contact zone of both early and recent human migrations in Asia. However, each single Orang Asli (indigenous) group was less diverse with a smaller effective population size (N(e)) than a European or an East Asian population, indicating a substantial isolation of some duration for these groups. All four MEGs were genetically more similar to Asian populations than to other continental groups, and the divergence time between MEGs and East Asian populations (12,000--6,000 years ago) was also much shorter than that between East Asians and Europeans. Thus, Malaysian Orang Asli groups, despite their significantly different features, may share a common origin with the other Asian groups. Nevertheless, we identified traces of recent gene flow from non-Asians to MEGs. Finally, natural selection signatures were detected in a batch of genes associated with immune response, human height, skin pigmentation, hair and facial morphology and blood pressure in MEGs. Notable examples include SYN3 which is associated with human height in all Orang Asli groups, a height-related gene (PNPT1) and two blood pressure-related genes (CDH13 and PAX5) in Negritos. We conclude that a long isolation period, subsequent gene flow and local adaptations have jointly shaped the genetic architectures of MEGs, and this study provides insight into the peopling and human migration

  6. Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

    Zhixian Yang

    2014-01-01

    Full Text Available Background electroencephalography (EEG, recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE and sample entropy (SampEn in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved.

  7. Adaptive neuro-fuzzy inference system for classification of background EEG signals from ESES patients and controls.

    Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang

    2014-01-01

    Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3-9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. PMID:24790547

  8. A Novel Algorithm for Fault Classification on Transmission Lines using a Combined Adaptive Network-based Fuzzy Inference System

    Yeo, S.M.; Kim, C.H. [Sungkyunkwan University (Korea); Chai, Y.M. [Chungju National University (Korea); Choi, J.D. [Daelim College (Korea)

    2001-07-01

    Accurate detection and classification of faults on transmission lines is vitally important. High impedance faults (HIF) in particular pose difficulties for the commonly employed conventional overcurrent and distance relays, and if not detected, can cause damage to expensive equipment, threaten life and cause fire hazards. Although HIFs are far less common than LIFs, it is imperative that any protection device should be able to satisfactorily deal with both HIFs and LIFs. This paper proposes an algorithm for fault detection and classification for both LIFs and HIFs using Adaptive Network-based Fuzzy Inference System(ANFIS). The performance of the proposed algorithm is tested on a typical 154[kV] Korean transmission line system under various fault conditions. Test results show that the ANFIS can detect and classify faults including (LIFs and HIFs) accurately within half a cycle. (author). 11 refs., 7 figs., 3 tabs.

  9. An Adaptive Strategy for the Classification of G-Protein Coupled Receptors

    Mohamed, S.; Rubin, D.; Marwala, T.

    2007-01-01

    One of the major problems in computational biology is the inability of existing classification models to incorporate expanding and new domain knowledge. This problem of static classification models is addressed in this paper by the introduction of incremental learning for problems in bioinformatics. Many machine learning tools have been applied to this problem using static machine learning structures such as neural networks or support vector machines that are unable to accommodate new informa...

  10. Composition-based classification of short metagenomic sequences elucidates the landscapes of taxonomic and functional enrichment of microorganisms

    Liu, Jiemeng; Wang, Haifeng; Yang, Hongxing; Zhang, Yizhe; Wang, Jinfeng; Zhao, Fangqing; Qi, Ji

    2012-01-01

    Compared with traditional algorithms for long metagenomic sequence classification, characterizing microorganisms’ taxonomic and functional abundance based on tens of millions of very short reads are much more challenging. We describe an efficient composition and phylogeny-based algorithm [Metagenome Composition Vector (MetaCV)] to classify very short metagenomic reads (75–100 bp) into specific taxonomic and functional groups. We applied MetaCV to the Meta-HIT data (371-Gb 75-bp reads of 109 h...

  11. Using ASTER Imagery in Land Use/cover Classification of Eastern Mediterranean Landscapes According to CORINE Land Cover Project

    Recep Gundogan; Abdullah E. Akay; Alaaddin Yüksel

    2008-01-01

    The satellite imagery has been effectively utilized for classifying land cover types and detecting land cover conditions. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor imagery has been widely used in classification process of land cover. However, atmospheric corrections have to be made by preprocessing satellite sensor imagery since the electromagnetic radiation signals received by the satellite sensors can be scattered and absorbed by the atmospheric gases...

  12. Response and adaptation of grapevine cultivars to hydrological conditions forced by a changing climate in a complex landscape

    De Lorenzi, Francesca; Bonfante, Antonello; Alfieri, Silvia Maria; Monaco, Eugenia; De Mascellis, Roberto; Manna, Piero; Menenti, Massimo

    2014-05-01

    Soil water availability is one of the main components of the terroir concept, influencing crop yield and fruit composition in grapes. The aim of this work is to analyze some elements of the "natural environment" of terroir (climate and soil) in combination with the intra-specific biodiversity of yield responses of grapevine to water availability. From a reference (1961-90) to a future (2021-50) climate case, the effects of climate evolution on soil water availability are assessed and, regarding soil water regime as a predictor variable, the potential spatial distribution of wine-producing cultivars is determined. In a region of Southern Italy (Valle Telesina, 20,000 ha), where a terroir classification has been produced (Bonfante et al., 2011), we applied an agro-hydrological model to determine water availability indicators. Simulations were performed in 60 soil typological units, over the entire study area, and water availability (= hydrological) indicators were determined. Two climate cases were considered: reference (1961-90) and future (2021-2050), the former from climatic statistics on observed variables, and the latter from statistical downscaling of predictions by general circulation models (AOGCM) under A1B SRES scenario. Climatic data consist of daily time series of maximum and minimum temperature, and daily rainfall on a grid with a spatial resolution of 35 km. Spatial and temporal variability of hydrological indicators was addressed. With respect to temporal variability, both inter-annual and intra-annual (i.e. at different stages of crop cycle) variability were analyzed. Some cultivar-specific relations between hydrological indicators and characteristics of must quality were established. Moreover, for several wine-producing cultivars, hydrological requirements were determined by means of yield response functions to soil water availability, through the re-analysis of experimental data derived from scientific literature. The standard errors of estimated

  13. Necessity to adapt land use and land cover classification systems to readily accept radar data

    Drake, B.

    1977-01-01

    A hierarchial, four level, standardized system for classifying land use/land cover primarily from remote-sensor data (USGS system) is described. The USGS system was developed for nonmicrowave imaging sensors such as camera systems and line scanners. The USGS system is not compatible with the land use/land cover classifications at different levels that can be made from radar imagery, and particularly from synthetic-aperture radar (SAR) imagery. The use of radar imagery for classifying land use/land cover at different levels is discussed, and a possible revision of the USGS system to more readily accept land use/land cover classifications from radar imagery is proposed.

  14. Methods for improving accuracy and extending results beyond periods covered by traditional ground-truth in remote sensing classification of a complex landscape

    Mueller-Warrant, George W.; Whittaker, Gerald W.; Banowetz, Gary M.; Griffith, Stephen M.; Barnhart, Bradley L.

    2015-06-01

    Successful development of approaches to quantify impacts of diverse landuse and associated agricultural management practices on ecosystem services is frequently limited by lack of historical and contemporary landuse data. We hypothesized that ground truth data from one year could be used to extrapolate previous or future landuse in a complex landscape where cropping systems do not generally change greatly from year to year because the majority of crops are established perennials or the same annual crops grown on the same fields over multiple years. Prior to testing this hypothesis, it was first necessary to classify 57 major landuses in the Willamette Valley of western Oregon from 2005 to 2011 using normal same year ground-truth, elaborating on previously published work and traditional sources such as Cropland Data Layers (CDL) to more fully include minor crops grown in the region. Available remote sensing data included Landsat, MODIS 16-day composites, and National Aerial Imagery Program (NAIP) imagery, all of which were resampled to a common 30 m resolution. The frequent presence of clouds and Landsat7 scan line gaps forced us to conduct of series of separate classifications in each year, which were then merged by choosing whichever classification used the highest number of cloud- and gap-free bands at any given pixel. Procedures adopted to improve accuracy beyond that achieved by maximum likelihood pixel classification included majority-rule reclassification of pixels within 91,442 Common Land Unit (CLU) polygons, smoothing and aggregation of areas outside the CLU polygons, and majority-rule reclassification over time of forest and urban development areas. Final classifications in all seven years separated annually disturbed agriculture, established perennial crops, forest, and urban development from each other at 90 to 95% overall 4-class validation accuracy. In the most successful use of subsequent year ground-truth data to classify prior year landuse, an

  15. Swarm Intelligence Approach Based on Adaptive ELM Classifier with ICGA Selection for Microarray Gene Expression and Cancer Classification

    T. Karthikeyan

    2014-05-01

    Full Text Available The aim of this research study is based on efficient gene selection and classification of microarray data analysis using hybrid machine learning algorithms. The beginning of microarray technology has enabled the researchers to quickly measure the position of thousands of genes expressed in an organic/biological tissue samples in a solitary experiment. One of the important applications of this microarray technology is to classify the tissue samples using their gene expression representation, identify numerous type of cancer. Cancer is a group of diseases in which a set of cells shows uncontrolled growth, instance that interrupts upon and destroys nearby tissues and spreading to other locations in the body via lymph or blood. Cancer has becomes a one of the major important disease in current scenario. DNA microarrays turn out to be an effectual tool utilized in molecular biology and cancer diagnosis. Microarrays can be measured to establish the relative quantity of mRNAs in two or additional organic/biological tissue samples for thousands/several thousands of genes at the same time. As the superiority of this technique become exactly analysis/identifying the suitable assessment of microarray data in various open issues. In the field of medical sciences multi-category cancer classification play a major important role to classify the cancer types according to the gene expression. The need of the cancer classification has been become indispensible, because the numbers of cancer victims are increasing steadily identified by recent years. To perform this proposed a combination of Integer-Coded Genetic Algorithm (ICGA and Artificial Bee Colony algorithm (ABC, coupled with an Adaptive Extreme Learning Machine (AELM, is used for gene selection and cancer classification. ICGA is used with ABC based AELM classifier to chose an optimal set of genes which results in an efficient hybrid algorithm that can handle sparse data and sample imbalance. The

  16. Travelling in the eastern Mediterranean with landscape character assessment

    Abu Jaber, N.; Abunnasr, Y.; Abu Yahya, A.; Boulad, N.; Christou, O.; Dimitropoulos, G.; Dimopoulos, T.; Gkoltsiou, K.; Khreis, N.; Manolaki, P.; Michael, K.; Odeh, T.; Papatheodoulou, A.; Sorotou, A.; Sinno, S.; Suliman, O.; Symons, N.; Terkenli, T.; Trigkas, Vassilis; Trovato, M. G.; Victora, M.; Zomeni, M.; Vogiatzakis, I. N.

    2015-06-01

    Following its application in Northern Europe, Landscape Character Assessment has also been implemented in Euro-Mediterranean countries as a tool for classifying, describing and assessing landscapes. Many landscape classifications employed in the Euro-Mediterranean area are similar in philosophy and application to the ones developed in Northern Europe. However, many aspects of landform, climate, land-use and ecology, as well as socio-economic context are distinctive of Mediterranean landscapes. The paper discusses the conceptual and methodological issues faced during landscape mapping and characterisation in four East-Mediterranean countries (within the MEDSCAPES project): Cyprus, Greece, Jordan and Lebanon. The major hurdles to overcome during the first phase of methodology development include variation in availability, quality, scale and coverage of spatial datasets between countries and also terminology semantics around landscapes. For example, the concept of landscape - a well-defined term in Greek and English - did not exist in Arabic. Another issue is the use of relative terms like 'high mountains,' `uplands' `lowlands' or ' hills'. Such terms, which are regularly used in landscape description, were perceived slightly differently in the four participating countries. In addition differences exist in nomenclature and classification systems used by each country for the dominant landscape-forming factors i.e. geology, soils and land use- but also in the cultural processes shaping the landscapes - compared both to each other and to the Northern-European norms. This paper argues for the development of consistent, regionally adapted, relevant and standardised methodologies if the results and application of LCA in the eastern Mediterranean region are to be transferable and comparable between countries.

  17. Wavelength-Adaptive Dehazing Using Histogram Merging-Based Classification for UAV Images

    Inhye Yoon; Seokhwa Jeong; Jaeheon Jeong; Doochun Seo; Joonki Paik

    2015-01-01

    Since incoming light to an unmanned aerial vehicle (UAV) platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image a...

  18. Testing the hydrological landscape unit classification system and other terrain analysis measures for predicting low-flow nitrate and chloride in watersheds.

    Poor, Cara J; McDonnell, Jeffrey J; Bolte, John

    2008-11-01

    Elevated nitrate concentrations in streamwater are a major environmental management problem. While land use exerts a large control on stream nitrate, hydrology often plays an equally important role. To date, predictions of low-flow nitrate in ungauged watersheds have been poor because of the difficulty in describing the uniqueness of watershed hydrology over large areas. Clearly, hydrologic response varies depending on the states and stocks of water, flow pathways, and residence times. How to capture the dominant hydrological controls that combine with land use to define streamwater nitrate concentration is a major research challenge. This paper tests the new Hydrologic Landscape Regions (HLRs) watershed classification scheme of Wolock and others (Environmental Management 34:S71-S88, 2004) to address the question: Can HLRs be used as a way to predict low-flow nitrate? We also test a number of other indexes including inverse-distance weighting of land use and the well-known topographic index (TI) to address the question: How do other terrain and land use measures compare to HLR in terms of their ability to predict low-flow nitrate concentration? We test this for 76 watersheds in western Oregon using the U.S. Environmental Protection Agency's Environmental Monitoring and Assessment Program and Regional Environmental Monitoring and Assessment Program data. We found that HLRs did not significantly improve nitrate predictions beyond the standard TI and land-use metrics. Using TI and inverse-distance weighting did not improve nitrate predictions; the best models were the percentage land use-elevation models. We did, however, see an improvement of chloride predictions using HLRs, TI, and inverse-distance weighting; adding HLRs and TI significantly improved model predictions and the best models used inverse-distance weighting and elevation. One interesting result of this study is elevation consistently predicted nitrate better than TI or the hydrologic classification

  19. An Adaptive Strategy for the Classification of G-Protein Coupled Receptors

    Mohamed, S; Marwala, T

    2007-01-01

    One of the major problems in computational biology is the inability of existing classification models to incorporate expanding and new domain knowledge. This problem of static classification models is addressed in this paper by the introduction of incremental learning for problems in bioinformatics. Many machine learning tools have been applied to this problem using static machine learning structures such as neural networks or support vector machines that are unable to accommodate new information into their existing models. We utilize the fuzzy ARTMAP as an alternate machine learning system that has the ability of incrementally learning new data as it becomes available. The fuzzy ARTMAP is found to be comparable to many of the widespread machine learning systems. The use of an evolutionary strategy in the selection and combination of individual classifiers into an ensemble system, coupled with the incremental learning ability of the fuzzy ARTMAP is proven to be suitable as a pattern classifier. The algorithm ...

  20. DOA Estimation of Low Altitude Target Based on Adaptive Step Glowworm Swarm Optimization-multiple Signal Classification Algorithm

    Zhou Hao

    2015-06-01

    Full Text Available The traditional MUltiple SIgnal Classification (MUSIC algorithm requires significant computational effort and can not be employed for the Direction Of Arrival (DOA estimation of targets in a low-altitude multipath environment. As such, a novel MUSIC approach is proposed on the basis of the algorithm of Adaptive Step Glowworm Swarm Optimization (ASGSO. The virtual spatial smoothing of the matrix formed by each snapshot is used to realize the decorrelation of the multipath signal and the establishment of a fullorder correlation matrix. ASGSO optimizes the function and estimates the elevation of the target. The simulation results suggest that the proposed method can overcome the low altitude multipath effect and estimate the DOA of target readily and precisely without radar effective aperture loss.

  1. A novel algorithm for fault classification in transmission lines using a combined adaptive network and fuzzy inference system

    Yeo, S.M.; Kim, C.H.; Hong, K.S. [Sungkyunkwan Univ., Suwon (Korea). School fo Information and Computer Engineering; Lim, Y.B. [LG Electronics CDMA Handsets Lab., Seoul (Korea); Aggarwal, R.K.; Johns, A.T. [University of Bath (United Kingdom). Dept. of Electronic and Electrical Engineering; Choi, M.S. [Myongji Univ., Yongin (Korea). Division of Electrical and Information Control Engineering

    2003-11-01

    Accurate detection and classification of faults on transmission lines is vitally important. In this respect, many different types of faults occur, inter alia low impedance faults (LIF) and high impedance faults (HIF). The latter in particular pose difficulties for the commonly employed conventional overcurrent and distance relays, and if not detected, can cause damage to expensive equipment, threaten life and cause fire hazards. Although HIFs are far less common than LIFs, it is imperative that any protection device should be able to satisfactorily deal with both HIFs and LIFs. Because of the randomness and asymmetric characteristics of HIFs, the modelling of HIF is difficult and many papers relating to various HIF models have been published. In this paper, the model of HIFs in transmission lines is accomplished using the characteristics of a ZnO arrester, which is then implemented within the overall transmission system model based on the electromagnetic transients programme. This paper proposes an algorithm for fault detection and classification for both LIFs and HIFs using Adaptive Network-based Fuzzy Inference System (ANFIS). The inputs into ANFIS are current signals only based on Root-Mean-Square values of three-phase currents and zero sequence current. The performance of the proposed algorithm is tested on a typical 154 kV Korean transmission line system under various fault conditions. Test results show that the ANFIS can detect and classify faults including (LIFs and HIFs) accurately within half a cycle. (author)

  2. Social-Ecological Transformation for Ecosystem Management: the Development of Adaptive Co-management of a Wetland Landscape in Southern Sweden

    Per Olsson

    2004-12-01

    Full Text Available We analyze the emergence of an adaptive co-management system for wetland landscape governance in southern Sweden, a process where unconnected management by several actors in the landscape was mobilized, renewed, and reconfigured into ecosystem management within about a decade. Our analysis highlights the social mechanisms behind the transformation toward ecosystem management. The self-organizing process was triggered by perceived threats among members of various local stewardship associations and local government to the area’s cultural and ecological values. These threats challenged the development of ecosystem services in the area. We show how one individual, a key leader, played an instrumental role in directing change and transforming governance. The transformation involved three phases: 1 preparing the system for change, 2 seizing a window of opportunity, and 3 building social-ecological resilience of the new desired state. This local policy entrepreneur initiated trust-building dialogue, mobilized social networks with actors across scales, and started processes for coordinating people, information flows and ongoing activities, and for compiling and generating knowledge, understanding, and management practices of ecosystem dynamics. Understanding, collaborative learning, and creating public awareness were part of the process. A comprehensive framework was developed with a shared vision and goals that presented conservation as development, turned problems into possibilities, and contributed to a shift in perception among key actors regarding the values of the wetland landscape. A window of opportunity at the political level opened, which made it possible to transform the governance system toward a trajectory of ecosystem management. The transformation involved establishing a new municipal organization, the Ecomuseum Kristianstads Vattenrike (EKV. This flexible organization serves as a bridge between local actors and governmental bodies and

  3. Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization

    Yudong Zhang

    2011-05-01

    Full Text Available This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM based texture features. Then, the features were reduced by principle component analysis (PCA. Finally, a two-hidden-layer forward neural network (NN was constructed and trained by adaptive chaotic particle swarm optimization (ACPSO. K-fold cross validation was employed to enhance generation. The experimental results on Flevoland sites demonstrate the superiority of ACPSO to back-propagation (BP, adaptive BP (ABP, momentum BP (MBP, Particle Swarm Optimization (PSO, and Resilient back-propagation (RPROP methods. Moreover, the computation time for each pixel is only 1.08 × 10−7 s.

  4. Simulations suggest that social and natural sciences differ in their research strategies adapted to work for different knowledge landscapes

    Jaffe, Klaus

    2014-01-01

    Do different field of knowledge require different research strategies? A numerical model exploring different virtual knowledge landscapes, revealed different optimal search strategies. Trend following is maximized when the popularity of new discoveries determine the number of individuals researching it. This strategy works best when many researchers explore few large areas of knowledge. In contrast, individuals or small groups of researchers are better in discovering small bits of information in dispersed knowledge landscapes. The best technique for all situations simulated, is to adjust the number of researchers needed to explore a knowledge cluster according to the opportunities and the level of crowding in that cluster. Bibliometric data of scientific publications showed a continuous bipolar distribution of these strategies, ranging from natural sciences, with highly cited publications in journals containing a large number of articles, to the social sciences, with rarely cited publications in journals cont...

  5. Metapopulation-Level Adaptation of Insect Host plant Preference and Extinction-Colonization Dynamics in Heterogenous Landscapes

    Hanski, I.; Heino, M.

    2003-01-01

    Species living in highly fragmented landscapes typically occur as metapopulations with frequent turnover of local populations. The turnover rate depends on population sizes and connectivities, but it may also depend on the phenotypic and genotypic composition of populations. The Glanville fritillary butterfly ("Melitaea cinxia") in Finland uses two host plant species, which show variation in their relative abundances at two spatial scales: locally among individual habitat patches and regional...

  6. 景观生态学原理在城市土地利用分类中的应用%Applying landscape ecological concepts in urban land use classification

    李伟峰; 欧阳志云; 肖燚

    2011-01-01

    根据城市相同土地利用类型具有相似景观格局特征的原理,探讨了融合景观格局特征指数和遥感技术的城市土地利用信息提取的新方法.以北京市五环内建城区为例,研究表明,在斑块类型水平和景观水平上,居住用地和非居住用地内景观斑块的大小、形状、边缘特征、空间连接度、核心区面积特征、多样性、均匀性等特征都有极显著的差异.进一步融合TM遥感影像和这些景观格局特征指数,提取了居民用地和非居民用地类型,总分类精度是79.7%,Kappa系数达到59.8%.研究揭示,景观生态学原理的引入,为传统的遥感技术应用提供了新的思路,在格局复杂的城市土地利用信息提取中有很大的应用发展潜力.%Generally, land uses are the ways in which lands are used by human activities. Especially in cities with dense population, land use is the direct results of various living requirements by the people. Therefore, urban land use is a very important indicator that can be used to measure the development of urbanization and their impacts on ecosystems. Hence,accurate extraction of urban land uses is critical for urban planning, land management and environmental protection. It has been proved that remote sensing technique is most efficient to extract land covers with high accuracy since different land covers have the specific physical properties which can be discriminated easily by the spectral bands of remote sensing data.However, considering the difference in land use and land cover, it is still a big challenge to extract urban land uses directly from remote sensing data with fast and satisfied accuracy because every land use is usually composed of different land covers. This study developed a new approach on urban land use classification by introducing landscape ecology theory into the application of traditional remote sensing techniques. According to the landscape ecology concepts, the landscape patterns

  7. Improved Correlation of the Neuropathologic Classification According to Adapted World Health Organization Classification and Outcome After Radiotherapy in Patients With Atypical and Anaplastic Meningiomas

    Purpose: To evaluate the correlation between the 1993 and 2000/2007 World Health Organization (WHO) classification with the outcome in patients with high-grade meningiomas. Patients and Methods: Between 1985 and 2004, 73 patients diagnosed with atypical or anaplastic meningiomas were treated with radiotherapy. Sections from the paraffin-embedded tumor material from 66 patients (90%) from 13 different pathology departments were re-evaluated according to the first revised WHO classification from 1993 and the revised classifications from 2000/2007. In 4 cases, the initial diagnosis meningioma was not reproducible (5%). Therefore, 62 patients with meningiomas were analyzed. Results: All 62 tumors were reclassified according to the 1993 and 2000/2007 WHO classification systems. Using the 1993 system, 7 patients were diagnosed with WHO grade I meningioma (11%), 23 with WHO grade II (37%), and 32 with WHO grade III meningioma (52%). After scoring using the 2000/2007 system, we found 17 WHO grade I meningiomas (27%), 32 WHO grade II meningiomas (52%), and 13 WHO grade III meningiomas (21%). According to the 1993 classification, the difference in overall survival was not statistically significant among the histologic subgroups (p = .96). Using the 2000/2007 WHO classifications, the difference in overall survival became significant (p = .02). Of the 62 reclassified patients 29 developed tumor progression (47%). No difference in progression-free survival was observed among the histologic subgroups (p = .44). After grading according to the 2000/2007 WHO classifications, significant differences in progression-free survival were observed among the three histologic groups (p = .005). Conclusion: The new 2000/2007 WHO classification for meningiomas showed an improved correlation between the histologic grade and outcome. This classification therefore provides a useful basis to determine the postoperative indication for radiotherapy. According to our results, a comparison of the

  8. 基于决策树分类的云南省迪庆地区景观类型研究%Exploring Landscapes Based on Decision Tree Classification in the Diqin Region, Yunnan Province

    李亚飞; 刘高焕; 黄翀

    2011-01-01

    Decision tree classification is a type of supervised classification method based on spatial data mining and knowledge discovery. In this paper, the authors examined the landscape pattern of the Diqin region by building the classification decision tree in Yunnan province and using Landsat TM imagery and digital elevation models (DEMs). Subsequently, a landscape distribution map was made. In order to look at the reliability and robustness of the decision tree classification method,the traditional supervised classification was used to derive a landscape distribution map over the region. A multitude of field sampling points were used to evaluate the accuracy of the two classification methods, covering the whole Diqing region and consisting of information regarding geographic coordinates, elevations, and the description of the major landscape types. Results indicate that the overall classification accuracies of the decision tree classification and the traditional supervised classification were 85.5% and 67.4% , respectively. The landscape distribution map derived by the decision tree classification method seems to be reliable in terms of the achievable accuracy. Several conclusions could be drawn by analyzing the derived landscape distribution map as follows. Landscape types in the Diqin region primarily included valley shrub,coniferous forest, sub alpine shrub meadow, alpine snow and ice, bare land, and water body,accounting for 5.5%, 36.16%, 3.4%, 3.7%, 25.4%, and 4.4% of the Diqin region area, respectively.Except bare land and water body, other landscape types varied essentially with elevation and aspect of maintains. The landscape of the largest area was found to be coniferous forest, which was consistent with the landform of alpine and canyon. Coniferous forest was the major landscape in the region, which was distributed over 3000 m above the sea level. In terms of different elevations,the coniferous forest could be conceptually divided into three

  9. PHONETIC CLASSIFICATION BY ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM AND SUBTRACTIVE CLUSTERING

    Samiya Silarbi

    2014-09-01

    Full Text Available This paper presents the application of Adaptive Network Based Fuzzy Inference System ANFIS on speech recognition. The primary tasks of fuzzy modeling are structure identification and parameter optimization, the former determines the numbers of membership functions and fuzzy if-then rules while the latter identifies a feasible set of parameters under the given structure. However, the increase of input dimension, rule numbers will have an exponential growth and there will cause problem of “rule disaster”. Thus, determination of an appropriate structure becomes an important issue where subtractive clustering is applied to define an optimal initial structure and obtain small number of rules. The appropriate learning algorithm is performed on TIMIT speech database supervised type, a pre-processing of the acoustic signal and extracting the coefficients MFCCs parameters relevant to the recognition system. Finally, hybrid learning combines the gradient decent and least square estimation LSE of parameters network. The results obtained show the effectiveness of the method in terms of recognition rate and number of fuzzy rules generated.

  10. The 'Functional Landscape Approach': Building a socio-ecological evidence base for its contribution to adaptation and resilience in wetland catchments.

    Carrie, Rachael; Dixon, Alan

    2015-04-01

    Sustainable land management is increasingly taking a landscape approach to advocate simultaneously for local and multiple stakeholder-negotiated development and environmental objectives. Landscape approaches advance earlier frameworks that failed to acknowledge or reconcile either biodiversity or societal trade-offs, and that often tended toward externally-derived or imposed management interventions. Most recently, the management of land to balance biodiversity, food security and ecosystem services outcomes has been informed by socio-ecological systems thinking that seeks to promote an interdisciplinary understanding of any given 'landscape' where environmental and social factors continually interact in complex, adaptive and resilient ways. Reflecting these concepts, and integrating local and external scientific knowledge, the Functional Landscape Approach (FLA) was developed by Wetland Action, focussing on wetland systems in rural sub-Saharan Africa to contribute to environmentally sensitive and climate resilient strategies for safeguarding essential ecosystem services and improving livelihoods and well-being. In particular, the FLA stresses the ways in which land productivity can be improved through supporting, strengthening or re-establishing functional linkages between wetlands and their catchments and provides a basis for local identification of specific interventions to improve the sustainability of land use. Crucially, it also emphasises the need for community-based institutional support and the importance of incentives through market linkages and value-chain development. In this paper we will describe our experiences of developing and implementing the FLA in Ethiopia, Zambia and Malawi over the past two decades. Drawing on successful and less-successful elements of participatory planning, monitoring and evaluation, and the facilitation of long-term sustainable benefits, we will discuss some of the accomplishments and challenges that can be associated with

  11. Climate variables explain neutral and adaptive variation within salmonid metapopulations: The importance of replication in landscape genetics

    Hand, Brian K; Muhlfeld, Clint C.; Wade, Alisa A.; Kovach, Ryan; Whited, Diane C.; Narum, Shawn R; Matala, Andrew P; Ackerman, Michael W.; Garner, B. A.; Kimball, John S; Stanford, Jack A.; Luikart, Gordon

    2016-01-01

    Understanding how environmental variation influences population genetic structure is important for conservation management because it can reveal how human stressors influence population connectivity, genetic diversity and persistence. We used riverscape genetics modelling to assess whether climatic and habitat variables were related to neutral and adaptive patterns of genetic differentiation (population-specific and pairwise FST) within five metapopulations (79 populations, 4583 individuals) of steelhead trout (Oncorhynchus mykiss) in the Columbia River Basin, USA. Using 151 putatively neutral and 29 candidate adaptive SNP loci, we found that climate-related variables (winter precipitation, summer maximum temperature, winter highest 5% flow events and summer mean flow) best explained neutral and adaptive patterns of genetic differentiation within metapopulations, suggesting that climatic variation likely influences both demography (neutral variation) and local adaptation (adaptive variation). However, we did not observe consistent relationships between climate variables and FST across all metapopulations, underscoring the need for replication when extrapolating results from one scale to another (e.g. basin-wide to the metapopulation scale). Sensitivity analysis (leave-one-population-out) revealed consistent relationships between climate variables and FST within three metapopulations; however, these patterns were not consistent in two metapopulations likely due to small sample sizes (N = 10). These results provide correlative evidence that climatic variation has shaped the genetic structure of steelhead populations and highlight the need for replication and sensitivity analyses in land and riverscape genetics.

  12. Applications of network analysis for adaptive management of artificial drainage systems in landscapes vulnerable to sea level rise

    Poulter, Benjamin; Goodall, Jonathan L.; Halpin, Patrick N.

    2008-08-01

    SummaryThe vulnerability of coastal landscapes to sea level rise is compounded by the existence of extensive artificial drainage networks initially built to lower water tables for agriculture, forestry, and human settlements. These drainage networks are found in landscapes with little topographic relief where channel flow is characterized by bi-directional movement across multiple time-scales and related to precipitation, wind, and tidal patterns. The current configuration of many artificial drainage networks exacerbates impacts associated with sea level rise such as salt-intrusion and increased flooding. This suggests that in the short-term, drainage networks might be managed to mitigate sea level rise related impacts. The challenge, however, is that hydrologic processes in regions where channel flow direction is weakly related to slope and topography require extensive parameterization for numerical models which is limited where network size is on the order of a hundred or more kilometers in total length. Here we present an application of graph theoretic algorithms to efficiently investigate network properties relevant to the management of a large artificial drainage system in coastal North Carolina, USA. We created a digital network model representing the observation network topology and four types of drainage features (canal, collector and field ditches, and streams). We applied betweenness-centrality concepts (using Dijkstra's shortest path algorithm) to determine major hydrologic flowpaths based off of hydraulic resistance. Following this, we identified sub-networks that could be managed independently using a community structure and modularity approach. Lastly, a betweenness-centrality algorithm was applied to identify major shoreline entry points to the network that disproportionately control water movement in and out of the network. We demonstrate that graph theory can be applied to solving management and monitoring problems associated with sea level rise

  13. The farmer as a landscape steward

    Raymond, Christopher M.; Bieling, Claudia; Fagerholm, Nora;

    2016-01-01

    We develop a landscape stewardship classification which distinguishes between farmers’ understanding of landscape stewardship, their landscape values, and land management actions. Forty semi-structured interviews were conducted with small-holder (<5 acres), medium-holders (5–100 acres), and large...

  14. Automatic Brain Lesion Detection and Classification Based on Diffusion-Weighted Imaging using Adaptive Thresholding and a Rule-Based Classifier

    N.M. Saad

    2014-12-01

    Full Text Available In this paper, a brain lesion detection and classification approach using thresholding and a rule-based classifier is proposed. Four types of brain lesions based on diffusion-weighted imaging i.e. acute stroke, solid tumor, chronic stroke, and necrosis are analyzed. The analysis is divided into four stages: pre-processing, segmentation, feature extraction, and classification. In the detection and segmentation stage, the image is divided into 8x8 macro-block regions. Adaptive thresholding technique is applied to segment the lesion’s region. Statistical features are measured on the region of interest. A rulebased classifier is used to classify four types of lesions. Jaccard’s similarity index of the segmentation results for acute stroke, solid tumor, chronic stroke, and necrosis are 0.8, 0.55, 0.27, and 0.42, respectively. The classification accuracy is 93% for acute stroke, 73% for solid tumor, 84% for chronic stroke, and 60% for necrosis. Overall, adaptive thresholding provides high segmentation performance for hyper-intensity lesions. The best segmentation and classification performance is achieved for acute stroke. The establishment of the technique could be used to automate the diagnosis and to clearly understand major brain lesions.

  15. Functional decoupling between flowers and leaves in the Ameroglossum pernambucense complex can facilitate local adaptation across a pollinator and climatic heterogeneous landscape.

    Wanderley, A M; Galetto, L; Machado, I C S

    2016-03-01

    Decoupling between floral and leaf traits is expected in plants with specialized pollination systems to assure a precise flower-pollinator fit, irrespective of leaf variation associated with environmental heterogeneity (functional modularity). Nonetheless, developmental interactions among floral traits also decouple flowers from leaves regardless of selection pressures (developmental modularity). We tested functional modularity in the hummingbird-pollinated flowers of the Ameroglossum pernambucense complex while controlling for developmental modularity. Using two functional traits responsible for flower-pollinator fit [floral tube length (TL) and anther-nectary distance (AN)], one floral trait not linked to pollination [sepal length (SL), control for developmental modularity] and one leaf trait [leaf length (LL)], we found evidence of flower functional modularity. Covariation between TL and AN was ca. two-fold higher than the covariation of either of these traits with sepal and leaf lengths, and variations in TL and AN, important for a precise flower-pollinator fit, were smaller than SL and LL variations. Furthermore, we show that previously reported among-population variation of flowers associated with local pollinator phenotypes was independent from SL and LL variations. These results suggest that TL and AN are functionally linked to fit pollinators and sufficiently decoupled from developmentally related floral traits (SL) and vegetative traits (LL). These results support previous evidences of population differentiation due to local adaptation in the A. pernambucense complex and shed light on the role of flower-leaf decoupling for local adaptation in species distributed across biotic and abiotic heterogeneous landscapes. PMID:26663030

  16. Do we know how to reconcile preservation of landscapes with adaptation of agriculture to climate change? A case-study in a hilly area in Southern Italy

    Menenti, Massimo; Alfieri, Silvia; Basile, Angelo; Bonfante, Antonello; Monaco, Eugenia; Riccardi, Maria; De Lorenzi, Francesca

    2013-04-01

    Limited impacts of climate change on agricultural yields are unlikely to induce any significant changes in current landscapes. Larger impacts, unacceptable on economic or social ground, are likely to trigger interventions towards adaptation of agricultural production systems by reducing or removing vulnerabilities to climate variability and change. Such interventions may require a transition to a different production system, i.e. complete substitution of current crops, or displacement of current crops at their current location towards other locations, e.g. at higher elevations within the landscape. We have assessed the impacts of climate change and evaluated options for adaptation of a valley in Southern Italy, dominated by vine and olive orchards with a significant presence of wheat. We have first estimated the climatic requirements of several varieties for each dominant species. Next, to identify options for adaptation we have evaluated the compatibility of such requirements with indicators of a reference (current) climate and of future climate. This climate - compatibility assessment was done for each soil unit within the valley, leading to maps of locations where each crop is expected to be compatible with climate. This leads to identify both potential crop substitutions within the entire valley and crop displacements from one location to another within the valley. Two climate scenarios were considered: reference (1961-90) and future (2021-2050) climate, the former from climatic statistics, and the latter from statistical downscaling of general circulation models (AOGCM). Climatic data consists of daily time series of maximum and minimum temperature, and daily rainfall on a grid with a spatial resolution of 35 km. We evaluated the adaptive capacity of the "Valle Telesina" (Campania Region, Southern Italy). A mechanistic model of water flow in the soil-plant-atmosphere system (SWAP) was used to describe the hydrological conditions in response to climate for each

  17. Landscape management with a nature concern: the role of a Natura 2000 classification in awareness raising of land owners for the provision of public goods

    PINTO-CORREIA, Teresa; Barroso, Filipe Lucas; Menezes, Helena; Jerónimo, Silva; Michelin, Yves

    2011-01-01

    Introduction of the International Conference: Although a large majority of Europeans live in urban and peri-urban areas today, it would be difficult to surmise that the landscapes they live in were the object of serious, intentional efforts on the part of policymakers. These landscapes are often considered to be degraded and inhospitable, and are frequently associated with social exclusion and the deterioration of living conditions. The French Ministry of Ecology, Energy, Sustainable Developm...

  18. Metapopulation-level adaptation of insect host plant preference and extinction-colonization dynamics in heterogeneous landscapes.

    Hanski, Ilkka; Heino, Mikko

    2003-11-01

    Species living in highly fragmented landscapes typically occur as metapopulations with frequent turnover of local populations. The turnover rate depends on population sizes and connectivities, but it may also depend on the phenotypic and genotypic composition of populations. The Glanville fritillary butterfly (Melitaea cinxia) in Finland uses two host plant species, which show variation in their relative abundances at two spatial scales: locally among individual habitat patches and regionally among networks of patches. Female butterflies in turn exhibit spatial variation in genetically determined host plant preference within and among patch networks. Emigration, immigration and establishment of new populations have all been shown to be strongly influenced by the match between the host plant composition of otherwise suitable habitat patches and the host plant preference of migrating butterflies. The evolutionary consequences of such biased migration and colonization with respect to butterfly phenotypes might differ depending on spatial configuration and plant species composition of the patches in heterogeneous patch networks. Using a spatially realistic individual-based model we show that the model-predicted evolution of host plant preference due to biased migration explains a significant amount of the observed variation in host plant use among metapopulations living in dissimilar networks. This example illustrates how the ecological extinction-colonization dynamics may be linked with the evolutionary dynamics of life history traits in metapopulations. PMID:14522169

  19. Landscape Evolution Modelling-LAPSUS

    Landscape evolution modelling can make the consequences of landscape evolution hypotheses explicit and theoretically allows for their falsification and improvement. ideally, landscape evolution models (LEMs) combine the results of all relevant landscape forming processes into an ever-adapting digital landscape (e.g. DEM). These processes may act on different spatial and temporal scales. LAPSUS is such a LEM. Processes that have in different studies been included in LAPSUS are water erosion and deposition, landslide activity, creep, solidification, weathering, tectonics and tillage. Process descriptions are as simple and generic as possible, ensuring wide applicability. (Author) 25 refs.

  20. Landscape Evolution Modelling-LAPSUS

    Baartman, J. E. M.; Temme, A. J. A. M.; Schoorl, J. M.; Claessens, L.; Viveen, W.; Gorp, W. van; Veldkamp, A.

    2009-07-01

    Landscape evolution modelling can make the consequences of landscape evolution hypotheses explicit and theoretically allows for their falsification and improvement. ideally, landscape evolution models (LEMs) combine the results of all relevant landscape forming processes into an ever-adapting digital landscape (e.g. DEM). These processes may act on different spatial and temporal scales. LAPSUS is such a LEM. Processes that have in different studies been included in LAPSUS are water erosion and deposition, landslide activity, creep, solidification, weathering, tectonics and tillage. Process descriptions are as simple and generic as possible, ensuring wide applicability. (Author) 25 refs.

  1. Multi-temporal land cover classification of the Konya Basin, south-central Turkey, based on a LANDSAT TM-derived NDVI/NDMI time series: satellite remote sensing in support of landscape-scale soil biogeochemistry research

    Mayes, M. T.; Ozdogan, M.; Marin-Spiotta, E.

    2010-12-01

    Recently, terrestrial biogeochemists and soil scientists have called for new approaches to study human impacts on soil biogeochemical properties at landscape-wide, 100-1000 km2 spatial scales (Trumbore and Czimczik 2008). Here, we use satellite remote sensing to map land cover across a 16,000 km2 region in the Konya Basin, south-central Turkey, in support of research into agricultural and pastoral land use impacts on soil biogeochemistry. Our land cover classification is based on time series analysis of Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) data, derived from eight LANDSAT TM images spanning the 2006-2007 growing seasons. Using a hierarchical, binary-split classification approach and a support vector machine (SVM) algorithm, we map five land cover classes that correspond with the following dominant land-use categories: 1) annual cultivated row-crops, 2) perennial orchards/cultivated woody vegetation, 3) fallow fields, 4) uncultivated woody vegetation, 5) steppe vegetation/rangeland. The final map has an overall classification accuracy of 87.4% (kappa = 0.842), determined via traditional confusion-matrix analysis and over 150 site visits during summer 2010. Classes 1 and 2, which have the highest per-pixel NDVI and NDMI sums across image dates, attain the highest producer and consumer accuracies (>95%). We also compare the relative contributions and efficacy of NDVI and NDMI in separating land cover classes, and the influence of radiometric correction and calibration across image dates on classification accuracies. Our results support previous research showing that NDVI time series can effectively classify agricultural landscapes in semi-arid to arid environments (Simonneaux et al. 2008; Pax-Lenny et al. 1996). By combining our land cover map with other geospatial information in a GIS, we demonstrate how satellite remote sensing can help expand spatial scales of terrestrial biogeochemistry research from

  2. Segmentation and object-oriented classification of wetlands in a karst Florida landscape using multi-season Landsat-7 ETM+ Imagery

    Segmentation and object-oriented processing of single-season and multi-season Landsat-7 ETM+ data was utilized for the classification of wetlands in a 1560 km2 study area of north central Florida. This segmentation and object-oriented classification outperformed the traditional ...

  3. 网络适应性改造对《中图法》未来发展的启示%Web Adaptability Upgrading——Its Revelation to the Future Development of Chinese Library Classification

    邱君瑞

    2001-01-01

    The efforts made by OCLC researchers to enhance Dewey Decimal Classification (DDC) so as to adapt it to Web environment are described. From the experience of OCLC researchers, the author puts forward some proposals for the future development of Chinese Library Classification.

  4. Adapt

    Bargatze, L. F.

    2015-12-01

    Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted

  5. Complex Adaptive Systems, soil degradation and land sensitivity to desertification: A multivariate assessment of Italian agro-forest landscape.

    Salvati, Luca; Mavrakis, Anastasios; Colantoni, Andrea; Mancino, Giuseppe; Ferrara, Agostino

    2015-07-15

    Degradation of soils and sensitivity of land to desertification are intensified in last decades in the Mediterranean region producing heterogeneous spatial patterns determined by the interplay of factors such as climate, land-use changes, and human pressure. The present study hypothesizes that rising levels of soil degradation and land sensitivity to desertification are reflected into increasingly complex (and non-linear) relationships between environmental and socioeconomic variables. To verify this hypothesis, the Complex Adaptive Systems (CAS) framework was used to explore the spatiotemporal dynamics of eleven indicators derived from a standard assessment of soil degradation and land sensitivity to desertification in Italy. Indicators were made available on a detailed spatial scale (773 agricultural districts) for various years (1960, 1990, 2000 and 2010) and analyzed through a multi-dimensional exploratory data analysis. Our results indicate that the number of significant pair-wise correlations observed between indicators increased with the level of soil and land degradation, although with marked differences between northern and southern Italy. 'Fast' and 'slow' factors underlying soil and land degradation, and 'rapidly-evolving' or 'locked' agricultural districts were identified according to the rapidity of change estimated for each of the indicators studied. In southern Italy, 'rapidly-evolving' districts show a high level of soil degradation and land sensitivity to desertification during the whole period of investigation. On the contrary, those districts in northern Italy are those experiencing a moderate soil degradation and land sensitivity to desertification with the highest increase in the level of sensitivity over time. The study framework contributes to the assessment of complex local systems' dynamics in affluent but divided countries. Results may inform thematic strategies for the mitigation of land and soil degradation in the framework of action

  6. Potentials of RapidEye time series for improved classification of crop rotations in heterogeneous agricultural landscapes: experiences from irrigation systems in Central Asia

    Conrad, Christopher; Machwitz, Miriam; Schorcht, Gunther; Löw, Fabian; Fritsch, Sebastian; Dech, Stefan

    2011-11-01

    In Central Asia, more than eight Million ha of agricultural land are under irrigation. But severe degradation problems and unreliable water distribution have caused declining yields during the past decades. Reliable and area-wide information about crops can be seen as important step to elaborate options for sustainable land and water management. Experiences from RapidEye classifications of crop in Central Asia are exemplarily shown during a classification of eight crop classes including three rotations with winter wheat, cotton, rice, and fallow land in the Khorezm region of Uzbekistan covering 230,000 ha of irrigated land. A random forest generated by using 1215 field samples was applied to multitemporal RapidEye data acquired during the vegetation period 2010. But RapidEye coverage varied and did not allow for generating temporally consistent mosaics covering the entire region. To classify all 55,188 agricultural parcels in the region three classification zones were classified separately. The zoning allowed for including at least three observation periods into classification. Overall accuracy exceeded 85 % for all classification zones. Highest accuracies of 87.4 % were achieved by including five spatiotemporal composites of RapidEye. Class-wise accuracy assessments showed the usefulness of selecting time steps which represent relevant phenological phases of the vegetation period. The presented approach can support regional crop inventory. Accurate classification results in early stages of the cropping season permit recalculation of crop water demands and reallocation of irrigation water. The high temporal and spatial resolution of RapidEye can be concluded highly beneficial for agricultural land use classifications in entire Central Asia.

  7. Evaluation of dimensions Responsiveness and Requirement of grandparents perceived for teen grandchildren: Adaptation of an instrument to classification of grandparent styles

    Alessandra Ribeiro Ventura Oliveira

    2015-02-01

    Full Text Available The study show the adaptation of the instrument characterized for Likert scales to assess the responsiviness and requirement dimensions. The instrument was applicated to 28 adolescents of both sexes aged between 10 to 19 years old with grandchildren of grandparents school students from Ceilândia (DF. The classification of grandparents styles was realized through the results obtained by the grandparents participants of the study in the responsiviness and requirement dimensions. The proportion of grandparents styles observed in the sample was 10,3 authoritarian, 39,3 authoritative, 10,3 indulgent, 93,3 negligent.  

  8. Classification of the types of markets in landscape architecture%关于风景园林行业市场领域分类的思考

    肖茂福; 谢正根

    2012-01-01

      从园林投资主体和使用目的的角度,把园林市场划分为市政类园林、地产类园林、企事业单位类园林、生态修复类园林、私家园林(艺)、园林苗木和养护7种主要类型,并对前5种概念给出定义,可供园林行业市场领域研究参考。%  The market of Landscape Architecture (L. A.) is booming now, which is attracting an increasingly number of investors. But on the contrary, there are few studies on the market in the academic field. In terms of the investors and the purpose of use, the L. A. market can mainly be divided into 7 types: the urban public space, the real estate, the enterprises and institutions, the ecological restoration, the private gardens, the flowers and trees, and the landscape maintenance. This paper gives the definitions of the first 5 market types, hoping to provide a reference for the future market researches in the field of Landscape Architecture.

  9. Hydrologic Landscape Characterization for the Pacific Northwest, USA

    Hydrologic classification can help address some of the challenges facing catchment hydrology. Wigington et al. (2013) developed a hydrologic landscape (HL) approach to classification that was applied to the state of Oregon. Several characteristics limited its applicability outs...

  10. Laser Raman detection of platelets for early and differential diagnosis of Alzheimer’s disease based on an adaptive Gaussian process classification algorithm

    Early and differential diagnosis of Alzheimer’s disease (AD) has puzzled many clinicians. In this work, laser Raman spectroscopy (LRS) was developed to diagnose AD from platelet samples from AD transgenic mice and non-transgenic controls of different ages. An adaptive Gaussian process (GP) classification algorithm was used to re-establish the classification models of early AD, advanced AD and the control group with just two features and the capacity for noise reduction. Compared with the previous multilayer perceptron network method, the GP showed much better classification performance with the same feature set. Besides, spectra of platelets isolated from AD and Parkinson’s disease (PD) mice were also discriminated. Spectral data from 4 month AD (n = 39) and 12 month AD (n = 104) platelets, as well as control data (n = 135), were collected. Prospective application of the algorithm to the data set resulted in a sensitivity of 80%, a specificity of about 100% and a Matthews correlation coefficient of 0.81. Samples from PD (n = 120) platelets were also collected for differentiation from 12 month AD. The results suggest that platelet LRS detection analysis with the GP appears to be an easier and more accurate method than current ones for early and differential diagnosis of AD. (paper)

  11. Image-based ATR utilizing adaptive clutter filter detection, LLRT classification, and Volterra fusion with application to side-looking sonar

    Aridgides, Tom; Fernández, Manuel

    2010-04-01

    An improved automatic target recognition (ATR) processing string has been developed. The overall processing string consists of pre-processing, subimage adaptive clutter filtering, detection, feature extraction, optimal subset feature selection, feature orthogonalization and classification processing blocks. The objects that are classified by three distinct ATR strings are fused using the classification confidence values and their expansions as features, and using "summing" or log-likelihood-ratio-test (LLRT) based fusion rules. These three ATR processing strings were individually developed and tuned by researchers from different companies. The utility of the overall processing strings and their fusion was demonstrated with an extensive side-looking sonar dataset. In this paper we describe a new processing improvement: six additional classification features are extracted, using primarily target shadow information and a feature extraction window whose length is now made variable as a function of range. This new ATR processing improvement resulted in a 3:1 reduction in false alarms. Two advanced fusion algorithms are subsequently applied: First, a nonlinear Volterra expansion (2nd order) feature-LLRT fusion algorithm is employed. Second, a repeated application of a subset Volterra feature selection / feature orthogonalization / LLRT fusion block is utilized. It is shown that cascaded Volterra feature- LLRT fusion of the ATR processing strings outperforms baseline "summing" and single-stage Volterra feature-LLRT fusion algorithms, yielding significant improvements over the best single ATR processing string results, and providing the capability to correctly call the majority of targets while maintaining a very low false alarm rate.

  12. The Effect of Adaptive Gain and Adaptive Momentum in Improving Training Time of Gradient Descent Back Propagation Algorithm on Classification Problems

    Norhamreeza Abdul Hamid

    2011-01-01

    Full Text Available The back propagation algorithm has been successfully applied to wide range of practical problems. Since this algorithm uses a gradient descent method, it has some limitations which are slow learning convergence velocity and easy convergence to local minima. The convergence behaviour of the back propagation algorithm depends on the choice of initial weights and biases, network topology, learning rate, momentum, activation function and value for the gain in the activation function. Previous researchers demonstrated that in ‘feed forward’ algorithm, the slope of the activation function is directly influenced by a parameter referred to as ‘gain’. This research proposed an algorithm for improving the performance of the current working back propagation algorithm which is Gradien Descent Method with Adaptive Gain by changing the momentum coefficient adaptively for each node. The influence of the adaptive momentum together with adaptive gain on the learning ability of a neural network is analysed. Multilayer feed forward neural networks have been assessed. Physical interpretation of the relationship between the momentum value, the learning rate and weight values is given. The efficiency of the proposed algorithm is compared with conventional Gradient Descent Method and current Gradient Descent Method with Adaptive Gain was verified by means of simulation on three benchmark problems. In learning the patterns, the simulations result demonstrate that the proposed algorithm converged faster on Wisconsin breast cancer with an improvement ratio of nearly 1.8, 6.6 on Mushroom problem and 36% better on  Soybean data sets. The results clearly show that the proposed algorithm significantly improves the learning speed of the current gradient descent back-propagatin algorithm.

  13. Laser Raman detection for oral cancer based on an adaptive Gaussian process classification method with posterior probabilities

    Du, Zhanwei; Yang, Yongjian; Bai, Yuan; Wang, Lijun; Su, Le; Chen, Yong; Li, Xianchang; Zhou, Xiaodong; Jia, Jun; Shen, Aiguo; Hu, Jiming

    2013-03-01

    The existing methods for early and differential diagnosis of oral cancer are limited due to the unapparent early symptoms and the imperfect imaging examination methods. In this paper, the classification models of oral adenocarcinoma, carcinoma tissues and a control group with just four features are established by utilizing the hybrid Gaussian process (HGP) classification algorithm, with the introduction of the mechanisms of noise reduction and posterior probability. HGP shows much better performance in the experimental results. During the experimental process, oral tissues were divided into three groups, adenocarcinoma (n = 87), carcinoma (n = 100) and the control group (n = 134). The spectral data for these groups were collected. The prospective application of the proposed HGP classification method improved the diagnostic sensitivity to 56.35% and the specificity to about 70.00%, and resulted in a Matthews correlation coefficient (MCC) of 0.36. It is proved that the utilization of HGP in LRS detection analysis for the diagnosis of oral cancer gives accurate results. The prospect of application is also satisfactory.

  14. Laser Raman detection for oral cancer based on an adaptive Gaussian process classification method with posterior probabilities

    The existing methods for early and differential diagnosis of oral cancer are limited due to the unapparent early symptoms and the imperfect imaging examination methods. In this paper, the classification models of oral adenocarcinoma, carcinoma tissues and a control group with just four features are established by utilizing the hybrid Gaussian process (HGP) classification algorithm, with the introduction of the mechanisms of noise reduction and posterior probability. HGP shows much better performance in the experimental results. During the experimental process, oral tissues were divided into three groups, adenocarcinoma (n = 87), carcinoma (n = 100) and the control group (n = 134). The spectral data for these groups were collected. The prospective application of the proposed HGP classification method improved the diagnostic sensitivity to 56.35% and the specificity to about 70.00%, and resulted in a Matthews correlation coefficient (MCC) of 0.36. It is proved that the utilization of HGP in LRS detection analysis for the diagnosis of oral cancer gives accurate results. The prospect of application is also satisfactory. (paper)

  15. Study of settlement distribution pattern in the Kolkheti lowland (Black Sea coast of Georgia) starting from early Bronze Age - natural and human influence and adaptation to landscape evolution

    Elashvili, Mikheil; Akhvlediani, Dimitri; Navrozashvili, Levan; Sukhishvili, Lasha; Kirkitadze, Giorgi; Kelterbaum, Daniel; Laermans, Hannes

    2015-04-01

    archaeological datasets are collected in the joint-venture project and in addition with known historical and old topographic maps of the region they represent a good start for the research. There are typical ancient settlements in the Kolkheti lowland, called locally "Dikhagudzuba", which are still identifiable on aerial imagery. Their structure, physical dimensions and locations were analyzed from aerial and on site studies. Data from existing archaeological studies and recent field works were analyzed to create a reliable database on the distribution of Bronze Age settlements. Changes in paleoclimate, sea level and river deltas represent the main components to form a paleolandscape of the study area. Based on the results of recent fieldwork and the analyses of regional historical maps in addition with the general geological and geomorphological settings paleogeographical scenarios were constructed. Proposed models of past landscape changes and human settlement pattern were merged and analyzed. From one hand the human settlement distribution (taking into account tells relation with the local landscape of the same period) help us to identify the best suitable scenario from the set of paleolandscape patterns. Moreover, paleogeographical scenarios provide a better understanding on the erection of human settlements in the past, and their influence and adaptation to ongoing changes.

  16. Relationship of vegetation degradation classification and landscape accessibility classification in Shenzhen%深圳市植被受损分级评价及其与景观可达性的关系

    刘语凡; 陈雪; 李贵才; 曾辉

    2011-01-01

    Analysis of vegetation degradation and identification of its causes are among the most important issues in plant ecology of the fast urbanizing areas. Previous research mainly focused on the patterns, mechanisms and restoration techniques in degraded communities at the ecosystem scale, but similar analyses are rare at the regional scale. The current study focused on the correlation of human disturbance and vegetation degradation on a regional scale, using Shenzhen,which is a fast-urbanizing area, as an example. We used aviation image and IRS satellite data and 1:100,000 digital topographical map of Shenzhen of the year 2007 and we defined the entire vegetation in Shenzhen, which mainly included forest, plantation, orchard, farm, shrub, bare land and unutilized areas, into twelve vegetation types using both visual interpretation and field observation. Forest, plantations and orchards were further classified into good and poor by their difference in vegetation coverage, tree height, dominant species, and plant diversity, whereas the bare lands were defined as bare land buildings, bare land roads and bare land quarries. We also used NDVI as a complementary tool to put the twelve vegetation types into six degradation classes using ANOVA and mean value distribution. Additionally, landscape accessibility was established as an index to indicate the intensity of human disturbance using altitude, slop, urban density,and the shortest distance to the nearest road, and was classified into five categories. Finally, the correlation of vegetation degradation class and landscape accessibility class was overlaid and Pearson correlation coefficients were calculated.Our results showed that ( 1 ) the correlation between degradation claas and landscape accessibility was significant with a correlation coefficient of 0. 794; (2) Forests in remote areas with good soil and topographical conditions were well preserved. Plantations and orchards were in the state of moderate disturbance and

  17. Adapting Landscape Mosaics of medIteranean Rainfed Agrosystems for a sustainable management of crop production, water and soil resources: the ALMIRA project.

    Jacob, Frédéric; Mekki, Insaf; Chikhaoui, Mohamed

    2014-05-01

    In the context of mitigating the pressures induced by global change combined with demography and market pressures, there is increasing societal demand and scientific need to understand the functioning of Mediterranean Rainfed Agrosystems (MRAs) for their potential to provide various environmental and economic services of importance such as food production, preservation of employment and local knowhow, downstream water delivery or mitigation of rural exodus. Efficient MRAs management strategies that allow for compromises between economic development and natural resources preservation are needed. Such strategies require innovative system based research, integration across approaches and scales. One of the major challenges is to make all contributions from different disciplines converging towards a reproducible transdisciplinary approach. The objective of this communication is to present the ALMIRA project, a Tunisian - Moroccan - French project which lasts four years (2014 - 2017). The communication details the societal context, the scientific positioning and the related work hypothesis, the study areas, the project structure, the expected outcomes and the partnership which capitalizes on long term collaborations. ALMIRA aims to explore the modulation of landscape mosaics within MRAs to optimize landscape services. To explore this new lever, ALMIRA proposes to design, implement and test a new Integrated Assessment Modelling approach that explicitly i) includes innovations and action means into prospective scenarii for landscape evolutions, and ii) addresses landscape mosaics and processes of interest from the agricultural field to the resource governance catchment. This requires tackling methodological challenges in relation to i) the design of spatially explicit landscape evolution scenarii, ii) the coupling of biophysical processes related to agricultural catchment hydrology, iii) the digital mapping of landscape properties and iv) the economic assessment of the

  18. Modelling the Relationship Between Land Surface Temperature and Landscape Patterns of Land Use Land Cover Classification Using Multi Linear Regression Models

    Bernales, A. M.; Antolihao, J. A.; Samonte, C.; Campomanes, F.; Rojas, R. J.; dela Serna, A. M.; Silapan, J.

    2016-06-01

    The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC) and land surface temperature (LST). Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric "Effective mesh size" was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas) and looking for common predictors between LSTs of these two different farming periods.

  19. Electromagnetic Landscape

    Cermak, Daniel; Okutsu, Ayaka; Jørgensen, Stina Marie Hasse

    2015-01-01

    Daniel Cermak-Sassenrath, Ayaka Okutsu, Stina Hasse. Electromagnetic Landscape - In-between Signal, Noise and Environment. Installation and artist talk. 21th International Symposium on Electronic Art (ISEA) 2015, Vancouver, CAN, Aug 14-18, 2015.......Daniel Cermak-Sassenrath, Ayaka Okutsu, Stina Hasse. Electromagnetic Landscape - In-between Signal, Noise and Environment. Installation and artist talk. 21th International Symposium on Electronic Art (ISEA) 2015, Vancouver, CAN, Aug 14-18, 2015....

  20. Adaptive and context-aware detection and classification of potential QoS degradation events in biomedical wireless sensor networks

    Abreu, Carlos; Miranda, Francisco; Mendes, Paulo M.

    2016-06-01

    The use of wireless sensor networks in healthcare has the potential to enhance the services provided to citizens. In particular, they play an important role in the development of state-of-the-art patient monitoring applications. Nevertheless, due to the critical nature of the data conveyed by such patient monitoring applications, they have to fulfil high standards of quality of service in order to obtain the confidence of all players in the healthcare industry. In such context, vis-à-vis the quality of service being provided by the wireless sensor network, this work presents an adaptive and context-aware method to detect and classify performance degradation events. The proposed method has the ability to catch the most significant and damaging variations on the metrics being used to quantify the quality of service provided by the network without overreacting to small and innocuous variations on the metric's value.

  1. Changing Landscapes, Changing Landscape's Story

    Lapka, Miloslav; Cudlínová, Eva

    2003-01-01

    Roč. 28, č. 3 (2003), s. 323-328. ISSN 0142-6397. [Symposium on Sustainable Landscapes in an Enlarged Europe.. Nové Hrady, 12.09.2001-14.09.2001] R&D Projects: GA MŠk ME 530 Grant ostatní: GA-(XE) QLK5-CT-2000-01211-SPRITE Institutional research plan: CEZ:AV0Z5039906 Keywords : Landscape stability * narrative approach * socio-economic typology Subject RIV: DO - Wilderness Conservation

  2. Managing the carnivore comeback: assessing the adaptive capacity of the Eurasian lynx (Lynx lynx) to cohabit with humans in shared landscapes

    Bouyer, Yaëlle

    2015-01-01

    Conflicts between humans and large carnivores are one of the most visible examples of the challenges that arise when seeking to achieve coexistence between humans and wildlife. With their large spatial requirements and predatory behavior, large carnivores are among the most difficult species to preserve in our modern day landscapes. Although large carnivores are usually considered as the epitomes of wilderness, because of human population growth and habitat fragmenta...

  3. Time in the Landscape: Designing for Perpetuity

    Taylor, Louise H

    2004-01-01

    Landscape is dynamic. All the elements in the landscape are in a continual process of change. There is growth, expansion, weathering, disintegration, decay and renewal. Change is the very substance of things and it is through these changes that we register the passage of time. This thesis explores the concept of material duration and its application to landscape design. Duration is a complex measure of time. This thesis adapts a definition of duration developed in the field of finance to expl...

  4. Measuring the Evolvability Landscape to study Neutrality

    Verel, Sébastien; Clergue, Manuel

    2007-01-01

    This theoretical work defines the measure of autocorrelation of evolvability in the context of neutral fitness landscape. This measure has been studied on the classical MAX-SAT problem. This work highlight a new characteristic of neutral fitness landscapes which allows to design new adapted metaheuristic.

  5. 一种基于自适应软分配的图像分类方法%Image classification based on adaptive soft assignment

    王挺进; 赵永威; 李弼程

    2015-01-01

    视觉词袋模型(BoVW)是当前图像分类领域的主流方法,然而,视觉单词同义性和歧义性问题严重制约了该模型的性能,进而降低图像分类准确率。针对该问题,本文提出一种基于自适应软分配的图像分类方法。该方法首先对尺度不变特征变换(SIFT)特征映射到视觉单词的距离进行分析,按一定的规则进行归类,并针对具有不同模糊程度的 SIFT特征采用自适应的分配策略;然后,通过卡方模型分析各个视觉单词与图像类别之间的相关性,并依此去除视觉停用词(VSW),重构视觉单词统计直方图;最后,输入到支持向量机(SVM)完成分类。实验结果表明,该优化方法能有效地降低视觉单词同义性和歧义性问题带来的影响,增强视觉单词的区分性,进而提高图像分类准确率。%Bag of Visual Words(BoVW) is the main solution in the current image classification field, whereas the synonymity and ambiguity of the visual words restrict the semantic expression ability of the model and reduce the accuracy of image classification. Aiming to the problem, an adaptive soft assignment method is proposed. Firstly, it analyzes the distance of the Scale Invariant Feature Transform(SIFT) features mapping to visual words, classifies these SIFT features according to certain rules, and applies adaptive allocation strategies to SIFT features with different fuzziness. Then, this paper analyzes the correlations between visual words and image categories via Chi-square model, and then removes the Visual Stop Words(VSW) and reconstructs the histograms. Finally, the images are classified by Support Vector Machine(SVM). The experimental results show that, the method can effectively reduce the impact of the visual words synonymity and ambiguity, and enhance the distinction of visual words, so as to improve the image classification accuracy.

  6. Electromagnetic Landscape

    Cermak, Daniel; Okutsu, Ayaka; Hasse, Stina

    2015-01-01

    Electromagnetic Landscape demonstrates in direct, tangible and immediate ways effects of the disruption of the familiar. An ubiquitous technological medium, FM radio, is turned into an alien and unfamiliar one. Audience participation, the environment, radio signals and noise create a site......-specific, ragged sonic landscape. The work exhibits intrinsic, non-trivial, emerging behaviour, cyclic or wave-like, which converges and ebbs. It varies its sonic and visual display through a dynamic interaction of light sources, fog and light sensors. The system maintains a fluxing state of ambivalence between...

  7. Cuban Landscapes

    Scarpaci, Joseph L.; Portela, Armando

    This accessible book offers a vivid geographic portrait of Cuba, exploring the island’s streetscapes, sugar cane fields, beaches, and rural settlements; its billboards, government buildings, and national landmarks. The authors illuminate how natural and built landscapes have shaped Cuban identity...

  8. Changing Landscape

    Tunby Gulbrandsen, Ib; Kamstrup, Andreas; Koed Madsen, Anders;

    with an analysis of the changing organizational landscape created by new ICT’s like Google, Facebook, Wikipedia, iPods, smart phones and Wi-Fi. Based on five netno- and ethno-graphic investigations of the intertwinement of ICT’s and organizational work, we point to three features that have changed the scene: new...

  9. Quantitative analyses of empirical fitness landscapes

    The concept of a fitness landscape is a powerful metaphor that offers insight into various aspects of evolutionary processes and guidance for the study of evolution. Until recently, empirical evidence on the ruggedness of these landscapes was lacking, but since it became feasible to construct all possible genotypes containing combinations of a limited set of mutations, the number of studies has grown to a point where a classification of landscapes becomes possible. The aim of this review is to identify measures of epistasis that allow a meaningful comparison of fitness landscapes and then apply them to the empirical landscapes in order to discern factors that affect ruggedness. The various measures of epistasis that have been proposed in the literature appear to be equivalent. Our comparison shows that the ruggedness of the empirical landscape is affected by whether the included mutations are beneficial or deleterious and by whether intragenic or intergenic epistasis is involved. Finally, the empirical landscapes are compared to landscapes generated with the rough Mt Fuji model. Despite the simplicity of this model, it captures the features of the experimental landscapes remarkably well. (paper)

  10. From climate-smart agriculture to climate-smart landscapes

    Scherr Sara J; Shames Seth; Friedman Rachel

    2012-01-01

    Abstract Background For agricultural systems to achieve climate-smart objectives, including improved food security and rural livelihoods as well as climate change adaptation and mitigation, they often need to be take a landscape approach; they must become ‘climate-smart landscapes’. Climate-smart landscapes operate on the principles of integrated landscape management, while explicitly incorporating adaptation and mitigation into their management objectives. Results An assessment of climate ch...

  11. Cooling towers in the landscape

    The cooling tower as a large technical construction is one of the most original industrial buildings. It sticks out as an outlandish element in our building landscape, a giant which cannot be compared with the traditional forms of technical buildings. If it is constructed as a reinforced-concrete hyperboloid, its shape goes beyond all limits of building construction. Judgment of these highly individual constructions is only possible by applying a novel standard breaking completely with tradition. This new scale of height and dimension in industrial construction, and in particular the modern cooling tower, requires painstaking care and design and adaptation to the landscape around it. (orig.)

  12. Landscape complexity and vegetation dynamics in Riding Mountain National Park, Canada

    Walker, David John

    spatial complexity over time. Fragmentation and habitat losses in the region surrounding RMNP were found to be high, with only half of the forest present in 1950 remaining in the 1990's. Scale-invariant spatial dispersion of forest fragments decreased between the 1950's and 1990's. Thus, the study area is becoming increasingly isolated from other natural forested areas within the region. In creating maps of land cover for these analyses, it was found that structural composition of the canopy was often more important than floristics in determining spectral reflectance in Landsat data. A rule-based optimization procedure using multivariate analysis was developed to maximize the relationship between vegetation on the ground and spectral reflectance. Because of the high degree of spatial complexity in these systems, an alternative approach to map accuracy assessment utilizing multiple discriminant analysis (MDA) was developed. It was found that closed conifer stands composed of different softwood species were not easily discriminated during classification because of identical spectral signatures at the stand-level. It is suggested that the highly structured architecture and conical form of conifer stands results in the anechoic interception and absorption of light. This light interception strategy may have adaptive advantages in regions where sun angle is low, or where cloud cover is high, such as in the boreal forest and montane environments. The results of these investigations into landscape pattern suggest that ecosystem dynamics in the boreal forest produce scale-invariant landscape complexity.

  13. THE CONCEPT, ESSENCE AND CLASSIFICATION OF ADAPTIVE MANAGEMENT SYSTEMS WITH ORGANIZATIONAL COMPLEXITY Понятие, сущность и классификация адаптивного управления системами с организационной сложностью

    Zhmurko D. Y.

    2013-01-01

    The article examines the concept and essence of adaptive management. The classification of adaptive management system (AMS). Is an example of adaptive management system with complex organi-zation for integrated segments of sugar subcomplex of agriculture

  14. Nominal classification

    Senft, G.

    2007-01-01

    This handbook chapter summarizes some of the problems of nominal classification in language, presents and illustrates the various systems or techniques of nominal classification, and points out why nominal classification is one of the most interesting topics in Cognitive Linguistics.

  15. A solution to the challenge of optimization on ''golf-course''-like fitness landscapes.

    Hygor Piaget M Melo

    Full Text Available Genetic algorithms (GAs have been used to find efficient solutions to numerous fundamental and applied problems. While GAs are a robust and flexible approach to solve complex problems, there are some situations under which they perform poorly. Here, we introduce a genetic algorithm approach that is able to solve complex tasks plagued by so-called ''golf-course''-like fitness landscapes. Our approach, which we denote variable environment genetic algorithms (VEGAs, is able to find highly efficient solutions by inducing environmental changes that require more complex solutions and thus creating an evolutionary drive. Using the density classification task, a paradigmatic computer science problem, as a case study, we show that more complex rules that preserve information about the solution to simpler tasks can adapt to more challenging environments. Interestingly, we find that conservative strategies, which have a bias toward the current state, evolve naturally as a highly efficient solution to the density classification task under noisy conditions.

  16. Exploring the Visual Landscape

    Nijhuis, S.; Van Lammeren, R.; van Der Hoeven, F

    2011-01-01

    Exploring the Visual Landscape is about the combination of landscape research and planning, visual perception and Geographic Information Science. It showcases possible ways of getting a grip on themes like: landscape openness, cluttering of the rural landscape, high-rise buildings in relation to cityscape, historic landscapes and motorway panoramas. It offers clues for visual landscape assessment of spaces in cities, parks and rural areas. In that respect, it extends the long tradition in the...

  17. Contemporary Danish landscape research

    Vejre, Henrik; Brandt, Jesper

    2004-01-01

    Danish landscape research blossomed during the 1990’ies thanks to several transdisciplinary research programmes involving several institutions. The main themes of the programmes encompassed Landscape change, landscape and biological diversity, nature and landscape management, use and monitoring of...... the countryside. The values of the Danish landscape pertain mainly to the coastal landscapes. The threats include the industrilization of the agricultural landsclaes and,in places urban sprawl....

  18. Baroque formation in landscape

    Semerádová, Šárka

    2013-01-01

    Bachelor thesis investigates the importance and influence of Baroque landscape. Solved the historical landscape context and mapping the remains of these adjustments, determining rural character of the landscape with Baroque elements. Describes the effect of changes on the landscape and the continuing impact of the current landscape. It deals with changing the size and location of the development of settlements and buildings in the open countryside. It surveys the current landscape condition, ...

  19. Driving the Landscape

    Haff, P. K.

    2012-12-01

    destination—whereas the natural evolution of landscape has no such goal. Goals will become an essential feature of landscape prediction. The presence of a goal potentially increases our ability to predict, provided it is possible to use feedback (i.e., management) to nudge the system back in the "right" direction when it starts to stray. Under a regime of accelerating technology the closest we can get to predicting the longer term future of landscape is adaptive management, which at large scale is really geoengineer the system. The goal presumably would be to maintain a condition conducive to human well-being, for example to maintain a suitable fraction of global arable land. A successful "prediction" would be to stay within an envelope of states consistent with that goal. We cannot say, however, in what specific state the landscape will be at any time beyond the near future; this will depend on the future sequence of management decisions, which are, like the system they are managing, unpredictable, except shortly before they are implemented. The landscape of the future will thus likely be the result of a series of quick fixes to previous trends in landscape change. Similar comments apply to the prediction, or management, of climate. There is of course no guarantee that it will be possible to stay within the desired envelope of well-being.

  20. Sami herders’ classification system of reindeer winter pastures – A contribution to adapt forest management to reindeer herding in northern Sweden

    Samuel Roturier

    2011-01-01

    The system for classifying vegetation types currently used in Swedish forestry has two major deficiencies when identifying reindeer winter pastures: it uses lichen cover as the sole criterion for defining them, and it ignores the possible adverse effects of snow cover. Based on ethnological field studies, this paper examines Sami reindeer herders' classification of reindeer winter pastures, and compares it to the system used by foresters at different levels of classification. At the lower lev...

  1. Improving land cover classification using input variables derived from a geographically weighted principal components analysis

    Comber, Alexis J.; Harris, Paul; Tsutsumida, Narumasa

    2016-09-01

    This study demonstrates the use of a geographically weighted principal components analysis (GWPCA) of remote sensing imagery to improve land cover classification accuracy. A principal components analysis (PCA) is commonly applied in remote sensing but generates global, spatially-invariant results. GWPCA is a local adaptation of PCA that locally transforms the image data, and in doing so, can describe spatial change in the structure of the multi-band imagery, thus directly reflecting that many landscape processes are spatially heterogenic. In this research the GWPCA localised loadings of MODIS data are used as textural inputs, along with GWPCA localised ranked scores and the image bands themselves to three supervised classification algorithms. Using a reference data set for land cover to the west of Jakarta, Indonesia the classification procedure was assessed via training and validation data splits of 80/20, repeated 100 times. For each classification algorithm, the inclusion of the GWPCA loadings data was found to significantly improve classification accuracy. Further, but more moderate improvements in accuracy were found by additionally including GWPCA ranked scores as textural inputs, data that provide information on spatial anomalies in the imagery. The critical importance of considering both spatial structure and spatial anomalies of the imagery in the classification is discussed, together with the transferability of the new method to other studies. Research topics for method refinement are also suggested.

  2. Landscaping for energy efficiency

    NONE

    1995-04-01

    This publication by the National Renewable Energy Laboratory addresses the use of landscaping for energy efficiency. The topics of the publication include minimizing energy expenses; landscaping for a cleaner environment; climate, site, and design considerations; planning landscape; and selecting and planting trees and shrubs. A source list for more information on landscaping for energy efficiency and a reading list are included.

  3. Sami herders’ classification system of reindeer winter pastures – A contribution to adapt forest management to reindeer herding in northern Sweden

    Samuel Roturier

    2011-04-01

    Full Text Available The system for classifying vegetation types currently used in Swedish forestry has two major deficiencies when identifying reindeer winter pastures: it uses lichen cover as the sole criterion for defining them, and it ignores the possible adverse effects of snow cover. Based on ethnological field studies, this paper examines Sami reindeer herders' classification of reindeer winter pastures, and compares it to the system used by foresters at different levels of classification. At the lower level, which deals with identifying discrete entities, it is possible to find some correspondence between the representations of forest characteristics used by the Sami herders and the foresters. Reindeer herders discriminate the same factors – tree height, canopy enclosure, stem density, field-layer, bottom-layer – as forest manager, but the former use this knowledge to evaluate the effects on snow cover and ice, and thus on the accessibility of the lichen beneath. Inconsistencies appear at the second level of classification, which consists in ordering this variety of forest characteristics into a classificatory system. There is a mismatch between Sami herders and forester’s representations and classifications of pastures because Sami categories are ‘complex’, i.e. categories including many criteria that have to be combined and balanced before defining the pasture. Herders’ representation of pasture is thus holistic, rather than purely botanical. The comparison of the two classification systems demonstrates that it is impossible to define grazing quality solely in terms of lichen abundance, because of the multidimensional nature of reindeer winter pastures and consequent shifts (spatial and temporal in its quality.

  4. Drawing Sound as Landscape

    David N Buck

    2014-10-01

    Full Text Available This essay discusses the relationship between music notation, sound, and landscape. I explore how the notation of musical sounds might lead to new methods for drawing auditory landscapes. There are three components to the research: an analysis of the score of English composer Michael Finnissy’s composition Green Meadows (1977; a reference to ethnomusicology to explore how the notation of sound-as-music might allow us to draw sound-as-landscape; and a notation to investigate the composition of a landscape through sound. In conclusion, I question how auditory landscapes might be composed, and suggest ways in which both drawing and sound might be considered landscape.

  5. Research using energy landscape

    Energy landscape is a theoretical tool used for the study of systems where cooperative processes occur such as liquid, glass, clusters, and protein. Theoretical and experimental researches related to energy landscape are introduced in this review

  6. Assessment of Classification Accuracies of SENTINEL-2 and LANDSAT-8 Data for Land Cover / Use Mapping

    Hale Topaloğlu, Raziye; Sertel, Elif; Musaoğlu, Nebiye

    2016-06-01

    This study aims to compare classification accuracies of land cover/use maps created from Sentinel-2 and Landsat-8 data. Istanbul metropolitan city of Turkey, with a population of around 14 million, having different landscape characteristics was selected as study area. Water, forest, agricultural areas, grasslands, transport network, urban, airport- industrial units and barren land- mine land cover/use classes adapted from CORINE nomenclature were used as main land cover/use classes to identify. To fulfil the aims of this research, recently acquired dated 08/02/2016 Sentinel-2 and dated 22/02/2016 Landsat-8 images of Istanbul were obtained and image pre-processing steps like atmospheric and geometric correction were employed. Both Sentinel-2 and Landsat-8 images were resampled to 30m pixel size after geometric correction and similar spectral bands for both satellites were selected to create a similar base for these multi-sensor data. Maximum Likelihood (MLC) and Support Vector Machine (SVM) supervised classification methods were applied to both data sets to accurately identify eight different land cover/ use classes. Error matrix was created using same reference points for Sentinel-2 and Landsat-8 classifications. After the classification accuracy, results were compared to find out the best approach to create current land cover/use map of the region. The results of MLC and SVM classification methods were compared for both images.

  7. VISUAL LANDSCAPE PERCEPTION

    ASLAN, Fürüzan; Aslan, Edanur; Atik, Atilla

    2015-01-01

    Landscape, the way that people perceive, is described as areas the characteristics of which are made up as a result of the interaction and activity of natural and/or human factors. The composition which is formed as a result of this interaction of natural and cultural components sets forth the visual landscape conception.In the scope of study landscape, perception, visual landscape concepts, visual design factors and visual design principles in support of related descriptions and the visuals ...

  8. Adaptation and extinction in experimentally fragmented landscapes

    Fakheran, Sima; Paul-Victor, Cloé; Heichinger, Christian; Schmid, Bernhard; Grossniklaus, Ueli; Turnbull, Lindsay A.

    2010-01-01

    Competition and disturbance are potent ecological forces that shape evolutionary trajectories. These forces typically work in opposition: when disturbance is infrequent, densities are high and competition is intense. In contrast, frequent disturbance creates a low-density environment in which competition is weak and good dispersal essential. We exploited recent advances in genomic research to quantify the response to selection by these powerful ecological forces at the phenotypic and molecula...

  9. Consensus in landscape preference judgments: the effects of landscape visual aesthetic quality and respondents' characteristics.

    Kalivoda, Ondřej; Vojar, Jiří; Skřivanová, Zuzana; Zahradník, Daniel

    2014-05-01

    Landscape's visual aesthetic quality (VAQ) has been widely regarded as a valuable resource worthy of protection. Although great effort has been devoted to determining the factors driving aesthetic preferences, public consensus in judgments has been neglected in the vast majority of such studies. Therefore, the aim of our study was to analyze three main possible sources of judgment variance: landscape VAQ, landscape type, and variability among respondents. Based upon an extensive perception-based investigation including more than 400 hikers as respondents, we found that variance in respondents' judgments differed significantly among assessed landscape scenes. We discovered a significant difference in judgment variances within each investigated respondent characteristic (gender, age, education level, occupational classification, and respondent's type of residence). Judgment variance was at the same time affected by landscape VAQ itself - the higher the VAQ, the better the consensus. While differences caused by characteristics indicate subjectivity of aesthetic values, the knowledge that people better find consensus for positively perceived landscapes provides a cogent argument for legal protection of valuable landscape scenes. PMID:24594757

  10. The Campus Landscape.

    du Von, Jay

    1966-01-01

    All across the country, landscaping and site development are coming to the fore as essential and integral parts of university planning and development. This reprint concentrates on the function of landscape architecture, and briefly examines some of the major responsibilities of the landscape architect in planning a campus. Included are--(1)…

  11. Using remote sensing products to classify landscape. A multi-spatial resolution approach

    García-Llamas, Paula; Calvo, Leonor; Álvarez-Martínez, José Manuel; Suárez-Seoane, Susana

    2016-08-01

    The European Landscape Convention encourages the inventory and characterization of landscapes for environmental management and planning actions. Among the range of data sources available for landscape classification, remote sensing has substantial applicability, although difficulties might arise when available data are not at the spatial resolution of operational interest. We evaluated the applicability of two remote sensing products informing on land cover (the categorical CORINE map at 30 m resolution and the continuous NDVI spectral index at 1 km resolution) in landscape classification across a range of spatial resolutions (30 m, 90 m, 180 m, 1 km), using the Cantabrian Mountains (NW Spain) as study case. Separate landscape classifications (using topography, urban influence and land cover as inputs) were accomplished, one per each land cover dataset and spatial resolution. Classification accuracy was estimated through confusion matrixes and uncertainty in terms of both membership probability and confusion indices. Regarding landscape classifications based on CORINE, both typology and number of landscape classes varied across spatial resolutions. Classification accuracy increased from 30 m (the original resolution of CORINE) to 90m, decreasing towards coarser resolutions. Uncertainty followed the opposite pattern. In the case of landscape classifications based on NDVI, the identified landscape patterns were geographically structured and showed little sensitivity to changes across spatial resolutions. Only the change from 1 km (the original resolution of NDVI) to 180 m improved classification accuracy. The value of confusion indices increased with resolution. We highlight the need for greater effort in selecting data sources at the suitable spatial resolution, matching regional peculiarities and minimizing error and uncertainty.

  12. Combining QuickBird, LiDAR, and GIS topography indices to identify a single native tree species in a complex landscape using an object-based classification approach

    Pham, Lien T. H.; Brabyn, Lars; Ashraf, Salman

    2016-08-01

    There are now a wide range of techniques that can be combined for image analysis. These include the use of object-based classifications rather than pixel-based classifiers, the use of LiDAR to determine vegetation height and vertical structure, as well terrain variables such as topographic wetness index and slope that can be calculated using GIS. This research investigates the benefits of combining these techniques to identify individual tree species. A QuickBird image and low point density LiDAR data for a coastal region in New Zealand was used to examine the possibility of mapping Pohutukawa trees which are regarded as an iconic tree in New Zealand. The study area included a mix of buildings and vegetation types. After image and LiDAR preparation, single tree objects were identified using a range of techniques including: a threshold of above ground height to eliminate ground based objects; Normalised Difference Vegetation Index and elevation difference between the first and last return of LiDAR data to distinguish vegetation from buildings; geometric information to separate clusters of trees from single trees, and treetop identification and region growing techniques to separate tree clusters into single tree crowns. Important feature variables were identified using Random Forest, and the Support Vector Machine provided the classification. The combined techniques using LiDAR and spectral data produced an overall accuracy of 85.4% (Kappa 80.6%). Classification using just the spectral data produced an overall accuracy of 75.8% (Kappa 67.8%). The research findings demonstrate how the combining of LiDAR and spectral data improves classification for Pohutukawa trees.

  13. Planetary Landscape Geography

    Hargitai, H.

    INTRODUCTION Landscape is one of the most often used category in physical ge- ography. The term "landshap" was introduced by Dutch painters in the 15-16th cen- tury. [1] The elements that build up a landscape (or environment) on Earth consists of natural (biogenic and abiogenic - lithologic, atmospheric, hydrologic) and artificial (antropogenic) factors. Landscape is a complex system of these different elements. The same lithology makes different landscapes under different climatic conditions. If the same conditions are present, the same landscape type will appear. Landscapes build up a hierarchic system and cover the whole surface. On Earth, landscapes can be classified and qualified according to their characteristics: relief forms (morphology), and its potential economic value. Aesthetic and subjective parameters can also be considered. Using the data from landers and data from orbiters we can now classify planetary landscapes (these can be used as geologic mapping units as well). By looking at a unknown landscape, we can determine the processes that created it and its development history. This was the case in the Pathfinder/Sojourner panoramas. [2]. DISCUSSION Planetary landscape evolution. We can draw a raw landscape develop- ment history by adding the different landscape building elements to each other. This has a strong connection with the planet's thermal evolution (age of the planet or the present surface materials) and with orbital parameters (distance from the central star, orbit excentricity etc). This way we can build a complex system in which we use differ- ent evolutional stages of lithologic, atmospheric, hydrologic and biogenic conditions which determine the given - Solar System or exoplanetary - landscape. Landscape elements. "Simple" landscapes can be found on asteroids: no linear horizon is present (not differentiated body, only impact structures), no atmosphere (therefore no atmospheric scattering - black sky as part of the landscape) and no

  14. Spaghetti and noodles. Why is the developing country differentiation landscape so complex?

    Fialho de Oliveira Ramos, Djalita; Bergeijk, Peter

    2013-01-01

    textabstractThe plethora of country classifications that emerged since the star of the 1950s is a remarkable phenomena in the arena of development policymaking. In our sample of country classifications, consisting of classifications for 111 developing countries, the average number of classifications per country is 3.1 at the start of 2013. The developing country differentiation landscape is of staggering complexity. For instance, of the 49 countries categorised as Least Developed Countries (L...

  15. CT-examination and standardization of findings in occupation-related pulmonary and pleural changes using a system adapted from the ILO pneumoconiosis classification of 1980

    Almost 250000 individuals are registered in Germany as being or having been exposed to asbestos fibre dust at their working places. As in other industrial countries, it is generally acknowledged that high-resolution computerized tomography is an indispensable tool in the diagnosis of dust disease, although so far there exist no such instruments as standardized examination techniques or shared classification systems of findings. The system described here was developed to standardize findings from computerized tomography in pneumoconiosis and to provide a better basis for comparisons with the ILO classfications. The letters and symbols introduced merely serve as codes and have no pathohistological meaning so that the system is readily transferable to pulmonary or pleural changes urelated to working place conditions. (orig./UG)

  16. Landscape epidemiology of plant diseases

    Plantegenest, Manuel; Le May, Christophe; Fabre, Frédéric

    2007-01-01

    Many agricultural landscapes are characterized by a high degree of heterogeneity and fragmentation. Landscape ecology focuses on the influence of habitat heterogeneity in space and time on ecological processes. Landscape epidemiology aims at applying concepts and approaches originating from landscape ecology to the study of pathogen dynamics at the landscape scale. However, despite the strong influence that the landscape properties may have on the spread of plant diseases, landscape epidemiol...

  17. Evolution in a rugged fitness landscape

    Flyvbjerg, Henrik; Lautrup, Benny

    1992-11-01

    Kaufman's NK model for genetic evolution and adaption is analyzed for for K=N-1. In this case it describes adaptive walks on random fitness landscapes, and its dynamics is equivalent to the Metropolis algorithm for Derrida's random-energy model at zero temperature. We derive analytical expressions for the average length and duration of adaptive walks, and for the variance about these averages. The results are exact to leading order in N, the number of genes. We also find that the lengths of walks are Poisson distributed to leading order in 1/lnN, and that the duration of walks essentially is exponentially distributed to leading order in 1/N.

  18. An Outline of the Evolution of Rural Cultural Landscapes in Poland

    KRZYSZTOF KORELESKI

    2007-01-01

    Full Text Available The paper outlines the evolution of rural cultural landscapes in Poland against the background of landscape classification. It defines cultural landscape types and subtypes, based on several criteria of landscape classification, such as: genetic, morphological, functional, and economic. A review of rural landscapes, based on genetic criteria, considers the following historical periods: the primeval community, the feudalism, the manorial system, the industrial revolution, the interwar period of 1918 – 1939, the period of socialist economy, and market economy. The processes that most significantly influenced the contemporary shape of the rural landscape occurred just after the Second World War: urbanization and industrialization, settlement in western and northern territories, as well as structural and spatial transformations that took place after the year 1989 related to the promotion of sustainable and multifunctional development of rural areas.

  19. Why Landscape Beauty Matters

    Angelika Krebs

    2014-11-01

    Full Text Available This philosophical paper explores the aesthetic argument for landscape conservation. The main claim is that the experience of beautiful landscapes is an essential part of the good human life. Beautiful landscapes make us feel at home in the world. Their great and irreplaceable value lies therein. To establish this claim, the concepts of landscape and “Stimmung” are clarified. It is shown how “Stimmung” (in the sense of mood is infused into landscape (as atmosphere and how we respond to it aesthetically. We respond by resonating or feeling at home. The paper ends by indicating how art can help us to better appreciate landscape beauty. This is done by way of an example from contemporary nature poetry, Michael Donhauser’s Variationen in Prosa, which begins with “Und was da war, es nahm uns an” (“And what was there accepted us”.

  20. Spatially-Explicit Bayesian Information Entropy Metrics for Calibrating Landscape Transformation Models

    Kostas Alexandridis

    2013-06-01

    Full Text Available Assessing spatial model performance often presents challenges related to the choice and suitability of traditional statistical methods in capturing the true validity and dynamics of the predicted outcomes. The stochastic nature of many of our contemporary spatial models of land use change necessitate the testing and development of new and innovative methodologies in statistical spatial assessment. In many cases, spatial model performance depends critically on the spatially-explicit prior distributions, characteristics, availability and prevalence of the variables and factors under study. This study explores the statistical spatial characteristics of statistical model assessment of modeling land use change dynamics in a seven-county study area in South-Eastern Wisconsin during the historical period of 1963–1990. The artificial neural network-based Land Transformation Model (LTM predictions are used to compare simulated with historical land use transformations in urban/suburban landscapes. We introduce a range of Bayesian information entropy statistical spatial metrics for assessing the model performance across multiple simulation testing runs. Bayesian entropic estimates of model performance are compared against information-theoretic stochastic entropy estimates and theoretically-derived accuracy assessments. We argue for the critical role of informational uncertainty across different scales of spatial resolution in informing spatial landscape model assessment. Our analysis reveals how incorporation of spatial and landscape information asymmetry estimates can improve our stochastic assessments of spatial model predictions. Finally our study shows how spatially-explicit entropic classification accuracy estimates can work closely with dynamic modeling methodologies in improving our scientific understanding of landscape change as a complex adaptive system and process.

  1. Landscape Fragmentation in Iceland

    Einar Hjörleifsson 1988

    2014-01-01

    Landscape fragmentation measurements provide baseline data of direct human influence on landscape and habitat systems through land use. In 2011, the European Environment Agency, the EEA and the Swiss Federal Office for the Environment or FOEN created a comprehensive report on the status of landscape fragmentation in 28 European countries, excluding Iceland. This thesis builds on EEA and FOEN methodology in order to create comparable data for Iceland. The Icelandic data set had to be adjusted ...

  2. Another Paper Landscape?

    Radlak, Ted

    2001-01-01

    Describes the University of Toronto's extensive central campus revitalization plan to create lush landscapes that add to the school's image and attractiveness. Drawings and photographs are included. (GR)

  3. Marginalization and Exclusion: Unraveling Systemic Bias in Classification

    Mai, Jens-Erik

    2016-01-01

    This paper explores the knowledge organization landscape in which Hope Olson’s numerous contri- butions to the field are situated. The paper first explores some of the foundational conceptual notions within knowledge organization that today are well-accepted. The paper then reviews Hope Olson’s c...... large library classification has unraveled the systemic bias found in all classifications. The paper calls for stronger engagement between scholarship and practice to ad- dress marginalization and exclusion in further work on classification systems....

  4. Rural Landscape Anatomy: Public space and civil yards in Dutch rural landscapes of the future

    Roncken, P.A.

    2006-01-01

    Landscape Architecture is still maturing in the Netherlands. It fills gaps left by urban designers and provides integrated design examples that reflect current cultural conditions, yet at the same time this does not necessarily lead to specific and adaptive design strategies. When dealing with the f

  5. Landscape agronomy: A new field for addressing agricultural landscape dynamics

    Marraccini, Élisa; Moonen, Anna Camilla; Galli, Mariassunta; Lardon, Sylvie; Rapey, Hélène; Thenail, Claudine; Bonari, Enrico

    2012-01-01

    Landscape dynamics increasingly challenge agronomists to explain how and why agricultural landscapes are designed and managed by farmers. Nevertheless, agronomy is rarely included in the wide range of disciplines involved in landscape research. In this paper, we describe how landscape agronomy can help explain the relationship between farming systems and agricultural landscape dynamics. For this, we propose a conceptual model of agricultural landscape dynamics that illustrates the specific co...

  6. Strategic Classification

    Hardt, Moritz; Megiddo, Nimrod; Papadimitriou, Christos; Wootters, Mary

    2015-01-01

    Machine learning relies on the assumption that unseen test instances of a classification problem follow the same distribution as observed training data. However, this principle can break down when machine learning is used to make important decisions about the welfare (employment, education, health) of strategic individuals. Knowing information about the classifier, such individuals may manipulate their attributes in order to obtain a better classification outcome. As a result of this behavior...

  7. Flowscapes: Infrastructure as landscape, landscape as infrastructure. Graduation Lab Landscape Architecture 2012/2013

    Nijhuis, S.; Jauslin, D.; de Vries, C.

    2012-01-01

    Flowscapes explores infrastructure as a type of landscape and landscape as a type of infrastructure, and is focused on landscape architectonic design of transportation-, green- and water infrastructures. These landscape infrastructures are considered armatures for urban and rural development. With movement and flows at the core, these landscape infrastructures facilitate aesthetic, functional, social and ecological relationships between natural and human systems. Through transdisciplinary des...

  8. Exploring the fitness landscape of poliovirus

    Bianco, Simone; Acevedo, Ashely; Andino, Raul; Tang, Chao

    2012-02-01

    RNA viruses are known to display extraordinary adaptation capabilities to different environments, due to high mutation rates. Their very dynamical evolution is captured by the quasispecies concept, according to which the viral population forms a swarm of genetic variants linked through mutation, which cooperatively interact at a functional level and collectively contribute to the characteristics of the population. The description of the viral fitness landscape becomes paramount towards a more thorough understanding of the virus evolution and spread. The high mutation rate, together with the cooperative nature of the quasispecies, makes it particularly challenging to explore its fitness landscape. I will present an investigation of the dynamical properties of poliovirus fitness landscape, through both the adoption of new experimental techniques and theoretical models.

  9. A Hierarchical Approach to Forest Landscape Pattern Characterization

    Wang, Jialing; Yang, Xiaojun

    2012-01-01

    Landscape spatial patterns have increasingly been considered to be essential for environmental planning and resources management. In this study, we proposed a hierarchical approach for landscape classification and evaluation by characterizing landscape spatial patterns across different hierarchical levels. The case study site is the Red Hills region of northern Florida and southwestern Georgia, well known for its biodiversity, historic resources, and scenic beauty. We used one Landsat Enhanced Thematic Mapper image to extract land-use/-cover information. Then, we employed principal-component analysis to help identify key class-level landscape metrics for forests at different hierarchical levels, namely, open pine, upland pine, and forest as a whole. We found that the key class-level landscape metrics varied across different hierarchical levels. Compared with forest as a whole, open pine forest is much more fragmented. The landscape metric, such as CONTIG_MN, which measures whether pine patches are contiguous or not, is more important to characterize the spatial pattern of pine forest than to forest as a whole. This suggests that different metric sets should be used to characterize landscape patterns at different hierarchical levels. We further used these key metrics, along with the total class area, to classify and evaluate subwatersheds through cluster analysis. This study demonstrates a promising approach that can be used to integrate spatial patterns and processes for hierarchical forest landscape planning and management.

  10. From climate-smart agriculture to climate-smart landscapes

    Scherr Sara J

    2012-08-01

    Full Text Available Abstract Background For agricultural systems to achieve climate-smart objectives, including improved food security and rural livelihoods as well as climate change adaptation and mitigation, they often need to be take a landscape approach; they must become ‘climate-smart landscapes’. Climate-smart landscapes operate on the principles of integrated landscape management, while explicitly incorporating adaptation and mitigation into their management objectives. Results An assessment of climate change dynamics related to agriculture suggests that three key features characterize a climate-smart landscape: climate-smart practices at the field and farm scale; diversity of land use across the landscape to provide resilience; and management of land use interactions at landscape scale to achieve social, economic and ecological impacts. To implement climate-smart agricultural landscapes with these features (that is, to successfully promote and sustain them over time, in the context of dynamic economic, social, ecological and climate conditions requires several institutional mechanisms: multi-stakeholder planning, supportive landscape governance and resource tenure, spatially-targeted investment in the landscape that supports climate-smart objectives, and tracking change to determine if social and climate goals are being met at different scales. Examples of climate-smart landscape initiatives in Madagascar’s Highlands, the African Sahel and Australian Wet Tropics illustrate the application of these elements in contrasting contexts. Conclusions To achieve climate-smart landscape initiatives widely and at scale will require strengthened technical capacities, institutions and political support for multi-stakeholder planning, governance, spatial targeting of investments and multi-objective impact monitoring.

  11. BATS AND BT INSECT RESISTANCE ON AGRICULTURAL LANDSCAPES

    A landscape model that utilizes land cover classification data, insect life history, insect movement, and bat foraging pressure is developed that addresses the implementation of genetically modified crops in the Winter Garden region of Texas. The principal strategy for delaying r...

  12. Typology of Post-Industrial Landscape in Usti Region

    Kolejka, Jaromír; Klimánek, M.

    Brno: MU Brno, 2012 - (Svobodová, H.), s. 1-19 ISBN 978-80-210-5799-9. [International Conference on Geography and Geoinformatics: Challenge for Practise and Education /19./. Brno (CZ), 08.09.2011-09.09.2011] Institutional support: RVO:68145535 Keywords : post-industrial landscape * survey * genetic classification * depiction Subject RIV: DE - Earth Magnetism, Geodesy, Geography

  13. Qualifying Urban Landscapes

    Clemmensen, Thomas Juel; Nielsen, Tom; Daugaard, Morten

    The article presents an attempt to develop alternatives to the dominant planning and design principles used in building and rebuilding the contemporary urban landscape. The basic idea is that the ‘forces of modernisation’ driving current development might result in a broader and more interesting...... contemporary urban landscape design practice....

  14. Nature and landscape protection

    In accordance with National Council of the Slovak Republic Act N. 287/1994 Coll. on Nature and Landscape Protection, the system of complex nature landscape protection has been designed based on five levels of protection. Categories of protected areas as well as cultural monuments in the Slovak Republic are reviewed.Slovak contribution to the world heritage is included

  15. Towards noise classification of road pavements

    Freitas, Elisabete F.; Paulo, Joel; Coelho, J. L. Bento; Pereira, Paulo A. A.

    2008-01-01

    Noise classification of road surfaces has been addressed in many European countries. This paper presents the first approach towards noise classification of Portuguese road pavements. In this early stage, it aims at establishing guidelines for decision makers to support their noise reduction policies and the development of a classification system adapted to the European recommendations. A ranking to provide guidance on tire-road noise emission levels for immediate use by decisio...

  16. Chapter V: Secondary landscape structure

    This chapter deals with the secondary landscape structure of the Slovak Republic. It consists of next subchapters: (1) Land use pattern; (2) Special landscape structures; (3) Real vegetation. The secondary landscape structure consists of the elements influenced by man, created or recreated. They represent material elements with a particular spatial delimitation in the landscape. Vegetation, above all forest vegetation, is the indispensable part of the secondary landscape structure. Special space was given to the historical landscape structure

  17. Comparison of Adaptive Information Security Approaches

    Antti Evesti; Eila Ovaska

    2013-01-01

    Dynamically changing environments and threat landscapes require adaptive information security. Adaptive information security makes it possible to change and modify security mechanisms at runtime. Hence, all security decisions are not enforced at design-time. This paper builds a framework to compare security adaptation approaches. The framework contains three viewpoints, that is, adaptation, security, and lifecycle. Furthermore, the paper describes five security adaptation approaches and compa...

  18. Geomorpho-Landscapes

    Farabollini, Piero; Lugeri, Francesca; Amadio, Vittorio

    2014-05-01

    Landscape is the object of human perceptions, being the image of spatial organization of elements and structures: mankind lives the first approach with the environment, viewing and feeling the landscape. Many definitions of landscape have been given over time: in this case we refer to the Landscape defined as the result of interaction among physical, biotic and anthropic phenomena acting in a different spatial-temporal scale (Foreman & Godron) Following an Aristotelic approach in studying nature, we can assert that " Shape is synthesis": so it is possible to read the land features as the expression of the endogenous and exogenous processes that mould earth surfaces; moreover, Landscape is the result of the interaction of natural and cultural components, and conditions the spatial-temporal development of a region. The study of the Landscape offers results useful in order to promote sustainable development, ecotourism, enhancement of natural and cultural heritage, popularization of the scientific knowledge. In Italy, a very important GIS-based tool to represent the territory is the "Carta della Natura" ("Map of Nature", presently coordinated by the ISPRA) that aims at assessing the state of the whole Italian territory, analyzing Landscape. The methodology follows a holistic approach, taking into consideration all the components of a landscape and then integrating the information. Each individual landscape, studied at different scales, shows distinctive elements: structural, which depend on physical form and specific spatial organization; functional, which depend on relationships created between biotic and abiotic elements, and dynamic, which depend on the successive evolution of the structure. The identification of the landscape units, recognized at different scales of analysis, allows an evaluation of the state of the land, referring to the dual risk/resource which characterizes the Italian country. An interesting opportunity is to discover those areas of unusual

  19. Automated Stellar Spectral Classification

    Bailer-Jones, Coryn; Irwin, Mike; von Hippel, Ted

    1996-05-01

    Stellar classification has long been a useful tool for probing important astrophysical phenomena. Beyond simply categorizing stars it yields fundamental stellar parameters, acts as a probe of galactic abundance distributions and gives a first foothold on the cosmological distance ladder. The MK system in particular has survived on account of its robustness to changes in the calibrations of the physical parameters. Nonetheless, if stellar classification is to continue as a useful tool in stellar surveys, then it must adapt to keep pace with the large amounts of data which will be acquired as magnitude limits are pushed ever deeper. We are working on a project to automate the multi-parameter classification of visual stellar spectra, using artificial neural networks and other techniques. Our techniques have been developed with 10,000 spectra (B Analysis as a front-end compression of the data. Our continuing work also looks at the application of synthetic spectra to the direct classification of spectra in terms of the physical parameters of Teff, log g, and [Fe/H].

  20. Computerized Classification Testing with the Rasch Model

    Eggen, Theo J. H. M.

    2011-01-01

    If classification in a limited number of categories is the purpose of testing, computerized adaptive tests (CATs) with algorithms based on sequential statistical testing perform better than estimation-based CATs (e.g., Eggen & Straetmans, 2000). In these computerized classification tests (CCTs), the Sequential Probability Ratio Test (SPRT) (Wald,…

  1. How Good Are Statistical Models at Approximating Complex Fitness Landscapes?

    du Plessis, Louis; Leventhal, Gabriel E; Bonhoeffer, Sebastian

    2016-09-01

    Fitness landscapes determine the course of adaptation by constraining and shaping evolutionary trajectories. Knowledge of the structure of a fitness landscape can thus predict evolutionary outcomes. Empirical fitness landscapes, however, have so far only offered limited insight into real-world questions, as the high dimensionality of sequence spaces makes it impossible to exhaustively measure the fitness of all variants of biologically meaningful sequences. We must therefore revert to statistical descriptions of fitness landscapes that are based on a sparse sample of fitness measurements. It remains unclear, however, how much data are required for such statistical descriptions to be useful. Here, we assess the ability of regression models accounting for single and pairwise mutations to correctly approximate a complex quasi-empirical fitness landscape. We compare approximations based on various sampling regimes of an RNA landscape and find that the sampling regime strongly influences the quality of the regression. On the one hand it is generally impossible to generate sufficient samples to achieve a good approximation of the complete fitness landscape, and on the other hand systematic sampling schemes can only provide a good description of the immediate neighborhood of a sequence of interest. Nevertheless, we obtain a remarkably good and unbiased fit to the local landscape when using sequences from a population that has evolved under strong selection. Thus, current statistical methods can provide a good approximation to the landscape of naturally evolving populations. PMID:27189564

  2. A checklist for ecological management of landscapes for conservation.

    Lindenmayer, David; Hobbs, Richard J; Montague-Drake, Rebecca; Alexandra, Jason; Bennett, Andrew; Burgman, Mark; Cale, Peter; Calhoun, Aram; Cramer, Viki; Cullen, Peter; Driscoll, Don; Fahrig, Lenore; Fischer, Joern; Franklin, Jerry; Haila, Yrjo; Hunter, Malcolm; Gibbons, Philip; Lake, Sam; Luck, Gary; MacGregor, Chris; McIntyre, Sue; Nally, Ralph Mac; Manning, Adrian; Miller, James; Mooney, Hal; Noss, Reed; Possingham, Hugh; Saunders, Denis; Schmiegelow, Fiona; Scott, Michael; Simberloff, Dan; Sisk, Tom; Tabor, Gary; Walker, Brian; Wiens, John; Woinarski, John; Zavaleta, Erika

    2008-01-01

    The management of landscapes for biological conservation and ecologically sustainable natural resource use are crucial global issues. Research for over two decades has resulted in a large literature, yet there is little consensus on the applicability or even the existence of general principles or broad considerations that could guide landscape conservation. We assess six major themes in the ecology and conservation of landscapes. We identify 13 important issues that need to be considered in developing approaches to landscape conservation. They include recognizing the importance of landscape mosaics (including the integration of terrestrial and aquatic areas), recognizing interactions between vegetation cover and vegetation configuration, using an appropriate landscape conceptual model, maintaining the capacity to recover from disturbance and managing landscapes in an adaptive framework. These considerations are influenced by landscape context, species assemblages and management goals and do not translate directly into on-the-ground management guidelines but they should be recognized by researchers and resource managers when developing guidelines for specific cases. Two crucial overarching issues are: (i) a clearly articulated vision for landscape conservation and (ii) quantifiable objectives that offer unambiguous signposts for measuring progress. PMID:17927771

  3. Entertainment Landscape Planning

    Jurga Kučinskienė

    2012-03-01

    Full Text Available The entertainment society can not imagine the life without entertainment. It is not enough to a human just to come to an amusement park. He/she wants a theme park which is guided by the need not only for extreme experiences but also the environment that must be formed in such a way that satisfies all the five senses. Sensory stimulators that accompany the experiences have to maintain and enrich its theme. The more senses, the more effective and more memorable experiences, then the bigger part of society will be satisfied. To have such experiences there should be a suitable environment – the entertainment landscape. The article deals with the features of entertainment landscape planning, analyzes the performances of entertainment park and theme park design items; it contains the rules of specific landscape plan used for the entertainment landscape design and the entertainment landscape design techniques. The article is illustrated with the examples of entertainment landscape theme parks and analyzes the significance of entertainment landscape creation in modern experience society.DOI: http://dx.doi.org/10.5755/j01.erem.59.1.659

  4. Integration of architectural design in the landscape

    Dimoska, Aleksandra; Mitanoska, Ana; Sandeva, Vaska; Despot, Katerina; Mitev, Trajce

    2012-01-01

    This paper focuses on integration of the architectural form in the landscape, a topic that is related to the conservation of the environment with ecological and bioclimatic architecture. This report explores the forerunners of sustainable development, energy saving and environmental adaptation, and research developed within the subterranean living to large projects, with particular attention to the formal and practical potential of land use. The paper contains both, modern and historica...

  5. Condensed landscape experience

    Earon, Ofri

    2011-01-01

    demands, quality of space, mixture of functions, urban complexity, public life and cultural heritage. In order to launch such an approach, an understanding of the spatial, social and environmental significance of a radical re-thinking of relationships between architecture and landscape is necessary. This......‘Re-thinking interaction between landscape and urban buildings’ participates in an interdisciplinary discourse about the theoretical and practical advantages of openly juxtaposing landscape and architecture without having one more advanced in importance. Recently, the greenification of buildings is...

  6. Experiencing landscape while walking

    Jakobsson, Anna

    2009-01-01

    The main objective of this thesis is to contribute to a widening of knowledge on spas, on garden design in the late 19th century and on the constituents of landscape heritage. A purpose is to broaden the discussion on landscape heritage, using Ron-neby Spa as an example. The main research questions are how the experience of Ronneby Spa can be studied and described and how the medical spa philosophy and ideas on garden design interplayed when it came to designing the landscape of Ronneby Spa. ...

  7. Elaboration of the third-generation world map of terrestrial landscapes as a model of the landscape sphere of the Earth

    Romanova, Emma; Alexeeva, Nina; Arshinova, Marina; Klimanova, Oksana; Kovaleva, Tatiana; Kondratieva, Tatiana; Alyautdinov, Ali

    2016-04-01

    The first fundamental investigation aimed at the elaboration of the global map of terrestrial landscapes has resulted in a series of maps for the Physical-Geographical Atlas of the World (1964). Typological classification of landscapes and the concept of the zonal differentiation of terrestrial landscapes of the Earth became a basis for the maps of physical-geographical regions of individual continents and the global map of landscape types at the scale of 1:80 Mln. The next stage of research in the sphere of small-scale landscape regionalization and mapping of both natural and natural-anthropogenic landscapes has produced the global maps of Geographical Belts and Zonal Types of Terrestrial Landscapes (1988) and Present-Day Landscapes of the World (1992) at the scale of 1:15 Mln. By the end of the 1990-s similar maps of individual continents were compiled for the Nature and Resources of the Earth digital atlas. Recent decades saw further development of the idea of zone - sector - belt structure of the Earth's landscape sphere which includes several hierarchically subordinated natural-territorial levels. New theoretical studies and emergence of extensive information materials allowed starting the elaboration of a new (third-generation) map at the scales of 1:15 Mln to 1:5 Mln. A new classification of landscape units was suggested basing on the analysis of principal landscape-forming factors (climatic, lithogene and biogenic). A new cartographical model was developed specifying the following hierarchical levels: geographical belts, sectors, natural zones and sub-zones, classes and subclasses of landscapes. Classification criteria used for landscape systematization and mapping include both natural parameters (radiation balance, heat and moisture supply, structure of the vegetative period, biological productivity of vegetation, etc.) and anthropogenic indicators, thus providing for the evaluation of the geoecological state of landscapes (ecosystems of regional dimension

  8. Transporter Classification Database (TCDB)

    U.S. Department of Health & Human Services — The Transporter Classification Database details a comprehensive classification system for membrane transport proteins known as the Transporter Classification (TC)...

  9. Classifying Classification

    Novakowski, Janice

    2009-01-01

    This article describes the experience of a group of first-grade teachers as they tackled the science process of classification, a targeted learning objective for the first grade. While the two-year process was not easy and required teachers to teach in a new, more investigation-oriented way, the benefits were great. The project helped teachers and…

  10. Tissue Classification

    Van Leemput, Koen; Puonti, Oula

    2015-01-01

    Computational methods for automatically segmenting magnetic resonance images of the brain have seen tremendous advances in recent years. So-called tissue classification techniques, aimed at extracting the three main brain tissue classes (white matter, gray matter, and cerebrospinal fluid), are no...... software packages such as SPM, FSL, and FreeSurfer....