Measure Of Structural Complexity Research Articles

A novel photographic approach for monitoring the structural heterogeneity and diversity of grassland ecosystems

Aims Studies that investigate the space-filling heterogeneity of biological structures in plant communities remain scarce. The main objective of this study was to evaluate the relationship between newly developed photographic measures of structural heterogeneity in digital images and plant species composition in the context of a long-term grassland experiment. Methods We tested a close-range photographic protocol using measures of structural heterogeneity in gray-tone images, namely mean infor mation gain (MIG) and spatial anisotropy, to assess differences in the compositional (species richness) and functional characteristics (plant height and flowering) of 78 managed grassland communities. We also implemented a random placement model of community assembly to explore the links between our measures of structural complexity and the geometric pattern of plant communities. Important Findings MIG and spatial anisotropy correlated with the growth and species richness of grassland communities. Simulations showed that structural heterogeneity in gray-tone digital images is a function of the size distribution and orientation pattern of plant modules. This easy, fast and non-destructive methodological approach could eventually serve to monitor the diversity and integrity of various ecosystems at different resolutions across space and time.

ChemInform Abstract: Development of Structural Complexity by Liquid‐Crystal Self‐Assembly

AbstractReview: <415 refs.

A Granular Hierarchical Multiview Metrics Suite for Statecharts Quality

This paper presents a bottom-up approach for a multiview measurement of statechart size, topological properties, and internal structural complexity for understandability prediction and assurance purposes. It tackles the problem at different conceptual depths or equivalently at several abstraction levels. The main idea is to study and evaluate a statechart at different levels of granulation corresponding to different conceptual depth levels or levels of details. The higher level corresponds to a flat process view diagram (depth = 0), the adequate upper depth limit is determined by the modelers according to the inherent complexity of the problem under study and the level of detail required for the situation at hand (it corresponds to the all states view). For purposes of measurement, we proceed using bottom-up strategy starting with all state view diagram, identifying and measuring its deepest composite states constituent parts and then gradually collapsing them to obtain the next intermediate view (we decrement depth) while aggregating measures incrementally, until reaching the flat process view diagram. To this goal we first identify, define, and derive a relevant metrics suite useful to predict the level of understandability and other quality aspects of a statechart, and then we propose a fuzzy rule-based system prototype for understandability prediction, assurance, and for validation purposes.

Development of Structural Complexity by Liquid‐Crystal Self‐assembly

Since the discovery of the liquid-crystalline state of matter 125 years ago, this field has developed into a scientific area with many facets. This Review presents recent developments in the molecular design and self-assembly of liquid crystals. The focus is on new exciting soft-matter structures distinct from the usually observed nematic, smectic, and columnar phases. These new structures have enhanced complexity, including multicompartment and cellular structures, periodic and quasiperiodic arrays of spheres, and new emergent properties, such as ferroelctricity and spontaneous achiral symmetry-breaking. Comparisons are made with developments in related fields, such as self-assembled monolayers, multiblock copolymers, and nanoparticle arrays. Measures of structural complexity used herein are the size of the lattice, the number of distinct compartments, the dimensionality, and the logic depth of the resulting supramolecular structures.

∊-Machine spectral reconstruction theory: a direct method for inferring planar disorder and structure from X-ray diffraction studies

In previous publications [Varn et al. (2002). Phys. Rev. B, 66, 174110; Varn et al. (2007). Acta Cryst. B63, 169-182] we introduced and applied a new technique for discovering and describing planar disorder in close-packed structures directly from their diffraction patterns. Here, we provide the theoretical development behind those results, adapting computational mechanics to describe one-dimensional structure in materials. We show that the resulting statistical model of the stacking structure - called the ε-machine - allows the calculation of measures of memory, structural complexity and configurational entropy. The methods developed here can be adapted to a wide range of experimental systems in which power spectra data are available.

Extending the information-theoretic measures of the dynamic complexity of manufacturing systems

This paper revisits and extends the structural and dynamic complexity measures of manufacturing systems, which are based on an information-theoretic interpretation of the amount of information that is needed to describe the state of a manufacturing system. In this paper, a generic manufacturing system is modelled as a set of interacting resources and queues. At any moment in time, the combined specific states of the resources and queues define the overall state of the manufacturing system. The main contribution of this paper is a set of dynamic complexity measures that quantify the rate at which information is generated by the facility, and which may be applied with or without reference to the facility's schedule. A worked example demonstrates the step-by-step calculations to obtain values of the complexity measures, and guidance is provided on how to obtain the data needed to carry out the calculations. The links between these complexity measures and the behaviour of a facility are identified and described.

Measuring the impact of changes to the complexity and coupling properties of automotive software systems

BackgroundIn the past few decades exponential increase in the amount of software used in cars has been recorded together with enhanced requirements for functional safety of their embedded software. As the evolution of software systems in cars often entails changes to software architecture, it is important to be able to monitor their impact. MethodWe conducted a case study on a distributed software system in cars at Volvo Car Corporation with the goal to develop, apply and evaluate measures of complexity and coupling which could support software architects in monitoring changes. ResultsThe results showed that two metrics – structural complexity and coupling measures – can guide architectural work and turn attention of architects to most complex subsystems. The results were confirmed by monitoring a complete electrical system of a vehicle under two releases. ConclusionBy applying the metrics after each significant change in the architecture, it is possible to verify that certain quality attributes have not deteriorated and to identify new testing areas. Using these metrics increases the product quality with respect to stability, reliability, and maintainability and also has potential to reduce long-term software development/maintenance costs.

Digraph complexity measures and applications in formal language theory

Automata, Logic and Semantics We investigate structural complexity measures on digraphs, in particular the cycle rank. This concept is intimately related to a classical topic in formal language theory, namely the star height of regular languages. We explore this connection, and obtain several new algorithmic insights regarding both cycle rank and star height. Among other results, we show that computing the cycle rank is NP-complete, even for sparse digraphs of maximum outdegree 2. Notwithstanding, we provide both a polynomial-time approximation algorithm and an exponential-time exact algorithm for this problem. The former algorithm yields an O((log n)^(3/2))- approximation in polynomial time, whereas the latter yields the optimum solution, and runs in time and space O*(1.9129^n) on digraphs of maximum outdegree at most two. Regarding the star height problem, we identify a subclass of the regular languages for which we can precisely determine the computational complexity of the star height problem. Namely, the star height problem for bideterministic languages is NP-complete, and this holds already for binary alphabets. Then we translate the algorithmic results concerning cycle rank to the bideterministic star height problem, thus giving a polynomial-time approximation as well as a reasonably fast exact exponential algorithm for bideterministic star height.

Information-based measures of structural complexity: application to fluorite-related structures

Topological complexity of fluorite-related structures consisting of edge- and corner-sharing tetrahedra is investigated using information-based complexity measures: topological information content, IG, normalized topological information content, IG,norm, vertex-degree distribution information content, Ivd, and average edge complexity, Ea. The IG and IG,norm parameters are quite sensitive to complexity of structural units and work well for homologous series as demonstrated for finite clusters and multiple chains. In addition, these parameters have high discriminative power and can be used for identification of structural units and crystal structures in general. The Ivd and Ea parameters are additional useful measures of complexity that are sensitive to the coordination numbers of particular atoms within the structural units.

Invention of Prediction Structures and Categorization of Robust MPC Syntheses

This plenary paper is concerned with robust model predictive control (MPC) synthesis. In particular, the novel notion of prediction structures is introduced, and then utilized to derive a precise and compact overview of the existing robust MPC (RMPC) syntheses as well as to indicate direct improvements of their system theoretic properties. The prediction structures paradigm allows for a systematic, implementation and examples independent, comparison and classification of the existing RMPC syntheses. The corresponding categorization of the currently available RMPC syntheses is derived by: (i) introducing the adequate indices as measures of structural (including topological and system theoretic) properties and computational complexity, and (ii) analysing the trade–off between the guaranteed structural properties and the necessary computational complexity. The associated analysis is based on the classical game and utility theory notions; for simplicity, it is delivered by deploying the aggregated structural property and computational complexity indices; furthermore, it is complemented with an unambiguous analysis of the “holy–grail” trade–off between the quality of structural properties and the degree of computational complexity.

Preference of early juveniles of a coral reef fish for ­distinct lagoonal microhabitats is not related to common measures of structural complexity

Coral reef populations of a variety of fish and invertebrate species are replenished by individuals that use inshore coastal habitats as temporary juvenile habitats. These habitats vary greatly in their architecture, and different characteristics of structure could play a role in their selection and utilization by resident fauna. To solely investigate the role of structural complexity in microhabitat selection, in situ habitat preference of 48 individuals of the early juvenile stage of a common reef fish (Haemulon flavolineatum) was studied for 4 structurally very different lagoonal microhabitats (i.e. mangrove, seagrass, rubble, coral), using a multiple-choice experiment in field enclosures. This fish species was selected as it utilizes these habitats during different parts of its life cycle. The structural complexity of each microhabitat was changed in each replicate experiment and assessed on the basis of 7 commonly used measures of structure using digitized photographs. We tested the hypothesis that in isolation of other factors, fish prefer the structurally most complex microhabitat that is available, independent of habitat type. However, fish always preferred seagrass and coral microhabitats even when offered at low complexity, and this choice was rather consistent over a 24 h time period. Structural characteristics appeared to be marginally important for the seagrass microhabitat only. Therefore, the differential preference for distinct lagoonal microhabitats does not appear to be driven by measures of structural complexity that are known to be important at the level of individual habitat types. In this light, continuing loss of coral and seagrass habitats in lagoonal environments due to anthropogenic effects is alarming as it affects preferential habitat of certain stages of the life cycle of fishes.

Paradoxical Relationship between Chromosomal Instability and Survival Outcome in Cancer

Chromosomal instability (CIN) is associated with poor prognosis in human cancer. However, in certain animal tumor models elevated CIN negatively impacts upon organism fitness, and is poorly tolerated by cancer cells. To better understand this seemingly contradictory relationship between CIN and cancer cell biological fitness and its relationship with clinical outcome, we applied the CIN70 expression signature, which correlates with DNA-based measures of structural chromosomal complexity and numerical CIN in vivo, to gene expression profiles of 2,125 breast tumors from 13 published cohorts. Tumors with extreme CIN, defined as the highest quartile CIN70 score, were predominantly of the estrogen receptor negative (ER(-)), basal-like phenotype and displayed the highest chromosomal structural complexity and chromosomal numerical instability. We found that the extreme CIN/ER(-) tumors were associated with improved prognosis relative to tumors with intermediate CIN70 scores in the third quartile. We also observed this paradoxical relationship between CIN and prognosis in ovarian, gastric, and non-small cell lung cancer, with poorest outcome in tumors with intermediate, rather than extreme, CIN70 scores. These results suggest a nonmonotonic relationship between gene signature expression and HR for survival outcome, which may explain the difficulties encountered in the identification of prognostic expression signatures in ER(-) breast cancer. Furthermore, the data are consistent with the intolerance of excessive CIN in carcinomas and provide a plausible strategy to define distinct prognostic patient cohorts with ER(-) breast cancer. Inclusion of a surrogate measurement of CIN may improve cancer risk stratification and future therapeutic approaches.

Fractional order analysis of Sephadex gel structures: NMR measurements reflecting anomalous diffusion

We report the appearance of anomalous water diffusion in hydrophilic Sephadex gels observed using pulse field gradient (PFG) nuclear magnetic resonance (NMR). The NMR diffusion data was collected using a Varian 14.1 Tesla imaging system with a home-built RF saddle coil. A fractional order analysis of the data was used to characterize heterogeneity in the gels for the dynamics of water diffusion in this restricted environment. Several recent studies of anomalous diffusion have used the stretched exponential function to model the decay of the NMR signal, i.e., exp[−( bD) α ], where D is the apparent diffusion constant, b is determined the experimental conditions (gradient pulse separation, durations and strength), and α is a measure of structural complexity. In this work, we consider a different case where the spatial Laplacian in the Bloch–Torrey equation is generalized to a fractional order model of diffusivity via a complexity parameter, β, a space constant, μ, and a diffusion coefficient, D. This treatment reverts to the classical result for the integer order case. The fractional order decay model was fit to the diffusion-weighted signal attenuation for a range of b-values (0 < b < 4000 s mm −2). Throughout this range of b values, the parameters β, μ and D, were found to correlate with the porosity and tortuosity of the gel structure.

Mining Roles with Multiple Objectives

With the growing adoption of Role-Based Access Control (RBAC) in commercial security and identity management products, how to facilitate the process of migrating a non-RBAC system to an RBAC system has become a problem with significant business impact. Researchers have proposed to use data mining techniques to discover roles to complement the costly top-down approaches for RBAC system construction. An important problem is how to construct RBAC systems with low complexity. In this article, we define the notion of weighted structural complexity measure and propose a role mining algorithm that mines RBAC systems with low structural complexity. Another key problem that has not been adequately addressed by existing role mining approaches is how to discover roles with semantic meanings. In this article, we study the problem in two primary settings with different information availability. When the only information is user-permission relation, we propose to discover roles whose semantic meaning is based on formal concept lattices. We argue that the theory of formal concept analysis provides a solid theoretical foundation for mining roles from a user-permission relation. When user-attribute information is also available, we propose to create roles that can be explained by expressions of user-attributes. Since an expression of attributes describes a real-world concept, the corresponding role represents a real-world concept as well. Furthermore, the algorithms we propose balance the semantic guarantee of roles with system complexity. Finally, we indicate how to create a hybrid approach combining top-down candidate roles. Our experimental results demonstrate the effectiveness of our approaches.

GEO-MATHEMATICAL MODELLING OF SPATIAL-ECOLOGICAL COMPLEX SYSTEMS: AN EVALUATION

Assessing the complexity of landscapes is one of the top research priorities for Physical Geography and Ecology. This paper aims at a methodological evaluation of the discrete and analytical mathematical models hitherto available for quantitative assessments of spatial ecological complex systems. These models are derived from cellular automata and nonlinear dynamics. They describe complex features and processes in landscapes, such as spatial ecological nonlinear interactions, unpredictability and chaos, self-organization and pattern formation. Beginning with a distinction between two basic types of spatial ecological complexity (structural, functional), and after reviewing the quantitative methods so far available to assess it, the areas where the major challenges (and hence, difficulties) for future research arise are identified. These are: a) to develop measures of structural spatial-ecological complexity, b) to find Lyapunov functions for dynamical systems describing spatial interactions on the landscape (and related attractors), and c) to combine discrete time and continuous spatial data and models.

Structural complexity in digital images as an ecological indicator for monitoring forest dynamics across scale, space and time

Abstract The development of ecological indicators for actively monitoring an ecosystem at a high resolution in scale, space and time is a challenge of primary interest. In this context, measures of structural complexity derived from close-range repeat photography may form a part of the solution. Moreover, recent mathematical tools, such as recurrence plots and recurrence quantification analysis (RP-RQA), are becoming accessible for characterizing the multivariate dynamics of natural systems given short, stochastic and non-stationary series. In this study, a total of 9360 grey-level digital images were recorded on a weekly basis across 72 sites in an old-growth forest ecosystem and analyzed for structural complexity. Structural complexity was assessed using an information theoretic measure (mean information gain). The effect of the scene scale on the observed dynamics was verified across a gradient of forest descriptors and light habitats. Multiscale dynamics responded nonlinearly to changes in scene scale, whereas seasonal trends in structural complexity showed a range of deterministic and stochastic behaviours. The determinism of multiscale time-series was related to sapling density, tree cover, and tree species richness. The sensitivity and flexibility of the RP-RQA approach applied to proxy measures of structural complexity in digital images forms an efficient methodology which might be used for actively monitoring forest ecosystems. This field study is one of the first to demonstrate that old-growth forest ecosystems behave like complex systems exhibiting nonlinear vegetation structure and dynamics across scales.

Three-dimensional quantification of structures in trabecular bone using measures of complexity

The study of pathological changes of bone is an important task in diagnostic procedures of patients with metabolic bone diseases such as osteoporosis as well as in monitoring the health state of astronauts during long-term space flights. The recent availability of high-resolution three-dimensional (3D) imaging of bone challenges the development of data analysis techniques able to assess changes of the 3D microarchitecture of trabecular bone. We introduce an approach based on spatial geometrical properties and define structural measures of complexity for 3D image analysis. These measures evaluate different aspects of organization and complexity of 3D structures, such as complexity of its surface or shape variability. We apply these measures to 3D data acquired by high-resolution microcomputed tomography (microCT) from human proximal tibiae and lumbar vertebrae at different stages of osteoporotic bone loss. The outcome is compared to the results of conventional static histomorphometry and exhibits clear relationships between the analyzed geometrical features of trabecular bone and loss of bone density, but also indicate that the measures reveal additional information about the structural composition of bone, which were not revealed by the static histomorphometry. Finally, we have studied the dependency of the developed measures of complexity on the spatial resolution of the microCT data sets.

MLU and IPSyn measuring absolute complexity

This article compares the results of Mean Length of Utterance (MLU) and Index of Productive Syntax (IPSyn) with the structural complexity of spontaneous utterances produced by 30-month-old Finnish children in a semi-structured playing situation. The comparison was carried out in order to determine the aspects of structural complexity which can be detected with MLU and IPSyn. This research adopts the frameworks of absolute complexity together with a multidimensional view of utterance structure and, furthermore, applies it through Utterance Analysis (UA). The results of the comparison between the metrics and changes in structural complexity discovered by UA reveal that MLU and IPSyn do function as measures of structural complexity but only if used in close relation to each other. Because they focus on different aspects of utterances, the results of both metrics should be interpreted in relation to one another. DOI: http://dx.doi.org/10.5128/ERYa5.11

Global Reform of Personal Income Taxation, 1981-2005: Evidence from 189 Countries

In this paper we use a panel of 189 countries to describe the salient trends that have emerged in national personal income tax systems spanning the twenty five year period from 1981 to 2005. Using complete national income tax schedules with statutory rates, thresholds, country-specific tax formulas and other information, we calculate actual average and marginal tax rates at different income levels as well as time-varying measures of structural progressivity and complexity of national tax systems. Overall, our analysis shows a significant transition from complex, graduated tax schedules that featured multiple tax brackets and stair stepped tax rates to simpler, flatter tax schedules distinguished by fewer tax brackets and lower rates. The emerging trend toward flat tax schedules is also highlighted. We show that frequent alterations of tax structures have diminished the overall progressivity and complexity of national tax systems; however, the degree of this change varies considerably by country group.

The organization of intrinsic computation: Complexity-entropy diagrams and the diversity of natural information processing

Intrinsic computation refers to how dynamical systems store, structure, and transform historical and spatial information. By graphing a measure of structural complexity against a measure of randomness, complexity-entropy diagrams display the different kinds of intrinsic computation across an entire class of systems. Here, we use complexity-entropy diagrams to analyze intrinsic computation in a broad array of deterministic nonlinear and linear stochastic processes, including maps of the interval, cellular automata, and Ising spin systems in one and two dimensions, Markov chains, and probabilistic minimal finite-state machines. Since complexity-entropy diagrams are a function only of observed configurations, they can be used to compare systems without reference to system coordinates or parameters. It has been known for some time that in special cases complexity-entropy diagrams reveal that high degrees of information processing are associated with phase transitions in the underlying process space, the so-called "edge of chaos." Generally, though, complexity-entropy diagrams differ substantially in character, demonstrating a genuine diversity of distinct kinds of intrinsic computation.