A cognitive complexity metric applied to cognitive development

Two experiments tested predictions from a theory in which processing load depends on relational complexity (RC), the number of variables related in a single decision. Tasks from six domains (transitivity, hierarchical classification, class inclusion, cardinality, relative-clause sentence comprehension, and hypothesis testing) were administered to children aged 3-8 years. Complexity analyses indicated that the domains entailed ternary relations (three variables). Simpler binary-relation (two variables) items were included for each domain. Thus RC was manipulated with other factors tightly controlled. Results indicated that (i) ternary-relation items were more difficult than comparable binary-relation items, (ii) the RC manipulation was sensitive to age-related changes, (iii) ternary relations were processed at a median age of 5 years, (iv) cross-task correlations were positive, with all tasks loading on a single factor (RC), (v) RC factor scores accounted for 80% (88%) of age-related variance in fluid intelligence (compositionality of sets), (vi) binary- and ternary-relation items formed separate complexity classes, and (vii) the RC approach to defining cognitive complexity is applicable to different content domains. (C) 2002 Elsevier Science (USA). All rights reserved.

Fitting genetic models to twin data with binary and ordered categorical responses: A comparison of structural equation modelling and Bayesian hierarchical models

We compare Bayesian methodology utilizing free-ware BUGS (Bayesian Inference Using Gibbs Sampling) with the traditional structural equation modelling approach based on another free-ware package, Mx. Dichotomous and ordinal (three category) twin data were simulated according to different additive genetic and common environment models for phenotypic variation. Practical issues are discussed in using Gibbs sampling as implemented by BUGS to fit subject-specific Bayesian generalized linear models, where the components of variation may be estimated directly. The simulation study (based on 2000 twin pairs) indicated that there is a consistent advantage in using the Bayesian method to detect a correct model under certain specifications of additive genetics and common environmental effects. For binary data, both methods had difficulty in detecting the correct model when the additive genetic effect was low (between 10 and 20%) or of moderate range (between 20 and 40%). Furthermore, neither method could adequately detect a correct model that included a modest common environmental effect (20%) even when the additive genetic effect was large (50%). Power was significantly improved with ordinal data for most scenarios, except for the case of low heritability under a true ACE model. We illustrate and compare both methods using data from 1239 twin pairs over the age of 50 years, who were registered with the Australian National Health and Medical Research Council Twin Registry (ATR) and presented symptoms associated with osteoarthritis occurring in joints of the hand.

Selecting methods to solve multi-well master equations

There are several competing methods commonly used to solve energy grained master equations describing gas-phase reactive systems. When it comes to selecting an appropriate method for any particular problem, there is little guidance in the literature. In this paper we directly compare several variants of spectral and numerical integration methods from the point of view of computer time required to calculate the solution and the range of temperature and pressure conditions under which the methods are successful. The test case used in the comparison is an important reaction in combustion chemistry and incorporates reversible and irreversible bimolecular reaction steps as well as isomerizations between multiple unimolecular species. While the numerical integration of the ODE with a stiff ODE integrator is not the fastest method overall, it is the fastest method applicable to all conditions.

Environmental exposure to toxicants mixtures from a multi-purpose estuary: perspectives for a biomonitoring study in Portugal

This work was focused on a multi-purpose estuarine environment (river Sado estuary, SW Portugal) around which a number of activities (e.g., fishing, farming, heavy industry, tourism and recreational activities) coexist with urban centres with a total of about 200 000 inhabitants. Based on previous knowledge of the hazardous chemicals within the ecosystem and their potential toxicity to benthic species, this project intended to evaluate the impact of estuarine contaminants on the human and ecosystem health. An integrative methodology based on epidemiological, analytical and biological data and comprising several lines of evidence, namely, human contamination pathways, human health effects, consumption of local produce, estuarine sediments, wells and soils contamination, effects on commercial benthic organisms, and genotoxic potential of sediments, was used. The epidemiological survey confirmed the occurrence of direct and indirect (through food chain) exposure of the local population to estuarine contaminants. Furthermore, the complex mixture of contaminants (e.g., metals, pesticides, polycyclic aromatic hydrocarbons) trapped in the estuary sediments was toxic to human liver cells exposed in vitro, causing cell death, oxidative stress and genotoxic effects that might constitute a risk factor for the development of chronic-degenerative diseases, on the long term. Finally, the integration of data from several endpoints indicated that the estuary is moderately impacted by toxicants that affect also the aquatic biota. Nevertheless, the human health risk can only be correctly assessed through a biomonitoring study including the quantification of contaminants (or metabolites) in biological fluids as well as biomarkers of early biological effects (e.g., biochemical, genetic and omics-based endpoints) and genetic susceptibility in the target population. Data should be supported by a detailed survey to assess the impact of the contaminated seafood and local farm products consumption on human health and, particularly, on metabolic diseases or cancer development.

Response of a multi-domain continental margin to compression: Study from seismic reflection-refraction and numerical modelling in the Tagus Abyssal Plain

The effects of the Miocene through Present compression in the Tagus Abyssal Plain are mapped using the most up to date available to scientific community multi-channel seismic reflection and refraction data. Correlation of the rift basin fault pattern with the deep crustal structure is presented along seismic line IAM-5. Four structural domains were recognized. In the oceanic realm mild deformation concentrates in Domain I adjacent to the Tore-Madeira Rise. Domain 2 is characterized by the absence of shortening structures, except near the ocean-continent transition (OCT), implying that Miocene deformation did not propagate into the Abyssal Plain, In Domain 3 we distinguish three sub-domains: Sub-domain 3A which coincides with the OCT, Sub-domain 3B which is a highly deformed adjacent continental segment, and Sub-domain 3C. The Miocene tectonic inversion is mainly accommodated in Domain 3 by oceanwards directed thrusting at the ocean-continent transition and continentwards on the continental slope. Domain 4 corresponds to the non-rifted continental margin where only minor extensional and shortening deformation structures are observed. Finite element numerical models address the response of the various domains to the Miocene compression, emphasizing the long-wavelength differential vertical movements and the role of possible rheologic contrasts. The concentration of the Miocene deformation in the transitional zone (TC), which is the addition of Sub-domain 3A and part of 3B, is a result of two main factors: (1) focusing of compression in an already stressed region due to plate curvature and sediment loading; and (2) theological weakening. We estimate that the frictional strength in the TC is reduced in 30% relative to the surrounding regions. A model of compressive deformation propagation by means of horizontal impingement of the middle continental crust rift wedge and horizontal shearing on serpentinized mantle in the oceanic realm is presented. This model is consistent with both the geological interpretation of seismic data and the results of numerical modelling.

RFID together with multi-agent systems to control global value chains

Nowadays, the cooperative intelligent transport systems are part of a largest system. Transportations are modal operations integrated in logistics and, logistics is the main process of the supply chain management. The supply chain strategic management as a simultaneous local and global value chain is a collaborative/cooperative organization of stakeholders, many times in co-opetition, to perform a service to the customers respecting the time, place, price and quality levels. The transportation, like other logistics operations must add value, which is achieved in this case through compression lead times and order fulfillments. The complex supplier's network and the distribution channels must be efficient and the integral visibility (monitoring and tracing) of supply chain is a significant source of competitive advantage. Nowadays, the competition is not discussed between companies but among supply chains. This paper aims to evidence the current and emerging manufacturing and logistics system challenges as a new field of opportunities for the automation and control systems research community. Furthermore, the paper forecasts the use of radio frequency identification (RFID) technologies integrated into an information and communication technologies (ICT) framework based on distributed artificial intelligence (DAI) supported by a multi-agent system (MAS), as the most value advantage of supply chain management (SCM) in a cooperative intelligent logistics systems. Logistical platforms (production or distribution) as nodes of added value of supplying and distribution networks are proposed as critical points of the visibility of the inventory, where these technological needs are more evident.

Phase diagrams of binary mixtures of patchy colloids with distinct numbers and types of patches: Theempty fluid regime

We investigate the effect of distinct bonding energies on the onset of criticality of low functionality fluid mixtures. We focus on mixtures ofparticles with two and three patches as this includes the mixture where "empty" fluids were originally reported. In addition to the number of patches, thespecies differ in the type of patches or bonding sites. For simplicity, we consider that the patches on each species are identical: one species has threepatches of type A and the other has two patches of type B. We have found a rich phase behavior with closed miscibility gaps, liquid-liquid demixing, and negative azeotropes. Liquid-liquid demixing was found to pre-empt the "empty" fluid regime, of these mixtures, when the AB bonds are weaker than the AA or BB bonds. By contrast, mixtures in this class exhibit "empty" fluid behavior when the AB bonds are stronger than at least one of the other two. Mixtureswith bonding energies epsilon(BB) = epsilon(AB) and epsilon(AA) < epsilon(BB), were found to exhibit an unusual negative azeotrope. (C) 2011 American Institute of Physics. [doi:10.1063/1.3561396]

Multi-dimensional scaling applied to histogram-based DNA analysis

This paper aims to study the relationships between chromosomal DNA sequences of twenty species. We propose a methodology combining DNA-based word frequency histograms, correlation methods, and an MDS technique to visualize structural information underlying chromosomes (CRs) and species. Four statistical measures are tested (Minkowski, Cosine, Pearson product-moment, and Kendall τ rank correlations) to analyze the information content of 421 nuclear CRs from twenty species. The proposed methodology is built on mathematical tools and allows the analysis and visualization of very large amounts of stream data, like DNA sequences, with almost no assumptions other than the predefined DNA “word length.” This methodology is able to produce comprehensible three-dimensional visualizations of CR clustering and related spatial and structural patterns. The results of the four test correlation scenarios show that the high-level information clusterings produced by the MDS tool are qualitatively similar, with small variations due to each correlation method characteristics, and that the clusterings are a consequence of the input data and not method’s artifacts.

Multi-apprentice learning for meta-heuristics parameter tuning in a multi agent scheduling system

The scheduling problem is considered in complexity theory as a NP-hard combinatorial optimization problem. Meta-heuristics proved to be very useful in the resolution of this class of problems. However, these techniques require parameter tuning which is a very hard task to perform. A Case-based Reasoning module is proposed in order to solve the parameter tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance.

Sensing Cloud Optimization to Solve ED of Units with Valve-Point Effects and Multi-fuels

In this paper a solution to an highly constrained and non-convex economical dispatch (ED) problem with a meta-heuristic technique named Sensing Cloud Optimization (SCO) is presented. The proposed meta-heuristic is based on a cloud of particles whose central point represents the objective function value and the remaining particles act as sensors "to fill" the search space and "guide" the central particle so it moves into the best direction. To demonstrate its performance, a case study with multi-fuel units and valve- point effects is presented.

The usefulness of ultrasound in the classification of chronic liver disease

Chronic Liver Disease is a progressive, most of the time asymptomatic, and potentially fatal disease. In this paper, a semi-automatic procedure to stage this disease is proposed based on ultrasound liver images, clinical and laboratorial data. In the core of the algorithm two classifiers are used: a k nearest neighbor and a Support Vector Machine, with different kernels. The classifiers were trained with the proposed multi-modal feature set and the results obtained were compared with the laboratorial and clinical feature set. The results showed that using ultrasound based features, in association with laboratorial and clinical features, improve the classification accuracy. The support vector machine, polynomial kernel, outperformed the others classifiers in every class studied. For the Normal class we achieved 100% accuracy, for the chronic hepatitis with cirrhosis 73.08%, for compensated cirrhosis 59.26% and for decompensated cirrhosis 91.67%.

Constraint solving over multi-valued logics - application to digital circuits

Due to usage conditions, hazardous environments or intentional causes, physical and virtual systems are subject to faults in their components, which may affect their overall behaviour. In a ‘black-box’ agent modelled by a set of propositional logic rules, in which just a subset of components is externally visible, such faults may only be recognised by examining some output function of the agent. A (fault-free) model of the agent’s system provides the expected output given some input. If the real output differs from that predicted output, then the system is faulty. However, some faults may only become apparent in the system output when appropriate inputs are given. A number of problems regarding both testing and diagnosis thus arise, such as testing a fault, testing the whole system, finding possible faults and differentiating them to locate the correct one. The corresponding optimisation problems of finding solutions that require minimum resources are also very relevant in industry, as is minimal diagnosis. In this dissertation we use a well established set of benchmark circuits to address such diagnostic related problems and propose and develop models with different logics that we formalise and generalise as much as possible. We also prove that all techniques generalise to agents and to multiple faults. The developed multi-valued logics extend the usual Boolean logic (suitable for faultfree models) by encoding values with some dependency (usually on faults). Such logics thus allow modelling an arbitrary number of diagnostic theories. Each problem is subsequently solved with CLP solvers that we implement and discuss, together with a new efficient search technique that we present. We compare our results with other approaches such as SAT (that require substantial duplication of circuits), showing the effectiveness of constraints over multi-valued logics, and also the adequacy of a general set constraint solver (with special inferences over set functions such as cardinality) on other problems. In addition, for an optimisation problem, we integrate local search with a constructive approach (branch-and-bound) using a variety of logics to improve an existing efficient tool based on SAT and ILP.

ROM-less RNS-to-binary converter moduli {22N − 1, 22N + 1, 2N − 3, 2N + 3}

In this paper, a novel ROM-less RNS-to-binary converter is proposed, using a new balanced moduli set {22n-1, 22n + 1, 2n-3, 2n + 3} for n even. The proposed converter is implemented with a two stage ROM-less approach, which computes the value of X based only in arithmetic operations, without using lookup tables. Experimental results for 24 to 120 bits of Dynamic Range, show that the proposed converter structure allows a balanced system with 20% faster arithmetic channels regarding the related state of the art, while requiring similar area resources. This improvement in the channel's performance is enough to offset the higher conversion costs of the proposed converter. Furthermore, up to 20% better Power-Delay-Product efficiency metric can be achieved for the full RNS architecture using the proposed moduli set. © 2014 IEEE.

Design of a laminated composite multi-cel structure subjected to torsion

Laminate composite multi-cell structures have to support both axial and shear stresses when sustaining variable twist. Thus the properties and design of the laminate may not be the most adequate at all cross-sections to support the torsion imposed on the cells. In this work, the effect of some material and geometric parameters on the optimal mechanical behaviour of a multi-cell composite laminate structure is studied when torsion is present. A particle swarm optimization technique is used to maximize the multi-cell structure torsion constant that can be used to obtain the angle of twist of the composite laminate profile.

Wetland Habitat Studies using various Classification Techniques on Multi-Spectral Landsat Imagery: Case study: Tram chim National Park, Dong Thap Vietnam

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies