Studies on blends of LLDPE and ethylene-methacrylic acid random copolymer

Blends of linear low-density polyethylene (LLDPE) and poly(ethylene-co-methacrylic acid) (EMA) random copolymer were studied by differential scanning calorimetry (DSC), wide-angle X-ray diffraction (WAXD), and excimer fluorescence. In binary blends, crystallization of EMA was studied, and no modification of crystal structure was detected. In excimer fluorescence measurements, emission intensities of blends of EMA and naphthalene-labeled LLDPE were measured. The ratio of the excimer emission intensity (I-D) to the emission intensity of the isolated "monomer" (I-M) decreases upon addition of EMA, indicating that PE segments of EMA interpenetrate into the amorphous phase of LLDPE. (C) 1998 Published by Elsevier Science Ltd,. All rights reserved.

Studies on blends of LLDPE and polar polymers compatibilized by a random copolymer

Compatibilization of blends of Linear low-density polyethylene (LLDPE)-poly(methyl methacrylate) (PMMA) and LLDPE-copolymer of methyl methacrylate (MMA) and 4-vinylpyridine (poly(MMA-co-4VP) with poly(ethylene-co-methacrylic acid) (EMAA) have been studied. Mechanical properties of the LLDPE-PMMA blends increase upon addition of EMAA. In order to further improve interfacial adhesion of LLDPE and PMMA, 4-vinyl pyridine units are introduced into PMMA chains, or poly(MMA-co-4VP) is used as the polar polymer. In LLDPE-poly(MMA-co-4VP)-EMAA blends, interaction of MAA in EMAA with 4VP of poly(MMA-co-4VP) causes a band shift in the infrared (IR) spectra. Chemical shifts of N-1s binding energy in X-ray photoelectronic spectroscopy (XPS) experiments indicate a transfer of proton from MAA to 4VP. Scanning electron microscopy (SEM) pictures show that the morphology of the blends were improved upon addition of EMAA. Nonradiative energy transfer (NRET) fluorescence results attest that there exists interdiffusion of chromophore-labeled LLDPE chains and chromophore-labeled poly(MMA-co-4VP) chains in the interface. Based on experimental results, the mechanism of compatibilization is studied in detail. Compatibilization is realized through the interaction between MAA in EMAA with 4VP in poly(MMA-co-4VP). (C) 1999 John Wiley & Sons, Inc.

Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics

Struyf, J., Dzeroski, S. Blockeel, H. and Clare, A. (2005) Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics. In proceedings of the EPIA 2005 CMB Workshop

Consensus and Confusion in Molluscan Trees: Evaluating Morphological and Molecular Phylogenies

Mollusks are the most morphologically disparate living animal phylum, they have diversified into all habitats, and have a deep fossil record. Monophyly and identity of their eight living classes is undisputed, but relationships between these groups and patterns of their early radiation have remained elusive. Arguments about traditional morphological phylogeny focus on a small number of topological concepts but often without regard to proximity of the individual classes. In contrast, molecular studies have proposed a number of radically different, inherently contradictory, and controversial sister relationships. Here, we assembled a dataset of 42 unique published trees describing molluscan interrelationships. We used these data to ask several questions about the state of resolution of molluscan phylogeny compared to a null model of the variation possible in random trees constructed from a monophyletic assemblage of eight terminals. Although 27 different unique trees have been proposed from morphological inference, the majority of these are not statistically different from each other. Within the available molecular topologies, only four studies to date have included the deep-sea class Monoplacophora; but 36.4% of all trees are not significantly different. We also present supertrees derived from 2 data partitions and 3 methods, including all available molecular molluscan phylogenies, which will form the basis for future hypothesis testing. The supertrees presented here were not constructed to provide yet another hypothesis of molluscan relationships, but rather to algorithmically evaluate the relationships present in the disparate published topologies. Based on the totality of available evidence, certain patterns of relatedness among constituent taxa become clear. The internodal distance is consistently short between a few taxon pairs, particularly supporting the relatedness of Monoplacophora and the chitons, Polyplacophora. Other taxon pairs are rarely or never found in close proximity, such as the vermiform Caudofoveata and Bivalvia. Our results have specific utility for guiding constructive research planning in order to better test relationships in Mollusca as well as other problematic groups. Taxa with consistently proximate relationships should be the focus of a combined approach in a concerted assessment of potential genetic and anatomical homology, while unequivocally distant taxa will make the most constructive choices for exemplar selection in higher-level phylogenomic analyses.

Taking the LEED? Analyzing spatial variations in market penetration rates of eco-labeled properties

This paper investigates the impact of policies to promote the adoption of LEED-certified buildings across CBSA in the United States. Drawing upon a unique database that combines data from a large number of sources and using a number of regression procedures, the determinants of the proportion LEED-certified space for more than 170 CBSA in the US is modeled. LEED-certified space still accounts for a relatively small proportion of commercial stock in all markets. The average proportion is less than 1%. There is no conclusive evidence of a positive impact of policy intervention on the levels of LEED-certified space. However, after accounting for bias introduced by non-random assignment of policies, we find preliminary evidence of a positive impact of city-level green building incentives. There is a significant positive association between market size and indicators of economic vitality on proportions of LEED-certified space.

Random prism: an alternative to random forests

Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach. However, an alternative approach for the induction of rule based classifiers is the Prism family of algorithms. Prism algorithms produce modular classification rules that do not necessarily fit into a decision tree structure. Prism classification rulesets achieve a comparable and sometimes higher classification accuracy compared with decision tree classifiers, if the data is noisy and large. Yet Prism still suffers from overfitting on noisy and large datasets. In practice ensemble techniques tend to reduce the overfitting, however there exists no ensemble learner for modular classification rule inducers such as the Prism family of algorithms. This article describes the first development of an ensemble learner based on the Prism family of algorithms in order to enhance Prism’s classification accuracy by reducing overfitting.

Parallel random prism: a computationally efficient ensemble learner for classification

Generally classifiers tend to overfit if there is noise in the training data or there are missing values. Ensemble learning methods are often used to improve a classifier's classification accuracy. Most ensemble learning approaches aim to improve the classification accuracy of decision trees. However, alternative classifiers to decision trees exist. The recently developed Random Prism ensemble learner for classification aims to improve an alternative classification rule induction approach, the Prism family of algorithms, which addresses some of the limitations of decision trees. However, Random Prism suffers like any ensemble learner from a high computational overhead due to replication of the data and the induction of multiple base classifiers. Hence even modest sized datasets may impose a computational challenge to ensemble learners such as Random Prism. Parallelism is often used to scale up algorithms to deal with large datasets. This paper investigates parallelisation for Random Prism, implements a prototype and evaluates it empirically using a Hadoop computing cluster.

Random Prism: a noise-tolerant alternative to Random Forests

Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms that also induces classification rules. Compared with decision trees, Prism algorithms generate modular classification rules that cannot necessarily be represented in the form of a decision tree. Prism algorithms produce a similar classification accuracy compared with decision trees. However, in some cases, for example, if there is noise in the training and test data, Prism algorithms can outperform decision trees by achieving a higher classification accuracy. However, Prism still tends to overfit on noisy data; hence, ensemble learners have been adopted in this work to reduce the overfitting. This paper describes the development of an ensemble learner using a member of the Prism family as the base classifier to reduce the overfitting of Prism algorithms on noisy datasets. The developed ensemble classifier is compared with a stand-alone Prism classifier in terms of classification accuracy and resistance to noise.

A scalable expressive ensemble learning using Random Prism: a MapReduce approach

The induction of classification rules from previously unseen examples is one of the most important data mining tasks in science as well as commercial applications. In order to reduce the influence of noise in the data, ensemble learners are often applied. However, most ensemble learners are based on decision tree classifiers which are affected by noise. The Random Prism classifier has recently been proposed as an alternative to the popular Random Forests classifier, which is based on decision trees. Random Prism is based on the Prism family of algorithms, which is more robust to noise. However, like most ensemble classification approaches, Random Prism also does not scale well on large training data. This paper presents a thorough discussion of Random Prism and a recently proposed parallel version of it called Parallel Random Prism. Parallel Random Prism is based on the MapReduce programming paradigm. The paper provides, for the first time, novel theoretical analysis of the proposed technique and in-depth experimental study that show that Parallel Random Prism scales well on a large number of training examples, a large number of data features and a large number of processors. Expressiveness of decision rules that our technique produces makes it a natural choice for Big Data applications where informed decision making increases the user’s trust in the system.

Carbon-13 turnover in 'roxo de valinhos' fig trees

The aim of this study was to assess carbon-13 turnover in different organs of the fig tree, 'Roxo de Valinhos' cultivar. The experiment was carried out in an orchard at School of Agronomical Sciences, FCA/UNESP, Botucatu Campus, State of São Paulo, Brazil. The main photosynthetically active leaf was previously determined based on gas exchanges by means of an open portable photosynthesis system, IRGA. That leaf was placed in a chamber where the enriched gas injection occurred. The leaf enrichment time was 30 minutes. Treatments were constituted of seven fig trees removed from the soil after: 6; 24; 48; 72; 120; 168 and 360 hours of enrichment using (13)C, and their parts were sectioned into: apical bud, young leaves, adult leaves (photosynthetically active), lateral sprouts, fruits, and branch. The results allowed the establishment of the carbon-13 metabolism sequence in the studied parts: Young leaves > Fruits > Sprouts > Adult leaves > Apical bud > branch > Labeled leaf. 'Roxo de Valinhos' fig trees, had (13)C turnover of 24 hours and carbon-13 half-time shorter than 11 hours.

Extraction of Building Roof Contours From LiDAR Data Using a Markov-Random-Field-Based Approach

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Molecular characterization and relatedness of Haematobia irritans (horn fly) populations, by RAPD-PCR

Haematobia irritans is a hematophagous parasite of cattle that causes significant economic losses in many parts of the world, including Brazil. In the present work, one American and four Brazilian populations of this species were studied by Random Amplified Polymorpht DNA (RAPD) to assess basically genetic variability within and between populations. Ten different decamer random primers were employed in the genomic DNA amplification, yielding 117 fragments in the five H.. irritans populations. In Drosophila prosaltans, used as an outgroup, 81 fragments were produced. Forty-three of these fragments were shared by both species. Among the H. irritans samples, that from Rio Branco (Acre State, Brazil) produced the smallest numbers of fragments and polymorphic bands. This high genetic homogenity may be ascribed to its geographic origin (in the Northwest of Brazil), which causes high isolation and low gene flow, unlike the other Brazilian populations, from the South Central region, in which cattle trade is very intensive. Marker fragments (exclusive bands) detected in every sample enabled the population origin to be characterized, but they are also potentially useful for further approaches such as the putative origin of Brazilian populations from North America. Similarity indices [Nei & Li, 1979, Proc. Natl. Acad. Sci. USA 76: 5269-5273] and phylogenetic trees, rooted by using the outgroup and produced by the Phylogenetic Analysis using Parsimony (PAUP 4.0-Swofford, 2001) program showed the closest relationships between flies from Sao Jose do Rio Preto and Turiuba (both from São Paulo State, Brazil) while flies from the geographically distant Rio Branco showed the greatest differentiation relative to the others.

Spatial patterns or trees in the Picinguara coastal sandy forest (state of Sao-Paulo, Brazil)

The species of the sandy plains forests (forests of the ''restingas'') have not yet had their spatial patterns studied as aids to the understanding of the diversity found in the different physiognomies along the Brazilian coast. In this paper a 10 x 10 m quadrat framework laid in a hectare of a tree dominant forest in the sandy plains of the Picinguaba area of the Serra do Mar State Park (municipality of Ubatuba, state of São Paulo, Brazil) was used to assess the spatial pattern of distribution for the ten most important species : Pera glabrata, Euterpe edulis, Eugenia brasiliensis, Alchornea triplinervea, Guatteria australis, Myrcia racemosa, Jacaranda semiserrata, Guarea macrophylla, Euplassa cantareirae and Nectandra oppositifolia. The spatial patterns were inferred through the calculations of their T-Square Index (C) and Dispersal Distance Index (I). P. glabrata shows a random pattern, E. edulis aggregate, E. brasiliensis, A. triplinervia, G. australis, E. cantareirae and N. oppositifolia with a tendency between aggregate and uniform and, M. racemosa, J. semiserrata and G. macrophylla between aggregate and random. Although the indexes are dependent of the sample size and of the technique adjustments, the relationship of the pattern with the environmental factors is shown by clustering methods. The results give confirmation of how the spatial patterns bring associations between populations and shape of the vegetation physiognomy.

Functional and phylogenetic diversity of scattered trees in an agricultural landscape: Implications for conservation

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Automatic Design of Boolean Networks for Modelling Differentiation Trees

Real living cell is a complex system governed by many process which are not yet fully understood: the process of cell differentiation is one of these. In this thesis work we make use of a cell differentiation model to develop gene regulatory networks (Boolean networks) with desired differentiation dynamics. To accomplish this task we have introduced techniques of automatic design and we have performed experiments using various differentiation trees. The results obtained have shown that the developed algorithms, except the Random algorithm, are able to generate Boolean networks with interesting differentiation dynamics. Moreover, we have presented some possible future applications and developments of the cell differentiation model in robotics and in medical research. Understanding the mechanisms involved in biological cells can gives us the possibility to explain some not yet understood dangerous disease, i.e the cancer. Le cellula è un sistema complesso governato da molti processi ancora non pienamente compresi: il differenziamento cellulare è uno di questi. In questa tesi utilizziamo un modello di differenziamento cellulare per sviluppare reti di regolazione genica (reti Booleane) con dinamiche di differenziamento desiderate. Per svolgere questo compito abbiamo introdotto tecniche di progettazione automatica e abbiamo eseguito esperimenti utilizzando vari alberi di differenziamento. I risultati ottenuti hanno mostrato che gli algoritmi sviluppati, eccetto l'algoritmo Random, sono in grado di poter generare reti Booleane con dinamiche di differenziamento interessanti. Inoltre, abbiamo presentato alcune possibili applicazioni e sviluppi futuri del modello di differenziamento in robotica e nella ricerca medica. Capire i meccanismi alla base del funzionamento cellulare può fornirci la possibilità di spiegare patologie ancora oggi non comprese, come il cancro.