Valores culturais, orientações políticas e classes sociais : consequências nos valores de trabalho

Aborda-se a temática das estruturas de valores e consequente impacto nas atitudes e comportamentos na sociedade em geral e no trabalho em particular. Uma amostra diversificada de 157 participantes, recrutada em diferentes meios sociais, variando assim em termos etários, de qualificações académicas, de classe social e de simpatia partidária, respondeu a um questionário sobre valores, valores de trabalho e orientações políticas. Primeiro, verificámos que as orientações político-ideológicas são estruturadas em duas dimensões correlacionadas mas independentes: a esquerda-direita e o autoritarismo-liberdade. Também verificámos que diferentes dimensões supraordenadas de valores do modelo de valores universais de Schwartz estruturam os valores de trabalho e as orientações político-ideológicas, com impactos específicos sobre as funções laborais desejáveis e as preferências socioeconómico-culturais. Finalmente, os efeitos das dimensões de classe social (detenção de capital e qualificação académica) sobre as orientações político-ideológicas sugerem que as dimensões direita-esquerda e autoritarismo-liberdade, apesar de estarem correlacionadas, têm motivações e origens sociológicas diferentes.

Early experiences of children attending classes at Central Institute for the Deaf

This paper reviews the results of a questionnaire sent out to parents of children attending Central Institute for the Deaf.

The effectiveness of "Word of Mouth" classes on the use of communication strategies, communication attitudes and lipreading skills

This paper examines the effectiveness of aural rehabilitation on persons with an adventitious hearing loss.

Genesis and expansion of metazoan transcription factor gene classes

We know little about the genomic events that led to the advent of a multicellular grade of organization in animals, one of the most dramatic transitions in evolution. Metazoan multicellularity is correlated with the evolution of embryogenesis, which presumably was underpinned by a gene regulatory network reliant on the differential activation of signaling pathways and transcription factors. Many transcription factor genes that play critical roles in bilaterian development largely appear to have evolved before the divergence of cnidarian and bilaterian lineages. In contrast, sponges seem to have a more limited suite of transcription factors, suggesting that the developmental regulatory gene repertoire changed markedly during early metazoan evolution. Using whole- genome information from the sponge Amphimedon queenslandica, a range of eumetazoans, and the choanoflagellate Monosiga brevicollis, we investigate the genesis and expansion of homeobox, Sox, T- box, and Fox transcription factor genes. Comparative analyses reveal that novel transcription factor domains ( such as Paired, POU, and T- box) arose very early in metazoan evolution, prior to the separation of extant metazoan phyla but after the divergence of choanoflagellate and metazoan lineages. Phylogenetic analyses indicate that transcription factor classes then gradually expanded at the base of Metazoa before the bilaterian radiation, with each class following a different evolutionary trajectory. Based on the limited number of transcription factors in the Amphimedon genome, we infer that the genome of the metazoan last common ancestor included fewer gene members in each class than are present in extant eumetazoans. Transcription factor orthologues present in sponge, cnidarian, and bilaterian genomes may represent part of the core metazoan regulatory network underlying the origin of animal development and multicellularity.

Identifying problematic classes in text classification

Real-world text classification tasks often suffer from poor class structure with many overlapping classes and blurred boundaries. Training data pooled from multiple sources tend to be inconsistent and contain erroneous labelling, leading to poor performance of standard text classifiers. The classification of health service products to specialized procurement classes is used to examine and quantify the extent of these problems. A novel method is presented to analyze the labelled data by selectively merging classes where there is not enough information for the classifier to distinguish them. Initial results show the method can identify the most problematic classes, which can be used either as a focus to improve the training data or to merge classes to increase confidence in the predicted results of the classifier.

The global distribution of phytoplankton size spectrum and size classes from their light-absorption spectra derived from satellite data

The absorption spectra of phytoplankton in the visible domain hold implicit information on the phytoplankton community structure. Here we use this information to retrieve quantitative information on phytoplankton size structure by developing a novel method to compute the exponent of an assumed power-law for their particle-size spectrum. This quantity, in combination with total chlorophyll-a concentration, can be used to estimate the fractional concentration of chlorophyll in any arbitrarily-defined size class of phytoplankton. We further define and derive expressions for two distinct measures of cell size of mixed populations, namely, the average spherical diameter of a bio-optically equivalent homogeneous population of cells of equal size, and the average equivalent spherical diameter of a population of cells that follow a power-law particle-size distribution. The method relies on measurements of two quantities of a phytoplankton sample: the concentration of chlorophyll-a, which is an operational index of phytoplankton biomass, and the total absorption coefficient of phytoplankton in the red peak of visible spectrum at 676 nm. A sensitivity analysis confirms that the relative errors in the estimates of the exponent of particle size spectra are reasonably low. The exponents of phytoplankton size spectra, estimated for a large set of in situ data from a variety of oceanic environments (~ 2400 samples), are within a reasonable range; and the estimated fractions of chlorophyll in pico-, nano- and micro-phytoplankton are generally consistent with those obtained by an independent, indirect method based on diagnostic pigments determined using high-performance liquid chromatography. The estimates of cell size for in situ samples dominated by different phytoplankton types (diatoms, prymnesiophytes, Prochlorococcus, other cyanobacteria and green algae) yield nominal sizes consistent with the taxonomic classification. To estimate the same quantities from satellite-derived ocean-colour data, we combine our method with algorithms for obtaining inherent optical properties from remote sensing. The spatial distribution of the size-spectrum exponent and the chlorophyll fractions of pico-, nano- and micro-phytoplankton estimated from satellite remote sensing are in agreement with the current understanding of the biogeography of phytoplankton functional types in the global oceans. This study contributes to our understanding of the distribution and time evolution of phytoplankton size structure in the global oceans.

Knowledge-based design cost estimation through extending industry foundation classes

In order to overcome divergence of estimation with the same data, the proposed digital costing process adopts an integrated design of information system to design the process knowledge and costing system together. By employing and extending a widely used international standard, industry foundation classes, the system can provide an integrated process which can harvest information and knowledge of current quantity surveying practice of costing method and data. Knowledge of quantification is encoded from literatures, motivation case and standards. It can reduce the time consumption of current manual practice. The further development will represent the pricing process in a Bayesian Network based knowledge representation approach. The hybrid types of knowledge representation can produce a reliable estimation for construction project. In a practical term, the knowledge management of quantity surveying can improve the system of construction estimation. The theoretical significance of this study lies in the fact that its content and conclusion make it possible to develop an automatic estimation system based on hybrid knowledge representation approach.

Constraints from the old quasar APM 08279+5255 on two classes of Lambda(t)-cosmologies

The viability of two different classes of Lambda(t)CDM cosmologies is tested by using the APM 08279+5255, an old quasar at redshift z = 3.91. In the first class of models, the cosmological term scales as Lambda(t) similar to R(-n). The particular case n = 0 describes the standard Lambda CDM model whereas n = 2 stands for the Chen and Wu model. For an estimated age of 2 Gyr, it is found that the power index has a lower limit n > 0.21, whereas for 3 Gyr the limit is n > 0.6. Since n can not be so large as similar to 0.81, the Lambda CDM and Chen and Wu models are also ruled out by this analysis. The second class of models is the one recently proposed by Wang and Meng which describes several Lambda(t)CDM cosmologies discussed in the literature. By assuming that the true age is 2 Gyr it is found that the epsilon parameter satisfies the lower bound epsilon > 0.11 while for 3 Gyr, a lower limit of epsilon > 0.52 is obtained. Such limits are slightly modified when the baryonic component is included.

Proteomics of the neurotoxic fraction from the sea anemone Bunodosoma cangicum venom: Novel peptides belonging to new classes of toxins

In contrast to the many studies on the venoms of scorpions, spiders, snakes and cone snails, tip to now there has been no report of the proteomic analysis of sea anemones venoms. In this work we report for the first time the peptide mass fingerprint and some novel peptides in the neurotoxic fraction (Fr III) of the sea anemone Bunodosoma cangicum venom. Fr III is neurotoxic to crabs and was purified by rp-HPLC in a C-18 column, yielding 41 fractions. By checking their molecular masses by ESI-Q-Tof and MALDI-Tof MS we found 81 components ranging from near 250 amu to approximately 6000 amu. Some of the peptidic molecules were partially sequenced through the automated Edman technique. Three of them are peptides with near 4500 amu belonging to the class of the BcIV, BDS-I, BDS-II, APETx1, APETx2 and Am-II toxins. Another three peptides represent a novel group of toxins (similar to 3200 amu). A further three molecules (similar to similar to 4900 amu) belong to the group of type 1 sodium channel neurotoxins. When assayed over the crab leg nerve compound action potentials, one of the BcIV- and APETx-like peptides exhibits an action similar to the type 1 sodium channel toxins in this preparation, suggesting the same target in this assay. On the other hand one of the novel peptides, with 3176 amu, displayed an action similar to potassium channel blockage in this experiment. In summary, the proteomic analysis and mass fingerprint of fractions from sea anemone venoms through MS are valuable tools, allowing us to rapidly predict the occurrence of different groups of toxins and facilitating the search and characterization of novel molecules without the need of full characterization of individual components by broader assays and bioassay-guided purifications. It also shows that sea anemones employ dozens of components for prey capture and defense. (C) 2008 Elsevier Inc. All rights reserved.

A hybrid approach to learn with imbalanced classes using evolutionary algorithms

There is an increasing interest in the application of Evolutionary Algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when one or more classes heavily outnumber other classes. Frequently, classical machine learning (ML) classifiers are not able to learn in the presence of imbalanced data sets, inducing classification models that always predict the most numerous classes. In this work, we propose a novel hybrid approach to deal with this problem. We create several balanced data sets with all minority class cases and a random sample of majority class cases. These balanced data sets are fed to classical ML systems that produce rule sets. The rule sets are combined creating a pool of rules and an EA is used to build a classifier from this pool of rules. This hybrid approach has some advantages over undersampling, since it reduces the amount of discarded information, and some advantages over oversampling, since it avoids overfitting. The proposed approach was experimentally analysed and the experimental results show an improvement in the classification performance measured as the area under the receiver operating characteristics (ROC) curve.

Evolutionary model trees for handling continuous classes in machine learning

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. (C) 2010 Elsevier Inc. All rights reserved.

Twisted conjugacy classes in nilpotent groups

A group is said to have the R(infinity) property if every automorphism has an infinite number of twisted conjugacy classes. We study the question whether G has the R(infinity) property when G is a finitely generated torsion-free nilpotent group. As a consequence, we show that for every positive integer n >= 5, there is a compact nilmanifold of dimension n on which every homeomorphism is isotopic to a fixed point free homeomorphism. As a by-product, we give a purely group theoretic proof that the free group on two generators has the R(infinity) property. The R(infinity) property for virtually abelian and for C-nilpotent groups are also discussed.