An urban design perspective to classify knowledge precincts : a typological analysis of global best practices

Purpose: The paper seeks to investigate emerging knowledge precincts under the urban design lens in order to identify recurrent spatial patterns of urban forms and functions to gather an understanding of physical aspects that contribute to the creation of place quality. Scope: This paper focuses on the physical design and layout of specific precincts. Although socio-economic and other factors come into play imparting the distinctiveness; this paper only focuses on the spatial dimensions. Method: The research first develops a design typology framework through the lead of literature, and then utilizes it to identify recurrent elements in knowledge precinct design in order to develop taxonomy of patterns and layouts. Results: The research reported in this paper provides preliminary insights into the various form and functional factors playing role in the design of knowledge precincts and evaluates the elements that contribute to the success of these urban interventions. Recommendations: The paper recommends the use of particular design-based solutions in order to enhance the place making in knowledge precincts. Conclusions: The study concludes that despite the locational, regulatory and other contextual differences, the underlying driving principle of providing place quality to people leads to the emergence of identifiable spatial patterns across the knowledge precincts.

How to classify the Oldest Old according to Health Status: A study of 1160 subjects belonging to 552 90+ Italian sibships characterised by familial longevity recruited within the GeHA EU project

The health status of the oldest old, the fastest increasing population segment worldwide, progressively becomes more heterogeneous, and this peculiarity represents a major obstacle to their classification. We compared the effectiveness of four previously proposed criteria (Franceschi et al., 2000; Evert et al., 2003; Gondo et al., 2006; Andersen-Ranberg et al., 2001) in 1160 phenotypically fully characterized Italian siblings of 90 years of age and older (90+, mean age: 93 years; age range: 90–106 years) belonging to 552 sib-ships, recruited in Northern, Central and Southern Italy within the EU-funded project GEHA, followed for a six-year-survival. Main findings were: (i) ‘‘healthy’’ subjects varied within a large range, i.e. 5.2% (Gondo), 8.7% (Evert), 17.7% (Franceschi), and 28.5% (Andersen-Ranberg); (ii) Central Italy subjects showed better health than those from Northern and Southern Italy; (iii) mortality risk was correlated with health status independently of geographical areas; and (iv) 90+ males, although fewer in number, were healthier than females, but with no survival advantage. In conclusion, we identified a modified version of Andersen-Ranberg criteria, based on the concomitant assessment of two basic domains (cognitive, SMMSE; physical, ADL), called ‘‘Simple Model of Functional Status’’ (SMFS), as the most effective proxy to distinguish healthy from not-healthy subjects. This model showed that health status was correlated within sib-ships, suggesting a familial/genetic component.

Entropy-Based Approach to Analyze and Classify Mineral Aggregates

This paper presents an automatic method to detect and classify weathered aggregates by assessing changes of colors and textures. The method allows the extraction of aggregate features from images and the automatic classification of them based on surface characteristics. The concept of entropy is used to extract features from digital images. An analysis of the use of this concept is presented and two classification approaches, based on neural networks architectures, are proposed. The classification performance of the proposed approaches is compared to the results obtained by other algorithms (commonly considered for classification purposes). The obtained results confirm that the presented method strongly supports the detection of weathered aggregates.

A single multiplex PCR and SNaPshot minisequencing reaction of 42 SNPs to classify admixture populations into mitochondrial DNA haplogroups

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

A single multiplex SNaPshot reaction of 42 SNPs to classify admixture populations into mitochondrial DNA haplogroups

SNaPshot minisequencing reaction is in increasing use because of its fast detection of many polymorphisms in a single assay. In this work we described a highly sensitive single nucleotide polymorphisms (SNPs) typing method with detection of 42 mitochondrial DNA (mtDNA) SNPs in a single PCR and SNaPshot multiplex reaction in order to allow haplogroup classification in Latin American admixture population. We validated the panel typing 160 Brazilian individuals. DNA was extracted from blood spotted on filter paper using Chelex protocol. Forty SNPs were selected targeting haplogroup-specific mutations in Europeans, Africans and Asians (only precursors of Native Americans haplogroups A2, B2, C1, and D1) and two non-coding SNPs were chosen to increase the power of discrimination between individuals (SNPs positions 16,519 and 16,362). It was done using a modified version of a previously published multiplex SNaPshot minisequencing reaction established to resolve European haplogroups, adding SNPs targeting Africans (L0, L1, L2, L3, and L*) and Asians (A, B, C, and D) haplogroups based on SNPs described at PhyloTree.org build 2. PCR primers were designed using PerlPrimer software and checked with the Autodimer program. Thirty-three primer-pairs were used to amplify 42 SNPs. Using this panel, we were able to successfully classify 160 individuals into their correct haplogroups. Complete SNP profiles were obtained from 10. pg of total DNA. We conclude that it is possible to build and genotype more than 40 mtDNA SNPs in a single multiplex PCR and SNaPshot reaction, with sensitivity and reliability, resolving haplogroup classification in admixture populations. © 2011.

Dendritic thickness: a morphometric parameter to classify mouse retinal ganglion cells

To study the dendritic morphology of retinal ganglion cells in wild-type mice we intracellularly injected these cells with Lucifer yellow in an in vitro preparation of the retina. Subsequently, quantified values of dendritic thickness, number of branching points and level of stratification of 73 Lucifer yellow-filled ganglion cells were analyzed by statistical methods, resulting in a classification into 9 groups. The variables dendritic thickness, number of branching points per cell and level of stratification were independent of each other. Number of branching points and level of stratification were independent of eccentricity, whereas dendritic thickness was positively dependent (r = 0.37) on it. The frequency distribution of dendritic thickness tended to be multimodal, indicating the presence of at least two cell populations composed of neurons with dendritic diameters either smaller or larger than 1.8 µm ("thin" or "thick" dendrites, respectively). Three cells (4.5%) were bistratified, having thick dendrites, and the others (95.5%) were monostratified. Using k-means cluster analysis, monostratified cells with either thin or thick dendrites were further subdivided according to level of stratification and number of branching points: cells with thin dendrites were divided into 2 groups with outer stratification (0-40%) and 2 groups with inner (50-100%) stratification, whereas cells with thick dendrites were divided into one group with outer and 3 groups with inner stratification. We postulate, that one group of cells with thin dendrites resembles cat ß-cells, whereas one group of cells with thick dendrites includes cells that resemble cat a-cells.

Automatic method to classify images based on multiscale fractal descriptors and paraconsistent logic

In this study is presented an automatic method to classify images from fractal descriptors as decision rules, such as multiscale fractal dimension and lacunarity. The proposed methodology was divided in three steps: quantification of the regions of interest with fractal dimension and lacunarity, techniques under a multiscale approach; definition of reference patterns, which are the limits of each studied group; and, classification of each group, considering the combination of the reference patterns with signals maximization (an approach commonly considered in paraconsistent logic). The proposed method was used to classify histological prostatic images, aiming the diagnostic of prostate cancer. The accuracy levels were important, overcoming those obtained with Support Vector Machine (SVM) and Bestfirst Decicion Tree (BFTree) classifiers. The proposed approach allows recognize and classify patterns, offering the advantage of giving comprehensive results to the specialists.

Multivariate analyses of UV-Vis absorption spectral data from cachaca wood extracts: a model to classify aged Brazilian cachacas according to the wood species used

Multivariate analyses of UV-Vis spectral data from cachaca wood extracts provide a simple and robust model to classify aged Brazilian cachacas according to the wood species used in the maturation barrels. The model is based on inspection of 93 extracts of oak and different Brazilian wood species by a non-aged cachaca used as an extraction solvent. Application of PCA (Principal Components Analysis) and HCA (Hierarchical Cluster Analysis) leads to identification of 6 clusters of cachaca wood extracts (amburana, amendoim, balsamo, castanheira, jatoba, and oak). LDA (Linear Discriminant Analysis) affords classification of 10 different wood species used in the cachaca extracts (amburana, amendoim, balsamo, cabreuva-parda, canela-sassafras, castanheira, jatoba, jequitiba-rosa, louro-canela, and oak) with an accuracy ranging from 80% (amendoim and castanheira) to 100% (balsamo and jequitiba-rosa). The methodology provides a low-cost alternative to methods based on liquid chromatography and mass spectrometry to classify cachacas aged in barrels that are composed of different wood species.

A probabilistic approach to classify the levee reliability

This work aims to evaluate the reliability of these levee systems, calculating the probability of “failure” of determined levee stretches under different loads, using probabilistic methods that take into account the fragility curves obtained through the Monte Carlo Method. For this study overtopping and piping are considered as failure mechanisms (since these are the most frequent) and the major levee system of the Po River with a primary focus on the section between Piacenza and Cremona, in the lower-middle area of the Padana Plain, is analysed. The novelty of this approach is to check the reliability of individual embankment stretches, not just a single section, while taking into account the variability of the levee system geometry from one stretch to another. This work takes also into consideration, for each levee stretch analysed, a probability distribution of the load variables involved in the definition of the fragility curves, where it is influenced by the differences in the topography and morphology of the riverbed along the sectional depth analysed as it pertains to the levee system in its entirety. A type of classification is proposed, for both failure mechanisms, to give an indication of the reliability of the levee system based of the information obtained by the fragility curve analysis. To accomplish this work, an hydraulic model has been developed where a 500-year flood is modelled to determinate the residual hazard value of failure for each stretch of levee near the corresponding water depth, then comparing the results with the obtained classifications. This work has the additional the aim of acting as an interface between the world of Applied Geology and Environmental Hydraulic Engineering where a strong collaboration is needed between the two professions to resolve and improve the estimation of hydraulic risk.