Legacy and alternative halogenated flame retardants in Lake Geneva fish

Environmental Science and Pollution Research - Legacy (i.e., polybrominated diphenyl ethers (PBDEs) and hexabromocyclododecane (HBCDD)) and alternative halogenated flame retardants (HFRs) were...     

Comprehensive utilization of residues of Magnolia officinalis based on fiber characteristics

Journal of Material Cycles and Waste Management - The traditional Chinese medicinal plant Magnolia officinalis has a wide range of applications; including more than 200 kinds of patented Chinese...     

Genetic Diversity of Lepilemur mustelinus ruficaudatus, a Nocturnal Lemur of Madagascar

The genetic polymorphism of natural populations of Lepilemur mustelinus ruficaudatus was studied by protein electrophoresis. We sampled blood from 72 individuals from four populations separated by geographic or anthropogenic barriers from southwestern Madagascar. Six out of 22 enzyme loci showed genetic variation with a degree of polymorphism of 0.273. The expected and observed degree of genetic heterozygosity over all loci is similar to that of other primates (H_e = 0.058, H_o = 0.036). The F-statistics revealed that the four subpopulations were similar with respect to gene structure (F_ST = 0.065, p = 0.016), but the genotypic structures within subpopulations were inconsistent with random mating. For the total of the four subpopulations the proportion of heterozygous individuals was significantly smaller than expected under random mating (F_IS = 0.373, F_IT = 0.414, p < 0.01). These results correspond closely to what is expected considering the low migration ability of individuals of L. m ruficaudatus leading to small and rather isolated inbred populations.     

Using fuzzy set theory to address the uncertainty of susceptibility to drought

This paper presents the technical aspects of a new methodology for assessing the susceptibility of society to drought. The methodology consists of a combination of inference modelling and fuzzy logic applications. Four steps are followed: (1) model input variables are selected—these variables reflect the main factors influencing susceptibility in a social group, population or region, (2) fuzzification—the uncertainties of the input variables are made explicit by representing them as ‘fuzzy membership functions’, (3) inference modelling—the input variables are used to construct a model made up of linguistic rules, and (4) defuzzification—results from the model in linguistic form are translated into numerical form, also through the use of fuzzy membership functions. The disadvantages and advantages of this methodology became apparent when it was applied to the assessment of susceptibility from three disciplinary perspectives: Disadvantages include the difficulty in validating results and the subjectivity involved with specifying fuzzy membership functions and the rules of the inference model. Advantages of the methodology are its transparency, because all model assumptions have to be made explicit in the form of inference rules; its flexibility, in that informal and expert knowledge can be incorporated through ‘fuzzy membership functions’ and through the rules in the inference model; and its versatility, since numerical data can be converted to linguistic statements and vice versa through the procedures of ‘fuzzification’ and ‘defuzzification’.