Systems of possibilistic regressions: a case study in ecological inference

This work introduces how possibilistic regression can be used in the case of non symmetrical triangular membership functions, building a system of regressions, so that suitable restrictions for each particular problem can be incorporated. We apply this methodology to the problem of ecological inference, in particular to the estimation of the electoral transition matrix. An experimentation with several examples shows the benefits of the new approach

Systems of possibilistic regressions: a case study in ecological inference

This work introduces how possibilistic regression can be used in the case of non symmetrical triangular membership functions, building a system of regressions, so that suitable restrictions for each particular problem can be incorporated. We apply this methodology to the problem of ecological inference, in particular to the estimation of the electoral transition matrix. An experimentation with several examples shows the benefits of the new approach

Biofuels from microalgae: biomethane

The high cost of axenic microalgae cultivation in photobioreactors limits nowadays the potential uses of microalgal biomass as a feedstock for the production of biodiesel or bioethanol. In this context, microalgae-based wastewater treatment (WWT) has emerged as the leading method of cultivation for supplying microalgae at low cost and low environmental impacts, while achieving sewage treatment. Nonetheless, the year-round dynamics in microalgae population and cell composition when grown in WWTPs restrict the use of this low-quality biomass to biogas production via anaerobic digestion. Although the macromolecular composition of the microalgae produced during wastewater treatment is similar to that of sewage sludge, the recalcitrant nature of microalgae cell walls requires an optimisation of pretreatment technologies for enhancing microalgae biodegradability. In addition, the low C/N ratio, the high water content and the suspended nature of microalgae suggest that microalgal biomass will also benefit from anaerobic co-digestion with carbon-rich substrates, which constitutes a field for further research. Photosynthetic microalgae growth can also support an effective CO2 capture and H2S oxidation from biogas, which would generate a high-quality biomethane complying with most international regulations for injection into natural gas grids or use as autogas. This book chapter will critically review the most recent advances in biogas production from microalgae, with a special focus on pretreatment technologies, co-digestion opportunities, modelling strategies, biogas upgrading and process microbiology

Evaluating the effects of white matter multiple sclerosis lesions on the volume estimation of 6 brain tissue segmentation methods

BACKGROUND AND PURPOSE: The accuracy of automatic tissue segmentation methods can be affected by the presence of hypointense white matter lesions during the tissue segmentation process. Our aim was to evaluate the impact of MS white matter lesions on the brain tissue measurements of 6 well-known segmentation techniques. These include straightforward techniques such as Artificial Neural Network and fuzzy C-means as well as more advanced techniques such as the Fuzzy And Noise Tolerant Adaptive Segmentation Method, fMRI of the Brain Automated Segmentation Tool, SPM5, and SPM8. MATERIALS AND METHODS: Thirty T1-weighted images from patients with MS from 3 different scanners were segmented twice, first including white matter lesions and then masking the lesions before segmentation and relabeling as WM afterward. The differences in total tissue volume and tissue volume outside the lesion regions were computed between the images by using the 2 methodologies. RESULTS: Total gray matter volume was overestimated by all methods when lesion volume increased. The tissue volume outside the lesion regions was also affected by white matter lesions with differences up to 20 cm3 on images with a high lesion load (≈50 cm3). SPM8 and Fuzzy And Noise Tolerant Adaptive Segmentation Method were the methods less influenced by white matter lesions, whereas the effect of white matter lesions was more prominent on fuzzy C-means and the fMRI of the Brain Automated Segmentation Tool. CONCLUSIONS: Although lesions were removed after segmentation to avoid their impact on tissue segmentation, the methods still overestimated GM tissue in most cases. This finding is especially relevant because on images with high lesion load, this bias will most likely distort actual tissue atrophy measurements