Processes controlling the quantities of biogenic materials in lakes and reservoirs subject to cultural eutrophication

The processes which control the growth, composition, succession and loss from suspension of phytoplankton algae are briefly reviewed, with special reference to function in eutrophic reservoir systems. The ecology of larger algal biomasses supported by high nutrient loading rates are more likely to be subject to physical (wash-out, underwater light penetration, thermal stability and mixing) than to chemical constraints. Sudden changes in the interactions between physical factors temporarily impair the growth of dominant algal species, and advance the succession. Certain algae may be cropped heavily, but selectively, by zooplankton feeding, but they are rarely the species which cause problems in waterworks practice. Grazing, however, does influence succession. A deeper understanding of the operation of loss control mechanism is urgently required. Potentially, manipulation of the physical environment provides an important means of alleviating day-to-day algal problems in eutrophic reservoirs; in terms of cost effectiveness these may prove to be more attractive than reducing nutrient loads at source.

The impact of physical processes on algal growth

Mixing and transport processes in surface waters strongly influence the structure of aquatic ecosystems. The impact of mixing on algal growth is species-dependent, affecting the competition among species and acting as a selective factor for the composition of the biocoenose. Were it not for the ever-changing ”aquatic weather”, the composition of pelagic ecosystems would be relatively simple. Probably just a few optimally adapted algal species would survive in a given water-body. In contrast to terrestrial ecosystems, in which the spatial heterogeneity is primarily responsible for the abundance of niches, in aquatic systems (especially in the pelagic zone) the niches are provided by the temporal structure of physical processes. The latter are discussed in terms of the relative sizes of physical versus biological time-scales. The relevant time-scales of mixing and transport cover the range between seconds and years. Correspondingly, their influence on growth of algae is based on different mechanisms: rapid changes are relevant for the fast biological processes such as nutrient uptake and photosynthesis, and the slower changes are relevant for the less dynamic processes such as growth, respiration, mineralization, and settling of algal cells. Mixing time-scales are combined with a dynamic model of photosynthesis to demonstrate their influence on algal growth.

TRANSFER OF ANALYTICAL METHODS FOR THE CHARACTERISATION OF NATURAL PRODUCTS TO R&D PROCESSES OF IDOKI

IDOKI SCF Technologies S.L. is a technology-based company, set up on September 2006 in Derio (Biscay) with the main scope of developing extraction and purification processes based on the use of supercritical fluid extraction technology (SFE) in food processing, extraction of natural products and the production of personal care products. IDOKI¿s researchers have been working on many different R&D projects so far, most of them using this technology. However, the optimization of a SFE method for the different matrices cannot be performed unless we have an analytical method for the characterisation of the extracts obtained in each experiment. The analytical methods are also essential for the quality control of the raw materials that are going to be used and also for the final product. This PhD thesis was born to tackle this problem and therefore, it is based on the development of different analytical methods for the characterisation of the extracts and products. The projects that we could include in this thesis were the following: the extraction propolis, the recovery of agroindustrial residues (soy and wine) and the dealcoholisation of wine.On the one hand, for the extraction of propolis, several UV-Vis spectroscopic methods were used in order to measure the antioxidant capacity and the total polyphenol and flavonoid content of the extracts. A SFC method was also developed in order to measure more specific phenolic compounds. On the other hand, for the recovery of agroindustrial residues UV-Vis spectroscopy was used to determine the total polyphenol content and two SFC methods were developed to analyse different phenolic compounds. Extraction methods such as MAE, FUSE and rotary agitation were also evaluated for the characterisation of the raw materials.Finally, for the dealcoholisation of wine, the development of a SBSE-TD-GC-MS and DHS-TD-GC-MS methods for the analysis of aromas and a NIR spectroscopic method for the determination of ethanol content with the help of chemometrics was necessary. Most of these methods are typically used in IDOKI¿s lab as routine analyses apart from others not included in this PhD thesis.

Properties of convergence of a class of iterative processes generated by sequences of self-mappings with applications to switched dynamic systems

This article investigates the convergence properties of iterative processes involving sequences of self-mappings of metric or Banach spaces. Such sequences are built from a set of primary self-mappings which are either expansive or non-expansive self-mappings and some of the non-expansive ones can be contractive including the case of strict contractions. The sequences are built subject to switching laws which select each active self-mapping on a certain activation interval in such a way that essential properties of boundedness and convergence of distances and iterated sequences are guaranteed. Applications to the important problem of stability of dynamic switched systems are also given.

Molecular Antennas and Photoactive Nanomaterials based on Energy Transfer Processes

110 p.

Gaussian processes for machine learning (GPML) toolbox

The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance functions; we offer a library of simple mean and covariance functions and mechanisms to compose more complex ones. Several likelihood functions are supported including Gaussian and heavy-tailed for regression as well as others suitable for classification. Finally, a range of inference methods is provided, including exact and variational inference, Expectation Propagation, and Laplace’s method dealing with non-Gaussian likelihoods and FITC for dealing with large regression tasks.