Preparation and physico-chemical characterisation of microporous polysaccharidic hydrogels

A new technique to obtain microporous hydrogels was realised. It permits us to obtain a microporous structure directly on the already cross-linked hydrogel. It consists in stratifying the already cross-linked hydrogel on to a filter with known porosity and forcing the CO2 bubbles, derived from the addition of HCl to a porogen salt (NaHCO3), to cross through the filter first and then the matrix. By changing the porosity of the filter, it was possible to modulate the porous morphology of the hydrogels. The polysaccharides selected were hyaluronane, alginate, and carboxymethylcellulose. The influence of the porous morphology on the physico-chemical properties of the gel has been evaluated by FT-IR, FRAP, calorimetric, water uptake, and rheological analysis.     

Optimisation of the throttling system in a CO2 refrigerating machine

This paper is an answer to the need of finding the optimal solution for the throttling system in refrigerating machines using CO₂ as working fluid; such a solution must combine reliability, low installation cost and high energy efficiency. To this purpose, different expansion systems are compared by means of a simulation programme, including a new one, operating with a differential valve, a liquid receiver and a thermostatic valve. The typical compression refrigerating cycle performed by CO₂ involves transcritical operations and therefore the upper pressure needs to be adjusted to the optimal value, that, unlike the traditional cycle, is not determined by heat transfer. The innovative system here proposed shows an intrinsic self-adjusting capability that leads to COP values quite close to the maximum ones when a fixed suitable value of the differential pressure is chosen, even if the temperature of the secondary fluid varies to a large extent.     

Reduction of immunoreactivity of bovine beta-lactoglobulin upon combined physical and proteolytic treatment

Bovine beta-lactoglobulin was hydrolyzed with trypsin or chymotrypsin before, during and after treatment at 600 MPa and pH 6.8 for 10 min at 30, 37 and 44 degrees C. The extent of beta-lactoglobulin hydrolysis under pressure was noticeably higher than at atmospheric pressure, particularly when chymotrypsin was used. Addition of proteases at ambient pressure to previously pressure-treated beta-lactoglobulin gave only a modest increase in proteolysis with respect to the untreated protein. Products of enzyme hydrolysis under pressure were separated by reverse-phase HPLC, and were found to be different from those obtained at atmospheric pressure when chymotrypsin was used. The residual immunochemical reactivity of the products of combined pressure-enzyme treatment was assessed on the unresolved hydrolysates by ELISA tests using polyclonal and monoclonal antibodies, and on individual hydrolytic fractions by Western Blotting using sera of paediatric patients allergic to whey proteins in cow milk. The immunoreactivity of the whole hydrolysates was related to their content of residual intact beta-lactoglobulin, and no immunochemical reactivity was found for all the products of chymotrypsin hydrolysis under pressure. The results indicate that chymotrypsin effectively hydrolysed hydrophobic regions of beta-lactoglobulin that were transiently exposed during the pressure treatments and that were not accessible in the native protein or in the protein that had been previously pressure treated.     

Feedback Control of Spin Systems

The feedback stabilization problem for ensembles of coupled spin 1/2 systems is discussed from a control theoretic perspective. The noninvasive nature of the bulk measurement allows in principle for a fully unitary and deterministic closed loop. The Lyapunov-based feedback design presented does not require spins that are selectively addressable. With this method, it is possible to obtain control inputs also for difficult tasks, like suppressing undesired couplings in identical spin systems.      

A support vector machine integrated system for the classification of operation anomalies in nuclear components and systems

A support vector machine (SVM) approach to the classification of transients in nuclear power plants is presented. SVM is a machine-learning algorithm that has been successfully used in pattern recognition for cluster analysis. In the present work, single- and multiclass SVM are combined into a hierarchical structure for distinguishing among transients in nuclear systems on the basis of measured data. An example of application of the approach is presented with respect to the classification of anomalies and malfunctions occurring in the feedwater system of a boiling water reactor. The data used in the example are provided by the HAMBO simulator of the Halden Reactor Project.     

Multiclass classification with bandit feedback using adaptive regularization

We present a new multiclass algorithm in the bandit framework, where after making a prediction, the learning algorithm receives only partial feedback, i.e., a single bit indicating whether the predicted label is correct or not, rather than the true label. Our algorithm is based on the second-order Perceptron, and uses upper-confidence bounds to trade-off exploration and exploitation, instead of random sampling as performed by most current algorithms. We analyze this algorithm in a partial adversarial setting, where instances are chosen adversarially, while the labels are chosen according to a linear probabilistic model which is also chosen adversarially. We show a regret of $\mathcal{O}(\sqrt{T}\log T)$ , which improves over the current best bounds of $\mathcal{O}(T^{2/3})$ in the fully adversarial setting. We evaluate our algorithm on nine real-world text classification problems and on four vowel recognition tasks, often obtaining state-of-the-art results, even compared with non-bandit online algorithms, especially when label noise is introduced.     

Online Mesh Refinement for Parallel Atmospheric Models

Forecast precisions of climatological models are limited by computing power and time available for the executions. As more and faster processors are used in the computation, the resolution of the mesh adopted to represent the Earth’s atmosphere can be increased, and consequently the numerical forecast is more accurate. However, a finer mesh resolution, able to include local phenomena in a global atmosphere integration, is still not possible due to the large number of data elements to compute in this case. To overcome this situation, different mesh refinement levels can be used at the same time for different areas of the domain. Thus, our paper evaluates how mesh refinement at run time (online) can improve performance for climatological models.The online mesh refinement (OMR) increases dynamically mesh resolution in parts of a domain,when special atmosphere conditions are registered during the execution. Experimental results show that the execution of a model improved by OMR provides better resolution for the meshes, without any significant increase of execution time. The parallel performance of the simulations is also increased through the creation of threads in order to explore different levels of parallelism.     

Reference spectra to classify Amazon water types

Reference spectra extracted from spectral libraries can distinguish different water types in images when associated with limnological information. In this study, we compiled available databases into a single spectral library, using field water reflectance spectra and limnological data collected by different researchers and campaigns in the Amazonian region. By using an iterative clustering procedure based on the combination of reflectance and optically active components (OACs), reference spectra representative of the major Amazonian water types were defined from this library. Differences between the resultant limnological classes were also evaluated by paired t-tests at significance level 0.05. Finally, reference spectra were tested for Spectral Angle Mapper (SAM) classification of waters in Hyperion/Earth Observing-One (EO-1) and Medium Resolution Imaging Spectrometer (MERIS)/Environment Satellite (Envisat) images acquired simultaneously as the field campaigns. Results showed highly variable concentrations of OACs due to the complexity of the Amazonian aquatic environments. Ten classes were defined to represent this complexity, broadly grouped into four limnological characteristics: clear waters with low concentrations of OACs (class 1); black waters rich in dissolved organic carbon (DOC) (class 2); waters with large concentrations of inorganic suspended solids (ISSs) (classes 3–7); and waters dominated by chlorophyll-a (chl-a) (classes 8–10). Using the ten reference spectra, SAM classification of the field water curves produced an overall accuracy of 86% with the highest values observed for classes 3, 4, 6 and 7 and the lowest accuracy for classes 1 and 2. The results of paired t-tests confirmed the class differences based on the concentrations of OACs. SAM classification of the Hyperion and MERIS images using ground truth information resulted in overall classification accuracies of 48% and 67%, respectively, with the highest errors associated with specific portions of the scenes that were not adequately represented in the spectral library.