One‐Pot Synthesis of Functional Helicoidal Hybrid Organic–Inorganic Nanofibers with Periodically Organized Mesoporosity

The one‐pot synthesis and properties of multifunctional hybrid mesoporous organosilica fibers with helical shapes are described. These hybrid mesoporous fibers are prepared without chiral elements and functionalized with a large variety of organic R functions (R = alkylthiols, phenylsulfonates, alkylphosphonates, dansyl, aminopropyl, fluoroalkyl, etc.). The resulting nanomaterials are thoroughly characterized by a variety of techniques. The use of a synergetic combination of achiral molecules as co‐directing structuring agents, a surfactant, and an organofunctional silica precursor R‐Si(OR)₃ allows, via carefully tuning of the main synthesis parameters and processing conditions, to control the shape, which is the anisotropic factor, of the hybrid nanofibers. The functionalization of the hybrid materials with fluorescent molecules (dansyl) and gold nanoparticles opens possibilities for sensor and catalytic applications, respectively. Moreover, these hybrid nanofibers can be easily transferred in organic solvents or in a “green” solvent such as water to make stable colloidal dispersions. This tunable functionality of nanofibers also allows their transferability into a variety of polymeric hosts (PVDF, PVBu, and PVP) allowing the formation of functional homogeneous nanocomposite hybrid membranes.     

A new approach to the extraction of ANN rules and to their generalization capacity through GP

Various techniques for the extraction of ANN rules have been used, but most of them have focused on certain types of networks and their training. There are very few methods that deal with ANN rule extraction as systems that are independent of their architecture, training, and internal distribution of weights, connections, and activation functions. This article proposes a methodology for the extraction of ANN rules, regardless of their architecture, and based on genetic programming. The strategy is based on the previous algorithm and aims at achieving the generalization capacity that is characteristic of ANNs by means of symbolic rules that are understandable to human beings.     

Oxidation of cysteine and glutathione by soluble polymeric MnO2

The kinetics of reduction of soluble polymeric MnO2 by cysteine and glutathione has been studied in the pH range of 4.0-9.0. The concentration of thiols was varied between 1 and 2 mM, while the MnO2 concentration was varied between 2 and 12 microM. In this pH range, the reaction products were identified as Mn(II) and the corresponding disulfides (cystine and glutathione disulfide). Cysteic or cysteine sulfonic acid was formed only when pH < 2. Experimental data indicate that the rate law over the pH range of 4-9 is first-order in both MnO2 and thiol concentration. Eyring plots for both thiols reacting with MnO2 indicate that the reaction is associative (deltaS(double dagger) approximately -160 J mol(-1) K(-1)) and proceeds via an inner-sphere redox process. The reaction proceeds via the formation of two different inner-sphere complexes [triple bond]Mn(IV)SR- and [triple bond]Mn(IV)SR and their further reaction to products. Both surface species are linked to each other via acid-base equilibria, and the rate constant decreases as pH increases. The presence of two ligand surface species is determined using surface complexation modeling. A reaction mechanism in agreement with the experimental results is proposed.     

NORM workers: a challenge for internal dosimetry programmes

The mining, milling and processing of uranium and thorium bearing minerals may result in radiation doses to workers. The control of occupational exposures from these natural sources of radiation imposes a challenge to regulators and radiation protection advisers. A survey pilot programme, which included six mines in Brazil and a monazite plant, was established, consisting of the collection and analysis of concentrations of uranium, thorium and polonium in urine, faeces and air samples. Results from workers were compared to background data from their families living in the same area and from residents from the population of Rio de Janeiro. Positive exposure results were identified among the coal miners, the niobium miners and the monazite sand workers. Difficulties in the application of internal dosimetry programmes are discussed in relation to the control of NORM workers.     

Classifying unlabeled short texts using a fuzzy declarative approach

Web 2.0 provides user-friendly tools that allow persons to create and publish content online. User generated content often takes the form of short texts (e.g., blog posts, news feeds, snippets, etc). This has motivated an increasing interest on the analysis of short texts and, specifically, on their categorisation. Text categorisation is the task of classifying documents into a certain number of predefined categories. Traditional text classification techniques are mainly based on word frequency statistical analysis and have been proved inadequate for the classification of short texts where word occurrence is too small. On the other hand, the classic approach to text categorization is based on a learning process that requires a large number of labeled training texts to achieve an accurate performance. However labeled documents might not be available, when unlabeled documents can be easily collected. This paper presents an approach to text categorisation which does not need a pre-classified set of training documents. The proposed method only requires the category names as user input. Each one of these categories is defined by means of an ontology of terms modelled by a set of what we call proximity equations. Hence, our method is not category occurrence frequency based, but highly depends on the definition of that category and how the text fits that definition. Therefore, the proposed approach is an appropriate method for short text classification where the frequency of occurrence of a category is very small or even zero. Another feature of our method is that the classification process is based on the ability of an extension of the standard Prolog language, named Bousi~Prolog , for flexible matching and knowledge representation. This declarative approach provides a text classifier which is quick and easy to build, and a classification process which is easy for the user to understand. The results of experiments showed that the proposed method achieved a reasonably useful performance.     

Inactivation of Fusarium oxysporum Conidia in Soil with Gaseous Ozone – Preliminary Studies

ABSTRACT

In this study, the efficiency of gaseous ozone (O₃) injected in the soil as an oxidizing agent for the inactivation of F. oxysporum was evaluated under laboratory conditions. The results show the treatment reached an inactivation efficiency of 76% after an applied dose of 0.40 g O₃ kg ^?1 soil. This shows that the injection of O₃ can be a viable alternative to control pathogenic organisms in the soils. Nevertheless, it is clear that more studies on determining the effects of this treatment on soil quality are needed.     

Extracting Low-Frequency Spatio-Temporal Patterns in Ambient Power System Data Using Blind Source Separation

Wide-area techniques provide a powerful tool to extract spatio-temporal patterns from high-dimensional datasets and can be used for event detection and visualization, data fusion, stability assessment, and coherency analysis. In this paper, a novel blind source separation-based approach for extracting low-frequency spatio-temporal patterns from measured ambient power system data is proposed and a spatio-temporal visualization index is also suggested. This methodology combines a nonlinear hierarchical neural network with a Blind Source Separation (BSS) technique. The neural network allows reducing noise and removing the nonlinear relations among data (preserve dynamic features of interest), while the BSS technique permits extracting spatial and temporal patterns. In addition, the proposed approach takes advantage of the latest techniques in nonlinear estimation of non-stationary time series. Finally, application examples of the proposed framework on real test cases recorded from an actual power system by Phasor Measurement Units (PMUs) are presented. The obtained results show that the temporal patterns can be used for extracting and identifying the low-frequency oscillation modes and the spatial patterns can be used for identifying modes with the most contribution in original data. Compared to other BSS approaches, the proposed method has shown to be better for the analysis of real ambient data.     

Influence of Particle Size on the Drying Kinetics of Single Droplets Containing Mixtures of Nanoparticles and Microparticles: Modeling and Pilot-Scale Validation

The particle size of the primary particles is an important parameter influencing the drying behavior of droplets. In this work, the influence of particle size on the drying kinetics and grain properties was analyzed for droplets containing silica nanoparticles, microparticles, and mixtures of the two. The presence of microparticles was found to increase the drying rate and shrinkage of the droplet. The drying curves were modeled using a reaction engineering approach (REA) model. Finally, different suspensions were dried in a pilot-scale spray dryer in order to prove the influence of the particle size obtained in the levitator tests.