Maskless Preparation of Spatially-Resolved Plasmonic Nanoparticles on Polydimethylsiloxane via In Situ Fluoride-Assisted Synthesis

Here, a fluoride-assisted route for the controlled in-situ synthesis of metal nanoparticles (NPs) (i.e., AgNPs, AuNPs) on polydimethylsiloxane (PDMS) is reported. The size and coverage of the NPs on the PDMS surface are modulated with time and over space during the synthetic process, leveraging the improved yield (10×) and faster kinetics (100×) of NP formation in the presence of F⁻ ions, compared to fluoride-free approaches. This enables the maskless preparation of both linear and step gradients and patterns of NPs in 1D and 2D on the PDMS surface. As an application in flexible plasmonics/photonics, continuous and step-wise spatial modulations of the plasmonic features of PDMS slabs with 1D and 2D AgNP gradients on the surface are demonstrated. An excellent spatially resolved tuning of key optical parameters, namely, optical density from zero to 5 and extinction ratio up to 100 dB, is achieved with AgNP gradients prepared in AgF solution for 12 minutes; the performance are comparable to those of commercial dielectric/interference filters. When used as a rejection filter in optical fluorescence microscopy, the AgNP-PDMS slabs are able to reject the excitation laser at 405 nm and retain the green fluorescence of microbeads (100 µm) used as test cases.     

DCOP FOR SMART HOMES: A CASE STUDY

The aim of this article is to bring forth the issue of integrating the services provided by intelligent artifacts in Ambient Intelligence applications. Specifically, we propose a Distributed Constraint Optimization procedure for achieving a functional integration of intelligent artifacts in a smart home. To this end, we employ Adopt-N , a state-of-the-art algorithm for solving Distributed Constraint Optimization Problems (DCOP). This article attempts to state the smart home coordination problem in general terms, and provides the details of a DCOP-based approach by describing a case study taken from the RoboCare project. More specifically, we show how (1) DCOP is a convenient metaphor for casting smart home coordination problems, and (2) the specific features which distinguish Adopt-N from other algorithms for DCOP represent a strong asset in the smart home domain.     

A chloride method for the determination of the enthalpy of steam/water mixtures discharged from geothermal wells

Chloride concentrations and pressures (or temperatures) in two points of a geothermal test line have been used to determine the enthalpy of the steam/water mixture discharged from Nisyros I geothermal well (Greece). The results are in agreement with those obtained by the Russell James method. The chloride method described in this paper is fast, requires cheap, portable equipment and is thus particularly suitable for short-term well testing during drilling or immediately after well completion, especially in steam-rich mixtures.     

Insect antifeedant activity associated with compounds isolated from species ofLonchocarpus andTephrosia

The antifeedant activity of a series of 21 chalcones, flavanes, and flavanones isolated from the generaLonchocarpus andTephrosia (Leguminosae) was assessed by behavioral and electrophysiological bioassays against larvae ofSpodoptera littoralis andS. exempta. The antifeedant activity is related to the molecular structure of the compounds, and possible modes of interaction with the insect taste receptors are discussed.     

Optimal Operating Conditions For Lab-Scale Ozonation Reactors

A method for the assessment of the optimal operating conditions for a mechanically stirred gas–liquid reactor is presented. The method exploits both fluid dynamic and chemical information. First, the behavior of the specific stirring power as a function of the stirrer speed allows singling out the dispersion region, in which the most efficient gas–liquid mass transfer is achieved. Inside this region, the analysis of experimental data obtained when considering a chemical system reacting at moderate Hatta numbers (i.e., Ha < about 2) allows determination of the rate constants and the fluid dynamic parameters (i.e., the mass transfer coefficient in the absence of chemical reaction and the characteristic diffusion time).     

Development of a model for the simultaneous analysis of wheel and rail wear in railway systems

The prediction of wheel and rail wear is a fundamental issue in the railway field, both in terms of vehicle stability and in terms of economic costs (planning of maintenance interventions). In particular the need of an accurate wear model arises from the interest of Trenitalia S.p.A. and Rete Ferroviara Italiana in designing new wheel and rail profiles and new bogie architectures optimized from the wear viewpoint with the aim of improving the wear and stability behavior of the standard ORE S1002 wheel profile matched with the UIC60 rail profile canted at 1/20 rad (which represents the wheel–rail combination adopted by the Italian railway line). In this work the authors present a wear model specifically developed for the evaluation of the wheel and rail profile evolution, the layout of which is made up of two mutually interactive but separate units: a vehicle model for the dynamical analysis and a model for the wear evaluation. Subsequently the new model has been compared with the wear evaluation procedure implemented in Simpack, a widely tested and validated multibody software for the analysis of the railway vehicle dynamics; the comparison aims both to evaluate the model performance (in terms of accuracy and efficiency) and to further validate the wear model (just tested, as regards the wheel wear prediction, in previous works related to the critical Aosta–Pre Saint Didier line). The comparison has been carried out considering a benchmark train composed by a locomotive (E.464) and a passenger vehicle (Vivalto) provided by Trenitalia while the simulations have been performed on a mean Italian railway line (obtained by means of a statistical analysis of the data relative to the whole Italian railway network provided by Rete Ferroviaria Italiana (RFI)).     

Biclustering meets triadic concept analysis

Biclustering numerical data became a popular data-mining task at the beginning of 2000’s, especially for gene expression data analysis and recommender systems. A bicluster reflects a strong association between a subset of objects and a subset of attributes in a numerical object/attribute data-table. So-called biclusters of similar values can be thought as maximal sub-tables with close values. Only few methods address a complete, correct and non-redundant enumeration of such patterns, a well-known intractable problem, while no formal framework exists. We introduce important links between biclustering and Formal Concept Analysis (FCA). Indeed, FCA is known to be, among others, a methodology for biclustering binary data. Handling numerical data is not direct, and we argue that Triadic Concept Analysis (TCA), the extension of FCA to ternary relations, provides a powerful mathematical and algorithmic framework for biclustering numerical data. We discuss hence both theoretical and computational aspects on biclustering numerical data with triadic concept analysis. These results also scale to n-dimensional numerical datasets.     

A fast compound algorithm for mining generators,closed itemsets,and computing links between equivalence classes

In pattern mining and association rule mining, there is a variety of algorithms for mining frequent closed itemsets (FCIs) and frequent generators (FGs), whereas a smaller part further involves the precedence relation between FCIs. The interplay of these three constructs and their joint computation have been studied within the formal concept analysis (FCA) field yet none of the proposed algorithms is scalable. In frequent pattern mining, at least one suite of efficient algorithms has been designed that exploits basically the same ideas and follows the same overall computational schema. Based on an in-depth analysis of the aforementioned interplay that is rooted in a fundamental duality from hypergraph theory, we propose a new schema that should enable for a more parsimonious computation. We exemplify the new schema in the design of Snow-Touch, a concrete FCI/FG/precedence miner that reuses an existing algorithm, Charm, for mining FCIs, and completes it with two original methods for mining FGs and precedence, respectively. The performance of Snow-Touch and of its closest competitor, Charm-L, were experimentally compared using a large variety of datasets. The outcome of the experimental study suggests that our method outperforms Charm-L on dense data while on sparse one the trend is reversed. Furthermore, we demonstrate the usefulness of our method and the new schema through an application to the analysis of a genome dataset. The initial results reported here confirm the capacity of the method to focus on significant associations.