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.     

Magnetic resonance of the encephalon in 17 patients with ocular Beh?et''s disease

OBJECTIVE: To evaluate the relationships between patient and physician pretreatment expectations of pain relief and subsequent pain relief reported by chronic pain patients immediately after treatment. DESIGN: Prospective study of consecutive patients undergoing a procedure in a pain clinic for treatment of chronic pain. Patients rated their current pain level and their expectation of pain relief immediately prior to undergoing a procedure (e.g., intravenous drug infusion, nerve block) for the treatment of chronic pain. Simultaneously and independently, the treating physician completed a similar questionnaire. At completion of the procedure, patients rated their current pain level and degree of pain relief. SETTING: University of Washington Multidisciplinary Pain Center procedure suite. PATIENTS: Forty-six consecutive chronic pain patients. INTERVENTION: Intravenous drug infusions and nerve blocks. OUTCOME MEASURES: Current pain and pain relief ratings. RESULTS: Patients' pain relief expectation ratings were not correlated significantly with their postprocedure pain relief ratings or pre-post procedure changes in pain ratings. However, a statistically significant correlation was found between physician expectations of pain relief and patient pain relief ratings and patient pre-post procedure changes in pain. CONCLUSIONS: The results of this study suggest that physicians are better predictors than are patients of patients responses to these procedures and/or that physicians may somehow subtly communicate their expectations to patients during the procedure, and these expectations then influence patient response. Patient pretreatment expectations may not always play a significant role in nonspecific treatment effects.     

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.     

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).     

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.     

Generating Rare Association Rules Using the Minimal Rare Itemsets Family

Rare association rules correspond to rare, or infrequent, itemsets, as opposed to frequent ones that are targeted by conventional pattern miners. Rare rules reflect regularities of local, rather than global, scope that can nevertheless provide valuable insights to an expert, especially in areas such as genetics and medical diagnosis where some specific deviations/illnesses occur only in a small number of cases. The work presented here is motivated by the long-standing open question of efficiently mining strong rare rules, i.e., rules with high confidence and low support. We also propose an efficient solution for finding the set of minimal rare itemsets. This set serves as a basis for generating rare association rules.