This is the first published study to calculate a reference equation for peak nasal inspiratory flow in North African subjects. This equation enables objective evaluation of nasal airway patency in patients of North African origin.

Sulfide-functionalized bambus[4]urils ((RS) BU[4]) and bambus[6]urils ((RS) BU[6]) were synthesized through thiol-ene click coupling reactions (TEC) of allylbambus[n]urils. Thiosugars were grafted to BU[4] and BU[6]. Synthesis of BU[6] derivatives always requires the use of a template anion (iodide, chloride, or bromide), which is enclosed in the cavity of BU[6]. We show that this anion influences the reactivity of bambus[6]urils. An encapsulated iodide makes allyl functions of allyl BU[6] less reactive towards TEC and hydrogenation reactions in comparison to the corresponding chloride or bromide inclusion complexes. This is critical for the chemical reactivity of BU[6] and even more to determine their anion-binding properties. We report a new, facile and fast method using AgSbF to prepare anion-free BU[6]. NMR spectroscopic methods were used to estimate association constants of these new empty BU[6] with different anions. Quantum chemical calculations were employed to rationalize the observed results. These new functionalized bambusuril scaffolds in alternate conformations could find applications as multivalent binders.

This paper introduces a novel approach for the recognition of a wide vocabulary of Arabic handwritten words. Note that there is an essential difference between the global and analytic approaches in pattern recognition. While the global approach is limited to reduced vocabulary, the analytic approach succeeds to recognize a wide vocabulary but meets the problems of word segmentation especially for Arabic. Combining the neural approach with some linguistic characteristics of the Arabic, it is expected that we become able to recognize better and to handle a large vocabulary of Arabic handwritten words. The proposed approach invokes two transparent neural networks, TNN_1 and TNN_2, to respectively recognize roots, schemes and the elements of conjugation from the structural primitives of the words. The approach was evaluated using examples from a database established for this purpose. The results are promising, and suggestions for improvements are proposed.

Abstr actThe complexity of the Ara bic characters morphology makes resea rch in recognition of the ha ndwritten Arabic writing rema in an interesting topic. In this setting, a system for recognition of ha ndwritten Arabic words ba sed on a Transparent Neural Network, ca lled TNNDF is developed within the LSTS (1) laboratory. It uses structural fea tures to describe words and ma kes recourse to Fourier descriptors (DF) when encounters a n ambiguity. To enha nce recognition results of TNNDF, we suggest a neural approach to learn letters, pa rt of a ra bic words and words. Experiments conducted on 750 samples, of 50 city na mes, extracted from the standard IFN/ENIT (2) da taba se of handwritten Tunisian city na mes show a n improvement of recognition accura cy. The results are promising, a nd suggestions for improvements leading to recognition of larger voca bula ry are proposed.

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