Isomer-specific influences on the composition of reaction intermediates in dimethyl ether/propene and ethanol/propene flame

This work provides experimental evidence on how the molecular compositions of fuel-rich low-pressure premixed flames are influenced as the oxygenates dimethyl ether (DME) or ethanol are incrementally blended into the propene fuel. Ten different flames with a carbon-to-oxygen ratio of 0.5, ranging from 100% propene (phi = 1.5) to 100% oxygenated fuel (phi = 2.0), are analyzed with flame-sampling molecular-beam mass spectrometry employing electron- or photoionization. Absolute mole fraction profiles for flame species with masses ranging from m/z = 2 (H2) to m/z = 80 (C6H8) are analyzed with particular emphasis on the formation of harmful emissions. Fuel-specific destruction pathways, likely to be initiated by hydrogen abstraction, appear to lead to benzene from propene combustion and to formaldehyde and acetaldehyde through DME and ethanol combustion, respectively. While the concentration of acetaldehyde increases 10-fold as propene is substituted by ethanol, it decreases as propene is replaced with DME. In contrast, the formaldehyde concentration rises only slightly with ethanol replacement but increases markedly with addition of DME. Allyl and propargyl radicals, the dominant precursors for benzene formation, are likely to be produced directly from propene decomposition or via allene and propyne. Benzene formation through propargyl radicals formed via unsaturated C2 intermediates in the decomposition of DME and ethanol is negligibly small. As a consequence, DME and ethanol addition lead to similar reductions of the benzene concentration.     

Ensemble Approach for Detection of Depression Using EEG Features

Depression is a public health issue that severely affects one’s well being and can cause negative social and economic effects to society. To raise awareness of these problems, this research aims at determining whether the long-lasting effects of depression can be determined from electroencephalographic (EEG) signals. The article contains an accuracy comparison for SVM, LDA, NB, kNN, and D3 binary classifiers, which were trained using linear (relative band power, alpha power variability, spectral asymmetry index) and nonlinear (Higuchi fractal dimension, Lempel–Ziv complexity, detrended fluctuation analysis) EEG features. The age- and gender-matched dataset consisted of 10 healthy subjects and 10 subjects diagnosed with depression at some point in their lifetime. Most of the proposed feature selection and classifier combinations achieved accuracy in the range of 80% to 95%, and all the models were evaluated using a 10-fold cross-validation. The results showed that the motioned EEG features used in classifying ongoing depression also work for classifying the long-lasting effects of depression.     

Insertion/isomerization polymerization of 1,5-hexadiene: synthesis of functional propylene copolymers and block copolymers

Polymerization of 1,5-hexadiene with a bis(phenoxyimine) titanium catalyst system is reported. The microstructure of the polymer contains the expected methylene-1,3-cyclopentane units as well as the unexpected 3-vinyl tetramethylene units. A mechanism for formation of this polymer is proposed. This unusual reaction is also employed in the synthesis of vinyl-functional polypropylene copolymers and block copolymers with low polydispersity indices.     

Monotone norms and tensor products

Various refinements of orthant monotonicity for norms are studied. A partial order is induced in the tensor product of two partially ordered vector spaces. The induced norm in the tensor product is shown to be orthant monotone in certain cases.