The Method of Images applied to the grounded sphere: The problem of the ground wire

The Method of Images poses an important difficulty when used to solve the problem of a charge in the presence of a grounded conducting sphere. This arises from the fact that the sum of the inducing charge and the image charge is different from zero. As a consequence, there is a monopole field far from the system, and any ground wire physically connected to the sphere will carry an electric current, changing the initial balance of charges until a new equilibrium is reached. The approach taken in this paper assumed an infinite straight wire connecting the sphere to ground. The charge distribution over the surface of the conductors was calculated, and the results analyzed. It was shown that the thinner the wire, the lower will be its total charge, and the closer will be the calculated charge density at the surface of the sphere to the conventional solution by the Method of Images.     

Nanoscale mapping of plasmon resonances of functional multibranched gold nanoparticles

The optical response of multibranched gold nanoparticles is studied by means of electron energy-loss spectroscopy (EELS) in aberration corrected STEM mode. In every case the plasmon response is constant and variations in the maxima positions were found to be dependent on the branches aspect ratio. The good spatial resolution combined with the high energy resolution (0.18 eV) of the monochromated electron beam allows mapping the different plasmonic modes along the entire nanoparticles ranging from 0.7 eV up to 2.25 eV.     

Convex Quantum Logic

In this work we study the convex set of quantum states from a quantum logical point of view. We consider an algebraic structure based on the convex subsets of this set. The relationship of this algebraic structure with the lattice of propositions of quantum logic is shown. This new structure is suitable for the study of compound systems and shows new differences between quantum and classical mechanics. These differences are linked to the nontrivial correlations which appear when quantum systems interact. They are reflected in the new propositional structure, and do not have a classical analogue. This approach is also suitable for an algebraic characterization of entanglement and it provides a new entanglement criteria.     

Fluctuation Bounds for Chaos Plus Noise in Dynamical Systems

We are interested in time series of the form y _n =x _n +ξ _n where {x _n } is generated by a chaotic dynamical system and where ξ _n models observational noise. Using concentration inequalities, we derive fluctuation bounds for the auto-covariance function, the empirical measure, the kernel density estimator and the correlation dimension evaluated along y ₀,…,y _n , for all n. The chaotic systems we consider include for instance the Hénon attractor for Benedicks-Carleson parameters.     

Controlled Radical Polymerization of 2,3‐Epithiopropyl Methacrylate

We report first results on the controlled radical polymerization of 2,3‐epithiopropyl methacrylate (ETMA) also known as thiiran‐2‐ylmethyl methacrylate. Reversible addition‐fragmentation chain transfer (RAFT) of ETMA was carried out in bulk and in solution, using AIBN as initiator and the chain transfer agents: cyanopropyl dithiobenzoate (CPDB) and cumyl dithiobenzoate (CDB). A linear increase of the number‐average molecular weight and decrease of the polydispersity with monomer conversion were observed using CPDB as transfer agent, indicating a controlled process. Atom transfer radical polymerization (ATRP) of ETMA was performed under different reaction conditions using copper bromide complexed by tertiary amine ligands and ethyl 2‐bromoisobutyrate (EBiB) or 2‐bromopropionitrile (BPN) as initiator. All experiments lead to a crosslinked polymer. Preliminary studies in the absence of initiator showed that the CuBr/ligand complex alone initiates the ring‐opening polymerization of thiirane leading to a poly(propylene sulfide) with pendant methacrylate groups.

     

A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition

An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain–computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD may limit the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.     

Combining classification and regression approaches for the quantification of highly overlapping capillary electrophoresis peaks by using evolutionary sigmoidal and product unit neural networks

This is a study of the potential of neural networks built by using different transfer functions (sigmoidal, product and sigmoidal–product units) designed by an evolutionary algorithm to quantify highly overlapping electrophoretic peaks. To test this approach, two aminoglycoside antibiotics, amikacin and paramomycin, were quantified from samples containing either only one component or mixtures of them though capillary zone electrophoresis (CZE) with laser‐induced fluorescence (LIF) detection. The three models assayed used as input data the four‐parameter Weibull curve associated with the profile of the electrophoretic peak and in some cases the class label for each sample estimated by cluster analysis. The combination of classification and regression approaches allowed the establishment of straightforward network topologies enabling the analytes to be quantified with great accuracy and precision. The best models for mixture samples were provided by product unit neural networks (PUNNs), 4:4:1 (14 weights) for both analytes, after discrimination by cluster analysis, allowing the analytes to be quantified with great accuracy: 8.2% for amikacin and 5.6% for paromomycin within the standard error of prediction for the generalization test, SEP_G. For comparison, partial least square regression was also used for the resolution of these mixtures; it provided a minor accuracy: SEP_G 11.8 and 15.7% for amikacin and paramomycin, respectively. The reduced dimensions of the neural networks models selected enabled the derivation of simple quantification equations to transform the input variables into the output variable. These equations can be more easily interpreted from a chemical point of view than those provided by other ANN models. Copyright © 2007 John Wiley & Sons, Ltd.     

Analysis of Plant–Plant Interactions Reveals the Presence of Potent Antileukemic Compounds

A method to identify anticancer compounds in plants was proposed based on the hypothesis that these compounds are primarily present in plants to provide them with an ecological advantage over neighboring plants and other competitors. According to this view, identifying plants that contain compounds that inhibit or interfere with the development of other plant species may facilitate the discovery of novel anticancer agents. The method was developed and tested using Magnolia grandiflora, Gynoxys verrucosa, Picradeniopsis oppositifolia, and Hedyosmum racemosum, which are plant species known to possess compounds with cytotoxic activities. Plant extracts were screened for growth inhibitory activity, and then a thin-layer chromatography bioautography assay was conducted. This located the major antileukemic compounds 1, 2, 4, and 5 in the extracts. Once the active compounds were located, they were extracted and purified, and their structures were determined. The growth inhibitory activity of the purified compounds showed a significant correlation with their antileukemic activity. The proposed approach is rapid, inexpensive, and can easily be implemented in areas of the world with high biodiversity but with less access to advanced facilities and biological assays.     

Inhibition of Bacterial Adhesion and Biofilm Formation by Seed-Derived Ethanol Extracts from Persea americana Mill

The increase in antibiotic resistance demands innovative strategies to combat microorganisms. The current study evaluated the antibacterial and antivirulence effects of ethanol extracts from Persea americana seeds obtained by the Soxhlet (SE) and maceration (MaE) methods. The UHPLC-DAD-QTOF analysis showed mainly the presence of polyphenols and neolignan. Ethanol extracts were not cytotoxic to mammalian cells (CC₅₀ > 500 µg/mL) and displayed a moderate antibacterial activity against Pseudomonas aeruginosa (IC₅₀ = 87 and 187 µg/mL) and Staphylococcus aureus (IC₅₀ = 144 and 159 µg/mL). Interestingly, no antibacterial activity was found against Escherichia coli. SE and MaE extracts were also able to significantly reduce the bacterial adhesion to A549 lung epithelial cells. Additionally, both extracts inhibited the biofilm growth at 24 h and facilitated the release of internal cell components in P. aeruginosa, which might be associated with cell membrane destabilization. Real-time PCR and agarose electrophoresis gel analysis indicated that avocado seed ethanol extracts (64 µg/mL) downregulated virulence-related factors such as mexT and lasA genes. Our results support the potential of bioproducts from P. americana seeds as anti-adhesive and anti-biofilm agents.     

PtFe catalysts supported on hierarchical porous carbon toward oxygen reduction reaction in microbial fuel cells

Journal of Solid State Electrochemistry - A new family of PtFe catalysts supported on hierarchical porous carbon (HPC), with different porous sizes, was developed and tested as cathodes in oxygen...