Probing Spin-Charge Relation by Magnetoconductance in One-Dimensional Polymer Nanofibers

Polymer nanofibers are one-dimensional organic hydrocarbon systems containing conducting polymers where the non-linear local excitations such as solitons, polarons and bipolarons formed by the electron-phonon interaction were predicted. Magnetoconductance (MC) can simultaneously probe both the spin and charge of these mobile species and identify the effects of electron-electron interactions on these nonlinear excitations. Here we report our observations of a qualitatively different MC in polyacetylene (PA) and in polyaniline (PANI) and polythiophene (PT) nanofibers. In PA the MC is essentially zero, but it is present in PANI and PT. The universal scaling behavior and the zero (finite) MC in PA (PANI and PT) nanofibers provide evidence of Coulomb interactions between spinless charged solitons (interacting polarons which carry both spin and charge)

Improving human robot collaboration through Force/Torque based learning for object manipulation

Human–Robot Collaboration (HRC) is a term used to describe tasks in which robots and humans work together to achieve a goal. Unlike traditional industrial robots, collaborative robots need to be adaptive; able to alter their approach to better suit the situation and the needs of the human partner. As traditional programming techniques can struggle with the complexity required, an emerging approach is to learn a skill by observing human demonstration and imitating the motions; commonly known as Learning from Demonstration (LfD). In this work, we present a LfD methodology that combines an ensemble machine learning algorithm (i.e. Random Forest (RF)) with stochastic regression, using haptic information captured from human demonstration. The capabilities of the proposed method are evaluated using two collaborative tasks; co-manipulation of an object (where the human provides the guidance but the robot handles the objects weight) and collaborative assembly of simple interlocking parts. The proposed method is shown to be capable of imitation learning; interpreting human actions and producing equivalent robot motion across a diverse range of initial and final conditions. After verifying that ensemble machine learning can be utilised for real robotics problems, we propose a further extension utilising Weighted Random Forest (WRF) that attaches weights to each tree based on its performance. It is then shown that the WRF approach outperforms RF in HRC tasks.</p

Nanofiltration of aerobically-treated palm oil mill effluent: Characterization of the size of colour compounds using synthetic dyes and polyethylene glycols

Membrane-based separation is one of the emerging technologies that have garnered significant interest in recent years for the treatment process of palm oil mill effluent (POME). As documented in the literature, different types of membrane processes such as ultrafiltration (UF), nanofiltration (NF) and reverse osmosis (RO) were used for the POME treatment and the efficiency of separation varied depending on the membrane properties. Unlike the previous works that used membranes to treat POME, the main focus of this current work is to utilize NF membrane to characterize the size of colour compounds in the aerobically-treated POME (AT-POME). Two different markers, i.e., synthetic dyes and polyethylene glycols (PEGs) with molecular weight (MW) in the range of 200-1000 g/mol were used to characterize the colour compounds in the AT-POME. Results showed that dyes are more suitable compared to PEGs for the characterization because dyes possessed negative charge similar as the colour compounds in the AT-POME. By using dyes as the markers, it was found that the size of the colour compounds in the AT-POME was at MW of 300-400 g/mol. Precise determination of the size of colour compounds in the AT-POME is of importance as it could provide useful information on the selection of ideal membrane properties (in particular pore size or molecular weight cut-off) to achieve complete solute separation

Impacts of hydrophilic nanofillers on separation performance of thin film nanocomposite reverse osmosis membrane

The membrane technology is still considered a costly method to produce potable water. In view of this, RO membrane with enhanced water permeability without trade-off in salt rejection is desirable as it could further reduce the cost for water desalination. In this study, thin film nanocomposite (TFN) membranes containing 0.05 or 0.10 w/v% hydrophilic nanofillers in polyamide layer were synthesized via interfacial polymerization of piperazine and trimesoyl chloride monomers. The resultant TFN membranes were characterized and compared with a control thin film composite (TFC) membrane. Results from the filtration experiments showed that TFN membranes exhibited higher water permeability, salt rejection and fouling resistance compared to that of the TFC membrane. Excessive amount of nanofillers incorporated in the membrane PA layer however negatively affected the cross-linking in the polymer matrix, thus deteriorating the membrane salt rejection. TFN membrane containing 0.05 w/v% of nanofillers showed better performances than the TFC membrane, recording a pure water flux of 11.2 L/m2∙he membsalt rejection of 95.4%, 97.3% and 97.5% against NaCl, Na2SO4 and MgSO4, respectively

Neutrino Mass and Grand Unification

Seesaw mechanism appears to be the simplest and most appealing way to understand small neutrino masses observed in recent experiments. It introduces three right handed neutrinos with heavy masses to the standard model, with at least one mass required by data to be close to the scale of conventional grand unified theories. This may be a hint that the new physics scale implied by neutrino masses and grand unification of forces are one and the same. Taking this point of view seriously, I explore different ways to resolve the puzzle of large neutrino mixings in grand unified theories such as SO(10) and models based on its subgroup $SU(2)_L\times SU(2)_R\times SU(4)_c$.Comment: 17 pages, 5 figures; Invited talk at the Nobel Symposium 129 on Neutrinos at Haga Slott, Sweden, August, 200

Fluctuation-driven dynamics of the Internet topology

We study the dynamics of the Internet topology based on the empirical data on the level of the autonomous systems. It is found that the fluctuations occurring in the stochastic process of connecting and disconnecting edges are important features of the Internet dynamics. The network's overall growth can be described approximately by a single characteristic degree growth rate $g_{\rm eff} \approx 0.016$ and the fluctuation strength $\sigma_{\rm eff} \approx 0.14$, together with the vertex growth rate $\alpha \approx 0.029$. A stochastic model which incorporate these values and an adaptation rule newly introduced reproduces several features of the real Internet topology such as the correlations between the degrees of different vertices.Comment: Final version appeared in Phys. Rev. Let

Synthetic RNA Silencing of Actinorhodin Biosynthesis in Streptomyces coelicolor A3(2)

We demonstrate the first application of synthetic RNA gene silencers in Streptomyces coelicolor A3(2). Peptide nucleic acid and expressed antisense RNA silencers successfully inhibited actinorhodin production. Synthetic RNA silencing was target-specific and is a new tool for gene regulation and metabolic engineering studies in Streptomyces.Peer reviewe

A renormalizable SO(10) GUT scenario with spontaneous CP violation

We consider fermion masses and mixings in a renormalizable SUSY SO(10) GUT with Yukawa couplings of scalar fields in the representation 10 + 120 + 126 bar. We investigate a scenario defined by the following assumptions: i) A single large scale in the theory, the GUT scale. ii) Small neutrino masses generated by the type I seesaw mechanism with negligible type II contributions. iii) A suitable form of spontaneous CP breaking which induces hermitian mass matrices for all fermion mass terms of the Dirac type. Our assumptions define an 18-parameter scenario for the fermion mass matrices for 18 experimentally known observables. Performing a numerical analysis, we find excellent fits to all observables in the case of both the normal and inverted neutrino mass spectrum.Comment: 16 pages, two eps figure