Carleton University

About: Carleton University is a education organization based out in Ottawa, Ontario, Canada. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 15852 authors who have published 39650 publications receiving 1106610 citations.

Observation of a narrow resonance of mass 2.46 GeV/c2 decaying to D*s+π0 and confirmation of the D*sJ(2317) state

Abstract: Using 13.5 ${\mathrm{fb}}^{\ensuremath{-}1}$ of ${e}^{+}{e}^{\ensuremath{-}}$ annihilation data collected with the CLEO II detector, we have observed a narrow resonance decaying to ${D}_{s}^{*+}{\ensuremath{\pi}}^{0}$ with a mass near $2.46\mathrm{GeV}{/c}^{2}.$ The search for such a state was motivated by the recent discovery by the BaBar Collaboration of a narrow state at $2.32\mathrm{GeV}{/c}^{2},$ the ${D}_{\mathrm{sJ}}^{*}{(2317)}^{+},$ that decays to ${D}_{s}^{+}{\ensuremath{\pi}}^{0}.$ Reconstructing the ${D}_{s}^{+}{\ensuremath{\pi}}^{0}$ and ${D}_{s}^{*+}{\ensuremath{\pi}}^{0}$ final states in CLEO data, we observe peaks in both of the corresponding reconstructed mass difference distributions, $\ensuremath{\Delta}{M(D}_{s}{\ensuremath{\pi}}^{0}{)=M(D}_{s}{\ensuremath{\pi}}^{0})\ensuremath{-}{M(D}_{s})$ and $\ensuremath{\Delta}{M(D}_{s}^{*}{\ensuremath{\pi}}^{0}{)=M(D}_{s}^{*}{\ensuremath{\pi}}^{0})\ensuremath{-}{M(D}_{s}^{*}),$ both of them at values near $350\mathrm{MeV}{/c}^{2}.$ We interpret these peaks as signatures of two distinct states, the ${D}_{\mathrm{sJ}}^{*}{(2317)}^{+}$ plus a new state, designated as the ${D}_{\mathrm{sJ}}{(2463)}^{+}.$ Because of the similar $\ensuremath{\Delta}M$ values, each of these states represents a source of background for the other if photons are lost, ignored or added. A quantitative accounting of these reflections confirms that both states exist. We have measured the mean mass differences $〈\ensuremath{\Delta}{M(D}_{s}{\ensuremath{\pi}}^{0})〉=350.0\ifmmode\pm\else\textpm\fi{}1.2(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}1.0(\mathrm{syst})\mathrm{MeV}{/c}^{2}$ for the ${D}_{\mathrm{sJ}}^{*}{(2317)}^{+}$ state, and $〈\ensuremath{\Delta}{M(D}_{s}^{*}{\ensuremath{\pi}}^{0})〉=351.2\ifmmode\pm\else\textpm\fi{}1.7(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}1.0(\mathrm{syst})\mathrm{MeV}{/c}^{2}$ for the new ${D}_{\mathrm{sJ}}{(2463)}^{+}$ state. We have also searched, but find no evidence, for decays of the two states via the channels ${D}_{s}^{*+}\ensuremath{\gamma},{D}_{s}^{+}\ensuremath{\gamma},$ and ${D}_{s}^{+}{\ensuremath{\pi}}^{+}{\ensuremath{\pi}}^{\ensuremath{-}}.$ The observations of the two states at 2.32 and $2.46\mathrm{GeV}{/c}^{2},$ in the ${D}_{s}^{+}{\ensuremath{\pi}}^{0}$ and ${D}_{s}^{*+}{\ensuremath{\pi}}^{0}$ decay channels, respectively, are consistent with their interpretations as $c\overline{s}$ mesons with an orbital angular momentum $L=1$ and spin and parity ${J}^{P}{=0}^{+}$ and ${1}^{+}.$

Continuous myoelectric control for powered prostheses using hidden Markov models

Abstract: This paper represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal. The scheme described within uses a hidden Markov model (HMM) to process four channels of myoelectric signal, with the task of discriminating six classes of limb movement. The HMM-based approach is shown to be capable of higher classification accuracy than previous methods based upon multilayer perceptrons. The method does not require segmentation of the myoelectric signal data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. The computational complexity of the HMM in its operational mode is low, making it suitable for a real-time implementation. The low computational overhead associated with training the HMM also enables the possibility of adaptive classifier training while in use.

The NR-6: a new brief measure of nature relatedness.

Abstract: The construct of (dis)connection with nature or 'nature relatedness' has become increasingly useful in the study of environmental behaviour as well as psychological health and well-being. Strong nature relatedness is associated with greater happiness and ecologically sustainable behaviour. A number of scales reliably assess individual differences in nature relatedness, but some circumstances may necessitate a brief measure. We developed a short-form version of the nature relatedness scale (NR-6), comprised of 6 items from the 'self' and 'experience' dimensions, and tested the new scale's predictive ability across multiple samples and with longitudinal data in students, community members, and business people. The new NR-6 scale demonstrated good internal consistency, temporal stability, and predicted happiness, environmental concern, and nature contact. This new brief measure of connectedness may have advantages where time and space are limited and the research context requires an assessment of connectedness elements rather than environmental attitudes.