Microwave Induced Instability Observed in BSCCO 2212 in a Static Magnetic Field

We have measured the microwave dissipation at 10 GHz through the imaginary part of the susceptibility, $\chi^"$, in a BSCCO 2212 single crystal in an external static magnetic field $H$ parallel to the c-axis at various fixed temperatures. The characteristics of $\chi^"(H)$ exhibit a sharp step at a field $H_{step}$ which strongly depends on the amplitude of the microwave excitation $h_{ac}$. The characteristics of $h_{ac}$ vs. $H_{step}$, qualitatively reveal the behavior expected for the magnetic field dependence of Josephson coupling.Comment: 4 pages, 3 Postscript figure

Long-Range Plasmon Assisted Energy Transfer Between Fluorescent Emitters

We demonstrate plasmon assisted energy transfer between fluorophores located at distances up to $7 \, \mu$m on the top of a thin silver film. Thanks to the strong confinement and large propagation length of surface plasmon polaritons, the range of the energy transfer is almost two orders of magnitude larger than the values reported in the literature so far. The parameters driving the energy transfer range are thoroughly characterized and are in very good agreement with theoretically expected values.Comment: 5 pages, 4 figures, accepted for publication in Physical Review Letter

Fluctuations of the local density of states probe localized surface plasmons on disordered metal films

We measure the statistical distribution of the local density of optical states (LDOS) on disordered semi-continuous metal films. We show that LDOS fluctuations exhibit a maximum in a regime where fractal clusters dominate the film surface. These large fluctuations are a signature of surface-plasmon localization on the nanometer scale

Comparison of potato varieties between seasons and their potential for acrylamide formation

BACKGROUND: Acrylamide is a probable human carcinogen produced during food preparation, including frying of potato products. The aim of this study was to investigate the impact of seasonal variation on tuber composition and its acrylamide generation potential. RESULTS: The chemical composition of potato varieties used respectively for French fry (Bintje and Ramos) and crisp (Lady Rosetta and Saturna) production was studied throughout a storage period of 9 months during two growing seasons (2003 and 2004), in addition to their acrylamide generation potential during preparation of French fries. A significant impact of variable climatological conditions on the reducing sugar, dry matter, total free amino acid and free asparagine contents of tubers was observed. Exceptionally warm summers gave rise to a lower reducing sugar content (expressed on a dry matter basis) and thus a lower susceptibility to acrylamide generation during frying. CONCLUSION: It cannot be excluded that potato growers and the potato-processing industry are confronted with some harvests that are more prone to acrylamide generation than others owing to climatological variability, thus confirming the importance of a multifactorial approach to mitigate acrylamide generation in potato products.</p

Effect of transport-induced charge inhomogeneity on point-contact Andreev reflection spectra at ferromagnet-superconductor interfaces

We investigate the transport properties of a ferromagnet-superconductor interface within the framework of a modified three-dimensional Blonder-Tinkham-Klapwijk formalism. In particular, we propose that charge inhomogeneity forms via two unique transport mechanisms, namely, evanescent Andreev reflection and evanescent quasiparticle transmission. Furthermore, we take into account the influence of charge inhomogeneity on the interfacial barrier potential and calculate the conductance as a function of bias voltage. Point-contact Andreev reflection (PCAR) spectra often show dip structures, large zero-bias conductance enhancement, and additional zero-bias conductance peak. Our results indicate that transport-induced charge inhomogeneity could be a source of all these anomalous characteristics of the PCAR spectra.Comment: 9 pages, 6 figure

A first-in-human, randomized, controlled, subject- and reviewer-blinded multicenter study of Actamax™ Adhesion Barrier

Purpose: Post-surgical adhesions remain a significant concern following abdominopelvic surgery. This study was to assess safety, manageability and explore preliminary efficacy of applying a degradable hydrogel adhesion barrier to areas of surgical trauma following gynecologic laparoscopic abdominopelvic surgery. Methods: This first-in-human, prospective, randomized, multicenter, subject- and reviewer-blinded clinical study was conducted in 78 premenopausal women (18–46 years) wishing to maintain fertility and undergoing gynecologic laparoscopic abdominopelvic surgery with planned clinically indicated second-look laparoscopy (SLL) at 4–12 weeks. The first two patients of each surgeon received hydrogel, up to 30 mL sprayed over all sites of surgical trauma, and were assessed for safety and application only (n = 12). Subsequent subjects (n = 66) were randomized 1:1 to receive either hydrogel (Treatment, n = 35) or not (Control, n = 31); 63 completed the SLL. Results: No adverse event was assessed as serious, or possibly device related. None was severe or fatal. Adverse events were reported for 17 treated subjects (17/47, 36.2%) and 13 Controls (13/31, 41.9%). For 95.7% of treated subjects, surgeons found the device “easy” or “very easy” to use; in 54.5%, some residual material was evident at SLL. For 63 randomized subjects who completed the SLL, adjusted between-group difference in the change from baseline adhesion score demonstrated a 41.4% reduction for Treatment compared with Controls (p = 0.017), with a 49.5% reduction (p = 0.008) among myomectomy subjects (n = 34). Conclusion: Spray application of a degradable hydrogel adhesion barrier during gynecologic laparoscopic abdominopelvic surgery was performed easily and safely, without evidence of clinically significant adverse outcomes. Data suggest the hydrogel was effective in reducing postoperative adhesion development, particularly following myomectomy

Robust Long-Term Hand Grasp Recognition With Raw Electromyographic Signals Using Multidimensional Uncertainty-Aware Models

Hand grasp recognition with surface electromyography (sEMG) has been used as a possible natural strategy to control hand prosthetics. However, effectively performing activities of daily living for users relies significantly on the long-term robustness of such recognition, which is still a challenging task due to confused classes and several other variabilities. We hypothesise that this challenge can be addressed by introducing uncertainty-aware models because the rejection of uncertain movements has previously been demonstrated to improve the reliability of sEMG-based hand gesture recognition. With a particular focus on a very challenging benchmark dataset (NinaPro Database 6), we propose a novel end-to-end uncertainty-aware model, an evidential convolutional neural network (ECNN), which can generate multidimensional uncertainties, including vacuity and dissonance, for robust long-term hand grasp recognition. To avoid heuristically determining the optimal rejection threshold, we examine the performance of misclassification detection in the validation set. Extensive comparisons of accuracy under the non-rejection and rejection scheme are conducted when classifying 8 hand grasps (including rest) over 8 subjects across proposed models. The proposed ECNN is shown to improve recognition performance, achieving an accuracy of 51.44% without the rejection option and 83.51% under the rejection scheme with multidimensional uncertainties, significantly improving the current state-of-the-art (SoA) by 3.71% and 13.88%, respectively. Furthermore, its overall rejection-capable recognition accuracy remains stable with only a small accuracy degradation after the last data acquisition over 3 days. These results show the potential design of a reliable classifier that yields accurate and robust recognition performance

Reliability Analysis for Finger Movement Recognition With Raw Electromyographic Signal by Evidential Convolutional Networks

Hand gesture recognition with surface electromyography (sEMG) is indispensable for Muscle-Gesture-Computer Interface. The usual focus of it is upon performance evaluation involving the accuracy and robustness of hand gesture recognition. However, addressing the reliability of such classifiers has been absent, to our best knowledge. This may be due to the lack of consensus on the definition of model reliability in this field. An uncertainty-aware model has the potential to self-evaluate the quality of its inference, thereby making it more reliable. Moreover, uncertainty-based rejection has been shown to improve the performance of sEMG-based hand gesture recognition. Therefore, we first define model reliability here as the quality of its uncertainty estimation and propose an offline framework to quantify it. To promote reliability analysis, we propose a novel end-to-end uncertainty-aware finger movement classifier, i.e., evidential convolutional neural network (ECNN), and illustrate the advantages of its multidimensional uncertainties such as vacuity and dissonance. Extensive comparisons of accuracy and reliability are conducted on NinaPro Database 5, exercise A, across CNN and three variants of ECNN based on different training strategies. The results of classifying 12 finger movements over 10 subjects show that the best mean accuracy achieved by ECNN is 76.34%, which is slightly higher than the state-of-the-art performance. Furthermore, ECNN variants are more reliable than CNN in general, where the highest improvement of reliability of 19.33% is observed. This work demonstrates the potential of ECNN and recommends using the proposed reliability analysis as a supplementary measure for studying sEMG-based hand gesture recognition