1,685 research outputs found
Parametric analysis of hollow conductor parallel and coaxial transmission lines for high frequency space power distribution
A parametric analysis was performed of transmission cables for transmitting electrical power at high voltage (up to 1000 V) and high frequency (10 to 30 kHz) for high power (100 kW or more) space missions. Large diameter (5 to 30 mm) hollow conductors were considered in closely spaced coaxial configurations and in parallel lines. Formulas were derived to calculate inductance and resistance for these conductors. Curves of cable conductance, mass, inductance, capacitance, resistance, power loss, and temperature were plotted for various conductor diameters, conductor thickness, and alternating current frequencies. An example 5 mm diameter coaxial cable with 0.5 mm conductor thickness was calculated to transmit 100 kW at 1000 Vac, 50 m with a power loss of 1900 W, an inductance of 1.45 micron and a capacitance of 0.07 micron-F. The computer programs written for this analysis are listed in the appendix
Complications of Resection Arthroplasty in Two-Stage Revision for the Treatment of Periprosthetic Hip Joint Infection
Little data is available regarding complications associated with resection arthroplasty in the treatment of hip periprosthetic joint infection (PJI). We assessed complications during and after two-stage revision using resection arthroplasty. In this retrospective study, 93 patients undergoing resection arthroplasty for hip PJI were included. Patients were assigned to a prosthesis-free interval of ≤10 weeks (group 1; 49 patients) or >10 weeks (group 2; 44 patients). The complication rates between groups were compared using the chi-squared test. The revision-free and infection-free survival was estimated using a Kaplan-Meier survival analysis. Seventy-one patients (76%) experienced at least one local complication (overall 146 complications). Common complications were blood loss during reimplantation (n = 25) or during explantation (n = 23), persistent infection (n = 16), leg length discrepancy (n = 13) and reinfection (n = 9). Patients in group 1 experienced less complications after reimplantation (p = 0.012). With increasing severity of acetabular bone defects, higher incidence of complications (p = 0.008), periprosthetic bone fractures (p = 0.05) and blood loss (p = 0.039) was observed. The infection-free survival rate at 24 months was 93.9% in group 1 and 85.9% in group 2. The indication for resection arthroplasty needs to be evaluated carefully, considering the high rate of complications and reduced mobility, particularly if longer prosthesis-free intervals are used
PlanT: Explainable Planning Transformers via Object-Level Representations
Planning an optimal route in a complex environment requires efficientreasoning about the surrounding scene. While human drivers prioritize importantobjects and ignore details not relevant to the decision, learning-basedplanners typically extract features from dense, high-dimensional gridrepresentations containing all vehicle and road context information. In thispaper, we propose PlanT, a novel approach for planning in the context ofself-driving that uses a standard transformer architecture. PlanT is based onimitation learning with a compact object-level input representation. On theLongest6 benchmark for CARLA, PlanT outperforms all prior methods (matching thedriving score of the expert) while being 5.3x faster than equivalentpixel-based planning baselines during inference. Combining PlanT with anoff-the-shelf perception module provides a sensor-based driving system that ismore than 10 points better in terms of driving score than the existing state ofthe art. Furthermore, we propose an evaluation protocol to quantify the abilityof planners to identify relevant objects, providing insights regarding theirdecision-making. Our results indicate that PlanT can focus on the most relevantobject in the scene, even when this object is geometrically distant.<br
Sign segmentation with changepoint-modulated pseudo-labelling
The objective of this work is to find temporal boundaries between signs in continuous sign language. Motivated by the paucity of annotation available for this task, we propose a simple yet effective algorithm to improve segmentation performance on unlabelled signing footage from a domain of interest. We make the following contributions: (1) We motivate and introduce the task of source-free domain adaptation for sign language segmentation, in which labelled source data is available for an initial training phase, but is not available during adaptation. (2) We propose the Changepoint-Modulated Pseudo-Labelling (CMPL) algorithm to leverage cues from abrupt changes in motion-sensitive feature space to improve pseudo-labelling quality for adaptation. (3) We showcase the effectiveness of our approach for category-agnostic sign segmentation, transferring from the BSLCORPUS to the BSL-1K and RWTH-PHOENIX-Weather 2014 datasets, where we outperform the prior state of the art
Low-count Time Series Anomaly Detection
Low-count time series describe sparse or intermittent events, which are
prevalent in large-scale online platforms that capture and monitor diverse data
types. Several distinct challenges surface when modelling low-count time
series, particularly low signal-to-noise ratios (when anomaly signatures are
provably undetectable), and non-uniform performance (when average metrics are
not representative of local behaviour). The time series anomaly detection
community currently lacks explicit tooling and processes to model and reliably
detect anomalies in these settings. We address this gap by introducing a novel
generative procedure for creating benchmark datasets comprising of low-count
time series with anomalous segments. Via a mixture of theoretical and empirical
analysis, our work explains how widely-used algorithms struggle with the
distribution overlap between normal and anomalous segments. In order to
mitigate this shortcoming, we then leverage our findings to demonstrate how
anomaly score smoothing consistently improves performance. The practical
utility of our analysis and recommendation is validated on a real-world dataset
containing sales data for retail stores.Comment: 6 pages, 7 figures, to be published in IEEE 2023 Workshop on Machine
Learning for Signal Processing (MLSP
Interleukin-4 (IL-4) enhances and soluble interleukin-4 receptor (sIL-4R) inhibits histamine release from peripheral blood basophils and mast cells in vitro and in vivo
The aim of the study was to analyse the effect of interleukin-4 (IL-4) on allergen and anti-IgE mediated histamine release from basophils and human skin mast cells and to assess whether soluble recombinant interleukin-4 receptor (sIL4R) can inhibit these effects. Anti-IgE stimulated histamine release from peripheral blood basophils and mast cells of atopic donors was enhanced after preincubation with IL-4, whereas after preincubation with sIL-4R it was inhibited. These effects were even more pronounced when samples were stimulated with a clinically relevant allergen. In IL-4 preincubated skin mast cells, there was a similar enhancement of anti-IgE stimulated histamine release, which could again be inhibited by sIL-4R. The effects of IL-4 and sIL4R were dose- and time-dependent. Mice sensitized to ovalbumin and treated with soluble recombinant murine sIL-4R showed significantly reduced immediate-type cutaneous hypersensitivity responses compared with untreated mice.
These in vivo effects were IgE independent, since there were no significant differences in total and allergen specific IgE/IgG1 antibody titres between treated and untreated mice. This indicates that IL4 exerts priming effects on histamine release by effector cells of the allergic response and that these effects are potently antagonized by soluble IL-4R both
in vitro and in vivo
Readout of GEM Detectors Using the Medipix2 CMOS Pixel Chip
We have operated a Medipix2 CMOS readout chip, with amplifying, shaping and
charge discriminating front-end electronics integrated on the pixel-level, as a
highly segmented direct charge collecting anode in a three-stage gas electron
multiplier (Triple-GEM) to detect the ionization from Fe X-rays and
electrons from Ru. The device allows to perform moderate energy
spectroscopy measurements (20 % FWHM at 5.9 keV -rays) using only digital
readout and two discriminator thresholds. Being a truly 2D-detector, it allows
to observe individual clusters of minimum ionizing charged particles in
(70:30) and (70:30) mixtures and to achieve excellent
spatial resolution for position reconstruction of primary clusters down to
, based on the binary centroid determination method.Comment: 18 pages, 14 pictures. submitted to Nuclear Instruments and Methods
in Physics Research
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