390 research outputs found

    Experimental-numerical evaluation of a new butterfly specimen for fracture characterisation of AHSS in a wide range of stress states

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    Results of an experimental-numerical evaluation of a new butterfly specimen for fracture characterisation of AHHS sheets in a wide range of stress states are presented. The test on the new butterfly specimen is performed in a uniaxial tensile machine and provides sufficient data for calibration of common fracture models. In the first part, results of a numerical specimen evaluation are presented, which was performed with a material model of a dual-phase steel DP600 taken from literature with plastic flow and fracture descriptions. In the second part, results of an experimental-numerical specimen evaluation are shown, which was conducted on another dual-phase steel DP600, which was available with a description of plastic flow only and whose fracture behaviour was characterised in the frame of this work. The overall performance of the new butterfly specimen at different load cases with regard to characterisation of the fracture behaviour of AHSS was investigated. The dependency of the fracture strain on the stress triaxiality and Lode angle as well as space resolution is quantified. A parametrised CrachFEM ductile shear fracture model and modified Mohr-Coloumb ductile shear fracture model are presented as a result of this quantification. The test procedure and results analysis are believed to contribute to current discussions on requirements to AHSS fracture characterisation

    A Tutorial on Interference Exploitation via Symbol-Level Precoding: Overview, State-of-the-Art and Future Directions

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    IEEE Interference is traditionally viewed as a performance limiting factor in wireless communication systems, which is to be minimized or mitigated. Nevertheless, a recent line of work has shown that by manipulating the interfering signals such that they add up constructively at the receiver side, known interference can be made beneficial and further improve the system performance in a variety of wireless scenarios, achieved by symbol-level precoding (SLP). This paper aims to provide a tutorial on interference exploitation techniques from the perspective of precoding design in a multi-antenna wireless communication system, by beginning with the classification of constructive interference (CI) and destructive interference (DI). The definition for CI is presented and the corresponding mathematical characterization is formulated for popular modulation types, based on which optimization-based precoding techniques are discussed. In addition, the extension of CI precoding to other application scenarios as well as for hardware efficiency is also described. Proof-of-concept testbeds are demonstrated for the potential practical implementation of CI precoding, and finally a list of open problems and practical challenges are presented to inspire and motivate further research directions in this area

    Increased Physical Activity and Reduced Pain with Spinal Cord Stimulation: a 12-Month Study

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    International Journal of Exercise Science 13(3): 1583-1594, 2020. The purpose of this study was to assess changes in pain and physical activity after replacing a traditional spinal cord stimulation (SCS) implantable pulse generator with a next generation SCS in patients for whom traditional SCS was no longer providing adequate relief of low back and/or leg pain. Subjects (n = 19) who reported that they were no longer receiving adequate relief from traditional SCS were implanted with a next generation SCS. Eighteen additional patients who were receiving relief from traditional SCS were also followed as a control. Both groups (next generation, traditional) were assessed for low-back and limb pain (visual analog scale) and daily physical activity (wearable accelerometer) at baseline and three, six, nine and 12 months following the SCS implant. Relative to baseline, next generation SCS subjects exhibited reductions (p ≤ 0.05 for all) in low-back pain (average reduction of 22%) at every time point, in leg pain (average reduction of 23%) at every time point except six months and increased physical activity (average increase of 57%) at three, six and nine months. As expected, there were no changes in pain or physical activity in the traditional SCS subjects (p ≥ 0.1). In conclusion, pain decreased, and physical activity increased in patients receiving a next generation SCS. Physical activity may serve as an objectively measured marker of pain

    Truthful Online Double Auctions for Mobile Crowdsourcing:An On-demand Service Strategy

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    Double auctions play a pivotal role in stimulating active participation of a large number of users comprising both task requesters and workers in mobile crowdsourcing. However, most existing studies have concentrated on designing offline two-sided auction mechanisms and supporting single-type tasks and fixed auction service models. Such works ignore the need of dynamic services and are unsuitable for large-scale crowdsourcing markets with extremely diverse demands (i.e., types and urgency degrees of tasks required by different requesters) and supplies (i.e., task skills and online durations of different workers). In this paper, we consider a practical crowdsourcing application with an on-demand service strategy. Especially, we innovatively design three online service models, namely online single-bid single-task (OSS), online single-bid multiple-task (OSM) and online multiple-bid multiple-task (OMM) models to accommodate diversified tasks and bidding demands for different users. Furthermore, to effectively allocate tasks and facilitate bidding, we propose a truthful online double auction mechanism for each service model based on the McAfee double auction. By doing so, each user can flexibly select auction service models and corresponding auction mechanisms according to their current interested tasks and online duration. To illustrate this, we present a three-demand example to explain the effectiveness of our on-demand service strategy in realistic crowdsourcing applications. Moreover, we theoretically prove that our mechanisms satisfy truthfulness, individual rationality, budget balance and consumer sovereignty. Through extensive simulations, we show that our mechanisms can accommodate the various demands of different users and improve social utility including platform utility and average user utility. IEE

    Exhaustive assignment of compositional bias reveals universally prevalent biased regions: analysis of functional associations in human and Drosophila

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    BACKGROUND: Compositionally biased (CB) regions are stretches in protein sequences made from mainly a distinct subset of amino acid residues; such regions are frequently associated with a structural role in the cell, or with protein disorder. RESULTS: We derived a procedure for the exhaustive assignment and classification of CB regions, and have applied it to thirteen metazoan proteomes. Sequences are initially scanned for the lowest-probability subsequences (LPSs) for single amino-acid types; subsequently, an exhaustive search for lowest probability subsequences (LPSs) for multiple residue types is performed iteratively until convergence, to define CB region boundaries. We analysed > 40,000 CB regions with > 20 million residues; strikingly, nine single-/double- residue biases are universally abundant, and are consistently highly ranked across both vertebrates and invertebrates. To home in subpopulations of CB regions of interest in human and D. melanogaster, we analysed CB region lengths, conservation, inferred functional categories and predicted protein disorder, and filtered for coiled coils and protein structures. In particular, we found that some of the universally abundant CB regions have significant associations to transcription and nuclear localization in Human and Drosophila, and are also predicted to be moderately or highly disordered. Focussing on Q-based biased regions, we found that these regions are typically only well conserved within mammals (appearing in 60–80% of orthologs), with shorter human transcription-related CB regions being unconserved outside of mammals; they are also preferentially linked to protein domains such as the homeodomain and glucocorticoid-receptor DNA-binding domain. In general, only ~40–50% of residues in these human and Drosophila CB regions have predicted protein disorder. CONCLUSION: This data is of use for the further functional characterization of genes, and for structural genomics initiatives

    Pharmacological evaluation of 3-carbomethoxy fentanyl in mice

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    In many animal species, as well as in humans, high doses of fentanyl (F) produce marked neurotoxic effects, such as muscular rigidity and respiratory depression. The antinociception (hot-plate test), impairment of motor coordination (rotarod test) and acute toxicity of intraperitoneal newly synthesized analogs, (±)cis-3-carbomethoxy- fentanyl (C) and (±)trans-3-carbomethoxyfentanyl (T) were evaluated in mice. The compounds tested induced antinociception, impairment of performance on the rotarod, and lethality in a dosedependent manner. The relative order of antinociceptive potency was similar to motor impairment potency, as well as lethality: F gt C gt T. Naloxone hydrochloride (1 mg/kg; sc) abolished all the effects observed, suggesting that they are mediated via opioid receptors, most probably of μ type. There were no significant differences between the therapeutic indices of F, C and T. It is concluded, the introduction of 3-carbomethoxy group in the piperidine ring of the fentanyl skeleton reduced the potency, but did not affect tolerability and safety of the compound. © 2011 by the authors

    Predicting mostly disordered proteins by using structure-unknown protein data

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    BACKGROUND: Predicting intrinsically disordered proteins is important in structural biology because they are thought to carry out various cellular functions even though they have no stable three-dimensional structure. We know the structures of far more ordered proteins than disordered proteins. The structural distribution of proteins in nature can therefore be inferred to differ from that of proteins whose structures have been determined experimentally. We know many more protein sequences than we do protein structures, and many of the known sequences can be expected to be those of disordered proteins. Thus it would be efficient to use the information of structure-unknown proteins in order to avoid training data sparseness. We propose a novel method for predicting which proteins are mostly disordered by using spectral graph transducer and training with a huge amount of structure-unknown sequences as well as structure-known sequences. RESULTS: When the proposed method was evaluated on data that included 82 disordered proteins and 526 ordered proteins, its sensitivity was 0.723 and its specificity was 0.977. It resulted in a Matthews correlation coefficient 0.202 points higher than that obtained using FoldIndex, 0.221 points higher than that obtained using the method based on plotting hydrophobicity against the number of contacts and 0.07 points higher than that obtained using support vector machines (SVMs). To examine robustness against training data sparseness, we investigated the correlation between two results obtained when the method was trained on different datasets and tested on the same dataset. The correlation coefficient for the proposed method is 0.14 higher than that for the method using SVMs. When the proposed SGT-based method was compared with four per-residue predictors (VL3, GlobPlot, DISOPRED2 and IUPred (long)), its sensitivity was 0.834 for disordered proteins, which is 0.052–0.523 higher than that of the per-residue predictors, and its specificity was 0.991 for ordered proteins, which is 0.036–0.153 higher than that of the per-residue predictors. The proposed method was also evaluated on data that included 417 partially disordered proteins. It predicted the frequency of disordered proteins to be 1.95% for the proteins with 5%–10% disordered sequences, 1.46% for the proteins with 10%–20% disordered sequences and 16.57% for proteins with 20%–40% disordered sequences. CONCLUSION: The proposed method, which utilizes the information of structure-unknown data, predicts disordered proteins more accurately than other methods and is less affected by training data sparseness
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