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Effect of a Herringbone Mesostructure on the Electromechanical Properties of Piezofiber Composites for Energy Harvesting Applications
Piezoelectric materials are often used in energy harvesting devices that convert the waste mechanical energy into effective electrical energy. Polymer-based piezoelectric composites appear to be promising candidates for use in these devices, as they offer a number of advantages, such as sufficient flexibility and environmental compatibility. However, a major drawback associated with these composites may be that their effective electromechanical properties are usually weaker than those of the piezoelectric constituents used in them. In this paper, we propose a class of polymeric-based piezoelectric composites with a laminated mesostructure that offer improved electromechanical properties over unidirectional piezofiber composites and can even possess stronger electromechanical properties than their piezoelectric constituents for certain modes of operation. We present examples of enhanced properties of these composites including effective piezoelectric charge and voltage coefficients, as well as effective electromechanical coupling factors for two-dimensional operation modes. We conduct an optimization to identify the optimal microstructure for the highest values of the coupling coefficients within this class of composites. Our findings demonstrate the potential in designing piezoelectric composites with a hierarchical structure to achieve significantly amplified electromechanical properties for energy harvesting applications.Physic
Non-linear stress response of non-gap-spanning magnetic chains suspended in a Newtonian fluid under oscillatory shear test : A direct numerical simulation
The corresponding author wishes to express his sincerest thanks to the Iran National Science Foundation (INSF) for supporting this work under Contract Number 92021291.Peer reviewedPostprintPublisher PD
A SPH solver for simulating paramagnetic solid fluid interaction in the presence of an external magnetic field
Acknowledgment The first two authors wish to express their sincerest thanks to Iran National Science Foundation (INSF) for supporting this work under Contract Number 92021291.Peer reviewedPostprin
Local Semiconducting Transition in Armchair Carbon Nanotubes: The Effect of Periodic Bi-site Perturbation on Electronic and Transport Properties of Carbon Nanotubes
In carbon nanotubes, the most abundant defects, caused for example by
irradiation or chemisorption treatments, are small perturbing clusters, i.e.
bi-site defects, extending over both A and B sites. The relative positions of
these perturbing clusters play a crucial role in determining the electronic
properties of carbon nanotubes. Using bandstructure and electronic transport
calculations, we find out that in the case of armchair metallic nanotubes a
band gap opens up when the clusters fulfill a certain periodicity condition.
This phenomenon might be used in future nanoelectronic devices in which certain
regions of single metallic nanotubes could be turned to semiconducting ones.
Although in this work we study specifically the effect of hydrogen adatom
clusters, the phenomenon is general for different types of defects. Moreover,
we study the influence of the length and randomness of the defected region on
the electron transport through it.Comment: 5 Pages, 5 Figure
Phased Array Systems in Silicon
Phased array systems, a special case of MIMO systems, take advantage of spatial directivity and array gain to increase spectral efficiency. Implementing a phased array system at high frequency in a commercial silicon process technology presents several challenges. This article focuses on the architectural and circuit-level trade-offs involved in the design of the first silicon-based fully integrated phased array system operating at 24 GHz. The details of some of the important circuit building blocks are also discussed. The measured results demonstrate the feasibility of using integrated phased arrays for wireless communication and vehicular radar applications at 24 GHz
Training Curricula for Open Domain Answer Re-Ranking
In precision-oriented tasks like answer ranking, it is more important to rank
many relevant answers highly than to retrieve all relevant answers. It follows
that a good ranking strategy would be to learn how to identify the easiest
correct answers first (i.e., assign a high ranking score to answers that have
characteristics that usually indicate relevance, and a low ranking score to
those with characteristics that do not), before incorporating more complex
logic to handle difficult cases (e.g., semantic matching or reasoning). In this
work, we apply this idea to the training of neural answer rankers using
curriculum learning. We propose several heuristics to estimate the difficulty
of a given training sample. We show that the proposed heuristics can be used to
build a training curriculum that down-weights difficult samples early in the
training process. As the training process progresses, our approach gradually
shifts to weighting all samples equally, regardless of difficulty. We present a
comprehensive evaluation of our proposed idea on three answer ranking datasets.
Results show that our approach leads to superior performance of two leading
neural ranking architectures, namely BERT and ConvKNRM, using both pointwise
and pairwise losses. When applied to a BERT-based ranker, our method yields up
to a 4% improvement in MRR and a 9% improvement in P@1 (compared to the model
trained without a curriculum). This results in models that can achieve
comparable performance to more expensive state-of-the-art techniques.Comment: Accepted at SIGIR 2020 (long
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