426 research outputs found
All-IP wireless sensor networks for real-time patient monitoring
AbstractThis paper proposes the all-IP WSNs (wireless sensor networks) for real-time patient monitoring. In this paper, the all-IP WSN architecture based on gateway trees is proposed and the hierarchical address structure is presented. Based on this architecture, the all-IP WSN can perform routing without route discovery. Moreover, a mobile node is always identified by a home address and it does not need to be configured with a care-of address during the mobility process, so the communication disruption caused by the address change is avoided. Through the proposed scheme, a physician can monitor the vital signs of a patient at any time and at any places, and according to the IPv6 address he can also obtain the location information of the patient in order to perform effective and timely treatment. Finally, the proposed scheme is evaluated based on the simulation, and the simulation data indicate that the proposed scheme might effectively reduce the communication delay and control cost, and lower the packet loss rate
Bayesian optimization with active learning of Ta-Nb-Hf-Zr-Ti system for spin transport properties
Designing materials with enhanced spin charge conversion, i.e., with high
spin Hall conductivity (SHC) and low longitudinal electric conductivity (hence
large spin Hall angle (SHA)), is a challenging task, especially in the presence
of a vast chemical space for compositionally complex alloys (CCAs). In this
work, focusing on the Ta-Nb-Hf-Zr-Ti system, we confirm that CCAs exhibit
significant spin Hall conductivities and propose a multi-objective Bayesian
optimization approach (MOBO) incorporated with active learning (AL) in order to
screen for the optimal compositions with significant SHC and SHA. As a result,
within less than 5 iterations we are able to target the TaZr-dominated systems
displaying both high magnitudes of SHC (~-2.0 (10 cm))
and SHA (~0.03). The SHC is mainly ascribed to the extrinsic skew scattering
mechanism. Our work provides an efficient route for identifying new materials
with significant SHE, which can be straightforwardly generalized to optimize
other properties in a vast chemical space
Disentangled Contrastive Learning for Learning Robust Textual Representations
Although the self-supervised pre-training of transformer models has resulted
in the revolutionizing of natural language processing (NLP) applications and
the achievement of state-of-the-art results with regard to various benchmarks,
this process is still vulnerable to small and imperceptible permutations
originating from legitimate inputs. Intuitively, the representations should be
similar in the feature space with subtle input permutations, while large
variations occur with different meanings. This motivates us to investigate the
learning of robust textual representation in a contrastive manner. However, it
is non-trivial to obtain opposing semantic instances for textual samples. In
this study, we propose a disentangled contrastive learning method that
separately optimizes the uniformity and alignment of representations without
negative sampling. Specifically, we introduce the concept of momentum
representation consistency to align features and leverage power normalization
while conforming the uniformity. Our experimental results for the NLP
benchmarks demonstrate that our approach can obtain better results compared
with the baselines, as well as achieve promising improvements with invariance
tests and adversarial attacks. The code is available in
https://github.com/zjunlp/DCL.Comment: Work in progres
Homoepitaxial regrowth habits of ZnO nanowire arrays
Synthetic regrowth of ZnO nanowires [NWs] under a similar chemical vapor transport and condensation [CVTC] process can produce abundant ZnO nanostructures which are not possible by a single CVTC step. In this work, we report three different regrowth modes of ZnO NWs: axial growth, radial growth, and both directions. The different growth modes seem to be determined by the properties of initial ZnO NW templates. By varying the growth parameters in the first-step CVTC process, ZnO nanostructures (e.g., nanoantenna) with drastically different morphologies can be obtained with distinct photoluminescence properties. The results have implications in guiding the rational synthesis of various ZnO NW heterostructures
Magnetic properties of Nd6Fe13Cu single crystals
The understanding of coercivity mechanism in high performance Nd-Fe-B
permanent magnets relies on the analysis of the magnetic properties of all
phases present in the magnets. By adding Cu in such compounds, a new Nd6Fe13Cu
grain boundary phase is formed, however, the magnetic properties of this phase
and its role in the magnetic decoupling of the matrix Nd2Fe14B grains are still
insufficiently studied. In this work, we have grown Nd6Fe13Cu single crystals
by the reactive flux method and studied their magnetic properties in detail. It
is observed that below the N\'eel temperature (TN = 410 K), the Nd6Fe13Cu is
antiferromagnetic in zero magnetic field; whereas when a magnetic field is
applied along the a-axis, a spin-flop transition occurs at approx. 6 T,
indicating a strong competition between antiferromagnetic and ferromagnetic
interactions in two Nd layers below and above the Cu layers. Our atomistic spin
dynamics simulation confirms that an increase in temperature and/or magnetic
field can significantly change the antiferromagnetic coupling between the two
Nd layers below and above the Cu layers, which, in turn, is the reason for the
observed spin-flop transition. These results suggest that the role of
antiferromagnetic Nd6Fe13Cu grain boundary phase in the coercivity enhancement
of Nd-Fe-B-Cu magnets is more complex than previously thought, mainly due to
the competition between its antiferro- and ferro-magnetic exchange
interactions.Comment: 15 pages, 4 figure
Coordinating Multiple Resources for Optimal Postdisaster Operation of Interdependent Electric Power and Natural Gas Distribution Systems
Electric power and natural gas systems are not separated but rather are
increasingly connected physically and functionally interdependent due to the
continuing development of natural gas-fired generation and gas industry
electrification. Such interdependency makes these two systems interact with
each other when responding to disasters. The aggravated risk of cascading
failures across the two systems has been exposed in recent energy crises,
highlighting the significance of preparing these interdependent systems against
disasters and helping their impacted services quickly recover. This promotes us
to treat power and gas systems as one whole to fully capture their interactive
behaviors. In this paper, we focus on the interdependent electric power and
natural gas distribution systems (IENDS) and propose a "supply - demand -
repair" strategy to comprehensively help the IENDS tide over the emergency
periods after disasters by coordinating mobile or stationary emergency
resources for various uses. Specifically, 1) on the supply side, the fuel
supply issue of different types of generators for emergency use is considered
and the fuel delivery process among different fuel facilities is mathematically
formulated; 2) on the demand side, a zonewise method is proposed for integrated
dispatch of power and gas demand responses; and 3) in the repair process, a
varying efficiency related to the repair units at work is introduced to
accurately model repairs. The proposed strategy is formulated into a
mixed-integer second-order cone programming model to obtain a globally optimal
decision of deploying all of those resources in a coordinated and organized
manner. A series of case studies based on test systems are conducted to
validate the effectiveness of the proposed strategy.Comment: 31 pages, 9 figures, submitted to Applied Energ
Arginine Vasopressin Injected into the Dorsal Motor Nucleus of the Vagus Inhibits Gastric Motility in Rats
Background. Until now, the effect of arginine vasopressin (AVP) in the DMV on gastric motility and the possible modulating pathway between the DMV and the gastrointestinal system remain poorly understood. Objectives. We aimed to explore the role of AVP in the DMV in regulating gastric motility and the possible central and peripheral pathways. Material and Methods. Firstly, we microinjected different doses of AVP into the DMV and investigated its effects on gastric motility in rats. Then, the possible central and peripheral pathways that regulate gastric motility were also discussed by microinjecting SR49059 (a specific AVP receptor antagonist) into the DMV and intravenous injection of hexamethonium (a specific neuronal nicotinic cholinergic receptor antagonist) before AVP microinjection. Results. Following microinjection of AVP (180 pmol and 18 pmol) into the DMV, the gastric motility (including total amplitude, total duration, and motility index of gastric contraction) was significantly inhibited (P<0.05). Moreover, the inhibitory effect of AVP (180 pmol) on gastric motility could be blocked completely by both SR49059 (320 pmol) and hexamethonium (8 μmol). Conclusions. It is concluded that AVP inhibits the gastric motility by acting on the specific AVP receptor in the DMV, with the potential involvement of the parasympathetic preganglionic cholinergic fibers
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