66 research outputs found
Revealing the supercritical dynamics of dusty plasmas and their liquid-like to gas-like dynamical crossover
Dusty plasmas represent a powerful playground to study the collective
dynamics of strongly coupled systems with important interdisciplinary
connections to condensed matter physics. Due to the pure Yukawa repulsive
interaction between dust particles, dusty plasmas do not display a traditional
liquid-vapor phase transition, perfectly matching the definition of a
supercritical fluid. Using molecular dynamics simulations, we verify the
supercritical nature of dusty plasmas and reveal the existence of a dynamical
liquid-like to gas-like crossover which perfectly matches the salient features
of the Frenkel line in classical supercritical fluids. We present several
diagnostics to locate this dynamical crossover spanning from local atomic
connectivity, shear relaxation dynamics, velocity autocorrelation function,
heat capacity, and various transport properties. All these different criteria
well agree with each other and are able to successfully locate the Frenkel line
in both 2D and 3D dusty plasmas. In addition, we propose the unity ratio of the
instantaneous transverse sound speed to the average particle speed
, i.e., , as a new diagnostic to identify
this dynamical crossover. Finally, we observe an emergent degree of
universality in the collective dynamics and transport properties of dusty
plasmas as a function of the screening parameter and dimensionality of the
system. Intriguingly, the temperature of the dynamical transition is
independent of the dimensionality, and it is found to be always times of
the corresponding melting point. Our results open a new path for the study of
single particle and collective dynamics in plasmas and their interrelation with
supercritical fluids in general.Comment: v1: comments are welcom
APICom: Automatic API Completion via Prompt Learning and Adversarial Training-based Data Augmentation
Based on developer needs and usage scenarios, API (Application Programming
Interface) recommendation is the process of assisting developers in finding the
required API among numerous candidate APIs. Previous studies mainly modeled API
recommendation as the recommendation task, which can recommend multiple
candidate APIs for the given query, and developers may not yet be able to find
what they need. Motivated by the neural machine translation research domain, we
can model this problem as the generation task, which aims to directly generate
the required API for the developer query. After our preliminary investigation,
we find the performance of this intuitive approach is not promising. The reason
is that there exists an error when generating the prefixes of the API. However,
developers may know certain API prefix information during actual development in
most cases. Therefore, we model this problem as the automatic completion task
and propose a novel approach APICom based on prompt learning, which can
generate API related to the query according to the prompts (i.e., API prefix
information). Moreover, the effectiveness of APICom highly depends on the
quality of the training dataset. In this study, we further design a novel
gradient-based adversarial training method {\atpart} for data augmentation,
which can improve the normalized stability when generating adversarial
examples. To evaluate the effectiveness of APICom, we consider a corpus of 33k
developer queries and corresponding APIs. Compared with the state-of-the-art
baselines, our experimental results show that APICom can outperform all
baselines by at least 40.02\%, 13.20\%, and 16.31\% in terms of the performance
measures EM@1, MRR, and MAP. Finally, our ablation studies confirm the
effectiveness of our component setting (such as our designed adversarial
training method, our used pre-trained model, and prompt learning) in APICom.Comment: accepted in Internetware 202
Observation of fast sound in two-dimensional dusty plasma liquids
Equilibrium molecular dynamics simulations are performed to study
two-dimensional (2D) dusty plasma liquids. Based on the stochastic thermal
motion of simulated particles, the longitudinal and transverse phonon spectra
are calculated, and used to determine the corresponding dispersion relations.
From there, the longitudinal and transverse sound speeds of 2D dusty plasma
liquids are obtained. It is discovered that, for wavenumbers beyond the
hydrodynamic regime, the longitudinal sound speed of a 2D dusty plasma liquid
exceeds its adiabatic value, i.e., the so-called fast sound. This phenomenon
appears at roughly the same length scale of the cutoff wavenumber for
transverse waves, confirming its relation to the emergent solidity of liquids
in the non-hydrodynamic regime. Using the thermodynamic and transport
coefficients extracted from the previous studies, and relying on the Frenkel
theory, the ratio of the longitudinal to the adiabatic sound speeds is derived
analytically, providing the optimal conditions for fast sound, which are in
quantitative agreement with the current simulation results.Comment: v1: 7 pages, 6 figure
First Characterization of Sphingomyeline Phosphodiesterase Expression in the Bumblebee, Bombus lantschouensis
 The bumblebee (Bombus lantschouensis Vogt) is an important pollinator of wild plants. Sphingomyelin phosphodiesterase (SMPD) is a hydrolase that plays a major role in sphingolipid metabolism reactions. We report the preparation and characterization of a polyclonal antibody for bumblebee SMPD. We then use the polyclonal antiserum to detect the SMPD protein at different development stages and in different tissues. Our results showed that a 1228bp fragment homologous with the B. terrestris SMPD gene was successfully amplified. The molecular weight of the fusion protein was about 70 kDa by SDS-PAGE. An effective polyclonal antibody against SMPD was also obtained from mice and found to have a higher specificity for bumblebee SMPD. Western blotting detection showed that SMPD was expressed at a high level in queen ovaries, although expression was lower in the midgut and venom gland. SMPD expression decreased from the egg stage until the pdd stage. We interpret our results as showing that the development of an effective polyclonal antiserum for the SMPD protein of a bumblebee, which provides a tool for exploring the function of the SMPD gene. In addition, the work has confirmed that SMPD should be considered as an important enzyme during bumblebee egg and larval stages
Feasibility and efficacy of simultaneous integrated boost intensity-modulated radiation therapy in patients with limited-disease small cell lung cancer
Mesenchymal Stem Cell in the Intervertebral Disc
Degeneration of the intervertebral disc (IVD) is a major spinal disorder that causes back pain. Nucleus pulposus (NP) in the central of IVD dehydrates and become more fibrous in the IVD degeneration. NP cells undergo apoptosis with the degeneration of extracellular matrix (ECM) components. To replenish the NP cells and core ECM, bone marrow mesenchymal stromal cells (BMSCs) have been highlighted in the regeneration of IVD degeneration. BMSCs differentiate into NP-like cells with the secretion of ECM components, which may not only replenish the number of NP cells but also stimulate NP reconstruction. This further maintains tissue homeostasis. Up to date, the disc progenitor cells (DPCs) have been identified with the characteristics of multidifferentiation and stem cell phenotype. These cells are involved in the IVD diseases and show regenerative potentials. However, the differences between the BMSCs and DPCs remain elusive, in particular, the cellular connection in vivo. As such, this chapter will discuss the findings of the two cell types and propose a novel concept in the understanding of the biology of IVD
A Three-Dimensional Thermodynamically Based Function for the Progressive Failure of Unidirectional Composites
A thermodynamically-based work potential theory for modeling progressive damage for laminated, unidirectional composites assuming plane stress (2D Schaperystheory) is extended to three dimensioanl (3D). An internal state variable, S, is defined to account for the dissipated energy due to damage evolution in the form of microstructure changes in the matrix. With the stationary of the total work potential with respect to the internal state variable, a thermodynamically-consistent set of evolution equationsis derived. The internal state variable is related to the transverse and shear modulithrough microdamage functions. In the first part of this work, coupon specimensare prepared to conduct experiments to characterize the relations between the internalstate variable and the transverse modulus as well as shear modulus. The informationis subsequent used for the prediction of three point bending test. In the second partof this work, objectivity is studied. Three separate methods utilizing different definitions of a reduced internal state variable or of the order of the polynomials are used to represent the matrix micro-damage functions are employed. The three methods are implemented in a user defined subroutine within a commercial finite element method software package. Results from numerical simulations of a center-notched composites panel are compared. The agreement in the maximum stress predictions among the three methods indicates that objectivity, with respect to the functional form of themicrodamage functions, is satisfied
VAD: Vectorized Scene Representation for Efficient Autonomous Driving
Autonomous driving requires a comprehensive understanding of the surrounding
environment for reliable trajectory planning. Previous works rely on dense
rasterized scene representation (e.g., agent occupancy and semantic map) to
perform planning, which is computationally intensive and misses the
instance-level structure information. In this paper, we propose VAD, an
end-to-end vectorized paradigm for autonomous driving, which models the driving
scene as a fully vectorized representation. The proposed vectorized paradigm
has two significant advantages. On one hand, VAD exploits the vectorized agent
motion and map elements as explicit instance-level planning constraints which
effectively improves planning safety. On the other hand, VAD runs much faster
than previous end-to-end planning methods by getting rid of
computation-intensive rasterized representation and hand-designed
post-processing steps. VAD achieves state-of-the-art end-to-end planning
performance on the nuScenes dataset, outperforming the previous best method by
a large margin. Our base model, VAD-Base, greatly reduces the average collision
rate by 29.0% and runs 2.5x faster. Besides, a lightweight variant, VAD-Tiny,
greatly improves the inference speed (up to 9.3x) while achieving comparable
planning performance. We believe the excellent performance and the high
efficiency of VAD are critical for the real-world deployment of an autonomous
driving system. Code and models will be released for facilitating future
research.Comment: Code&Demos: https://github.com/hustvl/VA
VMA: Divide-and-Conquer Vectorized Map Annotation System for Large-Scale Driving Scene
High-definition (HD) map serves as the essential infrastructure of autonomous
driving. In this work, we build up a systematic vectorized map annotation
framework (termed VMA) for efficiently generating HD map of large-scale driving
scene. We design a divide-and-conquer annotation scheme to solve the spatial
extensibility problem of HD map generation, and abstract map elements with a
variety of geometric patterns as unified point sequence representation, which
can be extended to most map elements in the driving scene. VMA is highly
efficient and extensible, requiring negligible human effort, and flexible in
terms of spatial scale and element type. We quantitatively and qualitatively
validate the annotation performance on real-world urban and highway scenes, as
well as NYC Planimetric Database. VMA can significantly improve map generation
efficiency and require little human effort. On average VMA takes 160min for
annotating a scene with a range of hundreds of meters, and reduces 52.3% of the
human cost, showing great application value
Electrical impedance performance of metal dry bioelectrode with different surface coatings
To improve the electrical impedance performance of bioelectrodes, a novel metal dry bioelectrodes with different coating layers are developed with laser micromilling and electroplating technology. Based on the analysis of the coating layer on the bioelectrode surface, the effect of different coating layers on the electrical impedance performance of bioelectrodes is investigated. The results show that the silver content increases with electroplating time when the silver layer is coated on the bioelectrode surface. However, the decrease of silver layer weight is observed with much longer electroplating time, and the optimal electroplating time is 20 min. Compared with the uncoated bioelectrode, the bioelectrode coated with silver layer exhibits much lower impedance value and better impedance stability. Especially, when the silver-coated bioelectrode is subsequently coated with silver-silver chloride layer, the lowest impedance value and best impedance stability are obtained
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