856 research outputs found
Electrospun Thymosin Beta-4 Loaded PLGA/PLA Nanofiber/ Microfiber Hybrid Yarns for Tendon Tissue Engineering Application
Microfiber yarns (MY) have been widely employed to construct tendon tissue grafts. However, suboptimal ultrastructure and inappropriate environments for cell interactions limit their clinical application. Herein, we designed a modified electrospinning device to coat poly(lactic-co-glycolic acid) PLGA nanofibers onto polylactic acid (PLA) MY to generate PLGA/PLA hybrid yarns (HY), which had a well-aligned nanofibrous structure, resembling the ultrastructure of native tendon tissues and showed enhanced failure load compared to PLA MY. PLGA/PLA HY significantly improved the growth, proliferation, and tendon-specific gene expressions of human adipose derived mesenchymal stem cells (HADMSC) compared to PLA MY. Moreover, thymosin beta-4 (Tβ4) loaded PLGA/PLA HY presented a sustained drug release manner for 28 days and showed an additive effect on promoting HADMSC migration, proliferation, and tenogenic differentiation. Collectively, the combination of Tβ4 with the nano-topography of PLGA/PLA HY might be an efficient strategy to promote tenogenesis of adult stem cells for tendon tissue engineering
Enhancing Heat Transfer in Internal Combustion Engine by Applying Nanofluids
Nanofluids exhibit novel properties including significant heat transfer properties that make them potentially useful in internal combustion engine cooling. However, although there is a substantial number of mechanisms proposed, modeling works related to their enhanced thermal conductivity, systematic mechanisms, or models that are suitable for nanofluids are still lacked. With molecular dynamics simulations, thermal conductivities of nanofluids with various nanoparticles have been calculated. Influence rule of various factors for thermal conductivity of nanofluids has been studied. Through defining the ratio of thermal conductivity enhancement by nanoparticle volume fraction, Κ, the impacts of nanoparticle properties for thermal conductivity are further evaluated. Furthermore, the ratio of energetic atoms in nanoparticles, E, is proposed to be an effective criterion for judging the impact of nanoparticles for the thermal conductivity of nanofluids. Mechanisms of heat conduction enhancement are investigated by MD simulations. Altered microstructure and movements of nanoparticles in the base fluid are proposed to be the main reasons for thermal conductivity enhancement in nanofluids. Both the static and dynamic mechanisms for heat conduction enhancement in nanofluids have been considered to establish a prediction model for thermal conductivity. The prediction results of the present model are in good agreement with experimental results
Rethinking Masked Language Modeling for Chinese Spelling Correction
In this paper, we study Chinese Spelling Correction (CSC) as a joint decision
made by two separate models: a language model and an error model. Through
empirical analysis, we find that fine-tuning BERT tends to over-fit the error
model while under-fit the language model, resulting in poor generalization to
out-of-distribution error patterns. Given that BERT is the backbone of most CSC
models, this phenomenon has a significant negative impact. To address this
issue, we are releasing a multi-domain benchmark LEMON, with higher quality and
diversity than existing benchmarks, to allow a comprehensive assessment of the
open domain generalization of CSC models. Then, we demonstrate that a very
simple strategy, randomly masking 20\% non-error tokens from the input sequence
during fine-tuning is sufficient for learning a much better language model
without sacrificing the error model. This technique can be applied to any model
architecture and achieves new state-of-the-art results on SIGHAN, ECSpell, and
LEMON.Comment: Accepted by ACL'202
An inexact regularized proximal Newton method for nonconvex and nonsmooth optimization
This paper focuses on the minimization of a sum of a twice continuously
differentiable function and a nonsmooth convex function. We propose an
inexact regularized proximal Newton method by an approximation of the Hessian
involving the th power of the KKT residual. For
, we demonstrate the global convergence of the iterate sequence for
the KL objective function and its -linear convergence rate for the KL
objective function of exponent . For , we establish the
global convergence of the iterate sequence and its superlinear convergence rate
of order under an assumption that cluster points satisfy a
local H\"{o}lderian local error bound of order
on the strong stationary point set;
and when cluster points satisfy a local error bound of order on
the common stationary point set, we also obtain the global convergence of the
iterate sequence, and its superlinear convergence rate of order
if . A dual
semismooth Newton augmented Lagrangian method is developed for seeking an
inexact minimizer of subproblem. Numerical comparisons with two
state-of-the-art methods on -regularized Student's -regression,
group penalized Student's -regression, and nonconvex image restoration
confirm the efficiency of the proposed method
Optimatization of sample points for monitoring arable land quality by simulated annealing while considering spatial variations
This presentation was given as part of the GIS Day@KU symposium on November 16, 2016. For more information about GIS Day@KU activities, please see http://gis.ku.edu/gisday/2016/.Arable land is the basis of food production, the most valuable input in agricultural production, and an important factor in sustainable agricultural development and national food security. In China, the reduction and degradation of arable land due to industrialization and urbanization has gradually emerged as one of the most prominen challenges. In this context, the
long-term dynamic monitoring of arable land quality becomes important for protecting arable land resources. However, little consideration has been given to optimizing sample points number and layout in previous monitoring studies on arable land quality. When considering the optimization of sample points, various strategies are needed, depending on the indicators. In addition, the distributio of soil properties displays spatial variations. However, existing sampling studies have
paid little attention to spatial variations during scenarios with multiple indicators.Therefore, it is necessary to further investigate how to improve the efficiency and accuracy of arable land quality monitoring and evaluation by optimizing the number and layout of sample points when there are spatial variations in multiple indicators.Platinum Sponsors: KU Department of Geography and Atmospheric Science. Gold Sponsors: Enertech, KU Environmental Studies Program, KU Libraries. Silver Sponsors: Douglas County, Kansas, KansasView, State of Kansas Data Access & Support Center (DASC) and the KU Center for Global and International Studies
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