Simultaneous observation of small- and large-energy-transfer electron-electron scattering in three dimensional indium oxide thick films

In three dimensional (3D) disordered metals, the electron-phonon (\emph{e}-ph) scattering is the sole significant inelastic process. Thus the theoretical predication concerning the electron-electron (\emph{e}-\emph{e}) scattering rate $1/\tau_\varphi$ as a function of temperature $T$ in 3D disordered metal has not been fully tested thus far, though it was proposed 40 years ago [A. Schmid, Z. Phys. \textbf{271}, 251 (1974)]. We report here the simultaneous observation of small- and large-energy-transfer \emph{e}-\emph{e} scattering in 3D indium oxide thick films. In temperature region of $T\gtrsim100$\,K, the temperature dependence of resistivities curves of the films obey Bloch-Gr\"{u}neisen law, indicating the films possess degenerate semiconductor characteristics in electrical transport property. In the low temperature regime, $1/\tau_\varphi$ as a function of $T$ for each film can not be ascribed to \emph{e}-ph scattering. To quantitatively describe the temperature behavior of $1/\tau_\varphi$, both the 3D small- and large-energy-transfer \emph{e}-\emph{e} scattering processes should be considered (The small- and large-energy-transfer \emph{e}-\emph{e} scattering rates are proportional to $T^{3/2}$ and $T^2$, respectively). In addition, the experimental prefactors of $T^{3/2}$ and $T^{2}$ are proportional to $k_F^{-5/2}\ell^{-3/2}$ and $E_F^{-1}$ ($k_F$ is the Fermi wave number, $\ell$ is the electron elastic mean free path, and $E_F$ is the Fermi energy), respectively, which are completely consistent with the theoretical predications. Our experimental results fully demonstrate the validity of theoretical predications concerning both small- and large-energy-transfer \emph{e}-\emph{e} scattering rates.Comment: 5 pages and 4 figure

Wavelet and Fourier Transforms in Health Monitoring of Embedded Structures

Embedded structures are widely encountered in construction, such as anti-slide piles in a slope and reinforced concrete frames in a building. The embedded structures are vulnerable subjected to seismic effects. After the seismic load, the integrity of the embedded structures is weakened but the damage is unlikely to be directly observed by engineersas a result, damage detection of an embedded structure is critical. In this study, the identification of the structural damage is investigated with Wavelet and Fourier transforms. With these two approaches, the structural signal in different frequency domain can be analyzed. The advantages and disadvantages of these two approaches in identifying the damage locations are compared. An outstanding advantage of the Wavelet transform is that it is able to identify the damage location, which makes this approach attractive for engineering practice. This advantage is exemplified by a cantilever beam finite element model.This research is supported by the National Natural Science foundation of China (Grand No.: 51879203)

Probabilistic Characterization of 3-D Spatial Variability of Soils: Methodology and Strategy

The 3-D spatial variability of soils has significant impacts on the failure mechanism and reliability of geotechnical structures and deserves a quantitative characterization through site investigation. This study develops a probabilistic approach for characterizing the 3-D spatial variability of soils within the framework of maximum likelihood estimation, whose computational problem is addressed through a matrix decomposition technique. The sampling strategy to minimize the statistical uncertainty is explored systematically based on virtual site analysis. The empirical distance criterion and density criterion are proposed to control the statistical uncertainty to a practically acceptable low level.This work was supported by the National Key R&D Program of China (Project No. 2017YFC1501300), the National Natural Science Foundation of China (Project Nos. 51679174, and 51779189), and the Open Fund of Key Laboratory of Rock Mechanics in Hydraulic Structural Engineering, Ministry of Education of China (Project No. RMHSE1903). The financial support is gratefully acknowledged

Probabilistic Analysis of Ground Deformation Induced by Excavation based on Hypoplastic Constitutive Models

Empirical model and finite element method are two commonly-used methods for prediction of ground deformation induced by excavation. Compared with the former, the finite element method can not only predict the deformation of different modes, but also predict the distributed deformation of the whole site. However, results of finite element analysis depends on the constitutive model used in the analysis. This paper uses an advanced hypoplastic constitutive model and its improved edition, which considers the small-strain effect of soil, to represent the soil behavior. Uncertainties are unavoidable in excavation engineering, such as those in soil parameters, loads, and models, etc. These uncertainties have profound effects on the prediction of deformation induced by excavation obtained from the finite element analysis. In order to consider the effect of parameter uncertainty on the prediction results, random variables are used to characterize the parameter uncertainty. Direct Monte Carlo simulation (MCS) method was used to incorporate the parameter uncertainty into reliability analysis of the deformation induced by excavation. The computational costs and convergence issues of finite element method in together with advanced constitutive model result in significant computational challenges in MCS-based reliability analysis. In order to improve the computing efficiency and robustness, artificial neural network (ANN) is adopted as a surrogate model of the finite element method to compute the soil deformation for a given set of uncertain parameters. Results show that responses predicted by the improved hypoplastic model fit the real response better.This work was supported by the National Key R&D Program of China (Project No. 2016YFC0800200), and the National Natural Science Foundation of China (Project Nos. 51579190, 51528901, 51679174), and Young Elite Scientists Sponsorship Program by CAST (Project Nos. 2017QNRC001). The financial support is gratefully acknowledged

Revisiting the Relationship between Scale of Fluctuation and Mean Cross Distance

Estimating scale of fluctuation is an intriguing issue, for which several methods have been developed, such as simple estimators (e.g., 0.8d¯-estimator) based on the mean cross distance d¯ of a soil property profile, sample autocorrelation function method, maximum likelihood method, Bayesian method, etc. Among these methods, the 0.8d¯-estimator is the simplest one and can be readily used by geotechnical practitioners whose training in probability theory and statistics is usually limited. It, however, shall be noted that the 0.8d¯-estimator was derived from the normal random field with squared exponential correlation function, which is largely ignored in its practical applications. Effects of the distribution type (e.g., normal or lognormal) and correlation function on the performance of the 0.8d¯-estimator remain unexplored and, hence, unknown to geotechnical practitioners, which potentially leads to misuse of the simple relationship. This paper aims to highlight the theoretical assumptions underlying the 0.8d¯-estimator and to, systematically, explore the effects of these theoretical assumptions on its performance (i.e., unbiasedness and variability). It is found that the 0.8d¯-estimator provides reasonably unbiased estimation of scale of fluctuation for the normal random field with squared exponential correlation function when there are, at least, two sampling data within a distance of scale of fluctuation. Whereas, results from the 0.8d¯-estimator for other cases violating the assumptions are biased, and may lead to a significant underestimation of scale of fluctuation. It is also found that the variability of the 0.8d¯-estimator increases as the sampling length decreases.The work described in this paper was supported by grants from National Key R&D Program of China (Project No. 2017YFC1501300), and the National Natural Science Foundation of China (Project Nos. 51528901, 51679174, 51779189), and an open fund from State Key Laboratory Hydraulics and Mountain River Engineering, Sichuan University (Project No. SKHL1619). The financial support is gratefully acknowledged

Bayesian Updating of Embankment Settlement on Soft Soils with Finite Element Method

Prediction of responses (e.g., embankment settlement) of geotechnical structures on soft soils is a challenging task due to their complex mechanical behaviors. In face with such complexity, the finite element method (FEM) combined with advanced soil constitutive models (e.g., soft soil creep (SSC) model) is frequently used to predict the short-term and long-term responses of geotechnical structures on soft soils, which involves a number of model parameters. Determination of these model parameters depends on knowledge obtained from site investigation data and/or monitoring information. This paper develops a Bayesian sequential updating (BSU) framework that incorporates monitoring information obtained at different construction stages to update FEM model parameters and their corresponding stochastic responses. To address the computational issues in Bayesian analysis, No-UTurn Sampler (NUTS) Markov chain Monte Carlo (MCMC) algorithm is introduced to populate posterior samples, and multiple Hermite response surfaces are constructed for different monitoring phases to reduce the computational efforts costed by evaluating the likelihood function. The proposed method is illustrated by a settlement prediction example of Ballina trial embankment, New South Wales, Australia. Effects of different likelihood functions (namely with and without model bias factor (MBF)) on Bayesian updating of settlement predictions are investigated. Results showed that the proposed BSU framework improves the prediction accuracy of soft soil settlement compared with prior predictions. NUTS is much more efficient in generating posterior samples compared with Metropolis-Hastings (MH) algorithm as the number of model parameters is relatively large. When considering short-term settlement behaviors of soft soils, the likelihood function without MBF is preferred because the adopted SSC can properly characterize short-term behaviors of soft soils. On the other hand, the likelihood function with MBF is recommended because SSC is hard to represent long-term behaviors of soft soils.This work was supported by the National Key R&D Program of China (Project No. 2016YFC0800200), the National Natural Science Foundation of China (Project Nos. 51679174, 51579190, 51528901), and Young Elite Scientists Sponsorship Program by CAST (Project No. 2017QNRC001). The financial support is gratefully acknowledged

Umbilical cord blood: A promising source for allogeneic CAR-T cells

Chimeric antigen receptor T (CAR-T) cell therapy is an effective treatment for relapsed and refractory acute lymphoblastic leukemia (R/R ALL). However, autologous CAR-T cells derived from patients with B-ALL often show poor amplification ability, exhaustion, and anergy. To overcome these limitations, allogeneic CAR-T cells may be used as effective substitutes; however, which source would be the best substitute is unclear. In this study, we compared the immunophenotype and antitumor efficacy of anti-CD19 CAR-T cells derived from healthy donor cord blood (CB), healthy donor peripheral blood (PB), and PB of B-ALL patients [PB (patient)] in vitro and NOD-Prkdcem26cd52Il2rgem26Cd22/Nju (NCG)-immunodeficient mice, respectively. The results revealed that CAR-T cells derived from healthy donor CB and PB showed a higher proportion of naive T cells and longer tumor suppression in tumor-bearing mice than those of PB (patient). PB (patient) CAR-T cells had a higher proportion of regulatory T cells (Treg cells) and released high levels of interluekin-10 (IL-10), which also suggest a poor prognosis. Thus, CAR-T cells derived from healthy donors have better antitumor efficacy than CAR-T cells derived from PB (patient), and CB may be a good source of allogeneic CAR-T cells