Cosmological constraints from Radial Baryon Acoustic Oscillation measurements and Observational Hubble data

We use the Radial Baryon Acoustic Oscillation (RBAO) measurements, distant type Ia supernovae (SNe Ia), the observational $H(z)$ data (OHD) and the Cosmic Microwave Background (CMB) shift parameter data to constrain cosmological parameters of $\Lambda$CDM and XCDM cosmologies and further examine the role of OHD and SNe Ia data in cosmological constraints. We marginalize the likelihood function over $h$ by integrating the probability density $P\propto e^{-\chi^{2}/2}$ to obtain the best fitting results and the confidence regions in the $\Omega_{m}-\Omega_{\Lambda}$ plane.With the combination analysis for both of the {\rm $\Lambda$}CDM and XCDM models, we find that the confidence regions of 68.3%, 95.4% and 99.7% levels using OHD+RBAO+CMB data are in good agreement with that of SNe Ia+RBAO+CMB data which is consistent with the result of Lin et al's work. With more data of OHD, we can probably constrain the cosmological parameters using OHD data instead of SNe Ia data in the future.Comment: 8 pages, 6 figures, 2 tables, accepted for publication in Physics Letters

A Uniform Viscoelastic-Plastic Constitutive Model for MD-PMMA at a Wide Temperature Range

The deformation characteristics of MD-PMMA vary greatly at different temperatures. In the paper, whether a uniform model could be used to describe these complex characteristics was discussed. Tensile properties of MD-PMMA at the temperatures of -50 ̊C, -25 ̊C, 20 ̊C, 60 ̊C, 90 ̊C were experimentally investigated. The entire deformation processes of PMMA were divided into four stages: elastic stage, viscoelastic stage, yielding stage and post-yielding stage. Strain softening and strain hardening phenomenon occurred in the yielding and post-yielding stage, it was the results of the competition between loading rate and plastic strain rate. A nonlinear model of activation dashpot was constructed, in the model, the evolution rate of plastic deformation was defined by Eyring's theory, and the actual stress was the difference between external applied stress and internal resistance stress caused by plastic strain. The above activation dashpot serially connected with the standard linear model (SLM) to identify elastic and viscoelastic characteristics. A two iterations integral algorithm was proposed to simplify the inter-coupling between the internal stress and the plastic strain, and the unknown parameters in the model could be easily fitted by the experimental data. This uniform viscoelastic-plastic model was demonstrated that could predict different deformation behaviors at a wide temperature range

Applying Minimum-Risk Criterion to Stochastic Hub Location Problems

AbstractThis paper presents a new class of two-stage stochastic hub location (HL) programming problems with minimum-risk criterion, in which uncertain demands are characterized by random vector. Meanwhile we demonstrate that the twostage programming problem is equivalent to a single-stage stochastic P-model. Under mild assumptions, we develop a deterministic binary programming problem by using standardization, which is equivalent to a binary fractional programming problem. Moreover, we show that the relaxation problem of the binary fractional programming problem is a convex programming problem. Taking advantage of branch-and-bound method, we provide a number of experiments to illustrate the efficiency of the proposed modeling idea

Benchmarking Inverse Optimization Algorithms for Molecular Materials Discovery

Machine learning-based molecular materials discovery has attracted enormous attention recently due to its flexibility in dealing with black box models. Yet, metaheuristic algorithms are not as widely applied to materials discovery applications. We comprehensively compare 11 different optimization algorithms for molecular materials design with targeted properties. These algorithms include Bayesian Optimization (BO) and multiple metaheuristic algorithms. We performed 5000 material evaluations repeated 5 times with different randomized initialization to optimize defined target properties. By maximizing the bulk modulus and minimizing the Fermi energy through perturbing parameterized molecular representations, we estimated the unique counts of molecular materials, mean density scan of the objectives space, mean objectives, and frequency distributed over the materials' representations and objectives. GA, GWO, and BWO exhibit higher variances for materials count, density scan, and mean objectives; and BO and Runge Kutta optimization (RUN) display generally lower variances. These results unveil that nature-inspired algorithms contain more uncertainties in the defined molecular design tasks, which correspond to their dependency on multiple hyperparameters. RUN exhibits higher mean objectives whereas BO displayed low mean objectives compared with other benchmarked methods. Combined with materials count and density scan, we propose that BO strives to approximate a more accurate surrogate of the design space by sampling more molecular materials and hence have lower mean objectives, yet RUN will repeatedly sample the targeted molecules with higher objective values. Our work shed light on automated digital molecular materials design and is expected to elicit future studies on materials optimization such as composite and alloy design based on specific desired properties.Comment: 15 pages, 5 figures, for the main manuscrip

Dose-dependent effect of ghrelin on gastric emptying in rats and the related mechanism of action

AbstractThe aim of this study was to investigate the dose-dependent effect of ghrelin on gastric emptying in rats and the related mechanism of action. Sixty Wistar rats were randomized into control and test groups, which respectively received intraperitoneal injection of normal saline and ghrelin at different doses (0.5 nmol/kg, 1.0 nmol/kg, 1.5 nmol/kg, 2.0 nmol/kg, and 2.5 nmol/kg). After 45 minutes, all rats were gavaged with semisolid paste. The gastric emptying rate was determined 30 minutes later, and the plasma cholecystokinin level was tested by radioimmunoassay. The mean gastric emptying rate in the test groups was significantly higher than in the control group (38.24 ± 7.15% and 27.18 ± 2.37%, respectively, p < 0.05). Medium and high doses of ghrelin (1.0 nmol/kg, 1.5 nmol/kg, 2.0 nmol/kg, and 2.5 nmol/kg), but not low dose (0.5 nmol/kg), accelerated the gastric emptying. In addition, the plasma cholecystokinin level in the test groups was significantly higher than in the control group (p < 0.01). The gastric emptying rate was positively correlated with the plasma cholecystokinin level (p < 0.01). Intraperitoneal injection of ghrelin at medium and high doses significantly accelerated gastric emptying in rats

Multi-body dynamic simulation and vibration transmission characteristics of dual-rotor system for aeroengine with rubbing coupling faults

In this paper, a dual-rotor system multi-body dynamic model with rubbing coupling faults is established for practical aero-engine. In the model, the rubbing fault simulation method is introduced, the coupling effect between the internal and external rotor is considered. The numerical simulations of rotor vibration are accomplished by the utilization of multi-body dynamic platform, where the simulation model consists of discs unbalances and local rub-impact between discs and casing shells. The time-domain responses, the frequency spectra and the shaft-center trajectories of dual-rotor with different unbalance and different rubbing positions are obtained. The vibration and its transmission characteristics of the inner and outer rotors are calculated. Finally, the simulation results are compared with the measured vibration of a dual rotor tester with rubbing fault. The simulation results are consistent with the measured results, which confirms the feasibility of the established model and the multi-body dynamics simulation method in this paper. The application of multi-body dynamics simulation method in aero-engine can deepen the understanding of the internal operation nature and laws of aero-engine, reduce the repetition of physical tests, greatly improve the efficiency and quality of development, accelerate the research and manufacture process

Blind2Sound: Self-Supervised Image Denoising without Residual Noise

Self-supervised blind denoising for Poisson-Gaussian noise remains a challenging task. Pseudo-supervised pairs constructed from single noisy images re-corrupt the signal and degrade the performance. The visible blindspots solve the information loss in masked inputs. However, without explicitly noise sensing, mean square error as an objective function cannot adjust denoising intensities for dynamic noise levels, leading to noticeable residual noise. In this paper, we propose Blind2Sound, a simple yet effective approach to overcome residual noise in denoised images. The proposed adaptive re-visible loss senses noise levels and performs personalized denoising without noise residues while retaining the signal lossless. The theoretical analysis of intermediate medium gradients guarantees stable training, while the Cramer Gaussian loss acts as a regularization to facilitate the accurate perception of noise levels and improve the performance of the denoiser. Experiments on synthetic and real-world datasets show the superior performance of our method, especially for single-channel images