An experimental study on fracture mechanical behavior of rock-like materials containing two unparallel fissures under uniaxial compression

Strength and deformability characteristics of rock with pre-existing fissures are governed by cracking behavior. To further research the effects of pre-existing fissures on the mechanical properties and crack coalescence process, a series of uniaxial compression tests were carried out for rock-like material with two unparallel fissures.In the present study, cement, quartz sand, and water were used to fabricate a kind of brittle rock-like material cylindrical model specimen. The mechanical properties of rock-like material specimen used in this research were all in good agreement with the brittle rock materials. Two unparallel fissures（a horizontal fissure and an inclined fissure） were created by inserting steel during molding the model specimen.Then all the pre-fissured rock-like specimens were tested under uniaxial compression by a rock mechanics servocontrolled testing system. The peak strength and Young＇s modulus of pre-fissured specimen all first decreased and then increased when the fissure angle increased from 0？to 75？.In order to investigate the crack initiation, propagation and coalescence process, photographic monitoring was adopted to capture images during the entire deformation process.Moreover, acoustic emission（AE） monitoring technique was also used to obtain the AE evolution characteristic of prefissured specimen. The relationship between axial stress, AE events, and the crack coalescence process was set up： when a new crack was initiated or a crack coalescence occurred, thecorresponding axial stress dropped in the axial stress–time curve and a big AE event could be observed simultaneously.Finally, the mechanism of crack propagation under microscopic observation was discussed. These experimental results are expected to increase the understanding of the strength failure behavior and the cracking mechanism of rock containing unparallel fissures.

Morphology and structure of extremely red objects at z ～ 1 in the CANDELS-COSMOS field

Using high-resolution HST/Wide Field Camera 3 F125W imaging from the CANDELS-COSMOS field, we report the structural and morphological properties of extremely red objects （EROs） at -z 1. Based on the UVJ color criteria, we sepa- rate EROs into two types： old passive galaxies （OGs） and dusty star-forming galaxies （DGs）. For a given stellar mass, we find that the mean size of OGs （DGs） is smaller by a factor of - 2 （1.5） than that of present-day early-type （late-type） galaxies at a rest-frame optical wavelength. We derive the average effective radii of OGs and DGs, corresponding to 2.09 ± 1.13 kpc and 3.27± 1.14 kpc, respectively. Generally, the DGs are heterogeneous, with mixed features including bulges, disks and irregular structures, with relatively high M20, large size and low G. By contrast, OGs have elliptical-like compact morphologies with lower M20, smaller size and higher G, indicating a more concentrated and symmetric spatial extent of the stellar population distribution in OGs than DGs. These findings imply that OGs and DGs have different evolutionary processes, and that the minor merger scenario is the most likely mechanism for the structural properties of OGs. However, the size evolution of DGs is possibly due to the secular evolution of galaxies.

Scaling Behavior for the Susceptibility of the Vacuum

Using the two-component superfluid model of Winterberg for space, two models for the susceptibility of the cosmic vacuum as a function of the cosmic scale parameter, a, are presented. We also consider the possibility that Newton’s constant can scale, i.e., G-1=G-1(a), to form the most general scaling laws for polarization of the vacuum. The positive and negative values for the Planckion mass, which form the basis of the Winterberg model, are inextricably linked to the value of G, and as such, both G and Planck mass are intrinsic properties of the vacuum. Scaling laws for the non-local, smeared, cosmic susceptibility, , the cosmic polarization, , the cosmic macroscopic gravitational field, , and the cosmic gravitational field mass density, , are worked out, with specific examples. At the end of recombination, i.e., the era of last scattering, using the polarization to explain dark matter, and the gravitational field mass density to explain dark energy, we find that, . While this is an unconventional assignment, differing from the ΛCDM model, we believe this is correct, as localized dark matter (LDM) contributions can be much higher in this epoch than cosmic smeared values for susceptibility. All density parameter assignments in Friedmanns’ equation are cosmic averages, valid for distance scales in excess of 100 Mpc in the current epoch. We also evaluate the transition from ordinary matter dominance, to dark matter dominance, for the cosmos as a whole. We obtain for the transition points, z=1.66, for susceptibility model I, and, z=2.53 , for susceptibility model II.

Multi-objective optimization of feature selection using hybrid cat swarm optimization

With the pervasive generation of information from a wide range of sensors and devices,there always exist a large number of input features in databases,thus complicating machine learning problem formulation.However,certain features are relatively impertinent to specific problems,which may degrade the performances of classifiers in terms of prediction accuracy,sensitivity,specificity,and recall rate.The main goal of a multi-objective optimization problem is to identify the subsets of the given features.To this end,a hybrid cat swarm optimization(HCSO)algorithm is proposed in our paper for performance improvement of the basic cat swarm optimization(CSO)that incorporates guided and competitive&inherent characteristics into the original CSO.The performance of HCSO has been tested by finding the optimal feature subset for 15 benchmark datasets.The number of class labels for these datasets varies between 2 and 40.The time complexity analysis of both CSO and HCSO has also been evaluated.Moreover,the performance of the proposed algorithm has been compared with that of simple CSO and other state-ofthe-art techniques.The performances obtained by HCSO have an average 2.68%improvement with a standard deviation of 2.91.The maximum performance improvement is up to 10.09%in prediction accuracy.Tested on the same datasets,CSO has yielded improvements within the range of-7.27%to 8.51%with an average improvement 0.9%and standard deviation 3.96.The statistical tests carried out in the experiments prove that HCSO manifests a moderately better feature selection capacity than that of its counterparts.