Does being green facilitate product innovation? This study examines whether green management in firms operating in China fosters radical product innovation to a greater extent than it does incremental product innovation and investigates the underlying institutional mechanisms involved in the relationship between green management and product innovation. The findings show that green management is more likely to lead to radical product innovation than to incremental product innovation. Moreover, government support as a formal institutional benefit more strongly mediates the effect of (...) green management on radical product innovation than its effect on incremental product innovation; whereas social legitimacy as an informal institutional benefit more strongly mediates the effect of green management on incremental product innovation than its effect on radical product innovation. These findings provide important implications for explaining how firms employ green management to facilitate product innovation. (shrink)
Coal texture is important for predicting coal seam permeability and selecting favorable blocks for coalbed methane exploration. Drilled cores and mining seam observations are the most direct and effective methods of identifying coal texture; however, they are expensive and cannot be used in unexplored coal seams. Geophysical logging has become a common method of coal texture identification, particularly during the CBM mining stage. However, quantitative methods for identifying coal texture based on geophysical logging data require further study. The support vector (...) machine, a machine-learning method, has received great interest due to its remarkable generalization performance, and it has been used to quantitatively identify hard and soft coal using geophysical logging data. In this study, four well-logging curves, the acoustic time difference, caliper log, density, and natural gamma, were used for coal texture analysis. Hard coal exhibited higher DEN, GR, lower CAL, and lower AC than soft coal. The accuracy rate of coal texture identification was highest when the linear kernel function was applied, and the maximum training accuracy rate was achieved when the penalty parameter value of the linear kernel increased to 1. The results of verification with a newly cored CBM exploration well indicated that the SVM-based identification method was effective for coal texture analysis. With the increasing availability of data, this method can be used to distinguish hard and soft coal in a coal-bearing basin under numerous sample learning conditions. (shrink)
The extremely poor loading performance of a thin coal shearer drum affects the mining efficiency in thin seam mining seriously on account of the restriction by the complicated mining environment and seam thickness. The coal loading performance of the drum is influenced by several complex factors, such as motion parameters and structural parameters, including the structure and form of the hub. The form of the drum hub is cylindrical in general, and in order to study the influence of the hub (...) form on the coal loading rate of the drum, seven drums with different hub forms and structures were designed. The influence of the complexity of hub structures on the coal loading performance was studied by discrete element method simulation in this paper. The change curves with the research object of different drums, such as coal loading rate, velocity field distribution, and contact force between fallen coal particles, were obtained. The results showed that the conical hub drum can improve the coal loading performance than the cylindrical hub drum, and the curve-shaped hub drum had a more obvious promotion on the coal loading performance. The coal loading rate increased first and then decreased with the increase of hub cone angle. Compared with the conical hub drum, the curve-shaped hub drum can not only improve the coal loading rate, but also has a larger space containing coal. This study has proposed a drum with a new form hub which could increase the coal loading rate, and the methods and conclusions provide the guidance for drum hub design. (shrink)
If stem cell-based therapies are developed, we will likely confront a difficult problem of justice: for biological reasons alone, the new therapies might benefit only a limited range of patients. In fact, they might benefit primarily white Americans, thereby exacerbating long-standing differences in health and health care.
In order to comprehensively study and identify the electromagnetic torque difference among the single static air-gap eccentricity fault, the single stator interturn short circuit fault, and the combined fault composed of these two, this article investigates the EMT ripple properties due to the mentioned three faults. Different from other studies, this paper considers not only the effect of the single fault types but also the impact of the single fault combinations on the EMT ripple characteristics. Detailed EMT expressions for each (...) fault are firstly derived on the basis of the magnetic flux density analysis. Then, finite element calculation and experimental study on a CS-5 prototype generator with two poles at 3000 rpm, which is specifically designed and manufactured ourselves, are carried out to validate the analysis result. It is found that the three faults will induce different ripple components in EMT. The combined faults have the most intensive impact sensitivity on the EMT ripples, while the single SAGE fault ranks the last in the impact effect. (shrink)
Nonnegative Matrix Factorization is a significant big data analysis technique. However, standard NMF regularized by simple graph does not have discriminative function, and traditional graph models cannot accurately reflect the problem of multigeometry information between data. To solve the above problem, this paper proposed a new method called Hypergraph Regularized Discriminative Nonnegative Matrix Factorization, which captures intrinsic geometry by constructing hypergraphs rather than simple graphs. The introduction of the hypergraph method allows high-order relationships between samples to be considered, and the (...) introduction of label information enables the method to have discriminative effect. Both the hypergraph Laplace and the discriminative label information are utilized together to learn the projection matrix in the standard method. In addition, we offered a corresponding multiplication update solution for the optimization. Experiments indicate that the method proposed is more effective by comparing with the earlier methods. (shrink)
Real-world complex systems always interact with each other, which causes these systems to collapse in an avalanche or cascading manner in the case of random failures or malicious attacks. The robustness of multilayer networks has attracted great interest, where the modeling and theoretical studies of which always rely on the concept of multilayer networks and percolation methods. A straightforward and tacit assumption is that the interdependence across network layers is strong, which means that a node will fail entirely with the (...) removal of all links if one of its interdependent nodes in other network layers fails. However, this oversimplification cannot describe the general form of interactions across the network layers in a real-world multilayer system. In this paper, we reveal the nature of the avalanche disintegration of general multilayer networks with arbitrary interdependency strength across network layers. Specifically, we identify that the avalanche process of the whole system can essentially be decomposed into two microscopic cascading dynamics in terms of the propagation direction of the failures: depth penetration and scope extension. In the process of depth penetration, the failures propagate from layer to layer, where the greater the number of failed nodes is, the greater is the destructive power that will emerge in an interdependency group. In the process of scope extension, failures propagate with the removal of connections in each network layer. Under the synergy of the two processes, we find that the percolation transition of the system can be discontinuous or continuous with changes in the interdependency strength across network layers, which means that a sudden system-wide collapse can be avoided by controlling the interdependency strength across network layers. Our work not only reveals the microscopic mechanism of global collapse in multilayer infrastructure systems but also provides stimulating ideas on intervention programs and approaches for cascade failures. (shrink)
In cased-hole acoustic logging, estimating the formation velocity is often problematic when the casing is poorly bonded with the formation. The overwhelmingly large casing waves dominate the measured waveforms and overlap with the low-coherence, weak formation arrival, contributing to the failure of conventional semblance processing method. To tackle this problem, we have developed a filtered frequency semblance array waveform signal processing technique. The multiple filter technique is first used to filter the measured waveforms. We then apply the [Formula: see text]th (...) root stacking method to the filtered signals. Consequently, the coherence of the formation signal on the obtained 3D semblance correlogram is significantly enhanced and clearly separated with the casing waves. We have applied the method to process synthetic and field cased-hole acoustic waveform data. Our results indicate that the new method significantly enhances the coherence of the desired formation signal and simultaneously estimates the formation of the P- and S-wave velocity. (shrink)