China's Regional Inequality in Innovation Capability, 1995-2004

Relying on a recently developed decomposition framework, this paper explores spatial distribution of innovation capability in China. It is found that at the regional level, China's inequality in innovation capability increased from 1995 to 2004. At the provincial level, the inequality decreased from 1995 to 2000, but increased from 2000 to 2004. Location, industrialization and urbanization, human capital, and openness (foreign direct investment) are significant contributors to the inequality in innovation capability. Unbalanced development in high-tech parks exerts a growing explanatory power in driving innovation disparity, which implies that institutional factor plays a direct role.innovation, regional disparity, inequality, decomposition, Asia, China

Direction-of-Arrival Estimation Based on Sparse Recovery with Second-Order Statistics

Traditional direction-of-arrival (DOA) estimation techniques perform Nyquist-rate sampling of the received signals and as a result they require high storage. To reduce sampling ratio, we introduce level-crossing (LC) sampling which captures samples whenever the signal crosses predetermined reference levels, and the LC-based analog-to-digital converter (LC ADC) has been shown to efficiently sample certain classes of signals. In this paper, we focus on the DOA estimation problem by using second-order statistics based on the LC samplings recording on one sensor, along with the synchronous samplings of the another sensors, a sparse angle space scenario can be found by solving an $ell_1$ minimization problem, giving the number of sources and their DOA's. The experimental results show that our proposed method, when compared with some existing norm-based constrained optimization compressive sensing (CS) algorithms, as well as subspace method, improves the DOA estimation performance, while using less samples when compared with Nyquist-rate sampling and reducing sensor activity especially for long time silence signal

Numerical Study of Spacer Grid Geometry in a 5 X 5 Nuclear Fuel Rod Bundle

Reactor fuel rod bundles serve as the primary heat source in light water reactors (LWRs), commonly found in the aforementioned PWR plants. The fuel rod bundles’ structure consists of a collection of fuel rods put into a parallel grid configuration. The bundles also include fuel rod spacers, which hold the fuel rods in place, in accordance with the grid. Repeating structures of the fuel bundles create the meta-structure in the reactor. In other words, the grid configuration repeats until it fills the entire space of the reactor. This results in reactor fuel rods suspended in the working fluid domain, oriented parallel length-wise to the flow direction, by the spacer grids. The generated heat from the fission reactions within the fuel rod elements provide the primary heat source for the power cycle. As the working fluid, light water, in this case, flows through the reactor, the heat generated by the fuel rods’ fission reactions is transferred to the fluid, adding its potential to do work. Taking advantage buoyancy effects of the heated working fluid, reactors usually have the working fluid enter through the bottom, then pumped up vertically through the fuel rod bundles and spacers. Since the working fluid flows through a flow region inside the reactor, containing fuel rod elements and the spacer grids, the drag force caused by these obstacles requires extra pumping power to overcome. This need for extra pumping power lowers total thermal system efficiency. Fortunately, the spacers have extra geometries in the end called mixing vanes, which agitate the working fluid as it flows through the reactor, disturbing the hydraulic and thermal boundary layers. As these boundary layers are disturbed, heat transfer rate increases; which, in turn, increases the amount of energy added to the working fluid during the heat addition phase of the thermal power cycle, adding more potential to do work in the work output phase of the cycle. Focusing only on hydraulic performances, this study attempted to lower hydraulic pressure drop across the spacer grid by numerically simulating spacer grids with different changes to their geometries inside a flow field. Several geometrical variations were chosen due to their ease of manufacturing and minimal tooling changes required at the supplier level; these variations include spacer grid length, mixing vane angle, spacer grid entrance geometry, and mixing vane shape. This study used the sectional method proven by Conner et al. (2010) and Navarro et al. (2011), to establish its baseline. In order to save the limited resources in computational power, the results from Navarro et al. (2011) were first transferred from CFX to Fluent 18.2. This step required the numerical results from both software to be compared and benchmarked. Fluent’s segregated method of calculating velocity and pressure makes the calculations easier on the limited hardware. In addition, Fluent’s finite volume method with cell-centered scheme also allows solutions to more accurately reflect cases where unstructured meshes are used, such as this study. To justify the results, this study also introduced different fluid domain inlets, additional boundary layers, and finer mesh sizes than the previous studies that provided the baseline. Mesh independent study was done to find the correct mesh size for a good compromise between resolution and convergence time. The results show that an element count of 4.27 × 107 or greater yielded computational results independent from element counts. However, since going with a higher element count does not significantly lengthen the computational time, the highest element count 6.18 × 107 elements, along with its respective body-sizing, 3.0 × 10−4 m, were chosen. The resulting y+ values of this study was around 1.75, less than the value used by Navarro et al. (2011). The total pressure drop across the region of interest also closely replicated the previous results found by various studies. Lastly, the study compared results from each variation. Shortening the spacer grid length decreases the pressure drop across its span. However, with a 25% reduction in spacer grid length, the pressure drop only reduced by 10.2%. This implied that the reduction in spacer grid length does not scale in unity with the reduction in pressure drop. Fortunately, increasing mixing vane angle significantly increase the agitation of the boundary layers. Specifically, a 20% increase in the mixing vane angle resulted in a 15.6% increase in swirl-factor, with only around 9.28% increase in pressure drop. Meanwhile, adding a 45-degree chamfer to the entrance of the spacer grid, with depth that bisects its thickness, decreased the pressure drop by 47%, without adding significant manufacturing steps to the construction. Then, a curvature was added to the mixing vane’s profile to ease fluid flow’s transition back to the freestream. However, the curvature on the mixing vane had detrimental effects on the overall performance, increasing the pressure drop across the spacer by 31.3%, while decreasing swirl factor by 4.3%

Matching Users' Preference Under Target Revenue Constraints in Optimal Data Recommendation Systems

This paper focuses on the problem of finding a particular data recommendation strategy based on the user preferences and a system expected revenue. To this end, we formulate this problem as an optimization by designing the recommendation mechanism as close to the user behavior as possible with a certain revenue constraint. In fact, the optimal recommendation distribution is the one that is the closest to the utility distribution in the sense of relative entropy and satisfies expected revenue. We show that the optimal recommendation distribution follows the same form as the message importance measure (MIM) if the target revenue is reasonable, i.e., neither too small nor too large. Therefore, the optimal recommendation distribution can be regarded as the normalized MIM, where the parameter, called importance coefficient, presents the concern of the system and switches the attention of the system over data sets with different occurring probability. By adjusting the importance coefficient, our MIM based framework of data recommendation can then be applied to system with various system requirements and data distributions.Therefore,the obtained results illustrate the physical meaning of MIM from the data recommendation perspective and validate the rationality of MIM in one aspect.Comment: 36 pages, 6 figure