Growth technique for producing high quality superconducting YBa2Cu3O7-δ single crystals

High quality single crystals of YBa₂Cu₃O_7- (YBCO) have been grown in yttria-stabilized zirconia crucibles by using a flux method. This method yields crystals with area 3 to 4 times larger than a previous method, and 3 to 5 times more crystals. Several crystals as large as 3×4mm² in thea-b plane are routinely obtained from each crucible. The samples typically range from 10 to 60 m thick. The superconductivity transition temperatureT _c of the crystals, measured by magnetic susceptibility, falls in the range 92.5 to 93 K and the smallest transition width (10 to 90%) is less than 0.25 K. Compared with the previous method, the surface is generally smoother, with less flux, and larger untwinned regions of 1 x 1 mm² are usually found.     

Spin correlations for an Anderson impurity in conventional and unconventional superconductors

We present a theoretical study of the spin-spin correlation functionC(r) in a superconductor, whereC(r) = S · s(r);S is an impurity spin operator, ands(r) is the conduction electron spin density operator. We model the impurity using the Anderson Hamiltonian, and use the U , 1/N expansion to do the calculations. In addition to conventional superconductors, we consider unconventional superconductors, in which the order parameter (k) has a lower rotational symmetry, and has vanishing angular average. Of particular interest is the way that the behavior ofC(r) reflects two length scales, the Kondo length _k and the superconducting coherence length, ₀, and the way that its behavior is affected by the angular dependence of unconventional gaps.     

A study of a new data association and track initiation method with normalized distance squared ordering

In an underwater environment, measurements regarding true targets and false targets (clutter) can be made. Therefore, a suitable data association method to exactly detect and track a target and an efficient track initiation method for judging tracks formed by the target should be selected in this environment. This paper attempts to propose a new data association method and track initiation method to detect and track targets more effectively. Also, the performance of the new method is tested in a series of Monte Carlo simulation runs and is compared with the existing data association and track initiation methods in a cluttered environment.     

数据中心安全管理的创新构想

与运动员都渴望追求好成绩一样,人们都期待数据中心能够实现高性能运转。然而,无论在什么时候,实现成功都需要遵循一个既有的过程：目标设定、设计、规划、培训、执行和维护,这些都是必要的步骤。勾画成功的蓝图是实现预期结果的重要部分,但如果没有有效的措施加以支持,     

The effect of the aspect ratio on the hydrophobicity of microstructured polydimethylsiloxane (PDMS) robust surfaces

In this study, we have designed and fabricated robust hydrophobic surfaces that are composed of various micropillar arrays and investigated the effect of the aspect ratio (feature height/feature size) of the micropillar on the wettability of the fabricated surfaces. The robust, micropillar-arrayed surfaces were designed to yield the same Wenzel and Cassie water contact angles (CAs). According to our design rule, one can achieve an enhanced hydrophobic surface by increasing the height of the micropillars. The designed hydrophobic surfaces were fabricated by polydimethylsiloxane (PDMS) replica molding with photolithographically micropatterned SU-8 masters. The hydrophobicity properties of the fabricated PDMS surfaces were fully characterized theoretically and experimentally. From the theoretical and experimental results, it was found that the micropillars of an intrinsically hydrophobic material with a high aspect ratio enhance the hydrophobicity of the surface by increasing the surface roughness (in view of the Wenzel state) and the opportunities for the entrapment of air beneath a water droplet (the Cassie state).     

Optimization of optical lens-controlled scanning electron microscopic resolution using generalized regression neural network and genetic algorithm

A scanning electron microscope (SEM) is a sophisticated equipment employed for fine imaging of a variety of surfaces. In this study, prediction models of SEM were constructed by using a generalized regression neural network (GRNN) and genetic algorithm (GA). The SEM components examined include condenser lens 1 and 2 and objective lens (coarse and fine) referred to as CL1, CL2, OL-Coarse, and OL-Fine. For a systematic modeling of SEM resolution (R), a face-centered Box–Wilson experiment was conducted. Two sets of data were collected with or without the adjustment of magnification. Root-mean-squared prediction error of optimized GRNN models are GA 0.481 and 1.96×10^-12 for non-adjusted and adjusted data, respectively. The optimized models demonstrated a much improved prediction over statistical regression models. The optimized models were used to optimize parameters particularly under best tuned SEM environment. For the variations in CL2 and OL-Coarse, the highest R could be achieved at all conditions except a larger CL2 either at smaller or larger OL-Coarse. For the variations in CL1 and CL2, the highest R was obtained at all conditions but larger CL2 and smaller CL1.     

Optimization of silicon solar cell fabrication based on neural network and genetic programming modeling

This study describes techniques for the cascade modeling and the optimization that are required to conduct the simulator-based process optimization of solar cell fabrication. Two modeling approaches, neural networks and genetic programming, are employed to model the crucial relation for the consecutively connected two processes in solar cell fabrication. One model (Model 1) is used to map the five inputs (time, amount of nitrogen and DI water in surface texturing and temperature and time in emitter diffusion) to the two outputs (reflectance and sheet resistance) of the first process. The other model (Model 2) is used to connect the two inputs (reflectance and sheet resistance) to the one output (efficiency) of the second process. After modeling of the two processes, genetic algorithms and particle swarm optimization were applied to search for the optimal recipe. In the first optimization stage, we searched for the optimal reflectance and sheet resistance that can provide the best efficiency in the fabrication process. The optimized reflectance and sheet resistance found by the particle swarm optimization were better than those found by the genetic algorithm. In the second optimization stage, the five input parameters were searched by using the reflectance and sheet resistance values obtained in the first stage. The found five variables such as the texturing time, amount of nitrogen, DI water, diffusion time, and temperature are used as a recipe for the solar cell fabrication. The amount of nitrogen, DI water, and diffusion time in the optimized recipes showed considerable differences according to the modeling approaches. More importantly, repeated applications of particle swarm optimization yielded process conditions with smaller variations, implying greater consistency in recipe generation.     

A dispatching method for automated lifting vehicles in automated port container terminals

In automated container terminals, containers are transported from the marshalling yard to a ship and vice versa by automated vehicles. The automated vehicle type studied in this paper is an automated lifting vehicle (ALV) that is capable of lifting a container from the ground by itself. This study discusses how to dispatch ALVs by utilizing information about pickup and delivery locations and time in future delivery tasks. A mixed-integer programming model is provided for assigning optimal delivery tasks to ALVs. A procedure for converting buffer constraints into time window constraints and a heuristic algorithm for overcoming the excessive computational time required for solving the mathematical model are suggested. Numerical experiments are reported to compare the objective values and computational times by a heuristic algorithm with those by an optimizing method and to analyze the effects of dual cycle operation, number of ALVs, and buffer capacity on the performance of ALVs.