Effects of noise on a model of oscillatory chemical reaction

A simple oscillating reaction model subject to additive Gaussian white noise is investigated as the model is located in the dynamic region of oscillations. The model is composed of three ordinary differential equations representing the time evolutions of X, Y, and Z, respectively. Initially, a uniform random noise is separately added to the three equations to study the effect of noise on the oscillatory cycle of X, Y, and Z. For a given value of noise intensity, the amplitude of oscillation increases monotonically with time. Furthermore, the noise is added to any one of the three equations to study the impact of noise on one species on the bifurcation behavior of the other

Continuous-Variable Nonclassicality Detection under Coarse-Grained Measurement

Coarse graining is a common imperfection of realistic quantum measurement, obstructing the direct observation of quantum features. Under highly coarse-grained measurement, we experimentally detect the continuous-variable nonclassicality of both Gaussian and non-Gaussian states. Remarkably, we find that this coarse-grained measurement outperforms the conventional fine-grained measurement for nonclassicality detection: it detects nonclassicality beyond the reach of the variance criterion, and furthermore, it exhibits stronger statistical significance than the high-order moments method. Our work shows the usefulness of coarse-grained measurement by providing a reliable and efficient way of nonclassicality detection for quantum technologies

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Cryogenic machining uses liquid nitrogen (LN2) as a coolant. This machining process can reduce the cutting temperature and increase tool life. Titanium alloys have been widely used in the aerospace and automobile industries because of their high strength-to-weight ratio. However, they are difficult to machine because of their poor thermal properties, which reduce tool life. In this study, we applied cryogenic machining to titanium alloys. Orthogonal cutting experiments were performed at a low cutting speed (1.2 - 2.1 m/min) in three cooling conditions: dry, cryogenic, and cryogenic plus heat. Cutting force and friction coefficients were observed to evaluate the machining characteristics for each cooling condition. For the cryogenic condition, cutting force and friction coefficients increased, but decreased for the cryogenic plus heat condition

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The surface roughness and cutting forces are the important factors for the machine-part quality during the hard-turning process. The aim of this paper is to optimize hard-cutting conditions via implementation of response surface methodology (RSM). The experiments were conducted for the hard-turning process with the Box-Behnken design. The validation of the surface roughness and cutting forces was performed with the obtained 2nd order polynomial regression model. The results showed that the surface roughness was strongly dependent upon the RPM. The diminution of the cutting force was attributed to the low feed rate and the depth of cut. On the basis of the RSM, optimized cutting conditions of RPM, feed rate, and depth of cut are 3440, 0.0352 [mm/rev], and 0.03 [mm]. In this optimal cutting condition, the surface roughness can be around Ra= 0.202 ??m

Accelerating Large-Scale Graph-based Nearest Neighbor Search on a Computational Storage Platform

K-nearest neighbor search is one of the fundamental tasks in various applications and the hierarchical navigable small world (HNSW) has recently drawn attention in large-scale cloud services, as it easily scales up the database while offering fast search. On the other hand, a computational storage device (CSD) that combines programmable logic and storage modules on a single board becomes popular to address the data bandwidth bottleneck of modern computing systems. In this paper, we propose a computational storage platform that can accelerate a large-scale graph-based nearest neighbor search algorithm based on SmartSSD CSD. To this end, we modify the algorithm more amenable on the hardware and implement two types of accelerators using HLS- and RTL-based methodology with various optimization methods. In addition, we scale up the proposed platform to have 4 SmartSSDs and apply graph parallelism to boost the system performance further. As a result, the proposed computational storage platform achieves 75.59 query per second throughput for the SIFT1B dataset at 258.66W power dissipation, which is 12.83x and 17.91x faster and 10.43x and 24.33x more energy efficient than the conventional CPU-based and GPU-based server platform, respectively. With multi-terabyte storage and custom acceleration capability, we believe that the proposed computational storage platform is a promising solution for cost-sensitive cloud datacenters.Comment: Extension of FCCM 20201 and Accepted in Transaction on Computer

Effective promotions through social media: A case of Major League Baseball teams

This study examined if the impacts of Facebook promotions on fan engagement were moderated by team performance in the Major League Baseball. Two teams showing the opposite results in the 2015 seasons were selected. Likes, Shares, and Comments were measured as indicators of fan engagement. All posts were categorised in the five promotion types. A two-way MANOVA was carried out to see if there were any differences in Facebook Likes, Shares, and Comments for differing promotion types and team performance. The results revealed that there were statistically significant interaction effects between promotion types and team performance on Likes and Shares. For player/personnel promotion and team information, the numbers of Likes and Shares were significantly higher than the other promotion types for both teams. For Likes and Shares by the two significant promotion types, Royals had significantly higher mean scores than did Phillies