Thin-plate forming by thixo- and rheoforging

Copyright © 2014 Chul Kyu Jin et al. Thin plates with a thickness of 1.2 mm are fabricated using two processes, thixoforging and rheoforging, which are semisolid forming techniques. The die design, formability, microstructure, and mechanical properties of the fabricated thin plates are analysed. A fan-shaped gate is designed by analysing the filling behaviour using semisolid material, and uniform filling behaviour of material is obtained by arranging nine overflows in product area. semisolid metal is prepared through a semisolid process in which reheating, a thixoprocess, and cooling with stirring, a rheoprocess, are applied. The semisolid material is injected into a forging die and is formed into thin plate at a punch speed of 300 mm/s and under a pressure of 100 MPa. Since semisolid material with a solid fraction below 45% has mainly small primary α-Al particles, the formability of the thin plate is improved. The formed thin plate also has good mechanical properties since the small and globular grains are evenly distributed. The thin plate formed from semisolid material with a solid fraction above 50% has poor mechanical properties owing to the large quantity of coarse primary α-Al particles. A rheoforged thin plate exhibits poorer mechanical properties than a thixoforged thin plate, but rheoforging produces amore precise thin plate

Real-time Image Processing for Microscopy-based Label-free Imaging Flow Cytometry in a Microfluidic Chip

Imaging flow cytometry (IFC) is an emerging technology that acquires single-cell images at high-throughput for analysis of a cell population. Rich information that comes from high sensitivity and spatial resolution of a single-cell microscopic image is beneficial for single-cell analysis in various biological applications. In this paper, we present a fast image-processing pipeline (R-MOD: Real-time Moving Object Detector) based on deep learning for high-throughput microscopy-based label-free IFC in a microfluidic chip. The R-MOD pipeline acquires all single-cell images of cells in flow, and identifies the acquired images as a real-time process with minimum hardware that consists of a microscope and a high-speed camera. Experiments show that R-MOD has the fast and reliable accuracy (500 fps and 93.3% mAP), and is expected to be used as a powerful tool for biomedical and clinical applications.113Ysciescopu

No-rebound trend of sea ice reduction in the Arctic Ocean: Role of "inertia effect" of the ocean

第4回極域科学シンポジウム横断セッション：[IA] 「急変する北極気候システム及びその全球的な影響の総合的解明」―GRENE北極気候変動研究事業研究成果報告2013―11月12日（火） 国立極地研究所 ２階大会議

Parallel symbolic state-space exploration is difficult, but what is the alternative?

State-space exploration is an essential step in many modeling and analysis problems. Its goal is to find the states reachable from the initial state of a discrete-state model described. The state space can used to answer important questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a starting point for sophisticated investigations expressed in temporal logic. Unfortunately, the state space is often so large that ordinary explicit data structures and sequential algorithms cannot cope, prompting the exploration of (1) parallel approaches using multiple processors, from simple workstation networks to shared-memory supercomputers, to satisfy large memory and runtime requirements and (2) symbolic approaches using decision diagrams to encode the large structured sets and relations manipulated during state-space generation. Both approaches have merits and limitations. Parallel explicit state-space generation is challenging, but almost linear speedup can be achieved; however, the analysis is ultimately limited by the memory and processors available. Symbolic methods are a heuristic that can efficiently encode many, but not all, functions over a structured and exponentially large domain; here the pitfalls are subtler: their performance varies widely depending on the class of decision diagram chosen, the state variable order, and obscure algorithmic parameters. As symbolic approaches are often much more efficient than explicit ones for many practical models, we argue for the need to parallelize symbolic state-space generation algorithms, so that we can realize the advantage of both approaches. This is a challenging endeavor, as the most efficient symbolic algorithm, Saturation, is inherently sequential. We conclude by discussing challenges, efforts, and promising directions toward this goal

Color-tunable mixed photoluminescence emission from Alq3 organic layer in metal-Alq3-metal surface plasmon structure

This work reports the color-tunable mixed photoluminescence (PL) emission from an Alq(3) organic layer in an Au-Alq(3)-Au plasmonic structure through the combination of organic fluorescence emission and another form of emission that is enabled by the surface plasmons in the plasmonic structure. The emission wavelength of the latter depends on the Alq(3) thickness and can be tuned within the Alq(3) fluorescent spectra. Therefore, a two-color broadband, color-tunable mixed PL structure was obtained. Obvious changes in the Commission Internationale d’Eclairage (CIE) coordinates and the corresponding emission colors of Au-Alq(3)-Au samples clearly varied with the Alq(3) thickness (90, 130, and 156 nm)

Development of a clinical scoring system for appendicitis in children with presumed appendicitis

Purpose To develop a clinical scoring system for children with presumed appendicitis who visit the emergency department. Methods A registry based-retrospective study was conducted in the pediatric emergency department between September 2015 and December 2016. Patients aged 4 to 17 years who had a > 1 of 5 Likert scale for possibility of appendicitis were included. Multiple logistic regressions based on Akaike information criterion were performed using variables regarding clinical features and inflammatory markers to develop the clinical scoring system. Results A total of 233 patients were included, and 93 (39.9%) had the final diagnosis of appendicitis. The final model with the lowest Akaike information criterion (171.7) consisted of 5 variables, including vomiting (1 point), absence of watery diarrhea (1 point), duration of symptoms ≤ 3 days (1 point), rebound tenderness (1 point), and white blood cell count > 10.0 × 109/L (2 points). If the clinical score was ≥ 4 of 6 points, the area under the receiver operating characteristic curve was 0.78 (95% confidence interval, 0.71-0.86) with a 78.9% sensitivity, 66.7% specificity, positive and negative predictive values of 70.0% and 76.2%, respectively, and positive and negative likelihood ratios of 2.4 and 0.3, respectively. Conclusion The 5-item clinical scoring system shows a fair performance for prediction of pediatric appendicitis. This simple tool could be applied to predict the pediatric appendicitis, and to avoid the use of potentially unnecessary computed tomography