Event sequence datasets with high event cardinality and long sequences are difficult to visualize and analyze. In particular, it is hard to generate a high level visual summary of paths and volume of flow. Existing approaches of mining and visualizing frequent sequential patterns look promising, but have limitations in terms of scalability, interpretability and utility. We propose CoreFlow, a technique that automatically extracts and visualizes branching patterns in event sequences. CoreFlow constructs a tree by recursively applying a three‐step procedure: rank events, divide sequences into groups, and trim sequences by the chosen event. The resulting tree contains key events as nodes, and links represent aggregated flows between key events. Based on CoreFlow, we have developed an interactive system for event sequence analysis. Our approach can compute branching patterns for millions of events in a few seconds, with improved interpretability of extracted patterns compared to previous work. We also present case studies of using the system in three different domains and discuss success and failure cases of applying CoreFlow to real‐world analytic problems. These case studies call forth future research on metrics and models to evaluate the quality of visual summaries of event sequences.
Deconvolution is the most commonly used image processing method in optical imaging systems to remove the blur caused by the point‐spread function (PSF). While this method has been successful in deblurring, it suffers from several disadvantages, such as slow processing time due to multiple iterations required to deblur and suboptimal in cases where the experimental operator chosen to represent PSF is not optimal. In this paper, we present a deep‐learning‐based deblurring method that is fast and applicable to optical microscopic imaging systems. We tested the robustness of proposed deblurring method on the publicly available data, simulated data and experimental data (including 2D optical microscopic data and 3D photoacoustic microscopic data), which all showed much improved deblurred results compared to deconvolution. We compared our results against several existing deconvolution methods. Our results are better than conventional techniques and do not require multiple iterations or pre‐determined experimental operator. Our method has several advantages including simple operation, short time to compute, good deblur results and wide application in all types of optical microscopic imaging systems. The deep learning approach opens up a new path for deblurring and can be applied in various biomedical imaging fields.
The combustion and NO emission characteristics of Shenmu coal and char were investigated with a thermogravimetry−mass spectrometry (TG−MS) system and on a pilot platform of a 2 MW circulating fluidized bed (CFB), respectively. Unlike Shenmu coal, the disadvantages of Shenmu char combustion include late ignition, poor combustion stability, and poor burnout. Despite its well-developed pore structure, there is less volatile content and more carbon crystal for Shenmu char, resulting in lower combustion reactivity compared to Shenmu coal. The combustion of Shenmu char has a higher dependence upon the combustion temperature, and it improves when the combustion temperature exceeds 900 °C. NO emission from the combustion of Shenmu char decreases with an increase in the combustion temperature, which is in contrast with the combustion of Shenmu coal. However, with an increase in the excess air ratio and a decrease in the secondary air ratio and the height of the secondary air port, NO emission from the combustion of both Shenmu char and coal increases.
In the absence of additional mesoporous template, hierarchically structured zeolites (HSZs) with variable Si/Al ratios (30-150) have been successfully synthesized via a newly developed steam-assisted crystallization process. The synthesized materials were characterized with powder X-ray diffraction, nitrogen sorption measurement, scanning electron microscopy, transmission electron microscopy, inductively coupled plasma optical emission spectrometry, solid-state nuclear magnetic resonance, and ammonia temperature-programmed desorption. All these results prove that the synthesized materials feature high crystallinity (microporous framework) and auxiliary mesoporous structure. In the model reactions of isopropylbenzene and 1,3,5-triisopropylbenzene cracking, compared to purely microporous ZSM-5 counterparts, here synthesized HSZs exhibited markedly enhanced catalytic performances resulting from their enlarged external surface area and shortened diffusion length in the microporous system.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.