ABSTRACT
A high-performance planar structure metal-semiconductor-metal-type solar-blind photodetector (SBPD) was fabricated on the basis of (010)-plane ß-Ga2O3 thermally oxidized from nonpolar (110)-plane GaN. A full width at half maximum of 0.486° was achieved for the X-ray rocking curve associated with (020)-plane ß-Ga2O3, which is better than most reported results for the heteroepitaxially grown (-201)-plane ß-Ga2O3. As a result of the relatively high crystalline quality, a dark current as low as 6.30 × 10-12 A was achieved at 5 V, while the photocurrent reached 1.86 × 10-5 A under 254 nm illumination at 600 µW/cm2. As a result, the photo-to-dark current ratio, specific detectivity, responsivity, and external quantum efficiency were calculated to be 2.95 × 106, 2.39 × 1012 Jones, 3.72 A/W, and 1815%, respectively. Moreover, the SBPD showed excellent repeatability and stability in the time-dependent photoresponse characteristics with fast relaxation time constants for the rise and decay processes of only 0.238 and 0.062 s, respectively. This study provides a promising approach to fabricate the device-level (010)-plane ß-Ga2O3 film and a new way for the epitaxial growth of (010)-plane ß-Ga2O3 and (110)-plane GaN as mutual substrates.
ABSTRACT
Visual data storytelling is gaining importance as a means of presenting data-driven information or analysis results, especially to the general public. This has resulted in design principles being proposed for data-driven storytelling, and new authoring tools being created to aid such storytelling. However, data analysts typically lack sufficient background in design and storytelling to make effective use of these principles and authoring tools. To assist this process, we present ChartStory for crafting data stories from a collection of user-created charts, using a style akin to comic panels to imply the underlying sequence and logic of data-driven narratives. Our approach is to operationalize established design principles into an advanced pipeline that characterizes charts by their properties and similarities to each other, and recommends ways to partition, layout, and caption story pieces to serve a narrative. ChartStory also augments this pipeline with intuitive user interactions for visual refinement of generated data comics. We extensively and holistically evaluate ChartStory via a trio of studies. We first assess how the tool supports data comic creation in comparison to a manual baseline tool. Data comics from this study are subsequently compared and evaluated to ChartStory's automated recommendations by a team of narrative visualization practitioners. This is followed by a pair of interview studies with data scientists using their own datasets and charts who provide an additional assessment of the system. We find that ChartStory provides cogent recommendations for narrative generation, resulting in data comics that compare favorably to manually-created ones.
ABSTRACT
Most visual analytics systems assume that all foraging for data happens before the analytics process; once analysis begins, the set of data attributes considered is fixed. Such separation of data construction from analysis precludes iteration that can enable foraging informed by the needs that arise in-situ during the analysis. The separation of the foraging loop from the data analysis tasks can limit the pace and scope of analysis. In this paper, we present CAVA, a system that integrates data curation and data augmentation with the traditional data exploration and analysis tasks, enabling information foraging in-situ during analysis. Identifying attributes to add to the dataset is difficult because it requires human knowledge to determine which available attributes will be helpful for the ensuing analytical tasks. CAVA crawls knowledge graphs to provide users with a a broad set of attributes drawn from external data to choose from. Users can then specify complex operations on knowledge graphs to construct additional attributes. CAVA shows how visual analytics can help users forage for attributes by letting users visually explore the set of available data, and by serving as an interface for query construction. It also provides visualizations of the knowledge graph itself to help users understand complex joins such as multi-hop aggregations. We assess the ability of our system to enable users to perform complex data combinations without programming in a user study over two datasets. We then demonstrate the generalizability of CAVA through two additional usage scenarios. The results of the evaluation confirm that CAVA is effective in helping the user perform data foraging that leads to improved analysis outcomes, and offer evidence in support of integrating data augmentation as a part of the visual analytics pipeline.
ABSTRACT
The objective of this study was to determine the effects of increased AA and energy intake during late gestation on reproductive performance, milk composition, and metabolic and redox status of sows. A total of 118 Yorkshire sows (third through sixth parity) were randomly assigned to dietary treatments from day 90 of gestation until farrowing. Dietary treatments consisted of combinations of 2 standardized ileal digestible (SID) AA levels [14.7 or 20.6 g/d SID Lys, SID Lys and other AA met or exceeded the NRC (2012) recommendations] and 2 energy levels (28.24 or 33.78 MJ/d intake of NE) in a 2 × 2 factorial design. After parturition, all sows were fed a standard lactation diet. Blood samples were collected and analyzed for parameters on metabolism, redox status, and amino acid profile. The data were analyzed using the generalized linear mixed models to reveal the impact of dietary levels of energy, AA, and their interaction. Sows with increased intake of AA had greater BW gain (P < 0.01) during late gestation. Furthermore, the BW loss during lactation was increased in sows with increasing intake of energy (P < 0.05) or AA (P < 0.05). Sows fed high energy had higher total litter birth weights (20.2 kg vs. 18.4 kg, P < 0.05) and shorter duration of farrowing (261 min vs. 215 min, P < 0.05), compared with those fed low energy, which likely was due to higher (P < 0.05) plasma glucose and lower (P < 0.05) plasma lactate prior to parturition. High AA intake in late gestation increased the ADG of piglets during the following lactation (P < 0.05), and increased the concentrations of plasma urea, and the following AA: Lys, Met, Thr, Val, Ile, Leu, Phe, Asp, Ser, and Arg at farrowing (P < 0.05). In conclusion, the increased intake of energy increased total litter weight of newborns and shortened the farrowing duration, which likely was due to improved energy status at farrowing. Furthermore, sows with increased intake of AA led to higher growth rate of piglets during the following lactation, accompanying with the increasing levels of plasma urea and amino acids. Therefore, the higher energy intake in late gestation appeared to improve litter weight and farrowing duration, while higher AA intake may have positive effect on piglets performance in lactation.
