Insight into the formation of secondary inorganic aerosol based on high-time-resolution data during haze episodes and snowfall periods in Zhengzhou, China.

Episodic haze is frequently observed in Zhengzhou, China. Such haze typically contains secondary inorganic aerosols. In this paper, we explore the formation mechanisms of sulfate, nitrate, and ammonium (SNA) in Zhengzhou from January 3 to 25, 2018 based on the results of a series of online instruments and a size-segregated filter sampler. Our results document the remarkable contributions of SNA to winter haze episodes in Zhengzhou, where they account for about 50% of PM2.5 mass concentration. SNA were mainly concentrated in droplet-mode particles, which increased remarkably with the aggravation of the haze episode. In addition, KNO3 and NaNO3 were formed in droplet-mode particles and coarse-mode particles respectively with increasing PM2.5 concentration. The atmosphere during the observation period was ammonia-rich, and the aerosol was acidic under high PM2.5 concentration. Homogeneous reactions dominated the formation of nitrate. HONO photolysis played a more important role in the origin of OH radicals when O3 decreased during haze episode. Under high relative humidity (RH), nitrate formation was influenced by heterogeneous hydrolysis reactions of N2O5. Sulfates were mainly formed through aqueous-phase reactions, especially when the RH was higher than 60%. Under these conditions, there were amounts of liquid water content existed in aerosols. Finally, we observed enhanced conversion of SO2 and NO2 during snowfall periods. This effect may be attributable to the higher RH and O3 levels despite the unfavorable effects of wet deposition and low concentrations of gaseous precursors.

Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis.

Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data. For DR techniques to be useful in exploratory data analysis, they need to be adapted to human needs and domain-specific problems, ideally, interactively, and on-the-fly. Many visual analytics systems have already demonstrated the benefits of tightly integrating DR with interactive visualizations. Nevertheless, a general, structured understanding of this integration is missing. To address this, we systematically studied the visual analytics and visualization literature to investigate how analysts interact with automatic DR techniques. The results reveal seven common interaction scenarios that are amenable to interactive control such as specifying algorithmic constraints, selecting relevant features, or choosing among several DR algorithms. We investigate specific implementations of visual analysis systems integrating DR, and analyze ways that other machine learning methods have been combined with DR. Summarizing the results in a "human in the loop" process model provides a general lens for the evaluation of visual interactive DR systems. We apply the proposed model to study and classify several systems previously described in the literature, and to derive future research opportunities.

Wellformedness properties in Euler diagrams: which should be used?

Euler diagrams are often used to visualize intersecting data sets in applications such as criminology; genetics, medicine, and computer file systems. One interesting aspect of these diagrams is that some data sets cannot be drawn without breaking one or more "wellformedness properties," which are considered to reduce the user comprehension of the diagrams. However, it is possible to draw the same data with different diagrams, each of which breaks different wellformedness properties. Hence, some properties are "swappable," so motivating the study of which of the alternatives would be best to use. This paper reports on the two empirical studies to determine how wellformedness properties affect comprehension. One study was with abstract data, the other was with concrete data that visualized students' enrollment on university modules. We have results from both studies that imply that diagrams with concurrency or disconnected zones perform less well than other some other properties. Further, we have no results that imply that diagrams with brushing points adversely affect performance. Our data also indicate that nonsimple curves are preferred less than diagrams with other properties. These results will inform both human diagram designers and the developers of automated drawing systems on the best way to visualize data using Euler diagrams.

Inductively generating Euler diagrams.

Euler diagrams have a wide variety of uses, from information visualization to logical reasoning. In all of their application areas, the ability to automatically layout Euler diagrams brings considerable benefits. In this paper, we present a novel approach to Euler diagram generation. We develop certain graphs associated with Euler diagrams in order to allow curves to be added by finding cycles in these graphs. This permits us to build Euler diagrams inductively, adding one curve at a time. Our technique is adaptable, allowing the easy specification, and enforcement, of sets of well-formedness conditions; we present a series of results that identify properties of cycles that correspond to the well-formedness conditions. This improves upon other contributions toward the automated generation of Euler diagrams which implicitly assume some fixed set of well-formedness conditions must hold. In addition, unlike most of these other generation methods, our technique allows any abstract description to be drawn as an Euler diagram. To establish the utility of the approach, a prototype implementation has been developed.

A Weighted Sum Validity Function for clustering with a Hybrid Niching Genetic Algorithm.

Clustering is inherently a difficult problem, both with respect to the construction of adequate objective functions as well as to the optimization of the objective functions. In this paper, we suggest an objective function called the Weighted Sum Validity Function (WSVF), which is a weighted sum of the several normalized cluster validity functions. Further, we propose a Hybrid Niching Genetic Algorithm (HNGA), which can be used for the optimization of the WSVF to automatically evolve the proper number of clusters as well as appropriate partitioning of the data set. Within the HNGA, a niching method is developed to preserve both the diversity of the population with respect to the number of clusters encoded in the individuals and the diversity of the subpopulation with the same number of clusters during the search. In addition, we hybridize the niching method with the k-means algorithm. In the experiments, we show the effectiveness of both the HNGA and the WSVF. In comparison with other related genetic clustering algorithms, the HNGA can consistently and efficiently converge to the best known optimum corresponding to the given data in concurrence with the convergence result. The WSVF is found generally able to improve the confidence of clustering solutions and achieve more accurate and robust results.