Visualizing Historical Book Trade Data: An Iterative Design Study with Close Collaboration with Domain Experts

The circulation of historical books has always been an area of interest for historians. However, the data used to represent the journey of a book across different places and times can be difficult for domain experts to digest due to buried geographical and chronological features within text-based presentations. This situation provides an opportunity for collaboration between visualization researchers and historians. This paper describes a design study where a variant of the Nine-Stage Framework was employed to develop a Visual Analytics (VA) tool called DanteExploreVis. This tool was designed to aid domain experts in exploring, explaining, and presenting book trade data from multiple perspectives. We discuss the design choices made and how each panel in the interface meets the domain requirements. We also present the results of a qualitative evaluation conducted with domain experts. The main contributions of this paper include: 1) the development of a VA tool to support domain experts in exploring, explaining, and presenting book trade data; 2) a comprehensive documentation of the iterative design, development, and evaluation process following the variant Nine-Stage Framework; 3) a summary of the insights gained and lessons learned from this design study in the context of the humanities field; and 4) reflections on how our approach could be applied in a more generalizable way

Factors that affect the growth and photosynthesis of the filamentous green algae, Chaetomorpha valida, in static sea cucumber aquaculture ponds with high salinity and high pH

Chaetomorpha valida, dominant filamentous green algae, can be harmful to sea cucumber growth in aquaculture ponds of China. In order to understand the environmental factors affecting the growth of C. valida in sea cucumber aquaculture ecosystems, a combination of field investigations and laboratory experiments were conducted. Field surveys over one year revealed that C. valida survived in sea cucumber aquaculture ponds in salinities ranging from 24.3 ± 0.01‰ to 32.0 ± 0.02‰ and a pH range of 7.5 ± 0.02–8.6 ± 0.04. The high salinity and pH during the period of low C. valida biomass from January to May lay the foundation for its rapid growth in the following months of June to October. Many factors interact in the field environment, thus, laboratory experiments were conducted to determine the isolated effects of pH and salinity on C. valida growth. In laboratory experiments, samples were incubated under different salinity and pH conditions at 25 °C, with a light intensity of 108 μmol photon·m−2·s−1, and a photoperiod of 12 L:12 D. Results showed that salinity and pH significantly affect the growth and Fv/Fm (quantum yield of photosynthesis) of C. valida (p < 0.01). C. valida grew the longest at a salinity of 34‰ and a pH of 8.0. At 34‰ salinity, C. valida grew to 26.44 ± 5.89 cm in 16 days. At a pH of 8.0, C. valida grew to 67.96 ± 4.45 cm in 32 days. Fv/Fm was 0.635 ± 0.002 at a salinity of 32‰, and 0.550 ± 0.006 to 0.660± 0.001 at pH 7.0 to 8.5. Based on these results, we conclude that C. valida can bloom in sea cucumber ponds due to the high salinity and pH of coastal sea waters, which promote growth and maintain the photosynthetic activity of C. valida

Self-adaptive k-means based on a covering algorithm

The K-means algorithm is one of the ten classic algorithms in the area of data mining and has been studied by researchers in numerous fields for a long time. However, the value of the clustering number k in the K-means algorithm is not always easy to be determined, and the selection of the initial centers is vulnerable to outliers. This paper proposes an improved K-means clustering algorithm called the covering K-means algorithm (C-K-means). The C-K-means algorithm can not only acquire efficient and accurate clustering results but also self-adaptively provide a reasonable numbers of clusters based on the data features. It includes two phases: the initialization of the covering algorithm (CA) and the Lloyd iteration of the K-means. The first phase executes the CA. CA self-organizes and recognizes the number of clusters k based on the similarities in the data, and it requires neither the number of clusters to be prespecified nor the initial centers to be manually selected. Therefore, it has a “blind” feature, that is, k is not preselected. The second phase performs the Lloyd iteration based on the results of the first phase. The C-K-means algorithm combines the advantages of CA and K-means. Experiments are carried out on the Spark platform, and the results verify the good scalability of the C-K-means algorithm. This algorithm can effectively solve the problem of large-scale data clustering. Extensive experiments on real data sets show that the accuracy and efficiency of the C-K-means algorithm outperforms the existing algorithms under both sequential and parallel conditions

The impact of RandD employees' income onwork engagement in high-tech industries: Based on a moderated mediation model

Based&nbsp;on&nbsp;the&nbsp;social exchange theory and job demand-resource&nbsp;model, this paper discusses&nbsp;the&nbsp;relationship between&nbsp;the&nbsp;R&amp;D employees&#39;&nbsp;income&nbsp;and work&nbsp;engagement&nbsp;in&nbsp;high-tech&nbsp;industry, and explores&nbsp;the&nbsp;internal mechanism&nbsp;of&nbsp;job satisfaction, organizational commitment and sense&nbsp;of&nbsp;income&nbsp;equity.&nbsp;The&nbsp;results show that&nbsp;income&nbsp;has a significant positive effect&nbsp;on&nbsp;work&nbsp;engagement. It is also identified that job satisfaction and organizational commitment have a completely&nbsp;mediating&nbsp;effect between&nbsp;income&nbsp;and work&nbsp;engagement, and that&nbsp;the&nbsp;sense&nbsp;of&nbsp;income&nbsp;equity&nbsp;moderates&nbsp;the&nbsp;mediating&nbsp;role&nbsp;of&nbsp;job satisfaction and organizational commitment. This paper proposes a new approach to illustrate&nbsp;the&nbsp;impact&nbsp;of&nbsp;income&nbsp;on&nbsp;work&nbsp;engagement, which enriches&nbsp;the&nbsp;theoretical&nbsp;model&nbsp;framework&nbsp;of&nbsp;income&nbsp;and provides theoretical guidance for enterprises to improve employees&#39; work&nbsp;engagement.</p

Service Composition Optimization Method Based on Parallel Particle Swarm Algorithm on Spark

Web service composition is one of the core technologies of realizing service-oriented computing. Web service composition satisfies the requirements of users to form new value-added services by composing existing services. As Cloud Computing develops, the emergence of Web services with different quality yet similar functionality has brought new challenges to service composition optimization problem. How to solve large-scale service composition in the Cloud Computing environment has become an urgent problem. To tackle this issue, this paper proposes a parallel optimization approach based on Spark distributed environment. Firstly, the parallel covering algorithm is used to cluster the Web services. Next, the multiple clustering centers obtained are used as the starting point of the particles to improve the diversity of the initial population. Then, according to the parallel data coding rules of resilient distributed dataset (RDD), the large-scale combination service is generated with the proposed algorithm named Spark Particle Swarm Optimization Algorithm (SPSO). Finally, the usage of particle elite selection strategy removes the inert particles to optimize the performance of the combination of service selection. This paper adopts real data set WS-Dream to prove the validity of the proposed method with a large number of experimental results

Effects of intermittent cold stimulation on growth performance, meat quality, antioxidant capacity and liver lipid metabolism in broiler chickens

ABSTRACT: Intermittent cold stimulation (ICS) enhances broilers’ resistance to cold stress. Nonetheless, further research is needed to investigate the underlying mechanisms that enhance cold stress resistance. A total of 160 one-day-old male Ross 308 broilers were randomly divided into 2 groups (CC and CS5), with the CC group managing temperature according to the standard for broiler growth stages, while the CS5 group were subjected to cold stimulation at a temperature 3℃ lower than the CC group for 5 h, every 2 d from 15 to 35 d. Sampling was conducted at 36 d (36D), 50 d (50D) and after acute cold stress for 24 h (Y24). First, we examined the effects of ICS on broiler growth performance, meat quality, antioxidant capacity, and lipid metabolism. The results demonstrated that ICS enhanced the performance of broilers to a certain degree. Specifically, the average weight gain in the CS5 group was significantly higher than that of the CC group, and the feed conversion ratio significantly decreased compared to CC at 4 W and 6 W (P ≤ 0.05). Compared with the CC group, cold stimulation significantly reduced drip loss, shearing force, and yellowness (a* value) of chicken meat, while significantly increased redness (b* value) (P ≤ 0.05). At Y24, the levels of T-AOC and GSH-PX in the serum of the CS5 group were significantly higher than those of the CC group, while the level of MDA was significantly lower (P ≤ 0.05). The content of TG, FFA, and VLDL in the serum of the CS5 group was significantly elevated, whereas the level of TC and HDL was significantly lower (P ≤ 0.05). In addition, we further explored whether AMPK-mTOR pathway is involved in the regulation of changes in lipid metabolism and the possible regulatory mechanisms downstream of the signaling pathway. The results showed that ICS significantly upregulated the expression levels of AMPK mRNA and protein in the liver of the CS5 group at 36D and Y24, while significantly down-regulating mTOR (P ≤ 0.05). Compared with the CC group, ICS significantly down-regulated the mRNA expression levels of lipid synthesis and endoplasmic reticulum stress-related genes (SREBP1c, FAS, SCD, ACC, GRP78 and PERK) at 36D and Y24, while significantly up-regulating the mRNA expression levels of lipid decomposition and autophagy-related genes (PPAR and LC3) (P ≤ 0.05). In addition, at Y24, the protein expression levels of endoplasmic reticulum stress-related genes (GRP78) in the CS5 group were significantly lower, while autophagy-related genes (LC3 and ATG7) were significantly higher (P ≤ 0.05). ICS can affect meat quality and lipid metabolism in broilers, and when broilers are subjected to acute cold stress, broilers trained with cold stimulation have stronger lipid metabolism capacity