A Bridge Role Metric Model for Nodes in Software Networks

<div><p>A bridge role metric model is put forward in this paper. Compared with previous metric models, our solution of a large-scale object-oriented software system as a complex network is inherently more realistic. To acquire nodes and links in an undirected network, a new model that presents the crucial connectivity of a module or the hub instead of only centrality as in previous metric models is presented. Two previous metric models are described for comparison. In addition, it is obvious that the fitting curve between the results and degrees can well be fitted by a power law. The model represents many realistic characteristics of actual software structures, and a hydropower simulation system is taken as an example. This paper makes additional contributions to an accurate understanding of module design of software systems and is expected to be beneficial to software engineering practices.</p></div

Comparisons of the value between the betweenness and bridge role in two identical networks.

<p>(a) <i>N</i> = 13 and <i>M</i> = 12. (b) <i>N</i> = 13 and <i>M</i> = 16. Node 1 is also in the center in these two networks as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111613#pone-0111613-g003" target="_blank">Figure 3</a>.</p

Architecture and network of the hydropower simulation system.

<p>(a) Architecture. (b) Network (<i>N</i> = 310, <i>M</i> = 850). The software system is developed by Embedded Technology Key Lab in Northeastern University (<a href="http://www.netology.cn" target="_blank">www.netology.cn</a>) for the Fengman hydropower station in China.</p

The correlations between the metric model <i>Bre</i> values, closeness, betweenness and the degree <i>K</i>.

<p>The corresponding data obtained from Evolution-2.6.2 (<i>N</i> = 1445, <i>M</i> = 1129), JeditR-1.35 (<i>N</i> = 822, <i>M</i> = 718), Blender-2.42 (<i>N</i> = 2426, <i>M</i> = 2848) and Azureus_2.5.0.2 (<i>N</i> = 2375, <i>M</i> = 3278), which are well-known software packages. The data points (•,▪,▴) represent measurements of the three metric models.</p

The extraction from codes to software network.

<p>The process is as follows: The UML class diagram is first abstracted from the source code and subsequently converted to the undirected software network.</p

The correlations between the <i>CC</i>2 and <i>Bre</i> values.

<p>The corresponding data are also obtained from Evolution-2.6.2, JeditR-1.35, Blender-2.42 and Azureus_2.5.0.2. The data points • represent measurements of the models.</p

Comparisons of the value between closeness and bridge role in two identical networks.

<p>(a) <i>N</i> = 13 and <i>M</i> = 18. (b) <i>N</i> = 13 and <i>M</i> = 21, where there are three more edges in this network on the basis of the network in (a). Node 1 is in the center in these two networks.</p

Several cases with number of edges gradually increasing and the fixed nodes.

<p>In <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111613#pone-0111613-g002" target="_blank">Figure 2 (a)</a>, <i>N</i> = 30, <i>M</i> = 29, and  = 0.0345. In <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111613#pone-0111613-g002" target="_blank">Figure 2 (b)</a>, <i>N</i> = 30, <i>M</i> = 37, and  = 0.0577. In <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111613#pone-0111613-g002" target="_blank">Figure 2 (c)</a>, <i>N</i> = 30, <i>M</i> = 275, and  = 0.1319. In <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111613#pone-0111613-g002" target="_blank">Figure 2 (d)</a>, <i>N</i> = 30, <i>M</i> = 435, and  = 0.1332. The value of reflects the connectivity of the node 1.</p

Data_Sheet_1_Unveiling the microbiota of sauce-flavor Daqu and its relationships with flavors and color during maturation.docx

This study investigated the microbial community in three-color sauce-flavor Daqu (black, yellow, and white) throughout their maturation processes, together with their physicochemical factors, culturable microbes, flavor components, and fermenting vitalities. Results from high-throughput sequencing revealed distinct microbial diversity, with more pronounced variations in bacterial community than in fungal community. Firmicutes and Ascomycota emerged as the most dominant bacterial and fungal phyla, respectively, during maturation. Genus-level analysis identified Kroppenstedia, Virgibacillus, and Bacillus as dominant bacteria in black Daqu, yellow Daqu, and white Daqu, severally, while Thermoascus was shared as the core dominant fungi for these Daqu. Physicochemical factors, particularly acidity, were found to exert a significant impact on microbial community. Kroppenstedtia was the key bacteria influencing the color formation of these Daqu. Furthermore, correlations between dominant microbes and flavor compounds highlighted their role in Daqu quality. Molds (Aspergillus, Rhizomucor, and Rhizopus), excepting Bacillus, played a crucial role in the formation of pyrazine compounds. Consequently, this study offers innovative insights into the microbial perspectives on color and pyrazine formation, establishing a groundwork for future mechanized Daqu production and quality control of sauce-flavor baijiu.</p

High Time- and Size-Resolved Measurements of PM and Chemical Composition from Coal Combustion: Implications for the EC Formation Process

Inefficient coal combustion is a significant source of elemental carbon (EC) air pollution in China, but there is a limited understanding of EC’s formation processes. In this study, high time-resolved particle number size distributions (PNSDs) and size-resolved chemical compositions were obtained from the combustion of four bituminous coals burned in a quartz tube furnace at 500 and 800 °C. Based on the distinct characteristics of PNSD, the flaming stage was divided into the first-flaming stage (with a PNSD peak at 0.3–0.4 μm) and the second-flaming stage (with a PNSD peak at 0.1–0.15 μm). For the size-segregated EC and OC measurements, more soot-EC was observed in particles larger than 0.3 μm, whereas the smaller ones possessed more char-EC. The results indicated that gas-phase and direct-conversion EC generation mechanisms dominate different burning stages. The analysis of 16 parent PAHs showed more high-molecular-weight PAHs in the second-flaming stage particles, which supports the idea of different formation processes for char-EC and soot-EC. For all four coals, the PNSD and chemical compositions shared a similar trend, confirming that the different formation processes of EC in different flaming stages were common. This study provides novel information concerning EC formation