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Business model analysis for the scope of entrepreneurship in a solar drying field in the European region
Published in Zoltán Bartha, Tekla Szép, Katalin Lipták, Dóra Szendi, Entrepreneurship in the Raw Materials Sector, 2022
Baibhaw Kumar, László Berényi, Zoltán Szamosi, Gábor L. Szepesi
The P-graph framework is part of the PNS (Process Network Synthesis) which is a flowsheet design used generally in process optimization in various complex problems associated with industries. In the 1990s, Cabezas et al. (Cabezas et al., 2015) developed the P-graph tool for the PNS-related optimization problem. The P-graph in Figure 4. depicts the process flow for various sections of solar-dried products. The PNS Draw was used to generate the graph from www. p-graph.com and the raw material and process are shown in Figure 4. This process model is proposed for entrepreneurs or business startups in solar drying. In the proposed graph, the cost analysis in the process flow of solar-dried products can be performed by using the algorithm tree in the software. The raw materials, intermediate materials, finished goods, and the processes involved in solar drying can be fed to the system as raw material, processes, and finished products. Parallel, the economic values associated with every step could be added. This generates the solution for the process. The software allows pricing analysis in between the processes involved. The optimization could help in the visualization of process flow and gives the heuristic viewpoint to the entrepreneur.
Participation Factors
Published in Federico Milano, Ioannis Dassios, Muyang Liu, Georgios Tzounas, Eigenvalue Problems in Power Systems, 2020
Federico Milano, Ioannis Dassios, Muyang Liu, Georgios Tzounas
The Python module graph-tool[139] is utilized to generate a graph of the studied network. The resulting graph has 1,479 vertices, which correspond to the system buses and 1,851 edges, which correspond to lines and transformers. Note that the coordinates of the graph vertices and edges do not represent the actual geography of the system. For the considered modes, we calculate the participation matrices of the bus active power injections. Then, the sizes of the graph vertices are adjusted with respect to the magnitude of the calculated PFs.
Models of space
Published in Christopher M. Gold, Spatial Context: An Introduction to Fundamental Computer Algorithms for Spatial Analysis, 2018
However, before we can do this we need to translate these spatial entities and their spatial relationships into a structure we can code. We shall first look at spatial data structures in general, using the PAN (or VPAN) graph tool. Once we have decided on the properties that we want in our structure we can translate the results into a table-based or pointer-based implementation.
Modeling dynamic systems: contribution to the unsteady behavior of a condenser based on the pseudo-bond graph approach
Published in International Journal of Modelling and Simulation, 2020
M. Kebdani, G. Dauphin-Tanguy, A. Dazin
Based on the comparison with the existing modeling works, the contributions are: The use of bond graph tool to model the BPC is new. The bond graph is particularly useful in writing the system equations and in assessing the effects of changes in the model even before the equations are written [10] and [11].The proposed model takes into account the dynamic behavior, unlike the majority of classical models limited to the permanent regime.The transitional model pays particular attention to the heat transfer coefficients. The novelty, compared to previous work, is twofold: first, coefficients vary with the evolution of local thermodynamic conditions. Second, the thermal behavior of the condenser is experimentally validated without any use of recalibration of the set of used coefficients.Unlike most existing models, the proposed model considers the heat exchange with the outside and the longitudinal conduction along the walls.Gaussian and polynomial correlations of thermo physical properties are specially developed on the basis of data provided by NIST (site 1).
Equality of public transit connectivity: the influence of mass rapid transit services on individual buildings for Singapore
Published in Transportmetrica B: Transport Dynamics, 2019
Zengxiang Li, Shen Ren, Nan Hu, Yong Liu, Zheng Qin, Rick Siow Mong Goh, Liwen Hou, Bharadwaj Veeravalli
Closeness centrality could be calculated by many graph processing libraries, such as NetworkX (2017) and the urban network analysis toolbox on ArcGIS (Sevtsuk and Mekonnen 2012). However, it is very time-consuming to calculate closeness centrality of all vertices in a large graph for city-scale analysis. To speed up graph processing, we have adopted an efficient parallel graph processing toolkit, i.e. graph-tool (Peixoto 2017). Take the Current-MRT graph in Table 1 as an example; Figure 2 reports the average execution time and CPU usage of the closeness centrality algorithm in five repeated experiments. The CPU usage increases and the execution time decreases almost proportionally with respect to the increasing assigned CPU cores. For the cases with a large number of CPU cores, CPU resource could not be fully utilized as the hyper-threading technique is enabled, which accordingly affects graph-tool performance.
Exploring beneath the surface using interactive data transects
Published in Annals of GIS, 2021
The basic methodology of the data mining tools developed here is to examine the layers of data that lie beneath the surface displayed in a cross-sectional graph associated with a defined transect line. Two tools are developed for different representations of the cross-sections, a stacked profile tool, and a line graph tool. The stacked profile transect represents the data layers as strata piled on top of one another similar to geologic profiles of rock strata. In contrast, the line graph transect is simply a series of value lines superimposed over one another. By allowing the user to define a series of transect lines interactively using either tool, it is expected that the user will have a more thorough understanding of the forces that give rise to the surface values and features.