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In the last few years, there is an increasing interest to generate visual representations of very large scientific domains. A methodology based on the combined use of ISI-JCR category cocitation and social networks analysis through the use of the Pathfinder algorithm has demonstrated its ability to achieve high quality, schematic visualizations for these kinds of domains. Now, the next step would be to generate these scientograms in an on-line fashion. To do so, there is a need to significantly decrease the run time of the latter pruning technique when working with category cocitation matrices of a large dimension like the ones handled in these large domains (Pathfinder has a time complexity order of O([n.sup.4]), with n being the number of categories in the cocitation matrix, i.e., the number of nodes in the network). Although a previous improvement called Binary Pathfinder has already been proposed to speed up the original algorithm, its significant time complexity reduction is not enough for that aim. In this paper, we make use of a different shortest path computation from classical approaches in computer science graph theory to propose a new variant of the Pathfinder algorithm which allows us to reduce its time complexity in one order of magnitude, O([n.sup.3]), and thus to significantly decrease the run time of the implementation when applied to large scientific domains considering the parameter q = n - 1. Besides, the new algorithm has a much simpler structure than the Binary Pathfinder as well as it saves a significant amount of memory with respect to the original Pathfinder by reducing the space complexity to the need of just storing two matrices. An experimental comparison will be developed using large networks from real-world domains to show the good performance of the new proposal. Keywords: PFNETs; Pathfinder algorithms; Cocitation analysis; Information visualization; Large scientific domain visual maps; Graph shortest path algorithms

Photovoltaics (PV) utilize sunlight to generate electricity, thus playing a crucial role in generating clean energy and decreasing carbon emissions. Simultaneously, these systems encourage self-sufficiency in energy production. Consequently, it becomes imperative to monitor the performance of photovoltaic systems as an essential method for assessing and confirming these advantages. Precise measurement and analysis of performance data offer researchers and industry experts valuable insights into system effectiveness, power generation trends, as well as their overall ecological influence. The significance of PV monitoring in ensuring and enhancing system performance is emphasized by the research conducted. The ability to collect and analyze real-time data enables operators to identify inefficient modules, as well as shading or other obstacles that could hinder energy production. Furthermore, monitoring systems enable early identification of potential malfunctions, which allows for timely maintenance and repair actions. Ultimately, these practices enhance overall energy generation while extending the longevity of PV installations.

Ninety children with signs and symptoms of gastroesophageal (GE) reflux were studied. All of them had GE scintigraphy, barium, and endoscopy, and 50, in addition, ultrasound examination. The sensitivity of the GE scintigraphy, in comparison with the other methods, was 92.5%. It is a useful screening test for GE reflux.