Water is a basic natural resource for life and the sustainable development of society. Methods to assess water quality in freshwater ecosystems based on environmental quality bioindicators have proven to be low cost, reliable, and can be adapted to ecosystems with well-defined structures. The objective of this paper is to propose an interdisciplinary approach for the assessment of water quality in freshwater ecosystems through bioindicators. From the presence/absence of bioindicator organisms and their sensitivity/tolerance to environmental stress, we constructed a bipartite network, G. In this direction, we propose a new method that combines two research approaches, Graph Theory and Random Matrix Theory (RMT). Through the topological properties of the graph G, we introduce a topological index, called $JP(G)$ , to evaluate the water quality, and we study its properties and relationships with known indices, such as Biological Monitoring Working Party ( $BMWP$ ) and Shannon diversity ( $H^{\prime }$ ). Furthermore, we perform a scaling analysis of random bipartite networks with already specialized parameters for our case study. We validate our proposal for its application in the reservoir of Guájaro, Colombia. The results obtained allow us to infer that the proposed techniques are useful for the study of water quality, since they detect significant changes in the ecosystem.

The studies on the evolution of tourist destinations are not a new issue, however, most of them have been focused on consolidated destinations, whereas only a minimum has been done on tourism transformation in rural areas. The objective of this work is to diagnose the evolutionary process of tourism in Coastal Rural Communities (CRCs). To do this, we propose a model which combines two research approaches, Path Dependence and Social Network Analysis. The methodological approach is divided into three parts: design, application and validation, and it is based on collecting in situ and identifying key informants. In the first part, the stages of a Path Dependence for a CRC are conceptualized and bipartite graphs are constructed to show the relationships between: (1) the identified establishments built in a period of time and (2) positive and negative lock-ins with a greater degree of influence on the evolutionary process. On this basis, the resulting graphs are associated with the stages of the Path Dependence. In the second part, the model theoretically raised is applied as an empirical case at the CRC of Playa Ventura, Guerrero, in the Southern Pacific of Mexico. Finally, we validated the model based on the results obtained, which indicate that the model is suitable for the generation of knowledge about the evolutionary process of tourism in CRC’s, and therefore, it opens the possibility of being replicated in other communities with the same characteristics.

Sustainable water management is important to ensure its availability for future generations. The study of water quality is fundamental for this purpose. Assessing the health of aquatic ecosystems through bioindicators has been shown to be reliable and inexpensive. The objective of this work was to evaluate water quality through a biomathematical model that involves environmental stress indicator organisms and their close relationship with dissolved oxygen. In this direction, a system of differential equations describing the population dynamics of aquatic macroinvertebrates under the influence of dissolved oxygen is proposed. The model is validated by its application in the Coyuca Lagoon, Mexico. Likewise, population changes over time were represented, which allowed us to deduce that the increase or decrease in the aeration/oxygenation rate significantly affects the population dynamics of the bioindicator organisms. In addition, to classify water quality, a one-to-one correspondence was established between water quality and the equilibrium points of the system of differential equations. The results obtained allow inferring that the proposed techniques are useful for the study of water quality since they can predict significant changes in the ecosystem and provide researchers and water managers with tools for decision making.

The sustainable use of terrestrial ecosystems is crucial to ensure food security for future generations, and the formulation of plant growth–promoting bacterial consortia has proven useful not only in sustainable agriculture but also in applications such as phytopathogen biocontrol, bioremediation of contaminated soils and waters, biofertilization, organic waste degradation, and the production of industrially relevant enzymes. In this work, we developed a mathematical model to quantify and identify antagonism‐free consortia based on double‐agar‐layer assays with 20 and 33 bacterial strains from independent studies. Simultaneously, we implemented a graph‐based geometric model to analyse their topological properties and defined an index to quantify feasible consortia. The proposed methods were experimentally validated, identifying 36,851 and 376,126 subsets of compatible strains, for 20 and 33 strains, respectively. These subsets have the potential to promote plant growth and other biotechnological functions, thus providing a scalable tool for the management of agricultural resources, environmental restoration, and the design of sustainable biotechnological practices.

Background/Objectives: Gestational Diabetes Mellitus (GDM) is a global health issue with immediate and long-term maternal–fetal complications. Current diagnostic approaches, such as the Oral Glucose Tolerance Test (OGTT), have limitations in accessibility, sensitivity, and timing. This study aimed to identify key nodes and structural interactions associated with GDM using graph theory and network analysis to improve early predictive strategies. Methods: A literature review inspired by PRISMA guidelines (2004–2025) identified 44 clinically relevant factors. A directed graph was constructed using Python (version 3.10.12), and centrality metrics (closeness, betweenness, eigenvector), k-core decomposition, and a Minimum Dominating Set (MDS) were computed. The MDS, derived using an integer linear programming model, was used to determine the smallest subset of nodes with systemic dominance across the network. Results: The MDS included 20 nodes, with seven showing a high out-degree (≥4), notably Apo A1, vitamin D, vitamin D deficiency, and sedentary lifestyle. Vitamin D exhibited 15 outgoing edges, connecting directly to protective factors like HDL and inversely to risk factors such as smoking and obesity. Sedentary behavior also showed high structural influence. Closeness centrality highlighted triglycerides, insulin resistance, uric acid, fasting plasma glucose, and HDL as nodes with strong predictive potential, based on their high closeness and multiple incoming connections. Conclusions: Vitamin D and sedentary behavior emerged as structurally dominant nodes in the GDM network. Alongside metabolically relevant nodes with high closeness centrality, these findings support the utility of graph-based network analysis for early detection and targeted clinical interventions in maternal health.