Groundwater is an essential reserve for comprehensive water resources management. Territories such as the Manabí Hydrographic Demarcation (MHD) in Ecuador have considerable water scarcity during dry periods due to low rainfall, which generates significant losses for the agriculture sector. This work aims to map groundwater potential (GWP) in the MHD using a methodological approach combining remote sensing, geographic information systems (GIS), and analytic hierarchy process (AHP) modeling to support sustainable water management strategies. For the identification and reduction of variables, the multi-objective optimization (MOO) approach was used as a heuristic reference. By applying the Pareto principle, the variables with the greatest influence on the model were selected for the development of the final adjusted map of the groundwater potential index (GWPIF). The spatial effectiveness (SE) and predictability results based on the receiver operating characteristic (ROC) and area under the curve (AUC) revealed that the GWPIF (AUC = 72
Snake venoms are multilayered and chemically diverse mixtures with important ecological and medical relevance, particularly in many regions of the Americas. Although venom research has largely focused on proteinaceous toxins, venoms also contain low-molecular-weight components that remain poorly characterized. Among these, the lipidome represents one of the least explored layers of venom composition, and comparative information across species is limited. In this study, we applied an untargeted high-resolution mass spectrometry-based lipidomics approach to characterize and compare lipid signatures across nine viperid venoms representing eight American snake species from the genera Crotalus, Lachesis, and Bothrops. This multi-species analysis enabled the identification of both shared and divergent lipid features. Across all venoms, sphingolipids constituted a conserved and dominant lipid class, forming a common lipidomic core, while glycerophospholipids, glycerolipids, and fatty acid-derived lipids exhibited genus- and species-specific quantitative variation. Multivariate analyses revealed moderate but consistent lipidomic structuring associated with taxonomic grouping, with Bothrops and most Crotalus species clustering more closely, whereas Lachesis venoms displayed distinct lipidomic profiles. Supervised discrimination highlighted a restricted set of lipid species driving group separation, primarily through relative abundance differences rather than unique presence or absence. Notably, intra-genus variability was observed within Crotalus and between geographically distinct Lachesis muta populations. Together, these findings demonstrate that viperid venoms possess structured yet variable lipidomic landscapes that complement known proteomic diversity. This study expands the comparative framework of venom lipidomics and supports its integration with other omics approaches to achieve a more comprehensive understanding of venom composition and diversity.
The rapid growth of wind energy has increased the need for advanced condition monitoring (CM), predictive maintenance, and remaining useful life (RUL) estimation strategies for wind turbines. In this context, digital twins (DTs) have emerged as a key tool for improving reliability, availability, and operational efficiency by integrating physical models, operational data, and artificial intelligence (AI). This paper presents a systematic literature review (SLR) aimed at analyzing the state of the art, classifying the main applications, and identifying research gaps. A rigorous search protocol was applied across scientific databases, considering inclusion and exclusion criteria and analysis categories aligned with four research questions. The results show a high concentration of studies on critical wind turbine components, a predominance of hybrid physics-based and data-driven approaches, and an increasing use of deep learning (DL) models. However, several research gaps remain, including the predominance of component-level digital twin implementations rather than system-level architectures, the lack of standardized datasets and benchmarking frameworks, and challenges related to SCADA data heterogeneity and real-time scalability. It is concluded that DTs are evolving toward more autonomous and prescriptive systems; however, they still require further maturation for widespread industrial adoption.
Amazon rainforests face intensifying water stress due to increases in vapour pressure deficit and changing hydrological regimes. Embolism resistance (Ψ50) is a critical metric of tree survival under drought conditions, it is defined as a plant's capacity to resist disruption of xylem water flow due to air bubble formation from water stress. However, measurements of Ψ50 are only available for a limited number of Amazon locations and species. Conversely, data on forest taxonomic composition are abundant across Amazonia, and if Ψ50 is conserved phylogenetically, these data could provide a way to scale-up drought resistance patterns. Here we evaluate Ψ50 measurements across non-flooded Amazonian tree taxa and reveal a moderate phylogenetic signal, with phylogenetic conservatism evident at the family-level. Notably, Fabaceae is amongst the most embolism-resistant tree families in Amazonia. Leveraging the phylogenetic signal we use species composition and tree size data from 448 forest plots across Amazonia to produce a macroecological assessment of Amazonian vulnerability to embolism. The resulting estimate spatial pattern reveals that forests in the Brazilian and Guiana Shield regions, where Fabaceae abundance is high, show strong resistance to embolism. In contrast, tree communities in Western Amazonia appear more vulnerable to embolism, suggesting a reduced capacity to withstand future drought conditions.
This experimental study explores novel reinforcement alternatives for composite materials, aiming to reduce dependence on synthetic fibers and fossil fuel derivatives through the utilization of vegetable fibers. A mechanical characterization was conducted on epoxy matrix composite materials reinforced with glass fiber and natural fiber fabric derived from bamboo cane and cotton. Specimens were fabricated and tested by ASTM D-3039 (tensile), ASTM D-7264 (flexural), and ASTM D-5628 (impact) standards. The optimal configuration consisted of one layer of glass fiber plus two layers of natural fiber fabric, oriented at 0°, vacuum laminated, and oven cured. This configuration exhibited a maximum tensile strength of 131.65 MPa, a maximum flexural strength of 124.61 MPa, and an impact resistance of 6.01 J. This hybrid material demonstrates potential applications in the automotive, construction, and furniture manufacturing industries. The results indicate that incorporating natural fibers into hybrid composites can significantly enhance mechanical properties, offering a sustainable alternative to conventional materials .
