The constant increase in industrialization and urbanization has led to the regular discharge of wastewater into the environment in excessive amounts, which has caused significant impacts on both human and wildlife ecosystems. The sustainable management and treatment of wastewater, whether of industrial or domestic origin, represents a crucial challenge in this century. In this study, phytoremediation was employed as a wastewater treatment strategy using two species of aquatic macrophytes: water hyacinth (Eichhornia crassipes) and duckweed (Lemna minor). The study was conducted over seven consecutive evaluation periods, with five-day intervals between each. The objective was to apply the multivariate HJ-Biplot methodology to evaluate the effects of phytoremediation of two species of aquatic microphytes on the physicochemical characteristics of wastewater from Milagro canton, Ecuador. Additionally, a microbiological analysis was conducted to determine the effectiveness of the floating macrophytes. The analysis was based on the measurement of various physicochemical parameters, such as pH, electrical conductivity (EC), dissolved oxygen (DO), oxidation–reduction potential (ORP), salinity, total dissolved solids (TDSs), biochemical oxygen demand (BOD), chemical oxygen demand (COD), hardness, and temperature. The results showed that the highest efficiency in pollutant removal was achieved with duckweed (Lemna minor) in five out of nine measured parameters, suggesting that this species was the most effective compared to the control sample and Eichhornia crassipes. The capacity of these macrophytes for wastewater treatment was confirmed by this study. To ensure effective water purification, timely extraction of aquatic macrophytes from water bodies is recommended. If this collection is not properly carried out, the nutrients absorbed and stored in the plant tissues may be released back into the aquatic environment due to plant decomposition.

Circular-economy research in hospitality has expanded from isolated waste-reduction practices to questions of procurement, digital measurement, stakeholder participation, and organizational change. Yet the available evidence remains difficult for hotel managers to convert into a governed investment portfolio: published reviews classify practices and barriers, while implementation decisions require owners, baselines, decision gates, auditable indicators, and feedback routines. An integrative review was therefore conducted to connect circular-hospitality evidence with management and governance mechanisms. The synthesis began from an established bibliometric map of 71 hospitality publications (2018–2024), audited its principal references and claims against publisher and DOI metadata, and incorporated peer-reviewed studies published through August 2026 that materially extended evidence on food systems, furniture and procurement, digital leadership, institutional pressures, and human participation. Sources were coded by operational domain, decision level, actor, information requirement, barrier, and outcome. Four mutually dependent governance functions emerged: strategic direction, knowledge mobilization, process control, and portfolio integration. On this basis, the article develops a five-stage 5D framework—Diagnose, Design, Deploy, Digitize, and Demonstrate—and specifies deliverables, decision gates, responsibilities, and denominator-aligned indicators for each stage. The resulting model shifts circularity from a catalogue of environmental initiatives to a repeatable management system. Its contribution is conceptual and translational rather than causal: it offers a testable architecture for hotel groups, independent properties, and destination partnerships, while identifying where longitudinal validation, common measurement rules, and evidence from emerging economies remain necessary.

The rapid expansion of scientific publications indexed in multiple bibliographic databases has created new computational challenges for large-scale scientometric analysis. Differences in metadata schemas, identifier structures, and export formats across indexing systems such as Web of Science and Scopus introduce inconsistencies that may distort network-based bibliometric analyses. These issues affect duplicate detection, node identification, and network topology construction. This study proposes a reproducible computational pipeline for cross-database scientometric network construction. The framework formalizes the preprocessing workflow into explicit computational modules, including metadata harmonization, deterministic duplicate detection, sparse graph construction, normalization, and structural diagnostics. The proposed architecture separates preprocessing stages into reproducible algorithmic components, enabling transparent evaluation of methodological assumptions. Empirical evaluation using an interdisciplinary dataset of 317 publications (1990–2023) demonstrate that deterministic preprocessing significantly improves network stability and preserves clustering structure. Structural diagnostics based on modularity, Herfindahl–Hirschman Index, Shannon entropy, and Gini coefficient provide multi-dimensional evaluation of network topology. Scalability experiments confirm near-linear computational growth under sparse graph construction. The principal contribution of this work lies in the formalization of a transparent and extensible computational architecture for reproducible scientometric analysis. The proposed pipeline supports reliable cross-database integration and enables scalable knowledge-mapping applications in interdisciplinary research domains.

Cacao cultivation contributes significantly to the global economy. However, a decline in production is evident due to the presence of pathogens, especially within the fungi kingdom, where some remain unidentified. The objective of this study was to identify the presence of Diaporthe sp. in cacao plants of the CCN-51 cultivar in Ecuador. Samples of cacao branches with symptoms of rot and necrotic tissue were collected (cankers). The samples were disinfected and processed in the Microbiology Laboratory of the Milagro State University, Ecuador. Cambium fragments were cultured on potato dextrose agar (PDA) and incubated at 27°C. After culture purification, morphological characterization and molecular identification were performed using ITS and EF1-α barcoding methods. The sequences were compared with the NCBI GenBank database for validation. A phylogenetic analysis was performed between the strains found and those reported in Puerto Rico and Australia. Morphological identification placed the isolates within the genus Diaporthe, which was confirmed molecularly. Phylogenetic analysis demonstrates marked genetic diversity among isolates within the genus Diaporthe. These findings suggest that Diaporthe spp. is prevalent in Ecuadorian cacao plantations and that molecular methods are effective for its identification. The presence of this pathogen implies the need for management strategies to mitigate its impact on cacao production.