This study extends the existing Neutrosophic STATIS framework toward the dual analysis of relationships among financial variables observed over multiple periods. Financial uncertainty was operationalized as modeled indeterminacy derived from local temporal volatility and deviation from annual sectoral behavior. Each observation was represented through truth, indeterminacy, and falsity components and subsequently aggregated into a scalar neutrosophic score. Dual STATIS was then applied to annual matrices containing seven financial indicators for 25 Segment 1 Ecuadorian savings and credit cooperatives observed from 2016 to 2023. The resulting structure was compared with classical Dual STATIS applied to the original standardized indicators, and sensitivity analyses were conducted under alternative weighting specifications. The neutrosophic-score analysis revealed generally high temporal similarity among annual financial structures. ROA and ROE primarily defined the profitability dimension, LIQCOR and INTFIN characterized the second dimension, and DEP and SCC were principally represented by the third dimension. The first three dimensions explained 64.94% of the total inertia, compared with 64.23% in the classical analysis. The comparison showed that the neutrosophic transformation preserved the main profitability and institutional-activity relationships but modified the temporal similarity ordering, annual weight ranking, factorial configuration, and position of MORAMP. Sensitivity results indicated that the principal structural conclusions remained stable under moderate changes in the weighting parameters. The proposed approach provides a complementary exploratory tool for incorporating modeled indeterminacy into longitudinal financial-structure analysis and cooperative-sector supervision.

This article presents an innovative methodology that integrates Dual STATIS analysis with neutrosophic logic to evaluate the stability of the structure of relationships among financial variables in savings and credit cooperatives. The developed approach makes it possible to incorporate the uncertainty present in financial indicators and analyze their temporal evolution. Unlike classical STATIS, which focuses on similarity among cooperatives, the dual approach analyzes the covariance structure among the variables, identifying stable latent dimensions and turning points in the cooperative system. The results reveal patterns of structural stability and significant changes associated with relevant economic events, including the impact of the COVID-19 pandemic. The proposed methodology constitutes a useful alternative for strengthening financial supervision processes and supporting strategic decision-making in the cooperative sector.

Interstitial Lung Diseases (ILDs) comprise a group of complex disorders characterized by pulmonary inflammation and fibrosis. ILDs represent a challenge in

Respiratory emergencies create recurrent seasonal pressure on hospital services, yet reproducible forecasting research is often hindered by the effort required to retrieve, interpret, harmonize, and document public administrative data. Chile-ED-Resp is a curated secondary dataset and reproducible processing pipeline derived from the official Chilean Ministry of Health/DEIS dataset “Atenciones de urgencias de causas respiratorias por semana epidemiológica”, produced by the Sistema de Atención Diaria de Urgencias (SADU). Version 1.1.0 uses the closed 2022–2025 period and was built from the official Parquet snapshot retrieved on 30 August 2026. After restriction to hospitals and hospital emergency units (UEH), the curated long artifact contains 449,844 rows and 32 variables, representing 180 hospitals and 12 official respiratory cause labels. A complementary forecasting artifact contains 37,620 hospital–week rows and 38 variables on a fixed 209-position statistical-week calendar (52 weeks in 2022–2024 and 53 in 2025); 178 of 180 hospitals (98.9%) satisfy the predefined ≥$95\%$ target-coverage criterion. Acute upper respiratory infection is retained under its official Spanish label and acronym, IRA Alta. Missing source records remain missing and are distinguished from explicitly reported zeros. Precomputed target-derived predictors use outcomes available through the preceding week and are intended for rolling-origin one-week-ahead evaluation, not an unqualified fixed-origin forecast of the entire test year. Reproducibility is supported through source metadata, SHA-256 checksums, machine-readable descriptive audits, 15 validation checks, unit tests, and an example that recomputes predictors at each forecast origin. The resource provides a transparent foundation for forecasting, benchmarking, health-informatics teaching, and secondary analyses while preserving source provenance and licensing conditions.