Los Andes Peruvian University

This study examines whether administrative simplification is associated with stronger document-management practices in a district-level local government organization, and why this matters for societal outcomes such as transparency and more equitable access to public services. Using a quantitative, cross-sectional, non-experimental design, we surveyed officials and administrative staff with validated Likert-type instruments (62 items for administrative simplification; 17 items for document management) and tested associations using Spearman’s rho. Results show a positive, modest relationship between simplification and document management (ρ ≈ 0.37; p < 0.001). Stage-level analyses indicate consistently positive correlations, with stronger associations in later, institutionalization-oriented stages (implementation, monitoring/evaluation, continuous improvement, and sustainability). The study contributes to debates on administrative burden and digital-era governance by linking staged simplification efforts to the organizational backbone of records flows. Practically, findings suggest that resource-constrained municipalities can improve governance quality by treating document management not as a back-office function but as an enabling infrastructure for user-centered services, accountability, and compliance with digital-government guidance. Limitations include the single-organization design and reliance on staff perceptions; future research should test citizen-level outcomes and service-equity effects.

Water scarcity in Peru is increasingly shaped by competing sectoral demands, particularly between large-scale mining and agriculture. Both sectors rely heavily on limited freshwater resources in arid coastal and Andean basins, generating complex trade-offs between economic productivity, environmental sustainability, and social equity. This review synthesizes and critically evaluates current knowledge on water footprint (WF) dynamics within mining–agriculture systems, integrating hydrosocial theory, water–energy–food nexus thinking, and sustainability transition frameworks. Mining activities in Peru are characterized by high blue and grey water footprints, associated with intensive extraction processes and contamination risks, while agriculture exhibits diverse water footprints depending on crop type, irrigation efficiency, and climatic conditions. The interaction of these sectors creates hydrosocial conflicts driven by unequal water allocation, environmental degradation, and institutional fragmentation. This paper identifies key drivers of conflict and evaluates emerging pathways for sustainability transitions, including technological innovation, nature-based solutions, and participatory governance mechanisms. An integrative conceptual framework derived from a thematic synthesis of the reviewed literature is proposed. The findings provide actionable insights for policymakers and researchers seeking to reconcile economic development with water sustainability in resource-constrained environments.

[This corrects the article DOI: 10.3389/fpubh.2026.1762195.].

Background The Ichu River serves as the primary water source for urban consumption, agricultural irrigation, and several local industrial operations in the Huancavelica region; however, increasing anthropogenic pressures including untreated municipal wastewater, mining effluents, agricultural runoff, and expanding urbanization have significantly deteriorated its water quality. These combined stressors highlight the need for an integrated assessment to understand the extent of contamination and associated human health risks. Methods The investigation measured water quality and health-related risks by analyzing physicochemical parameters, heavy metals, and microbial pollutants at eight sampling points, site 1 (S1) through (S8). Results The research data showed that water quality worsened progressively from upstream to downstream locations such as turbidity, TDS, conductivity, and BOD levels increased. Oil pollution and oxygen depletion arose from a reduction in dissolved oxygen from 6.3 to 4.5 mg/L at the different sampling sites (S1 to S8). Heavy metals (As, Pb, Cd, and Cr) in the samples exceeded the standards established by the World Health Organization (WHO) established standards because of mining and industrial wastewater and local wastewater discharge. The presence of excessive Escherichia coli (E. coli) and total coliforms in microbial tests proved that the water was severely contaminated by fecal matter. Principal Component Analysis showed that heavy metals exist with microbial pollution and organic load as the main sources of water quality decline, and pollution indicators were found to establish powerful relationships with depleted oxygen levels. Conclusion The severe contamination risks found in this study justify immediate pollution control measures, wastewater treatment enforcement, and sustainable watershed management practices. Urgent action is necessary because vital parameters surpass the standards set by the WHO and (United States Environmental Protection Agency (USEPA) to avoid enduring environmental damage and health problems. This research demonstrates the value of continuing water quality assessments while enforcing policies and raising public awareness to improve the water quality of the Ichu River.

Artificial intelligence personalizes nutrition; however, research remains fragmented, lacking integrated frameworks linking multimodal data, prediction, recommendations, and validation. This systematic literature review analyses primary human evidence on machine-learning-driven dietary personalization and develops a decision-support framework to distinguish prediction from recommendation. The literature search was conducted using Scopus, Web of Science plus, IEEE Xplore, PubMed/MEDLINE-indexed literature and Europe PMC and, via citation linkage, obtained a verified screening database of 53 unique records. After de-duplication, 53 unique records were identified. Twenty-one were excluded during title/abstract screening, leaving 32 records for further assessment. Five additional records were excluded before full-text retrieval, 27 reports were sought for retrieval, and six could not be retrieved. The remaining 21 full-text reports were assessed for eligibility; one was excluded because it was not directly related to the review question, yielding 20 studies for qualitative synthesis. The included studies comprised externally validated prediction studies, randomized dietary intervention trials, digital-twin interventions, recommender systems, microbiome-guided diets, metabolomics, and multi-omics nutrigenetics. Multimodal models had better predictive performance with regard to postprandial glycemia, triglyceride response, weight-loss response and metabolite response as compared with simpler dietary variables in selected settings. However, this predictive performance in itself did not constitute clinical benefit. The evidence was less consistent for clinical effectiveness. A number of randomized trials demonstrated an advantage with personalized strategies regarding glycemic control. In contrast, other ones failed to prove a significant advantage in terms of weight loss, LDL cholesterol, and severity of primary irritable bowel syndrome. These results suggest that the effectiveness of predictive accuracy should be considered as two distinct evidence questions. The review provides an evidence-informed, 5-layer decision-support architecture to define a clear separation between prediction, recommendation, validation, and governance of integrated multimodal nutrition evidence and clinical decision support to overcome fragmentation across genetic, metabolic, lifestyle, microbiome, and sensor-derived domains.