Outer membrane vesicles (OMVs) are tractable, cell-free microbial outputs that can shape innate immune programs. Here, we compared OMVs from the probiotic Escherichia coli Nissle 1917 (EcN) and the commensal strain ECOR12 in a paired within-donor model of human monocyte-derived dendritic cells (Mo-DCs) (N = 20). In the core integrated arm, Mo-DCs were exposed to iDC control, EcN OMVs, or ECOR12 OMVs (10 µg/mL, 24 h) and profiled for maturation markers (CD14, CD83, CD209), cytokines (IL-6, TNF-α, IL-10), and a targeted miRNA panel (miR-155-5p, let-7i-3p, miR-146b-5p, miR-29a-5p). Both OMV types promoted maturation (increased CD83 and reduced CD14), but generated distinct cytokine–miRNA configurations, with ECOR12 tending toward an IL-10–high profile and EcN toward higher IL-6/TNF-α tendencies. Multivariate integration separated conditions into reproducible, strain-specific immune fingerprints, supporting the key take-home that probiotic versus commensal E. coli OMVs imprint distinguishable coordinated response states in human DCs. In an extended phenotyping arm, ECOR63 OMVs were evaluated by ELISA and flow cytometry only and were not included in miRNA profiling or integrated PCA due to unavailable matched miRNA measurements.

Monthly cane supply anticipation is critical for harvest scheduling, mill intake coordination, transport allocation, labor planning, and maintenance organization in vertically integrated sugarcane agroindustrial systems. This study developed an interpretable monthly decision support framework using twelve years of original institutional records from Compañía Azucarera Valdez S.A., Milagro, Guayas, coastal Ecuador, covering January 2007 to December 2018. The primary endpoint was one-month-ahead monthly cane tonnage, predicted from variables available at the current monthly time point to avoid look-ahead bias. Complementary diagnostic layers were used to characterize recurrent operational states and to interpret monthly energy-saving behavior. A chronological validation design was applied, with 2007–2015 used for model training and 2016–2018 used for independent testing. The full linear and LASSO models achieved the strongest test performance, with R² values of 0.916 and 0.915 and mean absolute percentage errors near 5%, outperforming random forest and ANN/MLP benchmarks. The area-only baseline was weaker, while the no-area model retained predictive capacity, indicating that harvested area was important but insufficient to explain monthly cane tonnage. PCA and k-means clustering identified four recurrent operational states related to production scale, stress, crop quality, and energy performance conditions. Monthly energy saving showed ceiling-constrained behavior near 40% and was therefore interpreted as a diagnostic indicator rather than a robust forecasting target. Overall, the framework supports transparent monthly planning by combining leakage-aware forecasting, operational-state interpretation, and conservative energy performance diagnostics.

Introduction: This study compared the self-assessed quality of inclusive pre-service practicum systematization across four independent cohorts in an online Early Childhood Education program in Ecuador during 2024–2025. Methods: A quantitatively driven descriptive-comparative mixed-methods design was applied to 1,478 students. The Index of Quality of Inclusive Practicum Systematization (IQSIP) was defined a priori as the unweighted descriptive mean of six conceptually distinct ordinal criteria, rather than as a validated psychometric scale or direct measure of teaching competence. The Jonckheere–Terpstra test assessed the ordered cross-cohort trend, complemented by Kruskal–Wallis tests, Dunn–Holm comparisons, and epsilon-squared effect sizes. A documentary synthesis of 148 final reports, consolidating 3,552 open-ended responses, contextualized the quantitative findings. Results: IQSIP medians increased across successive cohorts (1.17, 2.17, 3.17, and 4.83). Significant ordered trends were identified for the global index and all three descriptive groupings ( p < 0.001). Omnibus and pairwise comparisons were also significant ( p < 0.001), with epsilon-squared values ranging from 0.914 to 0.933. Documentary findings described initial barriers, pedagogical adjustments, Universal Design for Learning, theory-practice reflection, and perceived changes in stakeholder participation. Discussion: The findings reveal a strong cross-cohort temporal pattern in self-assessed systematization quality. However, academic term was completely confounded with cohort, and score distributions were highly restricted and nearly non-overlapping. Consequently, the results cannot establish within-person development, teaching competence, or causal effects of the practicum. Further research should incorporate independent assessment, external validation, and longitudinal designs.

Monthly cane-supply anticipation is critical for harvest scheduling, mill-intake coordination, transport allocation, labor planning, and maintenance organization in vertically integrated sugarcane agroindustrial systems. This study developed an interpretable monthly decision-support framework using twelve years of original institutional records from Compañía Azucarera Valdez S.A., Milagro, Guayas, coastal Ecuador, covering January 2007 to December 2018. The primary endpoint was one-month-ahead monthly cane tonnage, predicted from variables available at the current monthly time point to avoid look-ahead bias. Complementary diagnostic layers were used to characterize recurrent operational states and to interpret monthly energy-saving behavior. A chronological validation design was applied, with 2007–2015 used for model training and 2016–2018 used for independent testing. The full linear and LASSO models achieved the strongest test performance, with R² values of 0.916 and 0.915 and mean absolute percentage errors near 5%, outperforming random forest and ANN/MLP benchmarks. The area-only baseline was weaker, while the no-area model retained predictive capacity, indicating that harvested area was important but insufficient to explain monthly cane tonnage. PCA and k-means clustering identified four recurrent operational states related to production scale, stress, crop quality, and energy-performance conditions. Monthly energy saving showed ceiling-constrained behavior near 40% and was therefore interpreted as a diagnostic indicator rather than a robust forecasting target. Overall, the framework supports transparent monthly planning by combining leakage-aware forecasting, operational-state interpretation, and conservative energy-performance diagnostics.

Public-sector performance systems integrate planning, monitoring, corrective-response, and execution indicators, yet their relative alignment with recorded administrative efficiency is rarely evaluated using validation that respects repeated observations. This study analyzes 1,620 de-identified repeated administrative records from monitoring and follow-up activities of EPUNEMI, an Ecuadorian public enterprise. The extract contains 81 recurring operational configurations, each represented by 20 observations, across five process types. It also includes analytical period labels spanning January 2024 to December 2026; these labels are treated only as de-identified temporal strata and not as verified calendar dates of data collection. Because de-identification prevents uninterrupted unit-level trajectories, the analysis is period-wise and dependence-aware rather than a causal longitudinal panel model. We combined exploratory multivariate profiling with grouped predictive validation, nested and ablation analyses, and MB-PLS block decomposition. Removing the lowest-communality quartile increased variance represented by the first two principal components from 44.95 to 60.90%; the validated global solution contained two exploratory profiles (silhouette = 0.387). A four-variable linear reconstruction using physical progress, budget execution, action closure, and delay achieved R ² = 0.958, RMSE = 2.051, and MAE = 1.629. Because the official institutional formula was unavailable, this result is interpreted as an internal audit of substantial statistical overlap rather than the institutional formula or independent prediction. MB-PLS yielded comparable accuracy and added block-level interpretation. Public value remained a normative horizon because citizen-facing outcomes were unavailable.

Introduction: This study explores the scientific development of human factor dimensions in industrial maintenance management. As organizations transition toward predictive, digital, and human-centered maintenance systems, human factors such as error, reliability, ergonomics, safety culture, competencies, and human–technology interaction have become central to maintenance performance and industrial safety. Methods: A systematic literature review and bibliometric analysis were conducted using 355 documents indexed in Scopus and Web of Science between 2010 and 2025. Data processing, performance analysis, and thematic analysis were carried out in R 4.6.0 within RStudio 2026.04.0 using bibliometrix 5.3.0; VOSviewer 1.6.20 was used only for co-authorship, keyword co-occurrence, and reference co-citation visualizations. A directed qualitative content synthesis complemented the quantitative mapping by examining research streams, mechanisms, gaps, and future priorities. Results: Findings reveal a rapidly expanding field, with a compound annual growth rate of 11.69%, particularly after 2019. China, India, and Canada lead scientific production. Co-citation analysis confirms Reason’s human error model as the dominant theoretical reference, while keyword analysis identifies “maintenance”, “human factors”, and “human error” as the conceptual core. Industry 4.0, predictive maintenance, artificial intelligence, and Industry 5.0 emerge as key growth areas. Structured content synthesis identified four interdependent streams: human error and reliability; organizational, ergonomic, and competency factors; workforce planning and optimization; and human-technology interaction in Industry 4.0/5.0. Discussion: The results show a transition from retrospective human error analysis toward prospective approaches integrating emerging technologies with human capabilities. The field remains fragmented in terms of authorship and geographically uneven, with Latin America and much of Africa underrepresented. This review positions human-centered maintenance management as an emerging socio-technical field linking human reliability, digital transformation, ergonomics, organizational safety, and workforce development.