Background/Objectives: Efficient management of intensive care unit (ICU) resources is a critical challenge for modern healthcare systems, which must balance high-quality patient care with operational and financial performance. ICU length of stay (LOS) is a key metric of clinical complexity and hospital efficiency. However, traditional methods for predicting LOS often fail to capture the complex, nonlinear interactions among physiological, demographic, and treatment-related variables. Machine learning (ML) and deep learning (DL) models have emerged as promising tools for enhancing predictive accuracy and supporting data-driven decision-making. Methods: This study presents a systematic review and meta-analysis of ML and DL approaches for predicting ICU LOS in adult patients. Following PRISMA guidelines, eight scientific databases were searched, yielding 33 eligible studies published between 2015 and 2025. Results: Mixed medical–surgical ICUs were the most common setting (51.5%), and 45.5% of datasets were sourced from public repositories. Most studies (19/33) focused on binary classification of prolonged stays, although thresholds ranged from >48 h to ≥14 days. The pooled results from ten studies yielded an AUROC of 0.9005 (95% CI: 0.8890–0.9121), indicating strong predictive capability across diverse clinical contexts. Subgroup analyses showed comparable performance between specialized surgical and general ICUs. Conclusions: These findings suggest that AI-driven LOS prediction models exhibit strong discriminatory power for ICU LOS prediction, supporting hospital capacity planning. However, to translate this into reliable clinical support, the methodological heterogeneity, scarcity of external validation, and near absence of calibration reporting identified in this review need to be addressed.

Purpose: This research aims to implement Lean Manufacturing (LM) techniques and tools (T&T) in a retail distribution center located in Mariano Roque Alonso, Paraguay, targeting waste reduction and efficiency improvements in fresh and frozen product (FFP) handling to reduce long lead times.Design/methodology/approach: This study was conducted in two phases. First, a literature review revealed the key gaps to be addressed, the most commonly faced problems, and the LM tools applicable to solving these issues. Second, a case study was conducted in which LM T&T were applied to reduce the long lead time and propose solutions to address difficulties in the FFP process. Five problem-detection tools (flowchart, brainstorming, Ishikawa diagram, Pareto, and value stream mapping) and three improvement tools (kaizen, 5S, and Plan-Do-Check-Act) were applied to streamline FFP process from order generation to store reception.Findings: The analysis revealed significant inefficiencies: four were workforce-related, ten related to work methods, six environmental, and three product-related. Addressing these issues could substantially reduce operational bottlenecks and improve process throughput.Practical implications: The application of LM techniques significantly minimized waste, enhancing time management and human resource utilization, which led to a 96% improvement in FFP processing efficiency. These changes are expected to shorten lead times, bolster competitive advantage, and increase customer satisfaction.Originality/value: This study underscores the versatility of LM techniques, adapting them from manufacturing to retail distribution. The innovative combination of kaizen with 5S and PDCA offers a robust framework for ongoing improvements, promising for broader adoption in the retail sector.

Introduction. Cardiovascular disease represents a substantial global health burden, and delays in diagnosis can further compromise clinical outcomes. Objective. This review aimed to evaluate the diagnostic effectiveness of artificial intelligence models across major cardiovascular conditions, with a specific focus on coronary artery disease, acute coronary syndromes and myocardial infarction, and heart failure. Method. Following PRISMA guidelines, we conducted a systematic review and applied meta-analytical techniques to pool measures of diagnostic performance. Random effects models with restricted maximum likelihood and Hartung–Knapp adjustments were used to calculate summary estimates, and analyses were stratified by cardiovascular condition. Only effects with variance (reported 95% CI or derivable denominators) were pooled. Areas under the receiver operating characteristic curve without variance were synthesized narratively. Results. Out of 39,383 records, thirty-five studies were eligible. The pooled area under the receiver operating characteristic curve was 0.823 (95% confidence interval 0.754–0.892). Heterogeneity was considerable (I² = 98.4%), and the 95% prediction interval was 0.59 to 1. However, 9 of 35 included studies reported external/independent validation. The pooled estimate therefore represents an overall descriptive average, reflecting the expected range of diagnostic performance rather than a single universal value. Stratified results showed pooled values of 0.88 for acute coronary syndromes and myocardial infarction, 0.87 for heart failure, and 0.86 for coronary artery disease. Sensitivity often reached 0.90 or higher in electrocardiography deep learning models, while specificity remained variable. Conclusion. Artificial intelligence models demonstrated promising discrimination across cardiovascular conditions, with variable specificity and predictive value, indicating that clinical utility requires external validation and local calibration.

Engineering education is crucial to the scientific and technological development of society. This study addresses the need to improve the teaching of Six Sigma (SS) to help undergraduate students understand the concepts in real industry environments. This study aims to evaluate the effectiveness of gamification and active learning in teaching SS at the university level. Because of the suboptimal results obtained with traditional teaching methods, an innovative pedagogical strategy was implemented under a Halloween theme, which integrates the SS DMAIC cycle with gamification elements, project-based learning, roleplaying, teamwork, experiential learning, and flipped learning. Academic performance improved from 70% to 81%, and a reduction in the variability of results was observed, with a decrease in standard deviation from 18% to 7%. The results showed that this methodology improved students' motivation, engagement, and retention of the SS concepts. Thus, it is expected to provide relevant information for educators and professionals, as it is a replicable model for various higher education programs.

Highly sensitive industrial processes require precise control to ensure product quality and to reduce operating costs. In the continuous manufacturing of polyisocyanurate (PIR) core sandwich insulation panels, the current manual monitoring of the critical foam expansion time presents variability and generates structural defects, resulting in a loss of up to 9 percent. This study integrated the Six Sigma statistical approach with an advanced technological improvement based on a laser distance sensor and angular control by servomechanism, and developed an automated system for the accurate determination of the contact time during the expansion of the material. Preliminary tests showed that the proposed technology significantly reduces the variability of this critical time, enabling the process to meet demanding specifications and projecting a reduction in material waste to less than 4 percent. These results highlight the technical feasibility and high implementation potential of the developed system in real industrial production lines.