Virtual sensing is crucial in order to provide feasible and economical alternatives when physical measuring instruments are not available. Developing model-based virtual sensors to calculate real-time information at each targeted location is a complex endeavor in terms of sensing technology. This paper proposes a new approach for model-based virtual sensor development using computational fluid dynamics (CFD) and control. Its main objective is to develop a three-dimensional (3D) real-time simulator using virtual sensors to monitor the temperature in a greenhouse. To conduct this study, a small-scale greenhouse was designed, modeled, and fabricated. The controller was based on the convection heat transfer equation under specific assumptions and conditions. To determine the temperature distribution in the greenhouse, a CFD analysis was conducted. Only one well-calibrated and controlled physical sensor (temperature reference) was enough for the CFD analysis. After processing the result that was obtained from the real sensor output, each virtual sensor had learned the associative transfer function that estimated the output from given input data, resulting in a 3D real-time simulator. This study has demonstrated, for the first time, that CFD analysis and a control strategy can be combined to obtain system models for monitoring the temperature in greenhouses. These findings suggest that, generally, virtual sensing can be applied in large greenhouses for monitoring the temperature using a 3D real-time simulator.

The automotive industry is increasingly focused on waste management, elimination, and reduction to achieve sustainability and cost reduction. This focus drives the industry towards resource-efficient operations that minimize environmental impact while exceeding customer expectations. Meeting these demands necessitates the adoption of more efficient production methodologies, such as the PDCA cycle. This work presents a case study that illustrates the application of the PDCA methodology to minimize scrap generation due to process variability in a multinational company that manufactures electric motors for the automotive industry. The aim was to demonstrate how the PDCA methodology can improve quality standards by minimizing scrap generated during the manufacture of electrical armatures. Notably, the organization in this case study set a waste target of 0.7%, which was significantly exceeded. Finally, the implementation of this methodology can deliver significant economic benefits, with a total annual cost reduction of approximately USD 135,000.

The current study presents a non-random, quantitative experimental investigation detailing the steps required to accredit a non-regulated test method (referred to as its own) under the ISO/IEC 17025:2017 standard and the criteria set by the Mexican Accreditation Entity (EMA). The focus is on the methodology employed to validate the test method, particularly emphasizing the precision of the measurement system, along with the total variation in and tolerance of its components. For the measurement analysis, repeatability and reproducibility (r&R) studies were conducted using a variance analysis method variant (ANOVA). This variant is highlighted for its ability to estimate deviations more accurately. Furthermore, the chosen model incorporates random effect measurements for all factors or components of system variation (operators, parts, interaction, and instrument). This approach demonstrates the reliability, accuracy, and precision of the proposed measurement system within the test method, leading to its subsequent accreditation under ISO/IEC 17025:2017 as a conformity assessment body.

This research explores the effectiveness of Quality Management Systems (QMSs) certified under the ISO 9001 standard by applying Hotelling’s multivariate statistical test T². This research focuses on organizations in central Mexico, evaluating whether the adoption of the ISO 9001 standard generates the promised benefits (by the International Standardization Organization itself), such as process improvement, increased customer satisfaction, higher sales, and increased revenues. Using a comprehensive framework grounded in ISO 10014 and incorporating statistical tools, such as descriptive analysis, regression, simulation, and Hotelling’s T² test, this study examined performance differences across sectors and pinpointed the critical factors impacting QMS outcomes. The results demonstrated notable advantages, including average improvements exceeding 20% in anticipated benefits. Furthermore, the analysis underscored the importance of QMS maturity, process enhancement, and customer satisfaction as pivotal drivers of QMS success. Sector-specific patterns also emerged, revealing that public organizations prioritize process efficiency and customer satisfaction, whereas private entities emphasize sales and revenue growth. By employing multivariate techniques, this research offers valuable insights into the interconnected factors affecting QMS effectiveness and provides actionable recommendations for organizations to enhance their QMS performance.

Labor dynamics have changed in recent years, and women have increased their participation in various activities within the production and business industries. One of these sectors is mining, which presents both challenges and opportunities to promote gender equality and decent work. In light of this, the objective of the present study is to identify the limitations faced by women in the mining sector, framed within personal, family, social, and labor aspects, in relation to their educational level. Due to the nature of the study, a cross-sectional, descriptive, and non-experimental investigation was proposed. The sample consisted of more than 50 companies distributed throughout the Mexican Republic, with a total of 316 female workers in the mining sector. The main results show greater limitations in labor and family aspects. Moreover, educational levels directly affect how women perceive the impact of working in mining on the various areas in which they operate.