Product design has become a critical process for the healthcare technology industry, given the ever-changing demands, vague customer requirements, and interrelations among design criteria. This paper proposed a novel integration of fuzzy Kano, Analytic Hierarchy Process (AHP), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Quality Function Deployment (QFD) to translate customer needs into product characteristics and prioritize design alternatives considering interdependence and vagueness. First, the customer requirements were established. Second, the fuzzy KANO was applied to calculate the impact of each requirement, often vague, on customer satisfaction. Third, design alternatives were defined, while the requirements’ weights were calculated using AHP. DEMATEL was later implemented for evaluating the interdependence among alternatives. Finally, QFD was employed to select the best design. A hip replacement surgery aid device for elderly people was used for validation. In this case, collateral issues were the most important requirement, while code change was the best-ranked design.

In this paper, an analytic review of the recent methodologies tackling the problem of dynamic allocation of ambulances was carried out. Considering that state-of-the-art is moving to deal with more extensive and dynamic problems to address in a better way real-life instances, this research looks to identify the evolution and recent applications of this kind of problem once the basic models are explored. This extensive review allowed us to identify the most recent developments in this problem and the most critical gaps to be addressed. In this sense, it is essential to point out that the dynamic location of emergency medical services (EMS) is nowadays a relevant topic considering its impact on the healthcare system outcomes. Issues related to forecasting, simulation, heterogeneous fleets, robustness, and solution speed for real-life problems, stand out in the identified gaps. Applications of machine learning the deployment challenges during epidemic outbreaks such as SARS and COVID-19 were also explored. At the same time, a proposed notation tries to tackle the fact that the word problem in this kind of work refers to a model on many occasions. The proposed notation eases the comparison between the different model proposals found in the literature.

Measuring the quality of management in education. Review article

Human activity recognition (HAR) is a popular field of study. The outcomes of the projects in this area have the potential to impact on the quality of life of people with conditions such as dementia. HAR is focused primarily on applying machine learning classifiers on data from low level sensors such as accelerometers. The performance of these classifiers can be improved through an adequate training process. In order to improve the training process, multivariate outlier detection was used in order to improve the quality of data in the training set and, subsequently, performance of the classifier. The impact of the technique was evaluated with KNN and random forest (RF) classifiers. In the case of KNN, the performance of the classifier was improved from 55.9% to 63.59%.

Impresión 3D utilizando Modelado por Deposición Fundida (MDF) es una de las tecnologías para la fabricación de piezas unitarias con aplicaciones particulares o específicas que regularmente no se encuentran en el mercado a gran escala. El tiempo de impresión puede demorar horas o días, lo cual es una debilidad en la tecnología de Impresión 3D. El tiempo de impresión fue analizado desde dos enfoques: volumen y forma para piezas de tamaño regularmente pequeños. A través de un software de preparación de impresión 3D se simuló un tiempo de impresión estimado, configurando parámetros que pueden influir tanto en los tiempos de impresión como en la calidad de la impresión. A través del presente estudio, bajo la metodología Diseño de Experimento Taguchi, se identificó que la Densidad de Relleno y la Velocidad de Impresión del Relleno son los parámetros más influyentes en el tiempo de impresión para este tipo de piezas.

Emergency medical services (EMSs) are critical to reducing fatalities and improving patient outcomes in emergencies such as traffic accidents, where response time is a decisive factor. This study proposes a comprehensive and systematic approach to designing and optimizing EMS systems tailored for urban traffic accidents. By integrating Geographic Information Systems (GISs), hypercube queuing models, Economic Value Added (EVA) calculations, and multi-criteria decision-making (MCDM) techniques, we developed a model that balances service efficiency, financial sustainability, and equitable access to emergency care. The hypercube queuing model was applied to estimate key performance metrics, such as response time, coverage, and the GINI index for equity, under varying numbers of ambulances and demand scenarios. In addition, EVA was calculated for different configurations of leased and owned ambulances, offering a financial perspective to assess the viability of public–private partnerships (PPPs) in EMSs. Using the fuzzy Analytic Hierarchy Process (AHP) and CoCoSo (Combined Compromise Solution) methods, this study identified the optimal number of ambulances required to minimize response time, maximize coverage, and ensure financial sustainability. The proposed approach has been applied to a real case in Colombia. Furthermore, integrating leased ambulances offers a financially viable solution with positive EVA values that guarantee the long-term sustainability of the public–private partnership. This paper advances the literature by providing a practical framework for optimizing EMS systems, particularly in developing countries where financial constraints and resource limitations represent significant challenges. The proposed methodology improves service efficiency and economic sustainability and ensures equity in access to life-saving care.