Traffic jams are one of the major transportation problems. The United States spends USD billions to mitigate the problem, and not always with good outcomes. This problem increases and has effects on sustainable transport, such as life quality, pollution, perishables, and costs. Large cities reduce traffic jams through congestion charges. This paper aims to reduce urban traffic congestion by estimating the charge through a multivariable model. It studies the main jammed areas in Santiago, Chile. The data came from published surveys. The model evaluation included Fisher multiple regression (F) and the determination coefficient (R²). These validations showed that the model is statistically significant. They also showed that the parameter estimation was good. Finally, this model contributes to improving the Sustainable Development Goals, such as SDG 3, SDG 11, and SDG 13, which may be successfully applied to Santiago City, as well as to any city worldwide.

This research investigates the optimal integration of Blockchain Technology (BT) in Supply Chain Management (SCM) within Chile’s maritime ports. Utilizing fuzzy Logarithmic Methodology of Additive Weights (LMAW) and Double Normalization-based Multiple Aggregation Methods (DNMA), the study systematically identifies, prioritizes, and ranks key factors influencing BT adoption in SCM. The study’s findings highlight crucial factors like enhanced transaction security, good supply chain practices, and risk management. Furthermore, it ranks the application of ports as prime candidates for BT integration. The research contributes theoretically by developing a hybrid model combining MCDA methods, and practically by guiding the strategic application of BT in the maritime logistics sector, aligning with the principles of Industry 5.0. This paper presents a novel approach that explores the utilization of BT in maritime supply chain management, incorporating MCDA in a vague environment. The research gap of this study lies in defining new contexts in both theoretical and practical literature reviews for extending the use of BT in SCM in the ports of Chile, according to Industry 5.0, to increase the efficiency and effectiveness of all aspects of operations in these places. The contribution of this research is applying hybrid MCDA methods in an uncertain environment to assist decision-makers (DMs) in better implementing BT in SCM in Chilean ports, according to Industry 5.0.

The core problem of a multimodal transportation system is integrating various transportation modes into a cohesive, efficient, and user-friendly network. This study introduces a novel centralized load concentration approach for regions facing geographic challenges. The principal aim is improving multimodal transportation systems by mitigating CO₂ emissions and improving operational efficiency. This will significantly reduce high logistics costs and the environmental impact caused by greenhouse gas emissions, particularly in land transportation, aligning with the global sustainable development goals and offering a promising path towards a more sustainable future. The proposed method implicates direct cargo transportation from its origin to the export ports without passing through intermediate centers. The mathematical model determines the most efficient means of transportation for each route, considering variables such as distance, volume, and type of cargo. Research results indicate that multiple hubs may not be necessary in scenarios with high freight concentration, which could streamline transportation and logistics operations. The modal preferences vary depending on regional dynamics and cargo characteristics, with rail and sea transport emerging as preferable options in specific circumstances, outperforming road transport. The proposed model shows reductions in logistics costs and CO₂ emissions compared to road-focused scenarios. This study provides an adaptable framework for optimizing multimodal transportation systems in regions with similar geographic and logistical attributes. It offers a versatile solution to various contexts and needs. Lastly, the strategic integration of multiple modes of transportation is fundamental to improving efficiency and sustainability.

This article is about an architectural hardware-software model creation—home-based smart health model—to boost healthcare to a higher position within society. As an emerging field, smart health modeling is still insufficient. Current smart health services are hospital centered, data are scattered and application dependent, and health service provision presents attention delays. Analyses of Internet of things, Internet of medical things, and smart health applications potentials are the bases for the proposed home-based smart health model. The model aims to facilitate the smart health development and strengthening. To evaluate whether the model does what it must do, first, check lists on how the model complies with current and future devices and applications, smart health impacts and smart health potentialities, and second, a case study analyses the model conformity through an Internet of things sophisticated cloud app.

This paper provides a multicriteria evaluation model, based on the AHP methodology for the evaluation of the Circular Economy 4.0’s, which develops the metric called “Circularity indicator for urban road projects (CIROAD)”. The main contribution of this work is to provide a measurement scale specifically designed for urban road infrastructure projects. It is a useful tool to assess the degree of implementation of the principles of the Circular Economy (CE) and the Fourth Industrial Revolution (I4.0) in an integrated manner in these types of projects, generating valuable information for all stakeholders and contributing to the objective of accelerating the transition towards a Circular Economy 4.0 model in the construction industry. The model is defined with twenty-five sub-criteria and seven general criteria, which are: (1) Circular Materials; (2) Circular Design Approaches; (3) Circular Construction Approaches; (4) Circular Operation Approaches; (5) Approaches to Deconstruction and Resource Recovery; (6) Social Value Creation; and (7) Economic Performance. The developed CIROAD model was applied to three projects in the urban transport area of the Chilean Ministry of Housing and Urbanism (MINVU/SERVIU) in the Santiago Metropolitan Region (RM). In these three projects, low performance was observed in terms of CIROAD scores (between 21% and 28% of a maximum of 100%); that is, there is a significant opportunity for improvement by incorporating more circular practices in the development of projects by the studied organization. To accelerate the transition to a circular economy model in the development of its projects, it is proposed that the organization prioritize improving the following circular practices (in order of importance): (1) the design of pavements with environmental criteria; (2) preserving value; (3) conducting cost–benefit analysis (CBA) of waste management; (4) environmental declaration of materials (EPD); (5) the used of recycled materials; and (6) BIM-based design. Finally, the suggestion for the organization in charge of these projects is to use the developed CIROAD model as a tool to support decision making regarding the prioritization of its project portfolio. That is, the organization should use CIROAD to generate a ranking score for each project and allocate resources for investment in the initiatives that show the best circularity performances, as estimated by CIROAD.

La compleja problemática del abandono estudiantil representa una oportunidad para la aplicación de la tecnología y métodos de la minería de datos en educación superior. El objetivo de esta investigación es obtener el perfil de los estudiantes en riesgo de abandono y así generar planes de gestión estudiantil que impacten sobre las variables que explican esta situación. Para esto se propone utilizar una estructura metodológica CRISP-DM, aplicando herramientas estadísticas y del aprendizaje automático no supervisado. El análisis, de tipo transversal, se realizó sobre un universo de estudiantes de primer año en jornada diurna, en una universidad privada chilena. Las variables sociodemográficas y de comportamiento utilizadas, se basaron en la teoría sobre deserción y juicio de expertos, y los datos se obtuvieron de los registros históricos disponibles en la Institución. Para obtener las variables que más influían en el abandono se realizaron análisis de correlación y de componentes principales. La aplicación del clustering jerárquico aglomerativo y la técnica de conjuntos aproximados produjeron cuatro perfiles de estudiantes con sus reglas de asociación respectivas y cinco variables académicas que permitieron diseñar un sistema de apoyo para disminuir el abandono y fomentar la retención.