(Springer, 2026) Dawar, Ishaan; Singh, Vijay. P; Singhal, Tanu; Bhushan, Megha; Agarwal, Kshitij; Lenguajes y Sistemas Informáticos; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Junta de Andalucía; TIC276: Diverso Lab - International Computing
Air pollution poses a significant threat to public health and environmental sustainability, particularly in urban areas with rapid industrialization and high vehicular emissions. This study focuses on Ghaziabad, one of India’s most polluted cities, to predict the Air Quality Index (AQI) using hourly environmental parameters such as pollutant concentrations, temperature, wind speed, and humidity, recorded from three monitoring stations. The primary objective is to evaluate AQI prediction models that can assist policymakers and urban planners in taking timely measures to improve air quality and public health. Several Machine Learning (ML) algorithms are used for AQI prediction, including Linear Regression (LR), Random Forest Regressor (RFR), Long Short-Term Memory (LSTM), and Extreme Gradient Boosting Regressor (XGBR). Among these, XGBR delivered the best performance, achieving a Mean Absolute Error of 2.536, a Mean Absolute Percentage Error of 1.62%, a Root Mean Square Error of 5.495, and an R2 score of 0.997, indicating highly accurate predictions. Furthermore, these AQI values were classified into various categories using different models, in which the XGB classifier performed the best, achieving an accuracy of 97.62%. The findings demonstrate the efficacy of ML models in forecasting AQI with high precision using environmental variables. This research contributes directly to the United Nations Sustainable Development Goal (SDG) 11: Sustainable Cities and Communities by promoting data-driven urban air quality management. Integrating such predictive models into smart city frameworks is recommended to facilitate real-time air quality monitoring and proactive environmental policymaking.
(MDPI, 2024-11-25) Fernández-Bueno, Laura; Torres Enamorado, Dolores; Bravo Vázquez, Ana; Rodríguez Blanco, Cleofás; Bernal Utrera, Carlos; Fisioterapia; CTS954: Innovaciones en Salud y Calidad de Vida
Introduction: Population aging increases the risk of dependency among older adults, which in turn necessitates care, primarily provided by family caregivers. This situation leads to physical and emotional strain on these caregivers. New technologies, such as tele-education, digital platforms, or mobile applications, can offer an accessible and equitable alternative for caregiver training and self-care support. Objective: The objective of this review is to analyze interventions targeted at family caregivers, both for their own self-care and for the care of dependent individuals, using new technologies. Design: A scoping review was conducted, including a total of thirty-two articles extracted from three databases: CINAHL, Scopus, and PubMed. Articles in any language were included, with no fixed time limit, while articles with samples that included family caregivers of oncology patients were excluded. Results: Most of the interventions were conducted via videoconference, showing outcomes that indicated a reduction in depressive symptoms among family caregivers. Conclusions: The implementation of new technologies for the development of interventions presents a viable alternative to in-person sessions. These technologies have shown positive results, while also helping to overcome time and geographical barriers imposed by caregiving responsibilities.
A systematic search of the available literature was performed in July 2012 of Medline, PubMed, Cochrane Library, and relevant journals and reference lists using the above-listed search terms. The first search detected 78 relevant articles. In a second-level search, two articles were added. Twenty-six articles were thus used for this review.
