The Special Issue Smart Cities: Innovating in the Transformation of Urban Environments presents a collection of original research contributions that employ mathematical modeling, optimization, machine learning, and deep learning techniques to tackle urgent challenges in designing, monitoring, and operating smart urban systems. The articles in this special issue cover a range of topics, including environmental engineering, intelligent transportation, photovoltaic performance assessment, and urban mobility. All manuscripts share a commitment to rigorous quantitative methodologies and data-driven decision support, with results that directly impact ecological integrity, public health, and overall population well-being. Additionally, the articles emphasize the importance of providing context for each contribution within the broader field of smart city research. They highlight their relevance to the Mathematical Biosciences and Engineering journal, which focuses on complex data and information processing at the intersection of mathematics, engineering, and life sciences.
Citation: Diego Rossit, Sergio Nesmachnow, Luis Hernández Callejo, Pedro Moreno. Editorial: Smart Cities, Innovating in the Transformation of Urban Environments[J]. Mathematical Biosciences and Engineering, 2026, 23(6): 1768-1773. doi: 10.3934/mbe.2026064
The Special Issue Smart Cities: Innovating in the Transformation of Urban Environments presents a collection of original research contributions that employ mathematical modeling, optimization, machine learning, and deep learning techniques to tackle urgent challenges in designing, monitoring, and operating smart urban systems. The articles in this special issue cover a range of topics, including environmental engineering, intelligent transportation, photovoltaic performance assessment, and urban mobility. All manuscripts share a commitment to rigorous quantitative methodologies and data-driven decision support, with results that directly impact ecological integrity, public health, and overall population well-being. Additionally, the articles emphasize the importance of providing context for each contribution within the broader field of smart city research. They highlight their relevance to the Mathematical Biosciences and Engineering journal, which focuses on complex data and information processing at the intersection of mathematics, engineering, and life sciences.
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M. A. Fadhel, A. M. Duhaim, A. Saihood, A. Sewify, M. N. A. Al-Hamadani, A. S. Albahri, et al., Comprehensive systematic review of information fusion methods in smart cities and urban environments, Inf. Fusion, 107 (2024), 102317. https://doi.org/10.1016/j.inffus.2024.102317 doi: 10.1016/j.inffus.2024.102317
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F. Caro, D. Rossit, C. Santiviago, J. Ferreira, S. Nesmachnow, Cost-performance trade-off analysis of physicochemical phosphorus removal systems for wastewater treatment: A bi-objective optimization approach, Math. Biosci. Eng., 23 (2026), 124–147. https://doi.org/10.3934/mbe.2026006 doi: 10.3934/mbe.2026006
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Y. Deng, Z. Wu, J. Liu, H. Liu, TTSNet: Traffic sign recognition via a transformer by learning spectrogram structural features, Math. Biosci. Eng., 23 (2026), 722–752. https://doi.org/10.3934/mbe.2026028 doi: 10.3934/mbe.2026028
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H. F. Mateo-Romero, J. I. Morales Aragonés, L. Hernández-Callejo, M. Á. González-Rebollo, V. Cardeñoso-Payo, et al., CNN-based estimation of series resistance in photovoltaic cells from electroluminescence images with application to output power prediction, Math. Biosci. Eng., 23 (2026), 1269–1288. https://doi.org/10.3934/mbe.2026046 doi: 10.3934/mbe.2026046
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A. Ali, A. Salah, M. Bekhit, A. Fathalla, Divide-and-train: A new approach to improve the predictive tasks of bike-sharing systems, Math. Biosci. Eng., 21 (2024), 6471–6492. https://doi.org/10.3934/mbe.2024282 doi: 10.3934/mbe.2024282
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Diego Rossit, Sergio Nesmachnow, Luis Hernández Callejo, Pedro Moreno. Editorial: Smart Cities, Innovating in the Transformation of Urban Environments[J]. Mathematical Biosciences and Engineering, 2026, 23(6): 1768-1773. doi: 10.3934/mbe.2026064
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