Urban Heat Islands are a major environmental and public health concern, causing temperature increase in urban areas. This study used satellite imagery and machine learning to analyze the spatial and temporal patterns of land surface temperature distribution in the Metropolitan Area of Merida (MAM), Mexico, from 2001 to 2021. The results show that land surface temperature has increased in the MAM over the study period, while the urban footprint has expanded. The study also found a high correlation ($r>$ 0.8) between changes in land surface temperature and land cover classes (urbanization/deforestation). If the current urbanization trend continues, the difference between the land surface temperature of the MAM and its surroundings is expected to reach 3.12 °C ± 1.11 °C by the year 2030. Hence, the findings of this study suggest that the Urban Heat Island effect is a growing problem in the MAM and highlight the importance of satellite imagery and machine learning for monitoring and developing mitigation strategies.

A theoretical model that describe the effect of design and operational conditions on current density distribution in a bioelectrochemical reactor used as microbial electrolysis cell (MEC) is described in this study. This model is proposed considering an approach where a direct electron transfer mechanism from the biofilm to the electrode surface takes place (mechanism present in most of microbial systems) and is governed by a dual donor-acceptor Nernst-Monod bioelectrochemical kinetic expression. The bioelectrochemical reactor is modelled considering two flow electrochemical reactor designs (a reactor design based in literature reports and a modified system proposed by the authors) operating at different flow inlet velocities and electrical overpotentials. Results obtained from the numerical solution shows that flow distribution is an essential aspect that impact the reactor performance, since concentration profiles and electrical potential-current distributions are strongly dependent on flow regime. Modified inlet configuration displays a more homogeneous fluid distribution and this behavior directly affects the mass transport and current density performance, as a result higher current density values are obtained for such configuration. Finally, it is expected that the information obtained from the analysis carried out in this report will provide us with a theoretical basis to realize the construction of a bioelectrochemical reactor prototype to develop the MEC concept.

The study of dynamic models and their parameterization remains a relevant topic in research. Motors and their models have been extensively analyzed, studied, and parameterized using various techniques due to their broad applicability in motorering and industrial settings. However, most methods for obtaining model parameters require at least two averaged signals from the motor, such as torque, current, speed, position, or acceleration. In this work, we propose the parameterization of a motor’s dynamic model using only the speed signal and the steady-state values of the variables. Through evolutionary computation, the mechanical and electrical equations of the motor are reconstructed based on this signal. This approach offers a significant advantage, as it enables parameter estimation without requiring the instrumentation needed for full current signal measurement or, alternatively, torque measurement. To achieve this, the transfer function representing the motor’s speed is utilized. The function reconstruction is performed with a Root Mean Square Error (RMSE) of less than 1% for both the speed and current signals. Since the original current signal is not required for this estimation, this work presents an innovative approach to estimating a system of dynamic equations using only a single measured variable and the dynamic relationships of its step-input response.