RESUMO
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.
RESUMO
The electrical and electromechanical responses of ~200 µm thick extruded nanocomposite films comprising of 4 wt.% and 5 wt.% multiwall carbon nanotubes mixed with polypropylene are investigated under an alternating current (AC) and compared to their direct current (DC) response. The AC electrical response to frequency (f) and strain (piezoimpedance) is characterized using two configurations, namely one that promotes resistive dominance (resistive configuration) and the other that promotes the permittivity/capacitive contribution (dielectric configuration). For the resistive configuration, the frequency response indicated a resistive-capacitive (RC) behavior (negative phase angle, θ), with a significant contribution of capacitance for frequencies of 104 Hz and above, depending on the nanotube content. The piezoimpedance characterization in the resistive configuration yielded an increasing impedance modulus (|Z|) and an increasing (negative) value of θ as the strain increased. The piezoimpedance sensitivity at f = 10 kHz was ~30% higher than the corresponding DC piezoresistive sensitivity, yielding a sensitivity factor of 9.9 for |Z| and a higher sensitivity factor (~12.7) for θ. The dielectric configuration enhanced the permittivity contribution to impedance, but it was the least sensitive to strain.
RESUMO
The emergence of modern technologies, such as Wireless Sensor Networks (WSNs), the Internet-of-Things (IoT), and Machine-to-Machine (M2M) communications, involves the use of batteries, which pose a serious environmental risk, with billions of batteries disposed of every year. However, the combination of sensors and wireless communication devices is extremely power-hungry. Energy Harvesting (EH) is fundamental in enabling the use of low-power electronic devices that derive their energy from external sources, such as Microbial Fuel Cells (MFC), solar power, thermal and kinetic energy, among others. Plant Microbial Fuel Cell (PMFC) is a prominent clean energy source and a step towards the development of self-powered systems in indoor and outdoor environments. One of the main challenges with PMFCs is the dynamic power supply, dynamic charging rates and low-energy supply. In this paper, a PMFC-based energy harvester system is proposed for the implementation of autonomous self-powered sensor nodes with IoT and cloud-based service communication protocols. The PMFC design is specifically adapted with the proposed EH circuit for the implementation of IoT-WSN based applications. The PMFC-EH system has a maximum power point at 0.71 V, a current density of 5 mA cm - 2 , and a power density of 3.5 mW cm - 2 with a single plant. Considering a sensor node with a current consumption of 0.35 mA, the PMFC-EH green energy system allows a power autonomy for real-time data processing of IoT-based low-power WSN systems.