Thermoelectricity energy harvesting based on the thermoelectric generator (TEG) has had a prominent role in energy harvesting solutions since they have dramatically grown recently due to the existence of significant thermal gradients $\Delta{{\mathrm {T}}}$ in both natural and industrial environments. However, to prove that a given environment is adequate to generate energy is a hard task. In order to early evaluate the potential of a given environment to get enough thermal energy to be converted to electric energy, we propose in this paper an automatic emulation system of thermal energy that can be used with any thermal source in a simple way. Experimental results show that the proposed platform can emulate any thermal gradient pattern, apply this pattern on a TEG and measure the actual electric energy generated by the TEG. An example of application is described, and the results are shown. The example is an experiment to harvest energy from tree trunks.

Biosensors based on the surface plasmon resonance (SPR) technique are widely used for analyte detection due to their high selectivity and real-time detection capabilities. However, conventional SPR spectrum analysis can be affected by experimental noise and environmental variations, reducing the accuracy of results. To address these limitations, this study presents the development of an open-source computational tool to optimize SPR biosensor characterization, implemented using MATLAB App Designer (Version R2024b). The tool enables the importation of experimental data, application of different smoothing methods, and integration of traditional and hybrid approaches to enhance accuracy in determining the resonance angle. The proposed tool offers several innovations, such as integration of both traditional and hybrid (angle vs wavelength) analysis modes, implementation of four advanced curve smoothing techniques, including Gaussian filter, Savitzky–Golay, smoothing splines, and EWMA, as well as a user-friendly graphical interface supporting real-time data visualization, experimental data import, and result export. Unlike conventional approaches, the hybrid framework enables multidimensional optimization of SPR parameters, resulting in greater accuracy and robustness in detecting resonance conditions. Experimental validation demonstrated a marked reduction in spectral noise and improved consistency in resonance angle detection across conditions. The results confirm the effectiveness and practical relevance of the tool, contributing to the advancement of SPR biosensor analysis.

Nesse artigo, é apresentado um estudo do comportamento da comunicação subterrânea via LoRa entre dois dispositivos, um deles posicionado sob a superfície do solo e outro acima dela. Foi analisado o desempenho da tecnologia LoRa em ambiente subterrâneo avaliando a interferência da profundidade e de outras variáveis como a distância, fator de espalhamento e características do ambiente na qualidade da comunicação. Este estudo foi realizado para verificar a adequação da tecnologia LoRa para monitoramento de variáveis físicas em ambientes subterrâneos, visando a aplicação de detecção de vazamentos em tempo real em tubulações de sistemas de distribuição de água ou outras aplicações subterrâneas.