The temperature is one of the most important factors which affect the performance of the photovoltaic cells and panels along with the irradiance. The current voltage characteristics, I-V, are measured at different temperatures from 25°C to 87°C and at different illumination levels from 400 to 1000 W/m², because there are locations where the upper limit of the photovoltaic cells working temperature exceeds 80°C. This study reports the influence of the temperature and the irradiance on the important parameters of four commercial photovoltaic cell types: monocrystalline silicon—mSi, polycrystalline silicon—pSi, amorphous silicon—aSi, and multijunction InGaP/InGaAs/Ge (Emcore). The absolute and normalized temperature coefficients are determined and compared with their values from the related literature. The variation of the absolute temperature coefficient function of the irradiance and its significance to accurately determine the important parameters of the photovoltaic cells are also presented. The analysis is made on different types of photovoltaics cells in order to understand the effects of technology on temperature coefficients. The comparison between the open-circuit voltage and short-circuit current was also performed, calculated using the temperature coefficients, determined, and measured, in various conditions. The measurements are realized using the SolarLab system, and the photovoltaic cell parameters are determined and compared using the LabVIEW software created for SolarLab system.

Artificial Intelligence (AI) and its real-life applications are among the most effervescent research topics of the last couple of years. In the past decade, stakeholders such as private companies, public institutions, non-profit entities, and even individuals, have developed and used various AI algorithms to solve a wide range of problems. Given the extended applicability and the disruption potential of this technology, it was just a matter of time until it connected to the field of cultural heritage. This paper presents the development of an intelligent conversational agent which was built to improve the accessibility to information inside a history museum. We present the cultural context, the application architecture, the implementation challenges, and the visitors’ feedback. We created a smart virtual agent that interacts with users in natural spoken language. This involved the design and implementation of the artificial intelligence-based core responsible for understanding the Romanian language. A short survey regarding the tourist acceptance of the system was conducted at the premises of our partners, the Museum “Casa Mureșenilor” from Brașov, shows good acceptance levels from both visitors and museum staff. Given the flexibility of the implementation, the system can be used by a large array of stakeholders with minor modifications.

Principal Component Analysis (PCA) is a method based on statistics and linear algebra techniques, used in hyperspectral satellite imagery for data dimensionality reduction required in order to speed up and increase the performance of subsequent hyperspectral image processing algorithms. This paper introduces the PCA approximation method based on a geometric construction approach (gaPCA) method, an alternative algorithm for computing the principal components based on a geometrical constructed approximation of the standard PCA and presents its application to remote sensing hyperspectral images. gaPCA has the potential of yielding better land classification results by preserving a higher degree of information related to the smaller objects of the scene (or to the rare spectral objects) than the standard PCA, being focused not on maximizing the variance of the data, but the range. The paper validates gaPCA on four distinct datasets and performs comparative evaluations and metrics with the standard PCA method. A comparative land classification benchmark of gaPCA and the standard PCA using statistical-based tools is also described. The results show gaPCA is an effective dimensionality-reduction tool, with performance similar to, and in several cases, even higher than standard PCA on specific image classification tasks. gaPCA was shown to be more suitable for hyperspectral images with small structures or objects that need to be detected or where preponderantly spectral classes or spectrally similar classes are present.

Two new hybrid systems incorporating photovoltaic, thermoelectric, and solar collector components were designed and characterized under natural sunlight conditions. Two types of photovoltaic cells were used: monocrystalline silicon and polycrystalline silicon connected on the hot side of the thermoelectric generator. The two types of solar collectors used were connected on the cold side of the thermoelectric generator. The experimental setup is presented in this paper. The behavior of each component related to irradiance, temperature, and wind speed was studied. The performances of the hybrid systems were determined and compared. The best performance was observed for the hybrid system with the monocrystalline photovoltaic panel. The maximum power of the photovoltaic cells increased by more than 11% when they were integrated in a hybrid system, due to a decrease in temperature. The photovoltaic cells’ temperature decreased by 6°C for the solar radiation of $440\mathrm{W}/{\mathrm{m}}^2$ , to 19°C for $\mathrm{1,000}\mathrm{W}/{\mathrm{m}}^2$ . The time stabilization was determined at 7–8 min for both hybrid systems in clear sky conditions. The software for the system control, data acquisition, and determination of the components’ parameters was developed using a graphical language program.