In this article, Au nanoparticles in polyvinylpyrrolidone (PVP) solution were prepared by gamma radiation at different concentrations. The solutions were irradiated at doses of 50 kGy for making different sizes. The average sizes of particle in the prepared samples were measured using the nanophox machine. A dual-beam mode-mismatched thermal lens (TL) method was used to investigate the effect of thermal diffusivity of samples. The TL measurement was carried out using a green diode laser (wavelength 532 nm, 60 mW) and a He–Ne laser (wavelength 632.8 nm, 0.5 mW) for excitation source and probe beam, respectively. The results showed that the thermal diffusivity of samples enhances with the growth of particle size and density of solutions. This increment can be attributed to phonon scattering at interface of particles–liquid and contact between the nanoparticles and surrounded liquid.

Increasing the number of cores to enhance computing power of processors leads to an increase in temperature for multicore systems. Thermal management is significant challenge in these processors. A proactive dynamic thermal management uses a thermal model to predict the temperature before processor temperature reaches the threshold. In this paper, some appropriate features for thermal model are read by using system measurement tools. Other features as historical and control features are created using the proposed processes. An online thermal model based on several thermal phase is proposed. For each phase, a neural network is used to forecast temperature. Different thermal phases are identified according to the parameters affecting the processor temperature using the adaptive resonance theory network. For each of the neural networks, the minimum number of proper features is selected based on the mutual information between the features. The proposed thermal model is able to detect new thermal phase at run time. Then, appropriate neural network is created for new phase. The proposed model has been evaluated to predict temperature for different time distances. The results shows the mean absolute error is less than 1 ° C.

This study investigates the temperature distribution across solar cells on the surface of a solar panel, specifically focusing on the effect of anti-reflective coating homogeneity. The research aims to analyze how temperature variations affect power output and performance ratio under high solar radiation and ambient temperature conditions in Baghdad, Iraq. Using a photovoltaic (PV) analyzer and a thermal imaging camera, the study measures the electrical characteristics and thermal distribution of an 80 Wp monocrystalline solar panel. The results reveal a significant decrease in performance ratio as solar cell temperatures rise, with values such as 88.7% at 35.5 W, 85.2% at 40.88 W, and 78.8% at 44.56 W. These findings underscore the importance of maintaining uniform anti-reflective coatings and effective heat dissipation to enhance solar panel efficiency. The study provides valuable insights for optimizing solar panel performance in high-temperature environments and suggests directions for future research on improving thermal management in photovoltaic systems.