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Highlights: What are the main findings? A comprehensive simulation model including particle flow and radiation transfer was developed for a CPC photocatalytic reactor. The ray tracing method was utilized to determine the radiation reaching the surface of the receiving tube, while the discrete ordinates method (DOM) was also employed to solve the radiative transfer equation (RTE), which shows the complete process of solar energy transfer. What is the implication of the main finding? Local volume radiative power absorption (LVRPA) and total radiative power absorption (TRPA) inside the receiving tube was obtained by this study, which is critical data for the photocatalytic reactor. Natural convection with intermittent disturbances is demonstrated to be effective operating mode for the CPC photocatalytic reactor. Compound parabolic concentrator (CPC) photocatalytic reactors are commonly used for photocatalytic water splitting in hydrogen production. This study aimed to gain a better understanding of the physical processes in CPC photocatalytic reactors and provide theoretical support for their design, optimization, and operation. The analysis involved the ray tracing approach, Euler–Euler two-fluid model, and discrete ordinates method (DOM) to study solar radiation transfer and particle flow in the reactor. The distribution of solar radiation on the receiving tube's surface after CPC concentration was obtained by conducting the ray tracing approach. This solar radiation distribution was then coupled into the Euler–Euler two-fluid model to solve for the natural convection flow field, the temperature field, and particle phase volume fraction distribution inside the receiving tube over a period of 120 s. Lastly, the discrete ordinates method (DOM) was used to analyze the transfer of radiation inside the receiving tube at different times, obtaining the distribution of local volume radiative power absorption (LVRPA) and the total radiative power absorption (TRPA) inside the tube. The results showed that the TRPA reached its maximum at 120 s, accounting for 66.61% of the incident solar UV radiation. According to the above results, it could be suggested that adopting an intermittent operation mode in CPC photocatalytic reactors is reasonable and efficient. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid car-following model: the physics-informed conditional generative adversarial network (PICGAN), designed to enhance multi-step car-following modeling in mixed traffic flow scenarios. This hybrid model leverages the strengths of both physics-based and deep-learning-based models. By taking advantage of the inherent structure of GAN, the PICGAN eliminates the need for an explicit weighting parameter typically used in the combination of traditional physics-based and data-driven models. The effectiveness of the proposed model is substantiated through case studies using the NGSIM I-80 dataset. These studies demonstrate the model's superior trajectory reproduction, suggesting its potential as a strong contender to replace conventional models in trajectory prediction tasks. Furthermore, the deployment of PICGAN significantly enhances the stability and efficiency in mixed traffic flow environments. Given its reliable and stable results, the PICGAN framework contributes substantially to the development of efficient longitudinal control strategies for connected autonomous vehicles (CAVs) in real-world mixed traffic conditions. [ABSTRACT FROM AUTHOR]

In order to reveal the heat transfer performance of nanofluids in solar collectors, the thermal conductivity and dynamic viscosity of five kinds of graphene oxide nanofluids, with a mass fraction of 0.002% to 0.01%, were studied in the temperature range of 25–50 °C. To ensure the dispersion and stability of the prepared nanofluids, UV–Vis absorption spectrum, zeta potential and particle size distribution were employed for nanofluid characterization. Agglomeration and sedimentation of the prepared nanofluids after standing for 20 days were observed, showing the good stability of the prepared graphene oxide–water nanofluid. The dynamic viscosity and thermal conductivity were measured. They show that with the increase in temperature, the dynamic viscosity of nanofluids decreases and the thermal conductivity increases. With the increase in mass concentration, the viscosity and thermal conductivity are improved. The highest thermal conductivity increase is obtained when the nanofluid concentration is 0.01% and the temperature is 50 °C. Finally, and most importantly, considering the inaccuracy of the existing experimental correlations to the predicted values of thermal conductivity, we propose our new mathematical model of correlation and carry out a series of tests to verify its reliability. The experimental correlations with temperature and concentration as independent variables show good agreement and accuracy with the experimental data. [ABSTRACT FROM AUTHOR]

It is very often the case that at some moment a time series process abruptly changes its underlying structure and, therefore, it is very important to accurately detect such change-points. In this problem, which is called a change-point (or break-point) detection problem, we need to find a method that divides the original nonstationary time series into a piecewise stationary segments. In this paper, we develop a flexible method to estimate the unknown number and the locations of change-points in autoregressive time series. In order to find the optimal value of a performance function, which is based on the Minimum Description Length principle, we develop a Cross-Entropy algorithm for the combinatorial optimization problem. Our numerical experiments show that the proposed approach is very efficient in detecting multiple change-points when the underlying process has moderate to substantial variations in the mean and the autocorrelation coefficient. We also apply the proposed method to real data of daily AUD/CNY exchange rate series from 2 January 2018 to 24 March 2020. [ABSTRACT FROM AUTHOR]

This paper uses quantitative eye tracking indicators to analyze the relationship between images of paintings and human viewing. First, we build the eye tracking fixation sequences through areas of interest (AOIs) into an information channel, the gaze channel. Although this channel can be interpreted as a generalization of a first-order Markov chain, we show that the gaze channel is fully independent of this interpretation, and stands even when first-order Markov chain modeling would no longer fit. The entropy of the equilibrium distribution and the conditional entropy of a Markov chain are extended with additional information-theoretic measures, such as joint entropy, mutual information, and conditional entropy of each area of interest. Then, the gaze information channel is applied to analyze a subset of Van Gogh paintings. Van Gogh artworks, classified by art critics into several periods, have been studied under computational aesthetics measures, which include the use of Kolmogorov complexity and permutation entropy. The gaze information channel paradigm allows the information-theoretic measures to analyze both individual gaze behavior and clustered behavior from observers and paintings. Finally, we show that there is a clear correlation between the gaze information channel quantities that come from direct human observation, and the computational aesthetics measures that do not rely on any human observation at all. [ABSTRACT FROM AUTHOR]

Today, eye trackers are extensively used in studying human cognition. However, it is hard to analyze and interpret eye movement data from the cognitive comprehension perspective of poster reading. To find quantitative links between eye movements and cognitive comprehension, we tracked observers' eye movement for reading scientific poster publications. We model in this paper eye tracking fixation sequences between content-dependent Areas of Interests (AOIs) as a Markov chain. Furthermore, we use the fact that a Markov chain is a special case of information or communication channel. Then, the gaze transition can be modeled as a discrete information channel, the gaze information channel. Next, some traditional eye tracking metrics, together with the gaze entropy and mutual information of the gaze information channel are calculated to quantify cognitive comprehension for every participant. The analysis of the results demonstrate that the gaze entropy and mutual information from individual gaze information channel are related to participants' individual differences. This is the first study that eye tracking technology has been used to assess the cognitive comprehension of poster reading. The present work provides insights into human cognitive comprehension by using the novel gaze information channel methodology. [ABSTRACT FROM AUTHOR]