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Accurate outdoor illumination estimation is not easy due to extremely complicated sky appearance and the mutual interference of the sun and sky. In view of these challenges, we present a new deep approach for the estimation of all-weather outdoor illumination. The key to our approach is a novel dual attention autoencoder (DAA) with two independent branches to compress the sun and sky lighting information from an input HDR panorama, respectively. This enables more accurate lighting estimation as evidenced by our experiments since the mutual interference between the sun and sky can be precluded effectively. In DAA, we design the adaptive feature pyramid and the attention module to promote its accuracy in compression. We further develop a sun-sky predictor, a masked network, to learn the sun and sky lighting conditions from an FoV-limited image. Comprehensive qualitative and quantitative experiments verify the effectiveness of our proposed approach and show its superiority over the state of the arts.

The rapid global spread of coronavirus disease 2019 (COVID-19) has promoted concern over human pathogens and their significant threats to public health security. The monitoring and control of human pathogens in public sanitation and health facilities are of great importance. Excessive sludge is an inevitable byproduct of sewage that contains human and animal feces in wastewater treatment plants (WWTPs). It is an important sink of different pollutants and pathogens, and the proper treatment and disposal of sludge are important to minimize potential risks to the environment and public health. However, there is a lack of comprehensive analysis of the diversity, exposure risks, assessment methods and inactivation techniques of pathogenic microorganisms in sludge. Based on this consideration, this review summarizes the control performance of pathogenic microorganisms such as enterovirus, Salmonella spp., and Escherichia coli by different sludge treatment technologies, including composting, anaerobic digestion, aerobic digestion, and microwave irradiation, and the mechanisms of pathogenic microorganism inactivation in sludge treatment processes are discussed. Additionally, this study reviews the diversity, detection methods, and exposure risks of pathogenic microorganisms in sludge. This review advances the quantitative assessment of pathogenic microorganism risks involved in sludge reuse and is practically valuable to optimize the treatment and disposal of sludge for pathogenic microorganism control.

In this paper, we propose a novel non-expected route travel time (NERTT) model, which belong to the rank-dependent expected utility model. The NERTT consists of two parts, which are the route travel time distribution and the distortion function. With the strictly increasing and strictly concave distortion function, we can prove that the route travel time in the proposed model is risk-averse, which is the main focus of this paper. We show two different reduction methods from the NERTT model to the travel time budget model and mean-excess travel time model. One method is based on the properly selected distortion functions and the other one is based on a general distortion function. Besides, the behavioral inconsistency of the expected utility model in the route choice can be overcome with the proposed model. The NERTT model can also be generalized to the non-expected disutility (NED) model, and some relationship between the NED model and the route choice model based on the cumulative prospect theory can be shown. This indicates that the proposed model has some generality. Finally, we develop a non-expected risk-averse user equilibrium model and formulate it as a variational inequality (VI) problem. A heuristic gradient projection algorithm with column generation is used to solve the VI. The proposed model and algorithm are tested on some hypothetical traffic networks and on some large-scale traffic networks.