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Large-eddy and direct numerical simulations generate vast data sets that are challenging to interpret, even for simple geometries at low Reynolds numbers. This has increased the importance of automatic methods for extracting significant features to understand physical phenomena. Traditional techniques like the proper orthogonal decomposition (POD) have been widely used for this purpose. However, recent advancements in computational power have allowed for the development of data-driven modal reduction approaches. This paper discusses four applications of deep neural networks for aerodynamic applications, including a convolutional neural network autoencoder, to analyze unsteady flow fields around a circular cylinder at Re = 100 and a supersonic boundary layer with Tollmien–Schlichting waves. The autoencoder results are comparable to those obtained with POD and spectral POD. Additionally, it is demonstrated that the autoencoder can compress steady hypersonic boundary-layer profiles into a low-dimensional vector space that is spanned by the pressure gradient and wall-temperature ratio. This paper also proposes a convolutional neural network model to estimate velocity and temperature profiles across different hypersonic flow conditions.

Thanks to sensor developments, unmanned aircraft system (UAS) are the most promising modern technologies used to collect imagery datasets that can be utilized to develop agricultural applications in these days. UAS imagery datasets can grow exponentially due to the ultrafine spatial and high temporal resolution capabilities of UAS and sensors. One of the main obstacles to processing UAS data is the intensive computational resource requirements. The structure from motion (SfM) is the most popular algorithm to generate 3D point clouds, orthomosaic images, and digital elevation models (DEMs) in agricultural applications. Recently, the SfM algorithm has been implemented in parallel computing to process big UAS data faster for certain applications. This study evaluated the performance of parallel SfM processing on public cloud computing and on-premise cluster systems. The UAS datasets collected over cropping fields were used for performance evaluation. We used multiple computing nodes and centralized network storage with different network environments for the SfM workflow. In single-node processing, an instance with the most computing power in the cloud computing system performed approximately 20 and 35 percent faster than in the most powerful machine in the on-premises cluster. The parallel processing results showed that the cloud-based system performed better in speed-up and efficiency metrics for scalability, although the absolute processing time was faster in the on-premise cluster. The experimental results also showed that the public cloud computing system could be a good alternative computing environment in UAS data processing for agricultural applications.

Recent portrayals of ancient Egypt in popular culture have renewed attention concerning the historical accuracy of how race and racism appear in representations of antiquity. Historians of the antiquity have robustly dismissed racist claims of whitewashing or blackwashing historical and cultural material in both scholarship and in popular culture. The 2017 video game Assassin’s Creed: Origins is a noteworthy site to examine this debate, as the game was designed with the assistance of historians and cultural experts, presenting players with an “historically accurate” ancient Egypt. Yet, if race is a fantasy, as Karen Fields and Barbara Fields’ “racecraft” articulates, then what historians have speculated in their study of race and racism are presentations of a proto-racecraft, borrowing from historian Benjamin Isaac. This essay argues that Assassin’s Creed: Origins racecrafts through the paradigm of historical speculation. As historians have speculated on meanings and operations of “race” and racism in ancient Egypt, Origins has made those speculations visible through its depiction of a racially diverse Ptolemaic Egypt. Yet, this racecraft is paradoxically good, as the game does so to push back against the hegemony of whiteness and whitewashing in contemporary popular culture.

Household air pollution (HAP) is of public health concern, with ~3 billion people worldwide (including >15 million in the US) exposed. HAP from coal use is a human lung carcinogen, yet the epidemiological evidence on carcinogenicity of HAP from biomass use, primarily wood, is not conclusive. To robustly assess biomass’s carcinogenic potential, prospective studies of individuals experiencing a variety of HAP exposures are needed. We have built a global consortium of 13 prospective cohorts (HAPCO: Household Air Pollution Consortium) that have site- and disease-specific mortality and solid fuel use data, for a combined sample size of 587,257 participants and 57,483 deaths. HAPCO provides a novel opportunity to assess the association of HAP with lung cancer death while controlling for important confounders such as tobacco and outdoor air pollution exposures. HAPCO is also uniquely positioned to determine the risks associated with cancers other than lung as well as nonmalignant respiratory and cardiometabolic outcomes, for which prospective epidemiologic research is limited. HAPCO will facilitate research to address public health concerns associated with HAP-attributed exposures by enabling investigators to evaluate sex-specific and smoking status-specific effects under various exposure scenarios.

Industrial pavements are thin slabs on a continuous support subjected to restrained shrinkage and loads. The use of fibers as an alternative reinforcement to steel welded wire mesh and rebars is today an extensive practice for the reinforcement of concrete slabs-on-grade. Despite the widespread use of fiber reinforcement, the corresponding benefits in controlling cracking phenomena due to shrinkage are generally not considered in the design process of Fiber Reinforced Concrete (FRC) slabs-on-grade. The post-cracking performance provided by glass macro-fibers at low crack openings is particularly convenient in structures with a high degree of redundancy. Referring to service conditions, it is well known that concrete shrinkage as well as thermal effects tend to be the principal reasons for the initial crack formation in slabs-on-grade. A numerical study on the risk of cracking due to shrinkage in ground-supported slabs is presented herein. Special attention is devoted to the evaluation of the beneficial effects of glass fibers in controlling cracking phenomena due to shrinkage. The numerical analyses are carried out on jointless pavements of different sizes. Since shrinkage stresses in slabs-on-grade are considerably influenced by external constraints which limit the contractions, different subgrade conditions have been also considered.