The classical gradient-diffusion hypothesis has known deficiencies when applied to cooling applications. In this paper, the gene-expression programming (GEP) method, a machine learning approach, has been applied to develop scalar-flux models via symbolic regression. The scalar-flux, the unclosed term of the mean passive-scalar transport equation, is treated by considering the polynomial basis and scalar invariants available from computable Reynolds-averaged quantities. This method has been applied to develop and then assess a model for the test case of jet in crossflow. A high-fidelity database was first probed for insight into which of the candidate bases are the most suitable as modelling terms. The high dimensionality of the function space, spanned by the basis, was then reduced by basic statistical techniques. The resulting data-driven model is presented and tested for a range of different jet in crossflow cases. Compared with eddy-diffusivity models, the new model is shown to produce reliably more accurate results. This demonstrates that the current framework can be used for scalar-flux modelling in complex three-dimensional flows and has potential to provide generalized form closures with improved predictive accuracy for the same classes of flows they were trained on.
Zhou F, Bo Y, Ciais P, Dumas P, Tang Q, Wang X, Liu J, Zheng C, Polcher J, Yin Z, et al.Deceleration of China’s human water use and its key drivers. Proceedings of the National Academy of Sciences of the United States of America [Internet]. 2020;117:doi: 10.1073/pnas.1909902117. 访问链接Abstract
Increased human water use combined with climate change have aggravated water scarcity from the regional to global scales. However, the lack of spatially detailed datasets limits our understanding of the historical water use trend and its key drivers. Here, we present a survey-based reconstruction of China’s sectoral water use in 341 prefectures during 1965 to 2013. The data indicate that water use has doubled during the entire study period, yet with a widespread slowdown of the growth rates from 10.66 km3·y−2 before 1975 to 6.23 km3·y−2 in 1975 to 1992, and further down to 3.59 km3·y−2 afterward. These decelerations were attributed to reduced water use intensities of irrigation and industry, which partly offset the increase driven by pronounced socioeconomic development (i.e., economic growth, population growth, and structural transitions) by 55% in 1975 to 1992 and 83% after 1992. Adoptions for highly efficient irrigation and industrial water recycling technologies explained most of the observed reduction of water use intensities across China. These findings challenge conventional views about an acceleration in water use in China and highlight the opposing roles of different drivers for water use projections.
Thinking tools that assist by externalizing thought processes and conceptual structures so they can be manipulated potentially improve user learning. We propose the design of a sensemaking assistant that integrates many such tools. Our design emerged from an intensive study of sensemaking by users working on real tasks, providing a link from users to developers. Sensemaking is the process of forming meaningful representations and working with them to gain understanding, possibly communicated in a report, to support planning, decision‑making, problem‑solving, and informed action. At the heart of our design is a set of tightly integrated tools for representing and manipulating a conceptual space: tools for producing and maintaining concept maps, causal maps/influence diagrams, argument maps, with support through self-organizing semantic maps, importing concepts and relationships from external Knowledge Organization Systems, and inferring connections between texts; further a tool for organizing information items (documents, text passages notes, images) linked to the concept map. The sensemaking assistant we envision guides users through the sensemaking process; for each function it suggests appropriate cognitive processes and provides tools that automate tasks. The comprehensive sensemaking model introduced in specifies functions in the iterative process of sensemaking: Task analysis and planning; Gap identification (tools for both: brainstorming, finding documents on the task); information acquisition, data seeking and structure seeking (search tool: finding databases, query expansion, passage retrieval; summarization tool); information organization, building structure, instantiating structure, information synthesis / new ideas / emerging sense (conceptual space tools mentioned above); information presentation, creating reports (from concept map to outline, guide through the writing process, analyze draft writing for coherence and clarity). The system tracks sources. Users using a sensemaking assistant may well internalize good ways for intellectual processes and good conceptual organization in addition to learning a useful application. The paper will provide some evidence from the literature and propose further testing.
The world is facing the dual challenge of closing a vast urban infrastructure financing gap and making urban infrastructure more climate resilient. As estimated by the OECD, USD 95 trillion will be required to develop transport, energy, water, and telecoms from 2016 to 2030 in developing countries. With the temperature rise, the extreme weather will have direct physical harm on infrastructure as the aging infrastructures would be vulnerable to storm surges and sea level rise. In order to keep global temperature rise this century well below 2 degrees Celsius above pre-industrial levels, an additional 10% of investment will be needed to develop climate resilient infrastructure, adding to the USD 6.9 trillion needed per year by 2030.
Based on the concepts of “ancient China studies” and “digital humanities” (DH) in the context of China, this paper first gives a brief review on the development and practice of DH cyberinfrastructure. Under a series of reflections and a brief investigation on ancient Chinese literatures and traditional humanistic activities, this paper puts forward a new DH cyberinfrastructure conceptual model for ancient China studies that can bring people, information, and computational tools together and allow humanistic scholars to perform in a new way and with higher efficiency. On the premise of actual practices to turn a conceptual model into reality, this paper discusses DH cyberinfrastructure and the future of academic libraries.