Analysis of internal flow field for a three-stage centrifugal fan under various operating conditions

Centrifugal fan in series is the core facility for pneumatic transportation. Its operation is required to be adjusted real time as the flow rate of transported materials changes. For optimisation performance, internal flow of a type 700 three-stage fan at rated rotation speed is analysed with FLUENT6.3 for various total pressure. Multiple reference frame (MRF) model is used for the impellers rotation in simulation, and Re-Normalisation group (RNG) κ-ɛ turbulence model and Roe-FDS flux type with first-order upwind difference scheme are used for calculation of compressible flow under various total pressure. Flow characteristics are obtained and differences of total pressure and velocity distribution in every impeller are analysed in different conditions, and velocity distribution on meridian plane as well as section of wind guide plates and volute are compared. Results show that there are many vortexes and backflows in fan with higher total pressure, and the lower pressure, the more smoothly flows, besides flow at inlet and outlet of impellers and in guide plates is seriously influenced by total pressure. Curves of P-Q and P-η at 4600 r/min are predicted based on simulation, which is helpful reducing the impact caused by off-design operation and improving efficiency

A Satellite Imagery Dataset for Long-Term Sustainable Development in United States Cities

Cities play an important role in achieving sustainable development goals (SDGs) to promote economic growth and meet social needs. Especially satellite imagery is a potential data source for studying sustainable urban development. However, a comprehensive dataset in the United States (U.S.) covering multiple cities, multiple years, multiple scales, and multiple indicators for SDG monitoring is lacking. To support the research on SDGs in U.S. cities, we develop a satellite imagery dataset using deep learning models for five SDGs containing 25 sustainable development indicators. The proposed dataset covers the 100 most populated U.S. cities and corresponding Census Block Groups from 2014 to 2023. Specifically, we collect satellite imagery and identify objects with state-of-the-art object detection and semantic segmentation models to observe cities’ bird’s-eye view. We further gather population, nighttime light, survey, and built environment data to depict SDGs regarding poverty, health, education, inequality, and living environment. We anticipate the dataset to help urban policymakers and researchers to advance SDGs-related studies, especially applying satellite imagery to monitor long-term and multi-scale SDGs in cities.Peer reviewe

A Satellite Imagery Dataset for Long-Term Sustainable Development in United States Cities

Abstract Cities play an important role in achieving sustainable development goals (SDGs) to promote economic growth and meet social needs. Especially satellite imagery is a potential data source for studying sustainable urban development. However, a comprehensive dataset in the United States (U.S.) covering multiple cities, multiple years, multiple scales, and multiple indicators for SDG monitoring is lacking. To support the research on SDGs in U.S. cities, we develop a satellite imagery dataset using deep learning models for five SDGs containing 25 sustainable development indicators. The proposed dataset covers the 100 most populated U.S. cities and corresponding Census Block Groups from 2014 to 2023. Specifically, we collect satellite imagery and identify objects with state-of-the-art object detection and semantic segmentation models to observe cities’ bird’s-eye view. We further gather population, nighttime light, survey, and built environment data to depict SDGs regarding poverty, health, education, inequality, and living environment. We anticipate the dataset to help urban policymakers and researchers to advance SDGs-related studies, especially applying satellite imagery to monitor long-term and multi-scale SDGs in cities

Ring-Fusion of Perylene Diimide Acceptor Enabling Efficient Nonfullerene Organic Solar Cells with a Small Voltage Loss

We report a novel small molecule acceptor (SMA) named FTTB-PDI4 obtained via ring-fusion between the thiophene and perylene diimide (PDI) units of a PDI-tetramer with a tetrathienylbezene (TTB) core. A small voltage loss of 0.53 V and a high power conversion efficiency of 10.58% were achieved, which is the highest value reported for PDI-based devices to date. By comparing the fused and nonfused SMAs, we show that the ring-fusion introduces several beneficial effects on the properties and performances of the acceptor material, including more favorable energy levels, enhanced light absorption and stronger intermolecular packing. Interestingly, morphology data reveal that the fused molecule yields higher domain purity and thus can better maintain its molecular packing and electron mobility in the blend. Theoretical calculations also demonstrate that FTTB-PDI4 exhibits a “double-decker” geometry with two pairs of mostly parallel PDI units, which is distinctively different from reported PDI-tetramers with highly twisted geometries and can explain the better performance of the material. This work highlights the promising design of PDI-based acceptors by the ring-fusion strategy

Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears

Technical advancements have significantly improved early diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various practical factors. Here, the authors develop an artificial intelligence assistive diagnostic solution to improve cervical liquid-based thin-layer cell smear diagnosis according to clinical TBS criteria in a large multicenter study