Reconstruction and fast prediction of a 3D flow field based on a variational autoencoder

Reconstruction and fast prediction of flow fields are important for the improvement of data center operations and energy savings. In this study, an artificial neural network (ANN) and variational autoencoder (VAE) composite model is proposed for the reconstruction and prediction of 3D flowfields with high accuracy and efficiency. The VAE model is trained to extract features of the problem and to realize 3D physical field reconstruction. The ANN is employed to achieve the constructability of the extracted features. A dataset of steady temperature/velocity fields is acquired by computational fluid dynamics and heat transfer (CFD/HT) and fed to train the deep learning model. The proposed ANN-VAE model is experimentally proven to achieve promising field prediction accuracy with a significantly reduced computational cost. Compared to the CFD/HT method, the ANN-VAE method speeds up the physical field prediction by approximately 380,000 times, with mean accuracies of 97.3% for temperature field prediction and 97.9% for velocity field prediction, making it feasible for real-time physical field acquisition.Comment: 43 pages, 23 figure

Single-cell RNA sequencing reveals different chondrocyte states in femoral cartilage between osteoarthritis and healthy individuals

BackgroundCartilage injury is the main pathological manifestation of osteoarthritis (OA). Healthy chondrocyte is a prerequisite for cartilage regeneration and repair. Differences between healthy and OA chondrocyte types and the role these types play in cartilage regeneration and OA progression are unclear.MethodThis study conducted single-cell RNA sequencing (scRNA-seq) on the cartilage from normal distal femur of the knee (NC group) and OA femur (OA group) cartilage, the chondrocyte atlas was constructed, and the differences of cell subtypes between the two groups were compared. Pseudo-time and RNA velocity analysis were both performed to verify the possible differentiation sequence of cell subtypes. GO and KEGG pathway enrichment analysis were used to explore the potential functional characteristics of each cell subtype, and to predict the functional changes during cell differentiation. Differences in transcriptional regulation in subtypes were explored by single-cell regulatory network inference and clustering (SCENIC). The distribution of each cell subtype in cartilage tissue was identified by immunohistochemical staining (IHC).ResultA total of 75,104 cells were included, they were divided into 19 clusters and annotated as 11 chondrocyte subtypes, including two new chondrocyte subtypes: METRNL+ and PRG4+ subtype. METRNL+ is in an early stage during chondrocyte differentiation, and RegC-B is in an intermediate state before chondrocyte dedifferentiation. With cell differentiation, cell subtypes shift from genetic expression to extracellular matrix adhesion and collagen remodeling, and signal pathways shift from HIF-1 to Hippo. The 11 subtypes were finally classified as intrinsic chondrocytes, effector chondrocytes, abnormally differentiated chondrocytes and dedifferentiated chondrocytes. IHC was used to verify the presence and distribution of each chondrocyte subtype.ConclusionThis study screened two new chondrocyte subtypes, and a novel classification of each subtype was proposed. METRNL+ subtype is in an early stage during chondrocyte differentiation, and its transcriptomic characteristics and specific pathways provide a foundation for cartilage regeneration. EC-B, PRG4+ RegC-B, and FC are typical subtypes in the OA group, and the HippO-Taz pathway enriched by these cell subtypes may play a role in cartilage repair and OA progression. RegC-B is in the intermediate state before chondrocyte dedifferentiation, and its transcriptomic characteristics may provide a theoretical basis for intervening chondrocyte dedifferentiation

Single-cell RNA sequencing reveals distinct chondrocyte states in femoral cartilage under weight-bearing load in Rheumatoid arthritis

IntroductionRheumatoid arthritis (RA) is a common autoimmune joint disease, the pathogenesis of which is still unclear. Cartilage damage is one of the main manifestations of the disease. Chondrocytes are the main functional component of articular cartilage, which is relevant to disease progression. Mechanical loading affects the structure and function of articular cartilage and chondrocytes, but the effect of weight bearing on chondrocytes in rheumatoid arthritis is still unclear.MethodsIn this paper, single-cell RNA sequencing (scRNA-seq) was performed on collected cartilage from the weight-bearing region (Fb group) and non-weight-bearing region (Fnb group) of the femur, and the differences between the Fb and Fnb groups were analyzed by cell type annotation, pseudotime analysis, enrichment analysis, cell interactions, single-cell regulatory network inference and clustering (SCENIC) for each cell type. ResultsA total of 87,542 cells were analyzed and divided into 9 clusters. Six chondrocyte subpopulations were finally identified by cellular annotation, and two new chondrocyte subtypes were annotated as immune-associated chondrocytes. The presence of each chondrocyte subpopulation and its distribution were verified using immunohistochemical staining (IHC). In this study, the atlas of femoral cartilage in knee rheumatoid arthritis and 2 new immune-related chondrocytes were validated using scRNA-seq and IHC, and chondrocytes in the weight-bearing and non-weight-bearing regions of the femur were compared. There might be a process of macrophage polarization transition in MCs in response to mechanical loading, as in macrophages.ConclusionTwo new immune-associated chondrocytes were identified. MCs have contrasting functions in different regions, which might provide insight into the role of immune and mechanical loading on chondrocytes in the development of knee rheumatoid osteoarthritis

Sciences for The 2.5-meter Wide Field Survey Telescope (WFST)

The Wide Field Survey Telescope (WFST) is a dedicated photometric survey facility under construction jointly by the University of Science and Technology of China and Purple Mountain Observatory. It is equipped with a primary mirror of 2.5m in diameter, an active optical system, and a mosaic CCD camera of 0.73 Gpix on the main focus plane to achieve high-quality imaging over a field of view of 6.5 square degrees. The installation of WFST in the Lenghu observing site is planned to happen in the summer of 2023, and the operation is scheduled to commence within three months afterward. WFST will scan the northern sky in four optical bands (u, g, r, and i) at cadences from hourly/daily to semi-weekly in the deep high-cadence survey (DHS) and the wide field survey (WFS) programs, respectively. WFS reaches a depth of 22.27, 23.32, 22.84, and 22.31 in AB magnitudes in a nominal 30-second exposure in the four bands during a photometric night, respectively, enabling us to search tremendous amount of transients in the low-z universe and systematically investigate the variability of Galactic and extragalactic objects. Intranight 90s exposures as deep as 23 and 24 mag in u and g bands via DHS provide a unique opportunity to facilitate explorations of energetic transients in demand for high sensitivity, including the electromagnetic counterparts of gravitational-wave events detected by the second/third-generation GW detectors, supernovae within a few hours of their explosions, tidal disruption events and luminous fast optical transients even beyond a redshift of 1. Meanwhile, the final 6-year co-added images, anticipated to reach g about 25.5 mag in WFS or even deeper by 1.5 mag in DHS, will be of significant value to general Galactic and extragalactic sciences. The highly uniform legacy surveys of WFST will also serve as an indispensable complement to those of LSST which monitors the southern sky.Comment: 46 pages, submitted to SCMP

Artificial intelligence for diagnosis and Gleason grading of prostate cancer: The PANDA challenge

Through a community-driven competition, the PANDA challenge provides a curated diverse dataset and a catalog of models for prostate cancer pathology, and represents a blueprint for evaluating AI algorithms in digital pathology. Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted kappa, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.KWF Kankerbestrijding ; Netherlands Organization for Scientific Research (NWO) ; Swedish Research Council European Commission ; Swedish Cancer Society ; Swedish eScience Research Center ; Ake Wiberg Foundation ; Prostatacancerforbundet ; Academy of Finland ; Cancer Foundation Finland ; Google Incorporated ; MICCAI board challenge working group ; Verily Life Sciences ; EIT Health ; Karolinska Institutet ; MICCAI 2020 satellite event team ; ERAPerMe

Improved Flutter Prediction for Turbomachinery Blades with Tip Clearance Flows

Recent design trends in steam turbines strive for high aerodynamic loading and high aspect ratio to meet the demand of higher efficiency. These design trends together with the low structural frequency in last stage steam turbines increase the susceptibility of the turbine blades to flutter. Flutter is the self-excited and self-sustained aeroelastic instability phenomenon, which can result in rapid growth of blade vibration amplitude and eventually blade failure in a short period of time unless adequately damped. To prevent the occurrences of flutter before the operation of new steam turbines, a compromise between aeroelastic stability and stage efficiency has to be made in the steam turbine design process. Due to the high uncertainty in present flutter prediction methods, engineers use large safety margins in predicting flutter which can rule out designs with higher efficiency. The ability to predict flutter more accurately will allow engineers to push the design envelope with greater confidence and possibly create more efficient steam turbines. The present work aims to investigate the influence of tip clearance flow on the prediction of steam turbine flutter characteristics. Tip clearance flow effect is one of the critical factors in flutter analysis for the majority of aerodynamic work is done near the blade tip. Analysis of the impact of tip clearance flow on steam turbine flutter characteristics is therefore needed to formulate a more accurate aeroelastic stability prediction method in the design phase.Besides the tip leakage vortex, the induced vortices in the tip clearance flow can also influence blade flutter characteristics. However, the spatial distribution of the induced vortices cannot be resolved by URANS method for the limitation of turbulence models. The Detached-Eddy Simulation (DES) calculation is thus applied on a realistic-scale last stage steam turbine model to analyze the structure of induced vortices in the tip region. The influence of the tip leakage vortex and the induced vortices on flutter prediction are analyzed separately. The KTH Steam Turbine Flutter Test Case is used in the flutter analysis as a typical realistic-scale last stage steam turbine model. The energy method based on 3D unsteady CFD calculation is applied in the flutter analysis. Two CFD solvers, an in-house code LUFT and a commercial software ANSYS CFX, are used in the flutter analysis as verification of each other. The influence of tip leakage vortex on the steam turbine flutter prediction is analyzed by comparing the aeroelastic stability of two models: one with the tip gap and the other without the tip gap. Comparison between the flutter characteristics predicted by URANS and DES approaches is analyzed to investigate the influence of the induced vortices on blade flutter characteristics. The multiple induced vortices and their relative rotation around the tip leakage vortex in the KTH Steam Turbine Flutter Test Case are resolved by DES but not by URANS simulations. Both tip leakage vortex and induced vortices have an influence on blade loading on the rear half of the suction side near the blade tip. The flutter analysis results suggest that the tip clearance flow has a significant influence on blade aerodynamic damping at the least stable interblade phase angle (IBPA), while its influence on the overall shape of the damping curve is minor. At the least stable IBPA, the tip leakage vortex shows a stabilization effect on rotor aeroelastic stabilities while the induced vortices show a destabilization effect on it. Meanwhile, a non-linear unsteady flow behavior is observed due to the streamwise motion of induced vortices during blade oscillation, which phenomenon is only resolved in DES results

Improved Flutter Prediction for Turbomachinery Blades with Tip Clearance Flows

Recent design trends in steam turbines strive for high aerodynamic loading and high aspect ratio to meet the demand of higher efficiency. These design trends together with the low structural frequency in last stage steam turbines increase the susceptibility of the turbine blades to flutter. Flutter is the self-excited and self-sustained aeroelastic instability phenomenon, which can result in rapid growth of blade vibration amplitude and eventually blade failure in a short period of time unless adequately damped. To prevent the occurrences of flutter before the operation of new steam turbines, a compromise between aeroelastic stability and stage efficiency has to be made in the steam turbine design process. Due to the high uncertainty in present flutter prediction methods, engineers use large safety margins in predicting flutter which can rule out designs with higher efficiency. The ability to predict flutter more accurately will allow engineers to push the design envelope with greater confidence and possibly create more efficient steam turbines. The present work aims to investigate the influence of tip clearance flow on the prediction of steam turbine flutter characteristics. Tip clearance flow effect is one of the critical factors in flutter analysis for the majority of aerodynamic work is done near the blade tip. Analysis of the impact of tip clearance flow on steam turbine flutter characteristics is therefore needed to formulate a more accurate aeroelastic stability prediction method in the design phase.Besides the tip leakage vortex, the induced vortices in the tip clearance flow can also influence blade flutter characteristics. However, the spatial distribution of the induced vortices cannot be resolved by URANS method for the limitation of turbulence models. The Detached-Eddy Simulation (DES) calculation is thus applied on a realistic-scale last stage steam turbine model to analyze the structure of induced vortices in the tip region. The influence of the tip leakage vortex and the induced vortices on flutter prediction are analyzed separately. The KTH Steam Turbine Flutter Test Case is used in the flutter analysis as a typical realistic-scale last stage steam turbine model. The energy method based on 3D unsteady CFD calculation is applied in the flutter analysis. Two CFD solvers, an in-house code LUFT and a commercial software ANSYS CFX, are used in the flutter analysis as verification of each other. The influence of tip leakage vortex on the steam turbine flutter prediction is analyzed by comparing the aeroelastic stability of two models: one with the tip gap and the other without the tip gap. Comparison between the flutter characteristics predicted by URANS and DES approaches is analyzed to investigate the influence of the induced vortices on blade flutter characteristics. The multiple induced vortices and their relative rotation around the tip leakage vortex in the KTH Steam Turbine Flutter Test Case are resolved by DES but not by URANS simulations. Both tip leakage vortex and induced vortices have an influence on blade loading on the rear half of the suction side near the blade tip. The flutter analysis results suggest that the tip clearance flow has a significant influence on blade aerodynamic damping at the least stable interblade phase angle (IBPA), while its influence on the overall shape of the damping curve is minor. At the least stable IBPA, the tip leakage vortex shows a stabilization effect on rotor aeroelastic stabilities while the induced vortices show a destabilization effect on it. Meanwhile, a non-linear unsteady flow behavior is observed due to the streamwise motion of induced vortices during blade oscillation, which phenomenon is only resolved in DES results

Seventh International Conference on Intelligent Systems and Knowledge Engineering - Foundations and Applications of Intelligent Systems

These proceedings present technical papers selected from the 2012 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2012), held on December 15-17 in Beijing. The aim of this conference is to bring together experts from different fields of expertise to discuss the state-of-the-art in Intelligent Systems and Knowledge Engineering, and to present new findings and perspectives on future developments. The proceedings introduce current scientific and technical advances in the fields of artificial intelligence, machine learning, pattern recognition, data mining, knowledge engineering, information retrieval, information theory, knowledge-based systems, knowledge representation and reasoning, multi-agent systems, and natural-language processing, etc. Furthermore they include papers on new intelligent computing paradigms, which combine new computing methodologies, e.g., cloud computing, service computing and pervasive computing with traditional intelligent methods. By presenting new methodologies and practices, the proceedings will benefit both researchers and practitioners who want to utilize intelligent methods in their specific fields.   Dr. Fuchun Sun is a professor at the Department of Computer Science & Technology, Tsinghua University, China. Dr. Tianrui Li is a professor at the School of Information Science & Technology, Southwest Jiaotong University, Chengdu, China. Dr. Hongbo Li also works at the Department of Computer Science & Technology, Tsinghua University, China

7th International Conference on Intelligent Systems and Knowledge Engineering

These proceedings present technical papers selected from the 2012 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2012), held on December 15-17 in Beijing. The aim of this conference is to bring together experts from different fields of expertise to discuss the state-of-the-art in Intelligent Systems and Knowledge Engineering, and to present new findings and perspectives on future developments. The proceedings introduce current scientific and technical advances in the fields of artificial intelligence, machine learning, pattern recognition, data mining, knowledge engineering, information retrieval, information theory, knowledge-based systems, knowledge representation and reasoning, multi-agent systems, and natural-language processing, etc. Furthermore they include papers on new intelligent computing paradigms, which combine new computing methodologies, e.g., cloud computing, service computing and pervasive computing with traditional intelligent methods. By presenting new methodologies and practices, the proceedings will benefit both researchers and practitioners who want to utilize intelligent methods in their specific fields.   Dr. Fuchun Sun is a professor at the Department of Computer Science & Technology, Tsinghua University, China. Dr. Tianrui Li is a professor at the School of Information Science & Technology, Southwest Jiaotong University, Chengdu, China. Dr. Hongbo Li also works at the Department of Computer Science & Technology, Tsinghua University, China