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Journal articles on the topic 'Cascade prediction'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Suzuki, Daiki, Sho Tsugawa, Keiichiro Tsukamoto, and Shintaro Igari. "On the effectiveness of a contrastive cascade graph learning framework: The power of synthetic cascade data." PLOS ONE 18, no. 10 (2023): e0293032. http://dx.doi.org/10.1371/journal.pone.0293032.

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Analyzing the dynamics of information diffusion cascades and accurately predicting their behavior holds significant importance in various applications. In this paper, we concentrate specifically on a recently introduced contrastive cascade graph learning framework, for the task of predicting cascade popularity. This framework follows a pre-training and fine-tuning paradigm to address cascade prediction tasks. In a previous study, the transferability of pre-trained models within the contrastive cascade graph learning framework was examined solely between two social media datasets. However, in o

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Huang, Ningbo, Gang Zhou, Mengli Zhang, Meng Zhang, and Ze Yu. "Modelling the Latent Semantics of Diffusion Sources in Information Cascade Prediction." Computational Intelligence and Neuroscience 2021 (September 29, 2021): 1–12. http://dx.doi.org/10.1155/2021/7880215.

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Predicting the information spread tendency can help products recommendation and public opinion management. The existing information cascade prediction models are devoted to extract the chronological features from diffusion sequences but treat the diffusion sources as ordinary users. Diffusion source, the first user in the information cascade, can indicate the latent topic and diffusion pattern of an information item to mine user potential common interests, which facilitates information cascade prediction. In this paper, for modelling the abundant implicit semantics of diffusion sources in info

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Lan, Xinyue, Liyue Wang, Cong Wang, Gang Sun, Jinzhang Feng, and Miao Zhang. "Prediction of Transonic Flow over Cascades via Graph Embedding Methods on Large-Scale Point Clouds." Aerospace 10, no. 12 (2023): 1029. http://dx.doi.org/10.3390/aerospace10121029.

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In this research, we introduce a deep-learning-based framework designed for the prediction of transonic flow through a linear cascade utilizing large-scale point-cloud data. In our experimental cases, the predictions demonstrate a nearly four-fold speed improvement compared to traditional CFD calculations while maintaining a commendable level of accuracy. Taking advantage of a multilayer graph structure, the framework can extract both global and local information from the cascade flow field simultaneously and present prediction over unstructured data. In line with the results obtained from the

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Sun, Ling, Yuan Rao, Xiangbo Zhang, Yuqian Lan, and Shuanghe Yu. "MS-HGAT: Memory-Enhanced Sequential Hypergraph Attention Network for Information Diffusion Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (2022): 4156–64. http://dx.doi.org/10.1609/aaai.v36i4.20334.

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Predicting the diffusion cascades is a critical task to understand information spread on social networks. Previous methods usually focus on the order or structure of the infected users in a single cascade, thus ignoring the global dependencies of users and cascades, limiting the performance of prediction. Current strategies to introduce social networks only learn the social homogeneity among users, which is not enough to describe their interaction preferences, let alone the dynamic changes. To address the above issues, we propose a novel information diffusion prediction model named Memory-enha

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Abufouda, Mohammed. "Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction." Complexity 2018 (October 9, 2018): 1–17. http://dx.doi.org/10.1155/2018/3873601.

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Recently, many online social networks, such as MySpace, Orkut, and Friendster, have faced inactivity decay of their members, which contributed to the collapse of these networks. The reasons, mechanics, and prevention mechanisms of such inactivity decay are not fully understood. In this work, we analyze decayed and alive subwebsites from the Stack Exchange platform. The analysis mainly focuses on the inactivity cascades that occur among the members of these communities. We provide measures to understand the decay process and statistical analysis to extract the patterns that accompany the inacti

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Cheng, Zhangtao, Xovee Xu, Ting Zhong, Fan Zhou, and Goce Trajcevski. "CasODE: Modeling Irregular Information Cascade via Neural Ordinary Differential Equations (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 16192–93. http://dx.doi.org/10.1609/aaai.v37i13.26956.

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Predicting information cascade popularity is a fundamental problem for understanding the nature of information propagation on social media. However, existing works fail to capture an essential aspect of information propagation: the temporal irregularity of cascade event -- i.e., users' re-tweetings at random and non-periodic time instants. In this work, we present a novel framework CasODE for information cascade prediction with neural ordinary differential equations (ODEs). CasODE generalizes the discrete state transitions in RNNs to continuous-time dynamics for modeling the irregular-sampled

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Wang, Zhengang, Xin Wang, Fei Xiong, and Hongshu Chen. "A Survey of Deep Learning-Based Information Cascade Prediction." Symmetry 16, no. 11 (2024): 1436. http://dx.doi.org/10.3390/sym16111436.

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Online social media have significantly boosted the creation and transmission of information, accelerating the dissemination and interaction of vast amounts of data, thereby making the prediction of information cascades increasingly important. In recent years, deep learning has been extensively applied in the domain of information cascade prediction. This paper primarily classifies, organizes, and summarizes the current research status and classic algorithms of information cascade prediction methods based on deep learning. According to the different focuses on characterizing information cascade

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Li, Gang, Tao Meng, Min Li, Mingle Zhou, and Delong Han. "A Dynamic Short Cascade Diffusion Prediction Network Based on Meta-Learning-Transformer." Electronics 12, no. 4 (2023): 837. http://dx.doi.org/10.3390/electronics12040837.

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The rise of social networks has greatly contributed to creating information cascades. Over time, new nodes are added to the cascade network, which means the cascade network is dynamically variable. At the same time, there are often only a few nodes in the cascade network before new nodes join. Therefore, it becomes a key task to predict the diffusion after the dynamic cascade based on the small number of nodes observed in the previous period. However, existing methods are limited for dynamic short cascades and cannot combine temporal information with structural information well, so a new model

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9

Zhou, Bangzhu, Xiaodong Feng, and Hemin Feng. "Structural-topic aware deep neural networks for information cascade prediction." PeerJ Computer Science 10 (February 19, 2024): e1870. http://dx.doi.org/10.7717/peerj-cs.1870.

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It is critical to accurately predict the future popularity of information cascades for many related applications, such as online opinion warning or academic influence evaluation. Despite many efforts devoted to developing effective prediction approaches, especially the recent presence of deep learning-based model, the structural information of the cascade network is ignored. Thus, to make use of the structural information in cascade prediction task, we propose a structural-topic aware deep neural networks (STDNN), which firstly learns the structure topic distribution of each node in the cascad

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Han, Jinyoung, Daejin Choi, Jungseock Joo, and Chen-Nee Chuah. "Predicting Popular and Viral Image Cascades in Pinterest." Proceedings of the International AAAI Conference on Web and Social Media 11, no. 1 (2017): 82–91. http://dx.doi.org/10.1609/icwsm.v11i1.14879.

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The word-of-mouth diffusion has been regarded as an important mechanism to advertise a new idea, image, technology, or product in online social networks (OSNs). This paper studies the prediction of popular and viral image diffusion in Pinterest. We first characterize an image cascade from two perspectives: (i) volume — how large the cascade is, that is, total number of users reached, and (ii) structural virality — how many users in the cascade are responsible for attracting other users. Our model predicts whether an image will be (a) popular in terms of the volume of its cascade, or (b) viral

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Littmann, Enno, and Helge Ritter. "Learning and Generalization in Cascade Network Architectures." Neural Computation 8, no. 7 (1996): 1521–39. http://dx.doi.org/10.1162/neco.1996.8.7.1521.

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Incrementally constructed cascade architectures are a promising alternative to networks of predefined size. This paper compares the direct cascade architecture (DCA) proposed in Littmann and Ritter (1992) to the cascade-correlation approach of Fahlman and Lebiere (1990) and to related approaches and discusses the properties on the basis of various benchmark results. One important virtue of DCA is that it allows the cascading of entire subnetworks, even if these admit no error-backpropagation. Exploiting this flexibility and using LLM networks as cascaded elements, we show that the performance

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Citavy´, J. "Performance Prediction of Straight Compressor Cascades Having an Arbitrary Profile Shape." Journal of Turbomachinery 109, no. 1 (1987): 114–22. http://dx.doi.org/10.1115/1.3262056.

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A semi-analytical method of predicting losses and deviation angle for an arbitrary straight compressor cascade has been derived. The method has been developed at SVU´SS and is based upon the solution of the direct cascade problem using the following sources: (i) a potential flow calculation; (ii) attached boundary layer techniques; and (iii) experimental cascade data for separated flow. As an example of an application of the method, simple relations between geometric and aerodynamic cascade parameters have been derived by doing a systematic set of calculations for about 100 cascade configurati

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Tang, Xiangyun, Dongliang Liao, Weijie Huang, Jin Xu, Liehuang Zhu, and Meng Shen. "Fully Exploiting Cascade Graphs for Real-time Forwarding Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (2021): 582–90. http://dx.doi.org/10.1609/aaai.v35i1.16137.

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Real-time forwarding prediction for predicting online contents' popularity is beneficial to various social applications for enhancing interactive social behaviors. Cascade graphs, formed by online contents' propagation, play a vital role in real-time forwarding prediction. Existing cascade graph modeling methods are inadequate to embed cascade graphs that have hub structures and deep cascade paths, or they fail to handle the short-term outbreak of forwarding amount. To this end, we propose a novel real-time forwarding prediction method that includes an effective approach for cascade graph embe

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Chen, Zhiqiang, Ming Zhou, Quanyong Xu, and Xudong Huang. "A Novel Quasi-3D Method for Cascade Flow Considering Axial Velocity Density Ratio." International Journal of Turbo & Jet-Engines 35, no. 1 (2018): 81–94. http://dx.doi.org/10.1515/tjj-2016-0025.

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AbstractA novel quasi-3D Computational Fluid Dynamics (CFD) method of mid-span flow simulation for compressor cascades is proposed. Two dimension (2D) Reynolds-Averaged Navier-Stokes (RANS) method is shown facing challenge in predicting mid-span flow with a unity Axial Velocity Density Ratio (AVDR). Three dimension (3D) RANS solution also shows distinct discrepancies if the AVDR is not predicted correctly. In this paper, 2D and 3D CFD results discrepancies are analyzed and a novel quasi-3D CFD method is proposed. The new quasi-3D model is derived by reducing 3D RANS Finite Volume Method (FVM)

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15

Pacciani, R., M. Marconcini, A. Arnone, and F. Bertini. "An assessment of the laminar kinetic energy concept for the prediction of high-lift, low-Reynolds number cascade flows." Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 225, no. 7 (2011): 995–1003. http://dx.doi.org/10.1177/0957650911412444.

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The laminar kinetic energy (LKE) concept has been applied to the prediction of low-Reynolds number flows, characterized by separation-induced transition, in high-lift airfoil cascades for aeronautical low-pressure turbine applications. The LKE transport equation has been coupled with the low-Reynolds number formulation of the Wilcox's k − ω turbulence model. The proposed methodology has been assessed against two high-lift cascade configurations, characterized by different loading distributions and suction-side diffusion rates, and tested over a wide range of Reynolds numbers. The aft-loaded T1

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Liu, Lin, Chunming Yang, Honghui Xiang, and Jiazhe Lin. "Plane Cascade Aerodynamic Performance Prediction Based on Metric Learning for Multi-Output Gaussian Process Regression." Symmetry 15, no. 9 (2023): 1692. http://dx.doi.org/10.3390/sym15091692.

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Multi-output Gaussian process regression measures the similarity between samples based on Euclidean distance and assigns the same weight to each feature. However, there are significant differences in the aerodynamic performance of plane cascades composed of symmetric and asymmetric blade shapes, and there are also significant differences between the geometry of the plane cascades formed by different blade shapes and the experimental working conditions. There are large differences in geometric and working condition parameters in the features, which makes it difficult to accurately measure the s

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Shen, Shaobo, Aiguo Song, Tao Li, and Huijun Li. "Time delay compensation for nonlinear bilateral teleoperation: A motion prediction approach." Transactions of the Institute of Measurement and Control 41, no. 16 (2019): 4488–98. http://dx.doi.org/10.1177/0142331219860928.

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This paper addresses the time delay compensation problem for nonlinear teleoperation system. A novel motion prediction approach is proposed based on a state observer with a cascade structure. The actual positions of master robot are estimated on the slave side by using delayed information of measurements. The prediction errors remain bounded under an appropriate set of assumptions for the system uncertainties. Moreover, an essential theorem for slave controller design is proposed to demonstrate performance recovery of the closed-loop system with cascade observer. By using the predictions, the

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Montgomery, M. D., J. M. Verdon, and S. Fleeter. "A Linearized Unsteady Aerodynamic Analysis for Real Blade Supersonic Cascades." Journal of Turbomachinery 119, no. 4 (1997): 686–94. http://dx.doi.org/10.1115/1.2841178.

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The prediction capabilities of a linearized unsteady potential analysis have been extended to include supersonic cascades with subsonic axial flow. The numerical analysis of this type of flow presents several difficulties. First, complex oblique shock patterns exist within the cascade passage. Second, the acoustic response is discontinuous and propagates upstream and downstream of the blade row. Finally, a numerical scheme based on the domain of dependence is required for numerical stability. These difficulties are addressed by developing a discontinuity capturing scheme and matching the numer

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Liu, Bao, Yaohua Sun, and Lei Gao. "An improved container-based deep forest model for predicting groundwater recharge." Journal of Physics: Conference Series 2816, no. 1 (2024): 012033. http://dx.doi.org/10.1088/1742-6596/2816/1/012033.

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Abstract This paper proposes ICDF, an improved container-based deep forest model, for effectively modeling and predicting groundwater recharge. The model consists of four points: the construction and expansion of the container module, the assignment of weights to the base model, the growth of the cascade layer, and the decision output. First, container modules are created and a maximization objective function is assigned to each container to control its growth. Next, different weights are assigned to each base model based on its contribution to container prediction. Cascade layers are built us

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Liu, Chaochao, Wenjun Wang, and Yueheng Sun. "Community structure enhanced cascade prediction." Neurocomputing 359 (September 2019): 276–84. http://dx.doi.org/10.1016/j.neucom.2019.05.069.

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Shang, Yingdan, Bin Zhou, Ye Wang, et al. "Popularity Prediction of Online Contents via Cascade Graph and Temporal Information." Axioms 10, no. 3 (2021): 159. http://dx.doi.org/10.3390/axioms10030159.

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Predicting the popularity of online content is an important task for content recommendation, social influence prediction and so on. Recent deep learning models generally utilize graph neural networks to model the complex relationship between information cascade graph and future popularity, and have shown better prediction results compared with traditional methods. However, existing models adopt simple graph pooling strategies, e.g., summation or average, which prone to generate inefficient cascade graph representation and lead to unsatisfactory prediction results. Meanwhile, they often overloo

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Kharat, J. P. "Comparative Study of Various Neural Network Architectures for MPEG-4 Video Traffic Prediction." International Journal of Advances in Applied Sciences 6, no. 4 (2017): 283. http://dx.doi.org/10.11591/ijaas.v6.i4.pp283-292.

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<p>Network traffic as it is VBR in nature exhibits strong correlations which make it suitable for prediction. Real-time forecasting of network traffic load accurately and in a computationally efficient manner is the key element of proactive network management and congestion control. This paper comments on the MPEG-4 video traffic predictions evaluated by different types of neural network architectures and compares the performance of the same in terms of mean square error for the same video frames. For that three types of neural architectures are used namely Feed forward, Cascaded Feed fo

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Sarkar, S. "Performance Prediction of a Mixed Flow Impeller." Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 206, no. 3 (1992): 189–96. http://dx.doi.org/10.1243/pime_proc_1992_206_029_02.

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A method based on two-dimensional cascade theory is presented here to predict the characteristics of mixed flow impellers of high specific speed having a conical flow path. The theoretical characteristics are compared with the experimental results. Agreement is fairly good in the normal operating range, but some uncertainties exist in the assessment of appropriate slip factors and losses in the field of mixed flow rotor cascades, which need further studies. In the present case, the flow is assumed to be incompressible.

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Wu, Liyin, Jingyang Zhou, Haining Jiang, Xi Yang, Yongzheng Zhan, and Yinhang Zhang. "Predicting the Characteristics of High-Speed Serial Links Based on a Deep Neural Network (DNN)—Transformer Cascaded Model." Electronics 13, no. 15 (2024): 3064. http://dx.doi.org/10.3390/electronics13153064.

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The design level of channel physical characteristics has a crucial influence on the transmission quality of high-speed serial links. However, channel design requires a complex simulation and verification process. In this paper, a cascade neural network model constructed of a Deep Neural Network (DNN) and a Transformer is proposed. This model takes physical features as inputs and imports a Single-Bit Response (SBR) as a connection, which is enhanced through predicting frequency characteristics and equalizer parameters. At the same time, signal integrity (SI) analysis and link optimization are a

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Chen, Ninghan, Xihui Chen, Zhiqiang Zhong, and Jun Pang. "Exploring Spillover Effects for COVID-19 Cascade Prediction." Entropy 24, no. 2 (2022): 222. http://dx.doi.org/10.3390/e24020222.

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An information outbreak occurs on social media along with the COVID-19 pandemic and leads to an infodemic. Predicting the popularity of online content, known as cascade prediction, allows for not only catching in advance information that deserves attention, but also identifying false information that will widely spread and require quick response to mitigate its negative impact. Among the various information diffusion patterns leveraged in previous works, the spillover effect of the information exposed to users on their decisions to participate in diffusing certain information has not been stud

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Han, Delong, Tao Meng, and Min Li. "Dynamic End-to-End Information Cascade Prediction Based on Neural Networks and Snapshot Capture." Electronics 12, no. 13 (2023): 2875. http://dx.doi.org/10.3390/electronics12132875.

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Knowing how to effectively predict the scale of future information cascades based on the historical trajectory of information dissemination has become an important topic. It is significant for public opinion guidance; advertising; and hotspot recommendation. Deep learning technology has become a research hotspot in popularity prediction, but for complex social platform data, existing methods are challenging to utilize cascade information effectively. This paper proposes a novel end-to-end deep learning network CAC-G with cascade attention convolution (CAC). This model can stress the global inf

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Benner, M. W., S. A. Sjolander, and S. H. Moustapha. "An Empirical Prediction Method For Secondary Losses In Turbines—Part II: A New Secondary Loss Correlation." Journal of Turbomachinery 128, no. 2 (2005): 281–91. http://dx.doi.org/10.1115/1.2162594.

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A new empirical prediction method for design and off-design secondary losses in turbines has been developed. The empirical prediction method is based on a new loss breakdown scheme, and as discussed in Part I, the secondary loss definition in this new scheme differs from that in the conventional one. Therefore, a new secondary loss correlation for design and off-design incidence values has been developed. It is based on a database of linear cascade measurements from the present authors’ experiments as well as cases available in the open literature. The new correlation is based on correlating p

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Aravamudan, Akshay, Xi Zhang, and Georgios C. Anagnostopoulos. "Anytime User Engagement Prediction in Information Cascades for Arbitrary Observation Periods." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4999–5009. http://dx.doi.org/10.1609/aaai.v37i4.25627.

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Predicting user engagement -- whether a user will engage in a given information cascade -- is an important problem in the context of social media, as it is useful to online marketing and misinformation mitigation just to name a couple major applications. Based on split population multi-variate survival processes, we develop a discriminative approach that, unlike prior works, leads to a single model for predicting whether individual users of an information network will engage a given cascade for arbitrary forecast horizons and observation periods. Being probabilistic in nature, this model retai

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Liu, Baojie, Hengtao Shi, and Xianjun Yu. "A new method for rapid shock loss evaluation and reduction for the optimization design of a supersonic compressor cascade." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 13 (2017): 2458–76. http://dx.doi.org/10.1177/0954410017715277.

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A one-dimensional analytical shock loss prediction method was proposed to tailor the shock system, i.e. the strength of the first and second passage shock, and reduce the shock loss in a supersonic cascade. To develop the one-dimensional analytical model, the shock system in a supersonic cascade was divided into four processes which can be seen in most supersonic compressor cascade, i.e. the flow upstream the extending-external shock, the flow between the extending-external shock and the first passage shock, the accelerating flow from the first passage shock to the second passage shock, and th

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Malik, Hasmat, Yadav Amit Kumar, Márquez F. P. García, and PÉREZ JESÚS MARÍA PINAR. "Novel application of Relief Algorithm in cascaded artificial neural network to predict wind speed for wind power resource assessment in India." Energy Strategy Reviews 41 (May 23, 2022): 100864. https://doi.org/10.1016/j.esr.2022.100864.

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The paper is published in Energy Strategy Reviews journal <em>(ELSEVIER)</em>.  Cite as: <em>Malik, H., Yadav, A. K., García Márquez, F. P., & Pinar-Pérez, J. M. (2022). Novel application of Relief Algorithm in cascaded artificial neural network to predict wind speed for wind power resource assessment in India. Energy Strategy Reviews, 41, 100864. (DOI:https://doi.org/10.1016/j.esr.2022.100864)</em>   <strong><em>Abstract:</em></strong> Wind power generated by wind has non-schedule nature due to stochastic nature of meteorological variable. Hence energy

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Cao, Ren-Meng, Xiao Fan Liu, and Xiao-Ke Xu. "Why cannot long-term cascade be predicted? Exploring temporal dynamics in information diffusion processes." Royal Society Open Science 8, no. 9 (2021): 202245. http://dx.doi.org/10.1098/rsos.202245.

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Predicting information cascade plays a crucial role in various applications such as advertising campaigns, emergency management and infodemic controlling. However, predicting the scale of an information cascade in the long-term could be difficult. In this study, we take Weibo, a Twitter-like online social platform, as an example, exhaustively extract predictive features from the data, and use a conventional machine learning algorithm to predict the information cascade scales. Specifically, we compare the predictive power (and the loss of it) of different categories of features in short-term an

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Davis, R. L., D. E. Hobbs, and H. D. Weingold. "Prediction of Compressor Cascade Performance Using a Navier–Stokes Technique." Journal of Turbomachinery 110, no. 4 (1988): 520–31. http://dx.doi.org/10.1115/1.3262226.

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An explicit, time marching, multiple-grid Navier–Stokes technique is demonstrated for the prediction of quasi-three-dimensional turbomachinery compressor cascade performance over the entire incidence range. A numerical investigation has been performed in which the present Navier–Stokes procedure was used to analyze a series of compressor cascade viscous flows for which corresponding experimental data are available. Results from these calculations show that the current viscous flow procedure is capable of predicting cascade profile loss and airfoil pressure distributions with high accuracy. The

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Moss, R. W., R. W. Ainsworth, and T. Garside. "Effects of Rotation on Blade Surface Heat Transfer: An Experimental Investigation." Journal of Turbomachinery 120, no. 3 (1998): 530–40. http://dx.doi.org/10.1115/1.2841750.

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Measurements of turbine blade surface heat transfer in a transient rotor facility are compared with predictions and equivalent cascade data. The rotating measurements involved both forward and reverse rotation (wake-free) experiments. The use of thin-film gages in the Oxford Rotor Facility provides both time-mean heat transfer levels and the unsteady time history. The time-mean level is not significantly affected by turbulence in the wake; this contrasts with the cascade response to free-stream turbulence and simulated wake passing. Heat transfer predictions show the extent to which such pheno

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Salleh, Faridah Hani Mohamed, Suhaila Zainudin, and Shereena M. Arif. "Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems." Advances in Bioinformatics 2017 (January 29, 2017): 1–14. http://dx.doi.org/10.1155/2017/4827171.

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Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the e

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Zeng, Zhiliang, Shouwei Zhao, Yu Peng, Xiang Hu, and Zhixiang Yin. "Cascade Forest-Based Model for Prediction of RNA Velocity." Molecules 27, no. 22 (2022): 7873. http://dx.doi.org/10.3390/molecules27227873.

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In recent years, single-cell RNA sequencing technology (scRNA-seq) has developed rapidly and has been widely used in biological and medical research, such as in expression heterogeneity and transcriptome dynamics of single cells. The investigation of RNA velocity is a new topic in the study of cellular dynamics using single-cell RNA sequencing data. It can recover directional dynamic information from single-cell transcriptomics by linking measurements to the underlying dynamics of gene expression. Predicting the RNA velocity vector of each cell based on its gene expression data and formulating

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Zhao, Yuhui, Ning Yang, Tao Lin, and Philip S. Yu. "Deep Collaborative Embedding for information cascade prediction." Knowledge-Based Systems 193 (April 2020): 105502. http://dx.doi.org/10.1016/j.knosys.2020.105502.

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Li, Hui, Sourav S. Bhowmick, Aixin Sun, and Jiangtao Cui. "Affinity-driven blog cascade analysis and prediction." Data Mining and Knowledge Discovery 28, no. 2 (2013): 442–74. http://dx.doi.org/10.1007/s10618-013-0307-0.

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Gou, Chengcheng, Huawei Shen, Pan Du, Dayong Wu, Yue Liu, and Xueqi Cheng. "Learning sequential features for cascade outbreak prediction." Knowledge and Information Systems 57, no. 3 (2018): 721–39. http://dx.doi.org/10.1007/s10115-017-1143-0.

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Hu, Zewen, Tao Wang, Hongcai Chen, Kanjian Zhang, and Haikun Wei. "Improved prediction of surface roughness in grinding process: a cascade of theoretical model and regularized extreme learning machine." Journal of Instrumentation 20, no. 06 (2025): P06008. https://doi.org/10.1088/1748-0221/20/06/p06008.

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Abstract Surface roughness is a key indicator of product quality, and developing a precise prediction model contributes to optimizing processes and enhancing production efficiency. Current models for predicting surface roughness primarily include theoretical and data-driven models. However, due to the complex nature of the grinding process, theoretical models that rely on simplified assumptions often fail to estimate surface roughness accurately. Additionally, data-driven models lack physical interpretation and exhibit a high dependence on data, which limits their practical application. To add

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Gehrer, A., and H. Jericha. "External Heat Transfer Predictions in a Highly Loaded Transonic Linear Turbine Guide Vane Cascade Using an Upwind Biased Navier–Stokes Solver." Journal of Turbomachinery 121, no. 3 (1999): 525–31. http://dx.doi.org/10.1115/1.2841347.

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External heat transfer predictions are performed for two-dimensional turbine blade cascades. The Reynolds-averaged Navier–Stokes equations with algebraic (Arnone and Pacciani, 1998), one-equation (Spalart and Allmaras, 1994), and two-equation (low-Re k–ε, Biswas and Fukuyama, 1994) turbulence closures are solved with a fully implicit time-marching finite volume method. Comparisons with measurements (Arts et al., 1990; Arts, 1994) for a highly loaded transonic turbine nozzle guide vane cascade show good agreement in some cases, but also reveal problems with transition prediction and turbulence

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Furuya, Okitsugu, and Shin Maekawa. "An Experimental and Theoretical Study on Cavitating Cascades and Their Application to Cavitating Propellers." Journal of Ship Research 29, no. 01 (1985): 23–38. http://dx.doi.org/10.5957/jsr.1985.29.1.23.

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In order to develop an analytical tool for predicting the off-design performance of supercavitating propellers over a wide range of operating conditions, a lifting-line theory was combined with a two-dimensional supercavitating cascade theory. The results of this simple method provided fairly accurate predictions for the performance at fully developed cavitating conditions. It was indicative that the fully developed supercavitating (s/c) propellers had strong cascade effects on their performance, and also that the three-dimensional propeller geometry corrections could properly be made by the l

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Burgess, B. H., R. K. Scott, and T. G. Shepherd. "Kraichnan–Leith–Batchelor similarity theory and two-dimensional inverse cascades." Journal of Fluid Mechanics 767 (February 18, 2015): 467–96. http://dx.doi.org/10.1017/jfm.2015.26.

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AbstractWe study the scaling properties and Kraichnan–Leith–Batchelor (KLB) theory of forced inverse cascades in generalized two-dimensional (2D) fluids (${\it\alpha}$-turbulence models) simulated at resolution $8192^{2}$. We consider ${\it\alpha}=1$ (surface quasigeostrophic flow), ${\it\alpha}=2$ (2D Euler flow) and ${\it\alpha}=3$. The forcing scale is well resolved, a direct cascade is present and there is no large-scale dissipation. Coherent vortices spanning a range of sizes, most larger than the forcing scale, are present for both ${\it\alpha}=1$ and ${\it\alpha}=2$. The active scalar f

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Jing, Xin, Yichen Jing, Yuhuan Lu, Bangchao Deng, Xueqin Chen, and Dingqi Yang. "CasFT: Future Trend Modeling for Information Popularity Prediction with Dynamic Cues-Driven Diffusion Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11906–14. https://doi.org/10.1609/aaai.v39i11.33296.

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The rapid spread of diverse information on online social platforms has prompted both academia and industry to realize the importance of predicting content popularity, which could benefit a wide range of applications, such as recommendation systems and strategic decision-making. Recent works mainly focused on extracting spatiotemporal patterns inherent in the information diffusion process within a given observation period so as to predict its popularity over a future period of time. However, these works often overlook the future popularity trend, as future popularity could either increase expon

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Nourani, Vahid, Gholamreza Andalib, Fahreddin Sadikoglu, and Elnaz Sharghi. "Cascade-based multi-scale AI approach for modeling rainfall-runoff process." Hydrology Research 49, no. 4 (2017): 1191–207. http://dx.doi.org/10.2166/nh.2017.045.

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Abstract In this paper, runoff time series of the sub-basins in a cascade form were decomposed by Wavelet Transform (WT) to extract their dynamical and multi-scale features for modeling Multi-Station (MS) rainfall-runoff (R-R) process of the Little River Watershed (LRW) in USA. A Self-Organizing Map (SOM) clustering technique was also employed to find homogeneous extracted sub-series' clusters. As a complementary feature, extraction criterion of mutual information (MI) was utilized for proper cluster agent choice to impose to the artificial intelligence (AI) models (Feed Forward Neural Network

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Borello, Domenico, Kemal Hanjalic, and Franco Rispoli. "Prediction of Cascade Flows With Innovative Second-Moment Closures." Journal of Fluids Engineering 127, no. 6 (2005): 1059–70. http://dx.doi.org/10.1115/1.2073267.

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We report on the performances of two second-moment turbulence closures in predicting turbulence and laminar-to-turbulent transition in turbomachinery flows. The first model considered is the one by Hanjalic and Jakirlic (HJ) [Comput. Fluids, 27(2), pp. 137–156 (1998)], which follows the conventional approach with damping functions to account for the wall viscous and nonviscous effect. The second is an innovative topology-free elliptic blending model, EBM [R. Manceau and K. Hanjalic, Phys. Fluids, 14(3), pp. 1–11 (2002)], here presented in a revised formulation. An in-house finite element code

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Kobayashi, Ryota, and Renaud Lambiotte. "TiDeH: Time-Dependent Hawkes Process for Predicting Retweet Dynamics." Proceedings of the International AAAI Conference on Web and Social Media 10, no. 1 (2021): 191–200. http://dx.doi.org/10.1609/icwsm.v10i1.14717.

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Online social networking services allow their users to post content in the form of text, images or videos. The main mechanism driving content diffusion is the possibility for users to re-share the content posted by their social connections, which may then cascade across the system. A fundamental problem when studying information cascades is the possibility to develop sound mathematical models, whose parameters can be calibrated on empirical data, in order to predict the future course of a cascade after a window of observation. In this paper, we focus on Twitter and, in particular, on the tempo

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Bloch, G. S., W. W. Copenhaver, and W. F. O’Brien. "A Shock Loss Model for Supersonic Compressor Cascades." Journal of Turbomachinery 121, no. 1 (1999): 28–35. http://dx.doi.org/10.1115/1.2841231.

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Loss models used in compression system performance prediction codes are often developed from the study of two-dimensional cascades. In this paper, compressible fluid mechanics has been applied to the changes in shock geometry that are known to occur with back pressure for unstarted operation of supersonic compressor cascades. This physics-based engineering shock loss model is applicable to cascades with arbitrary airfoil shapes. Predictions from the present method have been compared to measurements and Navier–Stokes analyses of the LO30-4 and L030-6 cascades, and very good agreement was demons

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Prandoni, P., and M. Vetterli. "An FIR cascade structure for adaptive linear prediction." IEEE Transactions on Signal Processing 46, no. 9 (1998): 2566–71. http://dx.doi.org/10.1109/78.709548.

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Wang, Shijie, Lihua Zhou, and Bing Kong. "Information cascade prediction based on T-DeepHawkes model." IOP Conference Series: Materials Science and Engineering 715 (January 3, 2020): 012042. http://dx.doi.org/10.1088/1757-899x/715/1/012042.

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Korenberg, M., D. McGaughey, and G. J. M. Aitken. "Parallel cascade prediction of turbulence induced wavefront tilt." Electronics Letters 32, no. 14 (1996): 1315. http://dx.doi.org/10.1049/el:19960855.

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