Relevant bibliographies by topics / Diffusion prediction

Academic literature on the topic 'Diffusion prediction'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Diffusion prediction"

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Rasero, Javier, Amy Isabella Sentis, Fang-Cheng Yeh, and Timothy Verstynen. "Integrating across neuroimaging modalities boosts prediction accuracy of cognitive ability." PLOS Computational Biology 17, no. 3 (March 5, 2021): e1008347. http://dx.doi.org/10.1371/journal.pcbi.1008347.

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Variation in cognitive ability arises from subtle differences in underlying neural architecture. Understanding and predicting individual variability in cognition from the differences in brain networks requires harnessing the unique variance captured by different neuroimaging modalities. Here we adopted a multi-level machine learning approach that combines diffusion, functional, and structural MRI data from the Human Connectome Project (N = 1050) to provide unitary prediction models of various cognitive abilities: global cognitive function, fluid intelligence, crystallized intelligence, impulsivity, spatial orientation, verbal episodic memory and sustained attention. Out-of-sample predictions of each cognitive score were first generated using a sparsity-constrained principal component regression on individual neuroimaging modalities. These individual predictions were then aggregated and submitted to a LASSO estimator that removed redundant variability across channels. This stacked prediction led to a significant improvement in accuracy, relative to the best single modality predictions (approximately 1% to more than 3% boost in variance explained), across a majority of the cognitive abilities tested. Further analysis found that diffusion and brain surface properties contribute the most to the predictive power. Our findings establish a lower bound to predict individual differences in cognition using multiple neuroimaging measures of brain architecture, both structural and functional, quantify the relative predictive power of the different imaging modalities, and reveal how each modality provides unique and complementary information about individual differences in cognitive function.
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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 (September 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 and long-term prediction tasks. Among the features that describe the user following network, retweeting network, tweet content and early diffusion dynamics, we find that early diffusion dynamics are the most predictive ones in short-term prediction tasks but lose most of their predictive power in long-term tasks. In-depth analyses reveal two possible causes of such failure: the bursty nature of information diffusion and feature temporal drift over time. Our findings further enhance the comprehension of the information diffusion process and may assist in the control of such a process.
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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 information cascade prediction, we propose a Diffusion Source latent Semantics-Fused cascade prediction framework, named DSSF. Specifically, we firstly apply diffusion sources embedding to model the special role of the source users. To learn the latent interaction between users and diffusion sources, we proposed a co-attention-based fusion gate which fuses the diffusion sources’ latent semantics with user embedding. To address the challenge that the distribution of diffusion sources is long-tailed, we develop an adversarial training framework to transfer the semantics knowledge from head to tail sources. Finally, we conduct experiments on real-world datasets, and the results show that modelling the diffusion sources can significantly improve the prediction performance. Besides, this improvement is limited for the cascades from tail sources, and the adversarial framework can help.
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Pinholt, Henrik D., Søren S. R. Bohr, Josephine F. Iversen, Wouter Boomsma, and Nikos S. Hatzakis. "Single-particle diffusional fingerprinting: A machine-learning framework for quantitative analysis of heterogeneous diffusion." Proceedings of the National Academy of Sciences 118, no. 31 (July 28, 2021): e2104624118. http://dx.doi.org/10.1073/pnas.2104624118.

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Single-particle tracking (SPT) is a key tool for quantitative analysis of dynamic biological processes and has provided unprecedented insights into a wide range of systems such as receptor localization, enzyme propulsion, bacteria motility, and drug nanocarrier delivery. The inherently complex diffusion in such biological systems can vary drastically both in time and across systems, consequently imposing considerable analytical challenges, and currently requires an a priori knowledge of the system. Here we introduce a method for SPT data analysis, processing, and classification, which we term “diffusional fingerprinting.” This method allows for dissecting the features that underlie diffusional behavior and establishing molecular identity, regardless of the underlying diffusion type. The method operates by isolating 17 descriptive features for each observed motion trajectory and generating a diffusional map of all features for each type of particle. Precise classification of the diffusing particle identity is then obtained by training a simple logistic regression model. A linear discriminant analysis generates a feature ranking that outputs the main differences among diffusional features, providing key mechanistic insights. Fingerprinting operates by both training on and predicting experimental data, without the need for pretraining on simulated data. We found this approach to work across a wide range of simulated and experimentally diverse systems, such as tracked lipases on fat substrates, transcription factors diffusing in cells, and nanoparticles diffusing in mucus. This flexibility ultimately supports diffusional fingerprinting’s utility as a universal paradigm for SPT diffusional analysis and prediction.
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Kim, Do Gyeum. "An Experimental Research for Developing Prediction Program for Time to Corrosion Reinforcing Steel." Advanced Materials Research 1052 (October 2014): 335–45. http://dx.doi.org/10.4028/www.scientific.net/amr.1052.335.

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This research has attempted to predict the level of corrosion of reinforcing bar depending on diffusion speed of chloride in concrete to develop prediction program for the time in which corrosion of reinforcing bar in concrete structure at coast occurs. Based on the results, diffusion algorithm of chloride has been formulated and corrosion prediction system has been developed by utilizing the prediction model for diffusion of chloride. The results from experiment and field investigation on coastal structure indicate that the developed program can predict diffusion speed of chloride relatively accurately, The majority of estimated values are coincide with experimental value apart from those of the surface regarding prediction on content of chloride according to different depth. Therefore, the newly developed program has been found to be useful for interpreting and predicting diffusion of chloride.
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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 (January 31, 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 studied. In this paper, we focus on the diffusion of information related to COVID-19 preventive measures due to its special role in consolidating public efforts to slow down the spread of the virus. Through our collected Twitter dataset, we validate the existence of the spillover effects. Building on this finding, we propose extensions to three cascade prediction methods based on Graph Neural Networks (GNNs). Experiments conducted on our dataset demonstrated that the use of the identified spillover effects significantly improves the state-of-the-art GNN methods in predicting the popularity of not only preventive measure messages, but also other COVID-19 messages.
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Qian, Fei, Li Chen, Jun Li, Chao Ding, Xianfu Chen, and Jian Wang. "Direct Prediction of the Toxic Gas Diffusion Rule in a Real Environment Based on LSTM." International Journal of Environmental Research and Public Health 16, no. 12 (June 17, 2019): 2133. http://dx.doi.org/10.3390/ijerph16122133.

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Predicting the diffusion rule of toxic gas plays a distinctly important role in emergency capability assessment and rescue work. Among diffusion prediction models, the traditional artificial neural network has exhibited excellent performance not only in prediction accuracy but also in calculation time. Nevertheless, with the continuous development of deep learning and data science, some new prediction models based on deep learning algorithms have been shown to be more advantageous because their structure can better discover internal laws and external connections between input data and output data. The long short-term memory (LSTM) network is a kind of deep learning neural network that has demonstrated outstanding achievements in many prediction fields. This paper applies the LSTM network directly to the prediction of toxic gas diffusion and uses the Project Prairie Grass dataset to conduct experiments. Compared with the Gaussian diffusion model, support vector machine (SVM) model, and back propagation (BP) network model, the LSTM model of deep learning has higher prediction accuracy (especially for the prediction at the point of high concentration values) while avoiding the occurrence of negative concentration values and overfitting problems found in traditional artificial neural network models.
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Halnes, Geir, Tuomo Mäki-Marttunen, Klas H. Pettersen, Ole A. Andreassen, and Gaute T. Einevoll. "Ion diffusion may introduce spurious current sources in current-source density (CSD) analysis." Journal of Neurophysiology 118, no. 1 (July 1, 2017): 114–20. http://dx.doi.org/10.1152/jn.00976.2016.

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Current-source density (CSD) analysis is a well-established method for analyzing recorded local field potentials (LFPs), that is, the low-frequency part of extracellular potentials. Standard CSD theory is based on the assumption that all extracellular currents are purely ohmic, and thus neglects the possible impact from ionic diffusion on recorded potentials. However, it has previously been shown that in physiological conditions with large ion-concentration gradients, diffusive currents can evoke slow shifts in extracellular potentials. Using computer simulations, we here show that diffusion-evoked potential shifts can introduce errors in standard CSD analysis, and can lead to prediction of spurious current sources. Further, we here show that the diffusion-evoked prediction errors can be removed by using an improved CSD estimator which accounts for concentration-dependent effects. NEW & NOTEWORTHY Standard CSD analysis does not account for ionic diffusion. Using biophysically realistic computer simulations, we show that unaccounted-for diffusive currents can lead to the prediction of spurious current sources. This finding may be of strong interest for in vivo electrophysiologists doing extracellular recordings in general, and CSD analysis in particular.
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Natsir, Bulkis, Faisal Yunus, and Triya Damayanti. "The Correlations Between Measurement of Lung Diffusing Capacity for Carbon Monoxide and The Severity Group of Asthma Patients in Persahabatan Hospital Jakarta." Jurnal Respirologi Indonesia 42, no. 1 (January 8, 2022): 58–66. http://dx.doi.org/10.36497/jri.v42i1.296.

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Introduction: Airway remodeling in asthma which involve small airway can affect until alveoli and cause abnormalities in the lung parenchyma. This study tries to find lung parenchymal abnormalities in patients with asthma through the examination diffusion capacity with a single breath DLCO method.Methods: A cross-sectional study by dividing asthma based on the degree of severity into two major groups, namely mild asthma (intermittent and mild persistent) and severe (persistent moderate and severe). The amount of each group is 31 subjects and 29 subjects, which are taken consecutively from stable asthma patients without comorbid who are seeking treatment in Persahabatan Hospital in December 2015 - May 2016.Results: The average value of DLCO /predictions in mild asthma group is 92,74 ± 15,70% and decreased in the severe asthma group is 77,45 ± 16,78%. Some spirometry value showed significant positive correlation with the value of DLCO/prediction, namely: FVC/prediction, FEV1 /prediction and FEF25-75 % / prediction with p < 0.05. Correlation analysis showed FVC/prediction could dramatically affect the diffusion capacity of asthmatic patients. There is a significant relationship between abnormalities in lung function (p=0,004) and severity of asthma (p=0.000) with a corresponding decrease DLCO / prediction (DLCO/ prediction ≤75 %).Conclusion: The severity of asthma has a relationship with the diffusion capacity of the lungs, increased severity will decrease the diffusion capacity in asthma patients. Decreasing diffusion capacity showed that abnormalities in asthma not only occur in the respiratory tract but also in the lung parenchyma.
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van der Aa, Niek E., Alexander Leemans, Frances J. Northington, Henrica L. van Straaten, Ingrid C. van Haastert, Floris Groenendaal, Manon J. N. L. Benders, and Linda S. de Vries. "Does Diffusion Tensor Imaging-Based Tractography at 3 Months of Age Contribute to the Prediction of Motor Outcome After Perinatal Arterial Ischemic Stroke?" Stroke 42, no. 12 (December 2011): 3410–14. http://dx.doi.org/10.1161/strokeaha.111.624858.

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Background and Purpose— After perinatal arterial ischemic stroke, diffusion-weighted imaging (DWI) and early evaluation of spontaneous motor behavior can be used to predict the development of unilateral motor deficits. The aim of this study was to investigate whether diffusion tensor imaging-based tractography at 3 months of age contributes to this prediction. Methods— Twenty-two infants with unilateral perinatal arterial ischemic stroke were included and scanned during the neonatal period. DWI was used to assess restricted diffusion in the cerebral peduncle. At the age of 3 months, diffusion tensor imaging-based tractography of the corticospinal tracts was performed along with assessment of the movement repertoire. The role of DWI, diffusion tensor imaging, and motor assessment in predicting unilateral motor deficits were compared by calculating the positive and negative predictive values for each assessment. Results— Eleven infants (50%) showed abnormal motor behavior at 3 months with subsequent development of unilateral motor deficits in 8 as determined at follow-up (9–48 months, positive predictive value 73%). Diffusion tensor imaging-based tractography correctly predicted the development of unilateral motor deficits in all 8 infants (positive predictive value 100%). A diagnostic neonatal DWI was available in 20 of 22 (91%) infants. Seven infants showed an abnormal DWI, resulting in unilateral motor deficits in 6 infants (positive predictive value 86%). All assessments had a negative predictive value of 100%. Conclusions— Diffusion tensor imaging-based tractography at 3 months can be used to predict neurodevelopmental outcome after perinatal arterial ischemic stroke. It has a similar predictive value as DWI in the neonatal period and can especially be of additional value in case of an indecisive neonatal DWI or unexpected abnormal early motor development.
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Dissertations / Theses on the topic "Diffusion prediction"

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D’Agostino, Carmine, Geoff D. Moggridge, Lynn F. Gladden, and Mick D. Mantl. "Prediction of mutual diffusion coefficients in non-ideal binary mixtures from PFG-NMR diffusion measurements." Diffusion fundamentals 20 (2013) 109, S. 1-2, 2013. https://ul.qucosa.de/id/qucosa%3A13698.

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D’Agostino, Carmine, Geoff D. Moggridge, Lynn F. Gladden, and Mick D. Mantl. "Prediction of mutual diffusion coefficients in non-ideal binary mixtures from PFG-NMR diffusion measurements." Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-184023.

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Nowicki, Timothy. "Statistical model prediction of fatigue life for diffusion bonded Inconel 600 /." Online version of thesis, 2008. http://hdl.handle.net/1850/7984.

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Pirot, Fabrice. "Analyse, mesure et prediction de la diffusion dans le stratum corneum humain." Besançon, 1996. http://www.theses.fr/1996BESA3710.

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Zhu, Qingyong, Geoffrey D. Moggridge, and Carmine D’Agostino. "A local composition model for the prediction of mutual diffusion coefficients in binary liquid mixtures from tracer diffusion coefficients." Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-198798.

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Zhu, Qingyong, Geoffrey D. Moggridge, and Carmine D’Agostino. "A local composition model for the prediction of mutual diffusion coefficients in binary liquid mixtures from tracer diffusion coefficients: A local composition model for the prediction of mutual diffusioncoefficients in binary liquid mixtures from tracer diffusion coefficients." Diffusion fundamentals 24 (2015) 58, S. 1, 2015. https://ul.qucosa.de/id/qucosa%3A14577.

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Long, Xiaoyan. "Prediction of shear strength and vertical movement due to moisture diffusion through expansive soils." Texas A&M University, 2006. http://hdl.handle.net/1969.1/4313.

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This dissertation presents an investigation of engineering behavior of expansive soils. An analytical study was undertaken for the development and modification of a Windows-based two-dimensional finite element computer program FLODEF that performs a sequentially coupled flow-displacement analysis for the prediction of moisture diffusion and the induced volume change in soils supporting various elements of civil infrastructure. The capabilities of the model are illustrated through case studies of shear strength envelope forecast and parametric studies of transient flow-deformation prediction in highway project sites to evaluate the effectiveness of engineering treatment methods to control swell-shrink deformations beneath highway pavements. Numerical simulations have been performed to study the field moisture diffusivity using a conceptual model of moisture diffusion in a fractured soil mass. A rough correlation between field and the laboratory measurements of moisture diffusion coefficients has been presented for different crack depth patterns.
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Reynier, Alain. "Modelisation et prediction de la migration des additifs des emballages alimentaires." Reims, 2000. http://www.theses.fr/2000REIMS004.

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La prediction de la migration par modelisation mathematique peut etre envisagee pour verifier la conformite d'un materiau plastique utilise en contact alimentaire et pour preciser l'incidence des phenomenes physico-chimiques impliques dans la migration. Dans une premiere partie, la prediction du coefficient de diffusion d'une molecule dans une matrice polymere a ete etudiee a partir de mesures experimentales de coefficients de diffusion. Un panel de composes a ete teste dans quatre polyolefines (vierges puis gonflees par un simulateur d'aliment gras). Ceci a permis de mettre en evidence l'effet du gonflement sur la diffusion. La correlation des diffusivites a la masse molaire des migrants a permis de determiner des groupes de comportement (diffusion rapide par reptation ou plus lente par sauts) selon la forme des molecules. Le concept de volume fractionne pondere par un facteur de forme a ete introduit afin de tenir compte de ces differences liees a la geometrie des migrants. Nous avons ensuite propose une modelisation de la migration qui, pour la premiere fois, prend en compte simultanement les principaux phenomenes impliques dans la migration (evolution de la diffusivite liee au gonflement du materiau par le simulateur sorbe, limitations cinetiques de transfert de matieres a l'interface). Ce modele a ete valide par comparaison a un essai de migration et a permis de montrer les limites d'utilisation des modeles simplifies. Il a de plus ete adapte au cas de l'extraction par solvant en introduisant la notion de solubilite potentielle du solvant dans le polymere, variant au cours du temps en fonction de la concentration en solvant sorbe. Enfin, ce travail propose une evolution de l'approche predictive en discussion dans le cadre de la reglementation europeenne en s'appuyant sur la prediction statistique de coefficients de diffusions surestimes et sur l'utilisation d'un modele adapte a la complexite du processus de migration.
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Chang, Hojoon. "Prediction of Soot Formation in Laminar Opposed Diffusion Flame with Detailed and Reduced Reaction Mechanisms." Thesis, Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/4922.

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The present work focuses on a computational study of a simplified soot model to predict soot production and destruction in methane/oxidizer (O2 and N2) and ethylene/air flames using a one-dimensional laminar opposed diffusion flame setup. Two different detailed reaction mechanisms (361 reactions and 61 species for methane/oxidizer flame and 527 reactions and 99 species for ethylene/air flame) are used to validate the simplified soot model in each flame. The effects of strain rate and oxygen content on the soot production and destruction are studied, and the soot related properties such as soot volume fraction, particle number density and particle diameter are compared with published results. The results show reasonable agreement with data and that the soot volume fraction decreases with higher strain rate and lower oxygen content. The simplified soot model has also been used with two reduced reaction mechanisms (12-step, 16-species for methane flame and 20-species for ethylene flame) since such reduced mechanisms are computationally more efficient for practical application. The profiles of the physical properties and the major species are in excellent agreement with the results using the detailed reaction mechanisms. However, minor hydrocarbon-species such as acetylene (C2H2) that is the primary pyrolysis species in the simplified soot model is significantly over predicted and this, in turn, results in an over-prediction of soot production. Finally, the reduced reaction mechanism is modified to get more accurate prediction of the minor hydrocarbon-species. The modified reduced reaction mechanism shows that the soot prediction can be improved by improving the predictions of the key minor species.
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Atwood, Robert Carl. "A combined cellular automata and diffusion model for the prediction of porosity formation during solidification." Thesis, Imperial College London, 2001. http://hdl.handle.net/10044/1/11433.

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Books on the topic "Diffusion prediction"

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Phillips, Norman A. Dispersion processes in large-scale weather prediction. Geneva, Switzerland: Secretariat of the World Meteorological Organization, 1990.

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Phillips, Norman A. Dispersion processes in large scale weather prediction. [Geneva]: World Meteorological Organization, 1990.

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Jiang, L. Y. Prediction of axisymmetric turbulent diffusion flames and comparison with laser-Doppler velocimetry data. [Downsview, Ont.]: Institute for Aerospace Studies, 1987.

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Elazar, Yekutiel. A mapping of the viscous flow behavior in a controlled diffusion compressor cascade using laser doppler velocimetry and preliminary evaluation of codes for the prediction of stall. Monterey, California: Naval Postgraduate School, 1988.

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Christopher, Layne, Arquilla John, Rand Corporation, and Arroyo Center, eds. Predicting military innovation. Santa Monica, CA: RAND, 1999.

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1934-, Hoffman Joe D., and United States. National Aeronautics and Space Administration., eds. The prediction of nozzle performance and heat transfer in hydrogen/oxygen rocket engines with transpiration cooling, film cooling, and high area ratios. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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O, Demuren A., and Lewis Research Center. Institute for Computational Mechanics in Propulsion., eds. On bi-grid local mode analysis of solution techniques for 3-D Euler and Navier-Stokes equations. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, Institute for Computational Mechanics in Propulsion, 1994.

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On bi-grid local mode analysis of solution techniques for 3-D Euler and Navier-Stokes equations. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, Institute for Computational Mechanics in Propulsion, 1994.

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On bi-grid local mode analysis of solution techniques for 3-D Euler and Navier-Stokes equations. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, Institute for Computational Mechanics in Propulsion, 1994.

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Konrad, Kerstin, Adriana Di Martino, and Yuta Aoki. Brain volumes and intrinsic brain connectivity in ADHD. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198739258.003.0006.

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Neuroimaging studies have increased our understanding of the neurobiological underpinnings of ADHD. Structural brain imaging studies demonstrate widespread changes in brain volumes, in particular in frontal-striatal-cerebellar networks. Based on the widespread nature of structural and functional brain abnormalities, approaches able to capture the organizing principles of large-scale neural systems have been used in ADHD. These include diffusion magnetic resonance imaging (MRI) and resting state functional MRI (R-fMRI). Complementary to findings of volumetric studies, diffusion investigations have reported structural connectivity abnormalities in frontal-striatal-cerebellar networks. In parallel, R-fMRI studies point towards abnormalities in the interaction of multiple networks, extending the functional territory of explorations beyond cognitive and motor control. In the future, a deep phenotypic characterization beyond diagnostic categories combined with longitudinal study designs and novel analytical approaches will accelerate the pace towards clinical translations of neuroimaging to improve the detection and prediction of neural trajectories and treatment response in ADHD.
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Book chapters on the topic "Diffusion prediction"

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Talwani, Pradeep, and Steve Acree. "Pore Pressure Diffusion and the Mechanism of Reservoir-Induced Seismicity." In Earthquake Prediction, 947–65. Basel: Birkhäuser Basel, 1985. http://dx.doi.org/10.1007/978-3-0348-6245-5_14.

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Berlyand, M. E. "Anomalously hazardous conditions of pollutant diffusion." In Prediction and Regulation of Air Pollution, 51–73. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3768-3_3.

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Altshuler, Yaniv, Wei Pan, and Alex Pentland. "Trends Prediction Using Social Diffusion Models." In Social Computing, Behavioral - Cultural Modeling and Prediction, 97–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29047-3_12.

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Yang, Cheng, Chuan Shi, Zhiyuan Liu, Cunchao Tu, and Maosong Sun. "Network Embedding for Information Diffusion Prediction." In Network Embedding, 171–87. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-01590-8_12.

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Kim, Jaeil, Yoonmi Hong, Geng Chen, Weili Lin, Pew-Thian Yap, and Dinggang Shen. "Graph-Based Deep Learning for Prediction of Longitudinal Infant Diffusion MRI Data." In Computational Diffusion MRI, 133–41. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05831-9_11.

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Fu, Wai-Tat, and Q. Vera Liao. "Information and Attitude Diffusion in Networks." In Social Computing, Behavioral - Cultural Modeling and Prediction, 205–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29047-3_25.

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Lakkaraju, Kiran. "A Preliminary Model of Media Influence on Attitude Diffusion." In Social Computing, Behavioral-Cultural Modeling, and Prediction, 327–32. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16268-3_38.

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Wu, Yao, Hong Huang, and Hai Jin. "Information Diffusion Prediction with Personalized Graph Neural Networks." In Knowledge Science, Engineering and Management, 376–87. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55393-7_34.

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Lv, Ruilin, Chengxi Zang, Wai Kin (Victor) Chan, and Wenwu Zhu. "Analyzing WeChat Diffusion Cascade: Pattern Discovery and Prediction." In Smart Service Systems, Operations Management, and Analytics, 379–90. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30967-1_34.

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Arjomandi, M., S. H. Sadati, H. Khorsand, and H. Abdoos. "Austenite Formation Temperature Prediction in Steels Using an Artificial Neural Network." In Diffusion in Solids and Liquids III, 335–41. Stafa: Trans Tech Publications Ltd., 2008. http://dx.doi.org/10.4028/3-908451-51-5.335.

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Conference papers on the topic "Diffusion prediction"

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Zitouni, Bayrem, and Lennart von Germersheim. "Desorption, diffusion, reflections, and reemission: the outgassing molecules elusive behaviors." In Systems Contamination: Prediction, Control, and Performance 2020, edited by Carlos E. Soares, Eve M. Wooldridge, and Bruce A. Matheson. SPIE, 2020. http://dx.doi.org/10.1117/12.2568077.

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Yang, Cheng, Jian Tang, Maosong Sun, Ganqu Cui, and Zhiyuan Liu. "Multi-scale Information Diffusion Prediction with Reinforced Recurrent Networks." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/560.

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Information diffusion prediction is an important task which studies how information items spread among users. With the success of deep learning techniques, recurrent neural networks (RNNs) have shown their powerful capability in modeling information diffusion as sequential data. However, previous works focused on either microscopic diffusion prediction which aims at guessing the next influenced user or macroscopic diffusion prediction which estimates the total numbers of influenced users during the diffusion process. To the best of our knowledge, no previous works have suggested a unified model for both microscopic and macroscopic scales. In this paper, we propose a novel multi-scale diffusion prediction model based on reinforcement learning (RL). RL incorporates the macroscopic diffusion size information into the RNN-based microscopic diffusion model by addressing the non-differentiable problem. We also employ an effective structural context extraction strategy to utilize the underlying social graph information. Experimental results show that our proposed model outperforms state-of-the-art baseline models on both microscopic and macroscopic diffusion predictions on three real-world datasets.
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Balali, Ali, Aboozar Rajabi, Sepehr Ghassemi, Masoud Asadpour, and Hesham Faili. "Content diffusion prediction in social networks." In 2013 5th Conference on Information and Knowledge Technology (IKT). IEEE, 2013. http://dx.doi.org/10.1109/ikt.2013.6620114.

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Wang, Zhitao, Chengyao Chen, and Wenjie Li. "Attention Network for Information Diffusion Prediction." In Companion of the The Web Conference 2018. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3184558.3186931.

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Chen, Xueqin, Fan Zhou, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, and Fengli Zhang. "Information Diffusion Prediction via Recurrent Cascades Convolution." In 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE, 2019. http://dx.doi.org/10.1109/icde.2019.00074.

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Wang, Jia, Vincent W. Zheng, Zemin Liu, and Kevin Chen-Chuan Chang. "Topological Recurrent Neural Network for Diffusion Prediction." In 2017 IEEE International Conference on Data Mining (ICDM). IEEE, 2017. http://dx.doi.org/10.1109/icdm.2017.57.

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Zhou, Fan, Xovee Xu, Kunpeng Zhang, Goce Trajcevski, and Ting Zhong. "Variational Information Diffusion for Probabilistic Cascades Prediction." In IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. IEEE, 2020. http://dx.doi.org/10.1109/infocom41043.2020.9155349.

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Lee, Jong-Ryul, and Chin-Wan Chung. "A new correlation-based information diffusion prediction." In the 23rd International Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2567948.2579241.

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Gu, Tianpei, Guangyi Chen, Junlong Li, Chunze Lin, Yongming Rao, Jie Zhou, and Jiwen Lu. "Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.01660.

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Kim, Sung-Soo, Moonyoung Chung, and Young-Kuk Kim. "Urban Traffic Prediction using Congestion Diffusion Model." In 2020 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia). IEEE, 2020. http://dx.doi.org/10.1109/icce-asia49877.2020.9276823.

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Reports on the topic "Diffusion prediction"

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Gareau, Paul, and Brian K. Rutt. Prediction of Malignancy in Breast Tumors Using Diffusion Weighted Magnetic Resonance Imaging. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada390993.

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Lyon, B. F. Prediction of external corrosion for steel cylinders at the Paducah Gaseous Diffusion Plant: Application of an empirical method. Office of Scientific and Technical Information (OSTI), February 1996. http://dx.doi.org/10.2172/212460.

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Or, Dani, Shmulik Friedman, and Jeanette Norton. Physical processes affecting microbial habitats and activity in unsaturated agricultural soils. United States Department of Agriculture, October 2002. http://dx.doi.org/10.32747/2002.7587239.bard.

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experimental methods for quantifying effects of water content and other dynamic environmental factors on bacterial growth in partially-saturated soils. Towards this end we reviewed critically the relevant scientific literature and performed theoretical and experimental studies of bacterial growth and activity in modeled, idealized and real unsaturated soils. The natural wetting-drying cycles common to agricultural soils affect water content and liquid organization resulting in fragmentation of aquatic habitats and limit hydraulic connections. Consequently, substrate diffusion pathways to soil microbial communities become limiting and reduce nutrient fluxes, microbial growth, and mobility. Key elements that govern the extent and manifestation of such ubiquitous interactions include characteristics of diffusion pathways and pore space, the timing, duration, and extent of environmental perturbations, the nature of microbiological adjustments (short-term and longterm), and spatial distribution and properties of EPS clusters (microcolonies). Of these key elements we have chosen to focus on a manageable subset namely on modeling microbial growth and coexistence on simple rough surfaces, and experiments on bacterial growth in variably saturated sand samples and columns. Our extensive review paper providing a definitive “snap-shot” of present scientific understanding of microbial behavior in unsaturated soils revealed a lack of modeling tools that are essential for enhanced predictability of microbial processes in soils. We therefore embarked on two pronged approach of development of simple microbial growth models based on diffusion-reaction principles to incorporate key controls for microbial activity in soils such as diffusion coefficients and temporal variations in soil water content (and related substrate diffusion rates), and development of new methodologies in support of experiments on microbial growth in simple and observable porous media under controlled water status conditions. Experimental efforts led to a series of microbial growth experiments in granular media under variable saturation and ambient conditions, and introduction of atomic force microscopy (AFM) and confocal scanning laser microscopy (CSLM) to study cell size, morphology and multi-cell arrangement at a high resolution from growth experiments in various porous media. The modeling efforts elucidated important links between unsaturated conditions and microbial coexistence which is believed to support the unparallel diversity found in soils. We examined the role of spatial and temporal variation in hydration conditions (such as exist in agricultural soils) on local growth rates and on interactions between two competing microbial species. Interestingly, the complexity of soil spaces and aquatic niches are necessary for supporting a rich microbial diversity and the wide array of microbial functions in unsaturated soils. This project supported collaboration between soil physicists and soil microbiologist that is absolutely essential for making progress in both disciplines. It provided a few basic tools (models, parameterization) for guiding future experiments and for gathering key information necessary for prediction of biological processes in agricultural soils. The project sparked a series of ongoing studies (at DTU and EPFL and in the ARO) into effects of soil hydration dynamics on microbial survival strategy under short term and prolonged desiccation (important for general scientific and agricultural applications).
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Baral, Aniruddha, Jeffrey Roesler, M. Ley, Shinhyu Kang, Loren Emerson, Zane Lloyd, Braden Boyd, and Marllon Cook. High-volume Fly Ash Concrete for Pavements Findings: Volume 1. Illinois Center for Transportation, September 2021. http://dx.doi.org/10.36501/0197-9191/21-030.

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High-volume fly ash concrete (HVFAC) has improved durability and sustainability properties at a lower cost than conventional concrete, but its early-age properties like strength gain, setting time, and air entrainment can present challenges for application to concrete pavements. This research report helps with the implementation of HVFAC for pavement applications by providing guidelines for HVFAC mix design, testing protocols, and new tools for better quality control of HVFAC properties. Calorimeter tests were performed to evaluate the effects of fly ash sources, cement–fly ash interactions, chemical admixtures, and limestone replacement on the setting times and hydration reaction of HVFAC. To better target the initial air-entraining agent dosage for HVFAC, a calibration curve between air-entraining dosage for achieving 6% air content and fly ash foam index test has been developed. Further, a digital foam index test was developed to make this test more consistent across different labs and operators. For a more rapid prediction of hardened HVFAC properties, such as compressive strength, resistivity, and diffusion coefficient, an oxide-based particle model was developed. An HVFAC field test section was also constructed to demonstrate the implementation of a noncontact ultrasonic device for determining the final set time and ideal time to initiate saw cutting. Additionally, a maturity method was successfully implemented that estimates the in-place compressive strength of HVFAC through wireless thermal sensors. An HVFAC mix design procedure using the tools developed in this project such as the calorimeter test, foam index test, and particle-based model was proposed to assist engineers in implementing HVFAC pavements.
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Bodanapally, Uttam, Andrew Choi, and Robert Shin. Diffusion-Weighted Imaging of Traumatic Optic Neuropathy: Diagnosis and Predicting the Prognosis. Fort Belvoir, VA: Defense Technical Information Center, January 2014. http://dx.doi.org/10.21236/ada601984.

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Caturla, M., M. D. Johnson, and J. Zhu. Toward a predictive atomistic model of ion implantation and dopant diffusion in silicon. Office of Scientific and Technical Information (OSTI), September 1998. http://dx.doi.org/10.2172/2853.

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Andrade, José E., and John W. Rudnicki. Multiscale framework for predicting the coupling between deformation and fluid diffusion in porous rocks. Office of Scientific and Technical Information (OSTI), December 2012. http://dx.doi.org/10.2172/1057395.

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Friedman, Shmuel, Jon Wraith, and Dani Or. Geometrical Considerations and Interfacial Processes Affecting Electromagnetic Measurement of Soil Water Content by TDR and Remote Sensing Methods. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7580679.bard.

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Time Domain Reflectometry (TDR) and other in-situ and remote sensing dielectric methods for determining the soil water content had become standard in both research and practice in the last two decades. Limitations of existing dielectric methods in some soils, and introduction of new agricultural measurement devices or approaches based on soil dielectric properties mandate improved understanding of the relationship between the measured effective permittivity (dielectric constant) and the soil water content. Mounting evidence indicates that consideration must be given not only to the volume fractions of soil constituents, as most mixing models assume, but also to soil attributes and ambient temperature in order to reduce errors in interpreting measured effective permittivities. The major objective of the present research project was to investigate the effects of the soil geometrical attributes and interfacial processes (bound water) on the effective permittivity of the soil, and to develop a theoretical frame for improved, soil-specific effective permittivity- water content calibration curves, which are based on easily attainable soil properties. After initializing the experimental investigation of the effective permittivity - water content relationship, we realized that the first step for water content determination by the Time Domain Reflectometry (TDR) method, namely, the TDR measurement of the soil effective permittivity still requires standardization and improvement, and we also made more efforts than originally planned towards this objective. The findings of the BARD project, related to these two consequential steps involved in TDR measurement of the soil water content, are expected to improve the accuracy of soil water content determination by existing in-situ and remote sensing dielectric methods and to help evaluate new water content sensors based on soil electrical properties. A more precise water content determination is expected to result in reduced irrigation levels, a matter which is beneficial first to American and Israeli farmers, and also to hydrologists and environmentalists dealing with production and assessment of contamination hazards of this progressively more precious natural resource. The improved understanding of the way the soil geometrical attributes affect its effective permittivity is expected to contribute to our understanding and predicting capability of other, related soil transport properties such as electrical and thermal conductivity, and diffusion coefficients of solutes and gas molecules. In addition, to the originally planned research activities we also investigated other related problems and made many contributions of short and longer terms benefits. These efforts include: Developing a method and a special TDR probe for using TDR systems to determine also the soil's matric potential; Developing a methodology for utilizing the thermodielectric effect, namely, the variation of the soil's effective permittivity with temperature, to evaluate its specific surface area; Developing a simple method for characterizing particle shape by measuring the repose angle of a granular material avalanching in water; Measurements and characterization of the pore scale, saturation degree - dependent anisotropy factor for electrical and hydraulic conductivities; Studying the dielectric properties of cereal grains towards improved determination of their water content. A reliable evaluation of the soil textural attributes (e.g. the specific surface area mentioned above) and its water content is essential for intensive irrigation and fertilization processes and within extensive precision agriculture management. The findings of the present research project are expected to improve the determination of cereal grain water content by on-line dielectric methods. A precise evaluation of grain water content is essential for pricing and evaluation of drying-before-storage requirements, issues involving energy savings and commercial aspects of major economic importance to the American agriculture. The results and methodologies developed within the above mentioned side studies are expected to be beneficial to also other industrial and environmental practices requiring the water content determination and characterization of granular materials.
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