Journal articles: '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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Prazeres, D. M. F. "Prediction of diffusion coefficients of plasmids." Biotechnology and Bioengineering 99, no. 4 (2008): 1040–44. http://dx.doi.org/10.1002/bit.21626.

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Tyn, Myo T., and Todd W. Gusek. "Prediction of diffusion coefficients of proteins." Biotechnology and Bioengineering 35, no. 4 (February 20, 1990): 327–38. http://dx.doi.org/10.1002/bit.260350402.

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Chan, K. S., N. S. Cheruvu, and G. R. Leverant. "Coating Life Prediction for Combustion Turbine Blades." Journal of Engineering for Gas Turbines and Power 121, no. 3 (July 1, 1999): 484–88. http://dx.doi.org/10.1115/1.2818498.

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A life prediction method for combustion turbine blade coatings has been developed by modeling coating degradation mechanisms including oxidation, spallation, and aluminum loss due to inward diffusion. Using this model, the influence of cycle time on coating life is predicted for GTD-111 coated with an MCrAlY, PtAl, or aluminide coating. The results are used to construct a coating life diagram that depicts failure and safe regions for the coating in a log-log Plot of number of startup cycles versus cycle time. The regime where failure by oxidation, spallation, and inward diffusion dominates is identified and delineated from that dominated by oxidation and inward diffusion only. A procedure for predicting the remaining life of a coating is developed. The utility of the coating life diagram for predicting the failure and useful life of MCrAlY, aluminide, or PtAl coatings on the GTD-111 substrate is illustrated and compared against experimental data.

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Bengtsson, Lisa, Sander Tijm, Filip Váňa, and Gunilla Svensson. "Impact of Flow-Dependent Horizontal Diffusion on Resolved Convection in AROME." Journal of Applied Meteorology and Climatology 51, no. 1 (January 2012): 54–67. http://dx.doi.org/10.1175/jamc-d-11-032.1.

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AbstractHorizontal diffusion in numerical weather prediction models is, in general, applied to reduce numerical noise at the smallest atmospheric scales. In convection-permitting models, with horizontal grid spacing on the order of 1–3 km, horizontal diffusion can improve the model skill of physical parameters such as convective precipitation. For instance, studies using the convection-permitting Applications of Research to Operations at Mesoscale model (AROME) have shown an improvement in forecasts of large precipitation amounts when horizontal diffusion is applied to falling hydrometeors. The nonphysical nature of such a procedure is undesirable, however. Within the current AROME, horizontal diffusion is imposed using linear spectral horizontal diffusion on dynamical model fields. This spectral diffusion is complemented by nonlinear, flow-dependent, horizontal diffusion applied on turbulent kinetic energy, cloud water, cloud ice, rain, snow, and graupel. In this study, nonlinear flow-dependent diffusion is applied to the dynamical model fields rather than diffusing the already predicted falling hydrometeors. In particular, the characteristics of deep convection are investigated. Results indicate that, for the same amount of diffusive damping, the maximum convective updrafts remain strong for both the current and proposed methods of horizontal diffusion. Diffusing the falling hydrometeors is necessary to see a reduction in rain intensity, but a more physically justified solution can be obtained by increasing the amount of damping on the smallest atmospheric scales using the nonlinear, flow-dependent, diffusion scheme. In doing so, a reduction in vertical velocity was found, resulting in a reduction in maximum rain intensity.

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Chi, Cheng-Ting, Ming-Han Lee, Ching-Feng Weng, and Max K. Leong. "In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach." International Journal of Molecular Sciences 20, no. 13 (June 28, 2019): 3170. http://dx.doi.org/10.3390/ijms20133170.

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Oral administration is the preferred and predominant route of choice for medication. As such, drug absorption is one of critical drug metabolism and pharmacokinetics (DM/PK) parameters that should be taken into consideration in the process of drug discovery and development. The cell-free in vitro parallel artificial membrane permeability assay (PAMPA) has been adopted as the primary screening to assess the passive diffusion of compounds in the practical applications. A classical quantitative structure–activity relationship (QSAR) model and a machine learning (ML)-based QSAR model were derived using the partial least square (PLS) scheme and hierarchical support vector regression (HSVR) scheme to elucidate the underlying passive diffusion mechanism and to predict the PAMPA effective permeability, respectively, in this study. It was observed that HSVR executed better than PLS as manifested by the predictions of the samples in the training set, test set, and outlier set as well as various statistical assessments. When applied to the mock test, which was designated to mimic real challenges, HSVR also showed better predictive performance. PLS, conversely, cannot cover some mechanistically interpretable relationships between descriptors and permeability. Accordingly, the synergy of predictive HSVR and interpretable PLS models can be greatly useful in facilitating drug discovery and development by predicting passive diffusion.

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Wang, William Yi, Bi Cheng Zhou, Jia Jia Han, Hua Zhi Fang, Shun Li Shang, Yi Wang, Xi Dong Hui, and Zi Kui Liu. "Prediction of Diffusion Coefficients in Liquid and Solids." Defect and Diffusion Forum 364 (June 2015): 182–91. http://dx.doi.org/10.4028/www.scientific.net/ddf.364.182.

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Our activities in predicting diffusion coefficients in fcc, bcc, and hcp solid solutions using first-principles calculations and in liquid usingabinitiomolecular dynamics are reviewed. These include self-diffusion coefficients [1-4], tracer diffusion coefficients in dilute solutions [5-7], calculation of migration entropy [8], tracer diffusion coefficients in metallic and oxide liquid [9, 10], and effects of vacancy on diffusion of oxygen [11, 12]. The effects of exchange correlation functionals are examined in some cases along with charge transfer between solute and solvent elements. The dominant contribution of diffusion on the effects of Re addition on the creep properties of Ni-base superalloys is discussed [13].

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Yang, R. T., Y. D. Chen, and Y. T. Yeh. "Prediction of cross-term coefficients in binary diffusion: Diffusion in zeolite." Chemical Engineering Science 46, no. 12 (1991): 3089–99. http://dx.doi.org/10.1016/0009-2509(91)85012-m.

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Shang, Yidan, Kiao Inthavong, Dasheng Qiu, Narinder Singh, Fajiang He, and Jiyuan Tu. "Prediction of nasal spray drug absorption influenced by mucociliary clearance." PLOS ONE 16, no. 1 (January 28, 2021): e0246007. http://dx.doi.org/10.1371/journal.pone.0246007.

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Evaluation of nasal spray drug absorption has been challenging because deposited particles are consistently transported away by mucociliary clearance during diffusing through the mucus layer. This study developed a novel approach combining Computational Fluid Dynamics (CFD) techniques with a 1-D mucus diffusion model to better predict nasal spray drug absorption. This integrated CFD-diffusion approach comprised a preliminary simulation of nasal airflow, spray particle injection, followed by analysis of mucociliary clearance and drug solute diffusion through the mucus layer. The spray particle deposition distribution was validated experimentally and numerically, and the mucus velocity field was validated by comparing with previous studies. Total and regional drug absorption for solute radius in the range of 1 − 110nm were investigated. The total drug absorption contributed by the spray particle deposition was calculated. The absorption contribution from particles that deposited on the anterior region was found to increase significantly as the solute radius became larger (diffusion became slower). This was because the particles were consistently moved out of the anterior region, and the delayed absorption ensured more solute to be absorbed by the posterior regions covered with respiratory epithelium. Future improvements in the spray drug absorption model were discussed. The results of this study are aimed at working towards a CFD-based integrated model for evaluating nasal spray bioequivalence.

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Dorman, L. I. "Prediction of galactic cosmic ray intensity variation for a few (up to 10-12) years ahead on the basis of convection-diffusion and drift model." Annales Geophysicae 23, no. 9 (November 22, 2005): 3003–7. http://dx.doi.org/10.5194/angeo-23-3003-2005.

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Abstract. We determine the dimension of the Heliosphere (modulation region), radial diffusion coefficient and other parameters of convection-diffusion and drift mechanisms of cosmic ray (CR) long-term variation, depending on particle energy, the level of solar activity (SA) and general solar magnetic field. This important information we obtain on the basis of CR and SA data in the past, taking into account the theory of convection-diffusion and drift global modulation of galactic CR in the Heliosphere. By using these results and the predictions which are regularly published elsewhere of expected SA variation in the near future and prediction of next future SA cycle, we may make a prediction of the expected in the near future long-term cosmic ray intensity variation. We show that by this method we may make a prediction of the expected in the near future (up to 10-12 years, and may be more, in dependence for what period can be made definite prediction of SA) galactic cosmic ray intensity variation in the interplanetary space on different distances from the Sun, in the Earth's magnetosphere, and in the atmosphere at different altitudes and latitudes.

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Kim, Yoon-Chul, Hyung Jun Kim, Jong-Won Chung, In Gyeong Kim, Min Jung Seong, Keon Ha Kim, Pyoung Jeon, et al. "Novel Estimation of Penumbra Zone Based on Infarct Growth Using Machine Learning Techniques in Acute Ischemic Stroke." Journal of Clinical Medicine 9, no. 6 (June 24, 2020): 1977. http://dx.doi.org/10.3390/jcm9061977.

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While the penumbra zone is traditionally assessed based on perfusion–diffusion mismatch, it can be assessed based on machine learning (ML) prediction of infarct growth. The purpose of this work was to develop and validate an ML method for the prediction of infarct growth distribution and volume, in cases of successful (SR) and unsuccessful recanalization (UR). Pre-treatment perfusion-weighted, diffusion-weighted imaging (DWI) data, and final infarct lesions annotated from day-7 DWI from patients with middle cerebral artery occlusion were utilized to develop and validate two ML models for prediction of tissue fate. SR and UR models were developed from data in patients with modified treatment in cerebral infarction (mTICI) scores of 2b–3 and 0–2a, respectively. When compared to manual infarct annotation, ML-based infarct volume predictions resulted in an intraclass correlation coefficient (ICC) of 0.73 (95% CI = 0.31–0.91, p < 0.01) for UR, and an ICC of 0.87 (95% CI = 0.73–0.94, p < 0.001) for SR. Favorable outcomes for mismatch presence and absence in SR were 50% and 36%, respectively, while they were 61%, 56%, and 25%, respectively, for the low, intermediate, and high infarct growth groups. The presented method can offer novel and alternative insights into selecting patients for recanalization therapy and predicting functional outcome.

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Suga, Kazuhiro, Taro Moteki, and Masanori Kikuchi. "Corrosion Prediction under Flow Field." Key Engineering Materials 462-463 (January 2011): 1261–66. http://dx.doi.org/10.4028/www.scientific.net/kem.462-463.1261.

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This study evaluates effects of the diffusion layer thickness and the share stress on metal surface for the polarization curve under flow field on metal corrosion. We measure the polarization curves under the different diffusion boundary layer thickness and the wall share stress. Metal surface conditions are observed from microscopic and macroscopic view points to evaluate tendencty of corrosion and shape of corrosion product.

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Azad, Hassan, and Gary W. Siebein. "On the prediction of sound diffusion coefficient." Journal of the Acoustical Society of America 143, no. 3 (March 2018): 1896. http://dx.doi.org/10.1121/1.5036168.

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Zhao, Jiuhua, Qipeng Liu, Lin Wang, and Xiaofan Wang. "Prediction of competitive diffusion on complex networks." Physica A: Statistical Mechanics and its Applications 507 (October 2018): 12–21. http://dx.doi.org/10.1016/j.physa.2018.05.004.

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Bosse, Dennis, and Hans-Jörg Bart. "Prediction of Diffusion Coefficients in Liquid Systems." Industrial & Engineering Chemistry Research 45, no. 5 (March 2006): 1822–28. http://dx.doi.org/10.1021/ie0487989.

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Liu, Jin-Hu, Yu-Xiao Zhu, and Tao Zhou. "Improving personalized link prediction by hybrid diffusion." Physica A: Statistical Mechanics and its Applications 447 (April 2016): 199–207. http://dx.doi.org/10.1016/j.physa.2015.12.036.

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Siddiqi, M. A., and K. Lucas. "Correlations for prediction of diffusion in liquids." Canadian Journal of Chemical Engineering 64, no. 5 (October 1986): 839–43. http://dx.doi.org/10.1002/cjce.5450640519.

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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 (June 28, 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-enhanced Sequential Hypergraph Attention Networks (MS-HGAT). Specifically, to introduce the global dependencies of users, we not only take advantages of their friendships, but also consider their interactions at the cascade level. Furthermore, to dynamically capture user' preferences, we divide the diffusion hypergraph into several sub graphs based on timestamps, develop Hypergraph Attention Networks to learn the sequential hypergraphs, and connect them with gated fusion strategy. In addition, a memory-enhanced embedding lookup module is proposed to capture the learned user representations into the cascade-specific embedding space, thus highlighting the feature interaction within the cascade. The experimental results over four realistic datasets demonstrate that MS-HGAT significantly outperforms the state-of-the-art diffusion prediction models in both Hits@K and MAP@k metrics.

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Coogan, Timothy J., and David O. Kazmer. "Healing simulation for bond strength prediction of FDM." Rapid Prototyping Journal 23, no. 3 (April 18, 2017): 551–61. http://dx.doi.org/10.1108/rpj-03-2016-0051.

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Purpose The purpose of this paper is to present a diffusion-controlled healing model for predicting fused deposition modeling (FDM) bond strength between layers (z-axis strength). Design/methodology/approach Diffusion across layers of an FDM part was predicted based on a one-dimensional transient heat analysis of the interlayer interface using a temperature-dependent diffusion model determined from rheological data. Integrating the diffusion coefficient across the temperature history with respect to time provided the total diffusion used to predict the bond strength, which was compared to the measured bond strength of hollow acrylonitrile butadiene styr (ABS) boxes printed at various processing conditions. Findings The simulated bond strengths predicted the measured bond strengths with a coefficient of determination of 0.795. The total diffusion between FDM layers was shown to be a strong determinant of bond strength and can be similarly applied for other materials. Research limitations/implications Results and analysis from this paper should be used to accurately model and predict bond strength. Such models are useful for FDM part design and process control. Originality/value This paper is the first work that has predicted the amount of polymer diffusion that occurs across FDM layers during the printing process, using only rheological material properties and processing parameters.

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Apte, Mandar S., Ahmadbazlee Matzain, Hong-Quan Zhang, Michael Volk, James P. Brill, and Jeff L. Creek. "Investigation of Paraffin Deposition During Multiphase Flow in Pipelines and Wellbores—Part 2: Modeling." Journal of Energy Resources Technology 123, no. 2 (January 15, 2001): 150–57. http://dx.doi.org/10.1115/1.1369359.

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A Joint Industry Project to investigate paraffin deposition in multiphase flowlines and wellbores was initiated at The University of Tulsa in May 1995. As part of this JIP, a computer program, based on the molecular diffusion theory, was developed for prediction of wax deposition during multiphase flow in pipelines and wellbores. The program is modular in structure and assumes a steady-state, one-dimensional flow, energy conservation principle. This paper will describe the simulator developed for predicting paraffin deposition during multiphase flow that includes coupling of multiphase fluid flow, solid-liquid-vapor thermodynamics, multiphase heat transfer, and flow pattern-dependent paraffin deposition. Predictions of the simulator are compared and tuned to the experimental data by adjusting the film heat transfer and diffusion coefficients and the thermal conductivity of the wax deposit.

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KINOSHITA, Shinichi, Hitoshi SHIOTANI, and Toshimi TAKAGI. "Numerical Prediction of Diffusion Flames Using Multicomponent Diffusion and the Accuracy Evaluation." Transactions of the Japan Society of Mechanical Engineers Series B 66, no. 647 (2000): 1859–64. http://dx.doi.org/10.1299/kikaib.66.647_1859.

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Liu, Huashan, Hang Wang, Wenjun Zhu, Xiaoma Tao, and Zhanpeng Jin. "Prediction of formation of intermetallic compounds in diffusion couples." Journal of Materials Research 22, no. 6 (June 2007): 1502–11. http://dx.doi.org/10.1557/jmr.2007.0212.

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Formation of intermetallic compounds (IMCs) at the interface between two metals during soldering processing exerts much influence on the electrical and mechanical performance of integrate circuits (ICs). Considering both of the thermodynamic and kinetic factors (including nucleation and growth) on phase formation, a new model capable of predicting phase formation sequence at the interface between two metals with different structures has been proposed in this work. Application of this new model on the interfacial reactions between pure elemental pairs of metals such as Ni/Sn, Cu/In, Cu/Sn, and Co/Sn at different temperatures shows good agreement between predictions by this model and experimental observations.

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Salunkhe, Sanchit, Oumnia El Fajri, Shanti Bhushan, David Thompson, Daphne O’Doherty, Tim O’Doherty, and Allan Mason-Jones. "Validation of Tidal Stream Turbine Wake Predictions and Analysis of Wake Recovery Mechanism." Journal of Marine Science and Engineering 7, no. 10 (October 11, 2019): 362. http://dx.doi.org/10.3390/jmse7100362.

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This paper documents the predictive capability of rotating blade-resolved unsteady Reynolds averaged Navier-Stokes (URANS) and Improved Delayed Detached Eddy Simulation (IDDES) computations for tidal stream turbine performance and intermediate wake characteristics. Ansys/Fluent and OpenFOAM simulations are performed using mixed-cell, unstructured grids consisting of up to 11 million cells. The thrust, power and intermediate wake predictions compare reasonably well within 10% of the experimental data. For the wake predictions, OpenFOAM performs better than Ansys/Fluent, and IDDES better than URANS when the resolved turbulence is triggered. The primary limitation of the simulations is under prediction of the wake diffusion towards the turbine axis, which in return is related to the prediction of turbulence in the tip-vortex shear layer. The shear-layer involves anisotropic turbulent structures; thus, hybrid RANS/LES models, such as IDDES, are preferred over URANS. Unfortunately, IDDES fails to accurately predict the resolved turbulence in the near-wake region due to the modeled stress depletion issue.

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Luyt, Charles-Edouard, Damien Galanaud, Vincent Perlbarg, Audrey Vanhaudenhuyse, Robert D. Stevens, Rajiv Gupta, Hortense Besancenot, et al. "Diffusion Tensor Imaging to Predict Long-term Outcome after Cardiac Arrest." Anesthesiology 117, no. 6 (December 1, 2012): 1311–21. http://dx.doi.org/10.1097/aln.0b013e318275148c.

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Background Prognostication in comatose survivors of cardiac arrest is a major clinical challenge. The authors' objective was to determine whether an assessment with diffusion tensor imaging, a brain magnetic resonance imaging sequence, increases the accuracy of 1 yr functional outcome prediction in cardiac arrest survivors. Methods Prospective, observational study in two intensive care units. Fifty-seven comatose survivors of cardiac arrest underwent brain magnetic resonance imaging. Fractional anisotropy (FA), a diffusion tensor imaging value, was measured in predefined white matter regions, and apparent diffusion coefficient was assessed in predefined grey matter regions. Prediction of unfavorable outcome at 1 yr was compared using four prognostic models: FA global, FA selected, apparent diffusion coefficient, and clinical classifiers. Results Of the 57 patients included in the study, 49 had an unfavorable outcome at 12 months. Areas under the receiver operating characteristic curve (95% CI) to predict unfavorable outcome for the FA global, FA selected, clinical, and apparent diffusion coefficient models were 0.92 (0.82-0.98), 0.96 (0.87-0.99), 0.78 (0.65-0.88), and 0.86 (0.74-0.94), respectively. The FA selected model had the best overall accuracy for predicting outcome, with a score above 0.44 having 94% (95% CI, 83-99%) sensitivity and 100% (95% CI, 63-100%) specificity for the prediction of unfavorable outcome. Conclusion Quantitative diffusion tensor imaging indicates that white matter damage is widespread after cardiac arrest. A prognostic model based on FA values in selected white matter tracts seems to predict accurately 1 yr functional outcome. These preliminary results need to be confirmed in a larger population.

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Bouvier, J. M., and M. Gelus. "Diffusion of Heavy Oil in a Swelling Elastomer." Rubber Chemistry and Technology 59, no. 2 (May 1, 1986): 233–40. http://dx.doi.org/10.5254/1.3538196.

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Abstract The swelling of SBR by an aromatic oil has been experimentally studied at temperatures ranging from ambient to 200°C with thermally stable networks. A model based on Fick's law was developed, and the change of geometry of the elastomer sample was taken into account. The proposed approach is global or macroscopic, and a constant diffusion coefficient has been defined. The diffusion number, ND, defined by two characteristics of a solvent-polymer system, tf the swelling time and tD the diffusion time, represents an important result for engineering applications; it allows prediction of the behavior of amorphous elastomers in contact with a diffusing organic liquid.

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Nilsson, Lars-Olof. "Chloride profiles with a peak – why and what are the consequences for predictions?" MATEC Web of Conferences 364 (2022): 02024. http://dx.doi.org/10.1051/matecconf/202236402024.

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Chloride ingress profiles do almost always have a peak at some depth but most prediction models are missing this peak. Some prediction models, such as the fib model, simply “cut off” a slice of the concrete up to the peak in further predictions. Other prediction models use data only from the profiles beyond the peak but include the concrete up to the peak as if it has the same properties as the rest of the concrete. A physical model has been developed to quantify the local changes because of leaching and the consequences of these changes with time. The model uses Fick’s 1st law for chloride diffusion and linear chloride binding. The depth of leaching with time is modelled with a simple square-root equation. The consequences of leaching are assumed to be linear from the surface into the maximum depth of leaching. The consequences of leaching are modelled as depth-dependent changes of porosity, chloride binding and the diffusion coefficient in Fick’s first law.

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Luo, Zijun, Rui Zeng, and Pan Wang. "Air Quality Prediction Based on Quadratic Prediction Model." Learning & Education 10, no. 5 (March 13, 2022): 52. http://dx.doi.org/10.18282/l-e.v10i5.2669.

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In order to improve the performance of the prediction model of air quality prediction,a secondary prediction mathematical model is established in this paper.The first is to clean the data and find the potential model relationship between variables through data mining and correlation methods,so as to establish the limit learning machine model.The model needs to be able to explain the influence of meteorological index variables on pollutant concentration diffusion to a certain extent.Then,the EML model is optimized by genetic algorithm,rolling optimization and other methods to reduce noise and make the data as accurate as possible.

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Li, Yangyang, Hao Jin, Xiangyi Yu, Haiyong Xie, Yabin Xu, Huajun Xu, and Huacheng Zeng. "Intelligent Prediction of Private Information Diffusion in Social Networks." Electronics 9, no. 5 (April 27, 2020): 719. http://dx.doi.org/10.3390/electronics9050719.

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In the information age, leaked private information may cause significant physical and mental harm to the relevant parties, leading to a negative social impact. In order to effectively evaluate the impact of such information leakage in today’s social networks, it is necessary to accurately predict the scope and depth of private information diffusion. By doing so, it would be feasible to prevent and control the improper spread and diffusion of private information. In this paper, we propose an intelligent prediction method for private information diffusion in social networks based on comprehensive data analysis. We choose Sina Weibo, one of the most prominent social networks in China, to study. Firstly, a prediction model of message forwarding behavior is established by analyzing the characteristic factors that influence the forwarding behavior of the micro-blog users. Then the influence of users is calculated based on the interaction time and topological structure of users relationship, and the diffusion critical paths are identified. Finally, through the user forwarding probability transmission, we determine the micro-blog diffusion cut-off conditions. The simulation results on Sina Weibo data set show that the prediction accuracy is 86.9%, which indicates that our method is efficient to predict the message diffusion in real-world social networks.

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Silverman, D. C. "Dimensionless Groups in the Modeling and Prediction of Corrosion Processes." Corrosion 41, no. 12 (December 1, 1985): 679–87. http://dx.doi.org/10.5006/1.3583003.

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Abstract Coupled differential equations that are difficult to solve, yet have predictive value, will arise when modeling a combination of general and localized corrosion in the presence of flow. These equations are impossible to solve if the flow is turbulent. By making the equations dimensionless, certain groups arise that can show the relative magnitudes of the processes involved. Three dimensionless groups arise from modeling the localized area. These show the ratios of the magnitudes of migration to diffusion, convective mass transport to diffusive mass transport, and surface reaction rate to mass transfer rate. The meanings of the groups are independent of geometry and flow conditions. These groups can provide a “back-of-the-envelope” type of guidance for experimental design and corrosion prediction even though the equations from which they are derived cannot be solved.

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Hou, Zhi Hong, and Shi Yong Luo. "Simulation and Computation of Diffusion in Solids." Applied Mechanics and Materials 182-183 (June 2012): 933–36. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.933.

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A computation software on diffusion computation in solids was developed. The software includes two sub-modules of “database management system (DBMS)” and "Evaluation & prediction". The “DBMS” deals with the diffusion coefficients gathered from reported documents and the data evaluated according to some rules, besides, it can provide users with retrieval of diffusion coefficients. Based on the solutions to the Fick’s first law and the Fick’s second law in the four typical critical conditions, the "Evaluation & prediction" sub-module gives the predication of concentration distribution after diffusion process in solids or computation for diffusion coefficient.

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Dunlop, Peter J., and C. M. Bignell. "Prediction of gaseous diffusion coefficients from thermal diffusion measurements and other experimental data." Journal of Chemical Physics 102, no. 14 (April 8, 1995): 5781–84. http://dx.doi.org/10.1063/1.469309.

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Chan, K. S., N. S. Cheruvu, and G. R. Leverant. "Coating Life Prediction Under Cyclic Oxidation Conditions." Journal of Engineering for Gas Turbines and Power 120, no. 3 (July 1, 1998): 609–14. http://dx.doi.org/10.1115/1.2818189.

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The hot gas path section components of land based turbines require materials with superior mechanical properties and good hot corrosion and oxidation resistance. These components are generally coated with either a diffusion coating (aluminide or platinum aluminide) or with an overlay coating (MCrAlY) to provide additional hot corrosion and/or oxidation protection. These coatings degrade due to inward and outward diffusion of elements during service. Outward diffusion of aluminum results in formation of a protective oxide layer on the surface. When the protective oxide spalls, Aluminum in the coating diffuses out to reform the oxide layer. Accelerated oxidation and failure of coating occur when the Al content in the coating is insufficient to reform a continuous alumina film. This paper describes development of a coating life predictions model that accounts for both oxidation and oxide spallation under thermal mechanical loading as well as diffusion of elements that dictate the end of useful life. Cyclic oxidation data for aluminide and platinum aluminide coatings were generated to determine model constants. Applications of this model for predicting cyclic oxidation life of coated materials are demonstrated. Work is underway to develop additional material data and to qualify the model for determining actual blade and vane coating refurbishment intervals.

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AlArfaj, Abeer Abdulaziz, and Hanan Ahmed Hosni Mahmoud. "Deep Learning Model for Prediction of Diffusion in Defect Substances." Processes 10, no. 8 (July 24, 2022): 1446. http://dx.doi.org/10.3390/pr10081446.

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Actual diffusion activity function is an important metric utilized to describe the diffusion activities of a vacancy defect substance. In this paper, we propose a deep learning three-dimensional convolutional CNN model (D3-CNN). A 3D convolution has its kernel slides in three dimensions as opposed to two dimensions with 2D convolutions. 3D convolution is more suitable for three-dimensional data. We also propose an amplification learning technique to predict the actual diffusion activity of a vacancy defect substance, which is impacted by the geometrical parameters of the defect substance and the vacancy distribution function. In this model, the geometric parameters of a three-dimensional constructed vacancy defect substance are generated. The 3D dataset is obtained by the atoms diffusion defect (ADD) simulation model. The geometric parameters of the 3D vacancy defect substance are computed by the proposed amplification technique. The 3D geometric parameters and the diffusion activity values are applied to a deep learning model for training. The actual diffusion activity values of a substance with a vacancy size ranging from size 0.52 mm to 0.61 mm are used for training, and the actual diffusion activity values of substance vacancy of size between 0.41 and 1.01 are classified by the three-dimensional network. The model can realize high speed and accuracy for the actual diffusion activity value. The mean relative absolute errors between the D3-CNN and the ADD models are 0.028–7.85% with a vacancy size of 0.41 to 0.81. For a usual sample with a vacancy of size equal to 0.6, the CPU computation load required by our model is 14.2 × 10−2 h, while the time required is 15.16 h for the ADD model. These results indicate that our proposed deep learning model has a strong learning capability and can function as an influential model to classify the diffusion activity of compound vacancy defect substances.

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Cuffey, Kurt M., and Eric J. Steig. "Isotopic diffusion in polar firn: implications for interpretation of seasonal climate parameters in ice-core records, with emphasis on central Greenland." Journal of Glaciology 44, no. 147 (1998): 273–84. http://dx.doi.org/10.3189/s0022143000002616.

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AbstractIf it were possible to properly extract seasonal information from ice-core isotopic records, paleoclimate researchers could retrieve a wealth of new information concerning the nature of climate changes and the meaning of trends observed in ice-core proxy records. It is widely recognized, however, that the diffusional smoothing of the seasonal record makes a “proper extraction" very difficult. In this paper, we examine the extent to which seasonal information (specifically the amplitude and shape of the seasonal cycle) is irrecoverably destroyed by diffusion in the firn. First, we show that isotopic diffusion firn is reasonably well understood. We do this by showing that a slightly modified version of the Whillans and Grootes (1985) theory makes a tenable a priori prediction of the decay of seasonal isotopic amplitudes with depth at the GISP2 site, though a small adjustment to one parameter significantly improves the prediction. Further, we calculate the amplitude decay at various other ice-core sites and show that these predictions compare favorably with published data from South Pole and locations in southern and central Greenland and the Antarctic Peninsula. We then present numerical experiments wherein synthetic ice-core records are created, diffused, sampled, reconstituted and compared to the original. These show that, alter diffusive mixing in the entire fini column, seasonal amplitudes can be reconstructed to within about 20% error in central Greenland but that all information about sub-annual signals is permanently lost there. Furthermore, most of the error in the amplitude reconstructions is due to the unknowable variations in the sub-annual signal. Finally, we explore how these results can be applied to other locations and suggest that Dye 3 has a high potential for meaningful seasonal reconstructions, while Siple Dome has no potential at all. An optimal ice-core site for seasonal reconstructions has a high accumulation rate and a low temperature.

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Cuffey, Kurt M., and Eric J. Steig. "Isotopic diffusion in polar firn: implications for interpretation of seasonal climate parameters in ice-core records, with emphasis on central Greenland." Journal of Glaciology 44, no. 147 (1998): 273–84. http://dx.doi.org/10.1017/s0022143000002616.

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AbstractIf it were possible to properly extract seasonal information from ice-core isotopic records, paleoclimate researchers could retrieve a wealth of new information concerning the nature of climate changes and the meaning of trends observed in ice-core proxy records. It is widely recognized, however, that the diffusional smoothing of the seasonal record makes a “proper extraction" very difficult. In this paper, we examine the extent to which seasonal information (specifically the amplitude and shape of the seasonal cycle) is irrecoverably destroyed by diffusion in the firn. First, we show that isotopic diffusion firn is reasonably well understood. We do this by showing that a slightly modified version of the Whillans and Grootes (1985) theory makes a tenable a priori prediction of the decay of seasonal isotopic amplitudes with depth at the GISP2 site, though a small adjustment to one parameter significantly improves the prediction. Further, we calculate the amplitude decay at various other ice-core sites and show that these predictions compare favorably with published data from South Pole and locations in southern and central Greenland and the Antarctic Peninsula. We then present numerical experiments wherein synthetic ice-core records are created, diffused, sampled, reconstituted and compared to the original. These show that, alter diffusive mixing in the entire fini column, seasonal amplitudes can be reconstructed to within about 20% error in central Greenland but that all information about sub-annual signals is permanently lost there. Furthermore, most of the error in the amplitude reconstructions is due to the unknowable variations in the sub-annual signal. Finally, we explore how these results can be applied to other locations and suggest that Dye 3 has a high potential for meaningful seasonal reconstructions, while Siple Dome has no potential at all. An optimal ice-core site for seasonal reconstructions has a high accumulation rate and a low temperature.

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Ito, Kensuke, Shohei Ohsawa, and Hideyuki Tanaka. "Information Diffusion Enhanced by Multi-Task Peer Prediction." Journal of Data Intelligence 1, no. 1 (March 2020): 18–35. http://dx.doi.org/10.26421/jdi1.1-2.

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Our study aims to strengthen truthfulness of the two-path mechanism: an information diffusion algorithm to find an influential node in non-cooperative directed acyclic graphs (DAGs). This subject is important because the two-path mechanism ensures only weak truthfulness (i.e., nodes are indifferent between reporting true or false out-edges), which restricts node selection accuracy. To enhance the mechanism, we employed an additional reward layer based on a multi-task peer prediction, where an informative equilibrium provides strictly higher rewards than any other equilibrium in virtually all cases (strong truthfulness). Rewards, which are derived from a comparison of each report, encourage a node to report true out-edges without affecting its own probability of being selected by the original two-path mechanism. We have also experimentally confirmed that our proposed {\em strongly truthful two-path mechanism} can sufficiently elicit true out-edges from each node.

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Ntemi, Myrsini, and Constantine Kotropoulos. "A jump-diffusion particle filter for price prediction." Signal Processing 183 (June 2021): 107994. http://dx.doi.org/10.1016/j.sigpro.2021.107994.

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Rosen, E. M., and D. C. Silverman. "Sorption/Diffusion Prediction in Nonmetallics Using Fick's Law." CORROSION 46, no. 11 (November 1990): 945–51. http://dx.doi.org/10.5006/1.3580864.

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Franzoni, Valentina, Andrea Chiancone, and Alfredo Milani. "A Multistrain Bacterial Diffusion Model for Link Prediction." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 11 (March 31, 2017): 1759024. http://dx.doi.org/10.1142/s0218001417590248.

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Topological link prediction is the task of assessing the likelihood of new future links based on topological properties of entities in a network at a given time. In this paper, we introduce a multistrain bacterial diffusion model for link prediction, where the ranking of candidate links is based on the mutual transfer of bacteria strains via physical social contact. The model incorporates parameters like efficiency of the receiver surface, reproduction rate and number of social contacts. The basic idea is that entities continuously infect their neighborhood with their own bacteria strains, and such infections are iteratively propagated on the social network over time. The probability of transmission can be evaluated in terms of strains, reproduction, previous transfer, surface transfer efficiency, number of direct social contacts i.e. neighbors, multiple paths between entities. The value of the mutual strains of infection between a pair of entities is used to rank the potential arcs joining the entity nodes. The proposed multistrain diffusion model and mutual-strain infection ranking technique have been implemented and tested on widely accepted social network data sets. Experiments show that the MSDM-LP and mutual-strain diffusion ranking technique outperforms state-of-the-art algorithms for neighbor-based ranking.

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Wu, Tao, Leiting Chen, Xingping Xian, and Yuxiao Guo. "Evolution prediction of multi-scale information diffusion dynamics." Knowledge-Based Systems 113 (December 2016): 186–98. http://dx.doi.org/10.1016/j.knosys.2016.09.024.

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Beets-Tan, R. "SP-0583 DIFFUSION MR PREDICTION: STANDARD OR RESEARCH." Radiotherapy and Oncology 103 (May 2012): S233. http://dx.doi.org/10.1016/s0167-8140(12)70921-0.

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Journal articles on the topic 'Diffusion prediction'

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