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Journal articles on the topic 'Stochastic processes with large dimension'

Author: Grafiati

Published: 12 October 2024

Last updated: 31 July 2025

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1

Panos, Aristeidis, Petros Dellaportas, and Michalis K. Titsias. "Large scale multi-label learning using Gaussian processes." Machine Learning 110, no. 5 (2021): 965–87. http://dx.doi.org/10.1007/s10994-021-05952-5.

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AbstractWe introduce a Gaussian process latent factor model for multi-label classification that can capture correlations among class labels by using a small set of latent Gaussian process functions. To address computational challenges, when the number of training instances is very large, we introduce several techniques based on variational sparse Gaussian process approximations and stochastic optimization. Specifically, we apply doubly stochastic variational inference that sub-samples data instances and classes which allows us to cope with Big Data. Furthermore, we show it is possible and bene

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Feitzinger, J. V. "Star Formation in the Large Magellanic Cloud." Symposium - International Astronomical Union 115 (1987): 521–33. http://dx.doi.org/10.1017/s0074180900096315.

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Methods used in pattern recognition and cluster analysis are applied to investigate the spatial distribution of the star forming regions. The fractal dimension of these structures is deduced. The new 21 cm, radio continuum (1.4 GHz) and IRAS surveys reveal scale structures of 700 pc to 1500 pc being identical with the optically identified star forming sites. The morphological structures delineated by young stars reflect physical parameters which determine the star formation in this galaxy. The formation of spiral arm filaments is understandable by stochastic selfpropagating star formation proc

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FRICKE, THOMAS, and DIETMAR WENDT. "THE MARKOFF AUTOMATON: A NEW ALGORITHM FOR SIMULATING THE TIME-EVOLUTION OF LARGE STOCHASTIC DYNAMIC SYSTEMS." International Journal of Modern Physics C 06, no. 02 (1995): 277–306. http://dx.doi.org/10.1142/s0129183195000216.

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We describe a new algorithm for simulating complex Markoff processes. We have used a reaction-cell method in order to simulate arbitrary reactions. It can be used for any kind of RDS on arbitrary topologies, including fractal dimensions or configurations not being related to any spatial geometry. The events within a single cell are managed by an event handler which has been implemented independently of the system studied. The method is exact on the Markoff level including the correct treatment of finite numbers of molecules. To demonstrate its properties, we apply it on a very simple reaction-

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Mazzolo, Alain, and Cécile Monthus. "Conditioning diffusion processes with killing rates." Journal of Statistical Mechanics: Theory and Experiment 2022, no. 8 (2022): 083207. http://dx.doi.org/10.1088/1742-5468/ac85ea.

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Abstract When the unconditioned process is a diffusion submitted to a space-dependent killing rate k ( x → ) , various conditioning constraints can be imposed for a finite time horizon T. We first analyze the conditioned process when one imposes both the surviving distribution at time T and the killing-distribution for the intermediate times t ∈ [0, T]. When the conditioning constraints are less-detailed than these full distributions, we construct the appropriate conditioned processes via the optimization of the dynamical large deviations at level 2.5 in the presence of the conditioning constr

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Honkonen, Juha. "Fractional Stochastic Field Theory." EPJ Web of Conferences 173 (2018): 01005. http://dx.doi.org/10.1051/epjconf/201817301005.

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Models describing evolution of physical, chemical, biological, social and financial processes are often formulated as differential equations with the understanding that they are large-scale equations for averages of quantities describing intrinsically random processes. Explicit account of randomness may lead to significant changes in the asymptotic behaviour (anomalous scaling) in such models especially in low spatial dimensions, which in many cases may be captured with the use of the renormalization group. Anomalous scaling and memory effects may also be introduced with the use of fractional

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Jonckheere, Matthieu, and Seva Shneer. "Stability of Multi-Dimensional Birth-and-Death Processes with State-Dependent 0-Homogeneous Jumps." Advances in Applied Probability 46, no. 1 (2014): 59–75. http://dx.doi.org/10.1239/aap/1396360103.

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We study the conditions for positive recurrence and transience of multi-dimensional birth-and-death processes describing the evolution of a large class of stochastic systems, a typical example being the randomly varying number of flow-level transfers in a telecommunication wire-line or wireless network. First, using an associated deterministic dynamical system, we provide a generic method to construct a Lyapunov function when the drift is a smooth function on ℝN. This approach gives an elementary and direct proof of ergodicity. We also provide instability conditions. Our main contribution cons

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Jonckheere, Matthieu, and Seva Shneer. "Stability of Multi-Dimensional Birth-and-Death Processes with State-Dependent 0-Homogeneous Jumps." Advances in Applied Probability 46, no. 01 (2014): 59–75. http://dx.doi.org/10.1017/s0001867800006935.

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We study the conditions for positive recurrence and transience of multi-dimensional birth-and-death processes describing the evolution of a large class of stochastic systems, a typical example being the randomly varying number of flow-level transfers in a telecommunication wire-line or wireless network. First, using an associated deterministic dynamical system, we provide a generic method to construct a Lyapunov function when the drift is a smooth function on ℝN. This approach gives an elementary and direct proof of ergodicity. We also provide instability conditions. Our main contribution cons

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8

Anantharam, Venkat, and François Baccelli. "Capacity and Error Exponents of Stationary Point Processes under Random Additive Displacements." Advances in Applied Probability 47, no. 1 (2015): 1–26. http://dx.doi.org/10.1239/aap/1427814578.

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Consider a real-valued discrete-time stationary and ergodic stochastic process, called the noise process. For each dimension n, we can choose a stationary point process in ℝn and a translation invariant tessellation of ℝn. Each point is randomly displaced, with a displacement vector being a section of length n of the noise process, independent from point to point. The aim is to find a point process and a tessellation that minimizes the probability of decoding error, defined as the probability that the displaced version of the typical point does not belong to the cell of this point. We consider

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Anantharam, Venkat, and François Baccelli. "Capacity and Error Exponents of Stationary Point Processes under Random Additive Displacements." Advances in Applied Probability 47, no. 01 (2015): 1–26. http://dx.doi.org/10.1017/s0001867800007679.

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Consider a real-valued discrete-time stationary and ergodic stochastic process, called the noise process. For each dimension n, we can choose a stationary point process in ℝ n and a translation invariant tessellation of ℝ n . Each point is randomly displaced, with a displacement vector being a section of length n of the noise process, independent from point to point. The aim is to find a point process and a tessellation that minimizes the probability of decoding error, defined as the probability that the displaced version of the typical point does not belong to the cell of this point. We consi

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EL BOUSTANI, SAMI, and ALAIN DESTEXHE. "BRAIN DYNAMICS AT MULTIPLE SCALES: CAN ONE RECONCILE THE APPARENT LOW-DIMENSIONAL CHAOS OF MACROSCOPIC VARIABLES WITH THE SEEMINGLY STOCHASTIC BEHAVIOR OF SINGLE NEURONS?" International Journal of Bifurcation and Chaos 20, no. 06 (2010): 1687–702. http://dx.doi.org/10.1142/s0218127410026769.

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Nonlinear time series analyses have suggested that the human electroencephalogram (EEG) may share statistical and dynamical properties with chaotic systems. During slow-wave sleep or pathological states like epilepsy, correlation dimension measurements display low values, while in awake and attentive subjects, there is no such low dimensionality, and the EEG is more similar to a stochastic variable. We briefly review these results and contrast them with recordings in cat cerebral cortex, as well as with theoretical models. In awake or sleeping cats, recordings with microelectrodes inserted in

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Dulfan, Anna, and Iryna Voronko. "Features of Spatial-Temporal Hierarchical Structures Formation." Lighting Engineering & Power Engineering 60, no. 2 (2021): 66–70. http://dx.doi.org/10.33042/2079-424x.2021.60.2.03.

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The degree of ordering of the structure of technologically important materials formed as a result of the evolution of complex physicochemical systems determines their physical properties, in particular optical. In this regard, the primary task for the theoretical study of methods for obtaining materials with predetermined physical properties is to develop approaches to describe the evolution of fractal (scale-invariant) objects in the formation of self-similar structures in systems exhibiting chaotic behavior. The paper forms an idea of the processes of evolution in materials formed as a resul

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Hashino, Tempei, and Gregory J. Tripoli. "The Spectral Ice Habit Prediction System (SHIPS). Part IV: Box Model Simulations of the Habit-Dependent Aggregation Process." Journal of the Atmospheric Sciences 68, no. 6 (2011): 1142–61. http://dx.doi.org/10.1175/2011jas3667.1.

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Abstract The purpose of this paper is to assess the prediction of particle properties of aggregates and particle size distributions with the Spectral Ice Habit Prediction System (SHIPS) and to investigate the effects of crystal habits on aggregation process. Aggregation processes of ice particles are critical to the understanding of precipitation and the radiative signatures of cloud systems. Conventional approaches taken in cloud-resolving models (CRMs) are not ideal to study the effects of crystal habits on aggregation processes because the properties of aggregates have to be assumed beforeh

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Hasal, Pavel, and Vladimír Kudrna. "Certain Problems with the Application of Stochastic Diffusion Processes for the Description of Chemical Engineering Phenomena. Numerical Simulation of One-Dimensional Diffusion Process." Collection of Czechoslovak Chemical Communications 61, no. 4 (1996): 512–35. http://dx.doi.org/10.1135/cccc19960512.

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Some problems are analyzed arising when a numerical simulation of a random motion of a large ensemble of diffusing particles is used to approximate the solution of a one-dimensional diffusion equation. The particle motion is described by means of a stochastic differential equation. The problems emerging especially when the diffusion coefficient is a function of spatial coordinate are discussed. The possibility of simulation of various kinds of stochastic integral is demonstrated. It is shown that the application of standard numerical procedures commonly adopted for ordinary differential equati

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Morzfeld, M., and A. J. Chorin. "Implicit particle filtering for models with partial noise, and an application to geomagnetic data assimilation." Nonlinear Processes in Geophysics 19, no. 3 (2012): 365–82. http://dx.doi.org/10.5194/npg-19-365-2012.

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Abstract. Implicit particle filtering is a sequential Monte Carlo method for data assimilation, designed to keep the number of particles manageable by focussing attention on regions of large probability. These regions are found by minimizing, for each particle, a scalar function F of the state variables. Some previous implementations of the implicit filter rely on finding the Hessians of these functions. The calculation of the Hessians can be cumbersome if the state dimension is large or if the underlying physics are such that derivatives of F are difficult to calculate, as happens in many geo

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Dong, Zhipeng, Yucheng Liu, Jianshe Kang, and Shaohui Zhang. "A Stochastic Learning Algorithm for Machine Fault Diagnosis." Shock and Vibration 2022 (February 18, 2022): 1–9. http://dx.doi.org/10.1155/2022/5790185.

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Industrial big data bring a large number of high-dimensional sample datasets. Although a deep learning network can well mine the internal nonlinear structure of the dataset, the construction of the deep learning model requires a lot of computing time and hardware facilities. At the same time, there are some nonlinear problems such as noise and fluctuation in industrial data, which make the deep architecture extremely complex and the recognition accuracy of the diagnosis model difficult to guarantee. To solve this problem, a new method, named stochastic learning algorithm (SL), is proposed in t

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Blăjină, Ovidiu, and Ionuţ Gabriel Ghionea. "On Solving Stochastic Optimization Problems." Mathematics 11, no. 21 (2023): 4451. http://dx.doi.org/10.3390/math11214451.

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Many optimization mathematical models, associated with the technical-economic processes of real-world problems, have elements of uncertainty in their structure, which places them in stochastic optimization programming. Their diversity and complexity, due to the large uncertainty space, require special methods of solving, because there is no general solution method. Within this context, in this paper we consider the category of optimization models that can contain random variable type coefficients and/or imposed probability levels on the constraints. The purpose of the paper is to propose a met

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Ullmann, Sebastian, Christopher Müller, and Jens Lang. "Stochastic Galerkin Reduced Basis Methods for Parametrized Linear Convection–Diffusion–Reaction Equations." Fluids 6, no. 8 (2021): 263. http://dx.doi.org/10.3390/fluids6080263.

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We consider the estimation of parameter-dependent statistics of functional outputs of steady-state convection–diffusion–reaction equations with parametrized random and deterministic inputs in the framework of linear elliptic partial differential equations. For a given value of the deterministic parameter, a stochastic Galerkin finite element (SGFE) method can estimate the statistical moments of interest of a linear output at the cost of solving a single, large, block-structured linear system of equations. We propose a stochastic Galerkin reduced basis (SGRB) method as a means to lower the comp

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Mürmann, Paula, Robin Jaccard, Maximilien Dreveton, Aryan Alavi Razavi Ravari, and Patrick Thiran. "Reducing Sensor Requirements by Relaxing the Network Metric Dimension." Proceedings of the ACM on Measurement and Analysis of Computing Systems 9, no. 2 (2025): 1–35. https://doi.org/10.1145/3727130.

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Source localization in graphs involves identifying the origin of a phenomenon or event, such as an epidemic outbreak or a misinformation source, by leveraging structural graph properties. One key concept in this context is the metric dimension, which quantifies the minimum number of strategically placed sensors needed to uniquely identify all vertices based on their distances. While powerful, the traditional metric dimension imposes a stringent requirement that every vertex must be uniquely identified, often necessitating a large number of sensors. In this work, we relax the metric dimension a

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Lin, Chang-Sheng, and Yi-Xiu Wu. "Response-Only Parametric Estimation of Structural Systems Using a Modified Stochastic Subspace Identification Technique." Applied Sciences 11, no. 24 (2021): 11751. http://dx.doi.org/10.3390/app112411751.

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The present paper is a study of output-only modal estimation based on the stochastic subspace identification technique (SSI) to avoid the restrictions of well-controlled laboratory conditions when performing experimental modal analysis and aims to develop the appropriate algorithms for ambient modal estimation. The conventional SSI technique, including two types of covariance-driven and data-driven algorithms, is employed for parametric identification of a system subjected to stationary white excitation. By introducing the procedure of solving the system matrix in SSI-COV in conjunction with S

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De Meulenaere, Roeland, Diederik Coppitters, Ale Sikkema, Tim Maertens, and Julien Blondeau. "Uncertainty Quantification for Thermodynamic Simulations with High-Dimensional Input Spaces Using Sparse Polynomial Chaos Expansion: Retrofit of a Large Thermal Power Plant." Applied Sciences 13, no. 19 (2023): 10751. http://dx.doi.org/10.3390/app131910751.

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The assessment of the future thermodynamics performance of a retrofitted heat and power production unit is prone to many uncertainties due to the large number of parameters involved in the modeling of all its components. To carry out uncertainty quantification analysis, alternatives to the traditional Monte Carlo method must be used due to the large stochastic dimension of the problem. In this paper, sparse polynomial chaos expansion (SPCE) is applied to the retrofit of a large coal-fired power plant into a biomass-fired combined heat and power unit to quantify the main drivers and the overall

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Yihe Lü, Bojie Fu, Liding Chen, Guohua Liu, and Wei Wei. "Nutrient transport associated with water erosion: progress and prospect." Progress in Physical Geography: Earth and Environment 31, no. 6 (2007): 607–20. http://dx.doi.org/10.1177/0309133307087085.

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Along with the major water erosion process, large amounts of soil nutrient can be transported to water bodies that may degrade the erosion sites and result in water pollution problems in the nutrient-receiving water bodies. Because of the far-reaching ecological and environmental impacts of the nutrient transport processes during water erosion, much research has been done on this topic. The progress of this research topic is reviewed from the perspective of nutrient sources, covering triggers, carriers and forms of nutrient transport, and the factors regulating nutrient transport. Although muc

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Comeau, Jules, and Eldon Gunn. "A neuro-dynamic programming approach to the optimal stand management problem." Canadian Journal of Forest Research 47, no. 6 (2017): 808–16. http://dx.doi.org/10.1139/cjfr-2016-0302.

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Some ideas of neuro-dynamic programming (NDP) are illustrated by considering the problem of optimally managing a forest stand under uncertainty. Because reasonable growth models require state information such as height (or age), basal area, and stand diameter, as well as an indicator variable for treatments that have been performed on the stand, they can easily lead to very large state spaces that include continuous variables. Realistic stand management policies include silvicultural options such as pre-commercial and commercial thinning as well as post-harvest treatments. We are interested in

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Brown, Mark, and Joel E. Cohen. "SQUARED COEFFICIENT OF VARIATION OF TAYLOR'S LAW FOR RANDOM ABSOLUTE DIFFERENCES." Probability in the Engineering and Informational Sciences 32, no. 4 (2017): 483–94. http://dx.doi.org/10.1017/s0269964817000304.

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In a family, parameterized by θ, of non-negative random variables with finite, positive second moment, Taylor's law (TL) asserts that the population variance is proportional to a power of the population mean as θ varies: σ2 (θ) = a[μ(θ)]b, a > 0. TL, sometimes called fluctuation scaling, holds widely in science, probability theory, and stochastic processes. Here we report diverse examples of TL with b = 2 (equivalent to a constant coefficient of variation) arising from a difference of random variables in normed vector spaces of dimension 1 and larger. In these examples, we compute a exactly

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Gavrilyuk, Ivan, and Boris N. Khoromskij. "Tensor Numerical Methods: Actual Theory and Recent Applications." Computational Methods in Applied Mathematics 19, no. 1 (2019): 1–4. http://dx.doi.org/10.1515/cmam-2018-0014.

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AbstractMost important computational problems nowadays are those related to processing of the large data sets and to numerical solution of the high-dimensional integral-differential equations. These problems arise in numerical modeling in quantum chemistry, material science, and multiparticle dynamics, as well as in machine learning, computer simulation of stochastic processes and many other applications related to big data analysis. Modern tensor numerical methods enable solution of the multidimensional partial differential equations (PDE) in {\mathbb{R}^{d}} by reducing them to one-dimension

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Ghaznavi, Ziam, Nicholas Butcher, Dragan Djurdjanovic, and S. V. Sreenivasan. "Roll-to-roll reactive ion etching of large-area nanostructure arrays in Si: Process development, characterization, and optimization." Journal of Vacuum Science & Technology B 41, no. 2 (2023): 022802. http://dx.doi.org/10.1116/6.0002261.

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Roll-to-roll (R2R) nanofabrication processes are recognized as key enabling-technologies for many next-generation applications in flexible electronics, displays, energy generation, storage, as well as healthcare. However, R2R processing techniques reported in the literature currently lack a scalable method of performing high-throughput nanoscale pattern transfer of geometry requiring a high degree of fidelity in terms of critical dimension resolution, etch uniformity, and aspect ratio. Reactive ion etching (RIE) addresses the need for sub-10 nm pattern transfer with large-area uniformity in wa

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Artyushenko, Mikhail, and Anna Khizhnyak. "ENTROPY METHODS OF SELF-ORGANIZING SYSTEMS MODELING USING HYPERSPECTRAL REMOTE SENSING DATA." Journal of Automation and Information sciences 2 (March 1, 2021): 67–78. http://dx.doi.org/10.34229/1028-0979-2021-2-6.

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Various mathematical models are created for exploring complex self-organizing systems. In geosystems, the deterministic nature of processes is due to their stochastic properties. In such systems, regular deterministic processes are formed by numerous random interelement interactions that occur at the micro level. In many cases, it is not possible to define correctly the deterministic law of evolution of the observable system or its part because of the large number of unpredictable and unknown factors that influence it. However, at the micro level, statistical distributions of system elements a

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Pinto, Helder, Yuri Antonacci, Gorana Mijatovic, et al. "Information-Theoretic Sequential Framework to Elicit Dynamic High-Order Interactions in High-Dimensional Network Processes." Mathematics 13, no. 13 (2025): 2081. https://doi.org/10.3390/math13132081.

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Complex networks of stochastic processes are crucial for modeling the dynamics of interacting systems, particularly those involving high-order interactions (HOIs) among three or more components. Traditional measures—such as mutual information (MI), interaction information (II), the redundancy-synergy index (RSI), and O-information (OI)—are typically limited to static analyses not accounting for temporal correlations and become computationally unfeasible in large networks due to the exponential growth of the number of interactions to be analyzed. To address these challenges, first a framework i

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López-Fernández, Estíbaliz, and José López. "The Impact of Tumor Eco-Evolution in Renal Cell Carcinoma Sampling." Cancers 10, no. 12 (2018): 485. http://dx.doi.org/10.3390/cancers10120485.

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Malignant tumors behave dynamically as cell communities governed by ecological principles. Massive sequencing tools are unveiling the true dimension of the heterogeneity of these communities along their evolution in most human neoplasms, clear cell renal cell carcinomas (CCRCC) included. Although initially thought to be purely stochastic processes, very recent genomic analyses have shown that temporal tumor evolution in CCRCC may follow some deterministic pathways that give rise to different clones and sub-clones randomly spatially distributed across the tumor. This fact makes each case unique

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Croci, Carolina, Gabriela Martínez de la Escalera, Carla Kruk, Angel Segura, Susana Deus Alvarez, and Claudia Piccini. "Selective enrichment of active bacterial taxa in the Microcystis associated microbiome during colony growth." PeerJ 13 (April 4, 2025): e19149. https://doi.org/10.7717/peerj.19149.

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The toxic cyanobacterium Microcystis causes worldwide health concerns, being frequently found in freshwater and estuarine ecosystems. Under natural conditions, Microcystis spp. show a colonial lifestyle involving a phycosphere populated by a highly diverse associated microbiome. In a previous study, we have proposed that colony formation and growth may be achieved through mechanisms of multispecies bacterial biofilm formation. Starting with single-cells, specific bacteria would be recruited from the environment to attach and create a buoyant biofilm or colony. This progression from a few singl

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Rato, Tiago J., Pedro Delgado, Cristina Martins, and Marco S. Reis. "First Principles Statistical Process Monitoring of High-Dimensional Industrial Microelectronics Assembly Processes." Processes 8, no. 11 (2020): 1520. http://dx.doi.org/10.3390/pr8111520.

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Modern industrial units collect large amounts of process data based on which advanced process monitoring algorithms continuously assess the status of operations. As an integral part of the development of such algorithms, a reference dataset representative of normal operating conditions is required to evaluate the stability of the process and, after confirming that it is stable, to calibrate a monitoring procedure, i.e., estimate the reference model and set the control limits for the monitoring statistics. The basic assumption is that all relevant “common causes” of variation appear well repres

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MIŁOŚ, PIOTR. "OCCUPATION TIMES OF SUBCRITICAL BRANCHING IMMIGRATION SYSTEMS WITH MARKOV MOTION, CLT AND DEVIATION PRINCIPLES." Infinite Dimensional Analysis, Quantum Probability and Related Topics 15, no. 01 (2012): 1250002. http://dx.doi.org/10.1142/s0219025712500026.

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In this paper we consider two related stochastic models. The first one is a branching system consisting of particles moving according to a Markov family in ℝd and undergoing subcritical branching with a constant rate of V > 0. New particles immigrate to the system according to a homogeneous space-time Poisson random field. The second model is the superprocess corresponding to the branching particle system. We study rescaled occupation time process and the process of its fluctuations under mild assumptions on the Markov family. In the general setting a functional central limit theorem as wel

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Klein, Marten, and Heiko Schmidt. "Exploring stratification effects in stable Ekman boundary layers using a stochastic one-dimensional turbulence model." Advances in Science and Research 19 (October 25, 2022): 117–36. http://dx.doi.org/10.5194/asr-19-117-2022.

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Abstract. Small-scale processes in atmospheric boundary layers are typically not resolved due to cost constraints but modeled based on physical relations with the resolved scales, neglecting expensive backscatter. This lack in modeling is addressed in the present study with the aid of the one-dimensional turbulence (ODT) model. ODT is applied as stand-alone column model to numerically investigate stratification effects in long-lived transient Ekman flows as canonical example of polar boundary layers by resolving turbulent winds and fluctuating temperature profiles on all relevant scales of the

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Bobkov, Vladimir I., Olga V. Bulygina, and Elizaveta K. Vereikina. "Using population algorithms to optimize the temperature regime of roasting phosphorite pellets." Journal Of Applied Informatics 17, no. 6 (2022): 94–101. http://dx.doi.org/10.37791/2687-0649-2022-17-6-94-101.

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The problem of rational energy resource use is especially acute for energy- intensive industries, which include high-temperature processing of mining chemical raw materials (for example, the production of phosphorite pellets from apatite-nepheline ore waste by drying and roasting). In this regard, the temperature modes of roasting conveyor machine should ensure not only the completion of the ongoing chemical-technological processes and the required product quality, but also energy and resource saving. Thus, there is an urgent scientific and practical task of optimizing charge heating modes bas

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Gao, Tianyun, Heiko Schmidt, Marten Klein, et al. "One-dimensional turbulence modeling of compressible flows: II. Full compressible modification and application to shock–turbulence interaction." Physics of Fluids 35, no. 3 (2023): 035116. http://dx.doi.org/10.1063/5.0137435.

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One-dimensional turbulence (ODT) is a simulation methodology that represents the essential physics of three-dimensional turbulence through stochastic resolution of the full range of length and time scales on a one-dimensional domain. In the present study, full compressible modifications are incorporated into ODT methodology, based on an Eulerian framework and a conservative form of the governing equations. In the deterministic part of this approach, a shock capturing scheme is introduced for the first time. In the stochastic part, one-dimensional eddy events are modeled and sampled according t

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Marelli, Enrico Piero, Maria Laura Parisi, and Marcello Signorelli. "Economic convergence in the EU and Eurozone." Journal of Economic Studies 46, no. 7 (2019): 1332–44. http://dx.doi.org/10.1108/jes-03-2019-0139.

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Purpose The purpose of this paper is to analyse whether several groups of European countries are on track for real “conditional” economic convergence in per capita income and the likely speed of convergence. The paper focusses also on the changes of the convergence processes over time. Design/methodology/approach Unlike the simple “absolute convergence”, it explores the concept of “conditional” or “club” convergence. Moreover, it adopts the approach of extending the univariate model to take into account the panel dimension over an extended time interval and endogeneity. Findings A process of r

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Younis, Haseeb, Paul Trust, and Rosane Minghim. "Understanding High Dimensional Spaces through Visual Means Employing Multidimensional Projections." International Journal on Engineering, Science and Technology 4, no. 2 (2022): 170–79. http://dx.doi.org/10.46328/ijonest.91.

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Data visualisation helps understanding data represented by multiple variables, also called features, stored in a large matrix where individuals are stored in lines and variable values in columns. These data structures are frequently called multidimensional spaces. A large set of mathematical tools, named frequently as multidimensional projections, aim to map such large spaces into 'visual spaces', that is, to 2 or 3 dimensions, where the aspect of that space can be visualised. While the final product is intuitive in that proximity between points - or iconic representation of points - indicate

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Gupta, Sangita Dutta, Ajitava Raychaudhuri, and Sushil Kumar Haldar. "Information technology and profitability: evidence from Indian banking sector." International Journal of Emerging Markets 13, no. 5 (2018): 1070–87. http://dx.doi.org/10.1108/ijoem-06-2017-0211.

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Purpose Information Technology has transformed the banking sector with respect to various systems and processes. Banks have adopted various measures to quicken their business activity and also save cost and time. That is why there has been large requirement of IT in the banking sector. The question arises whether this investment is enhancing the profitability of the bank or not. The purpose of this paper is to examine the presence of profitability paradox in Indian Banking Sector. Design/methodology/approach Data are collected from ten nationalized banks and three private sector banks from 200

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Schubert, Till, Jan Martin Brockmann, Johannes Korte, and Wolf-Dieter Schuh. "On the Family of Covariance Functions Based on ARMA Models." Engineering Proceedings 5, no. 1 (2021): 37. http://dx.doi.org/10.3390/engproc2021005037.

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In time series analyses, covariance modeling is an essential part of stochastic methods such as prediction or filtering. For practical use, general families of covariance functions with large flexibilities are necessary to model complex correlations structures such as negative correlations. Thus, families of covariance functions should be as versatile as possible by including a high variety of basis functions. Another drawback of some common covariance models is that they can be parameterized in a way such that they do not allow all parameters to vary. In this work, we elaborate on the affilia

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Nikolopoulos, S., P. Kapiris, K. Karamanos, and K. Eftaxias. "A unified approach of catastrophic events." Natural Hazards and Earth System Sciences 4, no. 5/6 (2004): 615–31. http://dx.doi.org/10.5194/nhess-4-615-2004.

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Abstract. Although there is an accumulated charge of theoretical, computational, and numerical work, like catastrophe theory, bifurcation theory, stochastic and deterministic chaos theory, there is an important feeling that these matters do not completely cover the physics of real catastrophic events. Recent studies have suggested that a large variety of complex processes, including earthquakes, heartbeats, and neuronal dynamics, exhibits statistical similarities. Here we are studying in terms of complexity and non linear techniques whether isomorphic signatures emerged indicating the transiti

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Murakami, H., X. Chen, M. S. Hahn, et al. "Bayesian approach for three-dimensional aquifer characterization at the hanford 300 area." Hydrology and Earth System Sciences Discussions 7, no. 2 (2010): 2017–52. http://dx.doi.org/10.5194/hessd-7-2017-2010.

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Abstract. This study presents a stochastic, three-dimensional characterization of a heterogeneous hydraulic conductivity field within DOE's Hanford 300 Area site, Washington, by assimilating large-scale, constant-rate injection test data with small-scale, three-dimensional electromagnetic borehole flowmeter (EBF) measurement data. We first inverted the injection test data to estimate the transmissivity field, using zeroth-order temporal moments of pressure buildup curves. We applied a newly developed Bayesian geostatistical inversion framework, the method of anchored distributions (MAD), to ob

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Wang, Zhizhen, Shuheng Tang, Zhifeng Yan, et al. "Three-Dimensional Geological Modeling of Coal Reservoirs and Analysis of Sensitivity Factors for Combined Mining Capacity." Processes 11, no. 12 (2023): 3448. http://dx.doi.org/10.3390/pr11123448.

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Due to the large non-homogeneity of coal reservoirs, there is a large uncertainty about the extent of the impact on coal bed methane production capacity. The Hanchengbei Block has the problems of early exploration, less available production data, and large variations in developed production capacity within a single well group during test production. Therefore, how to use the existing data to analyze the geological factors affecting the development of coalbed methane in the Hanchengbei Block is particularly important. In this paper, based on the coal seam properties and production characteristi

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Candel, Jasper H. J., Maarten G. Kleinhans, Bart Makaske, Wim Z. Hoek, Cindy Quik, and Jakob Wallinga. "Late Holocene channel pattern change from laterally stable to meandering – a palaeohydrological reconstruction." Earth Surface Dynamics 6, no. 3 (2018): 723–41. http://dx.doi.org/10.5194/esurf-6-723-2018.

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Abstract. River channel patterns may alter due to changes in hydrological regime related to changes in climate and/or land cover. Such changes are well documented for transitions between meandering and braiding rivers, whereas channel pattern changes between laterally stable and meandering rivers are poorly documented and understood. We hypothesize that many low-energy meandering rivers had relatively low peak discharges and were laterally stable during most of the Holocene, when climate was relatively stable and human impact was limited. Our objectives in this work are to identify a Late Holo

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Monterrubio-Velasco, Marisol, F. Ramón Zúñiga, José Carlos Carrasco-Jiménez, Víctor Márquez-Ramírez, and Josep de la Puente. "Modeling active fault systems and seismic events by using a fiber bundle model – example case: the Northridge aftershock sequence." Solid Earth 10, no. 5 (2019): 1519–40. http://dx.doi.org/10.5194/se-10-1519-2019.

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Abstract. Earthquake aftershocks display spatiotemporal correlations arising from their self-organized critical behavior. Dynamic deterministic modeling of aftershock series is challenging to carry out due to both the physical complexity and uncertainties related to the different parameters which govern the system. Nevertheless, numerical simulations with the help of stochastic models such as the fiber bundle model (FBM) allow the use of an analog of the physical model that produces a statistical behavior with many similarities to real series. FBMs are simple discrete element models that can b

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Morena, Carlos De La, Y. A. Nefedyev, A. O. Andreev, et al. "The analysis of Titan’s physical surface using multifractal geometry methods." Journal of Physics: Conference Series 2103, no. 1 (2021): 012017. http://dx.doi.org/10.1088/1742-6596/2103/1/012017.

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Abstract Titan makes up 95% of the mass of all 82 satellites of Saturn. Titan’s diameter is 5152 km, which means that it is larger than the Moon by 50%, and it is also significantly larger than Mercury. On the satellite, a subsurface ocean is possible, the theory of the presence of which has already been advanced earlier by some scientists. It is located under a layer of ice and consists of 10% ammonia, which is a natural antifreeze for it and does not allow the ocean to freeze. On the one hand, the ocean contains a huge amount of salt, which makes the likelihood of life in it hardly possible.

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Dudek, Gabriela, Przemysław Borys, Anna Strzelewicz, and Monika Krasowska. "Characterization of the Structure and Transport Properties of Alginate/Chitosan Microparticle Membranes Utilized in the Pervaporative Dehydration of Ethanol." Polymers 12, no. 2 (2020): 411. http://dx.doi.org/10.3390/polym12020411.

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The structure and transport properties of alginate/chitosan microparticle membranes used in ethanol dehydration processes were investigated. The membranes were characterized based on images obtained from high-resolution microscopy. The following parameters were determined: the observed total amount of void space, the average size of the void domains, their length and diameter, the fractal dimension, and the generalized stochastic fractal parameters. The total amount of void space was determined to be between 54% and 64%. The average size of the void domains is smaller for alginate membranes co

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Schertzer, D., and S. Lovejoy. "EGS Richardson AGU Chapman NVAG3 Conference: Nonlinear Variability in Geophysics: scaling and multifractal processes." Nonlinear Processes in Geophysics 1, no. 2/3 (1994): 77–79. http://dx.doi.org/10.5194/npg-1-77-1994.

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Abstract. 1. The conference The third conference on "Nonlinear VAriability in Geophysics: scaling and multifractal processes" (NVAG 3) was held in Cargese, Corsica, Sept. 10-17, 1993. NVAG3 was joint American Geophysical Union Chapman and European Geophysical Society Richardson Memorial conference, the first specialist conference jointly sponsored by the two organizations. It followed NVAG1 (Montreal, Aug. 1986), NVAG2 (Paris, June 1988; Schertzer and Lovejoy, 1991), five consecutive annual sessions at EGS general assemblies and two consecutive spring AGU meeting sessions. As with the other co

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Geiger, Bernhard C., and Tobias Koch. "On the Information Dimension of Stochastic Processes." IEEE Transactions on Information Theory 65, no. 10 (2019): 6496–518. http://dx.doi.org/10.1109/tit.2019.2922186.

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Howroyd, Douglas C., and Han Yu. "Assouad Dimension of Random Processes." Proceedings of the Edinburgh Mathematical Society 62, no. 1 (2018): 281–90. http://dx.doi.org/10.1017/s0013091518000433.

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AbstractIn this paper we study the Assouad dimension of graphs of certain Lévy processes and functions defined by stochastic integrals. We do this by introducing a convenient condition which guarantees a graph to have full Assouad dimension and then show that graphs of our studied processes satisfy this condition.

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LOWEN, STEVEN B., and MALVIN C. TEICH. "ESTIMATION AND SIMULATION OF FRACTAL STOCHASTIC POINT PROCESSES." Fractals 03, no. 01 (1995): 183–210. http://dx.doi.org/10.1142/s0218348x95000151.

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We investigate the properties of fractal stochastic point processes (FSPPs). First, we define FSPPs and develop several mathematical formulations for these processes, showing that over a broad range of conditions they converge to a particular form of FSPP. We then provide examples of a wide variety of phenomena for which they serve as suitable models. We proceed to examine the analytical properties of two useful fractal dimension estimators for FSPPs, based on the second-order properties of the points. Finally, we simulate several FSPPs, each with three specified values of the fractal dimensio

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Drach, I., M. Dykha, O. Babak, and O. Kovtun. "Modeling surface structure of tribotechnical materials." Problems of Tribology 29, no. 1/111 (2024): 16–24. http://dx.doi.org/10.31891/2079-1372-2024-111-1-16-24.

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Modern tribology makes it possible to correctly calculate, diagnose, predict and select appropriate materials for friction pairs, to determine the optimal mode of operation of the tribo-joint. The main parameter for solving friction problems and other problems of tribology is the topography of the surface. The main purpose of the models in these tasks is to display the tribological properties of engineering surfaces. In the framework of the classical approach, the topography of the surface is studied on the basis of its images from the point of view of functional and statistical characteristic

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