A general purpose parallel Fortran code for grid projected concentration reconstruction from multidimensional particle distributions

Kernel density estimation (KDE) is a widely used method in geography to study concentration of point pattern data. Geographical networks are 1.5 dimensional spaces with specific characteristics, analyzing events occurring on networks (accidents on roads, leakages of pipes, species along rivers, etc.). In the last decade, they required the extension of spatial KDE. Several versions of Network KDE (NKDE) have been proposed, each with their particular advantages and disadvantages, and are now used on a regular basis. However, scant attention has been given to the temporal extension of NKDE (TNKDE). In practice, when the studied events happen at specific time points and are constrained on a network, the methodologies used by geographers tend to overlook either the network or the temporal dimension. Here we propose a TNKDE based on the recent development of NKDE and the product of kernels. We also adapt classical methods of KDE (Diggle's correction, Abramson's adaptive bandwidth and bandwidth selection by leave‐one‐out maximum likelihood). We also illustrate the method with Montreal road crashes involving a pedestrian between 2016 and 2019.

Flux Corrected Transport (FCT) is a technique developed to reduce numerical diffusion around shock fronts which has not previously been used in reservoir engineering. The first section of this paper describes the application of FCT techniques to solving a one dimensional waterflooding problem, and shows how to include FCT in an IMPES simulator. A second feature of our program is the implicit timesplit treatment of capillary pressure effects. A comparison between laboratory data and simulator results is included, and good agreement is obtained.

An improved mean shift segmentation method featuring adaptive parameter selection is presented in this paper. We associate the bandwidths and weight for each point in a spatial-range feature space with boundary information in an image plane. Varying weight and bandwidth for each pixel are assigned according to a boundary map, which is obtained by learning multiple edge cues. We consider two groups of edge cues and two regressing modules, approach the cue combination as a supervised learning problem from the ground truth data (manually sketched boundary maps). From our preliminary results, the provided method can combine the top-down information got from regression models with the mean shift process and constrain over-clustering of pixels belonging different land objects.

Numerical simulation of three-dimensional (3D) seismic wavefields forms the basis of the research on the migration methods of 3D seismic data based on wave equations. Because the simulation precision of wavefield extrapolation determines the imaging accuracy to a certain extent, it is very important to study how to enhance the forward modeling precision of 3D seismic wavefields. Thus, we build on an optimized 3D staggered-grid finite-difference (SFD) method with high simulation precision based on two-dimensional (2D) seismic modeling. Since it generates the corresponding difference coefficients by utilizing the least square (LS) method to minimize the objective function constructed by the time-space domain dispersion relation of the 3D acoustic wave equation, our optimized time-space domain LS-based 3D SFD method can effectively enhance the modeling precision of the 3D seismic wavefields in theory compared with the 3D SFD methods based on the Taylor-series expansion (TE), especially for the large wavenumber range. Examining the numerical dispersion, algorithm stability and computational cost, we compare our optimized time-space domain LS-based 3D SFD method with three conventional TE-based and LS-based 3D SFD methods to illustrate and demonstrate its effectiveness and feasibility. The numerical examples from different 3D models suggest that our optimized time-space domain LS-based 3D SFD method can generate less numerical dispersion and higher simulation accuracy for 3D seismic wavefields than three other conventional 3D SFD methods, but its stability condition is stricter and its computational cost is slightly higher.

Mechanical interactions between flowing and coagulated blood (thrombus) are crucial in dictating the deformation and remodeling of a thrombus after its formation in hemostasis. We propose a fully-Eulerian, three-dimensional, phase-field model of thrombus that is calibrated with existing in vitro experimental data. This phase-field model considers spatial variations in permeability and material properties within a single unified mathematical framework derived from an energy perspective, thereby allowing us to study effects of thrombus microstructure and properties on its deformation and possible release of emboli under different hemodynamic conditions. Moreover, we combine this proposed thrombus model with a particle-based model which simulates the initiation of the thrombus. The volume fraction of a thrombus obtained from the particle simulation is mapped to an input variable in the proposed phase-field thrombus model. The present work is thus the first computational study to integrate the initiation of a thrombus through platelet aggregation with its subsequent viscoelastic responses to various shear flows. This framework can be informed by clinical data and potentially be used to predict the risk of diverse thromboembolic events under physiological and pathological conditions.

Diabetes mellitus poses a growing global health concern, particularly in low- and middle-income countries where early detection remains limited, demanding classification models that balance accuracy, interpretability, and adaptability to heterogeneous clinical data. This study proposes and evaluates the Adaptive Kernel Probability Model (AKPM), a novel nonparametric probabilistic classifier designed to enhance diabetes prediction by performing localized kernel density estimation with adaptive bandwidth selection via k-nearest neighbors. Implemented and tested on the Pima Indians Diabetes Dataset, AKPM outperformed conventional classifiers—Naïve Bayes and Gaussian Mixture Models (GMM)—across all evaluation metrics, achieving 87.5% accuracy, 83.3% precision, 76.9% recall, and an F1-score of 80.0% for the diabetic class, alongside 89.3% precision and 92.6% recall for the normal class. These results surpassed GMM (83.0% accuracy, 71.6% F1-score) and Naïve Bayes (80.0% accuracy, 66.6% F1-score), confirming AKPM’s superior capability to detect diabetic cases while minimizing false negatives. Offering transparent posterior inference and a modular design, AKPM emerges as a reliable and interpretable solution for clinical decision support systems and real-world healthcare applications.

The results of a study noise filtering in temperature time series measuring channels using basic frequency filtering algorithms are presented. The study was carried out from the perspective of high frequency random noise suppression. Temperature time series filtering was compared using sliding window procedures with adaptive bandwidth selection, median sliding window, and Baxter-King band-pass approximation. Experimental comparative analysis of filtering efficiency was carried out using the statistical programming language R and open libraries with sliding regression methods. The recorders of surface atmosphere parameters were ultrasonic meteorological stations located at the test site of IMKES SB RAS.

The narrow escape problem is a first-passage problem that concerns the calculation of the time needed for a Brownian particle to leave a domain with localized absorbing boundary traps, such that the measure of these traps is asymptotically small compared to the domain size. A common model for the mean first-passage time (MFPT) as a function of particle's starting location in a given domain with constant diffusivity is given by a Poisson partial differential equation subject to mixed Dirichlet-Neumann boundary conditions. The primary objective of this work is to perform direct numerical simulations of multiple particles undergoing Brownian motion in a three-dimensional spherical domain with boundary traps, compute MFPT values by averaging Brownian escape times, and compare these with explicit asymptotic results obtained previously by approximate solution of the Poisson problem. A close agreement of MFPT values is observed already at 10^{4} particle runs from a single starting point, providing a computational validation of the Poisson equation-based continuum model. Direct Brownian dynamics simulations are also used to study additional features of particle dynamics in narrow escape problems that cannot be captured in a continuum approach, such as average times spent by particles in a thin layer near the domain boundary, and effects of isotropic vs anisotropic near-boundary diffusion.

Development until the late 1980s and early 1990s on embodied the initiative of a typical science as the study of Papua titled Papuanistiecs as a course of study at the Department of Southeast Asia and Oceania, Faculty of Arts, University of Leiden-The Netherlands, while Melanesianology lead anthropology has been developed since 1896 when Boas carry out research works in the region of South Pacific and the Southwest Pacific. When Papuanistiecs sounds as a specific branch of the broader Melanesianology include anthropology, the course is not closed for any other specific subject areas, especially on the natural environment as physical nature (tangible) as well as the philosophy as the inner nature (intangible). A concept of intangible or philosophy of Papua-Melanesian on Socio-Cultural structure is what being proposed in this writing work as a "hidden-structure” or ‘covered structures'. The term or this formula was by the author uses to accommodate referrals various scholars who call the social structure of Papua-Melanesian as "loosely structure" or a missing structure and also as "confusing diversity" or as confuse or chaos diverse. That, "hidden structure" is said to be so because it is hidden to those outside the system which understanding as ethics while looking for people in the system who view of understanding the relationship of emics accused loose or off and confuse or confusing is very well lightly acknowledge. Melanesianology and Papuanistiecs very well coordinated by the Sydney University and the Australian National University in Canberra by enabling the University of Papua New Guinea/UPNG in Port Moresby PNG and the University of the South Pacific/USP, in Suva-Fiji. In addition to its general purpose by making Papuanistiecs and Melanesianology as a discipline of area regional study, the special purpose of the author also is about to raise these matters in connection with the State University of Cenderawasih who have declared itself as Anthropological Study Base, then the function of Papuanistiec and Melanesianology may be of the major studies take precedence in all disciplines.

There has been no prior general purpose brain-mind model for multiple events in complex backgrounds. I first discuss that although the age of brain-mind seems to have arrived, the current infrastructure does not well fit the need of research and peer-review for the challenging and important subject of brain-mind. Then, I present a general purpose model of the brain-mind, called the Epigenetic Developer (ED) network model. The model proposes five necessary “chunks” for the brain picture: development, architecture, area, space and time. The development chunk means that any practical brain, natural or artificial, needs to autonomously develop through interactions with the natural environments without any previously given set of tasks. The architecture chunk handles (1) multiple objects in complex backgrounds; (2) reasoning under abstract contexts; (3) multiple sensory modalities and multiple motor modalities and their integration. The area chunk addresses the issue of feature development and area representation, without rigidly specifying what each neuron does. The space chunk deals with spatial attention to individual objects in complex backgrounds, to satisfy the invariance and specificity criteria for type, location, and other concepts. The time chunk indicates that the brain uses its intrinsic spatial mechanisms to deals with time, without dedicated temporal components. The model copes with temporal contexts of events, to satisfy invariance and specificity criteria for time warping, time duration, temporal attention, and long temporal length. The theory and mechanisms are presented and some related experimental results are summarized.

Visual search requires humans to detect a great variety of target objects in scenes cluttered by other objects or the natural environment. It is unknown whether there is a general purpose neural detection mechanism in the brain that codes the presence of a wide variety of categories of objects embedded in natural scenes. We provide evidence for a feature-independent coding mechanism for detecting behaviorally relevant targets in natural scenes in the dorsal frontoparietal network. Pattern classifiers using single-trial fMRI responses in the dorsal frontoparietal network reliably predicted the presence of 368 different target objects and also the observer's choices. Other vision-related areas such as the primary visual cortex, lateral occipital complex, the parahippocampal, and the fusiform gyri did not predict target presence, while high-level association areas related to general purpose decision making, including the dorsolateral prefrontal cortex and anterior cingulate, did. Activity in the intraparietal sulcus, a main area in the dorsal frontoparietal network, correlated with observers' decision confidence and with the task difficulty of individual images. These results cannot be explained by physical differences across images or eye movements. Thus, the dorsal frontoparietal network detects behaviorally relevant targets in natural scenes independent of their defining visual features and may be the human analog of the priority map in monkey lateral intraparietal cortex.

Potential benefits in increased productivity and recovery efficiency coupled with advances in drilling and completion technology have roused considerable interest in horizontal well applications. Since horizontal well systems are characteristically less symmetric (compared to vertical ones), and horizontal wells typically penetrate the reservoir more and in paths that are more tortuous, the flow field and pressure distribution created by such wells can be expected to be three-dimensional in nature and thus be more susceptible to irregularities in geometry and other system properties in three dimensions. There is thus a need for developing predictive tools that adequately describe three-dimensional fluid flow through porous media in realistically complex situations. This need represents the main motivation of this work. Through the years, both analytical and numerical methods have been used to solve partial differential equations governing engineering problems. Analytical solutions are the most desirable since they are exact and continuous. Unfortunately, they can be obtained only for relatively simple and idealized cases. This has led to the development of various numerical methods which are able to solve more complex and realistic problems. The finite-difference and finite-element methods (FDM and FEM) have been the most widely used numerical techniques in general; in petroleum engineering, however, the use of the former is almost exclusive. The boundary-element method (BEM) is an emerging technique that has gained considerable research attention and popularity in recent years. It offers several potential advantages over the FDM and/or FEM, including Lesser modeling effort, due to the reduction of the dimensionality of multi-dimensional problems by one;Greater numerical accuracy, due to its "semi-analytic" nature and the elimination of grid orientation effects and numerical dispersion; andBetter problem representation, due to its ability to conform better to complex geometries and boundary conditions. The technique has been successfully applied to many diverse fields. It has only been used to a limited extent in petroleum engineering, however.

An edge-based smoothed finite element method (ES-FEM) for analyzing three-dimensional acoustic problems

The study of kinetic sorptive effects on the transport phenomena of reactive solute has numerous real-world applications, including in the industrial and environmental sectors. These kinds of investigations become more realistic when an oscillatory pressure gradient with both the reversible and irreversible reactions at the channel walls is considered in a magnetohydrodynamics flow. In the past, Ng and Yip [J. Fluid Mech. 446, 321–345 (2001)] studied the effect of sorptive phase exchange at boundaries on the solute transport phenomena in an open-channel flow using Mei's multiple-scale homogenization technique. They considered fluid flows without magnetic field and boundary absorption. This work uses the above-mentioned method to investigate the phase exchange kinetics of Taylor dispersion phenomena in a two-dimensional magnetohydrodynamics fluid flowing through a parallel channel. The paper discusses how various parameters and dimensionless numbers, such as the Hartmann, oscillatory Reynolds, and Damkohler, affect the flow velocity, transport coefficient, multi-dimensional concentration distributions, and transverse variation rate. Due to the strong magnetic field, the flow velocity and Taylor dispersivity are adversely affected and conspicuously reduced. Additionally, for large Damkohler numbers, the total dispersion coefficient and the Taylor dispersion coefficient both decrease. However, the longitudinal concentration distribution rises with the Hartmann number and partition coefficient. It is worth noting that in the presence of unequal boundary absorption, there is no occurrence of transverse symmetry in solute concentration at any given time. Controlling various processes of tracer dispersion in environmental systems, especially water purification and the chemical industry, may benefit from these intriguing findings.

This work presents an efficient method to map Motion Estimation (ME) algorithms onto General Purpose Graphic Processing Unit (GPGPU) architectures using CUDA programming model. Our method jointly exploits the massive parallelism available in current GPGPU devices and the parallelization potential of ME algorithms: Full Search (FS) and Diamond Search (DS). Our main goal is to evaluate the feasibility of achieving real-time high-definition video encoding performance running on GPUs. For comparison reasons, multi-core parallel and distributed versions of these algorithms were developed using OpenMP and MPI (Message Passing Interface) libraries, respectively. The CUDA-based solutions achieve the highest speed-up in comparison with OpenMP and MPI versions for both algorithms and, when compared to the state-of-the-art, our FS and DS solutions reach up to 18x and 11x speed-up, respectively.