Newton’s method has been widely used in simulation multiphase, multicomponent flow in porous media. In addition, to solve systems of linear equations in such problems, the generalized minimal residual method (GMRES) is often used. This paper analyzed the one-dimensional problem of multicomponent fluid flow in a porous medium and solved the system of the algebraic equation with the Newton-GMRES method. We calculated the linear equations with the GMRES, the GMRES with restarts after every m steps—GMRES (m) and preconditioned with Incomplete Lower-Upper factorization, where the factors L and U have the same sparsity pattern as the original matrix—the ILU(0)-GMRES algorithms, respectively, and compared the computation time and convergence. In the course of the research, the influence of the preconditioner and restarts of the GMRES (m) algorithm on the computation time was revealed; in particular, they were able to speed up the program.

Modern personalized medicine needs the integration of genetic, environmental, and spatial data for the comprehensive assessment of individual disease risk. However, combining disparate data sources into a single, interpretable result remains a complex bioinformatics challenge. Existing approaches to generating individual reports often rely on disparate analytical steps and partial manual processing, limiting their scalability, reproducibility, and standardization. Python pipeline was developed to integrate genetic marker filtering and annotation, individual polygenic risk score (PRS) calculation with percentile ranking, spatial air pollution estimates (LUR), and report generation via Jinja2 and WeasyPrint with separation of logic and visual presentation. The system generates an individual asthma research report with two blocks: genetic predisposition to bronchial asthma and genetic susceptibility to four pollutants corresponding to PM₂.₅, CO, NO₂ and SO₂. Interpretation is based on OR, PRS with positioning in the sampling distribution and an environmental exposure map linked to the participant’s location, ensuring reproducibility and reducing time costs. The proposed tool allows for standardized feedback from study participants, reduces the likelihood of errors in manual report preparation, and ensures transparent interpretation of genetic and environmental data, which is critical for bioinformatics and epidemiological work.

Reproducible high-performance computing pipelines for transcriptomic analysis are essential for population-scale precision oncology, yet most published workflows address only a single sequencing modality or lack benchmarking on underrepresented populations. We present KazRNA-Pipe, an open-source Nextflow v26.04.6 workflow processing both bulk and single-cell RNA sequencing (RNA-seq) within a unified Singularity-containerized environment with graphics processing unit (GPU) acceleration through the RAPIDS ecosystem. The pipeline was validated on 22 esophageal squamous cell carcinoma (ESCC) bulk RNA-seq samples from a Kazakhstan cohort (PRJNA608223) and on the largest public ESCC single-cell atlas (GSE160269). Quantification concordance across STAR, HISAT2, and Salmon reached Spearman ρ ≥ 0.90. BayesPrism deconvolution resolved cell-type proportions across all 22 samples.

This article considered the numerical simulation of multicomponent multiphase flow in porous media. The resulting system of nonlinear equations linearized by the Newton-Raphson method and solved with the iterative Generalized minimal residual method (GMRES) algorithm. To achieve better convergence, we used the ILU(0) preconditioner to the GMRES algorithm. As a result, we used a completely implicit scheme called the Newton-ILU0-GMRES algorithm to solve the problem of interest. Based on the obtained sequential algorithm, we implemented a parallel algorithm using Message Passing Interface (MPI) technology. Additionally, we made comparisons between the parallel program of the presented algorithm and the parallel program using the ready-made Portable Extensible Toolkit for Scientific Computation (PETSc) library. We developed an MPI parallel algorithm and tested it on the MVS-10P supercomputer of the Interdepartmental Supercomputer Center of the Russian Academy of Sciences.

This study investigates the classification of 5G network slice types using the public ”Network Slicing in 5G”dataset. Weaddressacritical scientific pitfall by identifying and mitigating evaluation leakage caused by near-deterministic rule-encoded binary indicators and heavy data duplication. By excluding these artificial context flags and utilizing only a minimal set of QoS-observable telemetry — specifically packet delay, packet loss rate, time, and equipment category — we establish a rigorous, leakage-aware evaluation protocol. Five representative classifier families were evaluated using a group-safe splitting strategy to ensure results reflect real-world operational conditions. Our experimental results demonstrate that tree-based ensembles significantly outperform linear models, with the strongest ensembles reaching about 95% accuracy (Histogram-based Gradient Boosting at 94.74% and Extra Trees marginally higher at 95.14%). This research underscores that while measurable network telemetry provides sufficient signal for high-accuracy slice recognition, non linear models are necessary to navigate the complex, stochastic overlaps inherent in real wireless environments.

The problem of generalization of the method is the main question that arises when studying the quality of iterative methods. The efficiency of solving systems using iterative methods directly depends on the assumptions about the system of equations to be solved. Prerequisites are used to provide a more efficient solution. Many types of prerequisites are currently known, for example, prerequisites based on the approximation of the system matrix: ILU, IQR, and ILQ; Prerequisites based on the approximation of the inverse matrix: a polynomial, rarely filled approximation of the inverse matrix (for example, AINV), an approximation in the factorized form of the inverse matrix (for example, FSAI, SPAI, etc.). This article analyzes the CG and CG methods with the preconditioner ILU (0) by the example of solving the two-dimensional Poisson equation. The CG method is usually used to solve any system of linear equations. ILU (0) was selected as a prerequisite for the article. The incomplete LU decomposition (ILU (0)) is an efficient precursor and is easily implemented. This suggests a system that can be solved to speed up the accumulation of CG and other iterative methods, that is, to reduce the number of iterations. The ILU (0) preconditioner is very easy to detect using the LU decomposition. Since the linear matrix was rarely filled, the CSR format was used to store the matrix in memory. ILU (0) + CG, i.e. the algorithm with a precondition, was assembled 5-8 times faster than the CG algorithm. Data on the number of iterations of convergence of the method without a preconditioner and with the ILU(0) preconditioner were obtained and analyzed.