South-West State University

The dynamics of magnetic fluid droplets formed in a microfluidic chip was investigated. The magnetic field was formed by two neodymium magnets located parallel to each other, which were moved relative to the microchannel during the experiment. The strength of the magnetic field acting on a magnetic drop was determined experimentally and using the FEMM 4.2 software package.

Modern multiclass image classification relies on high-dimensional convolutional-neural-network (CNN) feature vectors, which incur large memory and computational costs and provide little insight into the geometry and properties of the underlying vision data manifold. Existing graph-based spectral classifiers have shown promise on synthetic or binary classification problems, but they degrade on natural images with multiclasses because the feature manifolds possess nontrivial topology that destroys class separability when conventional graphs are used. We introduce a physics-inspired pipeline in which frozen MobileNetV2 features are interpreted as Ising spins placed on the vertices of a sparse multiedge type quasi-cyclic LDPC (MET-QC-LDPC) graph, thereby defining a random-bond Ising model (RBIM). The model is operated at its Nishimori temperature—identified as the unique point where the smallest eigenvalue of the Bethe–Hessian matrix vanishes. Two methodological pillars underpin the design. An exact spectral–topological correspondence that links local trapping sets in the Tanner graph to topological invariants (via poles of the Ihara–Bass zeta function), enabling systematic suppression of harmful substructures through permanent and Bethe-permanent bounds. Such a harmful subgraph reduces top-1 performance by more than a factor of four in multiclass classification. A fast quadratic–Newton estimator for the Nishimori temperature that converges in roughly nine Arnoldi iterations, providing a sixfold speed-up over standard bisection. This method enables spectral graph embedding training on large-scale datasets such as ImageNet-100. The resulting graph ensembles compress the original 1280 -dimensional MobileNetV2 representation to 32 dimensions for a ten-class ImageNet subset (ImageNet-10) and to 64 dimensions for a hundred-class subset (ImageNet-100). Despite this compression we achieve 98.7% top-1 accuracy on ImageNet-10, and up to 84.92 % top-1 accuracy on ImageNet-100 using three-graph soft ensemble. Compared with MobileNetV2, our hard-ensemble approach yields a 0.10 % increase in top-1 accuracy while reducing the number of FLOPs by a factor of 2.67. In contrast, when benchmark against ResNet-50, the soft-ensemble variant suffers only a 1.09 % drop in top-1 accuracy yet cuts FLOPs by 29 times, thereby achieving a favorable accuracy–efficiency trade-off. The novelty of this work lies in (a) establishing a rigorous link between graph trapping sets and algebraic-topological defects, (b) providing an efficient Nishimori-temperature estimator, and (c) demonstrating that topology-guided LDPC graph embedding yields highly compressed classifiers.

Metal-doped titanium dioxide nanoparticles are being used in many applications such as water treatment due to its high photocatalytic activity. Yttrium-doped titanium dioxide (TiO2) nanoparticles were synthesized by hydrothermal method at a temperature of 120°C. Various solvent treatment and thermal annealing methods were investigated to obtain nanoparticles of a specific size and minimal residual carbon content. The width of the band gap of nanoparticles was estimated using diffuse reflectance spectroscopy data. Photoluminescence properties of the materials were evaluated at an excitation wavelength of 473 nm. Doped TiO2 nanoparticles exhibit enhanced photocatalytic activity in the reaction of decomposition of methylene blue (MB), which is believed to be due to the presence of additional energy levels within the forbidden zone, as well as improved separation of electron-hole pairs with a decrease in recombination. These results contribute to a deeper understanding of the potential applications of Y-doped TiO2 nanoparticles in photocatalysis and underscore the importance of understanding the synthesis and characterization of such materials for future research and developments in improving water quality to make it appropriate for a specific end-use.

The operating conditions of steam turbines with large-size blades made of titanium alloy in a low-pressure cylinder are considered. For prevention of erosion-drop action and increasing of blades life, it is proposed to apply a protective coating using spark erosion. A mathematical model for coating formation on titanium alloys is developed.

A fuzzy controller for calculating sliding window parameters is proposed to prepare training data for large language models. Traditional approaches set the “window step” and “context length” as fixed constants across the entire text without considering linguistic characteristics of individual fragments, such as rich scientific text and monotonous repeating text. The proposed method utilizes two automatically extracted features: lexical diversity and average BPE token length. Operating on the Mamdani inference algorithm with a nine-rule base and center-of-gravity defuzzification, the fuzzy controller adaptively calculates the “window step” and “context length” parameters for each fragment. The proposed approach offers a cognitive interpretation by mimicking human reading mechanics, where readers naturally apply adaptive attention; for example, complex fragments are processed more attentively using smaller incremental steps.