June 30 · Article · Scientific Journal of Astana IT University · Electrical and Electronic Engineering
The migration from fifth generation (5G) to sixth generation (6G) mobile networks is driven by the Massive Internet of Things (IoT), which targets device densities of up to ten million units per square kilometer. Two scaling limits govern such systems: a data-volume limit, beyond which batch analytics must give way to distributed streaming, and a network-capacity limit, beyond which the radio access network can no longer carry the aggregate uplink. These limits have so far been treated independently, and no empirical anchor exists for Central Asia. This study makes three contributions. First, it derives a closed-form, dimensionless separation ratio that couples the two breakpoints through shared system parameters and determines which limit binds first. Second, the model is instantiated using 145 regulated 5G New Radio measurements from the commercial Tele2 network in Astana, Kazakhstan, across five load scenarios spanning 13 to 100,000 emulated devices. Third, the closed-form structure of the ratio suggests the splitting is a general trait of uplink-bound IoT domains, though this study empirically instantiates only the vertical-farming case. The instantiated model yields a separation ratio of about thirty-two, with breakpoints at twenty-eight nodes for network capacity and eight hundred eighty for data volume. The measured round-trip time of 124.8 milliseconds and the measured uplink of 0.19 megabits per second miss the International Mobile Telecommunications-2030 (IMT-2030) targets by factors of 1248 and about 5.3 × 10⁵, respectively. Because the network-capacity limit binds first, the analytics regime required for dense deployment is unreachable on 5G. For the measured single-operator deployment, 6G is a precondition rather than an enhancement; generalization to other operators and sites requires further measurement.
Kuanysh BakirovOpen access
June 30 · Article · Scientific Journal of Astana IT University · Computer Science Applications
Personalized education requires learning path recommendations that adapt to individual performance while satisfying prerequisite constraints. However, generating optimal learning sequences presents significant computational challenges due to the combinatorial complexity of curriculum graphs, heterogeneous learner profiles, and directed prerequisite dependencies that constrain valid traversal orders. This paper proposes a Hybrid Swarm Intelligence Framework combining Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC) for adaptive learning path recommendation. The hybrid architecture addresses distinct subproblems: PSO optimizes continuous feature weights and algorithm parameters, ACO constructs prerequisite-feasible paths through pheromone-guided graph traversal, and ABC refines solutions through local neighborhood exploration to escape suboptimal configurations. The curriculum is modeled as a directed weighted graph where nodes represent courses with learner-specific difficulty scores derived from behavioral features. Student behavioral data from 8,783 learners (6,968,707 records) inform learner-specific heuristics. The framework operates as a two-stage process: first, linear regression predicts GPA from behavioral features; second, the swarm intelligence pipeline selects paths maximizing predicted academic success. Experimental evaluation demonstrates a +20.69% improvement in mean path fitness scores compared to the Ridge baseline (p < 0.001, paired Cohen's dz = 3.48). We compared the framework against Deep Learning baselines (LSTM and GRU) configured as sequence-aware grade regressors. While the optimally tuned GRU emerged as a strong predictor (+11.88% over baseline), it fell significantly short of the Hybrid Swarm. Results suggest that for curriculum sequencing under prerequisite constraints, combinatorial swarm optimization can outperform greedy decoding from neural predictors when training data is limited. By demonstrating that swarm intelligence algorithms can be systematically combined to address prerequisite satisfaction and behavioral personalization, this work offers a scalable, interpretable alternative to opaque deep learning approaches.
Kazi Golam RabbanyOpen access
June 30 · Article · International Journal of Advanced Technology and Engineering Exploration · Mechanics of Materials
Composite materials (CM) are widely used in aerospace, transportation, and civil engineering due to their high specific strength, stiffness, and wear resistance. However, predicting their dynamic mechanical response under non-stationary loading conditions remains a significant challenge. Stress fields often exhibit singularities at corner points, contact discontinuities, and dissimilar material interfaces, which cannot be accurately resolved using classical homogenization approaches or conventional finite element methods (FEM). This paper presents a second-order accurate computational framework for analyzing the behavior of CM within a unified strip-halfplane configuration. Three mechanical scenarios are investigated: impact resistance (Problem A), tensile strength (Problem B), and bending behavior (Problem C). The proposed framework employs an explicit finite difference method (FDM) based on the method of spatial characteristics and a splitting technique. The numerical scheme is implemented in Python 3.12.3 using NumPy and Matplotlib libraries. A dedicated algorithm is developed to compute stress and velocity fields at singular points and is validated through a three-level grid convergence study. The results indicate that the stress concentration ratio at the singular point P reaches 3.0 times the applied boundary load in Problem A. Under tensile loading in Problem B, the normalized maximum normal stress, σ11k , attains a value of 0.63 at point P. Furthermore, sensitivity analysis reveals that the extremal stresses in the half-plane material vary by a factor of 3.0 for strip embedment depths of 5h, 10h, and 15h. Grid convergence is confirmed with a root mean square error (RMSE) of 4.3 × 10⁻³ between the medium and fine grids. The observed order of convergence at the singular point P is 1.2, which is consistent with the second-order accuracy of the trapezoidal bicharacteristic integration scheme. These findings provide quantitative reference data for the design and analysis of composite structures containing strip inclusions subjected to impact, tensile, and bending loads.
SZSerik ZhuzbayevOpen access
