The Tashkent University of Information Technologies named after Muhammad ibn Musa al-Khwarizmi (Uzbek: Muhammad ibn Muso al-Xorazmiy nomidagi Toshkent axborot texnologiyalari universiteti, Russian: Ташкентский Университет Информационных Технологий) often abbreviated as ТАТU or TUIT is one of the largest universities in Uzbekistan, located in its capital Tashkent. The Tashkent University of Information Technologies was founded as the Tashkent Electro Technical Institute of Communication in 1955 and it was the major and only producer of communication engineers for the Central Asian region. Today, it is one of the major universities to nurture ICT talent in Uzbekistan. The university was named after Al-Khwarizmi by a presidential resolution to further boost its role within the nation and abroad.
Cryptocurrency trading presents significant challenges due to extreme market volatility, rapid regime transitions, and non-stationary dynamics that render traditional trading strategies ineffective. Existing reinforcement learning approaches for cryptocurrency trading typically employ simplistic profit-based reward functions that fail to adequately capture risk management considerations, market microstructure costs, temporal dependencies, and regime-specific optimal behaviors. This limitation often results in strategies that perform well during favorable market conditions but suffer catastrophic losses during downturns. This paper introduces five novel reward functions grounded in economic utility theory, market microstructure, behavioral finance, adaptive risk management, and regime-conditional optimization. We systematically evaluate these reward functions across three reinforcement learning algorithms (Deep Q-Network, Proximal Policy Optimization, and Advantage Actor–Critic) and four distinct market regimes (bull, bear, high volatility, and recovery), using Bitcoin hourly data from 2018–2022. Our comprehensive experimental evaluation demonstrates that the Adaptive Risk Control reward function achieves exceptional performance, with a Sharpe ratio of 2.47, cumulative return of 26.4%, and maximum drawdown of only 16.8% during the predominantly bearish 2022 test period. Critically, regime-specific analysis reveals substantial performance heterogeneity: Adaptive Risk Control excels during high volatility (Sharpe ratio 3.21), while Temporal Coherence and Asymmetric Market-Conditional rewards dominate in trending and bear markets, respectively. These findings establish that sophisticated, theory-grounded reward engineering—rather than algorithmic innovations alone—constitutes the primary lever for improving RL trading systems, enabling positive risk-adjusted returns even during severe market downturns.
A nonlinear mathematical model is developed for unsteady groundwater flow in a three-layer heterogeneous aquifer system, comprising a confined aquifer, a covering layer, and a weakly permeable barrier. The model incorporates infiltration and evaporation governed by the M.M. Krylov–S.F. Averyanov law, where evaporation intensity depends on a critical groundwater level. Governing equations are nondimensionalized and solved using the alternating direction implicit (ADI) method with quasi-linearization to treat nonlinearities. Periodic variations in precipitation and evaporation are considered, alongside variable boundary permeabilities. The approach enables realistic simulation of multi-layer aquifer dynamics under diverse climatic and hydrogeological conditions, offering a robust tool for sustainable groundwater management, drought risk assessment, and aquifer protection strategies.
modern 3D assets increasingly travel together with audio-video (A/V) recordings: inspection meshes with synchronized video, reconstructed scenes linked to narration, and CAD-derived models packaged with capture logs. In practice, the most valuable metadata-session identifiers, timecodes and segment boundaries, frequency fingerprints, calibration parameters, and integrity statements-are stored out-of-band (sidecar JSON, database rows, container headers). During routine processing (NURBS-to-mesh tessellation, remeshing, decimation for level-of-detail, smoothing/fairing, and reduced-precision export), this external binding can silently break, causing metadata loss or enabling malicious transplant of provenance from one asset to another. This paper frames the dissertation problem of geometrically robust encoding and synchronization of A/V metadata inside parametric 3D surfaces (NURBS) and triangle meshes. We systematize the design space via a taxonomy that separates carrier (NURBS vs. mesh), embedding domain (spatial, invariant/differential, spectral, multiresolution), and synchronization strategy (intrinsic anchors, patch replication, parametric anchors). We formalize threat models that include both benign pipeline transformations (noise, quantization, smoothing, decimation) and adversarial actions (erasure, desynchronization, transplant). From these, we derive design requirements: bounded distortion (RMS/Hausdorff), anchor repeatability under topology change, redundancy with error-correcting codes, quantization-aware modulation, and cryptographic verification tying the decoded token to an A/V fingerprint and signed manifest. Finally, we report benchmark evidence using Bit Error Rate (BER) together with normalized RMS and approximate Hausdorff distance under representative attacks, supported by figures and tables to enable reproducibility.
The rapid development of mobile messaging applications has significantly transformed internal communications within banks, government institutions, and other critical information infrastructure (CII) facilities. However, the use of foreign public communication platforms creates risks associated with confidential data leakage, metadata exposure, dependence on foreign infrastructure, and insufficient administrative control. This paper proposes the architecture of PRIVATGRAM, a sovereign secure corporate messenger designed for the banking sector and critical infrastructure organizations. The proposed architecture is based on a modified Signal-class security model and integrates the X3DH key agreement protocol and the Double Ratchet algorithm. Unlike consumer-oriented solutions, PRIVATGRAM implements centralized administration, device-level session isolation, persistent ratchet-state storage, role-based access control, secure session recovery mechanisms, and sovereign on-premises deployment capabilities. The research demonstrates that sovereign secure messaging systems can significantly reduce cybersecurity risks, enhance digital sovereignty, and ensure compliance with the operational requirements of the banking sector and critical infrastructure facilities.
This paper examines the application of artificial intelligence methods to optimize the allocation of computing resources in network environments. The study proposes an AI-driven resource management framework that dynamically distributes processing loads across network nodes based on real-time demand analysis. The research evaluates the proposed system's performance in terms of latency reduction, throughput improvement, and energy efficiency. The results demonstrate that AI-based resource allocation significantly outperforms traditional static and rule-based methods, offering a scalable solution for modern high-performance computing networks.
