Tashkent Financial Institute

This study investigates the impact of macroeconomic factors—inflation, interest rates, exchange rates, money supply, and crisis events—on chemical industry index prices in the US and Germany, using the Autoregressive Distributed Lag (ARDL) model. The analysis identifies significant long-term relationships and short-term interdependence effects between the two markets. In the US, inflation and interdependence effects from Germany drive index prices, while Germany’s index is more sensitive to inflation, interest rates, and exchange rates. The US exerts a stronger influence on Germany, reflecting its global economic dominance. The findings also reveal faster adjustment to equilibrium in the US and greater resilience to crises compared to Germany. These insights provide valuable implications for investors, policymakers, and industry leaders.

This paper proposes a digital forensics-driven approach to ensure the integrity and privacy preservation of biometric systems in consumer electronics. Specifically, we develop a neural network-based forensic model to detect and analyze disguised speech attacks, which pose a significant threat to biometric authentication. The model leverages Multi-Layer Perceptron (MLP) architecture to identify speaker gender based on key acoustic parameters such as formant center frequency, bandwidth, and sound intensity. To enhance forensic accuracy, we employ L-BFGS optimization during model training. Experimental validation is conducted using SoundTouch-modified speech samples, simulating real-world biometric spoofing attempts. We further analyze the impact of network structure and activation functions on detection performance, as well as the model’s adaptability to various electronic disguise techniques. Results demonstrate that the proposed MLP-based forensic framework effectively differentiates between genuine and electronically disguised speech, providing a robust solution for biometric security in consumer electronics.

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The purpose of the study. In order to conduct a thorough examination of the biomechanical principles underlying lower limb functionality during softball batting, this study aims to specifically elucidate the contributions of ground reaction forces, patterns of muscle activation, and the temporal sequencing of these factors in influencing overall batting performance. Materials and methods. A systematic review was conducted following PRISMA guidelines, searching electronic databases for peer-reviewed research from January 2000 to December 2024. The study initially identified 156 potentially relevant articles, with 24 studies ultimately meeting the stringent inclusion criteria. Researchers utilized advanced measurement techniques including surface electromyography, motion capture systems, and force plate analysis to examine lower limb biomechanics. Results. Elite softball players demonstrated significantly more precise lower limb mechanics compared to intermediate players. Key findings include peak rear leg ground reaction forces ranging from 1.6 to 1.8 times body weight, compared to 1.2 to 1.4 times for intermediate players. Muscle activation showed a sequential pattern progressing from gluteal muscles through quadriceps to gastrocnemius, with a strong correlation to bat speed (r=0.72, p<0.01). Temporal sequencing was crucial, with elite players exhibiting a coefficient of variation of 8.3% compared to 15.7% for intermediate players. Conclusions. The study reveals that successful softball batting depends not on raw strength, but on the sophisticated coordination of lower limb biomechanics. Precise timing, muscle recruitment patterns, and efficient energy transfer through the kinetic chain are critical for optimal batting performance. The research provides evidence-based recommendations for targeted training interventions that focus on movement coordination and biomechanical efficiency.

This paper examines the significance and methodological foundations of organizing accounting in business entities. Properly organized accounting provides the necessary data for the accurate and effective management of a business’s financial condition. The paper analyzes the fundamental principles of accounting, relevant legislation and regulatory documents, as well as the problems encountered in establishing an accounting system and ways to address them. Efficient organization of accounting plays a vital role not only in fulfilling tax obligations but also in improving a company’s financial status, aiding decision-making, and preparing financial reports.

Soil samples were collected from newly irrigated meadow-alluvial soils in the Kegeyli district of northern Karakalpakstan to investigate their physical and mechanical properties, with particular focus on macro- and microaggregate composition. Comparative analysis of samples taken from two depth intervals (0–30 cm and 30–60 cm) revealed distinct soil characteristics. Porosity measurements showed values of 49.4% in the upper layer (0–30 cm) compared to 47.2% in the deeper layer (30–60 cm). Aggregate size distribution analysis demonstrated significant differences between depth layers. In the 0–30 cm samples, particles <0.25 mm and aggregates larger than 28.6 mm were classified as macroaggregates, while the 30–60 cm samples contained smaller quantities of both <0.25 mm particles and aggregates up to 10.0 mm, indicating variations in structural composition with depth. Further examination of soil layers at 0–25 cm and 25–33 cm depths revealed textural differences. The upper layer (0–25 cm) contained 24.9% physical clay and 36.0% physical sand, while the lower layer (25–33 cm) showed slightly reduced values of 23.0% physical clay and 32.0% physical sand. These findings provide valuable insights into the vertical heterogeneity of soil properties in newly irrigated meadow-alluvial soils of the study region.

For the set I(X) of probability measures on a compact Hausdorff space X, we propose a new way to introduce the topology by using the open subsets of the space X. Then, among other things, we give a new proof that for a compact Hausdorff space X, the space I(X) is also a compact Hausdorff space. For a Tychonoff space X, we consider the topological space Iτ(X) of τ-smooth idempotent probability measures on X and show that the space Iτ(X) is Čech-complete if and only if the given space X is Čech-complete.

This study aims to examine the perception of risk management and internal control in the public sector, specifically concerning preventing fraud. Specifically, this study examines the various aspects of fraud prevention among employees in the public sector, including goal formulation, internal environment, and communication of information, risk response, monitoring, risk assessment, and control actions. A total of 537 individuals working in the procurement and finance departments of federal ministries participated in a questionnaire survey conducted using the Committee of Sponsoring Organizations framework and focused on fraud prevention. This study shows that risk assessment, control actions, and information and communication exert a favorable influence on the prevention of fraudulent activities. This study revealed that risk assessment, control actions, and information and communication exert a favorable influence on the prevention of fraudulent activities. This study can contribute to the government's efforts in improving risk management, addressing corruption, and advancing the Sustainable Development Goal (SDG) 12 and 16 initiatives.

In the context of Secure Artificial Intelligence for 6G Consumer Electronics, accurately predicting vehicle behavior in dynamic traffic scenarios is a significant challenge in intelligent transportation. To avoid sending all raw data to a centralized cloud server, this study presents an artificial intelligence (AI) based distributed machine learning framework (AICEML) that can run on local edge devices. This method protects user privacy while minimizing transmission and processing delays. Accurate predictions are maintained despite the presence of many cars because to AICEML’s use of the model on edge devices, which incorporates edge-enhanced attention and graph convolutional neural network features to swiftly collect and transmit vehicle interaction information. Each edge device can adapt its neural network type and scale based on its computing capabilities, accommodating various application scenarios. Experimental results using the NGGSIM dataset demonstrate AICEML’s superiority, achieving precision, recall, and F1 scores of 0.9391, 0.9557, and 0.9473, respectively. With a 1-second prediction horizon, it maintains 91.21% accuracy and low time complexity even as the number of vehicles increases. This framework holds promise for enhancing intelligent transportation systems in the 6G era while prioritizing security and efficiency.