Automobile Sector Research Articles

Leveraging the power of Data Analysis in Automobile and Financial industries

In an era defined by digital transformation, data analysis has become a cornerstone of decision-making and innovation in both the automobile and financial sectors. This paper explores how these two industries leverage data analysis to enhance efficiency, foster innovation, and improve customer satisfaction. For the automobile industry, data-driven insights enable manufacturers to optimize supply chains, predict maintenance needs, enhance safety, and advance connected and autonomous vehicle technologies. In parallel, financial institutions employ data analysis to streamline risk management, personalize customer experiences, detect fraud, and refine investment strategies. Through a comparative framework, this study highlights the similarities and unique challenges these industries face, particularly in terms of data privacy, regulatory compliance, and the technical demands of big data integration. Additionally, the paper discusses case studies that exemplify successful data-driven initiatives and details emerging trends—such as AI-enhanced analytics, real-time decision-making, and the expanding role of machine learning. Key findings reveal that while data analysis is universally transformative, its applications and outcomes differ based on each industry's operational priorities and regulatory landscapes. The paper also underscores that as the volume, variety, and velocity of data continue to rise, the future of data analysis will hinge on overcoming data quality challenges and bridging skill gaps. This abstract offers a foundational overview of the paper, detailing the essential role data analysis plays in driving growth and competitiveness within the automobile and financial sectors.

Investigation on piezoresistive properties of reduced graphene oxide treated glass textiles based multifunctional sensors

AbstractGlass fabric‐reinforced composites (GFRC) and other revolutionary engineered materials find extensive application in the aerospace, construction, and automobile sectors. It is challenging to predict damage under real‐time stresses due to the anisotropic behavior of composite materials. This study presents the development of a glass textile‐based multifunctional composite sensor and demonstrates its application as a change in resistance or gauge factor in structural health monitoring (SHM) systems. This sensor is coated with reduced graphene oxide (rGO) nanomaterial. Its electrical resistance is evaluated by examining the concentration of nanoparticles and varying geometrical parameters. A three‐point bending method assessed the piezoresistive behavior of the integrated sensor in the GFRC. The impact of sensor width and relative placements in the material's thickness direction within the composite samples is evaluated. Cyclic flexural testing was also performed to demonstrate real‐world applications. The research concludes that it can be utilized as a damage assessment technique for GFRC. The developed sensor can be used to provide an electrically conductive path or as a resistor in the field of E‐textiles. The developed piezoresistive sensor has the flexibility to be used as a multifunctional sensor for multiple purposes, such as force, torque, weight, pressure, flow, acceleration sensors, etc.Highlights Glass textile‐based multifunctional materials are reported as strain‐monitoring sensors. Tailored sensor resistance could be achieved through rGO current conduction paths. Glass textile‐based sensors could potentially be used in the field of E‐textile.

Digitalization and Corporate Social Responsibility: A Case Study of the Moroccan Auto Insurance Sector

The aim of this article is to explore the impact of digitalization on corporate social responsibility (CSR) in the automobile insurance sector in Morocco. This article first explores the theoretical and conceptual foundations of digital transformation and CSR. A mixed methods approach is then used, combining qualitative interviews with a wider quantitative survey, to investigate how digital innovations influence CSR practices. Interview analysis provides the basis for the development of a conceptual framework and eight hypotheses, which are then tested using quantitative techniques to analyze survey data. The results reveal several links between the benefits of digitalization and CSR. Claims management platforms, digital roadside assistance tools, and digital vehicle assessment and inspection all positively impact policyholders’ well-being in terms of compensation and asset preservation, thereby enhancing the CSR profile of automobile insurers. Similarly, augmented reality (AR) and virtual reality (VR) training and simulation, as well as repair assistance, have positive impacts on policyholders’ well-being and advance the CSR positioning of automobile insurers. This article has limitations as it is based on a narrow industrial sector in a single country, but it nonetheless highlights certain relevant interrelationships between digitalization and CSR, contributing to the development of theory and practice in these research areas.

A BIONIC-INSPIRED DRAGONFLY ALGORITHM FOR PARAMETRIC OPTIMIZATION AND DAMAGE INVESTIGATION DURING MACHINING OF MODIFIED POLYMER NANOCOMPOSITE

Polymer nanocomposite is commonly used to develop structural components of space, aircraft, biomedical, sensor, automobile, and battery sector applications. It remarkably substitutes the heavyweight metallic and nonmetallic engineering materials. The machining principles of polymer nanocomposites are intensely different and complex from traditional metals and alloys. The nonhomogeneity, abrasive, and anisotropic nature differs its machining aspect from conventional metallic materials. This investigation aims to execute the CNC drilling of modified nanocomposite using Graphene–carbon (G-C) @ epoxy matrix. The process constraints, namely, cutting speed (S), feed (F), and wt.% of graphene oxide (GO) vary up to three levels and are designed according to the response surface methodology (RSM) array. The nonlinear model is created to predict surface roughness (Ra) and delamination (Fd) on regression analysis. It has been found that the average error for Ra is 0.94% and for Fd it is 3.27%, which is acceptable in model predictions. The metaheuristics-based evolutionary Dragonfly algorithm (DA) evaluated the optimal parametric condition. The optimal setting prediction for the DA is observed as cutting speed (S)-37.68[Formula: see text]m/min, feed (F)-80[Formula: see text]mm/min, and wt.% of graphene oxide (GO)-1%. This algorithm demonstrates a higher application potential than the previous efforts in controlling Ra and Fd values. Both the drilling response values are found to be minimized when the cutting speed increases and the feed decreases. The best fitness value for the DA is 1.626 for surface roughness and 5.086 for delamination. This study agreed with the prediction model’s outcomes and the process parameters’ optimal condition. The defects generated during the sample drilling, such as fiber pull out, uncut/burr, and fiber breakage, were examined using FE-SEM analysis. The optimal findings of the DA module significantly controlled the damages during machining.

Composite Recycling with Biocatalytic Thermoset Reforming.

Carbon fiber reinforced polymers (CFRPs, or composites) are increasingly replacing traditional manufacturing materials used in the automobile, aerospace, and energy sectors. With this shift, it is vital to develop end-of-life processes for CFRPs that retain the value of both the carbon fibers and the polymer matrix. Here we demonstrate a strategy to upcycle pre- and postconsumer polystyrene-containing CFRPs, cross-linked with unsaturated polyesters or vinyl esters, to benzoic acid. The thermoset matrix is upgraded via biocatalysis utilizing an engineered strain of the filamentous fungus Aspergillus nidulans, which gives access to valuable secondary metabolites in high yields, exemplified here by (2Z,4Z,6E)-octa-2,4,6-trienoic acid. Reactions are engineered to preserve the carbon fibers with much of their sizing so that the isolated carbon fiber plies are manufactured into new composite coupons that exhibit mechanical properties comparable to those of virgin manufacturing substrates. In sum, this represents the first system to reclaim a high value from both the fiber fabric and polymer matrix of a CFRP.

The window of opportunity: a case study of the battery electric vehicle in Thailand

Purpose Thailand, although a late entrant, has emerged as a significant center for the production and export of internal combustion engine (ICE) vehicles in Asia. With the arrival of a disruptive battery-electric vehicle (BEV), this study aims to investigate whether Thailand can exploit this window of opportunity to leapfrog. Design/methodology/approach This paper uses data sourced from Thailand’s Department of Intellectual Property to analyze electric vehicle (EV) patent applications. This paper collected qualitative data and conducted interviews with several EV manufacturers. Findings Although Japanese automakers dominate essential EV technology patents, they trail behind Chinese automakers, which benefited from the Free Trade Agreement (FTA) in EV sales. The incumbents, Japanese and Western, choose to expand their production of hybrid electric vehicles (HEVs) and plug-in hybrid vehicles. ICE technology does not constrain new entrants, such as Chinese automakers, Japanese newcomers and Thai newcomers. Compared to Japanese carmakers stuck with ICE and HEV technologies, they have been transitioning to BEVs at a faster pace. BEV has opened the door for large Thai indigenous energy corporations to enter the automotive industry by focusing on niche areas (nonfour-wheel vehicles) and supporting businesses like charging stations. In summary, Thailand has successfully attracted foreign direct investment (FDI) into the EV sector, but it has not yet succeeded in developing indigenous technologies related to EVs. The Thai automobile sector fails to leapfrog because it is too tied up with Japanese automotive production networks and has an unfavorable FTA with China. Originality/value This study sheds light on the limitations of an industrial development strategy relying on openness to trade and FDI without adequately strengthening indigenous technologies and firms, as the country adopting the strategy fails to leapfrog when the window of opportunities created by disruptive technologies arrives.

An expert system based decision-making framework for benchmarking industry in sustainable manufacturing

PurposeThe purpose of this study is to evaluate and enhance sustainable manufacturing practices across various industries, focusing on environmental, economic and social dimensions, to promote a comprehensive understanding and implementation of sustainability, thereby improving overall industry performance and fostering long-term ecological and economic health.Design/methodology/approachThe study uses multi-criteria decision-making-multivariate analysis technique to examine sustainable manufacturing practices (SMPs) in the Indian manufacturing sector. It identifies 11 SMP criteria through literature review and expert recommendations. Data are collected through questionnaires, expert committees and interviews. The study focuses on four key industries: automobile, steel, textile and plastic. Techniques like principal component analysis (PCA), technique for order preference by similarity to ideal solution (TOPSIS) and complex proportional assessment (COPRAS) are used to rank and assess performance.FindingsThe Indian automobile industry has shown the most effective SMPs compared to steel, textile and plastic sectors. The automobile sector is the benchmark for sustainable measures, emphasizing the importance of green practices for environmental, social and economic performance. Recommendations extend beyond the automobile sector to cement, electronics and construction.Practical implicationsThe research emphasizes the importance of SMPs across various industries, focusing on economic, environmental and social considerations. It advocates for a holistic approach that enhances resource efficiency and minimizes ecological footprint.Originality/valueThe study uses ranking methods like PCA-integrated TOPSIS and COPRAS to evaluate performance in different industries, focusing on the benchmarked automobile sector. The research offers valuable insights and advocates for the widespread adoption of sustainable policies beyond the studied sectors.

Engineering the Future: Robotics in the Automotive Industry Explained

The study provides an overview of the potential applications of industrial robots in the automobile sector, which is now the market's most significant client. An overview of the use of robots in global industry is provided in the first section of the article, which also discusses the state of industrial robot utilization. The most popular uses of robots, particularly in the automotive sector, are covered later, along with developments and outlooks for the field's future. The essay concluded with a specific project overview regarding robotized screw tightening on the car seat production line, which served as a model task from actual industrial practice.The many uses of robots in the automobile industry are examined in this review, including material handling, welding, painting, assembly line automation, and quality control. It emphasizes how collaborative robots (cobots) enhance human-robot interaction and how advances in machine learning and artificial intelligence maximize robotic performance. Keywords: Industrial robot; robotics in automotive; production of passenger cars; transport and production; automated assembly line

Advancing Vehicle Security: Deep Learning based Solution for Defending CAN Networks in the Internet of Vehicles

The Internet of Vehicle (IoV) is revolutionizing the automobile sector by allowing vehicles to interact with one another and with roadside infrastructure. The Controller Area Network (CAN) is a vital component of such smart vehicles, allowing communication between various Electronic Control Units (ECUs). However, the CAN protocol's intrinsic lack of security renders it open to a variety of cyber-attacks, posing substantial hazards to both safety and privacy.This research investigates the use of deep learning with multi-layer perceptron to improve the security of CAN networks inside the IoV framework. We discuss current threats to CAN networks, including spoofing, replay, and denial-of-service attacks, and how deep learning may be used to identify and mitigate these threats efficiently. We propose a unique deep learning-based defense mechanism for real-time threat detection. The suggested method is highly effective in identifying and mitigating potential risks, as evidenced by extensive testing on real-world CAN datasets. Based on our findings, the proposed solution has the potential to considerably enhance the security of CAN networks in the Internet of Vehicles, making car communication systems more secure and reliable.

Analysis and Exploration of Carbon Emission Dataset using Machine Learning Techniques

One of the primary environmental climate issues in metropolitan cities is that most people are faced with low air quality. Climate variance is impacting the world, triggering drought, storms, and extreme weather events. The primary contributor to climate change is affected greenhouse carbon gas emissions, of which carbon dioxide (CO2) and carbon monoxide (CO) make up the majority. The carbon emissions are expected to rise steadily worldwide. Many factors, including the burning of fossil fuels in transportation and manufacturing sectors, cause climate variance. Fast urbanization has used a high rate of motor vehicles as compared to a rural area. In metropolitan cities across the world, automobiles are the primary cause of air pollution. The use rate of vehicles keeps increasing, and that results in traffic congestion. This work is focusing on collecting two real-time vehicle emission datasets from two different devices to predict and compare the emissions of different light-duty vehicles by using the machine learning techniques. This research also analyzes the Canadian government emission dataset It shows the results of emission by fuel type and vehicle model. It shows the comparison graph of, mean square error, root mean square error and accuracy comparison of different machine learning algorithms. The work suggests some policies for reducing the carbon emissions. Even the government can adapt certain policies for mitigating the carbon emissions of individual vehicles. In this research work issues for reducing the emission of individual vehicles have been addressed. This work helps the automobile sector to reduce the emission and play a part to save the environment.

Effect of isobutanol/gasoline blends on the performance and emissions of an Otto engine-powered vehicle operating on a driving cycle

Abstract This study experimentally investigates the performance and emissions of a typical vehicle in the Latin American automobile sector–specifically; a 1.6 L spark ignition engine with port fuel injection (PFI) was used. The tests were performed using a Mustang MD150 chassis dynamometer under transient running conditions following the worldwide harmonised light test cycle (WLTC). Commercial gasoline (containing 10 vol. % ethanol; E10) was blended with 10, 20, and 30 vol. % of isobutanol. Results reveal that despite the reduction in the fuel lower heating value (LHV), adding the isobutanol B20 blend can improve the fuel economy by up to 6%. Similarly, when the alcohol content in the blend increased, the CO and HC emissions decreased by 10.5 and 10.2%, respectively. Furthermore, the B30 blend resulted in the lowest emissions but had the highest fuel consumption. Notably, these results were achieved without any adjustments to engine key components. Thus, the effects of isobutanol were consistent with the increase in octane and oxygenation of fuel blends.

Mixed Mode‐I/II interlaminar fracture toughness Characterization of woven Carbon–Kevlar interyarn hybrid textile composites

AbstractIn actual service conditions, the composite structures are subjected to the combined effect of Mode‐I and Mode‐II loadings. It has great significance in the aerospace, defense, military, automobile, and marine sectors. This study aims to determine the Mixed Mode‐I/II interlaminar fracture toughness (ILFT) of plain and twill woven carbon, Kevlar monolithic, and interyarn hybrid textile composites under several hybridization schemes. The tailored properties are obtained using the interyarn hybridization of high stiff carbon yarn and high tough Kevlar yarn. The effect of individual yarn in the length direction in different hybrid composite laminates is carefully investigated. The Mixed Mode‐I/II ILFT characterization is experimentally performed using a mixed mode bending (MMB) test specimen on the Instron 8862 system under 45%, 55%, and 65% Mixed Mode ratios (MMRs). The increase in mode mixture percentage decreases the lever length, increases the load‐bearing capacity, and increases the Mixed Mode‐I/II ILFT of the MMB test specimen. Plain woven composites exhibited a higher ILFT than twill woven textile composites. The plain woven carbon–Kevlar hybrid composite laminate with carbon yarn‐in‐length direction shows the highest Mixed Mode‐I/II ILFT after CP laminates. Hybridization significantly increases the Mixed Mode‐I/II ILFT compared to monolithic Kevlar fabric‐reinforced composites. Moreover, the failure mechanisms are investigated using scanning electron microscopy.Highlights Mixed Mode‐I/II ILFT of carbon–Kevlar interyarn hybrid textile composites. ILFT at nonlinearity (NL), VIS, and 5%/Max load points for 45%, 55%, and 65% MMR. Advantage of placing carbon yarn‐in‐length direction in hybrid configurations. Effect of carbon–Kevlar interyarn hybridization on Mixed Mode‐I/II ILFT. Fractography of tested specimens to investigate several failure mechanisms.

Synthesis and characterization of 4-layered functionally graded Al-Al2O3 MMC

Functionally graded materials (FGMs) have a gradient of properties along their dimensions. These materials are resistant to delamination, making them ideal for replacing conventional composites in various fields of engineering. Among FGMs, aluminium-based FGMs are receiving a lot of attention due to their versatility. Al-Al2O3 FGMs, in particular, are widely preferred in the aerospace and automobile sectors. This study involves synthesizing a 4-layered FG Al-Al2O3 MMC through PM. The material is subjected to impact, compression, and hardness tests, to assess its mechanical properties. To understand the characteristics of the material, Optical microscopy, SEM, and XRD are carried out. The powder metallurgy route produces FG MMCs with improved properties. The material exhibited an impressive hardness of 183 HV at the top layer, which is higher than similar FGMs and FGMs fabricated via Centrifugal Casting. The compressive strength of Al-Al2O3 FGM is 138 MPa and the impact strength is 16.25 kJ/m2. Owing to the improved properties and FGMs being resistant to delamination mode of failure the study recommends the material as a replacement for conventional Al-Al2O3.

Identifying extreme events in the stock market: A topological data analysis.

This paper employs Topological Data Analysis (TDA) to detect extreme events (EEs) in the stock market at a continental level. Previous approaches, which analyzed stock indices separately, could not detect EEs for multiple time series in one go. TDA provides a robust framework for such analysis and identifies the EEs during the crashes for different indices. The TDA analysis shows that L1, L2 norms and Wasserstein distance (WD) of the world leading indices rise abruptly during the crashes, surpassing a threshold of μ+4∗σ, where μ and σ are the mean and the standard deviation of norm or WD, respectively. Our study identified the stock index crashes of the 2008 financial crisis and the COVID-19 pandemic across continents as EEs. Given that different sectors in an index behave differently, a sector-wise analysis was conducted during the COVID-19 pandemic for the Indian stock market. The sector-wise results show that after the occurrence of EE, we have observed strong crashes surpassing μ+2∗σ for an extended period for the banking, automobile, IT, realty, energy, and metal sectors. While for the pharmaceutical and FMCG sectors, no significant spikes were noted. Hence, TDA also proves successful in identifying the duration of shocks after the occurrence of EEs. This also indicates that the banking sector continued to face stress and remained volatile even after the crash. This study gives us the applicability of TDA as a powerful analytical tool to study EEs in various fields.

Impact Loading on Polymer Nanocomposites - A Comprehensive Review

In recent days, the application of polymer nanocomposites (NC) has grown exponentially due to its varied applications and efficacy. However, it is also associated with various challenges and need of optimal solutions to address the same. Excellent properties in the area of high strain rate applications are usually addressed during fabrication of nanocomposites using graphene, nano fillers, nano clay and carbon nano tubes. Such NC offer good properties associated with thermal stability and unique desirable properties making them excellent replacements for conventional materials used so far. Their applications are found in defense, space and automobile sectors. Though NC have numerous positive characteristics, the reason behind their failure mechanism under loading conditions with static and dynamic is unanswered or not comprehensively clear. This article enumerates the collective review of the challenges and solutions addressed by various researchers. It includes techniques used for characterizing nanocomposites with respect to failure governing mechanism for both organic and inorganic NC.

Connectedness and Shock Propagation in South African Equity Sectors during Extreme Market Conditions

This study examined the connectedness and propagation of risk in the South African equity sectors during the Global Financial Crisis (GFC), the European Debt Crisis (EDC), the US–China trade war, and the COVID-19 pandemic. Daily returns of nine Johannesburg Stock Exchange (JSE) super sectors were examined from 3 January 2006 to 31 December 2021. Applying the connectedness matrix and time-varying parameter vector autoregressive (TVP-VAR) model, in full sample and sub-periods, the study showed that dynamic total connectedness of the super sectors is high in absolute form (62%). Furthermore, it was found that the highest volatility connectedness was during the EDC (68.83%) and during the COVID-19 pandemic (68.57%), followed by the GFC (63.16%) and lastly the US–China trade war (42.09%), respectively. This suggests that the tendency for a systemic risk is highest during the EDC, COVID-19, and GFC periods, and lowest during the US–China trade war. The financial sector was the primary net-transmitter of shocks during the COVID-19 period, while the automobile and parts sector was the strongest net-transmitter of shocks during the GFC, EDC, and US–China trade war. Similarly, the strongest net recipient of shocks during GFC, EDC, and COVID-19 is the chemical super sector. The study concludes that there is a significant volatility connectedness among JSE super sectors. In addition, the JSE super sectors exhibit time-varying connectedness during extreme events. Moreover, the net-transmitter and net-receiver of shock do not change significantly during different crisis periods. The policy implications of the findings are highlighted in the concluding section.

Application of Statistical Process Control in Automotive Manufacturing

This research looks at how “statistical process control”, or SPC, is used in the automotive sector and highlights how it can improve production efficiency, lower defect rates, and improve process quality. SPC is crucial to the monitoring and control of the complex assembly processes and strict quality standards that define the automobile sector. The efficiency of several SPC methods, including control charts and process capability analysis (Cp and Cpk), in preserving process stability and fulfilling quality criteria is assessed in this study. The study shows how these technologies may detect process variations and direct appropriate adjustments to optimize output using simulated data analysis. The results highlight how crucial SPC is to attaining consistent product quality and reliability, which raises performance and safety requirements in the automotive sector.

Debt Risk Analysis of Automotive Enterprises

In order to maintain their financial stability, businesses must recognize and manage the financial risks that are necessarily involved in their operations and output. Businesses may better comprehend their financial status and undertake efficient risk early warning by examining financial indicators. Academic research on the detection and mitigation of financial risks is extensive, and in an effort to increase the accuracy of assessments, an increasing number of academics are using multi-indicator systems for thorough analysis. Furthermore, non-statistical techniques including hierarchical analysis, B-S option pricing models, and artificial neural networks have shown extremely effective at handling complicated data and non-linear connections. As the economy transitions from high to medium-high growth, traditional and new energy vehicles in the automobile sector confront significant possibilities and problems. Traditional automakers aggressively converting to new energy cars include SAIC Motor, BAIC Group, and GAC Group. Companies that produce new energy vehicles, such XPeng, BYD, Tesla, Li Auto, and NIO, are becoming more competitive by developing new technologies and tapping into new markets. To enhance market competitiveness and sustainability, businesses must address issues like excessive inventory and price volatility that have been brought about by the new energy vehicle market’s explosive expansion through efficient financial risk management. The short- and long-term debt repayment capacities of traditional and new energy vehicle (NEV) enterprises are the main subjects of this study’s analysis of their financial statements. According to the findings, traditional automakers have more stable financial circumstances whereas NEV enterprises exhibit stronger short-term solvency and lower long-term financial risk in specific years with superior liquidity and lower debt ratios. The study attempts to offer resources for the automobile industry’s deleveraging, sustainable development, and inventory reduction.

Factors affecting flexural and impact strength of natural fiber reinforced polymer composites: a review

The surging demand for energy-efficient products and their environmental implications are important propelling factors contributing to the growing popularity of natural fiber-reinforced polymer composites (NFRPCs) in several domains. NFRP composites diversification significantly influenced the study and their development in recent years. The intrinsic characteristics of natural fibers have posed difficulties in the growth and use of NFRPCs. A significant amount of study has recently been performed to solve these sorts of problems in order to enhance the performance of NFRPCs and their uses. This review article aims to provide a comprehensive overview of the chemical composition of natural fibers, their physical and mechanical characteristics, and factors affecting the flexural and impact strength of NFRPCs, such as type of fiber, weight percentage of fiber, fiber geometry, surface treatments, stacking, hybridization, orientation, fillers, and information accomplished with them. The product life-cycle evaluation and sustainability development of plant-based NFRCs and the growing uses of NFRPCs, focusing on the automobile sector, are also explored. Finally, multiple perceptions were concluded with the expectation that this study may contribute to future improvements in the flexural and impact strength of NFRPCs.

Expanding the Areas of Attraction of Solutions to Singularly Perturbed Equations

The article analyzes the state and development prospects of the transport and logistics system of automobile transport in the Kyrgyz Republic. The authors consider the key features of the functioning of the automobile sector in the context of the country’s transition economy. The work assesses the current state of automobile transportation of goods and passengers, examines the main problems hindering the increase in the efficiency of the transport and logistics system. These include low technical equipment of the vehicle fleet, insufficient development of the logistics infrastructure, imperfection of legal regulation, personnel shortage. The authors propose a set of measures aimed at modernizing and optimizing the functioning of automobile transport in Kyrgyzstan. Among the key areas are the renewal of the vehicle fleet, the development of a network of logistics centers and multimodal transportation, the introduction of information technologies in transportation management, and the improvement of state regulation of the industry. According to the authors, the implementation of the proposed measures will contribute to increasing the competitiveness and customer focus of automobile transport in the Kyrgyz Republic, which in turn will have a positive impact on the development of the national economy.