In-situ monitoring and failure mechanism of PAI/PTFE lubricating coating under salt spray tribocorrosion

As the central actors in corporate strategy formulation and resource allocation, managers exert a profound influence on a company’s green development. Against the backdrop of China’s policy-driven push for corporate green transformation, this study examines the impact of managerial discretion on corporate green innovation and its underlying mechanisms. Drawing on a sample of Chinese A-share listed companies from 2014 to 2023, the analysis integrates agency theory and stewardship theory, focusing on the formal authority granted to Chief Executive Officer by their boards of directors. Empirical results indicate that managerial discretion exhibits an inverted U-shaped relationship with corporate green innovation: moderate power stimulates stewardship behavior among managers, thereby promoting green innovation, whereas excessive power induces agency problems, which in turn exerts a suppressing effect. Mechanism analysis reveals that R&D investment serves as a key transmission channel and itself exhibits an inverted U-shaped relationship with green innovation. Heterogeneity analysis reveals that this effect is more pronounced in state-owned enterprises and under conditions of high environmental uncertainty. Therefore, firms should reasonably delegate authority to management and actively establish monitoring mechanisms for green R&D investment thresholds to precisely enhance green innovation capabilities.

Large Language Models (LLMs) are increasingly deployed across professional and societal domains, introducing security, privacy, and governance challenges beyond traditional software vulnerabilities. Despite extensive research on individual risk categories, a unified lifecycle-oriented perspective connecting architectural properties, adversarial threats, and governance implications remains limited. This review examines security and privacy risks associated with LLMs through a lifecycle framework covering data acquisition, model training, alignment procedures, deployment, and post-deployment interaction. The study synthesizes prior research to construct a taxonomy of threats including prompt injection, jailbreaking, adversarial manipulation, training-stage attacks, privacy leakage, and socio-technical misuse. Ethical issues such as hallucination, bias amplification, and malicious use are analyzed alongside governance and regulatory frameworks. Results indicate that vulnerabilities in LLM systems arise primarily from probabilistic generation mechanisms, large-scale data ingestion, and complex deployment ecosystems rather than isolated implementation defects. Classical software vulnerability models therefore provide only partial coverage of risks associated with generative AI systems. The review is grounded in the concept of the alignment gap to explain how discrepancies between training objectives and real-world interaction contribute to persistent vulnerabilities. The findings highlight the need for lifecycle-oriented defense-in-depth strategies combining technical safeguards, privacy-preserving training, runtime monitoring, and governance mechanisms to support responsible deployment of LLM-based systems.

Introduction Equitable access to and service provision for oral health in Karnataka, India, remains challenging despite several policy and program initiatives. It was unclear whether the formulation and implementation of the oral health policy were coordinated and involved all stakeholders and institutions. Therefore, this study aimed to explore the gaps and barriers in oral health policy formulation and implementation in Karnataka. Methods This exploratory qualitative study employed a backward mapping approach and policy landscaping from 2000 to 2025. Content analysis was performed on 8 policy documents to identify how oral health was problematised in policies. The resultant policy matrix was subjected to assessing the implementation status of their recommendations and the perceptions of 31 stakeholders, including policymakers, administrators, program implementers, and dental health professionals, through in-depth interviews and focus group discussions. Theories of implementation were employed to guide data collection and analysis. Results Policy document analysis revealed inconsistencies in oral health policy, characterised by recommendations lacking actionable details, clear funding, or integration into broader health frameworks. Stakeholder consultations highlighted systemic neglect, including chronic underfunding, workforce shortages, and limited awareness of oral health policies. Weak accountability structures and the lack of monitoring and evaluation mechanisms further impeded implementation. Administrators, identified as key implementers, were often excluded from policymaking processes, resulting in limited policy ownership and effectiveness. Conclusion Although oral health has gained traction in policy discussions over two decades, implementation remains inconsistent. Oral health policymaking in Karnataka state has been fragmented, lacking a concerted effort involving all stakeholders and the institutions that support policy implementation. Addressing these challenges requires structural changes such as dedicated budgets, increased workforce capacity and stronger monitoring frameworks. These measures are critical to improving oral health services, particularly for marginalised and underserved communities, to achieve the health for all goal.

The article examines state industrial policy, focusing on its goals, challenges, and key directions. It analyzes management measures aimed at strengthening industrial potential, boosting competitiveness, stimulating innovation, and modernizing industries amid geopolitical and technological challenges in the early 21st century. The study also covers global challenges and related structural transformations of the Russian economy. The purpose of this study is to analyze and evaluate the effectiveness of implementing management software in the civilian industry of the Russian Federation, as well as to identify the key factors influencing its development and improvement. Results . As part of the study of the public administration mechanism, the need to distinguish between the concepts of “defense industry” and “civilian industry” was identified due to the organizational and structural differences between these sectors of the economy. The study also revealed the exceptional role of public programs in the transition from a resource management paradigm to a results-oriented management paradigm. However, there are still unresolved issues in public programming, such as difficulties in accessing funding and insufficient coordination between program participants. Conclusions . The Russian Government faces significant challenges in ensuring technological sovereignty and competitiveness of domestic goods. The key instrument is a set of government programs that guide budget formation and implement policy goals through various (not only financial) measures. In the civilian industrial sector, government programming includes the development and implementation of the following programs: “Development of Industry and Increasing Its Competitiveness,” “Development of the Aviation Industry,” “Development of Shipbuilding and Technologies for the Development of Offshore Fields,” and “Development of the Electronic and Radio-Electronic Industry.” Despite the high level of development of the methodology and monitoring mechanism, there are difficulties with access to funding and insufficient coordination between the participants in the program process.

Coal mining is one of the most hazardous industrial activities, where workers are constantly exposed to risks such as toxic gas leaks and unexpected seismic vibrations. Historically, mine safety relied on manual inspection methods and basic mechanical detectors, which were often slow, inaccurate, and unable to provide real-time alerts. With increasing mining depth and complexity, these traditional systems have become insufficient to ensure worker safety. The major problem lies in the lack of continuous monitoring and immediate warning mechanisms, which can lead to severe accidents, environmental damage, and loss of human lives. Conventional safety systems typically operate in isolation, lack remote monitoring capabilities, and require significant human intervention. Their limitations include delayed response, limited coverage, and inability to store or analyse data for preventive measures. Hence, there is a growing need for an intelligent, automated, and reliable monitoring system that can detect hazards in real time and communicate effectively. This research proposes a Zigbee-based IoT embedded system for earthquake and hazardous gas detection in coal mines. The system integrates sensors for gas and vibration detection with a microcontroller, enabling continuous monitoring of environmental conditions. Zigbee technology is used for efficient wireless communication between transmitter and receiver units, while IoT connectivity allows data to be uploaded to a remote server for real-time access and analysis. Additionally, alerts are provided through buzzers and display units to ensure immediate response. The proposed system enhances safety by providing early warning, reducing human dependency, and enabling remote monitoring. It offers a cost-effective, reliable, and scalable solution, significantly improving coal mine safety and helping prevent potential disasters.

The Innovative Fish Farming Monitoring System is an IoT-enabled smart aquaculture solution designed to continuously monitor and regulate critical water quality and environmental parameters in fish ponds or tanks. The system leverages an ESP-32 microcontroller integrated with multiple sensors — including the Ultrasonic sensor for water level detection, DHT11 for temperature and humidity measurement, RTC module for real-time clock management, SPO2 sensor for dissolved oxygen monitoring, and a pH sensor for acidity/alkalinity regulation — to provide real-time data acquisition and automated responses. Output peripherals include an LCD display for on-site parameter visualization, a buzzer for immediate alert notifications, and an IoT cloud module for remote monitoring and data logging via the internet. The entire system is powered by a Regulated Power Supply (RPS) ensuring consistent operation. The proposed system addresses the key challenges faced by traditional fish farming practices such as manual water quality testing, inadequate monitoring, and delayed responses to hazardous environmental changes. By integrating hardware components with a software layer running on the ESP-32, this system offers precision aquaculture that can reduce fish mortality rates, optimize growth conditions, and enhance the overall productivity and sustainability of fish farming operations. The system finds applicability in small-scale home aquariums, medium fish ponds, and large-scale commercial aquaculture farms.

Modern digital infrastructures including enterprise networks, cloud computing platforms, IoT ecosystems, and online service environments continuously generate vast volumes of real-time data such as network packets, authentication logs, and system performance metrics. This rapid data generation necessitates efficient, intelligent, and real-time security monitoring mechanisms. To address these challenges, this work proposes an automated and scalable AI-driven security framework for real-time anomaly detection and authentication threat analysis. Initially, machine learning models such as K-Nearest Neighbor (KNN) and Support Vector Classifier (SVC) are utilized to learn network behavior patterns and distinguish between normal and malicious activities. Although effective, these models exhibit limitations including high computational complexity, sensitivity to feature scaling, and inefficiency in handling large-scale or probabilistic data scenarios. To overcome these constraints, a Naive Bayes Classifier (NBC) is adopted as the primary probabilistic inference model within the framework. By leveraging Bayesian decision theory and modeling conditional feature dependencies, NBC enables efficient estimation of threat probabilities with reduced computational overhead and improved scalability for high-dimensional datasets. The proposed system incorporates data preprocessing, class balancing techniques, multi-model training, and deployment through a web-based Flask interface to facilitate real-time threat detection. Performance evaluation is conducted using standard metrics such as accuracy, precision, recall, and F1-score, demonstrating reliable anomaly detection and classification capabilities. The results validate that the proposed framework provides a robust, scalable, and efficient solution for enhancing real-time security intelligence in modern digital environments.

Telemedicine has become central to digital health strategies, yet the regulatory environment that shapes ethical safeguards and equitable access remains uneven and incompletely assessed across countries. Legal and normative instruments matter because they define requirements for privacy, consent, accountability, professional readiness, and barrier reduction. This study aimed to map the current global landscape of normative instruments related to telemedicine and identify which ethical and social safeguards are explicitly addressed, with particular attention to equity. We conducted a document analysis guided by the READ (ready the materials, extract data, analyze data, and distill findings) framework. From February 2024 to February 2026, we conducted a structured web-based search across all World Health Organization (WHO) member states with no language restrictions, using official government sources, statutory professional regulators, and institutional publication channels. Retrieval combined internal site searches, direct navigation, external search engine queries, and targeted snowball sampling to identify currently in-force instruments. Two researchers independently extracted and coded data using a predefined codebook. We operationalized 10 binary items covering regulatory presence and scope (questions 1 and 2), safeguards for data protection (question 3), consent and disclosure (questions 4 and 5), prior in-person prerequisites (question 6), monitoring (question 7), training requirements (question 8), and equity (questions 9 and 10). We summarized frequencies overall and stratified by WHO region and World Bank income group and conducted a qualitative thematic analysis of included normative instruments. Of the 194 WHO member states, 81 (41.8%) had at least one current normative instrument related to telemedicine in force. Among these, 72.8% (59/81) defined telemedicine or telehealth. Data protection provisions were most common (73/81, 90.1%), followed by mandatory informed consent (n=71, 87.7%) and monitoring mechanisms (n=65, 80.2%). Fewer countries required disclosure of telemedicine limitations (n=36, 44.4%) or mandated telemedicine-specific training (n=26, 32.1%). Prior in-person consultation requirements were uncommon (n=8, 9.9%). Equity-related safeguards were uneven: 51.9% (n=42) referenced justice, equity, or nondiscrimination, whereas 30.9% (n=25) included concrete barrier reduction provisions (eg, digital inclusion or accommodations for people with disabilities and minors). Telemedicine regulation is becoming more common, but both coverage and safeguarding content remain uneven. While privacy, consent, and monitoring are frequently addressed where regulation exists, disclosure, physician competency, and operational equity measures are less consistently specified. Strengthening telemedicine governance will require translating ethical commitments into enforceable standards that address digital determinants of access and protect groups at risk of exclusion.

This paper focuses on enhancing the security and reliability of digital payment transactions by developing an advanced UPI fraud detection system. The primary objective is to leverage machine learning algorithms and data analytics to analyse transaction patterns and detect anomalies that may indicate fraudulent activity. The system is designed to effectively address various types of UPI fraud, including phishing, identity theft, and unauthorized transactions. Additionally, it aims to incorporate a real-time monitoring mechanism capable of promptly identifying suspicious activities and triggering immediate alerts for intervention. The scope of this work extends to the integration of cutting-edge technologies such as artificial intelligence, machine learning, and data analytics, enabling the creation of a sophisticated fraud detection model. By analysing massive datasets of UPI transactions in real time, the model will uncover patterns, anomalies, and trends associated with fraudulent activities, thereby strengthening the defence against evolving digital payment threats.

ABSTRACT: The Enterprise Expense and Resource Management System (EERM) is a web-based application developed to automate and centralize the management of organizational expenses, budgets, and resources. In many organizations, these processes are handled manually using spreadsheets or disconnected systems, resulting in inefficiencies, lack of transparency, and increased chances of errors. The proposed system provides a unified platform with role-based access control, consisting of Admin, Manager, and Employee modules. Employees can submit expense claims and request resources, while managers review and approve them based on predefined policies and budget constraints. Administrators are responsible for managing users, resources, budgets, and system configurations. The system incorporates real-time budget monitoring, audit logging, and a pattern-based expense monitoring mechanism to detect unusual or abnormal spending behaviour. The backend is implemented using Python (Flask), and MySQL is used for structured data storage. The frontend is developed using HTML, CSS, JavaScript, and Bootstrap to ensure a responsive and user-friendly interface. The proposed system improves operational efficiency, enhances transparency, and ensures accountability within the organization. Experimental results indicate that the system effectively manages expense workflows, resource allocation, and audit tracking, making it a scalable and reliable solution for modern enterprise environments. Keywords— Expense Management, Resource Allocation, Audit Logging, Flask, MySQL, Role-Based Access Control

ABSTRACT This study investigates how the board's interpersonal diversity is associated with company tax avoidance behaviour. The ‘Out of Africa’ theory of human origin, which assesses how individual attributes (including cognitive capabilities, capacities and problem‐solving style) and interpersonal trust are shaped by human evolution, forms the basis of the interpersonal diversity scale. A broad literature affirms how diversity in cognitive skills and interpersonal trust enhances board performance and monitoring mechanisms within the board members. In this study, we analyse how the interpersonal diversity of the board members for UK firms over the period 1999 to 2019 mitigates agency problems and tax avoidance. Our main finding shows interpersonal diversity is associated with lower tax avoidance. These results emphasise the importance of the composition of boards with members from diverse genetic backgrounds. This could be encouraged through disclosure requirements, guidelines or incentives for companies to diversify their board composition.

ABSTRACT The banking sector has undergone a significant transformation with the rapid adoption of digital financial technologies. Indian Overseas Bank (IOB), a leading public sector bank in India, has actively integrated digital solutions to improve operational efficiency, service delivery, and customer experience. However, this shift has also introduced multiple financial risks, including cybersecurity threats, inaccuracies in credit assessment, operational disruptions, and regulatory compliance challenges. This study presents an analysis of the digital financial risk assessment methods adopted by IOB, focusing on their effectiveness, implementation challenges, and alignment with regulatory requirements. Primary data was collected from 103 respondents, including bank employees, banking professionals, and digital banking users, through a structured questionnaire. Secondary data was obtained from RBI reports, IOB annual reports, and relevant academic literature. The study evaluates key digital risk assessment tools such as AI-based credit scoring models, cybersecurity frameworks, operational risk management systems, compliance monitoring mechanisms, and data-driven early warning systems. Statistical techniques including percentage analysis, Likert scale evaluation, and chi-square tests were applied. The findings indicate that although IOB has made considerable progress in adopting digital risk management practices, several gaps remain in areas such as real-time fraud detection, customer awareness, cybersecurity infrastructure, and integrated risk governance. Keywords: Digital Financial Risk, Risk Assessment, IOB, Cybersecurity, AI Credit Scoring, Operational Risk, Regulatory Compliance

Urban waste mismanagement remains a critical public health concern, particularly in densely populated market areas where delayed garbage collection can increase the risk of disease outbreaks such as cholera, especially during the rainy season. This issue is largely attributed to the absence of an efficient monitoring mechanism that informs municipal authorities about the real-time status of waste bins. To address this challenge, this project presents an intelligent waste monitoring and collection system that integrates Internet of Things (IoT) technology with Global Positioning System (GPS) capabilities. The proposed system continuously tracks the fill level of garbage bins using embedded sensors and transmits this information to a centralized IoT server. Alongside filllevel data, the system also provides precise geographic location details of each bin through GPS integration. This enables waste management personnel to identify which bins require immediate attention without the need for manual inspection. Automated alerts are generated when bins reach critical capacity, ensuring that collection activities are carried out in a timely manner. Furthermore, the availability of real-time data supports route optimization for garbage collection vehicles, thereby reducing fuel consumption, operational costs, and environmental impact. By enabling data-driven decision-making, the system enhances overall efficiency in municipal waste management operations. Ultimately, this solution not only minimizes the risk of overflow and associated health hazards but also contributes to cleaner and more sustainable urban environments through improved waste collection practices.

In the joint operation of multiple virtual power plants, after day-ahead optimal dispatch is completed, some participants may engage in speculative behaviors such as misreporting profit contribution data to obtain greater benefits during profit distribution, thereby undermining fairness. To address this issue, this paper constructs a profit distribution model designed to prevent speculation. An improved Nash bargaining equilibrium algorithm based on a third-party trading intermediary is proposed to curb speculative actions. Furthermore, a dual-layer monitoring mechanism centered on profit deviation is established, which can effectively identify both single-day speculative behaviors and long-term systematic speculative trends, thereby triggering verification procedures. This forms a closed-loop management mechanism for speculation prevention—“detection, monitoring, analysis, verification”—ensuring fair profit distribution among participants within virtual power plants. Case study results demonstrate that the proposed method achieves an average deviation of only 2.32% compared to the profit distribution outcome under non-speculative conditions. In contrast, commonly used methods such as the Shapley value method, nucleolus method, and Nash–Harsanyi bargaining solution exhibit an average deviation as high as 18.44%. The research presented in this paper enables the detection of speculative behaviors among participants and facilitates verification, significantly enhancing the fairness and rationality of profit distribution.

This article examines international approaches to preventing the use of digital currencies and cryptoassets in money laundering processes, in the context of the transformation of the global financial environment. It analyzes the key risks arising from the development of decentralized financial technologies, the expanding use of stablecoins, the use of transaction mixing services, and other tools designed to increase the anonymity of financial transactions. It is argued that the growth of cross-border mobility of digital assets complicates the identification of ultimate beneficiaries and requires the development of comprehensive financial monitoring mechanisms at the international level. The paper summarizes international anti-money laundering standards in the virtual asset sector, particularly FATF recommendations, and examines current regulatory approaches, focusing on a comparative analysis of legal regulation models for providers of virtual asset-related services, licensing and registration mechanisms, procedural requirements for customer identification, the application of the Travel Rule, and the development of risk-based financial monitoring systems. The effectiveness of combating the legalization of criminal proceeds depends to a large extent on the level of institutional coordination between financial regulators, law enforcement agencies, and international organizations. The focus is on practical tools for identifying, seizing, and confiscating crypto assets within the framework of criminal proceedings, as well as on the role of international legal assistance and cross-border information exchange in combating transnational financial schemes. It has been demonstrated that the absence of effective procedures for the actual seizure of digital assets reduces the preventive effect of criminal law measures and preserves the economic attractiveness of illegal activities in the crypto sphere. Based on the conducted research, proposals have been formulated to improve Ukraine’s national financial security system, which include harmonizing legislation with European standards, strengthening the technological capabilities of financial monitoring, developing specialized digital forensics, and deepening international cooperation in the fight against money laundering. Implementing these proposed measures will contribute to the development of a more effective and sustainable model for preventing the use of digital currencies in criminal financial processes in the context of global digitalization.

This study provides a comprehensive analysis of the role, current status, and developmental prospects of public–private partnership (PPP) in Azerbaijan’s tourism sector. As tourism gains increased importance for the country’s economic diversification and international positioning, PPP emerges as a key mechanism for leveraging private investment, innovation, and expertise to complement public sector initiatives. Drawing on statistical data from the State Statistical Committee of Azerbaijan, legislative documents, and international reports from organizations such as the World Bank, UNWTO, OECD, and UNDP, this article evaluates how PPP models have been implemented in Azerbaijan’s tourism sector, their effectiveness in fostering infrastructure development, enhancing service quality, and promoting sustainable tourism in line with global standards. The paper systematically explores the legislative and institutional frameworks that underpin PPPs in Azerbaijan, compares them with international best practices, and identifies gaps and challenges—such as limited sector-specific guidelines, project diversification, and sustainability assessment. Through a detailed comparative analysis presented in tabular form, the article examines the strengths and weaknesses of current PPP practices in the Azerbaijani tourism industry. Key findings indicate that, while foundational progress has been made—especially in the development of tourism infrastructure and heritage conservation—there is significant potential for further enhancement, including expanding PPPs into digital tourism, event management, and marketing, as well as strengthening regulatory clarity and monitoring mechanisms. The study concludes with policy recommendations aimed at fostering a more dynamic, inclusive, and sustainable tourism environment.

Mongolia continues to experience the world's highest incidence of hepatocellular carcinoma (HCC), driven by chronic hepatitis B (HBV) and hepatitis C (HCV) infections. In response, the Mongolian Government has implemented comprehensive public health interventions, including the national 'Healthy Liver Programme,' to reduce viral hepatitis transmission and liver-related morbidity. This narrative review aims to evaluate national strategies, progress, and ongoing challenges in HBV and HCV control. Policy documents from government agencies, WHO, and NGOs were reviewed, along with scientific publications retrieved from PubMed, and Embase using terms such as 'HBV,' 'HCV,' and 'treatment access.' Grey literature was also analysed. The review focused on (i) national strategies and action plans; (ii) programmatic interventions, vaccination, screening, and treatment scale-up; and (iii) reported trends in coverage, treatment uptake, and progress towards WHO 2030 elimination targets. Findings were synthesised by comparing policy commitments with implementation outcomes and triangulating evidence across multiple sources. Forty-four peer-reviewed articles and 12 policy documents were included. Mongolia has achieved >95% infant HBV vaccination and 93.9% hepatitis A coverage, reducing HBsAg prevalence among children under five to 0.3% by 2023. Among adults, HBsAg prevalence declined from 6.9% in 2015 to 5.5% in 2023, while HCV prevalence decreased from 6.7% to 3.7%. More than 120,000 HCV infections have been diagnosed, with over half treated through national initiatives; by 2023, 4,700 HBV and 66,959 HCV patients had initiated therapy. Liver cancer incidence decreased from 39.1% in 2016 to 32.7% in 2021, and annual HCC cases fell from 857 in 2015 to 567 in 2022. Mongolia aims to reduce viral hepatitis prevalence by 90% and liver-related mortality by 85% in the coming decade. While substantial progress has been made, strengthened health system capacity and improved monitoring mechanisms are essential to close remaining gaps and accelerate progress towards hepatitis elimination. Policy implementation: Mongolia implemented several key policies, including the Targeted Prevention and Control Program to Eradicate Viral Hepatitis (1988-2000), mandatory HBV vaccination, and the Healthy Liver programmes (2016-2020 and 2022-2025).Vaccination programmes: Mongolia was among the first to introduce hepatitis B vaccination for newborns in 1991 and added a pentavalent vaccine in 2005. The HAV vaccine was introduced in 2012, with high coverage rates for children under one year.Healthy liver programme: This programme improved access to diagnostic and treatment options for viral hepatitis, including introducing antiviral medicines and reimbursement for antiviral therapy costs through national health insurance.Success achieved: The incidence of total viral hepatitis was reduced to 0.7 per 10,000 population by 2022, surpassing the goal of 10 per 10,000. HBsAg prevalence in children under five met WHO's regional goal, and HCV prevalence among adults significantly decreased.Treatment access: Patients in Mongolia have access to treatments for HBV, HCV, and HCC with significant reimbursement from the Health Insurance Fund. The affordability of these treatments has improved, making them accessible to a larger population.

We, the publisher, are issuing an expression of concern about Zhongchen Zhang, Xiaomei Wang (2024) Wearable Sports Smart Glasses Real-time Monitoring and Feedback Mechanism in Physical Education.https://doi.org/10.4108/eetpht.10.5531 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.

Examines the specifics of pre-trial proceedings in the Kyrgyz Republic in the context of modern requirements of criminal justice. Based on an analysis of legislation, law enforcement practice, and statistical data, the study identifies key problems in the functioning of the pre-trial stage: untimely registration of reports in the Unified Register of Crimes (URC), violations of the time limits and procedures for pre-investigation checks, restrictions on the rights of suspects and victims, insufficient judicial oversight, the formal nature of prosecutorial supervision, as well as staffing and resource shortages within investigative bodies. Special attention is given to violations of international standards of effective investigation established by the case law of the European Court of Human Rights. The paper proposes concrete measures for improving pre-trial proceedings, including the digitalization of procedures, strengthening judicial control, developing mechanisms for external monitoring, and ensuring the inadmissibility of evidence obtained in violation of the law. The conclusions emphasize the need for comprehensive modernization of the pre-trial stage as a key element in ensuring fairness in the criminal process of the Kyrgyz Republic.