This study aims to analyze Bitcoin price volatility after halving by considering interactions with Ethereum, Tether, Binance Coin, and USD Coin. This research was conducted because Bitcoin halving is a significant event affecting the dynamics of the crypto market, but its impact on price volatility is not fully understood. This research uses the EGARCH (Exponential Generalized Autoregressive Conditional Heteroskedasticity) method with purposive sampling technique. Daily price data from Bitcoin, Ethereum, Tether, Binance Coin, and USD Coin in the period May 11, 2020 to May 31, 2024. The analysis results show that Bitcoin price volatility increased significantly after the halving event. In addition, there is an effect of Bitcoin price volatility after halving, namely on the price of Ethereum, Tether and Binance Coin. While the price of USD Coin cannot be proven because the daily closing price data is homoskedasticity.
Ilham Ardhiyansyah, Ahmad Furqon, Mashilal Mashilal
Zakat is a fundamental component of Islamic social finance, intended to reduce inequality and strengthen community welfare. However, traditional zakat systems face recurring issues, including inefficiency, lack of transparency, and low public trust. This study aims to optimize zakat management using Ethereum blockchain technology, particularly the Layer 2 (Base) network, with a focus on its impact on cost efficiency and system transparency. Using a qualitative-descriptive approach, the research designs and simulates a blockchain-based zakat distribution model that incorporates smart contracts for automated fund allocation to eight categories of ashnaf, along with off-chain verification for Sharia compliance. A simulation of USDC 1,000 zakat fund distribution demonstrates that the blockchain system ensures accurate, traceable, and tamper-proof transactions, while reducing transaction costs by over 98% compared to conventional methods. Smart contracts automate the disbursement process, while all transaction records are stored on a public ledger, which supports real-time auditing and enhances institutional accountability. These results demonstrate that the integration of blockchain technology not only improves operational efficiency and transparency but also supports Islamic legal and ethical governance. In conclusion, this model provides a practical and scalable framework for modernizing zakat management with a strong emphasis on cost efficiency, public trust, and Sharia compliance.
Blockchain technology has transformed how data are stored and transactions are processed in a distributed environment. Blockchain assures data integrity by validating transactions through the consensus of a distributed ledger involving several miners as validators. Although blockchain provides multiple advantages, it has also been subject to some malicious attacks, such as a 51% attack, which is considered a potential risk to data integrity. These attacks can be detected by analyzing the anomalous node behavior of miner nodes in the network, and data analysis plays a vital role in detecting and overcoming these attacks to make a secure blockchain. Integrating machine learning algorithms with blockchain has become a significant approach to detecting anomalies such as a 51% attack and double spending. This study comprehensively analyzes various machine learning (ML) methods to detect anomalies in blockchain networks. It presents a Systematic Literature Review (SLR) and a classification to explore the integration of blockchain and ML for anomaly detection in blockchain networks. We implemented Random Forest, AdaBoost, XGBoost, K-means, and Isolation Forest ML models to evaluate their performance in detecting Blockchain anomalies, such as a 51% attack. Additionally, we identified future research directions, including challenges related to scalability, network latency, imbalanced datasets, the dynamic nature of anomalies, and the lack of standardization in blockchain protocols. This study acts as a benchmark for additional research on how ML algorithms identify anomalies in blockchain technology and aids ongoing studies in this rapidly evolving field.
This study provides a comprehensive analysis of the institutional and technological dimensions of anti-money laundering (AML) measures within the cryptocurrency space. The focus is on Financial Action Task Force (FATF) Recommendation 15 and its implementation in the Russian legal framework, as well as the regulation of digital financial assets and the digital ruble in the context of tightening international standards. A key issue addressed is the inherent contradiction between the fundamental properties of cryptocurrencies – namely, anonymity and decentralization – and the imperative to ensure transparency of financial transactions in accordance with AML/CFT (Countering Financing of Terrorism) requirements. The study systematizes modern approaches to minimizing this conflict, including customer identification mechanisms, transaction monitoring systems, and technological solutions to enhance operational transparency. Additionally, it examines the positions of Russian regulators concerning AML/CFT compliance in relation to virtual assets. Based on the conducted research, the study identifies emerging trends in the development of regulatory and legal frameworks and discusses prospects for adapting national legislation to meet global regulatory challenges.
Open access
Economic and Technological Developments in Russia
Economic Systems and Logistics Management
Economic, Social, and Public Health Issues in Russia and Globally
Ye Tian, Liangliang Song, Peng Qian, Yanbin Wang · 6 authors
The detection of malicious accounts on Ethereum - the preeminent DeFi platform - is critical for protecting digital assets and maintaining trust in decentralized finance. Recent advances highlight that temporal transaction evolution reveals more attack signatures than static graphs. However, current methods either fail to model continuous transaction dynamics or incur high computational costs that limit scalability to large-scale transaction networks. Furthermore, current methods fail to consider two higher-order behavioral fingerprints: (1) direction in temporal transaction flows, which encodes money movement trajectories, and (2) account clustering, which reveals coordinated behavior of organized malicious collectives. To address these challenges, we propose DiT-SGCR, an unsupervised graph encoder for malicious account detection. Specifically, DiT-SGCR employs directional temporal aggregation to capture dynamic account interactions, then coupled with differentiable clustering and graph Laplacian regularization to generate high-quality, low-dimensional embeddings. Our approach simultaneously encodes directional temporal dynamics, global topology, and cluster-specific behavioral patterns, thereby enhancing the discriminability and robustness of account representations. Furthermore, DiT-SGCR bypasses conventional graph propagation mechanisms, yielding significant scalability advantages. Extensive experiments on three datasets demonstrate that DiT-SGCR consistently outperforms state-of-the-art methods across all benchmarks, achieving F1-score improvements ranging from 3.62% to 10.83%.
Purpose Cryptocurrency’s novelty and volatility—combined with the absence of standardized reporting prior to 2023—created an opaque information environment. This study explores whether such conditions enabled assertive impression management in corporate reporting. We examine how firms not only varied the volume of cryptocurrency disclosures over time, but also strategically manipulated their readability . Additionally, we use this context to demonstrate the utility of machine learning and natural language processing tools for consistent analysis of complex financial narratives. Study design We analyze full-text annual reports, MD&A sections, and proxy statements from five publicly traded U.S. firms with diverse cryptocurrency involvements. Our methodology includes machine learning-based topic modeling, readability assessment using standardized indices, and visualization tools. Findings (i) Information Demand: Google search trends for target firms are strongly associated with Bitcoin price movements, reflecting external attention cycles. (ii) Impression Management: Firms increase both the frequency and readability of crypto disclosures in favorable markets and reduce or obscure them in downturns, consistent with strategic impression management. (iii) Readability: Crypto-related disclosures are significantly more readable than non-crypto sections from the same reports suggesting deliberate simplification. Contributions This study advances the limited literature on cryptocurrency disclosure by offering a textual and behavioral lens on corporate impression management. A key contribution is the integration of readability metrics, public attention signals, and NLP tools into disclosure analysis. We highlight how firms use both narrative framing and readability engineering as tools to influence perception—especially in periods of regulatory uncertainty. Implications Our findings have direct implications for policy and practice: (i) Policymakers should consider not only disclosure quantity but also its linguistic clarity and comparability, especially for volatile assets. (ii) Investors and analysts can use automated text analysis to detect subtle impression management tactics and to interpret the strategic use of clarity in disclosure narratives.
Haya R. Hasan, Khaled Salah, Ahmad Mayyas, Ahmad Musamih · 7 authors
Greenhouse gas emissions and carbon footprints have surged dramatically, with the transportation industry being a major contributor. While the UN aims to adopt zero-emission vehicles by 2040, the demand for electric vehicles (EVs) raises sustainability concerns about sourcing earth metals for batteries. Digital passports have emerged to track a product’s lifecycle, composition, certifications, origin, and recyclability. However, existing EV battery passport systems lack sufficient traceability, immutability, auditability, and are prone to manipulation due to their centralized nature. In this paper, we propose a decentralized blockchain-based digital passport for EV batteries using composable Non-Fungible Tokens (NFTs) to ensure traceable, tamper-proof records that promote transparency across the supply chain. Our solution enables standardized data sharing and interoperability while integrating off-chain storage for efficiency. We evaluate four cathode chemistries using ten sustainability Key Performance Indicators (KPIs) to compute an overall sustainability score per battery. We present a system architecture, smart contracts, and supporting algorithms are presented, with testing and validation in a simulated environment. A cost and security analysis confirms affordability and resilience against known attacks. Our approach offers practical value for sustainability assessments, regulatory audits, carbon footprint tracking, and circular economy initiatives by providing immutable, component-level provenance and real-time KPI updates. Limitations include simulation-based validation, lack of stakeholder testing, and scalability challenges in live deployments. While we address some of these, via Layer 2 scaling and modular smart contract design, further research is required to validate adoption in real-world EV supply chains. The smart contract code is made publicly available on GitHub.
Pedro Henrique Filgueiras dos Santos Oliveira, Daniel Muller Rezende, Saulo Moraes Villela, Heder S. Bernardino · 6 authors
One of the main events involving the world economy in 2022 was the beginning of the war between Russia and Ukraine. This event offers an opportunity to analyze how a large-magnitude world event can affect the use of cryptocurrencies. Ethereum is one of the most prominent and widely used cryptocurrency platforms and, as such, provides a valuable case study for this scenario. This work investigates the behavior of accounts and their transactions on the Ethereum network during this event. For this purpose, we collect all Ethereum transactions during two distinct periods: (i) during the month the conflict began, and (ii) during the previous year. We organized a dataset with the accounts involved in these transactions and the subset of these accounts that interacted with a service within Ethereum named Flashbots Auction. Flashbots Auction is crucial as it addresses issues regarding transaction ordering and miners exploiting that ordering to make profit. Then, we model temporal graphs in which each vertex represents an account, and each edge represents a transaction between two accounts. We analyzed the behavior of these accounts via graph metrics for both groups during each observed time window. The results show changes in account behavior and activity, as well as variations in daily transaction volume.
Abstract This study explores the factors that influence employees’ intention to use distributed ledger-based smart contracts in supply chains, addressing a gap in current research by applying the Unified Theory of Acceptance and Use of Technology (UTAUT) to this emerging technology within logistics contexts. Based on a quantitative survey of employees in German supply chain companies, the study measured constructs such as performance expectancy, effort expectancy, and social influence. Using Partial Least Squares Structural Equation Modelling (PLS-SEM), the findings reveal that performance expectancy is a key driver of behavioural intention. In contrast, effort expectancy and social influence have no significant impact. Additionally, demographic variables such as gender, age, experience, and voluntariness do not moderate these relationships. These results highlight the central role of perceived performance benefits in shaping adoption intentions. As the first study to apply an adoption framework to smart contract use specifically in supply chains, it offers valuable insights into the socio-technical dynamics of technology acceptance at the employee level. The findings suggest that supply chain managers should focus on communicating and demonstrating performance gains, supporting adoption through pilot implementations, and providing targeted training initiatives to encourage the integration of smart contracts in operational processes.
This paper unveils the LemniCoin ecosystem, advancing from a Binance Smart Chain (BSC) foundation to a quantum-resistant paradigm through the Lemniscate-AGM Isogeny (LAI) cryptosystem.Building on security audits conducted on March 27, 2025, and April 13, 2025, we introduce LemniCoin-QR (April 2025), a quantumhardened token; a secure wallet (May 2025); and LemniChain (December 2025), a Proof-of-Stake (PoS) blockchain.The Lemniscate-AGM Isogeny Problem (LAIP) underpins LAI, offering unparalleled resistance to quantum threats, surpassing Bitcoin (BTC) and Ethereum (ETH).We provide detailed proofs, audit insights, and implementation strategies, demonstrating superior security, transaction efficiency, and environmental sustainability-positioning LemniCoin as a premier investment in the post-quantum era.
The study aimed to analyse fine art photography as a form of visual art, its aesthetic characteristics, stylistic evolution, and contemporary developmental trends. The research was based on the analysis of art-historical works, exhibition catalogues, artistic practices of leading photographers, as well as content analysis of digital platforms and the non-fungible token market. The study established that fine art photography has transcended its traditional documentary function and acquired the status of an autonomous conceptual medium, integrating elements of painting, cinematography, performance, and digital technologies. It was found that the stylistic transformations of fine art photography have been accompanied by experiments with form, light, composition, and narrative structure, particularly evident in the development of such movements as conceptual, staged, and minimalist photography. The results confirmed that fine art photography has evolved into a multidimensional artistic practice that combines visual aesthetics with conceptual content and actively interacts with other art forms. It was revealed that contemporary photographers not only experiment with genres and techniques but also shape new modes of audience engagement through digital platforms, the non-fungible token technologies, and social media. The study demonstrated that digital technologies open new possibilities for photographers, particularly in algorithmic editing, generative art, and the non-fungible token economy, while simultaneously transforming the methods of presentation and artist-audience interaction. The findings may be applied in art education, curatorial practice, and the development of new digital exhibition formats.
Decentralized Finance (DeFi) has become as a world-shattering power in the financial sector, proposing a trustless, permission less, and decentralized substitute to traditional financial systems. This paper attempts to assess the impact of Decentralized Finance on traditional financial markets, emphasizing the benefits, challenges, and regulatory contemplations accompanied its implementation. The data has collected from both primary and secondary sources. This study provides experimental visions of growing impact of Decentralized Finance on investment strategies. From the study, it has been suggested, that even though Decentralized Finance provides better financial inclusion and transparency also it associated with risks related to security, instability, and regulatory uncertainty. The paper accomplishes with recommendations for integrating Decentralized Finance with traditional finance for a stronger and adaptive financial system.
As privacy and security concerns increase, Zero Knowledge Proof (ZKP) technology offers a promising solution for secure digital verification. ZKP addresses key privacy and security challenges across individual, business, and public sectors by enabling data protection without revealing sensitive information. The aim of this study is to analyse ZKP’s application areas by reviewing current literature and case studies, examining its strengths, limitations, and potential risks. Findings highlight the capability of ZKP to enhance privacy, security, and verification processes across various fields, including blockchain technology, identity authentication, secure data sharing, and digital voting systems. The paper provides a balanced perspective on ZKP’s benefits and challenges, including computational complexity and scalability issues. By suggesting practical use cases, this work aims to contribute to a deeper understanding of how ZKP technology can support innovation across various industries while addressing critical privacy and security needs.
The rapid adoption of Non-Fungible Tokens (NFTs) has revolutionised the digital art and collectibles markets. NFTs present novel opportunities for creators and investors alike. However, with such opportunities also comes the risk of money laundering through NFTs. The South African digital art market has not been spared from the rising phenomenon of NFTs. This rising phenomenon has brought with it questions regarding whether the South African anti-money laundering (AML) regime can adequately counter the challenge of money laundering through NFTs. This is particularly so if one considers that, generally, the AML regulatory framework for NFTs is still nascent, not only in South Africa, but also globally. Thus, this contribution comparatively examines the AML regulation of NFTs in South Africa to establish the adequacy and efficacy of the country’s AML regime. The paper concludes that while NFTs are still new, they can be dealt with under the blanket regulation for crypto assets and in specific use cases, AML regulations may be applied to them.
This paper formalizes the proves the security of the Bridgeless protocol, a protocol able to bridge tokens between various chains. The Bridgeless protocol is run by a set of validators, responsible for verifying deposit transactions on the source chain and generating the corresponding withdrawals on the target chain. The protocol is designed to be chain-agnostic and the validators interact with each supported chain via a chain client. It currently supports EVM-compatible chains, the Zano, and the Bitcoin chains. The paper formalizes all involved subprotocols and describes the conditions under which the protocol maintains safety and liveness.
Isaac Marroqui Penalva, Enrique Tomás Martínez Beltrán, Manuel Gil Pérez, Alberto Huertas Celdrán
Decentralized Federated Learning (DFL) enables nodes to collaboratively train models without a central server, introducing new vulnerabilities since each node independently selects peers for model aggregation. Malicious nodes may exploit this autonomy by sending corrupted models (model poisoning), delaying model submissions (delay attack), or flooding the network with excessive messages, negatively affecting system performance. Existing solutions often depend on rigid configurations or additional infrastructures such as blockchain, leading to computational overhead, scalability issues, or limited adaptability. To overcome these limitations, this paper proposes RepuNet, a decentralized reputation system that categorizes threats in DFL and dynamically evaluates node behavior using metrics like model similarity, parameter changes, message latency, and communication volume. Nodes' influence in model aggregation is adjusted based on their reputation scores. RepuNet was integrated into the Nebula DFL platform and experimentally evaluated with MNIST and CIFAR-10 datasets under non-IID distributions, using federations of up to 25 nodes in both fully connected and random topologies. Different attack intensities, frequencies, and activation intervals were tested. Results demonstrated that RepuNet effectively detects and mitigates malicious behavior, achieving F1 scores above 95% for MNIST scenarios and approximately 76% for CIFAR-10 cases. These outcomes highlight RepuNet's adaptability, robustness, and practical potential for mitigating threats in decentralized federated learning environments.
Data trading is one of the key focuses of Web 3.0. However, all the current methods that rely on blockchain-based smart contracts for data exchange cannot support large-scale data trading while ensuring data security, which falls short of fulfilling the spirit of Web 3.0. Even worse, there is currently a lack of discussion on the essential properties that large-scale data trading should satisfy. In this work, we are the first to formalize the property requirements for enabling data trading in Web 3.0. Based on these requirements, we are the first to propose Yotta, a complete batch data trading scheme for blockchain, which features a data trading design that leverages our innovative cryptographic workflow with IPFS and zk-SNARK. Our simulation results demonstrate that Yotta outperforms baseline approaches up to 130 times and exhibits excellent scalability to satisfy all the properties.
This paper presents a formal analysis of the Lightning Network as a monetary system structurally diverging from Bitcoin's base-layer settlement model. We demonstrate that under increasing transaction demand, BTC transaction fees rise superlinearly due to throughput constraints, while Lightning Network routing costs approach a bounded asymptote. Using mathematical modeling, game-theoretic proofs, and complexity analysis, we show that Lightning enables indefinite off-chain operation via the emergence of liquidity hub oligopolies. These hubs exhibit properties of unregulated financial intermediaries, including rent extraction, opacity, and systemic fragility. Strategic agent models show that channel closure becomes economically infeasible, and routing problems approach hardness limits in P-Space complexity. We conclude that Lightning does not merely extend Bitcoin, but constitutes a synthetic financial system with shadowbank characteristics, lacking reserve discipline, transparency, or enforceable settlement guarantees.
Daniël Reijsbergen, Eyasu Getahun Chekole, Howard Halim, Jianying Zhou
Biometric authentication relies on physiological or behavioral traits that are inherent to a user, making them difficult to lose, forge or forget. Biometric data with a temporal component enable the following authentication protocol: recent readings of the underlying biometrics are encoded as time series and compared to a set of base readings. If the distance between the new readings and the base readings falls within an acceptable threshold, then the user is successfully authenticated. Various methods exist for comparing time series data, such as Dynamic Time Warping (DTW) and the Time Warp Edit Distance (TWED), each offering advantages and drawbacks depending on the context. Moreover, many of these techniques do not inherently preserve privacy, which is a critical consideration in biometric authentication due to the complexity of resetting biometric credentials. In this work, we propose ZK-SERIES to provide privacy and efficiency to a broad spectrum of time series-based authentication protocols. ZK-SERIES uses the same building blocks, i.e., zero-knowledge multiplication proofs and efficiently batched range proofs, to ensure consistency across all protocols. Furthermore, it is optimized for compatibility with low-capacity devices such as smartphones. To assess the effectiveness of our proposed technique, we primarily focus on two case studies for biometric authentication: shake-based and blow-based authentication. To demonstrate ZK-SERIES's practical applicability even in older and less powerful smartphones, we conduct experiments on a 5-year-old low-spec smartphone using real data for two case studies alongside scalability assessments using artificial data. Our experimental results indicate that the privacy-preserving authentication protocol can be completed within 1.3 seconds on older devices.
Dmitry Mikhaylov, Andrei Kutin, Joseph Anderson, Maxim Falaleev · 6 authors
Purpose - given the increased international efforts to prevent illicit financial activity related to cryptocurrencies, the study intends to thoroughly examine the complex field of cryptocurrency laundering. The core of our study project is the complex relationships that exist between cutting-edge technologies and strong security protocols in the cryptocurrency space, a dandruff attack. This paper aims to disentangle the process of bitcoin laundering by exploring the intricate webs of deceit. Method - This is a case study applying observational and experimental methods. Result - we have discovered a pattern of cryptocurrency laundering. The first one saw the primary repository start a cyclical fund movement pattern that involved several new addresses. Equal sums are then systematically transferred over a network of new addresses. The criminal then distributed the stolen money among several new addresses after combining it with an equal quantity of money. It then split and merged, and one saw the resultant sum being transmitted to the BitTorrent blockchain. The cyclical trajectory and engagement with extra money were part of the follow-up return to the Tron blockchain. Observation of the ultimate combination of pilfered money with additional monies sent to the cryptocurrency service "JustLend.org" Originality – no research has been done on using a dandruff attack to launder cryptocurrency. Thus, it is essential to acknowledge the offender's activities to raise awareness in general.
This paper studies the existence of the long-run equilibrium relationship between the US M2 money supply (M2SL) and the price of Bitcoin (BTC) spanning January 2015 to April 2025. Utilizing a log-log model to focus on elasticity, this study employs a robust econometric methodology to examine the relationship between the US M2 money supply and Bitcoin (BTC) prices. The empirical findings confirm that the natural logarithms of M2 and BTC are integrated of order one, denoted as I (1). The Johansen test shows a long-run elasticity estimate of 2.65, suggesting that a 1% increase in the M2 money supply is associated with a 2.65% increase in the price of Bitcoin. The VECM analysis validates this long-run equilibrium, with a statistically significant error correction term (λ’ = -0.12), indicating that 12% of any deviation from the long-run path is corrected monthly. The cointegration tests for both variables provide strong evidence of a stable, long-run relationship. These results lead us to conclude that Bitcoin performs as a highly elastic asset with respect to changes in the M2 money supply.
In recent years, several research and development initiatives have focused on developing secure and trustworthy systems for the healthcare industry via pervasive and mobile healthcare (mHealth) solutions. State-of-the-art mHealth solutions primarily rely on centralized storage, such as cloud computing servers, which may escalate the maintenance costs, require ever-increasing storage infrastructure, and pose privacy and security risks to the health-critical data produced, consumed, and transmitted over ad hoc networks. To overcome these limitations, we conducted this study intending to synergize mobile computing (devices to process health-critical data) and blockchain technology (infrastructure to secure storage and retrieval of health-critical data), specifically addressing data security and privacy using a blockchain mHealth system. The research employs an incremental method by (i) developing a framework that acts as a blueprint to architect blockchain-enabled mHealth systems, (ii) implementing a suite of algorithms as a proof-of-concept to automate the framework, and (iii) experimental evaluations to validate the scalability, computation, and energy efficiency of the proposed solution. The proposed framework has been implemented as a frontend using a mobile application interface that exploits the backend via the InterPlanetary File System (IPFS) system and Ethereum blockchain for secure management of mHealth data. We use a case-study-based approach demonstrating how health units, medics, and patients can securely access and distribute health-critical data. For evaluation, we deployed a smart contract prototype on the Ethereum TESTNET network in a Windows environment to test the proposed framework. Results of the evaluation indicate (a) scalability with query response time (range: 10–41 ms), (b) computational performance (CPU utilization: 1.5% – 2.5%), and (c) energy efficiency (gas consumption: 40000 units for 1000 bytes). The proposed solution – framework, algorithms, and experimental evaluation – aims to advance state-of-the-art architecting and implementing cybersecurity mHealth solutions using blockchain technology.
In a time of rising cyber threats and widespread digital communication, protecting sensitive information is crucial. This paper offers a detailed survey and analysis of current methods in secure communication, focusing on the relationship between cryptographic systems and steganographic techniques. We base our work on foundational mathematics, especially commutative algebra, and use modern technologies like Generative Adversarial Networks (GANs), zero-knowledge proofs, and quantum-resistant algorithms. We present a layered approach to information security. We discuss how combining classical and modern cryptography with improved steganographic embedding and signal processing techniques highlights the need for hybrid and adaptable models to protect communication. This study aims to be a reference point for future research and development in secure digital systems. Key Words: Cryptography : from classical to post-quantum, Steganography and Data hiding Techniques, GAN-Based Steganography in wireless sensor network , Methodology Overview.
Ministry of Youth and Sports of Ukraine, Alina Hrushyna
The main mechanisms of targeted financing in Ukraine are revealed, and their role in ensuring socio-economic development under martial law is defined. The article analyzes the effectiveness of the implementation of active state targeted programs up to 2023, particularly in terms of the fulfillment of planned funding from both the state and local budgets, as well as the achievement of performance indicators. The key shortcomings in the system of program design, financing, and effectiveness evaluation are identified. The paper proposes ways to improve the effectiveness of program implementation, including through decentralization, enhanced monitoring, and independent auditing. Particular emphasis is placed on the importance of adapting programs to the challenges of war and national recovery, as well as the expediency of long-term programs in periods of limited public financial resources.