Blockchain Papers

Abhay Kumar Yadav, Virendra P. Vishwakarma

No abstract is available for this record.

Malla Jogarao, Ch. Kameswari

Abstract The quick implementation of technology in Industry 4.0 which includes the Internet of Things (IoT), artificial intelligence (AI), blockchain, and robotics leads to supply chain management (SCM) transformation as it alters workforce profiles. This chapter examines the fundamental links between human factors, like employee transition, and sustainable SCM in Industry 4.0 specifically for the United Nations Sustainable Development Goals (UNSDG) 2030 targets as well as the post-COVID-19 environment. It explores how technology transforms talents and promotes robotic–human teamwork as well as impacts workforce well-being. The analysis of job displacement challenges, ethical problems, and labour shortages, together with identifying opportunities for new skills development and resilient supply chain implementations. Special emphasis is placed on sustainable workforce solutions through actual implementations and practical frameworks for workforce protection through ongoing learning and efficient management of organizational change along with policy collaboration. It explores both ethical and social consequences through an examination of employee data security risks, social inequality problems, and employee mental health effects related to fast technology acceptance. The future research areas include AI autonomous driving vehicles in different fields, smart manufacturing, and health care sustainability. The extensive evaluation provides essential information that benefits scholars, policymakers, together with industrial leaders who need to understand human capital management in sustainable supply chains using Industry 4.0 technologies.

Nguyễn Thị Thanh Bình

This review synthesizes theoretical and empirical insights from 1055 peer-reviewed articles on artificial intelligence (AI), corporate governance, and ethics. Situated in the corporate governance and accounting literature, it develops a computational framework to identify thematic patterns and conceptual links among AI, transparency, accounting, governance, and ESG. Using latent Dirichlet allocation, co-occurrence network analysis, sentence-level semantic similarity, and exploratory regression, the study identifies three recurring configurations of conceptual association: (1) Ethics, Governance, and Transparency; (2) Machine Learning, Finance, Blockchain, and Accounting; and (3) Corporate, ESG, and Accounting. The findings indicate that these themes are repeatedly connected within the scholarly literature.

Iryna Kravchuk, Nataliia Valinkevych, Oksana Prysiazhniuk

This paper substantiates the theoretical and applied foundations of investment management for forming and developing resilient distribution networks in agribusiness. Under global food market transformations, systemic macroeconomic instability, and geopolitical shocks, conventional linear investment models prove ineffective for long-term planning. To bridge this gap, this research adapts advanced economic frameworks directly to agricultural supply chains, shifting the focus from discrete physical asset valuation to ecosystem-wide synergy. This is achieved by combining classic capital planning with portfolio diversification, real options valuation (ROV), behavioral finance, stakeholder-driven ESG metrics, and decentralized financial tools (DeFi). The study proposes a hierarchical digitization model of the investment process powered by artificial intelligence (AI) and Big Data. This system operates at three spatial levels: national (for comprehensive stress-testing against geopolitical shocks), regional (deploying predictive digital twins of logistics clusters to optimize infrastructure placement), and local (facilitating agile capital allocation and behavioral consumer analysis). This structure ensures capital flows efficiently into highperforming channels while minimizing bottlenecks. To address the trade-off between environmental requirements and financial risks, the study introduces the "Two-Factor Balanced Development Matrix." This model links financial credit scoring with multidimensional ESG profiling. Counterparties are categorized into operational quadrants (e.g., Green Leaders, Traditional Pragmatists, Eco-Startups) to determine customized trade credit lines and commercial terms. Finally, the research outlines integrated risk mitigation instruments, including green trade finance (IFC, EBRD), eco-premium forward contracts, and parametric climate insurance. These measures reduce non-performing loans, lower the cost of capital, and improve the Scope 3 emission rating for distributors.

Jyoti Kumari, Pradeep Yadav, Chandra Prakash Bhargava

The convergence of digital technologies and sustainability assessments is changing the dynamics of environmental governance and corporate accountability. This chapter analyzes the way in which artificial intelligence, IoT, blockchain, digital twins, cloud analytics, and robotic process automation technologies contribute to sustainability assessment through their use in measuring, monitoring, and reporting on the environmental and social performance of corporations. Relying on the latest academic research, legislation, and business practice in this field, the chapter considers theoretical background, real-world applications, and governance issues related to digital sustainability assessment. A comprehensive analytical structure is provided, comprising data gathering, analysis, verification, and reporting, alongside comparative tables of relevant technologies, methods, legislation, problems, and solutions.

Y. Su

With the rapid advancement of industrial Internet technologies and intelligent wireless sensing infrastructures, efficient data acquisition and information transmission have become fundamental to modern textile supply chain management. The integration of electromagnetic-enabled Internet of Things (IoT) devices, RFID technologies, and intelligent communication networks provides essential support for real-time financial monitoring and digital taxation services. Against this background, this paper investigates the application of intelligent finance and taxation in textile industry supply chains by proposing an integrated framework based on artificial intelligence, blockchain, cloud computing, and IoT technologies. The framework enables transparent financial management, automated tax compliance, dynamic supply chain finance, and end-to-end traceability through seamless integration of operational, financial, and logistics data. Key applications, including blockchain-based material provenance verification, AI-driven credit assessment, automated customs and tax processing, and intelligent risk management, are systematically analyzed. The proposed architecture improves supply chain transparency, operational efficiency, sustainability, and resilience while facilitating data-driven decision-making across textile production and distribution processes. Furthermore, the study demonstrates that intelligent finance and taxation can establish a unified digital ecosystem for financial governance and supply chain collaboration, providing valuable technical references for wireless industrial information acquisition, smart sensing, and communication-assisted digital management in future intelligent manufacturing environments.

Shahab Azim, Lala Rukh, Shakir Ullah

Crypto currency is one of most interesting financial innovation of 21st century. Crypto currency trading not only involve financial literacy while trading but also there are psychological factors affecting the decision of traders. Keeping in view the psychological factors and investors’ decision, this research study is designed to investigate the complex interplay between psychological triggers and market dynamics in the cryptocurrency sector in Pakistan, specifically examining how these elements coalesce to drive investor behavior and market volatility. While traditional financial models often attribute asset fluctuations to technological or fundamental shifts, this study posits that cryptocurrency markets are fundamentally driven by human perception and emotional reactivity. Utilizing a quantitative methodological approach, data was collected from a sample of 175 experienced traders to analyze the impact of emotional states, market sentiment, and behavioral discipline on trading outcomes. The empirical results, derived through multiple linear regression analysis, reveal that the model possesses a high level of explanatory power, accounting for 56% of the variance in emotional trading behavior (R2=0.56R2=0.56). Market sentiment emerged as the primary determinant of impulsive trading (β=0.48β=0.48), demonstrating that external social cues often exert a stronger influence on decision-making than internal emotional states. Among specific psychological variables, Fear, Uncertainty, and Doubt (FUD) were identified as the most significant predictors of rash choices (β=0.34β=0.34), while the Fear of Missing Out (FOMO) also demonstrated a substantial, though secondary, effect (β=0.21β=0.21). Conversely, the study found that trading experience and the application of systematic strategies serve as vital moderating factors that decrease emotional reactivity and enhance behavioral stability (β=−0.19β=−0.19). The findings contribute to the fields of behavioral finance and digital economics by illustrating that the volatility inherent in digital assets is a systemic byproduct of individual psychological biases aggregated through digital narratives. The research concludes that achieving a sustainable financial ecosystem requires moving beyond purely technical regulations. Instead, it advocates for the implementation of behaviorally-informed safeguards, such as algorithmic "cooling-off" periods and sentiment-aware trading tools, to mitigate the risks associated with reactive investing. Ultimately, this work provides a blueprint for a more resilient digital financial future by prioritizing human factors in market governance.

Smitha Rajagopal

AI-powered predictive systems for decision support are revolutionizing the way that smart enterprises and industrial organizations are analysing data, predicting future conditions, and making operational and strategic decisions. The systems include machine learning, deep learning, predictive analytics, prescriptive analytics, real-time monitoring, and intelligent recommendation systems to enhance decision-making accuracy, efficiency, and responsiveness. They are used in business forecasting, customer and financial analytics, supply-chain and inventory management, predictive maintenance, production optimization, quality control, energy management, workplace safety and asset monitoring. The addition of new technologies like the Internet of Things, Industrial Internet of Things, digital twins, cloud and edge computing, robotics, blockchain and next generation networks further improve system connectivity, scalability and real-time performance. The successful implementation of these steps needs a structured framework for problem identification, data collection, preprocessing, feature engineering, model selection, training, validation, system integration, deployment, and continual monitoring. Despite these progressions, data quality, interoperability, scalability, algorithmic bias, explainability, privacy, cybersecurity, organizational readiness, and regulatory compliance are all important challenges that still need to be addressed. There is still a need for human oversight, especially when dealing with safety-critical and high-impact decisions. It includes the technological foundations, system architecture, implementation processes, enterprise and industrial applications, performance evaluation, governance requirements, and future directions of AI-supported predictive decision support systems. It concludes that the systems that are trustworthy, secure, transparent, sustainable and intelligent are enterprise and industrial operations.

Prajakta Khule, K. Kumaraswamy, Puja Bhardwaj, Meghana Bhilare

Abstract The increasing complexity of global financial systems has exposed the limitations of conventional centralized banking infrastructures in managing transparency, operational efficiency, security, and real-time transaction processing. Distributed Ledger Technology (DLT), particularly blockchain, has emerged as a transformative digital architecture capable of addressing these structural challenges through decentralized record management, cryptographic security, and automated transaction validation. This study examines the architectural foundations and strategic viability of blockchain-enabled distributed ledger technologies within modern banking and corporate finance. Using a qualitative research approach based on an extensive review of recent scholarly literature, industry reports, and practical financial applications, the study evaluates how different blockchain architectures contribute to organizational transformation. Three representative case studies—consortium corporate lending and syndicate management, cross-border settlement systems, and decentralized Know Your Customer (KYC) identity management—are analyzed to demonstrate the practical implications of enterprise blockchain adoption. The findings indicate that permissioned and consortium blockchain architectures significantly enhance operational transparency, reduce intermediary dependence, improve data integrity, automate compliance through smart contracts, and accelerate financial transactions while strengthening governance and auditability. However, the study also identifies challenges associated with regulatory uncertainty, interoperability with legacy systems, scalability, and institutional readiness that continue to influence large-scale implementation. The research contributes to the growing body of knowledge by integrating architectural analysis with strategic business evaluation and proposes a comprehensive perspective on the role of distributed ledger technologies in reshaping banking operations and corporate financial management. The findings provide useful insights for researchers, financial institutions, technology professionals, and policymakers seeking to develop secure, efficient, and sustainable digital financial ecosystems. Keywords: Distributed Ledger Technology (DLT), Blockchain, Smart Contracts, Consortium Lending, Cryptographic Auditing, Cross-Border Clearance, Financial Disintermediation, Asymmetric Cryptography, Banking and Finance.

B. Srinivasan

Among the various problems that have persisted in global supply chains include data silos, information asymmetry, and vulnerability to fraud. In this paper, a blockchain-based intelligent management of the supply chain model has been suggested, involving distributed ledger technology, smart contracts, and Internet of Things for real-time tracking. The model features a four-tiered architecture consisting of data ingestion, blockchain network, smart contract automation, and application tiers. Tasks that include registering stakeholders, verifying the authenticity of the goods, transferring ownership, and verifying compliance can be automated through smart contracts. The solution offers the ability to process up to 200 transactions per second with an 18% reduction in gas costs as opposed to conventional solutions. Trace back time reduces from 95 seconds to 8 seconds, and the consumer trust index grows by 70%.

Saravanan Chinnappan, Sanjay Mallenahalli Basavaraj

This chapter examines the ways in which blockchain smart contracts and responsible artificial intelligence (AI) are transforming many sectors. At the moment, typical contracts in industries like manufacturing or supply chains face several inefficiencies, delays, and the possibility of errors or even fraud. The issue is that those smart contracts lack the intelligence required for real-world scenarios where things are constantly changing, even though blockchain has helped by making things more automated and transparent. The idea here is to make blockchain contracts less rigid by incorporating AI and real-time data analysis. Contracts would adjust in response to events rather than simply adhering to predetermined guidelines. Additionally, the chapter explores how smart contracts are established by fusing AI tools, data feeds from services like Chainlink, and platforms like Ethereum. In general, it involves creating systems that are responsible and intelligent, which seems to be the only viable option at the moment. This chapter examines the evolution of AI in industrial contexts, analyzing its role before and after the integration of smart contracts. It presents relevant industrial case studies, applies responsible AI principles to the development of blockchain-based smart contracts, and underscores the adoption of international frameworks and standards to promote ethical, transparent, and accountable implementation across industries.

R. Darmesh Ram., B. Yasodha Jagadeeswari.

Information and Communication Technologies such as blockchain can significantly contribute to achieving the Sustainable Development Goals (SDGs). Without a doubt, blockchain, as one of the most valuable technological advancements, has been introduced over the past decade and has played a significant role in the industrial revolution. Blockchain technology is progressively taking over the business world. Blockchain as a disruptive technology and a driver for social change has exhibited great potential to promote sustainable practices and help organizations and governments achieve the United Nations’ Sustainable Development Goals (SDGs). The emergence of other technologies derived from blockchain, such as decentralized finance (DeFi) and the Metaverse, has fundamentally transformed people’s daily lives and profoundly impacted future versions of digital businesses. The Blockchain technology revamped several industries, including Real Estate, Healthcare, Education, and Legal industry to name a few. It opened new doors of opportunities and profit for the entrepreneurs and established brands. The paper's main contribution is to advance knowledge about the role of blockchain for economic and sustainable development in countries of the world. Grounded in the innovation forecasting literature, this paper explores blockchain-based innovations and research in the context of economic and sustainable development.

Sanjana

Purpose: The rapid evolution of spatial computing has initiated a paradigm shift from traditional, two-dimensional e-commerce to immersive, three-dimensional virtual commerce (v-commerce). This paper conceptualizes the foundational drivers, structural mechanics, and strategic implications of consumer behavior within the emerging metaverse marketplace.Methodology/Approach: Synthesizing Social Presence Theory and the Technology Acceptance Model (TAM), this study provides a comprehensive conceptual framework analyzing how multi-sensory immersion, avatar-mediated identity expression, and decentralized economic frameworks alter consumer decision-making. Findings: The paper establishes that the metaverse fundamentally redefines digital consumer behavior by transforming standard transactions into identity-driven social expressions. It outlines how immersive experiential marketing stimuli (e.g., gamified storefronts, virtual try-ons) drive high emotional arousal and hedonic consumption patterns. Furthermore, the analysis maps the collapse of the traditional boundary between buyers and sellers via Play-to-Earn (P2E) and Create-to-Earn (C2E) models, re-contextualizing virtual consumers as active entrepreneurial producers within blockchain-secured economies. Research Implications: While presenting a robust conceptual blueprint for v-commerce engagement, the study highlights critical consumer inhibitors, including biometric data harvesting risks, infrastructural access barriers, and psychological virtual fatigue. Originality: This paper bridges the gap between conventional digital marketing theories and spatial mechanics. It provides actionable strategic imperatives for contemporary brands specifically detailing hybrid "phygital" retail systems, spatial analytics optimization, and community-centric governance via Decentralized Autonomous Organizations (DAOs) to effectively future-proof enterprise models.

Dileep Kumar Murala, Sandeep Kumar Panda

The Metaverse has the potential to revolutionise financial services, foster innovation, and enhance client engagement. The Metaverse offers innovative financial products, services, and ecosystems inside a virtual and decentralised environment for user involvement, transactions, and digital asset creation. Blockchain technology within the Metaverse facilitates decentralised, secure, and transparent financial services in virtual settings. The capacity of Blockchain to establish decentralised ecosystems, guarantee digital ownership through Non-Fungible Tokens (NFTs), and facilitate smart contracts is transforming Traditional Finance (TradFi) and promoting Decentralised Finance (DeFi). Users can execute borderless transactions, oversee digital assets, and engage in tokenised economies within the Metaverse, transforming financial services. The intersection of Blockchain technology and the Metaverse within the financial sector is explored via virtual banking, tokenised physical assets, and decentralised exchanges. Innovations such as Blockchain-based trustless transactions, digital identity, and virtual financial inclusion are emphasised. The Metaverse leverages Blockchain’s decentralisation to enhance financial services, establish new marketplaces, and revolutionise investment. The research also addresses legal compliance, cybersecurity hurdles, scalability constraints, and privacy concerns related to this integration. This chapter aims to comprehend the impact of Blockchain technology on financial services within the Metaverse by incorporating recent advancements and emerging trends. It illustrates how these technology advancements are generating novel corporate models and transforming global banking.

Shivaratri Narasimha Rao, Mohammed Ali Shaik, Dr. Imran Qureshi, Salman Ali Syed

The chapter examines the speedy innovations and overlaps of three disruptive technologies, blockchain, generative artificial intelligence (AI), and computer vision (CV) and how they can transform industries. The innovations of blockchain have been developed past cryptocurrencies to include consensus mechanisms, scalability and finance, supply chain, and healthcare applications. The discussion brings out the focus on decentralized finance (DeFi), smart contracts, and nonfungible tokens (NFTs), with a focus on new paradigms of transparency, ownership, and trust. Generative adversarial networks (GANs) and variational autoencoders (VAEs) are used to create generative AI, which can produce realistic text, images, video, and sound but can revolutionize entertainment, advertising, and art and poses an ethical challenge, including such issues as deepfakes and misinformation. With the support of deep learning and neural networks, CV is now capable of human-like performance in image recognition, object detection, and facial recognition to support the application in autonomous vehicles, medical imaging, and smart cities. This chapter also looks into the application of blockchain, generative AI, and CV to provide more data protection, model interpretability, and immersive virtual environment.

Divya S R, Bharathi Mohan G, Vinutha V

Cryptocurrencies have transformed the modern financial system by introducing decentralized digital payment methods that operate without the need for traditional banking institutions. Bitcoin, introduced in 2009 by Satoshi Nakamoto, was the first successful cryptocurrency and remains the most dominant digital currency in the market. Built on blockchain technology, Bitcoin enables secure peer-to-peer transactions through cryptographic techniques and distributed ledger systems. As the popularity of cryptocurrencies has grown, large volumes of transaction data, market trends, and online user activity have created opportunities for advanced data analysis. Artificial Intelligence (AI) and Machine Learning (ML) techniques are increasingly being applied to cryptocurrency-related challenges such as price prediction, trend analysis, fraud detection, volatility forecasting, portfolio management, and mining optimization. At the same time, issues such as privacy, security, scalability, and cyber threats continue to affect the cryptocurrency ecosystem. This paper explores the relationship between Bitcoin, blockchain technology, and artificial intelligence, while examining how AIbased approaches can improve the efficiency, reliability, and security of cryptocurrency systems. It also discusses important concepts such as blocks, blockchain structure, proof of work, and the Bitcoin mining process.

Tatiana Zalan, Efstathios Polyzos, Élissar Toufaily

No abstract is available for this record.

Бобоева Гулнисо Рузмат кизи Бобоева Гулнисо Рузмат кизи Boboyeva Gulniso Ruzmat qizi

Transaction monitoring and efficient audit management have become increasingly importantin modern financial systems. Traditional centralized databases and auditing methods often face challengesrelated to security vulnerabilities, fraudulent activities, and data manipulation. A blockchain-based financialtransaction monitoring system integrates smart contracts, decentralized ledgers, and audit trails to automatefinancial operations, enhance transparency, and reduce fraud risks. The proposed architecture is implementedon Ethereum and Hyperledger Fabric platforms, enabling automated transaction validation and executionthrough smart contracts. All transactions are stored in an immutable decentralized ledger, while audit trailsare generated and maintained automatically. Simulation results demonstrate a 40–60% reduction in fraudulentactivities and up to a 70% decrease in audit processing time compared with conventional approaches. Theapplication of cryptographic algorithms and Zero-Knowledge Proofs further strengthens data security andprivacy protection. The proposed solution contributes to the improvement of financial control and auditingsystems within the framework of the digital economy.

GaiXia Wang

This study adopts a conceptual and design-oriented approach to investigate blockchain integration into accounting informatization for improving transparency, reliability, and intelligence in enterprise financial services. A blockchain-based big data model is developed using distributed ledger, consensus, and encryption mechanisms to support secure and consistent financial data sharing. A personalized feedback system empowered by smart contracts and data analytics delivers real-time customized accounting information for service-oriented decision-making. Exploratory independence and reliability assessments examine data consistency and system robustness. Preliminary evidence indicates that the proposed framework tends to reduce data deviations, support decision efficiency, and improve user satisfaction over conventional systems. This study contributes to the service-oriented transformation of accounting informatization and offers a design framework for intelligent data-driven financial management systems enabled by blockchain.

Oleh Ivchenko, Iryna Ivchenko, Dmytro Grybeniuk

Research article: Blockchain-Based Tax Compliance — Smart Contracts for Automated VAT Collection

Dr. S.C.B. Samuel Anbuselvan, K. Saketh Ram

Abstract:The pervasive integration of digital technologies has fundamentally redefined the operational paradigms of commerce, finance, and accounting. This paper explores the multidimensional impact of Digital Transformation (DT) across these interconnected sectors, focusing on the adoption and efficacy of Artificial Intelligence (AI), Robotic Process Automation (RPA), and Blockchain technology. Through a systematic qualitative review of recent literature and industry frameworks, this study examines how traditional financial workflows are evolving into automated, data-driven ecosystems. The findings indicate that while DT significantly enhances real-time reporting, fraud detection, and transactional efficiency, organizations face substantial barriers, including high implementation costs, data security vulnerabilities, and a growing digital skills gap. The paper concludes that successful digital transformation requires not only technological investment but also a strategic realignment of organizational culture and regulatory compliance frameworks. Future research trajectories emphasize the need for standardized continuous auditing protocols and scalable decentralized finance (DeFi) architectures.

Nancy. F, Dr. A Geetha

This research paper examines the structural and philosophical changes in global commerce and management required because of the "Third Wave" of digital transformation (DT). While earlier versions of DT dealt with the digitization of analog records and the uptake of cloud computing, the modern age of Agentic AI, Industrial Metaverse and Decentralized Autonomous Organizations (DAOs) is demanding a fundamental re-engineering of the firm. Through a methodical approach of analyzing current technological trajectories and management frameworks this study identifies the existence of a critical "Agility Gap" between legacy led organizations and the digital native enterprises. The research proposes Integrated Digital-Managerial Framework (IDMF) as strategic roadmap of 2026 and onwards. Key findings suggest that "Digital Maturity" is no longer a technology benchmark but comes instead as a cultural and structural imperative, one determining whether a company survives in the marketplace in an increasingly automated commerce landscape.

Deepa Parasar, Dr. Priyanka Mishra, Aman Kumar Hamilton, Snigdha Madhab Ghosh, Dhanashree Rohan Kedare, Dr. Atowar ul Islam

Among the most influential technologies that predetermine the current digital society, artificial intelligence (AI) and blockchain have emerged as fast as the digitalization of technologies. This review examines the conceptual and practical implications and applications of AI and blockchain with particular attention to how the two can be used in the framework of digital governance, cybersecurity, and socio-cultural change. The analysis synthesizes existing literature to explore how AI enhances data processing, predictive analytics, and automated decision-making, while blockchain strengthens transparency, decentralization, and data integrity in digital systems. Their integration is shown to support more efficient governance frameworks, improved policy decision-making, secure digital infrastructures, and reliable identity management. At the same time, the review highlights broader societal impacts, including changes in digital trust, ownership structures, and creative and media industries. Despite these opportunities, several challenges remain, including governance fragmentation, ethical concerns, privacy risks, and limitations related to interoperability and institutional readiness. New research directions are also outlined in the form of trustworthy and explainable AI, sustainable technological infrastructures, and the adoption of AI and blockchain systems of Web3 and decentralized digital ecosystems. Altogether, AI and blockchain convergence is an important change in the structure of the digital space, and it will need harmonized governance structures and responsible innovation to establish safe, transparent and inclusive digital societies.

Muxtorov Maqsudbek Sherzodbek o'g'li Almardanov Samariddin Abdixoliq o'g'li

This paper examines the comprehensive impact of digital transformation on the finance and accounting sectors. With rapid advancements in cloud computing, artificial intelligence, blockchain technology, and automation tools, traditional paradigms of financial reporting, auditing, and managerial accounting are being fundamentally redefined. The study analyzes how digital technologies enhance accuracy, efficiency, transparency, and scalability of financial operations across organizations of various sizes and industries. The findings demonstrate that digital transformation facilitates real-time financial reporting, automated bookkeeping, predictive financial analytics, fraud detection systems, and data-driven strategic decision-making through robotic process automation (RPA), machine learning, big data analytics, and distributed ledger technologies. The paper concludes with policy recommendations and organizational guidelines for effective and responsible digital transformation in financial management, emphasizing human oversight, continuous upskilling, and regulatory alignment.