Abstract This study explores the role of Artificial Intelligence (AI) in transforming agricultural supply chain management in Bangladesh through a systematic comparative analysis of existing literature, institutional reports, and global case studies. AI technologies including predictive analytics, machine learning, blockchain, and precision agriculture are examined for their potential to address longstanding inefficiencies in Bangladesh’s agri-supply chain. The study finds that AI-driven demand forecasting models using LSTM and ARIMA achieved 89–92% crop yield prediction accuracy, representing a 37% improvement over traditional methods. Smart warehousing systems reduced operational costs by 25% and increased order processing speed by 40%, while blockchain integration cut payment cycles from 15 days to 2.3 days and increased smallholder farmer incomes by 22–25%. Precision agriculture technologies achieved 25% yield growth with 15–20% water savings and 30% fertilizer efficiency gains. Despite these promising outcomes, Bangladesh’s AI adoption rate remains at only 18%, significantly behind India (35%) and Vietnam (28%), primarily due to insufficient infrastructure, lack of digital literacy, and high implementation costs. The study proposes targeted policy interventions including IoT subsidies, farmer training programs, and public-private partnerships to enable inclusive and sustainable AI integration across Bangladesh’s agricultural sector.
Mingqian Li, Rong Du, Andrew Burton‐Jones, Jianing Xie
Purpose Grounded in signaling theory, this study examines whether traceability information displaces or complements incumbent quality cues and contrasts the relative efficacy of blockchain-enabled traceability technologies with traditional systems. Design/methodology/approach This study analyzes 18 months of product-level sales data from a global e-commerce platform using a staggered difference-in-differences design with robustness checks. We apply latent Dirichlet allocation topic modeling to consumer reviews and use a synthetic difference-in-differences approach to examine shifts in consumer attention after traceability implementation. Findings Traceability information increases product sales, particularly for lower-reputation brands and diminishes the effect of electronic word-of-mouth, suggesting that diagnostic quality signals matter more than social information signals. Although blockchain-enabled traceability should enhance signal credibility, its observed impact falls short of expectations. Research limitations/implications The sample is limited to the automotive engine oil context in China. Future research should examine other categories and national contexts. Practical implications Platform managers and emerging brands can deploy low-cost traceability labels to boost demand. Blockchain solutions may require consumer education to justify higher implementation costs. Social implications Augmenting supply-chain transparency and product traceability curbs counterfeit and substandard goods, improves consumer welfare, and supports regulatory and sustainability objectives. Originality/value This study systematically assesses the substitutive and complementary roles of traceability signals in a multi-cue setting, tempers optimism about blockchain-enabled traceability and extends research on digital supply-chain transparency and signaling theory.
While much has been written about the volatility of digital assets, academic scholarship has largely overlooked how blockchain technologies have been adopted and reimagined by LGBTQ+ communities. This article addresses that gap through a digital ethnography of queer NFT communities active during the crypto craze of 2022, combining online participant observation with semi-structured interviews. Drawing on José Esteban Muñoz’s concept of queer futurity, it examines how queer users imagined blockchain as a speculative platform for alternative economic and social possibility—despite the financial risks embedded in the technology’s libertarian and capitalist structures. The article interrogates the utopian rhetoric of inclusion, decentralisation, and wealth redistribution that was deployed within these communities to justify their interest in and holdings of non-fungible tokens (NFTs) and cryptocurrency. Queer leaders leveraged the blockchain to foster inclusive digital communities and promote wealth circulation amongst LGBTQ+ individuals, while community members embraced the technology as a risky opportunity for queer economic mobility. The article positions blockchain as a contested site where competing futurities collide—offering the illusion of liberation and the reproduction of existing inequalities. It argues that while queer users sought to make the blockchain ‘queer from the start,’ their efforts were ultimately constrained by the capitalist logics that underpin the technology.
Muhammad Farooq Shaikh, S. Hamza Hassan, Jawwad Shamsi, Alessia Maccaro · 5 authors
Background and objective The integration of blockchain and digital twin (DT) technologies is increasingly recognised as a promising approach for improving healthcare data integrity, interoperability, privacy, and clinical decision support. While digital twins enable dynamic patient modelling and predictive healthcare applications, blockchain provides secure data governance through decentralised trust, auditability, and access control. However, existing research remains fragmented, with limited synthesis of the architectural integration, regulatory readiness, ethical governance, and interoperability of blockchain-enabled healthcare digital twin systems. This systematic scoping review addresses these gaps by providing a comprehensive architectural and compliance-oriented analysis of the current evidence. Methods A systematic scoping review was conducted following PRISMA 2020 guidelines using Scopus, PubMed, and Web of Science. From 148 identified records, 55 eligible studies published between 2020 and 2025 were included after duplicate removal and eligibility screening. Data were extracted on digital twin functionality, blockchain architecture, healthcare application domains, consensus mechanisms, privacy-preserving strategies, and regulatory and ethical alignment. Structured Python-based visual mapping and comparative analyses were performed to identify architectural, governance, and compliance patterns across the literature. Results The findings demonstrate that blockchain is predominantly employed to provide access control, audit logging, data integrity, consent management, and secure data provenance within healthcare digital twin ecosystems. Patient-level and EHR-centred digital twins represented the most mature application areas, whereas cross-domain and infrastructure-level frameworks dominated early architectural exploration. The review identifies recurring compliance-oriented architectural patterns while revealing substantial gaps in clinically validated deployments, interoperability with established healthcare standards, decentralised governance models, and formal implementation of GDPR- and HIPAA-compliant engineering practices. Comparative heatmap analyses further highlight the uneven maturity of ethical governance and regulatory integration across blockchain functionalities. Conclusion This review provides the first comprehensive compliance-oriented architectural synthesis of blockchain-enabled healthcare digital twin systems by integrating technical architecture, regulatory readiness, ethical governance, and privacy-preserving design patterns within a unified analytical framework. The proposed architectural mapping identifies critical research gaps in interoperability, governance engineering, consensus optimisation, and real-world clinical validation, providing a foundation for the development of trustworthy, GDPR/HIPAA-aligned, FHIR-compatible, and clinically interoperable healthcare digital twin ecosystems.
K. Satheshkumar, S. Ramalingam, A. Suresh Babu, S. Murugesan
ABSTRACT Vehicular ad hoc networks (VANETs) are essential components of intelligent transportation systems that facilitate real‐time communication between vehicles (V2V) and between vehicles and infrastructure (V2I). Despite their importance, VANETs face challenges, such as high node mobility, energy limitations, security risks, and ever‐changing network topologies. Existing clustering and routing algorithms often struggle to manage the instability caused by mobility, energy disparities, and secure congestion‐free communication simultaneously. To address these challenges, this work introduced an integrated cross‐layer framework featuring three innovative algorithms: mobility‐aware black hole clustering (M‐BHC), energy‐aware piranha optimization algorithm (EPOA), and cross‐layer multi‐attribute blockchain routing with congestion control (CL‐MABRC). The M‐BHC algorithm enhances the stability of clusters and counters black‐hole attacks by forming clusters dynamically based on real‐time vehicle mobility patterns. EPOA optimizes the selection of cluster heads (CHs) by reducing energy consumption through a bio‐inspired resource allocation strategy modeled on piranha predation behavior. CL‐MABRC addresses network congestion and security using blockchain‐based verification and cross‐layer routing decisions informed by multi‐attribute metrics. Extensive simulations were conducted with a setting of 100 veh/km 2 . The proposed framework showed significant performance improvements over benchmark protocols, such as optimal security‐aware cluster‐based hybrid geographical and opportunistic routing (OSC‐GOR), enhanced location‐aided ant colony routing (ELAACR), trust‐based multi‐objective honey badger algorithm (TMOHBA), and robust cryptographic scheme for reliable data communication (RCSRC). It achieved a throughput of 99.89 Kbps, end‐to‐end delay of 3.9 ms, collision rate of 21.8%, energy consumption of 41.98%, and jitter of 0.05 ms. Together, the M‐BHC, EPOA, and CL‐MABRC algorithms create a robust, energy‐efficient, and secure communication framework for VANETs, enhancing scalability, reliability, and real‐time performance in transportation systems.
The use of a wireless sensor network is increasingly supporting e-governance functions such as municipal utility monitoring, environmental monitoring, grievance-based field reporting, and smart public service delivery. Most wireless sensor network architectures rely on a gateway or database. However, this introduces vulnerabilities to data integrity, node accountability, and auditability. This study examines transparency through a blockchain-enabled WSN architecture for e-governance. The study applies a reproducible Python-based Monte Carlo simulation with a fixed random seed, five node densities, three architectural scenarios, and 450 observations. The scenarios that are compared in this work are a normal WSN, a centralized secure WSN, and a permissioned blockchain-enabled WSN with smart-contract-based identity registration, hash-linked data records, trust scoring, and tamper verification. Descriptive statistics, one-way ANOVA, Welch t-tests, Pearson correlation, and multiple linear regression analysis. The blockchain-assisted WSN, as evidenced by the simulation findings of our project, produced the highest mean data integrity score, tampering detection rate, trust score, malicious node detection rate, and packet delivery ratio. The architecture also improved the composite service efficiency index relative to the conventional baseline, even though it introduced higher latency, transaction confirmation time, and energy consumption. The research indicates that the permissioned blockchain can enhance public-sector WSN transparency with edge aggregation and lightweight cryptographic operations along with carefully tuned endorsement rules. The methods presented in this study allow for scrutiny of secure WSN designs tailored for e-governance.
The development of blockchain technology has given rise to the Decentralized Autonomous Organization (DAO), a new business organizational model that operates through smart contracts in a decentralized manner, without a conventional management structure. The existence of DAOs has not been accommodated in the Indonesian corporate legal system, creating a legal vacuum regarding legal subject status, accountability, legal standing, taxation, and dispute resolution. This study aims to analyze the characteristics of DAOs from a corporate law perspective and the urgency of convergence between corporate law and blockchain technology in its regulation in Indonesia. The study employs a normative juridical method with statutory, conceptual, and comparative approaches. The results indicate the need for regulations that recognize and regulate DAOs as digital business entities to achieve legal certainty, legal protection, and a sustainable digital investment climate.
B Santhosh Kumar, P. Penchala Prasad, M. Raghavendra Reddy
Abstract Alzheimer’s disease is a neurodegenerative disorder that affects millions of individuals worldwide, making early diagnosis through Magnetic Resonance Imaging a significant clinical necessity. Existing medical image analysis techniques often suffer from limitations associated with inadequate preprocessing, reduced sensitivity to subtle abnormalities in the hippocampus and cortex, poor generalization across heterogeneous MRI acquisition systems, and insufficient mechanisms for secure medical data management. To address these challenges, this research proposes an integrated framework combining the Internet of Medical Things (IoMT), Artificial Intelligence, and blockchain technology for secure and efficient Alzheimer’s disease monitoring. The proposed framework employs Feature Pooling VGG16 (FPVGG16) for discriminative feature extraction, while feature selection is optimized using the Wave Search Binary Waterwheel Plant Optimization algorithm. Subsequently, a feature-selective Coordinated Xception-based Convolutional Spatial Network (CXCSN) is utilized for accurate disease classification. Blockchain technology is incorporated to provide secure, tamper-resistant, and privacy-preserving management of patient information and MRI records. Experimental evaluations conducted on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets validate the effectiveness of the proposed framework, achieving accuracies of 99.31% and 99.21%, precisions of 99.28% and 99.25%, and recalls of 99.18% and 99.14 %, respectively. The results indicate that the proposed framework provides an effective solution for secure, reliable, and highly accurate Alzheimer’s disease diagnosis and monitoring.
Purpose This study aims to examine the feasibility of blockchain adoption during investment banks’ Know Your Customer (KYC) validation processes. It studies the role played by government in regulating the blockchain-based KYC process. Design/methodology/approach A framework based on the extended technology acceptance model (TAM) was conceptualised to formulate six hypotheses. Based on this, a structured questionnaire was developed and administered among the employees of investment banks through a multi-stage sampling technique. The final sample, comprising 605 responses, was analysed using a covariance-based structural equation modelling (Mediation Analysis) on JASP V.19. Findings The present research explains that the government, as a mediating variable, has a 45.7% direct impact and 54.3% indirect effect on investment banks in the adoption and actual usage of blockchain technology for KYC validation. The perceived ease of use, perceived usefulness and attitude to use technology are key factors that influence its adoption for front-office operations. Perceived ease of use is a dominant indicator within the model. Research limitations/implications This study contributes theoretically by extending the existing TAM model with its practical application in the KYC process during validation of customer documentation in the banking industry, adding practical relevance to the regulatory framework. Originality/value The research derives its originality from the mediating role of government regulation in implementing KYC through blockchain. It proposes a blueprint of a working model that can be internalised to optimise the processes, extending the existing theory and its application with practical relevance.
The rapid advancement of artificial intelligence (AI) and blockchain technologies has fundamentally transformed the normative foundations, authority structures, and legitimacy of contemporary legal systems. While these technologies are commonly portrayed as instruments for enhancing efficiency and legal certainty, their increasing integration into legal decision-making raises profound philosophical questions concerning the nature of law, justice, and human agency. This article critically examines how AI and blockchain reshape legal normativity through the lens of legal philosophy. Employing a normative juridical methodology supported by conceptual and philosophical approaches, the study analyzes the implications of algorithmic decision-making and decentralized technological infrastructures for the evolution of legal authority. The findings demonstrate a paradigmatic shift from human-centered normative reasoning toward computational rationality grounded in algorithmic logic. AI replaces interpretative legal reasoning with probabilistic prediction, privileging statistical inference over moral deliberation. Simultaneously, blockchain institutionalizes automated legal enforcement through smart contracts, thereby minimizing interpretative discretion and limiting the contextual flexibility traditionally required to achieve substantive justice. These developments contribute to the emergence of what this article conceptualizes as post-human legal normativity, in which legal authority increasingly resides within technological systems rather than human reasoning and institutional judgment. The study argues that this transformation generates significant challenges to justice, transparency, accountability, and democratic legitimacy. The growing reliance on algorithmic authority risks reducing law to a technical mechanism detached from its ethical and normative foundations. Consequently, the philosophy of law must be reconstructed to reaffirm the centrality of human agency in legal governance and to ensure that emerging technologies function as instruments serving legal values rather than autonomous sources of legal authority.
Transformer-based detectors for Solidity smart contracts almost universally encode a contract within a single 512-token window, then attribute performance differences to the choice of pre-trained encoder. We show this attribution is misplaced. On DIVE-25 (22,330 deployed contracts, eight DASP categories, multi-label at 2.46 labels per contract) the median contract occupies 2,994 sub-word tokens and only 5.48% fit a single window. We segment each contract at top-level declaration boundaries, pack the segments greedily into at most 24 chunks of 510 tokens for an effective context of 12,240 tokens covering 98.25% of the corpus, and recombine the chunk representations with a bidirectional LSTM under additive attention. Holding preprocessing, chunk budget, pooling, aggregator, loss, schedule, seeds and split identical, the extended context is worth +0.1038 micro-F1 and +0.1722 macro-F1 over single-window truncation, roughly four times the benefit of the best available encoder. The loss under truncation is markedly uneven: Front Running falls by 0.294 and Time manipulation by 0.262, while Access Control, whose indicators sit near the top of a file, loses 0.010. Fifty-one structural measurements and a five-relation contract graph enter the classifier through per-class gates initialised at σ(−4) ≈ 0.018, so any contribution must be learned; both open, and the resulting gain is 1.9 times larger on categories below 900 test instances. Under family-aware leakage-controlled partitioning the complete system reaches 0.8435 micro-F1 and0.7775 macro-F1, with the fusion gain significant under a paired bootstrap (macro-F1 +0.0173, 95% CI [+0.0113, +0.0236]). We report every result additionally on a twin-free test subset from which the 39.58% of test contracts sharing a structural twin with training are removed. Finally, evaluated against human-verified exploitability judgements the detector scores 0.455 mean AUC, below a baseline built from contract size and compiler version alone (0.735), bounding what any detector trained on analyser consensus can be claimed to do.
This chapter examines the transformative role of blockchain technology, artificial intelligence (AI), and smart contracts in reshaping trade finance and customs modernisation across Africa, with particular emphasis on the African Continental Free Trade Area (AfCFTA) framework. It explores how distributed ledger technologies are enhancing transparency and reducing transaction costs in cross-border trade, while AI-driven credit scoring and risk assessment models are expanding financial inclusion for underserved enterprises. The chapter analyses the deployment of smart contracts for automating trade documentation and compliance processes, and evaluates the impact of these technologies on fraud reduction and supply chain traceability. Drawing on real-world cases from the Pan-African Payment and Settlement System (PAPSS), Flutterwave, Kifiya, and the AfCFTA Digital Trade Protocol, the chapter highlights both opportunities and governance challenges, including data privacy, regulatory fragmentation, scalability constraints, and the digital infrastructure divide that characterises much of the continent. Policy recommendations for harmonised regulatory frameworks and capacity building are advanced.
Law, logistics, and international trade
Legal, Health, Environmental and COVID-19 Challenges
Land ownership management is a critical administrative process that requires secure record maintenance, transparent ownership verification, and efficient property transfer mechanisms. Conventional land registry systems primarily depend on centralized databases and paper-based documentation, making them susceptible to document forgery, unauthorized modifications, duplicate ownership claims, lengthy verification procedures, and administrative inefficiencies. These limitations often result in ownership disputes, reduced public trust, and delays in property transactions. This paper presents a Blockchain-Enabled Secure Land Registry Framework that leverages blockchain technology to establish a decentralized, transparent, and tamper-resistant platform for land registration and ownership management. The proposed system integrates a React.js-based user interface with a Node.js and Express.js backend, while Firebase Authentication and Firebase Firestore manage user authentication, supporting documents, and application data. Ethereum smart contracts developed using Solidity are employed to securely record land registration, government verification, and ownership transfer transactions on the blockchain, with Ganache serving as the blockchain testing environment. Every approved transaction generates a unique blockchain transaction hash, enabling secure verification, complete traceability, and immutable ownership history. The hybrid architecture combines the scalability of cloud-based data management with the integrity of blockchain technology to ensure efficient record retrieval while preventing unauthorized alterations. The implemented framework demonstrates secure land registration, transparent ownership transfer, simplified government verification, and reliable auditability with minimal operational complexity. The proposed solution provides a scalable and cost-effective approach for modern digital land administration and establishes a strong foundation for future integration with national land registries, electronic identity verification, GIS-based property mapping, and mobile-enabled citizen services.
Secure and transparent attendance management has become increasingly important in educational institutions as conventional attendance systems often face challenges such as proxy attendance, unauthorized record modification, and limited traceability. Most existing solutions rely on centralized databases, making them susceptible to data tampering, accidental loss, and single-point failures. This paper presents a Blockchain-Based Attendance Management System that leverages blockchain technology to provide a decentralized and immutable mechanism for recording and verifying attendance information. The proposed framework integrates a React.js-based user interface with a Node.js and Express.js backend, while Firebase Authentication and Firestore manage user authentication and application data. Attendance records are securely stored through Ethereum smart contracts executed on the Ganache blockchain network, with transaction hashes linked to Firebase for efficient retrieval and verification. This hybrid architecture combines the scalability of cloud-based data management with the integrity and transparency of blockchain technology. Once attendance is recorded, the information cannot be altered without detection, ensuring reliable auditability and improved trust among students, faculty members, and administrators. The implemented system demonstrates secure attendance recording, fast verification, and efficient transaction management while reducing the possibility of record manipulation. The proposed solution offers a practical, scalable, and cost-effective approach for modern attendance management and provides a strong foundation for future enhancements such as biometric authentication, QR code-based attendance, and cloud-enabled blockchain deployment.
The study investigated digital currency and blockchain technology in the 21st century financial ecosystem. The empirical study adopted a descriptive survey design. A questionnaire was used for data collection in a sample size of 121 selected randomly from the staff and students of Abia State Polytechnic, Aba. The data collected from the respondents were analyzed with the frequency distribution table and chi-square (x2 ) statistical technique. The findings revealed the imperativeness of digital currency and blockchain technology in the 21st century financial ecosystem. In other words, digital currency and blockchain technology has significant effect with financial ecosystem. The study, therefore, recommended among others that Central bank of Nigeria, legislators and financial stakeholders should collaborate to establish compliance standards and best practices for digital currency and blockchain integration in financial ecosystem. These standards should ensure that digital currency algorithms and blockchain technology conform with regulatory requirements and ethical principles, while promoting transparency and accountability.
Hadeer Khayoon Ashour, Noor Salah Alramadan, Hamid Mohsin Jadah
There is growing interest in using blockchain technology to overcome the flaws of legacy payment systems and banking operations, few empirical efforts have examined the possible use of blockchain by large institutions. The study examines how blockchain is changing the payment systems and banking services with a focus on Citigroup (Citi) and various Citi blockchain projects, specifically Citi Token Services. The study aims to assess the impact of blockchain’s adoption on efficiency, cost reduction, customer confidence and service accessibility. A quantitative research study was conducted in a longitudinal design, and data were analysed using multiple linear regression in the SPSS program from 2020–2024 to check the relationship between variables. The results indicate that blockchain implementation offers considerable transaction speed, operational and transactional cost reduction (up to 80 percent), increased customer trust and broader service access with 24/7 transactions. The regression model explains 51.9 percent of the variance in performance. Although promising, blockchain for banking is still in its infancy and facing a variety of challenges that need to be solved for wider application, such as scalability, regulatory compliance, and integration with existing systems.
The rapid growth of digital education and online recruitment has significantly increased the demand for reliable academic credential verification. Conventional certificate verification methods are often centralized, time-consuming, and susceptible to document forgery, unauthorized modification, and administrative delays. To address these challenges, this paper presents a Blockchain-Enabled Decentralized Framework for Secure Academic Certificate Issuance and Real-Time Verification. The proposed framework utilizes Ethereum blockchain technology through Solidity smart contracts to establish an immutable and transparent repository of certificate records, ensuring that issued credentials cannot be altered without detection. A SHA-256 cryptographic hashing mechanism is employed to generate unique digital fingerprints for each certificate, while Firebase Authentication and Cloud Firestore provide secure identity management and efficient off-chain metadata storage. The user interface is developed using React.js, enabling educational institutions to issue certificates and allowing employers, universities, and other stakeholders to verify credentials instantly through a simple web-based platform. During verification, the system recomputes the certificate hash and compares it with the blockchain record to detect tampering and validate authenticity in real time. Experimental evaluation on a local Ethereum network demonstrates reliable certificate issuance, rapid verification with sub-second response times, secure transaction handling, and effective resistance against certificate forgery. The proposed framework enhances transparency, trust, and operational efficiency while minimizing manual verification efforts. Furthermore, its modular architecture facilitates future migration to public blockchain networks and decentralized storage platforms, making it suitable for scalable deployment across educational institutions and digital credential ecosystems.
Dodi Setiawan, Sri Sutjiningtyas, A. Eka Hermia Fitrianingsy, Ronald Naibaho · 5 authors
Blockchain consensus mechanisms are critical for ensuring security, efficiency, and scalability in decentralized networks. This study qualitatively examines ten widely used consensus algorithms—Proof of Work (PoW), Proof of Stake (PoS), Delegated PoS (DPoS), PBFT, Raft, Proof of Authority (PoA), Hybrid PoW/PoS, DAG/IOTA, Hashgraph, and Tendermint—within the research context of Bintan, Riau Islands, Indonesia. Performance was evaluated through literature review and simulated network observations, focusing on transaction throughput (TPS), latency, energy consumption, and network stability. Results indicate that DAG/IOTA and Hashgraph achieve the highest throughput with minimal latency, making them suitable for IoT and enterprise-scale applications. PoS and PoA offer energy-efficient alternatives, while PoW provides high security at the cost of high energy usage. Hybrid PoW/PoS demonstrates balanced performance across multiple metrics. Qualitative analysis highlights trade-offs among energy efficiency, throughput, latency, and decentralization. These findings provide practical guidance for selecting consensus mechanisms according to network requirements, operational constraints, and sustainability considerations, contributing a consolidated perspective on blockchain efficiency and scalability.
Pipelines that pair a large language model with a static analyzer, feeding findings back as repair instructions, appear throughout recent smart contract repair research. They rest on a rarely examined assumption: that the analyzer output serving as the oracle faithfully records what the analyzer found. I report three ways that assumption fails, identified during a four-contract instrument-validation exercise preceding a planned repair study. First, Mythril v0.24.8 can exit without reaching the analysis phase while returning exit status zero, empty standard error, and a findings array byte-identical to that of a genuinely clean scan; the failure is reported in a sibling JSON field that finding-extraction code has no reason to read. Second, 12 of 23 Slither findings in my validation set fell outside the high, medium, and low impact bands, so an unfiltered count measures a composite whose components may not behave alike under repair. Third, keying finding identity on source location breaks across repair rounds. On the one contract carried through three rounds, location-based keying inflated resolved findings from 7 to 12 and introduced findings from 2 to 7. The underlying instability is established in the warning-tracking literature; my contribution is its consequence for repair metrics, where it biases both transition counts upward and can confound comparison between methods producing differently sized .patches. I separately report an executed exploit showing a specification-level authorization defect that produced no high or medium impact finding. I propose calibration procedures for each hazard and release the harness, contracts, and raw analyzer output at doi:10.5281/zenodo.21586404.
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.