Cryptocurrency is conceptualized as digital assets designed to function as mediums of exchange in Todays’ world. The objective of the study was to; evaluate the technological infrastructures and perception of Tanzanian consumers, as determinants to the adoption of cryptocurrencies in Tanzania’s commercial banks. The study employed a quantitative research design using surveys administered to 350 selected bank staff from ten commercial banks in Dar es Salaam and Dodoma. Data were collected through structured questionnaires and analyzed using descriptive and inferential statistical techniques. The findings indicate that both consumer perception and technological infrastructure significantly influence cryptocurrency adoption in Tanzania's commercial banks, with consumer perception having a more dominant impact. The study concludes that although banks possess strong infrastructure readiness, increasing public awareness and understanding is crucial to promoting wider adoption of cryptocurrency. Commercial banks are advised to invest in advanced and secure technological infrastructures to support the growing adoption and safe integration of cryptocurrencies. Future studies can adopt a mixed research approach, incorporating qualitative methods to gain deeper insights into the factors determining the adoption of cryptocurrencies in Tanzania's commercial banking sector.
Dimitris Kastoris, Dimitris Papadopoulos, Konstantinos C. Giotopoulos
Mathematical modeling plays a crucial role in supporting decision-making across a wide range of scientific disciplines. These models often involve multiple parameters, the estimation of which is critical to assessing their reliability and predictive power. Recent advancements in artificial intelligence have made it possible to efficiently estimate such parameters with high accuracy. In this study, we focus on modeling the dynamics of cryptocurrency market shares by employing a Lotka-Volterra system. We introduce a methodology based on a deep neural network (DNN) to estimate the parameters of the Lotka-Volterra model, which are subsequently used to numerically solve the system using a fourth-order Runge-Kutta method. The proposed approach, when applied to real-world market share data for Bitcoin, Ethereum, and alternative cryptocurrencies, demonstrates excellent alignment with empirical observations. Moreover, our method outperforms ARIMA models in terms of accuracy, showcasing its effectiveness for crypto market forecasting. The entire framework, including neural network training and Runge-Kutta integration, was implemented in MATLAB.
This article proposes a hybrid framework that integrates technological and legal solutions to automate compliance and dispute resolution in international personal data transfers. The approach leverages smart contracts built on blockchain technology, incorporating standardized contractual clauses (SCC/MCC) and non-fungible tokens (NFTs) to trigger complaint procedures. By involving supervisory authorities as escrow agents, the system ensures transparency, efficiency, and regulatory compliance, thereby overcoming the limitations of traditional methods. Through comparative analysis and a case study, the article demonstrates the viability of a scalable and interoperable solution that enhances data subjects’ rights while aligning with the GDPR and other international regulatory frameworks.
In today’s automotive industry, characterized by demand volatility, process uncertainty, supply chain complexity, and information ambiguity, achieving operational excellence has become increasingly challenging. To address these issues, we propose a novel framework that integrates blockchain technology into the life cycle of an automobile. Specifically, our solution employs Delegated Proof of Stake (DPoS) as a consensus mechanism and utilizes a Private Interplanetary File System (IPFS) for data storage and retrieval. This framework aims to enhance transparency, traceability, and data integrity across all stages of an automobile’s life cycle. We provide a detailed delegate’s election process in DPoS and data storage and retrieval on Private IPFS through sequence diagrams. By mitigating vulnerabilities and reducing uncertainties, our approach improves operational efficiency and stakeholder satisfaction, offering a robust solution to the challenges within the VUCA (volatility, uncertainty, complexity, ambiguity) realm in the automotive industry.
Well construction in the oil and gas industry generates substantial emissions, necessitating precise tracking to meet environmental regulations and sustainability targets. This paper explores an innovative approach combining numerical modeling with distributed ledger technology (DLT) to monitor and manage emissions throughout the well construction process. Unlike traditional methods, which often rely on retrospective data collection, this method leverages real-time simulations and a decentralized data framework to provide actionable insights. By focusing on predictive modeling and data integration, we propose a system that enhances emissions accountability and supports operational efficiency. Case studies demonstrate its practical application, while the discussion addresses implementation challenges and future potential.
Current blockchain protocols (e.g., Proof-of-Work and Proof-of-Stake) secure the ledger yet cannot measure validator trustworthiness, allowing subtle misconduct that is especially damaging in decentralized-finance (DeFi) settings. We introduce Proof-of-Behavior (PoB), a consensus model that (i) gives each action a layered utility score -- covering motivation and outcome, (ii) adapts validator weights using recent scores, and (iii) applies decentralized verification with proportional slashing. The reward design is incentive-compatible, yielding a Nash equilibrium in which honest behavior maximizes long-run pay-offs. Simulated DeFi experiments (loan-fraud detection, reputation-weighted validation) show that PoB cuts fraud acceptance by more than 90%, demotes malicious validators within two rounds, and improves proposer fairness versus standard PoS, all with no more than a 5% throughput overhead. By linking consensus influence to verifiably trustworthy conduct, PoB offers a scalable, regulation-friendly foundation for secure and fair blockchain governance in financial applications.
The cryptocurrency market presents both significant investment opportunities and higher risks relative to traditional financial assets. This study examines the tail behavior of daily returns for two leading cryptocurrencies, Bitcoin and Ethereum, using seven-parameter estimates from prior research, which applied the Generalized Tempered Stable (GTS) distribution. Quantile-quantile (Q-Q) plots against the Normal distribution reveal that both assets exhibit heavy-tailed return distributions. However, Ethereum consistently shows a greater frequency of extreme values than would be expected under its Bitcoin-modeled counterpart, indicating more pronounced tail risk.
Cryptocurrency transaction fraud detection faces the dual challenges of increasingly complex transaction patterns and severe class imbalance. Traditional methods rely on manual feature engineering and struggle to capture temporal and structural dependencies in transaction networks. This paper proposes an Augmented Temporal-aware Graph Attention Network (ATGAT) that enhances detection performance through three modules: (1) designing an advanced temporal embedding module that fuses multi-scale time difference features with periodic position encoding; (2) constructing a temporal-aware triple attention mechanism that jointly optimizes structural, temporal, and global context attention; (3) employing weighted BCE loss to address class imbalance. Experiments on the Elliptic++ cryptocurrency dataset demonstrate that ATGAT achieves an AUC of 0.9130, representing a 9.2% improvement over the best traditional method XGBoost, 12.0% over GCN, and 10.0% over standard GAT. This method not only validates the enhancement effect of temporal awareness and triple attention mechanisms on graph neural networks, but also provides financial institutions with more reliable fraud detection tools, with its design principles generalizable to other temporal graph anomaly detection tasks.
Ryan Croote, Islam El-Ashi, Thomas Locher, Yvonne-Anne Pignolet
There is growing interest in providing programmatic access to the value locked in Bitcoin, which famously offers limited programmability itself. Various approaches have been put forth in recent years, with the vast majority of proposed mechanisms either building new functionality on top of Bitcoin or leveraging a bridging mechanism to enable smart contracts that make use of ``wrapped'' bitcoins on entirely different platforms. In this work, an architecture is presented that follows a different approach. The architecture enables the execution of Turing-complete Bitcoin smart contracts on the Internet Computer (IC), a blockchain platform for hosting and executing decentralized applications. Instead of using a bridge, IC and Bitcoin nodes interact directly, eliminating potential security risks that the use of a bridge entails. This integration requires novel concepts, in particular to reconcile the probabilistic nature of Bitcoin with the irreversibility of finalized state changes on the IC, which may be of independent interest. In addition to the presentation of the architecture, we provide evaluation results based on measurements of the Bitcoin integration running on mainnet. The evaluation results demonstrate that, with finalization in a few seconds and low execution costs, this integration enables complex Bitcoin-based decentralized applications that were not practically feasible or economically viable before.
We develop a monetary macro accounting theory (MoMaT) and its software specification for a consistent national accounting. In our money theory money functions primarily as a medium of payment for obligations and debts, not as a medium of exchange, originating from the temporal misalignment where producers pay suppliers before receiving revenue. MoMaT applies the legal principles of Separation and Abstraction to model debt, contracts, property rights, and money to understand their nature. Monetary systems according to our approach operate at three interconnected levels: micro (division of labor), meso (banking for risk-sharing), and macro (GDP sharing, money issuance). Critical to money theory are macro debt relations, hence the model focuses not on the circulation of money but on debt vortices: the ongoing creation and resolution of financial obligations. The Bill of Exchange (BoE) acts as a unifying contractual instrument, linking debt processes and monetary issuance across fiat and gold-based systems. A multi-level BoE framework enables liquidity exchange, investments, and endorsements, designed for potential implementation in blockchain smart contracts and AI automation to improve borrowing transparency. Mathematical rigor can be ensured through category theory and sheaf theory for invariances between economic levels and homology theory for monetary policy foundations. Open Games can structure macroeconomic analysis with multi-agent models, making MoMaT applicable to blockchain economic theory, monetary policy, and supply chain finance.
This study investigates the impact of data source diversity on the performance of cryptocurrency forecasting models by integrating various data categories, including technical indicators, on-chain metrics, sentiment and interest metrics, traditional market indices, and macroeconomic indicators. We introduce the Crypto100 index, representing the top 100 cryptocurrencies by market capitalization, and propose a novel feature reduction algorithm to identify the most impactful and resilient features from diverse data sources. Our comprehensive experiments demonstrate that data source diversity significantly enhances the predictive performance of forecasting models across different time horizons. Key findings include the paramount importance of on-chain metrics for both short-term and long-term predictions, the growing relevance of traditional market indices and macroeconomic indicators for longer-term forecasts, and substantial improvements in model accuracy when diverse data sources are utilized. These insights help demystify the short-term and long-term driving factors of the cryptocurrency market and lay the groundwork for developing more accurate and resilient forecasting models.
To cater to the needs of (Zero Knowledge) proofs for (mathematical) proofs, we describe a method to transform formal sentences in 2x2-matrices over multivariate polynomials with integer coefficients, such that usual proof-steps like modus-ponens or the substitution are easy to compute from the matrices corresponding to the terms or formulas used as arguments. By evaluating the polynomial variables in random elements of a suitably chosen finite field, the proof is replaced by a numeric sequence. Only the values corresponding to the axioms have to be computed from scratch. The values corresponding to derived formulas are computed from the values corresponding to their ancestors by applying the homomorphic properties. On such sequences, various Zero Knowledge methods can be applied.
The advent of 5G and beyond has brought increased performance networks, facilitating the deployment of services closer to the user. To meet performance requirements such services require specialized hardware, such as Field Programmable Gate Arrays (FPGAs). However, FPGAs are often deployed in unprotected environments, leaving the user's applications vulnerable to multiple attacks. With the rise of quantum computing, which threatens the integrity of widely-used cryptographic algorithms, the need for a robust security infrastructure is even more crucial. In this paper we introduce a hybrid hardware-software solution utilizing remote attestation to securely configure FPGAs, while integrating Post-Quantum Cryptographic (PQC) algorithms for enhanced security. Additionally, to enable trustworthiness across the whole edge computing continuum, our solution integrates a blockchain infrastructure, ensuring the secure storage of any security evidence. We evaluate the proposed secure configuration process under different PQC algorithms in two FPGA families, showcasing only 2% overheard compared to the non PQC approach.
The hallucination problem of Large Language Models (LLMs) has increasingly drawn attention. Augmenting LLMs with external knowledge is a promising solution to address this issue. However, due to privacy and security concerns, a vast amount of downstream task-related knowledge remains dispersed and isolated across various "silos," making it difficult to access. To bridge this knowledge gap, we propose a blockchain-based external knowledge framework that coordinates multiple knowledge silos to provide reliable foundational knowledge for large model retrieval while ensuring data security. Technically, we distill knowledge from local data into prompts and execute transactions and records on the blockchain. Additionally, we introduce a reputation mechanism and cross-validation to ensure knowledge quality and provide incentives for participation. Furthermore, we design a query generation framework that provides a direct API interface for large model retrieval. To evaluate the performance of our proposed framework, we conducted extensive experiments on various knowledge sources. The results demonstrate that the proposed framework achieves efficient LLM service knowledge sharing in blockchain environments.
This paper develops a formal game-theoretic model to examine how protocol mutability disrupts cooperative mining behaviour in blockchain systems. Using a repeated game framework with stochastic rule shocks, we show that even minor uncertainty in institutional rules increases time preference and induces strategic deviation. Fixed-rule environments support long-term investment and stable equilibrium strategies; in contrast, mutable protocols lead to short-termism, higher discounting, and collapse of coordinated engagement. Simulation results identify instability zones in the parameter space where rational mining gives way to extractive or arbitrage conduct. These findings support an Austrian economic interpretation: calculability requires rule stability. Institutional noise undermines the informational basis for productive action. We conclude that protocol design must be treated as a constitutional economic constraint, not a discretionary variable, if sustainable cooperation is to emerge in decentralised systems.
This paper integrates Austrian capital theory with repeated game theory to examine strategic miner behaviour under different institutional conditions in blockchain systems. It shows that when protocol rules are mutable, effective time preference rises, undermining rational long-term planning and cooperative equilibria. Using formal game-theoretic analysis and Austrian economic principles, the paper demonstrates how mutable protocols shift miner incentives from productive investment to political rent-seeking and influence games. The original Bitcoin protocol is interpreted as an institutional anchor: a fixed rule-set enabling calculability and low time preference. Drawing on the work of Bohm-Bawerk, Mises, and Hayek, the argument is made that protocol immutability is essential for restoring strategic coherence, entrepreneurial confidence, and sustainable network equilibrium.
Penelitian ini dilatarbelakangi oleh maraknya fenomena investasi berbasis digital seperti Bitcoin di Indonesia, yang memunculkan polemik hukum dalam perspektif Islam, khususnya mazhab Syafi’i dan fatwa Majelis Ulama Indonesia (MUI). Tujuan penelitian ini adalah untuk menganalisis status hukum investasi Bitcoin ditinjau dari prinsip-prinsip muamalah Islam serta mengevaluasi apakah aset digital tersebut dapat dikategorikan halal atau haram. Penelitian menggunakan pendekatan kualitatif dengan metode studi pustaka, melalui analisis literatur akademik, fatwa-fatwa resmi, dan kajian hukum Islam kontemporer. Hasil penelitian menunjukkan bahwa secara umum Bitcoin dinilai haram karena mengandung unsur gharar, maysir, dan tidak memiliki underlying asset yang jelas, namun masih terdapat kemungkinan kehalalan jika dipenuhi syarat-syarat tertentu seperti transparansi, kepastian nilai, dan penggunaan sebagai komoditas bukan alat tukar, dalam kerangka maqāṣid al-sharī‘ah.
The NANOSPRESSO project, described by Senti et al. in their lead article (Senti et al. 2025), is being developed to address the immense unmet medical need in the rare disease and rare cancers communities that are currently underserved by pharmaceutical industry and healthcare providers. The current situation in rare diseases, with small patient numbers (sometimes with only a single case reported worldwide) and substantial drug development costs, makes it difficult for the pharmaceutical companies to recoup their investment in this field.These commercial disincentives, combined with an uncharted regulatory landscape, impede the translation to the clinic of the scientific advances in development of nucleic acid-based therapeutics (NBTs) achieved in recent years that could have already significantly improved the lives of many patients with rare diseases and some rare cancers (Cheerie et al. 2023, Qian et al. 2025, Khorkova et al. 2023).Although the exact proportion of genetic diseases treatable by formulated NBTs (fNBTs) potentially produced by NANOSPRESSO is hard to calculate, a rough estimate based on the known types of pathogenic mutations and the proportions of genes amenable to regulation by multiple endogenous mechanisms that can be engaged by NBTs shows that it could be significant. However, in spite of the plethora of known NBT-based approaches that can be used in the treatment of genetic diseases caused by point mutations or insertions/deletions, substantial current practical challenges in this field can limit their impact (see Supplementary Materials).Although the first successful example of an "n=1" NBT (i.e., one produced for a single individual) was published in 2019 (Kim et al. 2019) it has not been widely replicated, with only 26 other cases over 16 years (Cheerie et al. 2025), and the currently available NBT treatments mostly focus on more common cases (e.g., nusinersen for spinal muscular atrophy) (Moultrie et al. 2025). The interdisciplinary team needed for development of such personalized medicines would require members with expertise in diagnosing the diseases, sequencing the mutations, designing and manufacturing NBTs to pharmaceutical standards, resolving patent issues, ensuring regulatory compliance, and administering the treatmentwith all of this provided in time to stop the progression of the disease. Assembling such a team for each individual case in limited available time is practically impossible under current conditions. This process however could be facilitated if there were institutions providing the necessary infrastructure, such as organizations capable of all the above functions.Establishing and financing such institutions represents probably the biggest challenge in translation of achievements in personalized medicine to the clinic. Other problems in this case include the need for regulatory bodies to develop the appropriate framework for personalized drugs and maintain specialized personnel for inspections, as well as multiple other legal and societal implications including treatment cost, patent issues, and ethical considerations.Senti et al. propose the NANOSPRESSO project as a solution to the challenges posed by translation of personalized NBTs to the clinic (Senti et al. 2025). NANOSPRESSO promotes decentralized personalized production of formulated NBTs, construed as drugs for magistral preparation in hospital pharmacies. Magistral preparation is used for individual patients when no approved alternatives exist, which is the case in many rare diseases.Senti et al. outline a production protocol that includes encapsulation of NBTs in lipid nanoparticles (LNPs) using microfluidics technology on location at hospital pharmacies. The NBTs will have to be custom-designed for individual patients, or small groups of patients.This could represent the most labor-intensive, unpredictable, and expensive step in the production process. The designed NBTs will then be manufactured at pharmaceutical-grade nucleic acid synthesis facilities (potentially including CelluTx LLC, siTOOLs Biotech, or Anjarium Biosciences AG). The authors envision that the final step of NBT drug manufacture, its encapsulation in LNPs (potentially designed and manufactured by companies such as Lipoid or NanoVation Therapeutics), will be accomplished at the hospital pharmacies using a specialized piece of microfluidics equipment (potentially by Solstice Act (FDCA) exempts pharmacy compounding performed for a specific patient from FDCA requirements such as compliance with current good manufacturing practices (cGMP), appropriate labeling, and US Food and Drug Administration (FDA) approval. However, due to complex and innovative nature of NBTs the regulatory bodies may need to develop the appropriate framework for personalized NBTs and maintain purpose-trained personnel for routine inspections of facilities in hospital pharmacies. Importantly, the authors stress their on-going communication with European and state regulatory authorities (Senti et al. 2025).Notably, the NANOSPRESSO project considers the implications of the patent laws, ethical considerations around gene editing and the implications for social equity and human diversity, all of which are essential for the wide adoption of personalized NBTs. Notably, the NANOSPRESSO project also plans to develop a unified platform for technology and data sharing with blockchain security and consistent data formats across the network. Such platform can ensure patient privacy and intellectual property protection and provide easy interface with AI tools.The current NANOSPRESSO-NL project is supported by a Netherlands Science Agenda-Netherlands Organization for Scientific Research grant. However, it is only a 6-year project, which does not allow sufficient time given the scope of the undertaking. Other financing sources may be available. Notably, the potential market for personalized drug technology is substantial, as more than 300 million people worldwide suffer from genetic disorders (The Lancet Global Health, 2024, Cavaller-Bellaubi et al. 2023). Additionally, new genetic disease-and cancer-causing mutations are discovered every year. From the reimbursement point of view, treatment with NBTs could significantly decrease lifelong spending compared to the currently available treatments. For example, average inpatient admission costs over a 12 month period post treatment with nusinersen (an NBT for spinal muscular atrophy (SMA))were reduced by 63% in pediatric patients and by 79% in adult patients as compared to the 12 months pre-nusinersen treatment with prior standard care (Zhu et al. 2024). Treatment with single injection gene therapy drug for SMA, onasemnogene abeparvovec, further reduced the annual numbers of inpatient admissions (by 66%) and emergency department visits (by 50%) compared to nusinersen treatment (Toro et al. 2023). These numbers are especially significant given that direct non-healthcare informal cost for families of SMA patients can reach 63% of total annual disease cost (Landfeldt et al. 2023;López-Bastida et al. 2017).Experience gained in the NANOSPRESSO project may help obtain a more realistic estimate of the benefits afforded by personalized drugs.Furthermore, data and expertise gained in the treatment of rare diseases could be instrumental in developing drugs for common diseases. The 'intervention-outcome' type datasets in uniform format generated by NANOSPRESSO could be essential for training AI tools for discovery of novel gene-disease associations, structure-activity relationships, assessing delivery efficiency of the LNP formulations and toxicity of NBTs, etc. Reports on drug preparation procedures and treatment outcomes provided by clinics utilizing the NANOSPRESSO approach are likely to be an important asset generated by the NANOSPRESSO program. Licensing access to data and reports generated in the NANOSPRESSO project to pharmaceutical and biotechnology companies could provide revenue to extend the project beyond the current 6-year funding period. However, fundraising (and associated expenses) may be needed to engage these and other financing sources, including rare disease foundations, charitable organizations, and government programs.At present the NBT treatments might not lead to a complete cure, due to late diagnoses, incomplete knowledge of disease biology, poor delivery to target tissues, suboptimal dosing regimens, toxicity, immunogenicity, etc. However, these issues can be resolved with more experience using NBTs in the clinic. Importantly, even in their current form, NBTs can bring significant improvements in patients' and caregivers' quality of life and relief to the financial burden (Zhu et. al 2024, Toro et al. 2023).As significant as the challenges with magistral production of personalized fNBTs are, they are surmountable given the measures proposed by NANOSPRESSO. Furthermore, as Senti et al. point out, there are successful precedents of personalized technologies being used at the point of care, e.g., the Prodigy system (Miltenyi Biotec, Inc) that automated the entire process of chimeric antigen receptor (CAR)-T cell manufacturing from cell activation to reinfusion.The NANOSPRESSO project could also tap into a pro-active network of rare disease foundations. Notably, NANOSPRESSO could enhance community participation by improving their website and posting frequent updates on the project plans, progress, crowdsourcing efforts and citizen scientists' initiatives, as the website could be the project's
Syed Abrar Ahmed, Ricardo Correia, Anderson Oliveira do Carmo, Henrique Martins
Increasingly, across geographies, citizens are requiring access to and control of their health data. This paper examines the "Logging Component" proposed by the European Health Data Space (EHDS) regulation and its crucial role in facilitating secure and transparent access to electronic health records (EHR) and health data. We analysed the proposal for the five elements of the Logging Component (LC): identification of data accessors, identification of data subjects, categorisation of accessed data, temporal logging, and data origin tracking. Explored how these elements contribute towards enhanced accountability and compliance in health data management. We experimented with distributed ledger technology (DLT) to support the "data origin tracking element", reaching the demonstration level which can be presented. We used hybrid DLT to develop a system for immutable storage of access logs, and for using smart contracts to maintain a self-governing decentralised access control list (ACL) directly integrated with EHR and PHR systems. We found that the LC is more than a regulatory requirement. It can serve as a framework for the integration of advanced technologies, e.g. DLT and others, increasingly mature and potentially foundational to building new networks of trust among stakeholders, while ensuring data privacy in cross-border and intra-border healthcare scenarios. The study also identified shortcomings of the LC, such as the absence of "purpose logging", which was conceptualised and proposed. This study contributes to the understanding of how logging mechanisms can enhance transparency and accountability in electronic health record systems within the European healthcare landscape, but with potential usefulness for the "Global EHR". In conclusion, our findings suggest that the successful implementation of the five elements of the Logging Component are mandatory and can benefit from mature advance technologies, but the sixth element proposed by us would be critical for achieving the EHDS's broader objectives of harmonised health data sharing in Europe and beyond while maintaining robust security standards.
The pandemic outbreak has revealed significant flaws in the complex and highly fragmented Healthcare Supply Chain’s (HSC’s). However, two major issues persist in the HSCs, leading to inefficiencies: transparency in vaccine distribution and accuracy in demand forecasting. The recent pandemic has highlighted and intensified existing vulnerabilities in HSC’s, leading to the effective utilization of digital technologies to manage them. This research proposes a novel framework that merges Blockchain (BC) and Machine Learning (ML) to bolster the HSCs amidst pandemics, by developing a framework named the Predictive BlockVax Distribution Network (PBDN) model. The proposed PBDN model utilizes BC for securing transactions and Long Short-Term Memory (LSTM), for precise demand prediction. Leveraging Hyperledger Besu, which represents an Ethereum client that is accessible for public use, the PBDN framework ensures BC’s privacy, scalability, and efficient network operations, while LSTM’s advanced forecasting outperforms traditional models and Deep Learning (DL) techniques. This integration showcases a significant leap in managing vaccine distribution and enhancing system resilience, fairness, and transparency. The proposed PBDN model illustrates the potential of BC and ML together to tackle pandemic-induced Supply Chains (SC’s) disruptions, providing a decentralized solution that supports autonomous, informed decision-making without third-party dependency. This approach not only addresses immediate challenges but also sets a precedent for future crisis response, emphasizing the need for robust, Transparent Supply Chain’s (TSC’s).
In the evolving landscape of digital technologies, blockchain has emerged as a cornerstone for building decentralized, transparent, and tamper-proof systems. Central to the security and reliability of blockchain is the use of advanced cryptographic techniques, which ensure data integrity, user privacy, and resistance to malicious attacks. This paper provides a comprehensive analysis of cutting-edge cryptographic technologies that are shaping the future of blockchain networks. Key mechanisms discussed include zero-knowledge proofs, homomorphic encryption, ring signatures, and post-quantum cryptography. These technologies not only enhance privacy and scalability but also prepare blockchain systems to withstand future computational threats, including those posed by quantum computing. By exploring real-world implementations and potential applications, this research underscores the critical role of advanced cryptography in enabling secure, scalable, and future-ready decentralized infrastructures. The study also highlights ongoing challenges and future research directions to optimize the integration of these technologies in both public and private blockchain environments. Key Words: Blockchain, Cryptography, Zero-Knowledge Proofs, Post-Quantum Cryptography, Homomorphic Encryption, Ring Signatures, Decentralized Security, Privacy-Preserving Technologies
Makine öğrenmesi ve derin öğrenme, son yıllarda finansal piyasalarda tahminleme süreçlerinde sıklıkla başvurulan yöntemler arasında yer almaktadır. Bu yöntemler, özellikle yüksek oynaklığa sahip kripto para piyasalarında, yatırım kararlarını destekleyici araçlar olarak öne çıkmaktadır. Denetimli öğrenme algoritmaları, geçmiş verilere dayanarak gelecekteki fiyatları tahmin etmeye yönelik güçlü çözümler sunarken; derin öğrenme yaklaşımları, özellikle sıralı veri yapılarındaki karmaşık ilişkileri yakalamada avantaj sağlamaktadır. Bitcoin gibi dijital varlıkların ekonomik değişkenlerle olan ilişkisini anlamak, hem bireysel yatırımcılar hem de finansal kurumlar açısından stratejik bir gereklilik haline gelmiştir. Bu bağlamda açıklanabilir yapay zeka (XAI) teknikleri, tahmin modellerinin iç mantığını şeffaf biçimde ortaya koyarak karar destek sistemlerine katkı sunmaktadır. Bu çalışmada, Bitcoin’in (BTC) günlük kapanış fiyatları, altın, USDX, VIX ve Brent petrol gibi ekonomik göstergeler dikkate alınarak SVR, LSTM, XGBoost ve ANN algoritmaları ile tahmin edilmiştir. 2014–2024 dönemini kapsayan veri setiyle gerçekleştirilen analizde, MAPE, MAE ve R² gibi performans ölçütleri üzerinden karşılaştırma yapılmıştır. Sonuçlara göre, en yüksek doğruluk oranını SVR modeli göstermiştir. Ayrıca SHAP analizi kullanılarak BTCY (yükseliş), BTCD (düşüş) ve BTCA (açılış) değişkenlerinin tahmin sürecinde pozitif katkı sunduğu belirlenmiştir. Buna karşılık, AK (altın) ve BrPK (petrol) gibi dışsal faktörlerin olumsuz etkiler yarattığı gözlemlenmiştir. Elde edilen bulgular ile hem model doğruluğunu hem de değişkenlerin etkisini şeffaf biçimde ortaya koyarak literatüre önemli katkılar sunulmuştur.
The development of high-load computing systems in modern information environments represents a critical aspect of technological progress, necessitating the creation of innovative approaches to cybersecurity. The increasing intensity of data exchange, the complexity of computational processes, and the growing number of interacting nodes pose significant challenges to traditional information security methods. Classical centralized security models are gradually losing their effectiveness due to the high risk of data processing center compromise, the vulnerability to denial-of-service (DDoS) attacks, unauthorized access, and system exploits. These risks emphasize the necessity of implementing decentralized security mechanisms that can withstand emerging cyber threats and ensure the reliability of high-load computing infrastructures. This article presents a conceptual approach to enhancing the cybersecurity of high-load computing systems through the integration of blockchain technology and smart contracts. A comprehensive analysis of current threats and risks inherent in such systems has been conducted, along with an investigation into the efficiency of smart contracts in user authentication, access control, data verification, and attack prevention. Particular attention is given to the advantages of decentralized security solutions, including the elimination of single points of failure, the enhancement of transparency in security processes, and the automation of control mechanisms. The study also evaluates the resilience of blockchain-based security frameworks in mitigating both internal and external cyber threats. The proposed architectural model leverages smart contracts for managing access to computing resources, verifying transaction integrity, and minimizing the impact of external threats. The analysis of recent research in blockchain technologies and their application in high-load environments provides insights into the feasibility and advantages of such an approach. By utilizing smart contracts, it becomes possible to automate security procedures, reduce reliance on centralized authentication servers, and ensure that system interactions remain tamper-proof and resistant to adversarial attacks. The research findings indicate that integrating smart contracts into high-load computing systems enhances cybersecurity by automating data verification processes, eliminating intermediaries in transactions, and strengthening resilience against attacks. Furthermore, the study outlines promising directions for future research, including the optimization of smart contract execution mechanisms to reduce computational overhead and the integration of blockchain-based solutions into hybrid security models that combine decentralized and centralized approaches. This approach offers a strategic pathway for developing robust, scalable, and resilient cybersecurity frameworks tailored to the needs of high-performance computing infrastructures.
Open access
Economic and Technological Systems Analysis
Advanced Research in Systems and Signal Processing
As large language models (LLMs) are used in sensitive fields, accurately verifying their computational provenance without disclosing their training datasets poses a significant challenge, particularly in regulated sectors such as healthcare, which have strict requirements for dataset use. Traditional approaches either incur substantial computational cost to fully verify the entire training process or leak unauthorized information to the verifier. Therefore, we introduce ZKPROV, a novel cryptographic framework allowing users to verify that the LLM's responses to their prompts are trained on datasets certified by the authorities that own them. Additionally, it ensures that the dataset's content is relevant to the users' queries without revealing sensitive information about the datasets or the model parameters. ZKPROV offers a unique balance between privacy and efficiency by binding training datasets, model parameters, and responses, while also attaching zero-knowledge proofs to the responses generated by the LLM to validate these claims. Our experimental results demonstrate sublinear scaling for generating and verifying these proofs, with end-to-end overhead under 3.3 seconds for models up to 8B parameters, presenting a practical solution for real-world applications. We also provide formal security guarantees, proving that our approach preserves dataset confidentiality while ensuring trustworthy dataset provenance.