This thesis addresses two research areas: scalable distributed ledgers for micro-transactions, and the automation of assembly planning in manufacturing industries. Established blockchain solutions are robust and reliable. Being distributed and decentralized, they avoid a single point of failure, and fault-tolerant consensus mechanisms ensure that the system works as intended even when some participants are faulty or malicious. However, their main weakness is scalability. The two most popular and well-known blockchain solutions, Bitcoin and Ethereum, require all nodes to store all transactions, and their transaction throughput is far too low to compete with traditional, centralized transaction processing systems. To improve scalability, systems have been developed that split the network nodes into groups that can process transactions in parallel, a technique known as sharding. We propose a sharded system called ScaleGraph that uses a novel architecture with one transaction per block and one shard per account, designed to maximize parallelism. The design is inspired by concepts from distributed hash tables, particularly to define shards based on a logical distance metric for node IDs and account IDs. Nodes store and process only transactions involving those accounts that are close to the node according to the distance metric. This greatly reduces the storage burden on each node and allows any number of transactions involving distinct accounts to be validated in parallel. We also design a new cross-shard transaction commit protocol for this architecture. The protocol offers global serializability and inevitable atomic commit, without the need for an abort path. This is achieved using only shard-local consensus and certificate exchange, rather than global or joint cross-shard consensus. Manufacturing is a highly complex process in many industries and involves many different planning problems where increasing automation has the potential to make manufacturing more efficient. This thesis presents a proof-of-concept solution to the kitting layout problem, where a list of parts has to be placed on a kitting wagon for delivery to an assembly line station. However, some problems have proven difficult to automate in practice, despite decades of research. One such problem, assembly line balancing, is analyzed in depth. We identify fundamental challenges that make the goal of complete automation implausible in some industries, such as automotive manufacturing. Human intervention is thus unavoidable, suggesting that bridging the gap between theory and practice requires decision support systems for assisted, iterative, and interactive planning. The thesis also includes preliminary work on the product sequencing problem, limited to framing the use case, assumptions, and requirements. Subsequent ongoing work suggests strong parallels to assembly line balancing, indicating that the identified challenges and possibilities for addressing them reflect a broader pattern in industrial planning automation.
This article is devoted to the contribution of cryptocurrencies and blockchain to the transformation of the world economy. It describes the basic principles of blockchain functioning, the evolution of major cryptocurrencies such as Bitcoin and Ethereum, as well as their practical application in the areas of decentralized finance, cross-border payments, and supply chain management. The study also analyzes the advantages of these technologies and their current limitations.
Open access
Security, Politics, and Digital Transformation
Digitalization and Economic Development in Agriculture
In the digital age, Bitcoin remains the first and most notable cryptocurrency. Over the years, its value has increased, making it a desirable digital asset with millions of enthusiasts who trade and invest daily. Bitcoin is highly volatile in comparison with traditional assets and in absolute terms. Understanding its volatility history helps investors decide whether to buy, sell, or hold. A mathematical model that accounts for volatility is essential for these decisions. Unfortunately, Bitcoin’s vast profit potential for investors comes with the dilemma of its negative impact on global environmental health, which needs serious attention. This study aims to model Bitcoin’s return volatility that can support investment decisions and, on the other hand, the negative impact of Bitcoin mining and outline the actions necessary to mitigate it.
This paper proposes a polar-coordinate framework for Bitcoin prices around the halving cycle, where each revolution is one halving epoch and radial distance represents log price. This alignment reveals recurring patterns conventional time-series charts obscure. We develop five statistics, spanning seasonality amplitude, phase concentration, intercycle repeatability, radial growth, and bull-bear asymmetry, each paired with an explicit null hypothesis and, given the small number of completed cycles, block-bootstrap or randomization inference. To test whether the structure is specific to Bitcoin's supply schedule, we apply the identical clock to Ethereum and the S&P 500 as placebo assets. Bitcoin shows significant halving-phase seasonality (p
Cryptocurrency markets exhibit complex microstructural dynamics characterized by high-frequency volatility bursts, rapid regime switching, and long-range temporal dependencies, which expose several limitations of existing volatility forecasting approaches. In particular, attention-based models suffer from prohibitive quadratic computational cost on long high-frequency sequences, while many recurrent architectures struggle to adapt to regime transitions, asymmetric volatility responses, and risk-aware uncertainty estimation. To address these gaps, this paper proposesCryptoMamba-SSM, a novel volatility prediction framework built upon Mamba-based state space models with linear computational complexity. CryptoMamba-SSM integrates selective memory mechanisms with structured state space representations to effectively capture critical market microstructure signals arising from liquidity shocks and sentiment transitions, while dynamically adjusting memory retention across different volatility regimes. This design enables efficient modeling of long-sequence dependencies inherent in cryptocurrency price movements without incurring the computational bottlenecks of traditional attention-based architectures. Through comprehensive experiments on Bitcoin historical data spanning multiple market regimes, we demonstrate that CryptoMamba-SSM consistently outperforms conventional LSTM, GRU, and Transformer baselines, achieving up to a 23.7% reduction in Mean Absolute Error and a 31.2% improvement in directional accuracy. The selective memory mechanism effectively captures regime-switching behaviors and microstructural anomalies, leading to more reliable short-term volatility risk quantification. Moreover, the linear-time complexity of CryptoMamba-SSM enables real-time processing of high-frequency trading data while maintaining strong generalization across diverse market conditions.
This study assesses whether Bitcoin’s linkage with AI equities remains robust after accounting for equity risk sentiment. To this end, the study employs the multiscale quantile‐on‐quantile correlation (MSQQC) and multiscale quantile‐on‐quantile partial correlation (MSQQPC) approaches, using daily data covering 02/01/2019–16/06/2025. The results indicate that BTC–AI comovement is strongly state‐ and frequency‐dependent rather than stable across the joint distribution or across horizons. In the high‐frequency band, dependence is weak and only intermittently significant, with localised negative regions around BTC ≈ 0.20 with AI ≈ 0.30–0.50 and BTC ≈ 0.30 with AI ≈ 0.70. In the mid‐frequency band, significance concentrates in the tails, showing negative dependence under downside stress conditions such as BTC ≈ 0.10–0.30 with AI ≈ 0.10, alongside sign changes when BTC is in upper‐tail states. In the low‐frequency band, dependence becomes broadly positive and significant across most quantile combinations, with limited decoupling when AI is highly elevated (≈ 0.80–0.90) and BTC is also in upper quantiles (≈ 0.70–0.90). Importantly, conditioning on VIX and VVIX does not materially alter these patterns, suggesting that sentiment influences segments of short‐run dependence but does not overturn the longer‐run BTC–AI linkage. The study derives policy recommendations from these findings.
This thesis examines whether cryptocurrencies can function as diversification or risk-reducing assets relative to the Swedish equity market during periods of financial stress. Using daily data for Bitcoin, Ethereum and Ripple from 2018 to 2024, their dynamic relationship with the OMX30 index is analyzed. To provide a broader benchmark, gold, the German DAX index, and the U.S. S&P 500 index are included as comparison assets. Periods of financial stress are identified as episodes in which the OMX30 declines by at least 10 percent from a recent peak. Time-varying correlations are estimated using a Dynamic Conditional Correlation GARCH (DCC-GARCH) model, allowing the analysis of how interasset relationships evolve over time. In addition, hedge effectiveness measures are employed to assess the cryptocurrencies practical ability to reduce portfolio risk.The results show that Bitcoin, Ethereum and Ripple exhibit weak but positive correlations with the Swedish equity market, implying that they may serve as diversifiers but not ashedges or safe-havens. During periods of financial stress, correlations tend to increase rather than decrease, indicating limited protective properties. Hedge effectiveness estimates further suggest that the risk-reducing capacity of cryptocurrencies is unstable and generally weak. Incontrast, gold displays more consistent negative correlations and superior hedging performance. Overall, the findings suggest that cryptocurrencies offer limited diversification benefits for Swedish investors and should not be considered reliable risk-mitigating assets during market stress.
The rise of Bitcoin has revolutionized the financial landscape, but it has also opened the door to a new era of criminal activities. Criminals take advantage of the anonymity provided by Bitcoin to conduct illicit transactions and engage in fraudulent activities. To address this issue, this paper proposes a detection model using Graph Neural Networks (GNNs) to detect fraudulent activities in the complex financial systems of Bitcoin. From the GNNs, we use EvolveGCN and EvolveGGCN to compare between them and find a powerful model that can investigate the network construction of financial transactions and capture patterns and anomalies that traditional methods may miss. In the literature, there have been a limited number of studies on Bitcoin fraud detection using GNNs, especially EvolveGGCN. Therefore, in this paper, we focus on the detection of fraud in the Bitcoin network using EvolveGCN and EvolveGGCN. In addition, we used a more recent dataset called Elliptic++, which is an extension of the Elliptic Dataset. The dataset provides valuable information on the behavior and patterns of fraudulent actions in the Bitcoin network. The results show that EvolveGGCN outperforms other models in terms of precision, recall, F1 score, and micro-F1 score. With an F1-score of 0.90 and micro-F1 of 0.93 for detecting illicit transactions in the early time steps.
This paper examines whether social media sentiment derived from Twitter and Reddit improves the explanation and prediction of cryptocurrency volatility. Using Bitcoin and Ethereum as benchmark assets, we combine sentiment indicators with GARCH-type models and the HAR-RV framework. Results suggest that cryptocurrency volatility is primarily driven by internal market dynamics rather than social media sentiment.
This paper challenges the conventional divide between productive and non-productive assets by proposing that scarcity, rather than internal cash flow generation, is the fundamental source of value across all asset classes. Interim payments such as dividends, rents, or coupons, represent one modality of monetizing scarcity, but terminal resale and other mechanisms serve equivalent roles. We develop a valuation framework in which scarcity is modeled as a latent, time-varying state variable shaped by economic pressures on demand and supply. A class of monetization functions, characterized by monotonicity and curvature, maps scarcity states into observable or forecast cash flows. This formulation allows discounted cash flow (DCF) logic to be reinterpreted as a general pricing mechanism for intertemporal scarcity. The framework accommodates both terminal-value assets, such as Bitcoin or gold, and income-generating assets, such as equities or bonds. We formally demonstrate the equivalence between terminal and periodic payoff structures and introduce a classification of assets according to their scarcity mechanism, whether physical, contractual, algorithmic, or reputational. By embedding scarcity at the core of valuation, this approach dissolves artificial distinctions in asset classification and establishes a unified foundation for pricing financial claims across diverse contexts.
The advent of sufficiently powerful quantum computers poses an existential cryptographic threat to elliptic-curve-based public key infrastructure, upon which major blockchain networks depend for transaction security and identity. This paper conducts a rigorous comparative analysis of quantum risk exposure for Bitcoin and Ethereum, examining the structural, governance, and economic dimensions of post-quantum cryptographic (PQC) transition for each protocol. We analyze the mathematical incompatibility of leading NIST standardized PQC signature schemes with current blockchain scalability constraints, with particular attention to signature size inflation (30-100× current schemes), the loss of algebraic linearity preventing signature aggregation, and the resulting implications for block space, fee markets, node economics, and validator infrastructure. We subsequently contrast Ethereum's upgrade-oriented, stake-weighted governance model and its modular cryptographic architecture against Bitcoin's deliberately ossified, consensus-driven governance structure. Our findings indicate that while Ethereum possesses the structural and institutional prerequisites for a credible, phased transition to post-quantum cryptography, Bitcoin's governance model and architectural constraints render such a transition highly contested and potentially irresolvable without chain fragmentation. We conclude that Bitcoin's structural limitations, compounded by deep ideological fractures and the irreversible nature of PQC deployment, place it at significant risk of prolonged governance stagnation or chain split, undermining its position as a reliable store of value and 'digital gold' standard in the medium term.
Penelitian ini bertujuan untuk menganalisis dan membandingkan kinerja investasi Bitcoin, Ethereum, emas, dan Indeks LQ45 selama periode 2020-2024 dilihat dari sisi return, risiko, dan rasio Sharpe. Data yang digunakan merupakan harga penutupan bulanan yang diperoleh dari situs resmi investing.com. Metode analisis yang digunakan yaitu uji ANOVA dilanjutkan dengan uji lanjut post-hoc Tamhane’s T2 dan Tukey HSD. Hasil penelitian menunjukkan bahwa secara agregat terdapat perbedaan return antar instrumen, namun perbedaan tersebut tidak signifikan secara statistik pada uji post-hoc. Risiko merupakan pembeda utama dalam perbandingan keempat instrumen, dengan Ethereum sebagai aset paling berisiko, disusul oleh Bitcoin, Indeks LQ45, dan emas sebagai aset paling stabil. Pada rasio Sharpe, hanya terdapat perbedaan antara aset Bitcoin dan Ethereum dengan Indeks LQ45, di mana Bitcoin dan Ethereum menunjukkan efisiensi kinerja lebih baik dalam menghasilkan return terhadap risiko dibanding Indeks LQ45.