Many poor long-term financial decisions are not “choices” but symptoms of a psychological state called Learned Helplessness. This is the belief, often learned from past setbacks, that one has no control over outcomes, leading to passivity and avoidance. In finance, this manifests as a belief that “it doesn’t matter what I do, I’ll never get ahead.” This is academically defined as an External Locus of Control, the belief that one’s financial future is in the hands of luck or external forces, not personal effort. An External Locus of Control can be directly linked to saving significantly less for retirement and avoiding proactive financial planning. In this research, we attempt to predict cryptocurrency engagement, given it's attractiveness for people who feel that traditional, effort-based financial structures are futile, as a function of beliefs about people's locus of control of their finances, planning horizon, and self-efficacy.
This study focuses on cryptocurrencies. At the beginning it explains what cryptocurrency is, its main features and main areas of its significance for the economy. In this section it deals with the possibility of cryptocurrency one day replacing traditional money, trading opportunities cryptocurrencies offer, possibility to finance a business with digital coins and its availability to people without the access to banking services. A brief overview of cryptocurrency history and a definition of the technology of blockchain are also provided. The practical part of the thesis is analysing cryptocurrencies Bitcoin, Ethereum and Litecoin. Firstly, these are described in terms of their origin, emission, circulation, price development and process of mining. Secondly, the impact of selected factors on the price fluctuation of selected cryptocurrencies is evaluated using statistical methods and econometric models. The analysis showed the cryptocurrency prices are more dependent on the internal factors such as the transaction volume, transaction fee, total supply, demand and hashrate, than on the external factors such as interest rates, exchange rates, stock prices and the price of gold.
In this article, we provide an in-depth analysis of the different causes of food fraud, food safety, and food waste, which are recognized as the main challenges in agri-food supply chains. Leveraging technologies like blockchain- and IoT-enabled traceability systems presents promising solutions to overcome these challenges. Using the PRISMA methodology, we conducted a comprehensive literature review of 41 selected articles to assess the effectiveness of such solutions. The findings reveal that only 48% target primarily improved food safety, while food waste (9%) and food fraud (21%) played a less important role. Only a few papers actively incorporate these attributes into their architectural design and many papers lack practical implementation details, leaving significant gaps in understanding their practical applicability. One finding was that 61% of the proposed solution were build on a public permissionless blockchain (Ethereum) and 39% where build on a permissioned private or consortium blockchain (mainly Hyperledger Fabric and Sawtooth). Critical aspects such as data privacy, confidentiality, final infrastructure governance, or legal frameworks are in most cases missing, e.g., 75% did not discuss the governance of the solution at all. To address the identified limitations, we propose a modular reference architecture that balances transparency and confidentiality through a hybrid blockchain approach. It incorporates a trusted platform layer with secure data storage, publicly verifiable summaries, and role-based access control. The architecture is illustrated through multiple use cases and qualitatively evaluated against characteristics of existing solutions, highlighting its conceptual suitability for regulated agri-food ecosystems.
Subramanya V. Odeyar, P. K. Lolakshi, L. Swetha, K. M. Thejaswini · 6 authors
Abstract The Bitcoin has recently garnered significant media and public attention due to its dramatic price increases and declines. As a result, many researchers have examined the various factors influencing Bitcoin’s price and the patterns behind its fluctuations, often using machine learning techniques. This study explores several machine learning algorithms for Bitcoin price prediction, including logistic regression and long short-term memory (LSTM) models. While LSTM-based models have shown superior performance in predicting Bitcoin prices (regression), this research provides a detailed investigation into Bitcoin’s evolution and a comprehensive review of the machine learning methods used for price prediction. Additionally, the study includes a Bitcoin price prediction model, which is developed using specific algorithms to forecast Bitcoin’s price, along with insights into the factors affecting its price movements. The proposed LSTM model has achieved 98% accuracy.
Autonomous software agents on blockchains coordinate complex behavior by reading shared ledger state instead of exchanging direct messages. Arbitrage bots, liquidation keepers, and MEV searchers all watch balances, contract storage, and event logs; when conditions change, they act. This form of indirect coordination mirrors what Grassé called stigmergy in 1959: organisms coordinating through traces left in a shared environment, with no central plan. Stigmergy has mature formalizations in swarm intelligence and multi-agent systems, and on-chain agents already behave stigmergically in practice, but no framework bridges the two. We propose Coordinación indirecta basada en el estado del registro contable (“Indirect coordination grounded in ledger state”) as an applied definition that maps Grassé’s mechanism onto distributed ledger technology. We operationalize this with a state-transition formalism, derive four on-chain coordination patterns (State-Flag, Event-Signal, Threshold-Trigger, Commit-Reveal Sequencing), and evaluate a simulated task board against off-chain messaging and centralized orchestration baselines. The stigmergic approach matches the baselines on task completion under benign conditions while outperforming both under Byzantine adversarial pressure, at a gas-cost premium of roughly 1.5×.
Traditional digital trust architectures suffer from the “Library Problem”: dependency on pre-compiled, static lookup tables or binaries that must be trusted blindly, creating supply-chain vulnerabilities. This paper proposes a paradigm shift to Intrinsic Trust, where encoding infrastructure is mathematically regenerated at runtime rather than distributed. We introduce the 0MXI Calculus, a deterministic lattice system anchored on universal transcendental constants:the golden ratio Φ ≈ 1.618033988749895 and π ≈ 3.141592653589793, with a contraction ratio λ ≈ 0.339949771344778. Operations on a quantized F15 lattice ensure cross-platform determinism, bounded by a Prime Boundary Horizon (N = 23) that guarantees injective reversibility (Theorems 1 and 2).This framework underpins TreeOS, an operating system that bootstraps from a “Math Root-of-Trust” via autogenesis, regenerating a bijective Tick Table for byte encoding without stored dependencies. TreeBABEL, the verifiable data transport protocol, packages data as JSON artifacts with mathematical manifests for independent receiver validation. Extending this, the VMEM Node Architecture transforms online repositories into externalized memory banks, enabling AI models to scrape and derive OS state on demand, eliminating internal weight bloat and static knowledge cutoffs.We demonstrate adaptability to constrained ledgers (e.g., 280-character limits) for efficient chunking. Through rigorous proofs and a Python reference implementation, we show that trust can be calculated, not stored, decoupling systems from physical hardware and fostering entropy-neutral, zero-trust computation.
Contemporary governance theory confronts a tripartite crisis that existing frameworks address only in isolation. First, algorithmic systems are systematically eroding the cognitive, affective, and epistemic conditions for individual personhood - what this paper terms the Personhood Atrophy Model. Second, recommendation-engine-driven fragmentation has dissolved the shared cultural and epistemic spaces upon which collective purpose and democratic deliberation depend. Third, the structural asymmetry between the pace of technological change and the operational tempo of democratic institutions has produced a compounding legitimacy crisis for the sovereign nation-state, increasingly outflanked by corporate platforms exercising sovereign-equivalent power without democratic accountability. Political theory and science and technology studies have addressed each of these dimensions in isolation. No integrated analytical framework currently exists that connects the micro-level erosion of selfhood, the meso-level collapse of shared meaning, and the macro-level transformation of sovereignty into a unified theory of algorithmic governance. This paper introduces the Republic of Code framework, drawing on the monograph by Shaik (2026), and proposes three original theoretical constructs: (1) the Wet Code/Dry Code distinction as a governance epistemology tool, formalizing the fundamental incompatibility between human-interpretable and machine-enforced law; (2) the Personhood Atrophy Model mapping algorithmic erosion of agency across cognitive, affective, and epistemic vectors; and (3) the Five Futures Matrix, a two-axis typology of possible political arrangements under algorithmic conditions. The paper concludes by proposing a suite of constitutional innovations - including Proof of Humanity (whose mechanism design infrastructure is formally developed in Shaik, 2026b), Zero-Knowledge Justice, and High-Fidelity Democracy - necessary for the reconstruction of democratic legitimacy in what it terms the Republic of Code. The analysis carries implications for legal scholarship, platform governance policy (including industrial cyber-physical systems, examined in Shaik, 2026e), and the updating of social contract theory for an era in which digital exit costs approach zero.
This project is the public home of Relational Calculus, a meta‑mathematical framework that replaces the brute‑force logic of absolute‑scale computation with dimension‑less, capacity‑anchored blueprints. At its heart lies a simple but radical axiom: every system possesses an intrinsic maximum—a “North Star”—and by expressing all observations as fractions of that limit, complexity collapses, efficiency soars, and transfer across domains becomes automatic. The collection gathers the complete stack: the foundational theoretical paper, a ready‑to‑run Relational Decoder (an open‑source algorithm that probes any black‑box function and extracts its dimensionless template), and five applied case studies that prove the principle in wildly different arenas—number theory (deterministic prime pair lattices), symbolic artificial intelligence (a geometric chess engine that exhibits emergent strategy with zero domain knowledge, gaining 90%+ efficiency), high‑energy physics (scale‑invariant jet tagging that transfers zero‑shot across collision energies with +14.5% AUC), quantum chemistry (80% error reduction in cross‑molecule transfer), and precision oncology (a lightweight XGBoost that achieves 98.4% cross‑species diagnostic accuracy under a 70% hardware‑signal collapse, completely erasing batch effects). A companion paper extends the logic to large language models, proposing Relational‑CoT as a drop‑in replacement for resource‑intensive chain‑of‑thought reasoning. Every work converges on the same empirical signature: >90% reduction in computational cost, genuine zero‑shot generalization across scales and species, and the proof that Green AI is not an aspiration but an engineering reality. An integrated STEM curriculum for ages 10–14 ensures that the relational lens is taught before the continuous one, inoculating the next generation against the wasteful “math of deviation.” All code, data, and executable papers are open‑source. The project is intended not as a scholarly gesture but as an enablement instrument for the industrial shift from the Age of Fire—where more compute meant more extraction—to the Era of Relation, where measuring how full a system is replaces the endless pursuit of how much.
This thesis presents a comprehensive predictive maintenance system and application interface that integrates deep learning and blockchain technologies in order to enhance maintenance strategies in industrial systems. Traditional predictive maintenance systems have significant issues regarding data security and decentralization. This study aims to address these limitations by leveraging blockchain technology, with a specific focus on improving the reliability and verifiability of predictive maintenance processes. In this study, an LSTM-CNN hybrid model was developed to evaluate complex patterns in both time and features, thereby enabling high-accuracy fault prediction. The proposed model is designed to perform binary classification for fault prediction in industrial equipment. During the implementation phase of the study, an open-source dataset was used to train and test the developed model. The Randomized Search method was used in the hyperparameter optimization process to increase the prediction success of the proposed model. The hybrid model was trained with 5-fold cross-validation, and class weighting and threshold value optimization methods were applied to eliminate the class imbalance problem. In the threshold optimization phase, F1-score-based methods are applied to maximize recall at three predefined minimum precision levels (0.05, 0.2, and 0.85), while identifying the most balanced trade-off between precision and recall. In the proposed system, sensor data are stored in a database (SQLite3), and cryptographic proofs generated using zero-knowledge techniques are transmitted to the Ethereum network. The Poseidon hash function is used to ensure data integrity, and the Groth16 protocol is used for Zk-Snark proof generation. This approach enables secure verification of data validity without publicly disclosing sensor data and simultaneously addresses scalability concerns. The system architecture is designed to include manager, operator, and engineer nodes, and all smart contracts are implemented using Solidity. In addition, a graphical user interface is developed using the Tkinter library in Python. The experimental results demonstrate that the proposed LSTM–CNN hybrid model produces successful outcomes in terms of fault prediction performance. According to scenario where the decision threshold is optimized based on the F1-score, the model achieves an accuracy of 0.987, an AUC value of 0.979, and an F1-score of 0.794. In future studies, the proposed system is planned to be implemented on the Ethereum mainnet instead of a test network, with a comprehensive evaluation of on-chain operational costs. However, instead of Zk-Snark proofs, which have a centralized structure, the use of Zk-Stark proofs, which are transparent and do not violate the principle of decentralization, is planned.
Rapid urbanization in Tanzania has increased municipal solid waste generation and placed growing pressure on urban waste-management systems that remain focused mainly on collection, transport, and disposal. Given the high organic fraction of municipal waste generation. This paper examines Resource recovery from municipal waste through cost-effective biogas technologies in Tanzania, focusing on policy and institutional frameworks that support or constrain decentralized municipal organic waste-to-biogas systems that use appropriate standard procedures. The findings show that Tanzania has a broad policy framework for environmental protection, renewable energy, private-sector participation, and resource recovery, but this foundation has not been well translated into practice. Key constraints include fragmented mandates, limited biogas-specific standards, weak organic waste segregation, inadequate financing mechanisms, and insufficient formal inclusion of communities and informal waste actors. The paper argues that improving decentralized biogas implementation requires converting existing policy commitments into enforceable, financed, and locally coordinated municipal resource-recovery systems
Decentralized finance (DeFi) agents automate multi-transaction workflows such as swapping, lending, and vault management, but they also create process-level risk. A run can consist of individually valid calls while still becoming economically unsafe because an intermediate step leaves latent authority, weakens execution constraints, or accepts unverified external evidence. Existing defenses are often mismatched to this process-level risk. Off-chain preflight checks alone cannot protect against runtime deviations from the intended plan, and coarse on-chain allowlists are too weak to express the call-level intent that matters in DeFi. We present CheckpointAgent, a workflow-security architecture for checkpointed DeFi-agent execution. It composes manifest commitments, smart-account policy guards, post-state predicates, and attestation-gated advancement to constrain a run step by step and tie checkpoint advancement to verifiable evidence. Rather than judging safety only after a workflow finishes, CheckpointAgent checks whether each step remains consistent with the intended workflow and stops execution when the required conditions no longer hold. In the author-curated 27-scenario local-chain suite, the strongest evaluated setting preserves all 5 benign runs and prevents unsafe completion in all 22 adversarial runs, stopping them either through on-chain enforcement or through trusted-attestation advancement under the configured attester assumption. Under explicit trust assumptions and within the measured workflows and snapshots, checkpointed execution can materially reduce process-level risk without modifying target protocols.
The smart contracts facilitated by blockchains allow the decentralized and automated implementation of digital contracts, yet the current security measures in this space are mostly geared towards vulnerability detection and post-implementation functionality, which do not provide much defence against runtime attacks. The paper analyses the concept of smart contracts as a unified approach to cybersecurity, and provides a Hardened Smart Contract Model (HSCM) as a proactive and runtime security quotient model. The suggested model places policy-conscious logic, formal safety requirements, risk aversive execution, upgradability under control by governance, and unchangeable auditability directly in the design of contracts. The framework guarantees the elimination of unauthorized access, re-entrancy and logic abuse by providing runtime verification and automated response measures that avert such violations even before state transitions take place. A fair amount of experimental confirmation on an Ethereum-compatible system proves that there is a high security guarantee with tolerable load overhead, the deployed smart contracts could be hardened.
Svetlana Marković, Radovan Vladisavljević, Marko Marković
Smart contracts are one of the most prevalent and important blockchain-based technologies in the field of financial security, as the automatic execution of predefined rules provides additional efficiency, lowers costs and minimizes the involvement of intermediaries. This research will examine the use of smart contracts for process automation, risk reduction and organizational restructuring in the financial sector. In particular, the interaction between centralized and decentralized financial systems, the technology behind the implementation of blockchain and security issues associated with the use of smart contracts will be considered. At the same time, escrows will be presented as an example of the practical use of smart contracts for financial operations. The results of this analysis will show that smart contracts can be used as a means to increase the reliability of financial operations; however, their widespread use depends on proper regulation, security assessment and integration with the existing financial infrastructure.