Abstract Globalization has fundamentally transformed English literature publishing, creating a dynamic interplay between unprecedented market expansion and significant challenges to creative diversity. This seminar paper investigates how economic mergers, technological disruptions, and cultural flows have reshaped the industry since the 1980s, when independent presses gave way to the dominant "Big Five" conglomerates Penguin Random House, HarperCollins, and others that now control over 80% of English-language fiction sales. These giants prioritize profitable, transmedia blockbusters like the Harry Potter series, fostering a "winner-takes-all" economy where top titles capture 90% of profits, often at the expense of experimental or midlist works.Counterbalancing this consolidation, digital platforms such as Amazon Kindle Direct Publishing and e-books have democratized access, empowering self-published successes like The Martian and enabling diaspora authors from regions like Nigeria and the Philippines to reach global audiences. Translation booms, fueled by prizes like the Man Booker International, introduce hybrid narratives Salman Rushdie's multilingual *Midnight's Children*, Mohsin Hamid's migratory Exit West that embody HomiBhabha's "third space" of cultural negotiation.Yet, risks loom: algorithmic recommendations promote formulaic "McLiterature," English hegemony marginalizes non-translated voices , and profit motives encourage tokenistic diversity. Through case studies of Penguin Random House's global strategies, Amazon's market dominance, and indie rebels like Tilted Axis Press, alongside emerging AI tools and Web3 royalties, the paper evaluates impacts on content, authors, and pluralism. It concludes that while globalization amplifies voices, safeguarding literary risk-taking require balancing commercial imperatives with equitable, culturally rich publishing in our interconnected era.
The purpose of the study is to comprehensively assess the legal and economic prerequisites for integrating prediction markets into the US financial system, taking into account the regulation of derivative financial instruments, gambling legislation, and the characteristics of decentralized management models, as well as to determine the impact of legalization on the informational efficiency of markets and the stability of the financial system. The study uses formal legal analysis of regulatory acts and law enforcement practices of federal authorities, a comparative legal approach to the distinction between financial and gambling regulation, economic and mathematical modeling using autoregressive models with external variables and rational expectations theory, as well as elements of agent-based modeling to assess the risks of price manipulation. The source base consists of relevant scientific publications from 2022 to 2026, analytical materials, and regulatory documents. It has been established that the main barrier to integrating decentralized prediction markets into the US legal framework is the dual legal nature of event contracts, which creates competition between the regulatory regimes governing derivatives markets and the gambling sector. It has been proven that the absence of a centralized issuer in decentralized autonomous organizations complicates state supervision and the identification of the responsible entity. Economic analysis confirmed the ability of binary contracts to aggregate scattered information and form market expectations more efficiently than individual traditional indicators, while also revealing the risks of short-term price distortions. The need to introduce a mixed legal support model, combining distributed registries with a licensed organizational form of activity, is justified. Legalization of prediction markets, provided that there is a clear distinction between financial and gambling regulation, can increase the transparency of market expectations and expand the range of risk management tools without creating excessive systemic threats.
The rapid penetration of decentralized financial mechanisms into the structure of Ukraine's cryptocurrency market, where the volume of DeFi transactions consistently exceeds that of centralized platforms, highlights the need for effective tools to maintain the instant solvency of lending protocols during periods of sharp price fluctuations in digital assets. The purpose of this article is to systematize liquidity risks in decentralized financial systems, conduct a comparative assessment of algorithmic strategies for their minimization, and identify ways to enhance the stress resilience of protocols. The methodological basis of the study consists of a taxonomic analysis for classifying types of risks, a comparative analysis of the effectiveness of key liquidity management strategies, economic-mathematical modeling of cascading liquidation processes, and a correlation analysis of the relationship between the magnitude of cryptoasset price declines and the frequency of protocol failures. The empirical basis consists of on-chain data on the transaction activity of leading DeFi protocols for the period 2024â2026. The results show that hybrid configurationsâwhich combine dynamic interest rate regulation with overcollateralization and decentralized oracle networksâdemonstrate the highest resilience to extreme volatility. It was found that increasing collateral requirements proportionally reduces the probability of cascading liquidations, but simultaneously limits the protocol's capital efficiency, highlighting the need to optimize these parameters. It has been established that compositional links between protocols create a domino effect: a local liquidity shortage in one pool can trigger a chain of forced liquidations in adjacent systems within a critically short time frame. The scientific novelty lies in the development of a typologized scheme for neutralizing liquidity threats, which, unlike existing ones, integrates sentinel oracle, execution liquidation, and reserve insurance instruments into a unified protocol risk management system. The method for estimating margin call thresholds has been improved to account for the historical volatility of specific cryptoassets. The practical significance of the obtained results lies in their potential use by DeFi protocol architects and smart contract developers when designing risk management systems, configuring liquidation auction parameters, and selecting the optimal configuration of oracle networks for the Ukrainian crypto market.
Federated learning (FL) enables collaborative model training over distributed private data. However, sustaining open participation requires incentive mechanisms that compensate contributors for their resources and risks. Enabled by Web3 primitives, especially blockchains, recent FL proposals incorporate incentive mechanisms for open participation, yet most focus primarily on algorithmic design and overlook system-level challenges, including coordination efficiency, secure handling of model updates, and practical usability. We present FWeb3, a practical Web3-enabled FL framework for incentive-aware training in open environments. FWeb3 adopts a modular architecture that separates FL functions from Web3 support services, decoupling the off-chain training and data plane from on-chain settlement while preserving verifiable incentive execution. The framework supports pluggable aggregation and contribution evaluation methods and provides a browser-native DApp interface to lower the participation barrier. We evaluate FWeb3 in real-world settings and show that it supports end-to-end incentive-aware FL with transaction and data-transfer overheads of only 21.3% and 3.4% in WAN; FWeb3 also deploys from zero configuration in under 3 minutes and enables user onboarding in under 1 minute.
Rosa Pericàs Gornals, M. Magdalena Payeras Capellà , Victor Garcia Font, M. Puigserver ¡ 5 authors
The exchange of non-fungible tokens (NFTs) traditionally occurs on centralized NFT marketplaces, which often wield significant control over transactions, as the imposition of restrictions and fees. Additionally, the current NFT standard (ERC-721) lacks certain functionalities that hinder the seamless direct exchange of NFTs between owners. This article introduces a novel protocol that eliminates the need for an intermediary marketplace, as well as their intermediary smart contract or escrow contract, facilitating a fair exchange of NFTs as collectibles. The protocol is represented by an extension of the ERC-721 called Exchangeable NFTs (ExNFTs), which introduces the necessary functionalities to enable direct NFT exchanges. We analyze the limitations of the ERC-721 standard and build upon our previously proposed protocol of Rejectable NFTs (RejNFTs), which introduces a rejectable functionality empowering the receiver to reject the transfer of an NFT. Additionally, we discuss the reasons why achieving a fair direct exchange of NFTs is currently unattainable with the prevailing standards. We present the key functionalities of the ExNFT approach, along with a Solidity implementation of the ExNFTs. Furthermore, we evaluate the performance of the protocol, considering costs and security.
The article is devoted to the study of mechanisms for managing reputational risks in decentralized autonomous organizations (Decentralized Autonomous Organizations â DAO) operating on the basis of blockchain technologies. The relevance of the research is determined by the rapid development of decentralized digital ecosystems, the spread of algorithmic governance models, and the need to ensure trust among participants in an environment where traditional institutional mechanisms of centralized control are absent. Under such conditions, the issue of reputational risk management becomes particularly important, as the level of trust directly affects the stability and sustainability of decentralized organizations. The aim of the study is to identify and substantiate mechanisms for managing reputational risks in decentralized autonomous organizations based on blockchain technologies, taking into account the specific features of their functioning and the principles of decentralized governance. The methodological basis of the research includes methods of systemic analysis, institutional approach, comparative analysis, and modeling. To identify the key factors shaping reputational risks, the study employs the analysis of contemporary scientific publications, generalization of DAO project practices, and examination of digital governance tools within blockchain ecosystems. As a result of the study, the main sources of reputational risks in decentralized autonomous organizations were systematized, including information asymmetry among participants, insufficient transparency of decision-making procedures, technical vulnerabilities of smart contracts, and potential manipulation of voting mechanisms. The key mechanisms for managing reputational risks in the DAO environment were generalized, including participant reputation evaluation systems, transparent decentralized voting mechanisms, smart contract auditing, and moderation tools for digital communities. Based on the conducted analysis, a conceptual model for managing reputational risks in DAO was developed, which provides for the integration of blockchain transparency tools, collective control mechanisms, and procedures for evaluating the reputational behavior of participants in digital ecosystems. The scientific novelty of the study lies in substantiating a conceptual approach to reputational risk management in decentralized autonomous organizations, which combines the capabilities of blockchain infrastructure with self-regulation mechanisms of decentralized digital communities. The practical significance of the obtained results lies in the possibility of their application by developers of DAO projects, blockchain platforms, and digital ecosystems for the development of reputational risk management systems, increasing the level of trust among participants, and ensuring the stable functioning of decentralized organizations.
The paperâs subject is examining decentralized autonomous organizations (DAOs) governance models during the digital economic shift. This study aims to deeply analyze DAOs to find governance aspects, and to develop a model to help integrate them into global finance and public administration. To achieve this goal, the study addressed the following tasks: analyzing existing methods to DAO governance; identifying key risks and opportunities; modeling a hybrid organizational structure; and assessing the role of stablecoins as a stabilizing element. The studyâs novelty comes from the creation of a unique adaptive DAO governance model that combines elements of centralization and decentralization to enhance efficiency and legitimacy. The author used methods of comparative and systemic analysis, modeling, and real-world solutions and regulations. The results prove DAOs can complement traditional governance by making economic interactions more transparent and efficient. The studyâs main takeaway is that DAOs need a balanced regulatory framework and better governance. Government agencies, blockchain developers, and specialists in public and corporate governance can use the work results.
Open access
Blockchain Technology Applications and Security
Digitalization and Economic Development in Agriculture
The article examines digital asset inheritance in Web3 ecosystems, where the economic value of cryptocurrencies, NFTs, tokenised assets, cloud-stored intellectual property and high-value platform accounts is not supported by sufficiently reliable legal and technical mechanisms for intergenerational transfer. The relevance of the topic is determined by the fact that traditional inheritance law is oriented mainly toward tangible objects or documented property rights, whereas blockchain-native assets depend on private keys, platform accounts are restricted by terms of service, and the cross-border nature of digital portfolios complicates the determination of applicable law. The purpose of the study is to develop an integrated conceptual model of the Self-Sovereign Digital Heritage System (SSDHS), combining self-sovereign identity, decentralised identifiers, verifiable credentials, digital safes, smart-contract execution of inheritance conditions and regulatory compliance. The methodological basis includes comparative legal analysis, system analysis, functional modelling, conceptual design and regulatory impact assessment. The article substantiates a six-layer SSDHS architecture consisting of the identity layer, digital asset inventory layer, secure storage layer, blockchain layer, inheritance execution layer and legal compliance layer. It is shown that SSI addresses the problem of cryptographic heir authentication, whereas the digital safe ensures secure preservation of private keys, inheritance instructions, DID material and the digital testament. A comparative analysis of the regulatory frameworks of the United States, the European Union and Ukraine is conducted, including fiduciary access to digital assets, electronic wills, digital identity, crypto-asset markets, personal data protection, virtual assets and electronic identification. The study substantiates that SSDHS can serve as a legal-technological reference model for reducing the risk of digital asset loss caused by inaccessible private keys, improving heir identification reliability, reducing dependence on centralised intermediaries and preparing future legislative solutions for digital heritage.
Decentralized finance (DeFi) has emerged as a transformative paradigm, leveraging programmable blockchains to innovate upon traditional financial services without centralized intermediaries. However, DeFi introduces a unique and highly adversarial security landscape characterized by immutable transactions, complex protocol composability, and transparent execution environments. This survey provides a comprehensive systematization of DeFi security, categorizing vulnerabilities across three distinct layers: technical and code layer, economic and protocol layer, and infrastructure and cross-chain layer. Furthermore, we structure the defense mechanisms according to the protocol lifecycle, including pre-deployment prevention strategies, runtime mitigation techniques, and post-incident response and recovery mechanisms. We also delve into specific phenomena such as maximal extractable value, analyzing its dual role as both a market efficiency tool and a security vector. By synthesizing existing literature and incident reports, this survey establishes a holistic framework for understanding the interplay between code and finance. Finally, we identify critical open challenges and propose future research directions aimed at maturing the discipline of DeFi security and mitigating systemic risks.
Open access
Infrastructure Resilience and Vulnerability Analysis
Job-based smishing scams, where victims are recruited under the guise of remote job opportunities, represent a rapidly growing and understudied threat within the broader landscape of online fraud. In this paper, we present Anansi, the first scalable, end-to-end measurement pipeline designed to systematically engage with, analyze, and characterize job scams in the wild. Anansi combines large language models (LLMs), automated browser agents, and infrastructure fingerprinting tools to collect over 29,000 scam messages, interact with more than 1900 scammers, and extract behavioral, financial, and infrastructural signals at scale. We detail the operational workflows of scammers, uncover extensive reuse of message templates, domains, and cryptocurrency wallets, and identify the social engineering tactics used to defraud victims. Our analysis reveals millions of dollars in cryptocurrency losses, highlighting the use of deceptive techniques such as domain fronting and impersonation of well-known brands. Anansi demonstrates the feasibility and value of automating the engagement with scammers and the analysis of infrastructure, offering a new methodological foundation for studying large-scale fraud ecosystems.
We document the first systematic evidence of negative spillover effects in crypto asset returns across blockchains. Using on-chain data from Ethereum, Solana, Binance Smart Chain, Arbitrum, and Avalanche (2022-2025), we show that surges on one chain often coincide with declines on others, in contrast to the positive co-movements typical of equity markets. These spillovers intensify during attention shocks, proxied by chain activity and extreme return events, and persist after controlling for global equity returns, interest rates, and Bitcoin. Nonlinear factor models reveal that attention-driven capital reallocation, rather than common information, underlies these dynamics. Our findings introduce a new form of cross-market linkage, attention-induced substitution, that shapes risk transmission in crypto markets. The results carry implications for portfolio diversification, systemic risk measurement, and regulation of token launches that may trigger cross-chain capital flight.
Due to the scalability and portability, low-altitude intelligent networks (LAINs) are essential in various fields such as surveillance and disaster rescue. However, in LAINs, unmanned aerial vehicles (UAVs) are characterized by the distributed topology and high mobility, thus vulnerable to security threats, which may degrade routing performances for data transmissions. Hence, how to ensure the routing stability and security of LAINs is challenging. In this paper, we focus on the routing with multiple UAV clusters in LAINs. To minimize the damage caused by potential threats, we present the zero-trust architecture with the software-defined perimeter and blockchain techniques to manage the identify and mobility of UAVs. Besides, we formulate the routing problem to optimize the end-to-end (E2E) delay and transmission success ratio (TSR) simultaneously, which is an integer nonlinear programming problem and intractable to solve. Therefore, we reformulate the problem into a decentralized partially observable Markov decision process. We design the multi-agent double deep Q-network-based routing algorithms to solve the problem, empowered by the soft-hierarchical experience replay buffer and prioritized experience replay mechanisms. Finally, extensive simulations are conducted and the numerical results demonstrate that the proposed framework reduces the average E2E delay by 59\% and improves the TSR by 29\% on average compared to benchmarks, while simultaneously enabling faster and more robust identification of low-trust UAVs.
Multi-horizon price forecasting is central to portfolio allocation, risk management, and algorithmic trading, yet deep learning architectures have proliferated faster than rigorous financial benchmarks can evaluate them. This study provides a controlled comparison of nine architectures (Autoformer, DLinear, iTransformer, LSTM, ModernTCN, N-HiTS, PatchTST, TimesNet, and TimeXer) spanning Transformer, MLP, CNN, and RNN families across cryptocurrency, forex, and equity index markets at 4-hour and 24-hour horizons. A total of 918 experiments were conducted under a strict five-stage protocol including fixed-seed Bayesian hyperparameter optimization, configuration freezing per asset class, multi-seed retraining, uncertainty aggregation, and statistical validation. ModernTCN achieves the best mean rank (1.333) with a 75 percent first-place rate, followed by PatchTST (2.000). Results reveal a clear three-tier ranking structure and show that architecture explains nearly all performance variance, while seed randomness is negligible. Rankings remain stable across horizons despite 2 to 2.5 times error amplification. Directional accuracy remains near 50 percent across all configurations, indicating that MSE-trained models lack directional skill at hourly resolution. The findings highlight the importance of architectural inductive bias over raw parameter count and provide reproducible guidance for multi-step financial forecasting.
Dang Sy Duy, Nguyen Duy Chien, Kapil Dev, Jeff Nijsse
Graph neural networks (GNNs) offer a principled approach to financial fraud detection by jointly learning from node features and transaction graph topology. However, their effectiveness on real-world anti-money laundering (AML) benchmarks depends critically on training practices such as specifically weight initialisation and normalisation that remain underexplored. We present a systematic ablation of initialisation and normalisation strategies across three GNN architectures (GCN, GAT, and GraphSAGE) on the Elliptic Bitcoin dataset. Our experiments reveal that initialisation and normalisation are architecture-dependent: GraphSAGE achieves the strongest performance with Xavier initialisation alone, GAT benefits most from combining GraphNorm with Xavier initialisation, while GCN shows limited sensitivity to these modifications. These findings offer practical, architecture-specific guidance for deploying GNNs in AML pipelines for datasets with severe class imbalance. We release a reproducible experimental framework with temporal data splits, seeded runs, and full ablation results.
Decentralised finance (DeFi) has profoundly reshaped global capital markets, enabling automatic transactions, eliminating the need for intermediaries, and accelerating transaction settlement times. Despite these significant advancements, institutional involvement in DeFi remains very low. The lack of institutional participation can be attributed to the lack of an enforceable compliance mechanism at the protocol level; that is, once a transaction is confirmed as having been completed on the blockchain, it cannot be undone or disputed in any meaningful way. The existing compliance mechanisms are primarily retrospective, meaning that they generate alerts after a transaction has occurred instead of preventing illicit transfers in advance. Regulated financial institutions that transact in cryptocurrency bear the ultimate financial risk and regulatory burden. The UK FCA has made it very clear through CP25/41 that there are now specific regulatory expectations regarding the existence of adequate pre-settlement controls [2]. We introduce AMTTP Version 4.0, which has been designed to have a four-layer architecture explicitly intended to support deterministic compliance enforcement in DeFi institutions. Layer I provides SDKs, REST APIs, and web applications intended for programmatic and human interaction with AMTTP; Layer II provides a compliance orchestration layer that combines (i) machine learning risk scoring (ii) graph analysis (iii) sanctions screening, and (iv) policy adjudication into a single deterministic decision-making matrix; Layer III consists of an offline training pipeline with a Composite Teacher that uses an AutoencoderEnhanced XGBoost (w = 0.4), seven FATF AML Mode Patterns (w = 0.3), and graph structural properties (w = 0.3) in order to produce pseudo-labels (SLPs) for the Student pipeline across 2,640,000 transactions; and finally, Layer IV supports the physical infrastructure for AMTTP deployment, which consists of 18 smart contracts on Ethereum Sepolia, 17 containerised microservices, and a Database Persistence Tier (MongoDB, Redis, Memgraph, IPFS). The Infrastructure Security features multioracle threshold signatures, replay protection & zkNAF a zeroknowledge proof framework that allows for privacy preserving verification of KYC credentials, risk ranges & non-membership from sanctions. In addition, TLS Encryption, Rate Limiting, Cloudflare Tunnel integration & the UI Integrity Service provide an additional layer of protection at the infrastructure level. This paper aims to demonstrate that deterministic compliance can be integrated into decentralised finance at an architectural level. In order to support this assertion, the client SDKs (TypeScript and Python) are released as open source.1
In March 2025, the United States established a Strategic Bitcoin Reserve by executive order. Bhutan had been quietly mining Bitcoin with hydroelectric power, accumulating over $1 billion without public announcement. By February 2026, more than 145 publicly traded companies held Bitcoin on their balance sheets, collectively exceeding one million BTC. No established academic framework predicted these developments. In his 2023 MIT thesis Softwar, Major Jason Lowery proposed that Bitcoin is best understood not as money or a hedge but as a power projection technology rooted in thermodynamic proof of work. This paper presents the first empirical evaluation of that framework. Nine falsifiable predictions are tested against observed data; five have been confirmed and one partially realized within three years of publication. The developments that monetary, financial, environmental, and security models fail to explain (strategic reserves, geopolitical competition for hash rate, sovereign mining operations) are precisely those the power projection framework predicts.
Traditional paper-based voting system for student organization leaders election has issues related to security, transparency, and trust. This research addressed these issues by implementing a blockchain on e-voting system utilizing smart contracts to ensure the security and transparency of the voting process. The system was developed using the agile software development life cycle (SDLC) methodology and was tested using black-box and system usability scale (SUS) method to evaluate its functionality and usability. Security testing was conducted through unit testing on the smart contract and block verification within the Sepolia network. The results showed that the decentralized e-voting system could prevent vote manipulation and detecting duplicate voters, as evidenced by the unit testing of the smart contract, which confirmed that recorded votes could not be manipulated and attempts to submit multiple votes were detected and rejected. Meanwhile, system transparency was demonstrated through direct verification using a block explorer, showing that the entire voting process and the smart contract code were publicly accessible and transparent. The system was successfully simulated on a small scale within a student organization, and usability testing using the SUS method was conducted with 30 respondents. The test resulted in a score of 72 points, indicating that the system was in the good category and was well accepted by users. Therefore, the decentralized approach in this e-voting system has been proven to enhance transparency and overcome the problems of security issues in the voting process.
Shalini, Abhay Bhatia, Dr. Parag Jain, Dr. Lokesh Kumar
Exceptional connectivity across global networks has been driven by the expansion of Internet of Things devices, while significant weaknesses in security, scalability, and data management have emerged. Distributed ledger technology offers creative solutions to these fundamental limitations. This article reviews the blending of Blockchain technology with IoT, analyzing its potential, challenges and current advances. The article also highlights various applications and future research directions. This review aims to provide a comprehensive understanding by synthesizing existing knowledge, identifying research gaps, and establishing the context for future studies of blockchain-IoT integration, emphasizing critical design considerations and practical implementations.
Penelitian ini bertujuan untuk memetakan perkembangan dan arah evolusi riset mengenai perilaku investor di era digital melalui pendekatan bibliometrik. Data dikumpulkan dari basis data Scopus dan dianalisis menggunakan perangkat VOSviewer untuk mengidentifikasi pola publikasi, jaringan kolaborasi, serta struktur konseptual berdasarkan ko-occurence kata kunci. Hasil analisis menunjukkan bahwa tema investasi dan pasar keuangan tetap menjadi fondasi utama literatur, namun dalam beberapa tahun terakhir terjadi pergeseran signifikan menuju integrasi teknologi digital seperti machine learning, artificial intelligence, cryptocurrency, dan decentralized finance. Visualisasi overlay memperlihatkan bahwa topik berbasis algoritma dan sistem keuangan terdesentralisasi merupakan tema yang relatif lebih mutakhir. Analisis jaringan kolaborasi mengindikasikan bahwa produksi ilmiah masih terpusat pada beberapa negara dan institusi tertentu, dengan keterlibatan terbatas dari negara berkembang. Secara konseptual, studi ini menunjukkan bahwa perilaku investor di era digital berkembang dari pendekatan psikologis tradisional menuju kerangka yang lebih terintegrasi dengan transformasi teknologi dan analitik berbasis data. Temuan ini memberikan arah penelitian lanjutan terkait bias perilaku dalam lingkungan investasi yang semakin terdigitalisasi dan dimediasi algoritma.
O. Kravets, B. Martynenkov, A. Tcvetkov, E. Puzhanova ¡ 7 authors
The article discussed an algorithm for achieving mutual information coordination for a system with distributed ledger technology based on a blockchain. The goal is to develop a generalized approach to formalizing the operation of the distributed ledger technology blockchain system in the course of achieving mutual coordination, including taking into account the possibilities of implementing abnormal functions by the distributed ledger technology blockchain node of the system and grouping nodes. The rules of block chain formation in algorithms for achieving mutual information coordination are proposed. The process of achieving mutual information coordination is described. A mathematical model of the process of achieving mutual information coordination between the nodes of the distributed ledger technology blockchain system is proposed, which differs in the representation of the system by a team of finite automata with the possibility of creating associations (pools) and providing an assessment of the centralization of the system in the conditions of choosing different variants of behaviour strategy by automata.
Open access
Cybersecurity and Information Systems
Advanced Research in Systems and Signal Processing
Results-Based Financing (RBF) has been promoted as an innovative health financing mechanism to improve service delivery and health outcomes in low-resource settings. However, evidence on its impact within devolved health systems remains limited. This study examines how RBF influences key performance indicators (KPIs) in Zambia's devolved district health services. An embedded multiple-case study was conducted across 12 districts in Northern Province, Zambia. Mixed methods were employed, combining qualitative interviews with 44 stakeholders and quantitative analysis of health facility data. The study focused on maternal and child health indicators, service utilization patterns, and health worker motivation following RBF implementation. RBF implementation was associated with improvements in several KPIs: maternal health outcomes (100% of facilities reported improvements), medicine availability (91%), and the quality of primary healthcare services (67%). Health worker motivation increased, with 42% agreeing and 24% strongly agreeing that RBF positively affected service delivery. Community-based volunteers responded positively to incentive structures. However, challenges included delayed fund disbursement (91.7% reported), inadequate funding (83.3%), and monitoring gaps (50%). RBF can enhance key health indicators in devolved systems when properly designed and implemented. Success depends on timely incentive disbursement, robust monitoring systems, and integration with existing community health structures. The study provides evidence for policymakers considering RBF scale-up in decentralized health systems.
Open access
Global Maternal and Child Health
Health Systems, Economic Evaluations, Quality of Life
Zambia has implemented significant health-sector decentralization since 1992, culminating in the devolution of district health services to local authorities under the 2016 Constitutional Amendment. Results-Based Financing (RBF) has been piloted as a performance incentive mechanism, but its institutionalization within devolved structures remains largely unexplored. This study explores the opportunities and challenges of embedding RBF within Zambia's devolved health system, with a focus on Northern Province. A qualitative case study design was used, involving forty-four participants from twelve districts. Purposive sampling selected health workers from provincial and district health offices, local authority representatives, and national stakeholders. Data collection included semi-structured interviews, document reviews, and observations, with thematic analysis conducted using NVivo 9. Most respondents (82%) reported involvement in RBF implementation, and fifty-three% believed that increased Constituency Development Fund (CDF) allocations improved district health services. Key benefits cited were increased accountability (81.8%) and greater community participation (77.3%). Challenges included insufficient funding (83.3%), delays in disbursing funds (91.7%), and limited understanding among local authority implementers. Infrastructure development and procurement of medical equipment were identified as primary areas for CDF improvements (56%). Respondents also agreed (53%) that the Ministry of Health and the Ministry of Local Government would support the institutionalization of RBF. Effective integration of RBF into devolved systems requires harmonizing policies between health and local government ministries, building capacity within local authorities, and aligning RBF with other domestic financing mechanisms, such as CDFs. A phased approach to integration, with clearly defined governance structures, is recommended to ensure sustainable scaling.
Open access
Global Maternal and Child Health
Primary Care and Health Outcomes
Health Systems, Economic Evaluations, Quality of Life
The development of blockchain technology has given rise to smart contracts as a self-executing, decentralized, code-based contractual mechanism. Their presence poses conceptual and normative challenges in the Indonesian contract law system, particularly regarding the legal standing and construction of the parties' responsibilities amidst a normative vacuum. This study aims to analyze the legal status of smart contracts from the perspective of Indonesian civil law and to formulate a relevant legal liability model for blockchain-based transactions. The research method used is normative legal research with statutory, conceptual, and analytical approaches. Primary legal materials include the Civil Code, the Electronic Transactions and Transactions Law, and regulations related to electronic transactions, while secondary legal materials include doctrine, liability theory, and literature on blockchain. The results show that smart contracts can be qualified as agreements as long as they meet the valid requirements of an agreement as stipulated in Article 1320 of the Civil Code. However, their immutable and automated nature creates tensions with the principles of freedom of contract and good faith. The identification of legal subjects in the blockchain ecosystem includes users, developers, validators, and platforms, with liability models that can be based on fault liability or the possibility of strict liability under certain conditions. The absence of norms has the potential to give rise to disparities in interpretation and legal uncertainty, so that a normative reconstruction is needed that is adaptive to the character of decentralization to ensure legal certainty, justice, and benefits.
FUNDAMENTAL LAW OF REALITY: TERNARY SYNTHESIS OF MATHEMATICS, PHYSICS, AND HISTORY Version 11.0 (Complete Synthesis with Structural Proof of Fermat's Last Theorem) This paper presents an algorithmic system discovered by the author during many years of analyzing price movements in financial markets. Four software modules written in MQL4 revealed a universal ternary hierarchical structure possessing Zâ-symmetry. From the code analysis, the fundamental group Zâ Ă Zâ, generating 9 basic relations, and the formula for the number of intersection points in the hierarchy, P = N â 2K, were derived. The discovered structure has proven to be universal across various fields of knowledge: Mathematics: Zâ Ă Zâ is isomorphic to a subgroup of SU(3) and the nilpotent ring â[x,y]/(xÂł, yÂł); the system's fractal dimension is D = log 3 / log 2 â 1.585. Number Theory: The synchronization parameter Ď = 0 at the non-trivial zeros of the Riemann zeta function is equivalent to the Riemann Hypothesis, numerically confirmed on 4153 zeros (100% match). Physics: Zâ Ă Zâ â SU(3) describes the color symmetry of Quantum Chromodynamics; the 9 compactification moduli of string theory correspond to the 9 system relations; the Ď = 0 state is interpreted as a transition to 11-dimensional M-Theory. History: Using an inverse problem method on 251 key dates, the reference points Tâ = â5502, Tâ = â5501, Tâ = â5500 were determined. The formula D = Tâ + 3k + s describes all key historical events. Four epochal points (â5502, â3315, â1128, 1059) mark shifts in civilizational cycles. Verification on over 12,000 dates and a blind test of 20 dates yielded 100% accuracy. Markets: On BRENT oil data (1998â2026), 4 convergence points (2005, 2011, 2018, 2025) were found with an 81-month interval, corresponding to the historical epochal points. Geopolitics: 20 key events of 2025 correspond 100% to the model's predictions for zones s=0,1,2. Fermat's Last Theorem: A structural explanation is derived through the formula P = N - 2K: for n > 2, the hierarchy depth K ⼠2 leads to a critical shortage of intersection points for synchronizing three independent circuits (x, y, z). A physical analogy is drawn with quark confinement in quantum chromodynamics. The cumulative statistical significance of all confirmations is p < 10âťâšÂłâľ, which excludes random coincidence. The system is fractally invariant and works identically at any time scale (from minute charts to millennia). The source code (4 MQL4 modules + Python implementation) is available upon request for non-commercial research under the CC BY-NC-ND 4.0 license. Keywords: ternary hierarchy, Zâ Ă Zâ, intersection points, Riemann Hypothesis, Fermat's Last Theorem, SU(3), string theory, M-theory, historical periodization, fractals, algorithmic realism, power law distribution, confinement.