The evolution of the global digital financial system is generating two main forms of digital currencies: a centralized currency system, such as Central Bank Digital Currency (CBDC), and a decentralized cryptocurrency system, like Bitcoin and Ether. This study aims to analyze the conceptual differences between the centralized (CBDC) and decentralized (Bitcoin and Ether) models and each operating mechanism. The study also examines how both models impact the stability of the economy and adherence to Shariah principles. Using the qualitative approach and exploratory design, the study examines materials on CBDC, Bitcoin, and Ether. The study collects data from central bank reports, monetary policy documents, academic articles, and technical papers published by relevant institutions. The content analysis method should identify similarities and differences between the currencies in terms of system architecture, infrastructure, technological efficiency, energy, governance and compatibility with Shariah principles. According to the study, CBDC, Bitcoin and Ether represent three distinct paradigms: Bitcoin's decentralized system, through proof-of-work, produces rather limited functionality to emphasise individual freedom and privacy, while Ether innovates the system via a switch to proof-of-stake and smart contracts, which leads to greater functionality. CBDC, on the other hand, maintains a centralized system to ensure monetary stability, but with a compromise on users' privacy. Hence, while maintaining the value of blockchain transparency and traceability without sacrificing economic stability, the study proposes a hybrid approach in order to improve transaction efficiency. The study suggests implementing a regulatory sandbox involving authorities, economists and Shariah experts as an initial test measure of this innovation to ensure security for users and compliance with the principles of Shariah in the development of a healthier digital financial ecosystem.
We present Areon, a family of latency-friendly, stake-weighted, multi-proposer proof-of-stake consensus protocols. By allowing multiple proposers per slot and organizing blocks into a directed acyclic graph (DAG), Areon achieves robustness under partial synchrony. Blocks reference each other within a sliding window, forming maximal antichains that represent parallel ``votes'' on history. Conflicting subDAGs are resolved by a closest common ancestor (CCA)-local, window-filtered fork choice that compares the weight of each subDAG -- the number of recent short references -- and prefers the heavier one. Combined with a structural invariant we call Tip-Boundedness (TB), this yields a bounded-width frontier and allows honest work to aggregate quickly. We formalize an idealized protocol (Areon-Ideal) that abstracts away network delay and reference bounds, and a practical protocol (Areon-Base) that adds VRF-based eligibility, bounded short and long references, and application-level validity and conflict checks at the block level. On top of DAG analogues of the classical common-prefix, chain-growth, and chain-quality properties, we prove a backbone-style $(k,\varepsilon)$-finality theorem that calibrates confirmation depth as a function of the window length and target tail probability. We focus on consensus at the level of blocks; extending the framework to richer transaction selection, sampling, and redundancy policies is left to future work. Finally, we build a discrete-event simulator and compare Areon-Base against a chain-based baseline (Ouroboros Praos) under matched block-arrival rates. Across a wide range of adversarial stakes and network delays, Areon-Base achieves bounded-latency finality with consistently lower reorganization frequency and depth.
BACKGROUND: Kenya’s public tertiary healthcare is facing persistent quality of healthcare challenges characterized by acute shortage of healthcare workers, frequent industrial unrest, broken-down healthcare facilities, and erratic supply of essential commodities. To address these systemic challenges the government introduced the asset lease financing (ALF) mechanism aimed at strengthen tertiary hospitals through modern medical equipment and technologies. However, the effect of ALF on quality remains highly debated and controversial. This study examined the effect and constraints of ALF in improving quality of healthcare within Kenya’s tertiary hospitals. METHODS: A convergent parallel mixed-methods design was employed with quantitative data collected from 145 hospital managers, staff and patients. Descriptive statistics were used to summarize participants characteristics and indicators of study variables. Ordinary least square regression was then used to estimate the effect of ALF on quality of tertiary healthcare, controlling for existing traditional funding. Complementary qualitative insights were gathered from 26 policymakers, hospital managers, and health financing experts through semi-structured interviews and analyzed using thematic analysis to identify patterns in strengths and constraints. Integration of findings happened through triangulation to enhance interpretation and understanding. RESULTS: Analysis showed that asset lease financing had a significant positive effect on quality of tertiary healthcare (β = 0.587, p < 0.01), explaining 26% of the variance. When traditional funding was controlled, ALF remained significant (β = 0.495, p < 0.01), with the model explaining 33% of the variance. Respondents attributed this to improved access to advanced diagnostic and therapeutic equipment, as well as expanded service capacity. However, descriptive summaries and qualitative perspectives revealed several constraints limiting ALF optimal effect in improving tertiary healthcare quality in Kenya. Stakeholders noted high recurrent costs, under-utilized assets, weak contract negotiation, and top-down procurement processes that limited hospital autonomy and contribution. Operational gaps, including inadequate training and delayed maintenance, further constrained ALF effect on quality. CONCLUSIONS: ALF has the potential to enhance quality of healthcare and technological capacity in Kenya’s tertiary hospitals, but its effects are contingent on robust governance, effective contract design, and alignment with institutional capacity which seem lacking in the Kenyan context. Without these safeguards, current leasing arrangements risk becoming fiscally unsustainable with little quality enhancement. Policymakers should strengthen transparency, decentralize decision-making, and incorporate performance-based provisions into leasing contracts to maximize ALF effect in enhancing quality of care.
We present ZK IR, a novel 32-bit instruction set architecture (ISA) specifically designed for efficient zero-knowledge proof generation using STARK protocols. Unlike existing zkVMs that adapt general-purpose ISAs like RISC-V, ZK IR is designed from first principles to minimize proving overhead while maintaining compatibility with modern compiler toolchains. Our key contribution is a rigorous analysis demonstrating that a pure 32-bit register architecture with software-based multi-precision arithmetic outperforms designs with wider registers or specialized field arithmetic units. We achieve approximately 2× reduction in constraint count compared to naive approaches. ZK IR uses the Baby Bear field (31-bit prime) with Plonky3 for proving, and provides an LLVM-based compiler infrastructure enabling developers to write ZK applications in Rust, C, and C++.
This research examines how emerging forms of digital sovereignty, decentralized infrastructures, and anticipatory AI governance are reshaping nationhood in the algorithmic age. Drawing on the conceptual framework of Algorithmic Nations (Calzada 2018) and incorporating new empirical insights from embedded action research (2022–2025), the study analyses the Basque Country as a paradigmatic case of a “small stateless nation” navigating the global reconfiguration of power between states, corporations, and communities. The presentation synthesizes three competing post-Westphalian paradigms—Network States (Srinivasan 2022), Network Sovereignties (De Filippi 2024), and Algorithmic Nations (Calzada 2018)—as shown in the comparative table on page 19, highlighting their differing assumptions regarding governance, identity, participation, and technological control. Building on the diagnostic indicators of Europe’s digital dependence (page 10) and the transition from Gaia-X to EuroStack (page 11), the study evaluates the strategic implications of digital public infrastructures, data cooperatives, federated architectures, and Web3 ecosystems for stateless nations. Through comparative analysis of the Global North (e.g., Scotland, Quebec, Flanders), the Global South (e.g., Kurdistan, Sámi, Tamil, Amazigh), and the Basque Country (pages 16–17), the work demonstrates how communities with diverse geopolitical constraints can articulate forms of AI sovereignty grounded in rights-based, culturally rooted, and community-driven governance. The Basque case illustrates how fragmented digital systems (.eus, EJIE/Izenpe, Osakidetza, MUBIL, etc.) can evolve toward an interoperable, multi-scalar technopolitical architecture, aligning linguistic, territorial, and infrastructural dimensions. The analysis argues that AI-driven infrastructures, data governance, and decentralized architectures are not merely technical layers but emerging geopolitical terrains where stateless, indigenous, diasporic, and minority nations can renegotiate autonomy. The concept of Algorithmic Nations provides a framework for understanding how community sovereignty can be built through data commons, federated systems, and anticipatory governance, particularly in multilingual and culturally distinct territories such as the Basque Country. Overall, the study contributes to debates on global digital governance, digital sovereignty, and the future of nationhood by proposing that algorithmic infrastructures are becoming central to political organization. It calls for democratic, inclusive, and community-oriented models of AI governance capable of avoiding techno-authoritarianism, Big Tech dependency, and “sovereignty washing,” while enabling emancipatory, culturally anchored, and future-oriented forms of collective self-determination.
Behavioral finance has emerged as a critical framework for understanding market dynamics beyond traditional rational agent models. This research presents a comprehensive multimodal approach to behavioral finance analysis, integrating market data, macroeconomic indicators, news sentiment, cryptocurrency metrics, Web3 analytics, GitHub development activity, and social sentiment to test five advanced hypotheses regarding behavioral pattern identification and market anomaly detection. The study employs an ultra-comprehensive data pipeline processing 30,400 samples across seven distinct data sources, generating 91 engineered features representing behavioral biases, investment patterns, and market psychology. Advanced machine learning techniques including Principal Component Analysis, t-Distributed Stochastic Neighbor Embedding, Variational Autoencoders, K-Means, Hierarchical Clustering, DBSCAN, Isolation Forest, One-Class SVM, and Elliptic Envelope are applied to identify behavioral structures and detect anomalies. Statistical validation through chi-square tests, ANOVA, Granger causality analysis, and lagged correlation studies demonstrates that three of five hypotheses (60%) achieve statistical significance at p < 0.05. Key findings reveal that behavioral structures exist and correspond to canonical biases (chi-square = 3406.780, p < 0.001), cluster assignments maintain moderate stability across market regimes (Jaccard similarity = 0.300), and sentiment and macroeconomic factors exhibit 65 significant causal relationships with behavioral patterns. However, multimodal data integration does not uniformly improve clustering quality (Silhouette score decrease of 0.116), and cluster-conditioned anomaly detection fails to outperform global methods (F1-score decrease of 0.017). These findings contribute to behavioral finance theory while providing practical applications for investment management, fraud detection, and regulatory compliance.
Jiaxi Liu, Lin Sun, Tianyu Kang, Di Wu · 7 authors
Federated Learning (FL) enables model training on distributed devices while preserving data privacy. However, malicious clients can submit fabricated model updates to fraudulently obtain training rewards, a behavior known as free-rider attacks. Existing detection-based solutions analyze anomalies in model updates but lack direct evidence of local training, making it fail to fully prevent free-riders. To address this limitation, we propose zkVFL, a verifiable FL framework leveraging Zero-Knowledge Proofs (ZKP) to ensure the integrity of local training while preserving privacy. To reduce the computational overhead of proof generation in ZKP, zkVFL introduces two novel techniques: (i) anomaly-aware client sampling to selectively perform ZKP verification and (ii) A recursive ZKP protocol (ReMPoT), incorporating a pruning-based layer selection technique, reduces proof generation costs. Experimental results demonstrate that zkVFL improves the accuracy and convergence of FL training under free-rider attacks while significantly reducing the computational and memory overhead of proof generation on resource-constrained devices.
Permissionless blockchains have evolved beyond cryptocurrency into foundations for Web3 applications, decentralized finance (DeFi), and digital asset ownership, yet this rapid expansion has intensified privacy vulnerabilities. This study provides a comprehensive review of recent trends, emerging privacy threats, and mitigation strategies in permissionless blockchain ecosystems. We examine six developments reshaping the landscape: meme coin proliferation on high-throughput networks, real-world asset tokenization linking on-chain activity to regulated identities, perpetual derivatives exposing trading strategies, institutional adoption concentrating holdings under regulatory oversight, prediction markets creating permanent records of beliefs, and blockchain–AI integration enabling both privacy-preserving analytics and advanced deanonymization. Through this work and forensic analysis of documented incidents, we analyze seven critical privacy threats grounded in verifiable 2024–2025 transaction data: dust attacks, private key management failures, transaction linking, remote procedure call exposure, maximal extractable value extraction, signature hijacking, and smart contract vulnerabilities. Blockchain exploits reached $2.36 billion in 2024 and $2.47 billion in the first half of 2025, with over 80% attributed to compromised private keys and signature vulnerabilities. We evaluate privacy-enhancing technologies, including zero-knowledge proofs, ring signatures, and stealth addresses, identifying the gap between academic proposals and production deployment. We further propose a Secure Development Lifecycle framework incorporating measurable security controls validated against incident data. This work bridges the disconnect between privacy research and industrial practice by synthesizing current trends, providing insights, documenting real-world threats with forensic evidence, and providing actionable insights for both researchers advancing privacy-preserving techniques and developers building secure blockchain applications.
Denis Wapukha Walumbe, Gabriel Kamau, Jane Wanjiru Njuki
With the rising integration of blockchain in critical domains such as healthcare, designing efficient, lightweight, and privacy-preserving consensus mechanisms remain a significant challenge.Existing Proof-of-Stake (PoS) implementations often incur high computational and communication overhead, making them unsuitable for telemedicine systems.This study proposed LightweightPoS, a novel voting mechanism designed for this environment.The proposed mechanism incorporates a cluster-based voting to minimize message complexity, Byzantine Agreement protocol for robust fault tolerance and cryptographic sortition to ensure fairness and privacy.This implementation slashes global communication, reducing message complexity by over 95% compared to traditional PoS models.The study evaluated the proposed and baseline mechanisms through simulations using real-time telemedicine data sensors.The results demonstrated that the proposed mechanism consistently achieved sub-10ms latency, high transaction throughput (up to 2400 TPS) and low energy consumption (~0.002kWh per round).It significantly outperformed baseline mechanism like Algorand and Ouroboros.Furthermore, the system included an effective Byzantine node detection, ensuring reliability under adversarial conditions.This work contributes a practical consensus voting mechanism that balances privacy and regulatory compliance.It provides a robust foundation for deploying blockchain technology in privacy-sensitive telemedicine applications.
Giovanni Maria Cristiano, Salvatore D'Antonio, Jonah Giglio, Giovanni Mazzeo · 5 authors
The growing scalability demand of public Blockchains led to the rise of Layer-2 solutions, such as Rollups. Rollups improve transaction throughput by processing operations off-chain and posting the results on-chain. A critical component in Rollups is the Sequencer, responsible for receiving, ordering and batching transactions before they are submitted to the Layer-1 blockchain. While essential, the centralized nature of the Sequencer makes it vulnerable to attacks, such as censorship, transaction manipulation and tampering. To enhance its security, there are solutions in the literature that shield the Sequencer inside a Trusted Execution Environment (TEE). However, the attestation of TEEs introduces additional centralization, which is in contrast with the core Blockchain principle. In this paper, we propose a TEE-secured Sequencer equipped with a decentralized attestation mechanism. We outline the design and implementation of our solution, covering the system architecture, TEE integration, and the decentralization of the attestation process. Additionally, we present an experimental evaluation conducted on a realistic Rollup testnet. Our results show that this approach strengthens Sequencer integrity without sacrificing compatibility or deployability in existing Layer-2 architectures.
Wenbin Wu, Kejiang Qian, Alexis Lui, Christopher Jack · 8 authors
We curate the DeXposure dataset, the first large-scale dataset for inter-protocol credit exposure in decentralized financial networks, covering global markets of 43.7 million entries across 4.3 thousand protocols, 602 blockchains, and 24.3 thousand tokens, from 2020 to 2025. A new measure, value-linked credit exposure between protocols, is defined as the inferred financial dependency relationships derived from changes in Total Value Locked (TVL). We develop a token-to-protocol model using DefiLlama metadata to infer inter-protocol credit exposure from the token's stock dynamics, as reported by the protocols. Based on the curated dataset, we develop three benchmarks for machine learning research with financial applications: (1) graph clustering for global network measurement, tracking the structural evolution of credit exposure networks, (2) vector autoregression for sector-level credit exposure dynamics during major shocks (Terra and FTX), and (3) temporal graph neural networks for dynamic link prediction on temporal graphs. From the analysis, we observe (1) a rapid growth of network volume, (2) a trend of concentration to key protocols, (3) a decline of network density (the ratio of actual connections to possible connections), and (4) distinct shock propagation across sectors, such as lending platforms, trading exchanges, and asset management protocols. The DeXposure dataset and code have been released publicly. We envision they will help with research and practice in machine learning as well as financial risk monitoring, policy analysis, DeFi market modeling, amongst others. The dataset also contributes to machine learning research by offering benchmarks for graph clustering, vector autoregression, and temporal graph analysis.
P. Bhuvaneshwari, A Krishnaveni, Harold Robinson, E. Golden Julie
The deep learning technique has emerged as an exemplary model for managing the Artificial Intelligence-based Blockchain framework with technological enhancements to guarantee reliable data through the consensus procedure. The deep learning-enabled blockchain transaction model has involved the development of security to solve the problems of confidentiality and data anonymity. The Hybrid techniques of the Blockchain with the Deep Learning technique are proposed to generate enhanced data durability and its propagation through the enhanced convolutional temporal network (EnCTN) for transaction analysis in a blockchain-enabled Auto Encoder technique. The sliding window extraction technique is used to extract information from a particular window size to evaluate the needed input values from the temporal series. The dilated Convolution is used to capture the long-range dependencies. The proposed technique is implemented in the Ethereum environment using Python, and experimental results show that it has produced an improved performance than the relevant technique in several performance parameters. The anomaly classification accuracy is improved than the relevant technique and it is evaluated using the NSL-KDD dataset. The proposed framework delivers an efficient solution for the real-world anomaly detection application while accurate discovery of temporal anomalies and computational efficiency is enhanced.
The rapid expansion of Indonesia’s digital financial ecosystem has significantly advanced financial inclusion and innovation through the growth of fintech platforms, digital payments, and crypto-asset adoption. However, this transformation introduces multifaceted risks, including cyber threats, data breaches, digital fraud, regulatory uncertainty, and money-laundering vulnerabilities associated with crypto-assets and decentralized finance. This study employs a systematic literature review to examine the challenges and innovations in digital financial risk control within Indonesia’s fintech and digital asset sectors. Findings indicate that effective risk mitigation relies heavily on regulatory coordination, advanced supervisory technology, consumer digital literacy, and robust data protection practices. RegTech and SupTech innovations powered by artificial intelligence support real-time risk monitoring and enhance compliance with global standards such as FATF recommendations. Nevertheless, successful digital financial governance also requires algorithmic accountability and ethical technology deployment. This study underscores that safeguarding stability, trust, and consumer protection is essential to achieving a secure and inclusive digital financial system while enabling responsible innovation.
The fundamental limitation of blockchain architecture lies not in cryptographic primitives or consensus mechanisms, but in a conceptual mistake: the bundling of state transitions with asset custody. Every distributed ledger since Bitcoin has conflated these two concerns, creating an artificial ceiling on performance that no amount of clever engineering can overcome. This paper presents Virtual Rollups, a post-blockchain architecture that achieves what was previously thought impossible—sub-millisecond finality with full self-custody—by recognizing that state and escrow need not travel together. We formalize the Virtual Rollup construction, prove its security properties under Byzantine conditions, and demonstrate how its unified liquidity layer solves the multi-chain fragmentation problem that plagues decentralized finance. The result is not merely an incremental improvement but a categorical leap: trading venues can now match centralized exchanges in performance while exceeding them in security.
Adaptive models and mechanisms of project financing, which are becoming critical for ensuring the sustainability of entrepreneurial activity in Ukraine in conditions of unprecedented military uncertainty were explored and analyzed in the article. Particular attention was paid to the need to integrate risk-sharing instruments between the public and private sectors. The study focused on transforming traditional approaches to assessing investments that have proven to be unviable in conditions of systematic military risk and mass destruction of capital assets, and to identify factors that minimize fiscal pressure and facilitate the attraction of private capital to critical recovery sectors. The methodology was based on the analysis of empirical cases (the «5-7-9%» program, grant mechanisms) and their critical comparative analysis using the real options theory (ROT) as a strategic framework for assessing managerial flexibility (relocation, expansion options). Global regulatory requirements (IFRS, RDNA4) and institutional risk transfer mechanisms (MIGA and DFC) were also systematized. The hypothesis of a direct proportional dependence of financing efficiency on the synergy between state compensation for systemic risk and the ability of enterprises to quickly adapt was substantiated. The results confirm that business sustainability was achieved through a two-vector mechanism: centralized risk absorption (MIGA/DFC) provides an «external anchor», and decentralized flexibility mechanisms allow the implementation of managerial options at the enterprise level. Empirical analysis showed the effectiveness of state credit risk subsidy programs and identified key challenges, which allowed formulating recommendations for the transition to mechanisms for subsidizing the cost of insurance premiums. The scientific value of the article lies in the substantiation of an adaptive project financing model that integrates ROT and institutional de-risking, as well as in the systematization of requirements for investors and forecasting possible consequences of modern financing models in Ukraine.
Cryptocurrency exchanges are integral to the digital asset economy; however, their rapid growth has been accompanied by recurrent high-impact cyberattacks that erode trust and inflict substantial losses. Guided by the PRISMA-ScR framework, this review systematically screened peer-reviewed and industry sources to construct a validated dataset of 220 major incidents (2009–2024) across centralized (CEX) and decentralized (DEX) exchanges. We classify attack vectors, analyze repeated high-impact patterns, and identify systemic vulnerabilities spanning cryptographic mechanisms and exchange infrastructure. Across CEX platforms, four of ten identified attack types accounted for 62 of the 80 incidents and approximately $1.764 billion in losses (42.1% of the $4.191 billion CEX total). Across DEX platforms, five of eighteen attack types were responsible for 120 of 140 incidents, totaling $3.755 billion (87.3% of the $4.303 billion DEX total). The overall losses sum to $8.494 billion across 220 incidents (80 CEX; 140 DEX). Repeated vectors comprised 182/220 incidents and $5.519 billion (65.0%) of losses, dominated by wallet/key compromise (78 incidents; $2.394 billion) and DEX system/server/protocol exploits (56 incidents; $1.939 billion); these two classes account for 134/182 repeated incidents (79.1%) and $4.333 billion (78.5%) of repeated losses. We examine the susceptibility of cryptographic defenses to emerging quantum adversaries and assess the exchange readiness for post-quantum threats. This study is the first to systematically compile and quantitatively analyze cybercrime incidents affecting both centralized and decentralized cryptocurrency exchanges in a unified dataset, enabling unprecedented comparability of systemic risks with actionable insights for cybersecurity researchers, regulators, and exchange operators seeking quantum-safe infrastructure evolution.
Abstract This study examines the economic and geopolitical determinants of Indonesia’s defense expenditure from 1984 to 2022 using the Autoregressive Distributed Lag (ARDL) model to capture both short-term and long-term dynamics. Recognizing the contextual relevance of Indonesia’s Total People’s Defense and Security System (SISHANKAMRATA), the analysis relies on conventional military expenditure data (% of GDP) due to the absence of consolidated multi-ministerial records. The results show that in the short run, defense spending is highly sensitive to macroeconomic shocks: inflation, exchange rate volatility, and foreign direct investment exert negative effects, while debt, trade openness, and regional military expenditure strengthen budgetary allocations. In the long run, macroeconomic fundamentals (debt, growth, inflation, and foreign investment) together with neighboring countries’ military spending drive defense expenditure, whereas regional average spending has a negative effect and U.S. military expenditure does not show a structural impact. These findings underscore the dual pressures of fiscal fragility and regional security competition in shaping Indonesia’s defense budget. Policy implications highlight the importance of inflation-adjusted and exchange rate–resilient budgeting, sustainable financing mechanisms such as defense bonds or a Defense Sovereign Wealth Fund (D-SWF), and deeper ASEAN defense cooperation to balance security needs with fiscal discipline. This study contributes a macro-level perspective on defense economics under conditions of institutional fragmentation, offering a framework for future comparative and panel-based research across decentralized security systems.
Ashar Prima, Dewi Gayatri, Yati Afiyanti, Christantie Effendy
Background: Indonesia faces a growing double burden of non-communicable diseases, particularly cancer. The latest data from the Global Cancer Observatory (Globocan) indicates over 408,661 new cases and 242,099 cancer-related deaths in 2022, with a projected 63% increase in the case burden between 2025 and 2040 without strategic intervention. Although a new legal framework through Health Law No. 17 of 2023 and the Minister of Health Decree (KMK) No. HK.01.07/MENKES/2180/2023 has mandated palliative care as an integral component of health services, its implementation still faces significant systemic barriers. Policy and Implications: This policy brief analyzes the disconnection between the policy mandate and on-the-ground reality, identifying critical gaps in accessibility, healthcare workforce capacity particularly among nursesand financing mechanisms through the National Health Insurance (JKN) program. The failure to effectively integrate palliative care not only causes unnecessary suffering for millions of patients but also burdens the health system with inefficient costs and suboptimal end-of-life care, reflected in the high "financial toxicity" experienced by patients. Recommendations: We recommend a four-pillar strategy: (1) Formalize and standardize palliative services within the JKN benefits package with a clear financing model to address regulatory ambiguity; (2) Develop a national competency-based palliative education and training strategy for all health workers, with a focus on empowering nurses in primary care; (3) Implement a decentralized and tiered palliative care delivery model centered on Community Health Centers (Puskesmas) to ensure equitable access; and (4) Launch a national public education campaign to destigmatize palliative care and increase awareness. Conclusion: The integration of palliative care is not merely an option but a strategic and ethical imperative for achieving Universal Health Coverage (UHC) in Indonesia. It is a cost-effective investment to improve patients' quality of life, support families, and ensure the sustainability of the national health system in facing future non-communicable disease challenges.
Open access
Healthcare Systems and Reforms
Palliative Care and End-of-Life Issues
Health Systems, Economic Evaluations, Quality of Life
Yu Gao, Carlo Campajola, Nicolò Vallarano, Andreia Sofia Teixeira · 5 authors
IOTA is a distributed ledger technology that relies on a peer-to-peer (P2P) network for communications. Recently an auto-peering algorithm was proposed to build connections among IOTA peers according to their “Mana" endowment, which is an IOTA internal reputation system. This paper’s goal is to detect potential vulnerabilities and evaluate the resilience of the P2P network generated using IOTA auto-peering algorithm against eclipse attacks. In order to do so, we interpret IOTA’s auto-peering algorithm as a random network formation model and employ different network metrics to identify cost-efficient partitions of the network. As a result, we present a potential strategy that an attacker can use to eclipse a significant part of the network, providing estimates of costs and potential damage caused by the attack. On the side, we provide an analysis of the properties of IOTA auto-peering network ensemble, as an interesting class of homophile random networks in between 1D lattices and regular Poisson graphs.
The rapid growth of fintech start-ups has led to a drastic change in the financial ecosystem in India, but at the same time, they are under scrutiny from various regulatory bodies because of the volume of risk associated with digital finance (specifically financial fraud, data security, and transaction risk) associated with digital finance. While compliance with the various regulations has historically been a lengthy manual process that involved multiple compliance departments and therefore had a high level of inherent error risk, with the introduction of blockchain technology, there is now the potential to develop compliance systems that use automated and tamper-proof processes that allow for an increased amount of transparency, auditability, and operational efficiencies. Thus, the main focus of this research paper is to evaluate how a blockchain-based system for regulatory compliance might impact fintech start-ups. A case study was conducted on CryptoShield Solutions Pvt. Ltd., a Mumbai-based RegTech company specializing in distributed ledger–based compliance platforms. The data was collected during the internship through observation, workflow analysis, discussions with professionals, and review of anonymized compliance records.The findings indicate that fintech firms adopting blockchain compliance tools have observed 35–45% reduction in manual reporting hours, improved accuracy, faster audit completion cycles, and stronger trust among investors and regulators. However, awareness remains shallow due to skill gaps, cost perception, and lack of standardized guidelines. The final recommendations of the study on stronger digital adoption efforts, awareness programs, and capacity-building initiatives will significantly accelerate the pace of blockchain-enabled compliance transformation in India.
Open access
Blockchain Technology Applications and Security
Innovations and Analysis in Business and Education
Democratic institutions increasingly rely on verifiable digital trust to enable fair participation and evidence-based decisions. Truvry is a decentralised protocol that converts behaviour-based evidence (usage patterns, transaction integrity, peer attestations) into portable cryptographic proofs that remain independent of any single platform or identifier, allowing individuals to transfer trust capital across domains while preserving privacy. The current prototype is zero-knowledge–compatible; in this version we use hashed proof anchoring and field-level redaction (no zk-SNARK module is deployed), with configurable smart-contract verifiers. By decoupling trust from identity, Truvry widens citizen inclusion, mitigates gatekeeping bias, and supplies auditable inputs for AI-mediated governance. In prototype tests (n=112), end-to-end proof issuance averaged 3.7 s (fastest local 1.4 s), verifier parse+check averaged 1.8 s, and the current minimum anonymisation entropy is 8.9 bits; gas costs for optional on-chain anchoring remained below US$0.02. All results are based on simulated user streams; a production pilot is planned.
How do the emerging Web 3.0 technologies affect the survival of non-state armed groups (NSAGs) in their violent struggles vis-à-vis state entities? While techno-optimists argue that Web 3.0 can democratize the internet and curb monopolistic practices, its decentralized features, such as enhanced privacy, data ownership, and personalization, also present significant security challenges. These technologies can be weaponized by NSAGs to promote their efficiency and resilience. Borrowing insights from social movement theory, we construct a theoretical framework to explain how Web 3.0 applications affect the dynamics of NSAGs by impacting their organizational modes and strategies. It is argued that blockchain-based platforms, metaverse projects, and other Web 3.0 technologies promote the efficiency of the recruitment, training, financing, purchasing, and communication processes of NSAGs, increasing their capacities as social organizations, and thereby render these groups more resilient to collapse. We illustrate and corroborate our theoretical claims by examining the cases of how NSAGs such as the Islamic State utilize decentralized crypto exchanges and the Dark Web in their operations.
Open access
Terrorism, Counterterrorism, and Political Violence
The article examines the theoretical foundations for selecting algorithms and data structures to ensure secure storage and processing of metadata in IoT systems using the Ethereum blockchain. A classification of metadata types specific to heterogeneous IoT environments is presented, taking into account semantic significance, update frequency, and data criticality. Formal requirements for algorithms are formulated, covering resistance to forgery, computational complexity, scalability under high-intensity request loads, and resource efficiency in terms of gas costs and network throughput. A comparative analysis of data structures employed in the Ethereum infrastructure, including Merkle Tree, Merkle-Patricia Trie (MPT), Multi-State MPT, and GPU-accelerated modifications, is performed according to criteria such as asymptotic complexity, memory efficiency, and suitability for incremental updates. A conceptual model for organizing metadata exchange between IoT nodes and smart contracts is proposed, incorporating modules for encoding, verification, gas cost optimization, and standardized interaction interfaces. The presented results provide a theoretical basis for developing formally verified and energy-efficient solutions in the field of secure Ethereum blockchain integration with the Internet of Things.
The article examines the theoretical foundations for selecting algorithms and data structures to ensure secure storage and processing of metadata in IoT systems using the Ethereum blockchain. A classification of metadata types specific to heterogeneous IoT environments is presented, taking into account semantic significance, update frequency, and data criticality. Formal requirements for algorithms are formulated, covering resistance to forgery, computational complexity, scalability under high-intensity request loads, and resource efficiency in terms of gas costs and network throughput. A comparative analysis of data structures employed in the Ethereum infrastructure, including Merkle Tree, Merkle-Patricia Trie (MPT), Multi-State MPT, and GPU-accelerated modifications, is performed according to criteria such as asymptotic complexity, memory efficiency, and suitability for incremental updates. A conceptual model for organizing metadata exchange between IoT nodes and smart contracts is proposed, incorporating modules for encoding, verification, gas cost optimization, and standardized interaction interfaces. The presented results provide a theoretical basis for developing formally verified and energy-efficient solutions in the field of secure Ethereum blockchain integration with the Internet of Things.