Tržište kriptovaluta jedno je od najmlađih financijskih tržišta, koje se razvija usporedno s napretkom digitalnih tehnologija. Osnovna obilježja ovoga tržišta proizlaze iz njegove nereguliranosti i izražene volatilnosti cijena, pri čemu se posebno ističe značaj malih investitora i pojedinaca kao aktivnih sudionika. Cilj ovoga rada jest istražiti utjecaj različitih bihevioralnih faktora na investicijske odluke i namjere ulaganja u kriptovalute. U teorijskom dijelu prikazana su temeljna obilježja kriptovaluta i financijskih tržišta, kao i ključni koncepti bihevioralne ekonomije. Poseban naglasak stavljen je na kognitivne pristranosti poput pretjeranog samopouzdanja, efekta FOMO, averzije prema gubitku i žaljenju, utjecaja influencera, gamifikacije te zablude kockara. Empirijski dio rada temelji se na anketnom istraživanju u kojem je sudjelovalo 208 ispitanika, a prikupljeni podaci analizirani su pomoću metoda deskriptivne statistike i višestruke regresije. Rezultati pokazuju da su pojedini bihevioralni faktori, prije svega FOMO, gamifikacija i kockarska zabluda, statistički značajni u objašnjavanju investicijske namjere. Takvi nalazi potvrđuju teorijske pretpostavke bihevioralnih financija te pokazuju da emocionalni i kognitivni obrasci značajno oblikuju odluke investitora na tržištu kriptovaluta. Zaključno, istraživanje doprinosi boljem razumijevanju ponašanja ulagača u kontekstu kriptovaluta i ističe potrebu za daljnjim proučavanjem utjecaja psiholoških čimbenika na financijsko odlučivanje. Dobiveni rezultati ujedno pokazuju kako tradicionalna financijska teorija, koja pretpostavlja racionalnost investitora, nije dostatna za objašnjenje investicijskog ponašanja na suvremenim tržištima.
Globalized supply chains are strained by fragmented data, multi-tier opacity, counterfeit risks, and costly disputes. Blockchain—a shared, append-only ledger—has been proposed to enhance transparency, traceability, and operational efficiency, yet real-world adoption reveals both breakthroughs and bottlenecks. This paper develops a deploymentminded view that integrates GS1 EPCIS/CBV standards for interoperable event data, permissioned ledgers for governance, and privacy-preserving proofs (zero-knowledge) to reconcile transparency with business confidentiality. We synthesize evidence from systematic reviews and flagship pilots (e.g., Walmart–IBM Food Trust) and contrast them with lessons from initiatives that wound down (e.g., TradeLens), extracting adoption patterns, KPI impacts, and failure modes. We then describe a reference methodology—data acquisition via EPCIS events, Fabric-based channels, and role-based access—plus an evaluation rubric for trace time, recall precision, dispute cycle time, and data-reconciliation costs. Results from literature-anchored benchmarks indicate orders-of-magnitude traceability lead-time (TLT) reductions (days → seconds) and measurable reductions in manual reconciliation, with gains contingent on standards compliance and high-quality “oracle” data. Finally, we map future directions—zk-proof rollups, interoperable digital product passports, and policy-aligned sustainability metrics—alongside candid limitations around ecosystem incentives, privacy, scalability, and data veracity. We conclude that blockchain can shift chains from reactive to verifiable and auditable networks when combined with data standards, sound governance, and selective privacy technologies rather than “full transparency” alone.
K. Nirmala Devi, Lakshmi Narasimha, N. Sujatha, Y. Geetha · 6 authors
The integration of blockchain technology into financial markets has sparked significant scholarly interest, particularly in the context of stock market prediction. This bibliometric analysis aims to provide a comprehensive overview of research trends, influential publications, and emerging themes within this interdisciplinary domain from 2018 to 2025. Drawing data from Scopus the study utilizes bibliometric tools such as Biblioshiny and VOSviewer to analyse publication outputs, citation patterns, co-authorship networks, and keyword co-occurrence. The findings reveal a consistent growth in academic contributions, especially after 2019, reflecting blockchain’s increasing relevance in financial prediction and its convergence with machine learning, deep learning, and artificial intelligence. Key research clusters identified include algorithmic trading, decentralized finance (DeFi), cryptographic modelling, and predictive analytics. The analysis also highlights leading journals, authors, and institutions contributing to the advancement of this field. However, certain limitations are acknowledged. The focus on selected databases may have excluded valuable contributions from platforms such as IEEE Xplore, SSRN, or non-indexed proceedings. Additionally, the keyword-based search strategy may have overlooked studies using alternative terminologies. The temporal scope may also bias the analysis toward recent developments while underrepresenting foundational research. The study offers a valuable reference point for scholars and practitioners, mapping the intellectual structure and thematic progression of blockchain-based stock prediction research. Future studies are encouraged to adopt multi-database approaches, combine quantitative and qualitative methods, and explore regulatory and regional variations to enrich understanding and guide practical implementation.
Integration of Federated Learning (FL) with Blockchain technology to decentralized privacy-preserving, and scalable framework for strengthening cybersecurity. As cyber threats like ransomware, malware, and network intrusions grow in complexity, there is an increasing need for collaborative threat detection and mitigation. However, traditional collaborative approaches often involve sharing sensitive information across organizations, raising significant privacy concerns and regulatory challenges under frameworks like GDPR and HIPAA. FL works to solve these problems through enabling multiple entities to work together on training machine learning models without sharing their original information. Despite its advantages, FL faces challenges such as the risk of model tampering, trust deficits between participants, and dependence on a centralized server for model aggregation. To overcome these limitations the Blockchain technologies will be in used so blockchain technology provides a distributed, transparent, and non-mutable ledger that safely manages FL operations. It helps preserve the accuracy and trustworthiness of model updates via smart contracts along with consensus mechanisms, bypassing the requirement fora central aggregator. In addition, blockchain enables incentivization by introducing token-based rewards, encouraging active participation in collaborative threat detection networks. Privacy- preserving techniques to boost information security, techniques like differential privacy and homomorphic encryption are also put into practice. Such a integration of FL and blockchain is particularly impactful in securing distributed systems such as IoT devices, critical infrastructure, and enterprise networks, where privacy, trust, and scalability are crucial. This project aims to demonstrate the practical implementation of this framework, paving the way for adaptive and globally scalable cyber security systems to combat evolving threats.
The rapid advancement of artificial intelligence (AI) and large language models (LLMs) is profoundly reshaping higher education, shifting from institution-centered paradigms to learner-centric personalized learning environments (PLEs). However, PLEs face critical challenges in identity management, including data breaches, unauthorized access, and interoperability barriers, which undermine security and trust. This study proposes the Blockchain-based Student Identity Management System (BSIMS), a conceptual model integrating blockchain technology, xAPI standards, and OAuth2 protocols to uphold confidentiality, integrity, availability, authenticity, and non-repudiation (CIAAN) principles. Grounded in the Technology Acceptance Model (TAM) and Information Systems Success Model (ISSM), BSIMS was validated through mixed-methods research involving 90 students and 16 experts from five Yunnan Province universities. Results demonstrate BSIMS' superiority in user satisfaction (explaining 81.7% variance), CIAAN performance (M=4.58 vs. 3.18 for traditional systems, p<0.001), and reliability (zero downtime, 0.3 ms response time). Ethical and legal implications, such as immutability conflicts with privacy rights, are addressed via zero-knowledge proofs and off-chain storage. BSIMS offers a scalable framework for secure PLEs, advancing educational informatization in Yunnan and beyond.
Abstract By converting between currencies, cryptocurrency exchanges provide access between the traditional and cryptocurrency ecosystem, making them susceptible to money laundering. The European Union extended the scope of the 5 $$^{\text {th}}$$ Anti-Money Laundering Directive (AMLD5) to include cryptocurrency exchanges, requiring them to obtain a registration, conduct customer due diligence, and report unusual transactions. It is, however, unknown whether the measures introduced by the implementation of AMLD5 lead to less risk exposure and what impact it has on cryptocurrency exchanges. This paper uses a mixed-methods approach to explore the effects of the Dutch implementation of AMLD5 measures on cryptocurrency exchanges active in the Netherlands. We analyzed over 335,000 transactions and complemented them with seven qualitative interviews with Dutch cryptocurrency exchanges and the supervisory authority. We find that the Dutch implementation of AMLD5 imposed high administrative burdens and substantial fees on relatively small exchanges that do not pose high money laundering risks. This raises questions about the alignment of the goals and consequences of the regulation.
Wan Amir Azlan Wan Haniff, Redwan Yasin, Rahmawati Mohd Yusoff, Asma Hakimah Ab Halim · 6 authors
The article investigates the challenges and prospects of the ruling of Waqf Crowdfunding (Waqf-CF) scheme adoption in Malaysia as Shariah-compliant fintech successors deployed to mobilize Islamic endowment. However, the implementation of Waqf-CF is hindered by a number of challenges, such as the uncertainty of the legal aspects and fragmented governance, along with technology limitations and Shariah compliance issues. Using a qualitative approach, insights were gathered from seven experts 7 experts in finance, academia, and business to inform and guide our work. The results suggest that poor coordination of regulation between federal and state governments, varied modes of governance, and a lack of fintech literacy in waqf bodies are the barriers to successful implementation. In this regard, the paper examines the Waqf-CF models currently being used, including the Crowdfunding-Waqf Model and the Hasanah Platform, by highlighting the pros and cons of each. Based on these, the authors present a sophisticated hybrid model combining blockchain-based smart contracts, AI-led risk profiling, and real-time Shariah auditing for increased trust, transparency, and scalability. Finally, the paper calls for the need of a national regulatory framework and better institutional support to drive Waqf Crowdfunding as an ethical and sustainable funding option that is in line with Maqasid al-Shariah and the nation’s vision to be a global Islamic financial hub.
Christoph Hochrainer, Valentin Wüstholz, Maria Christakis
Zero-knowledge virtual machines (zkVMs) are increasingly deployed in decentralized applications and blockchain rollups since they enable verifiable off-chain computation. These VMs execute general-purpose programs, frequently written in Rust, and produce succinct cryptographic proofs. However, zkVMs are complex, and bugs in their constraint systems or execution logic can cause critical soundness (accepting invalid executions) or completeness (rejecting valid ones) issues. We present Arguzz, the first automated tool for testing zkVMs for soundness and completeness bugs. To detect such bugs, Arguzz combines a novel variant of metamorphic testing with fault injection. In particular, it generates semantically equivalent program pairs, merges them into a single Rust program with a known output, and runs it inside a zkVM. By injecting faults into the VM, Arguzz mimics malicious or buggy provers to uncover overly weak constraints. We used Arguzz to test six real-world zkVMs (RISC Zero, Nexus, Jolt, SP1, OpenVM, and Pico) and found eleven bugs in three of them. One RISC Zero bug resulted in a $50,000 bounty, despite prior audits, demonstrating the critical need for systematic testing of zkVMs.
ABSTRACT Despite the universal acknowledgment of financial profit expectations as an investment driver, environmental concern has been suggested as a factor influencing investors' decisions to purchase cryptocurrency. In this sense, this study investigates the impact of environmental information on investment allocation decisions to purchase different types of cryptocurrencies with different levels of environmental impacts (i.e., cryptocurrencies using Proof‐of‐Work (Bitcoin) and Proof‐of‐Stake (Ether) consensus algorithms). This study used an online survey involving 199 respondents in experimental groups (receiving environmental information before allocating decision) and control groups (receiving no environmental information before allocating decision) to split an imaginary fund into Bitcoin and Ether. No significant difference in allocating capital was found between the groups regardless of investment horizon, time of affiliation as a cryptocurrency investor, education level of the respondents, and perceived importance of environmental impacts. Possible explanations for this insensitivity are widespread prior knowledge about the environmental impact of Bitcoin, psychological reactance towards environmental information, and the assessed overall low perceived importance of environmental impact for investment decisions in cryptocurrencies. The lack of significant impact found in such an experimental study implies that environmental education alone cannot be sufficient to shift investor preferences. The findings offer initial insights into the impact of environmental awareness on cryptocurrency investment motivations and provide empirical evidence to understand cryptocurrency investment behaviors in the current research scene. The results suggest researchers and policymakers investigate further investors' motives while coming up with more restrictive policy instruments to mitigate the negative environmental impact of cryptocurrencies.
The smart grid is the next evolution of electrical power systems, a continuation of the old grids that involves a mix of digital and traditional power grid technologies to allow the potential to communicate in both directions, decentralized energy production and real-time monitoring. However, such a connection exposes it to cyber attacks, data fraud, and unauthorized access as well. Blockchain technology is one of these technologies because it is transparent, immutable, and decentralized to overcome these security obstacles. In this paper, an overview of blockchain technology smart grid security, architecture, consensus algorithm, and application are presented. Some of the most notable blockchain works in the smart grid include secure energy trading, decentralised identity management, detecting attacks and preserving privacy. When applied in smart grids, reviewed blockchain protocols also comprise Proof of Work (PoW) and Proof of Stake (PoS) along with Practical Byzantine Fault Tolerance (PBFT). Top of that, there are hybrid types of blockchain such as artificial intelligence (AI) and the Internet of things (IoT) that are also covered as the next picture to enable the system to become more scalable and interoperable. Power consumption, time wastage, and regulation hurdle is greatly considered. This paper has concluded that blockchain is a bottom-up technology, which can cause smart grid infrastructures to be much more resilient, transparent, and efficient.
Abstract This paper presents a systematic literature review of 137 peer-reviewed publications from 41 journals, examining the interconnectedness between cryptocurrencies and traditional financial markets. Using a rigorous three-stage methodology for study selection, we identify key research themes including spillover effects, volatility transmission, interdependence, hedge effectiveness, and safe-haven properties of cryptocurrencies. Our analysis reveals that GARCH-based models dominate early work on volatility and contagion, while more recent studies adopt advanced approaches, such as cross-quantilogram, wavelet coherence, and multifractal detrended cross-correlation, to capture non-linear, time-varying relationships without assuming stationarity. Our review offers three major contributions. First, we provide a comprehensive classification of the interconnectedness between different types of cryptocurrencies and financial markets, highlighting their evolving roles as hedges, safe havens, or diversifiers. Second, we synthesize empirical findings to show how spillovers, time-varying correlations, tail dependencies, and contagion risks intensify under major events, such as COVID-19, regulatory shifts, and geopolitical conflicts. Third, we draw attention to overlooked areas, including emerging market dynamics and macroeconomic determinants. We recommend that policymakers implement early warning systems and proactively monitor volatility and connectedness in crypto markets to reduce contagion risks and maintain financial stability. Policy frameworks should consider the unique features of crypto markets and the time-varying interlinkages between cryptos, commodities, fiat currencies, and equities. Investors, in turn, should track cryptocurrency price movements closely, as they provide valuable signals for forecasting broader market trends and improving portfolio risk management. These insights have practical implications for risk mitigation and decision-making in increasingly integrated financial systems.
Mohammed Ziaul Haider, Tayyaba Noreen, Mishah Uzziél Salman, Marcos Dias de Assunção · 5 authors
Cross-chain bridges and oracle DAOs represent some of the most vulnerable components of decentralized systems, with more than 2.8 billion lost due to trust failures, opaque validation behavior, and weak incentives. Current oracle designs are based on multisigs, optimistic assumptions, or centralized aggregation, exposing them to attacks and delays. Moreover, predictable committee selection enables manipulation, which threatens data integrity across chains. We propose V-ZOR, a verifiable oracle relay that integrates zero-knowledge proofs, quantum-grade randomness, and cross-chain restaking to mitigate these risks. Each oracle packet includes a Halo 2 proof verifying that the reported data was correctly aggregated using a deterministic median. To prevent committee manipulation, VZOR reseeds its VRF using auditable quantum entropy, ensuring unpredictable and secure selection of reporters. Reporters stake once on a shared restaking hub; any connected chain can submit a fraud proof to trigger slashing, removing the need for multisigs or optimistic assumptions. A prototype in Sepolia and Scroll achieves sub-300k gas verification, one-block latency, and a $\mathbf{1 0} \times$ increase in collusion cost. V-ZOR demonstrates that combining ZK attestation with quantum-randomized restaking enables a trust-minimized, high-performance oracle layer for cross-chain DeFi.
In disaster scenarios where conventional energy infrastructure is compromised, secure and traceable energy trading between solar-powered households and mobile charging units becomes a necessity. To ensure the integrity of such transactions over a blockchain network, robust and unpredictable nonce generation is vital. This study proposes an SDN-enabled architecture where machine learning regressors are leveraged not for their accuracy, but for their potential to generate randomized values suitable as nonce candidates. Therefore, it is newly called Proof of AutoML. Here, SDN allows flexible control over data flows and energy routing policies even in fragmented or degraded networks, ensuring adaptive response during emergencies. Using a 9000-sample dataset, we evaluate five AutoML-selected regression models - Gradient Boosting, LightGBM, Random Forest, Extra Trees, and K-Nearest Neighbors - not by their prediction accuracy, but by their ability to produce diverse and non-deterministic outputs across shuffled data inputs. Randomness analysis reveals that Random Forest and Extra Trees regressors exhibit complete dependency on randomness, whereas Gradient Boosting, K-Nearest Neighbors and LightGBM show strong but slightly lower randomness scores (97.6%, 98.8% and 99.9%, respectively). These findings highlight that certain machine learning models, particularly tree-based ensembles, may serve as effective and lightweight nonce generators within blockchain-secured, SDN-based energy trading infrastructures resilient to disaster conditions.
Ye Tian, Yifan Jia, Yanbin Wang, Jianguo Sun · 7 authors
The success of smart contracts has made them a target for attacks, but their closed-source nature often forces vulnerability detection to work on bytecode, which is inherently more challenging than source-code-based analysis. While recent studies try to align source and bytecode embeddings during training to transfer knowledge, current methods rely on graph-level alignment that obscures fine-grained structural and semantic correlations between the two modalities. Moreover, the absence of precise vulnerability patterns and granular annotations in bytecode leads to depriving the model of crucial supervisory signals for learning discriminant features. We propose ExDoS to transfer rich semantic knowledge from source code to bytecode, effectively supplementing the source code prior in practical settings. Specifically, we construct semantic graphs from source code and control-flow graphs from bytecode. To address obscured local signals in graph-level contract embeddings, we propose a Dual-Attention Graph Network introducing a novel node attention aggregation module to enhance local pattern capture in graph embeddings. Furthermore, by summarizing existing source-code vulnerability patterns and designing corresponding bytecode-level patterns for the three target vulnerabilities, we provide an aligned pattern framework that facilitates fine-grained cross-modal alignment and the capture of function-level vulnerability signals. Finally, we propose a dual-focus objective for our cross-modal distillation framework, comprising: a Global Semantic Distillation Loss for transferring graph-level knowledge and a Local Semantic Distillation Loss enabling expert-guided, fine-grained vulnerability-specific distillation. Experiments on real-world contracts demonstrate that our method achieves consistent F1-score improvements (3%--6%) over strong baselines.
Monero, the leading privacy-focused cryptocurrency, relies on a peer-to-peer (P2P) network to propagate transactions and blocks. Growing evidence suggests that non-standard nodes exist in the network, posing as honest nodes but are perhaps intended for monitoring the network and spying on other nodes. However, our understanding of the detection and analysis of anomalous peer behavior remains limited. This paper presents a first comprehensive study of anomalous behavior in Monero's P2P network. To this end, we collected and analyzed over 240 hours of network traffic captured from five distinct vantage points worldwide. We further present a formal framework which allows us to analytically define and classify anomalous patterns in P2P cryptocurrency networks. Our detection methodology, implemented as an offline analysis, provides a foundation for real-time monitoring systems. Our analysis reveals the presence of non-standard peers in the network where approximately 14.74% (13.19%) of (reachable) peers in the network exhibit non-standard behavior. These peers exhibit distinct behavioral patterns that might suggest multiple concurrent attacks, pointing to substantial shortcomings in Monero's privacy guarantees and network decentralization. To support reproducibility and enable network operators to protect themselves, we release our examination pipeline to identify and block suspicious peers based on newly captured network traffic.
Recent advances in Large Language Models (LLMs) have shown remarkable capabilities in financial reasoning and market understanding. Multi-agent LLM frameworks such as TradingAgent and FINMEM augment these models to long-horizon investment tasks by leveraging fundamental and sentiment-based inputs for strategic decision-making. However, these approaches are ill-suited for the high-speed, precision-critical demands of High-Frequency Trading (HFT). HFT typically requires rapid, risk-aware decisions driven by structured, short-horizon signals, such as technical indicators, chart patterns, and trend features. These signals stand in sharp contrast to the long-horizon, text-driven reasoning that characterizes most existing LLM-based systems in finance. To bridge this gap, we introduce QuantHarness, the first multi-agent LLM framework explicitly designed for high-frequency algorithmic trading. The system decomposes trading into four specialized agents--Indicator, Pattern, Trend, and Risk--each equipped with domain-specific tools and structured reasoning capabilities to capture distinct aspects of market dynamics over short temporal windows. Extensive experiments across nine financial instruments, including Bitcoin and Nasdaq futures, demonstrate that QuantHarness consistently outperforms baseline methods, achieving higher predictive accuracy at both 1-hour and 4-hour trading intervals across multiple evaluation metrics. Our findings suggest that coupling structured trading signals with LLM-based reasoning provides a viable path for traceable, real-time decision systems in high-frequency financial markets.
Lei Yu, Jingyuan Zhang, Xin Wang, Jiajia Ma · 6 authors
Smart contracts automate the management of high-value assets, where vulnerabilities can lead to catastrophic financial losses. This challenge is amplified in Large Language Models (LLMs) by two interconnected failures: they operate as unauditable "black boxes" lacking a transparent reasoning process, and consequently, generate code riddled with critical security vulnerabilities. To address both issues, we propose SmartCoder-R1 (based on Qwen2.5-Coder-7B), a novel framework for secure and explainable smart contract generation. It begins with Continual Pre-training (CPT) to specialize the model. We then apply Long Chain-of-Thought Supervised Fine-Tuning (L-CoT SFT) on 7,998 expert-validated reasoning-and-code samples to train the model to emulate human security analysis. Finally, to directly mitigate vulnerabilities, we employ Security-Aware Group Relative Policy Optimization (S-GRPO), a reinforcement learning phase that refines the generation policy by optimizing a weighted reward signal for compilation success, security compliance, and format correctness. Evaluated against 17 baselines on a benchmark of 756 real-world functions, SmartCoder-R1 establishes a new state of the art, achieving top performance across five key metrics: a ComPass of 87.70%, a VulRate of 8.60%, a SafeAval of 80.16%, a FuncRate of 53.84%, and a FullRate of 50.53%. This FullRate marks a 45.79% relative improvement over the strongest baseline, DeepSeek-R1. Crucially, its generated reasoning also excels in human evaluations, achieving high-quality ratings for Functionality (82.7%), Security (85.3%), and Clarity (90.7%).
NIDIA HELENA SANDOVAL MESA, Roberto Albeiro Pava Díaz
The drug supply chain is a critical process that involves multiple stakeholders, including pharmaceutical laboratories, logistics companies, pharmacies and patients. Distributed ledger technology such as blockchain can improve security and transparency in the drug supply chain, providing a decentralized and secure record of all transactions made in the process. Also, it allows defining mechanisms that help mitigate the falsification and manipulation of medicines, facilitating public health surveillance and providing a reliable environment for the acquisition and consumption of medicines. On the other hand, the implementation of blockchain tends to guarantee the security and privacy of the data associated with the actors in the supply chain, but one of its greatest challenges faces the limitations of interoperability, that is, the ability for different Blockchains can communicate with each other and share information. Finally, this article presents a bibliometric review of indexed publications in the period of time between 2018 and 2023, characterizing the line of publications in this area, with indicators associated with authors, publication sources and trends in keywords and topics associated.
Abstract Background Children with subtotally resected pediatric low-grade glioma (pLGG) often face multiple lines of treatment, which are seldom capable of eliminating the entire tumor. Genomics-based biomarkers are often used to select targeted therapies, but this paradigm only yields overall response rates of ∼50% optimally. Functional precision medicine (FPM), where patient-specific therapeutic efficacy is evaluated by directly treating individuals’ tumor outside their body, can predict individualized drug responses for some cancers, but pLGG is notoriously difficult to maintain outside the body, limiting development of FPM for pLGG. Methods We describe what is, to our knowledge, the first platform that can maintain, treat, and analyze zero-passage pLGG tumor tissue ex vivo , facilitating FPM testing. We engraft pLGG tumors onto a previously validated organotypic brain slice culture (OBSC) platform. After ensuring reproducible engraftment and maintenance of living pLGG tumor tissue on OBSCs, we measured MAPK pathway response to targeted therapies via immunoblotting. We then measured tumor ex vivo response to targeted therapies. Results Each zero-passage pLGG tumor tissue specimen exhibited reproducible growth on the OBSC platform. Western blot demonstrated each BRAF KIAA1549 fusion+ tumor exhibited expected paradoxical MAPK upregulation to dabrafenib treatment. Two of three tumors demonstrated cytotoxicity from trametinib as predicted, whereas one tumor did not. No clinical correlates were measured in this proof-of-concept study, though this mixed response to MEK inhibition may be in line with real-world clinical responses. Conclusion The OBSC platform supports ex vivo maintenance of passage-zero pLGG tumor tissue and enables personalized drug screening to yield a new functional biomarker of pLGG drug response.
Sudarsono Sudarsono, Adhi Surya Harahap, John Sihar Manurung
This study reviews recent international literature (2024–2025) on the application of blockchain in financial management. The findings indicate that blockchain contributes significantly across various areas, including supply chain finance (SCF), financial reporting, working capital management, asset tokenization, and decentralized finance (DeFi). In SCF, blockchain improves transaction traceability, reduces information asymmetry, and lowers the risk of supply chain disruptions. In financial reporting, blockchain-based e-invoicing enhances transparency, accountability, and reduces the cost of equity by improving investor confidence. The integration of blockchain into working capital management enables real-time synchronization of financial and operational data, thereby strengthening decision-making and liquidity optimization. Meanwhile, asset tokenization creates opportunities for democratizing investment access and diversifying funding sources. DeFi, while offering innovative financing alternatives and disrupting traditional financial intermediaries, remains strongly influenced by global macroeconomic dynamics and regulatory frameworks. Furthermore, blockchain enhances cross-border trade efficiency by streamlining document verification, reducing transaction delays, and fostering trust among international trading partners. Despite these substantial benefits, blockchain adoption continues to face challenges, such as regulatory uncertainty, cybersecurity risks, scalability limitations, and digital asset volatility. Overall, the synthesis of literature highlights blockchain not only as a technological innovation but also as a strategic pillar in modern financial governance. This study also suggests that future research should examine the integration of blockchain with global regulatory frameworks, green finance initiatives, and sustainable financial practices to ensure both scalability and long-term resilience.
This study examines the impact of cryptocurrency ownership on corporate volatility, focusing on external financial conditions, internal financial conditions, and liquidity crises. The research utilizes secondary data from publicly traded companies in the United States listed in the Refinitiv database for the period 2018-2023. To enhance the validity of the results, a matching procedure was implemented, in which each cryptocurrency-owning company was paired with a similar non-cryptocurrency-owning company to create a balanced control group. The analysis employed panel data regression on 384 publicly traded companies in the U.S. The findings indicate that the ratio of cryptocurrency ownership has a significant positive effect on corporate volatility. Additionally, liquidity levels also have a significant positive impact on the volatility of companies holding cryptocurrencies, suggesting that liquidity crises amplify the effect of cryptocurrency ownership fluctuations on corporate volatility. Internal financial conditions, measured by Return on Assets (ROA), exhibit a significant negative effect on the volatility of companies holding cryptocurrencies, implying that strong internal financial health mitigates the impact of cryptocurrency ownership fluctuations on volatility. Conversely, external factors such as company Beta do not influence increased volatility, which contrasts with the expectation that external factors would amplify the effect of cryptocurrency ownership fluctuations on corporate volatility. This study offers important implications for financial managers and regulators in designing risk mitigation strategies against digital asset price fluctuations.
Traceable ring signatures (TRSs) allow a signer to create a signature that maintains anonymity while enabling traceability if needed. It merges the characteristics of traditional ring signatures with the ability to trace signers, making it ideal for applications that demand both confidentiality and accountability. In a TRS scheme, a ring of potential signers generates a signature on a message without disclosing the actual signer’s identity. However, the identity can be traced if the signer uses the same tag for multiple signatures. This paper introduces a novel formal construction of TRS under universally composable (UC) security. We integrate verifiable random functions (VRFs) and zero-knowledge proofs for membership, employing Pedersen commitments. Our signature schemes maintain a logarithmic size while preserving the UC security guarantees. Additionally, we explore the potential to extend the property of one-time anonymity in TRS to K-time anonymity.
Decentralized Federated Learning (DFL) enables collaborative model training without a central server, but it remains vulnerable to privacy leakage because shared model updates can expose sensitive information through inversion, reconstruction, and membership inference attacks. Differential Privacy (DP) provides formal safeguards, yet existing DP-enabled DFL methods operate as black-boxes that cannot track cumulative noise added across clients and rounds, forcing each participant to inject worst-case perturbations that severely degrade accuracy. We propose PrivateDFL, a new explainable and privacy-preserving framework that addresses this gap by combining a HyperDimensional Computing (HD) model with a transparent DP noise accountant tailored to decentralized learning. HD offers structured, noise-tolerant high-dimensional representations, while the accountant explicitly tracks cumulative perturbations so each client adds only the minimal incremental noise required to satisfy its (epsilon, delta) budget. This yields significantly tighter and more interpretable privacy-utility tradeoffs than prior DP-DFL approaches. Experiments on MNIST (image), ISOLET (speech), and UCI-HAR (wearable sensor) show that PrivateDFL consistently surpasses centralized DP-SGD and Renyi-DP Transformer and deep learning baselines under both IID and non-IID partitions, improving accuracy by up to 24.4% on MNIST, over 80% on ISOLET, and 14.7% on UCI-HAR, while reducing inference latency by up to 76 times and energy consumption by up to 36 times. These results position PrivateDFL as an efficient and trustworthy solution for privacy-sensitive pattern recognition applications such as healthcare, finance, human-activity monitoring, and industrial sensing. Future work will extend the accountant to adversarial participation, heterogeneous privacy budgets, and dynamic topologies.
Smart contracts have emerged as a transformative force in contract law, leveraging blockchain technology to automate transactions and reduce reliance on human intermediaries. However, their widespread adoption is hindered by significant legal challenges, particularly in determining liability for transaction failures. This Note examines the accountability problems inherent in smart contracts, focusing on the critical role of oracles—third-party entities that feed external data into blockchain-based agreements. While existing scholarship explores the theoretical foundations and potential applications of smart contracts, this Note shifts focus to liability allocation and proposes a novel framework: default oracle liability. Under this proposal, oracles bear primary responsibility for transaction errors arising from inaccurate data sourcing or validation failures. If oracles demonstrate that they functioned correctly, liability shifts to smart contract developers, who are responsible for ensuring secure and error-free code. By clarifying accountability, this framework incentivizes higher standards for data accuracy and software integrity, ultimately fostering a more reliable and legally-viable environment for smart contracts to operate.